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Jain S, Bakolitsa C, Brenner SE, Radivojac P, Moult J, Repo S, Hoskins RA, Andreoletti G, Barsky D, Chellapan A, Chu H, Dabbiru N, Kollipara NK, Ly M, Neumann AJ, Pal LR, Odell E, Pandey G, Peters-Petrulewicz RC, Srinivasan R, Yee SF, Yeleswarapu SJ, Zuhl M, Adebali O, Patra A, Beer MA, Hosur R, Peng J, Bernard BM, Berry M, Dong S, Boyle AP, Adhikari A, Chen J, Hu Z, Wang R, Wang Y, Miller M, Wang Y, Bromberg Y, Turina P, Capriotti E, Han JJ, Ozturk K, Carter H, Babbi G, Bovo S, Di Lena P, Martelli PL, Savojardo C, Casadio R, Cline MS, De Baets G, Bonache S, Díez O, Gutiérrez-Enríquez S, Fernández A, Montalban G, Ootes L, Özkan S, Padilla N, Riera C, De la Cruz X, Diekhans M, Huwe PJ, Wei Q, Xu Q, Dunbrack RL, Gotea V, Elnitski L, Margolin G, Fariselli P, Kulakovskiy IV, Makeev VJ, Penzar DD, Vorontsov IE, Favorov AV, Forman JR, Hasenahuer M, Fornasari MS, Parisi G, Avsec Z, Çelik MH, Nguyen TYD, Gagneur J, Shi FY, Edwards MD, Guo Y, Tian K, Zeng H, Gifford DK, Göke J, Zaucha J, Gough J, Ritchie GRS, Frankish A, Mudge JM, Harrow J, Young EL, Yu Y, Huff CD, Murakami K, Nagai Y, Imanishi T, Mungall CJ, Jacobsen JOB, Kim D, Jeong CS, Jones DT, Li MJ, Guthrie VB, Bhattacharya R, Chen YC, Douville C, Fan J, Kim D, Masica D, Niknafs N, Sengupta S, Tokheim C, Turner TN, Yeo HTG, Karchin R, Shin S, Welch R, Keles S, Li Y, Kellis M, Corbi-Verge C, Strokach AV, Kim PM, Klein TE, Mohan R, Sinnott-Armstrong NA, Wainberg M, Kundaje A, Gonzaludo N, Mak ACY, Chhibber A, Lam HYK, Dahary D, Fishilevich S, Lancet D, Lee I, Bachman B, Katsonis P, Lua RC, Wilson SJ, Lichtarge O, Bhat RR, Sundaram L, Viswanath V, Bellazzi R, Nicora G, Rizzo E, Limongelli I, Mezlini AM, Chang R, Kim S, Lai C, O’Connor R, Topper S, van den Akker J, Zhou AY, Zimmer AD, Mishne G, Bergquist TR, Breese MR, Guerrero RF, Jiang Y, Kiga N, Li B, Mort M, Pagel KA, Pejaver V, Stamboulian MH, Thusberg J, Mooney SD, Teerakulkittipong N, Cao C, Kundu K, Yin Y, Yu CH, Kleyman M, Lin CF, Stackpole M, Mount SM, Eraslan G, Mueller NS, Naito T, Rao AR, Azaria JR, Brodie A, Ofran Y, Garg A, Pal D, Hawkins-Hooker A, Kenlay H, Reid J, Mucaki EJ, Rogan PK, Schwarz JM, Searls DB, Lee GR, Seok C, Krämer A, Shah S, Huang CV, Kirsch JF, Shatsky M, Cao Y, Chen H, Karimi M, Moronfoye O, Sun Y, Shen Y, Shigeta R, Ford CT, Nodzak C, Uppal A, Shi X, Joseph T, Kotte S, Rana S, Rao A, Saipradeep VG, Sivadasan N, Sunderam U, Stanke M, Su A, Adzhubey I, Jordan DM, Sunyaev S, Rousseau F, Schymkowitz J, Van Durme J, Tavtigian SV, Carraro M, Giollo M, Tosatto SCE, Adato O, Carmel L, Cohen NE, Fenesh T, Holtzer T, Juven-Gershon T, Unger R, Niroula A, Olatubosun A, Väliaho J, Yang Y, Vihinen M, Wahl ME, Chang B, Chong KC, Hu I, Sun R, Wu WKK, Xia X, Zee BC, Wang MH, Wang M, Wu C, Lu Y, Chen K, Yang Y, Yates CM, Kreimer A, Yan Z, Yosef N, Zhao H, Wei Z, Yao Z, Zhou F, Folkman L, Zhou Y, Daneshjou R, Altman RB, Inoue F, Ahituv N, Arkin AP, Lovisa F, Bonvini P, Bowdin S, Gianni S, Mantuano E, Minicozzi V, Novak L, Pasquo A, Pastore A, Petrosino M, Puglisi R, Toto A, Veneziano L, Chiaraluce R, Ball MP, Bobe JR, Church GM, Consalvi V, Cooper DN, Buckley BA, Sheridan MB, Cutting GR, Scaini MC, Cygan KJ, Fredericks AM, Glidden DT, Neil C, Rhine CL, Fairbrother WG, Alontaga AY, Fenton AW, Matreyek KA, Starita LM, Fowler DM, Löscher BS, Franke A, Adamson SI, Graveley BR, Gray JW, Malloy MJ, Kane JP, Kousi M, Katsanis N, Schubach M, Kircher M, Mak ACY, Tang PLF, Kwok PY, Lathrop RH, Clark WT, Yu GK, LeBowitz JH, Benedicenti F, Bettella E, Bigoni S, Cesca F, Mammi I, Marino-Buslje C, Milani D, Peron A, Polli R, Sartori S, Stanzial F, Toldo I, Turolla L, Aspromonte MC, Bellini M, Leonardi E, Liu X, Marshall C, McCombie WR, Elefanti L, Menin C, Meyn MS, Murgia A, Nadeau KCY, Neuhausen SL, Nussbaum RL, Pirooznia M, Potash JB, Dimster-Denk DF, Rine JD, Sanford JR, Snyder M, Cote AG, Sun S, Verby MW, Weile J, Roth FP, Tewhey R, Sabeti PC, Campagna J, Refaat MM, Wojciak J, Grubb S, Schmitt N, Shendure J, Spurdle AB, Stavropoulos DJ, Walton NA, Zandi PP, Ziv E, Burke W, Chen F, Carr LR, Martinez S, Paik J, Harris-Wai J, Yarborough M, Fullerton SM, Koenig BA, McInnes G, Shigaki D, Chandonia JM, Furutsuki M, Kasak L, Yu C, Chen R, Friedberg I, Getz GA, Cong Q, Kinch LN, Zhang J, Grishin NV, Voskanian A, Kann MG, Tran E, Ioannidis NM, Hunter JM, Udani R, Cai B, Morgan AA, Sokolov A, Stuart JM, Minervini G, Monzon AM, Batzoglou S, Butte AJ, Greenblatt MS, Hart RK, Hernandez R, Hubbard TJP, Kahn S, O’Donnell-Luria A, Ng PC, Shon J, Veltman J, Zook JM. CAGI, the Critical Assessment of Genome Interpretation, establishes progress and prospects for computational genetic variant interpretation methods. Genome Biol 2024; 25:53. [PMID: 38389099 PMCID: PMC10882881 DOI: 10.1186/s13059-023-03113-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2023] [Accepted: 11/17/2023] [Indexed: 02/24/2024] Open
Abstract
BACKGROUND The Critical Assessment of Genome Interpretation (CAGI) aims to advance the state-of-the-art for computational prediction of genetic variant impact, particularly where relevant to disease. The five complete editions of the CAGI community experiment comprised 50 challenges, in which participants made blind predictions of phenotypes from genetic data, and these were evaluated by independent assessors. RESULTS Performance was particularly strong for clinical pathogenic variants, including some difficult-to-diagnose cases, and extends to interpretation of cancer-related variants. Missense variant interpretation methods were able to estimate biochemical effects with increasing accuracy. Assessment of methods for regulatory variants and complex trait disease risk was less definitive and indicates performance potentially suitable for auxiliary use in the clinic. CONCLUSIONS Results show that while current methods are imperfect, they have major utility for research and clinical applications. Emerging methods and increasingly large, robust datasets for training and assessment promise further progress ahead.
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Valiente P, Nim S, Kim J, Kim PM. Computational Design of Potent and Selective d-Peptide Agonists of the Glucagon-like Peptide-2 Receptor. J Med Chem 2023; 66:10342-10353. [PMID: 37491005 PMCID: PMC10424673 DOI: 10.1021/acs.jmedchem.3c00464] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2023] [Indexed: 07/27/2023]
Abstract
Here, we designed three d-GLP-2 agonists that activated the glucagon-like peptide-2 receptor (GLP-2R) cyclic adenosine monophosphate (cAMP) accumulation without stimulating the glucagon-like peptide-1 receptor (GLP-1R). All the d-GLP-2 agonists increased the protein kinase B phosphorylated (p-AKT) expression levels in a time- and concentration-dependent manner in vitro. The most effective d-GLP-2 analogue boosted the AKT phosphorylation 2.28 times more effectively compared to the native l-GLP-2. The enhancement in the p-AKT levels induced by the d-GLP-2 analogues could be explained by GLP-2R's more prolonged activation, given that the d-GLP-2 analogues induce a lower β-arrestin recruitment. The higher stability to protease degradation of our d-GLP-2 agonists helps us envision their potential applications in enhancing intestinal absorption and treating inflammatory bowel illness while lowering the high dosage required by the current treatments.
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Affiliation(s)
- Pedro
A. Valiente
- Donnelly
Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario M5S 3E1, Canada
| | - Satra Nim
- Donnelly
Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario M5S 3E1, Canada
| | - Jisun Kim
- Donnelly
Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario M5S 3E1, Canada
| | - Philip M. Kim
- Donnelly
Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario M5S 3E1, Canada
- Department
of Molecular Genetics, University of Toronto, Toronto, Ontario M5S 3E1, Canada
- Department
of Computer Science, University of Toronto, Toronto, Ontario M5S 3E1, Canada
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Ichikawa DM, Abdin O, Alerasool N, Kogenaru M, Mueller AL, Wen H, Giganti DO, Goldberg GW, Adams S, Spencer JM, Razavi R, Nim S, Zheng H, Gionco C, Clark FT, Strokach A, Hughes TR, Lionnet T, Taipale M, Kim PM, Noyes MB. A universal deep-learning model for zinc finger design enables transcription factor reprogramming. Nat Biotechnol 2023; 41:1117-1129. [PMID: 36702896 PMCID: PMC10421740 DOI: 10.1038/s41587-022-01624-4] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2021] [Accepted: 11/17/2022] [Indexed: 01/27/2023]
Abstract
Cys2His2 zinc finger (ZF) domains engineered to bind specific target sequences in the genome provide an effective strategy for programmable regulation of gene expression, with many potential therapeutic applications. However, the structurally intricate engagement of ZF domains with DNA has made their design challenging. Here we describe the screening of 49 billion protein-DNA interactions and the development of a deep-learning model, ZFDesign, that solves ZF design for any genomic target. ZFDesign is a modern machine learning method that models global and target-specific differences induced by a range of library environments and specifically takes into account compatibility of neighboring fingers using a novel hierarchical transformer architecture. We demonstrate the versatility of designed ZFs as nucleases as well as activators and repressors by seamless reprogramming of human transcription factors. These factors could be used to upregulate an allele of haploinsufficiency, downregulate a gain-of-function mutation or test the consequence of regulation of a single gene as opposed to the many genes that a transcription factor would normally influence.
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Affiliation(s)
- David M Ichikawa
- Institute for Systems Genetics, NYU Grossman School of Medicine, New York, NY, USA
- Department of Biochemistry and Molecular Pharmacology, NYU Grossman School of Medicine, New York, NY, USA
| | - Osama Abdin
- Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada
| | - Nader Alerasool
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario, Canada
| | - Manjunatha Kogenaru
- Institute for Systems Genetics, NYU Grossman School of Medicine, New York, NY, USA
| | - April L Mueller
- Institute for Systems Genetics, NYU Grossman School of Medicine, New York, NY, USA
| | - Han Wen
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario, Canada
| | - David O Giganti
- Institute for Systems Genetics, NYU Grossman School of Medicine, New York, NY, USA
| | - Gregory W Goldberg
- Institute for Systems Genetics, NYU Grossman School of Medicine, New York, NY, USA
| | - Samantha Adams
- Institute for Systems Genetics, NYU Grossman School of Medicine, New York, NY, USA
| | - Jeffrey M Spencer
- Institute for Systems Genetics, NYU Grossman School of Medicine, New York, NY, USA
| | - Rozita Razavi
- Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario, Canada
| | - Satra Nim
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario, Canada
| | - Hong Zheng
- Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario, Canada
| | - Courtney Gionco
- Institute for Systems Genetics, NYU Grossman School of Medicine, New York, NY, USA
| | - Finnegan T Clark
- Institute for Systems Genetics, NYU Grossman School of Medicine, New York, NY, USA
| | - Alexey Strokach
- Department of Computer Science, University of Toronto, Toronto, Ontario, Canada
| | - Timothy R Hughes
- Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario, Canada
| | - Timothee Lionnet
- Institute for Systems Genetics, NYU Grossman School of Medicine, New York, NY, USA
| | - Mikko Taipale
- Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario, Canada
| | - Philip M Kim
- Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada.
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario, Canada.
- Department of Computer Science, University of Toronto, Toronto, Ontario, Canada.
| | - Marcus B Noyes
- Institute for Systems Genetics, NYU Grossman School of Medicine, New York, NY, USA.
- Department of Biochemistry and Molecular Pharmacology, NYU Grossman School of Medicine, New York, NY, USA.
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McFee M, Kim PM. GDockScore: a graph-based protein-protein docking scoring function. Bioinform Adv 2023; 3:vbad072. [PMID: 37359726 PMCID: PMC10290236 DOI: 10.1093/bioadv/vbad072] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/14/2023] [Revised: 05/30/2023] [Accepted: 06/10/2023] [Indexed: 06/28/2023]
Abstract
Summary Protein complexes play vital roles in a variety of biological processes, such as mediating biochemical reactions, the immune response and cell signalling, with 3D structure specifying function. Computational docking methods provide a means to determine the interface between two complexed polypeptide chains without using time-consuming experimental techniques. The docking process requires the optimal solution to be selected with a scoring function. Here, we propose a novel graph-based deep learning model that utilizes mathematical graph representations of proteins to learn a scoring function (GDockScore). GDockScore was pre-trained on docking outputs generated with the Protein Data Bank biounits and the RosettaDock protocol, and then fine-tuned on HADDOCK decoys generated on the ZDOCK Protein Docking Benchmark. GDockScore performs similarly to the Rosetta scoring function on docking decoys generated using the RosettaDock protocol. Furthermore, state-of-the-art is achieved on the CAPRI score set, a challenging dataset for developing docking scoring functions. Availability and implementation The model implementation is available at https://gitlab.com/mcfeemat/gdockscore. Supplementary information Supplementary data are available at Bioinformatics Advances online.
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Affiliation(s)
- Matthew McFee
- Department of Molecular Genetics, The University of Toronto, Toronto, ON M5S 1A8, Canada
- Donnelly Centre for Cellular and Biomolecular Research, The University of Toronto, Toronto, ON M5S 3E1, Canada
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Abstract
The generation of de novo protein structures with predefined functions and properties remains a challenging problem in protein design. Diffusion models, also known as score-based generative models (SGMs), have recently exhibited astounding empirical performance in image synthesis. Here we use image-based representations of protein structure to develop ProteinSGM, a score-based generative model that produces realistic de novo proteins. Through unconditional generation, we show that ProteinSGM can generate native-like protein structures, surpassing the performance of previously reported generative models. We experimentally validate some de novo designs and observe secondary structure compositions consistent with generated backbones. Finally, we apply conditional generation to de novo protein design by formulating it as an image inpainting problem, allowing precise and modular design of protein structure.
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Affiliation(s)
- Jin Sub Lee
- Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON, Canada
| | - Jisun Kim
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON, Canada
| | - Philip M Kim
- Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada.
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON, Canada.
- Department of Computer Science, University of Toronto, Toronto, ON, Canada.
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Nim S, O'Hara DM, Corbi-Verge C, Perez-Riba A, Fujisawa K, Kapadia M, Chau H, Albanese F, Pawar G, De Snoo ML, Ngana SG, Kim J, El-Agnaf OMA, Rennella E, Kay LE, Kalia SK, Kalia LV, Kim PM. Disrupting the α-synuclein-ESCRT interaction with a peptide inhibitor mitigates neurodegeneration in preclinical models of Parkinson's disease. Nat Commun 2023; 14:2150. [PMID: 37076542 PMCID: PMC10115881 DOI: 10.1038/s41467-023-37464-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2021] [Accepted: 03/14/2023] [Indexed: 04/21/2023] Open
Abstract
Accumulation of α-synuclein into toxic oligomers or fibrils is implicated in dopaminergic neurodegeneration in Parkinson's disease. Here we performed a high-throughput, proteome-wide peptide screen to identify protein-protein interaction inhibitors that reduce α-synuclein oligomer levels and their associated cytotoxicity. We find that the most potent peptide inhibitor disrupts the direct interaction between the C-terminal region of α-synuclein and CHarged Multivesicular body Protein 2B (CHMP2B), a component of the Endosomal Sorting Complex Required for Transport-III (ESCRT-III). We show that α-synuclein impedes endolysosomal activity via this interaction, thereby inhibiting its own degradation. Conversely, the peptide inhibitor restores endolysosomal function and thereby decreases α-synuclein levels in multiple models, including female and male human cells harboring disease-causing α-synuclein mutations. Furthermore, the peptide inhibitor protects dopaminergic neurons from α-synuclein-mediated degeneration in hermaphroditic C. elegans and preclinical Parkinson's disease models using female rats. Thus, the α-synuclein-CHMP2B interaction is a potential therapeutic target for neurodegenerative disorders.
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Affiliation(s)
- Satra Nim
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON, Canada
| | - Darren M O'Hara
- Krembil Research Institute, Toronto Western Hospital, University Health Network, Toronto, ON, Canada
| | - Carles Corbi-Verge
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON, Canada
| | - Albert Perez-Riba
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON, Canada
| | - Kazuko Fujisawa
- Krembil Research Institute, Toronto Western Hospital, University Health Network, Toronto, ON, Canada
| | - Minesh Kapadia
- Krembil Research Institute, Toronto Western Hospital, University Health Network, Toronto, ON, Canada
| | - Hien Chau
- Krembil Research Institute, Toronto Western Hospital, University Health Network, Toronto, ON, Canada
| | - Federica Albanese
- Krembil Research Institute, Toronto Western Hospital, University Health Network, Toronto, ON, Canada
| | - Grishma Pawar
- Krembil Research Institute, Toronto Western Hospital, University Health Network, Toronto, ON, Canada
| | - Mitchell L De Snoo
- Krembil Research Institute, Toronto Western Hospital, University Health Network, Toronto, ON, Canada
| | - Sophie G Ngana
- Krembil Research Institute, Toronto Western Hospital, University Health Network, Toronto, ON, Canada
| | - Jisun Kim
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON, Canada
| | - Omar M A El-Agnaf
- Neurological Disorders Research Center, Qatar Biomedical Research Institute (QBRI), Hamad Bin Khalifa University (HBKU), Qatar Foundation, Doha, Qatar
| | - Enrico Rennella
- Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada
| | - Lewis E Kay
- Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada
- Department of Biochemistry, University of Toronto, Toronto, ON, Canada
- Department of Chemistry, University of Toronto, Toronto, ON, Canada
- Program in Molecular Medicine, The Hospital for Sick Children Research Institute, Toronto, ON, Canada
| | - Suneil K Kalia
- Krembil Research Institute, Toronto Western Hospital, University Health Network, Toronto, ON, Canada.
- Division of Neurosurgery, Department of Surgery, University of Toronto, Toronto, ON, Canada.
| | - Lorraine V Kalia
- Krembil Research Institute, Toronto Western Hospital, University Health Network, Toronto, ON, Canada.
- Division of Neurology, Department of Medicine, University of Toronto, Toronto, ON, Canada.
- Tanz Centre for Research in Neurodegenerative Diseases, University of Toronto, Toronto, ON, Canada.
| | - Philip M Kim
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON, Canada.
- Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada.
- Department of Computer Science, University of Toronto, Toronto, ON, Canada.
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Khatami MH, Mendes UC, Wiebe N, Kim PM. Gate-based quantum computing for protein design. PLoS Comput Biol 2023; 19:e1011033. [PMID: 37043517 PMCID: PMC10124842 DOI: 10.1371/journal.pcbi.1011033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2022] [Revised: 04/24/2023] [Accepted: 03/17/2023] [Indexed: 04/13/2023] Open
Abstract
Protein design is a technique to engineer proteins by permuting amino acids in the sequence to obtain novel functionalities. However, exploring all possible combinations of amino acids is generally impossible due to the exponential growth of possibilities with the number of designable sites. The present work introduces circuits implementing a pure quantum approach, Grover's algorithm, to solve protein design problems. Our algorithms can adjust to implement any custom pair-wise energy tables and protein structure models. Moreover, the algorithm's oracle is designed to consist of only adder functions. Quantum computer simulators validate the practicality of our circuits, containing up to 234 qubits. However, a smaller circuit is implemented on real quantum devices. Our results show that using [Formula: see text] iterations, the circuits find the correct results among all N possibilities, providing the expected quadratic speed up of Grover's algorithm over classical methods (i.e., [Formula: see text]).
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Affiliation(s)
- Mohammad Hassan Khatami
- Terrence Donnelly Centre for Cellular & Biomolecular Research, University of Toronto, Toronto, Ontario, Canada
| | | | - Nathan Wiebe
- Department of Computer Science, University of Toronto, Toronto, Ontario, Canada
- Pacific Northwest National Laboratory, Richland, Washington, United States of America
- Department of Physics University of Washington, Seattle, Washington, United States of America
| | - Philip M Kim
- Terrence Donnelly Centre for Cellular & Biomolecular Research, University of Toronto, Toronto, Ontario, Canada
- Department of Computer Science, University of Toronto, Toronto, Ontario, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada
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8
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Xie X, Valiente PA, Kim PM. HelixGAN a deep-learning methodology for conditional de novo design of α-helix structures. Bioinformatics 2023; 39:6991169. [PMID: 36651657 PMCID: PMC9887083 DOI: 10.1093/bioinformatics/btad036] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2022] [Revised: 12/19/2022] [Accepted: 01/17/2023] [Indexed: 01/19/2023] Open
Abstract
MOTIVATION Protein and peptide engineering has become an essential field in biomedicine with therapeutics, diagnostics and synthetic biology applications. Helices are both abundant structural feature in proteins and comprise a major portion of bioactive peptides. Precise design of helices for binding or biological activity is still a challenging problem. RESULTS Here, we present HelixGAN, the first generative adversarial network method to generate de novo left-handed and right-handed alpha-helix structures from scratch at an atomic level. We developed a gradient-based search approach in latent space to optimize the generation of novel α-helical structures by matching the exact conformations of selected hotspot residues. The designed α-helical structures can bind specific targets or activate cellular receptors. There is a significant agreement between the helix structures generated with HelixGAN and PEP-FOLD, a well-known de novo approach for predicting peptide structures from amino acid sequences. HelixGAN outperformed RosettaDesign, and our previously developed structural similarity method to generate D-peptides matching a set of given hotspots in a known L-peptide. As proof of concept, we designed a novel D-GLP1_1 analog that matches the conformations of critical hotspots for the GLP1 function. MD simulations revealed a stable binding mode of the D-GLP1_1 analog coupled to the GLP1 receptor. This novel D-peptide analog is more stable than our previous D-GLP1 design along the MD simulations. We envision HelixGAN as a critical tool for designing novel bioactive peptides with specific properties in the early stages of drug discovery. AVAILABILITY AND IMPLEMENTATION https://github.com/xxiexuezhi/helix_gan. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Xuezhi Xie
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON M5S 3E1, Canada,Department of Computer Science, University of Toronto, Toronto, ON M5S 3E1, Canada
| | - Pedro A Valiente
- Department of Computer Science, University of Toronto, Toronto, ON M5S 3E1, Canada
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Cable J, Saphire EO, Hayday AC, Wiltshire TD, Mousa JJ, Humphreys DP, Breij ECW, Bruhns P, Broketa M, Furuya G, Hauser BM, Mahévas M, Carfi A, Cantaert T, Kwong PD, Tripathi P, Davis JH, Brewis N, Keyt BA, Fennemann FL, Dussupt V, Sivasubramanian A, Kim PM, Rawi R, Richardson E, Leventhal D, Wolters RM, Geuijen CAW, Sleeman MA, Pengo N, Donnellan FR. Antibodies as drugs-a Keystone Symposia report. Ann N Y Acad Sci 2023; 1519:153-166. [PMID: 36382536 PMCID: PMC10103175 DOI: 10.1111/nyas.14915] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Therapeutic antibodies have broad indications across diverse disease states, such as oncology, autoimmune diseases, and infectious diseases. New research continues to identify antibodies with therapeutic potential as well as methods to improve upon endogenous antibodies and to design antibodies de novo. On April 27-30, 2022, experts in antibody research across academia and industry met for the Keystone symposium "Antibodies as Drugs" to present the state-of-the-art in antibody therapeutics, repertoires and deep learning, bispecific antibodies, and engineering.
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Affiliation(s)
| | - Erica Ollmann Saphire
- Center for Infectious Disease and Vaccine Research, La Jolla Institute for Immunology, La Jolla, California, USA.,Department of Medicine, University of California San Diego, La Jolla, California, USA
| | - Adrian C Hayday
- Peter Gorer Department of Immunobiology, King's College London, London, UK.,Cancer Research UK Cancer Immunotherapy Accelerator, London, UK.,Immunosurveillance Laboratory, The Francis Crick Institute, London, UK
| | | | - Jarrod J Mousa
- Department of Infectious Diseases and Center for Vaccines and Immunology, College of Veterinary Medicine, Athens, Georgia, USA.,Department of Biochemistry and Molecular Biology, Franklin College of Arts and Sciences, University of Georgia, Athens, Georgia, USA.,Vanderbilt Vaccine Center, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | | | - Esther C W Breij
- Translational Research and Precision Medicine, Genmab BV, Utrecht, the Netherlands
| | - Pierre Bruhns
- Institut Pasteur, Université de Paris, Unit of Antibodies in Therapy and Pathology, Paris, France
| | - Matteo Broketa
- Institut Pasteur, Université de Paris, Unit of Antibodies in Therapy and Pathology, Paris, France
| | - Genta Furuya
- Department of Preventive Medicine and Department of Pathology, Graduate School of Medicine, University of Tokyo, Tokyo, Japan
| | - Blake M Hauser
- Ragon Institute of MGH, MIT, and Harvard, Cambridge, Massachusetts, USA
| | - Matthieu Mahévas
- Service de Médecine Interne, Centre de Référence des Cytopénies Auto-immunes de l'adulte, Centre Hospitalier Universitaire Henri-Mondor, Assistance Publique-Hôpitaux de Paris, Université Paris-Est Créteil, Créteil, France
| | - Andrea Carfi
- Moderna Inc., Cambridge, Massachusetts, USA.,Department of Pathology, Miller School of Medicine, University of Miami, Miami, Florida, USA
| | - Tineke Cantaert
- Immunology Unit, Institut Pasteur du Cambodge, The Pasteur Network, Phnom Penh, Cambodia
| | - Peter D Kwong
- Vaccine Research Center, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, Maryland, USA
| | - Prabhanshu Tripathi
- Vaccine Research Center, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, Maryland, USA
| | | | | | - Bruce A Keyt
- IGM Biosciences, Inc., Mountainview, California, USA
| | | | - Vincent Dussupt
- Emerging Infectious Diseases Branch, U.S. Military HIV Research Program, Walter Reed Army Institute of Research, Silver Spring, Maryland, USA.,Henry M. Jackson Foundation for the Advancement of Military Medicine, Bethesda, Maryland, USA
| | | | - Philip M Kim
- Department of Molecular Genetics, Donnelly Centre for Cellular and Biomolecular Research, Toronto, Ontario, Canada.,Department of Computer Science, University of Toronto, Toronto, Ontario, Canada
| | - Reda Rawi
- Vaccine Research Center, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, Maryland, USA
| | - Eve Richardson
- Department of Statistics, University of Oxford, Oxford, UK
| | | | - Rachael M Wolters
- Department of Pathology, Microbiology, and Immunology, Vanderbilt University Medical Center, Nashville, Tennessee, USA
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10
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Valiente P, Nim S, Lee J, Kim S, Kim PM. Targeting the Receptor-Binding Motif of SARS-CoV-2 with D-Peptides Mimicking the ACE2 Binding Helix: Lessons for Inhibiting Omicron and Future Variants of Concern. J Chem Inf Model 2022; 62:3618-3626. [PMID: 35875887 PMCID: PMC9344788 DOI: 10.1021/acs.jcim.2c00500] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2022] [Indexed: 11/30/2022]
Abstract
The COVID-19 pandemic continues to spread around the world, with several new variants emerging, particularly those of concern (VOCs). Omicron (B.1.1.529), a recent VOC with many mutations in the spike protein's receptor-binding domain (RBD), has attracted a great deal of scientific and public interest. We previously developed two D-peptide inhibitors for the infection of the original SARS-CoV-2 and its VOCs, alpha and beta, in vitro. Here, we demonstrated that Covid3 and Covid_extended_1 maintained their high-affinity binding (29.4-31.3 nM) to the omicron RBD. Both D-peptides blocked the omicron variant in vitro infection with IC50s of 3.13 and 5.56 μM, respectively. We predicted that Covid3 shares a larger overlapping binding region with the ACE2 binding motif than different classes of neutralizing monoclonal antibodies. We envisioned the design of D-peptide inhibitors targeting the receptor-binding motif as the most promising approach for inhibiting current and future VOCs of SARS-CoV-2, given that the ACE2 binding interface is more limited to tolerate mutations than most of the RBD's surface.
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Affiliation(s)
- Pedro
A. Valiente
- Donnelly
Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario M5S 3E1, Canada
| | - Satra Nim
- Donnelly
Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario M5S 3E1, Canada
| | - JinAh Lee
- Zoonotic
Virus Laboratory, Institut Pasteur Korea, 16, Daewangpangyo-ro 712 beon-gil
Bundang-gu, Seongnam-si, Gyeonggi-do 13488, Republic of Korea
| | - Seungtaek Kim
- Zoonotic
Virus Laboratory, Institut Pasteur Korea, 16, Daewangpangyo-ro 712 beon-gil
Bundang-gu, Seongnam-si, Gyeonggi-do 13488, Republic of Korea
| | - Philip M. Kim
- Donnelly
Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario M5S 3E1, Canada
- Department
of Molecular Genetics, University of Toronto, Toronto, Ontario M5S 3E1, Canada
- Department
of Computer Science, University of Toronto, Toronto, Ontario M5S 3E1, Canada
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11
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Abdin O, Nim S, Wen H, Kim PM. PepNN: a deep attention model for the identification of peptide binding sites. Commun Biol 2022; 5:503. [PMID: 35618814 PMCID: PMC9135736 DOI: 10.1038/s42003-022-03445-2] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2022] [Accepted: 05/03/2022] [Indexed: 11/09/2022] Open
Abstract
Protein-peptide interactions play a fundamental role in many cellular processes, but remain underexplored experimentally and difficult to model computationally. Here, we present PepNN-Struct and PepNN-Seq, structure and sequence-based approaches for the prediction of peptide binding sites on a protein. A main difficulty for the prediction of peptide-protein interactions is the flexibility of peptides and their tendency to undergo conformational changes upon binding. Motivated by this, we developed reciprocal attention to simultaneously update the encodings of peptide and protein residues while enforcing symmetry, allowing for information flow between the two inputs. PepNN integrates this module with modern graph neural network layers and a series of transfer learning steps are used during training to compensate for the scarcity of peptide-protein complex information. We show that PepNN-Struct achieves consistently high performance across different benchmark datasets. We also show that PepNN makes reasonable peptide-agnostic predictions, allowing for the identification of novel peptide binding proteins.
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Affiliation(s)
- Osama Abdin
- Department of Molecular Genetics, University of Toronto, Toronto, ON, M5S 3E1, Canada
| | - Satra Nim
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON, M5S 3E1, Canada
| | - Han Wen
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON, M5S 3E1, Canada
| | - Philip M Kim
- Department of Molecular Genetics, University of Toronto, Toronto, ON, M5S 3E1, Canada. .,Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON, M5S 3E1, Canada. .,Department of Computer Science, University of Toronto, Toronto, ON, M5S 3E1, Canada.
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12
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Castillo F, Corbi-Verge C, Murciano-Calles J, Candel AM, Han Z, Iglesias-Bexiga M, Ruiz-Sanz J, Kim PM, Harty RN, Martinez JC, Luque I. Phage display identification of nanomolar ligands for human NEDD4-WW3: Energetic and dynamic implications for the development of broad-spectrum antivirals. Int J Biol Macromol 2022; 207:308-323. [PMID: 35257734 DOI: 10.1016/j.ijbiomac.2022.03.010] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2021] [Revised: 03/01/2022] [Accepted: 03/02/2022] [Indexed: 11/29/2022]
Abstract
The recognition of PPxY viral Late domains by the third WW domain of the human HECT-E3 ubiquitin ligase NEDD4 (NEDD4-WW3) is essential for the budding of many viruses. Blocking these interactions is a promising strategy to develop broad-spectrum antivirals. As all WW domains, NEDD4-WW3 is a challenging therapeutic target due to the low binding affinity of its natural interactions, its high conformational plasticity, and its complex thermodynamic behavior. In this work, we set out to investigate whether high affinity can be achieved for monovalent ligands binding to the isolated NEDD4-WW3 domain. We show that a competitive phage-display set-up allows for the identification of high-affinity peptides showing inhibitory activity of viral budding. A detailed biophysical study combining calorimetry, nuclear magnetic resonance, and molecular dynamic simulations reveals that the improvement in binding affinity does not arise from the establishment of new interactions with the domain, but is associated to conformational restrictions imposed by a novel C-terminal -LFP motif in the ligand, unprecedented in the PPxY interactome. These results, which highlight the complexity of WW domain interactions, provide valuable insight into the key elements for high binding affinity, of interest to guide virtual screening campaigns for the identification of novel therapeutics targeting NEDD4-WW3 interactions.
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Affiliation(s)
- Francisco Castillo
- Department of Physical Chemistry, Institute of Biotechnology and Excelence Unit in Chemistry Applied to Biomedicine and Environment, School of Sciences, University of Granada, Campus Fuentenueva s/n 18071, Granada, Spain
| | - Carles Corbi-Verge
- Department of Physical Chemistry, Institute of Biotechnology and Excelence Unit in Chemistry Applied to Biomedicine and Environment, School of Sciences, University of Granada, Campus Fuentenueva s/n 18071, Granada, Spain; Donnelly Centre for Cellular and Biomolecular Research, Department of Molecular Genetics & Department of Computer Science, University of Toronto, Toronto, ON M5S 3E1, Canada
| | - Javier Murciano-Calles
- Department of Physical Chemistry, Institute of Biotechnology and Excelence Unit in Chemistry Applied to Biomedicine and Environment, School of Sciences, University of Granada, Campus Fuentenueva s/n 18071, Granada, Spain
| | - Adela M Candel
- Department of Physical Chemistry, Institute of Biotechnology and Excelence Unit in Chemistry Applied to Biomedicine and Environment, School of Sciences, University of Granada, Campus Fuentenueva s/n 18071, Granada, Spain
| | - Ziying Han
- Department of Pathobiology, School of Veterinary Medicine, University of Pennsylvania, 3800 Spruce St., Philadelphia, PA 19104, USA
| | - Manuel Iglesias-Bexiga
- Department of Physical Chemistry, Institute of Biotechnology and Excelence Unit in Chemistry Applied to Biomedicine and Environment, School of Sciences, University of Granada, Campus Fuentenueva s/n 18071, Granada, Spain
| | - Javier Ruiz-Sanz
- Department of Physical Chemistry, Institute of Biotechnology and Excelence Unit in Chemistry Applied to Biomedicine and Environment, School of Sciences, University of Granada, Campus Fuentenueva s/n 18071, Granada, Spain
| | - Philip M Kim
- Donnelly Centre for Cellular and Biomolecular Research, Department of Molecular Genetics & Department of Computer Science, University of Toronto, Toronto, ON M5S 3E1, Canada
| | - Ronald N Harty
- Department of Pathobiology, School of Veterinary Medicine, University of Pennsylvania, 3800 Spruce St., Philadelphia, PA 19104, USA
| | - Jose C Martinez
- Department of Physical Chemistry, Institute of Biotechnology and Excelence Unit in Chemistry Applied to Biomedicine and Environment, School of Sciences, University of Granada, Campus Fuentenueva s/n 18071, Granada, Spain
| | - Irene Luque
- Department of Physical Chemistry, Institute of Biotechnology and Excelence Unit in Chemistry Applied to Biomedicine and Environment, School of Sciences, University of Granada, Campus Fuentenueva s/n 18071, Granada, Spain.
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13
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Valiente PA, Wen H, Nim S, Lee J, Kim HJ, Kim J, Perez-Riba A, Paudel YP, Hwang I, Kim KD, Kim S, Kim PM. Computational Design of Potent D-Peptide Inhibitors of SARS-CoV-2. J Med Chem 2021; 64:14955-14967. [PMID: 34624194 PMCID: PMC8525337 DOI: 10.1021/acs.jmedchem.1c00655] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2021] [Indexed: 12/28/2022]
Abstract
Blocking the association between the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike protein receptor-binding domain (RBD) and the human angiotensin-converting enzyme 2 (ACE2) is an attractive therapeutic approach to prevent the virus from entering human cells. While antibodies and other modalities have been developed to this end, d-amino acid peptides offer unique advantages, including serum stability, low immunogenicity, and low cost of production. Here, we designed potent novel D-peptide inhibitors that mimic the ACE2 α1-binding helix by searching a mirror-image version of the PDB. The two best designs bound the RBD with affinities of 29 and 31 nM and blocked the infection of Vero cells by SARS-CoV-2 with IC50 values of 5.76 and 6.56 μM, respectively. Notably, both D-peptides neutralized with a similar potency the infection of two variants of concern: B.1.1.7 and B.1.351 in vitro. These potent D-peptide inhibitors are promising lead candidates for developing SARS-CoV-2 prophylactic or therapeutic treatments.
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Affiliation(s)
- Pedro A. Valiente
- Donnelly Centre for Cellular and Biomolecular
Research, University of Toronto, Toronto, Ontario M5S 3E1,
Canada
| | - Han Wen
- Donnelly Centre for Cellular and Biomolecular
Research, University of Toronto, Toronto, Ontario M5S 3E1,
Canada
| | - Satra Nim
- Donnelly Centre for Cellular and Biomolecular
Research, University of Toronto, Toronto, Ontario M5S 3E1,
Canada
| | - JinAh Lee
- Zoonotic Virus Laboratory, Institut
Pasteur Korea, 16, Daewangpangyo-ro 712 Beon-gil Bundang-gu, Seongnam-si,
Gyeonggi-do 13488, Republic of Korea
| | - Hyeon Ju Kim
- Zoonotic Virus Laboratory, Institut
Pasteur Korea, 16, Daewangpangyo-ro 712 Beon-gil Bundang-gu, Seongnam-si,
Gyeonggi-do 13488, Republic of Korea
| | - Jinhee Kim
- Zoonotic Virus Laboratory, Institut
Pasteur Korea, 16, Daewangpangyo-ro 712 Beon-gil Bundang-gu, Seongnam-si,
Gyeonggi-do 13488, Republic of Korea
| | - Albert Perez-Riba
- Donnelly Centre for Cellular and Biomolecular
Research, University of Toronto, Toronto, Ontario M5S 3E1,
Canada
| | - Yagya Prasad Paudel
- Donnelly Centre for Cellular and Biomolecular
Research, University of Toronto, Toronto, Ontario M5S 3E1,
Canada
| | - Insu Hwang
- Center for Convergent Research of Emerging Virus
Infection, Korea Research Institute of Chemical Technology,
Daejeon 34114, Republic of Korea
| | - Kyun-Do Kim
- Center for Convergent Research of Emerging Virus
Infection, Korea Research Institute of Chemical Technology,
Daejeon 34114, Republic of Korea
| | - Seungtaek Kim
- Zoonotic Virus Laboratory, Institut
Pasteur Korea, 16, Daewangpangyo-ro 712 Beon-gil Bundang-gu, Seongnam-si,
Gyeonggi-do 13488, Republic of Korea
| | - Philip M. Kim
- Donnelly Centre for Cellular and Biomolecular
Research, University of Toronto, Toronto, Ontario M5S 3E1,
Canada
- Department of Molecular Genetics,
University of Toronto, Toronto, Ontario M5S 3E1,
Canada
- Department of Computer Science,
University of Toronto, Toronto, Ontario M5S 3E1,
Canada
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14
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Abdin O, Kim PM. Rapid protein model refinement by deep learning. Nat Comput Sci 2021; 1:456-457. [PMID: 38217116 DOI: 10.1038/s43588-021-00104-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/15/2024]
Affiliation(s)
- Osama Abdin
- Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada
| | - Philip M Kim
- Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada.
- Donnelly Centre, University of Toronto, Toronto, Ontario, Canada.
- Department of Computer Science, University of Toronto, Toronto, Ontario, Canada.
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15
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Abstract
Computational generation of new proteins with a predetermined three-dimensional shape and computational optimization of existing proteins while maintaining their shape are challenging problems in structural biology. Here, we present a protocol that uses ProteinSolver, a pre-trained graph convolutional neural network, to quickly generate thousands of sequences matching a specific protein topology. We describe computational approaches that can be used to evaluate the generated sequences, and we show how select sequences can be validated experimentally. For complete details on the use and execution of this protocol, please refer to Strokach et al. (2020).
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Affiliation(s)
- Alexey Strokach
- Department of Computer Science, University of Toronto, Toronto, ON M5S 3E1, Canada
| | - David Becerra
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON M5S 3E1, Canada
| | - Carles Corbi-Verge
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON M5S 3E1, Canada
| | - Albert Perez-Riba
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON M5S 3E1, Canada
| | - Philip M. Kim
- Department of Computer Science, University of Toronto, Toronto, ON M5S 3E1, Canada
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON M5S 3E1, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, ON M5S 3E1, Canada
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16
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Strokach A, Lu TY, Kim PM. ELASPIC2 (EL2): Combining Contextualized Language Models and Graph Neural Networks to Predict Effects of Mutations. J Mol Biol 2021; 433:166810. [PMID: 33450251 DOI: 10.1016/j.jmb.2021.166810] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2020] [Revised: 12/19/2020] [Accepted: 01/03/2021] [Indexed: 12/21/2022]
Abstract
The ELASPIC web server allows users to evaluate the effect of mutations on protein folding and protein-protein interaction on a proteome-wide scale. It uses homology models of proteins and protein-protein interactions, which have been precalculated for several proteomes, and machine learning models, which integrate structural information with sequence conservation scores, in order to make its predictions. Since the original publication of the ELASPIC web server, several advances have motivated a revisiting of the problem of mutation effect prediction. First, progress in neural network architectures and self-supervised pre-trained has resulted in models which provide more informative embeddings of protein sequence and structure than those used by the original version of ELASPIC. Second, the amount of training data has increased several-fold, largely driven by advances in deep mutation scanning and other multiplexed assays of variant effect. Here, we describe two machine learning models which leverage the recent advances in order to achieve superior accuracy in predicting the effect of mutation on protein folding and protein-protein interaction. The models incorporate features generated using pre-trained transformer- and graph convolution-based neural networks, and are trained to optimize a ranking objective function, which permits the use of heterogeneous training data. The outputs from the new models have been incorporated into the ELASPIC web server, available at http://elaspic.kimlab.org.
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Affiliation(s)
- Alexey Strokach
- Department of Computer Science, University of Toronto, Toronto, ON M5S 3E1, Canada
| | - Tian Yu Lu
- Department of Computer Science, University of Toronto, Toronto, ON M5S 3E1, Canada
| | - Philip M Kim
- Department of Computer Science, University of Toronto, Toronto, ON M5S 3E1, Canada; Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON M5S 3E1, Canada; Department of Molecular Genetics, University of Toronto, Toronto, ON M5S 3E1, Canada.
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17
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Teyra J, Kelil A, Jain S, Helmy M, Jajodia R, Hooda Y, Gu J, D’Cruz AA, Nicholson SE, Min J, Sudol M, Kim PM, Bader GD, Sidhu SS. Large-scale survey and database of high affinity ligands for peptide recognition modules. Mol Syst Biol 2020; 16:e9310. [PMID: 33438817 PMCID: PMC7724964 DOI: 10.15252/msb.20199310] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2019] [Revised: 11/03/2020] [Accepted: 11/04/2020] [Indexed: 12/13/2022] Open
Abstract
Many proteins involved in signal transduction contain peptide recognition modules (PRMs) that recognize short linear motifs (SLiMs) within their interaction partners. Here, we used large-scale peptide-phage display methods to derive optimal ligands for 163 unique PRMs representing 79 distinct structural families. We combined the new data with previous data that we collected for the large SH3, PDZ, and WW domain families to assemble a database containing 7,984 unique peptide ligands for 500 PRMs representing 82 structural families. For 74 PRMs, we acquired enough new data to map the specificity profiles in detail and derived position weight matrices and binding specificity logos based on multiple peptide ligands. These analyses showed that optimal peptide ligands resembled peptides observed in existing structures of PRM-ligand complexes, indicating that a large majority of the phage-derived peptides are likely to target natural peptide-binding sites and could thus act as inhibitors of natural protein-protein interactions. The complete dataset has been assembled in an online database (http://www.prm-db.org) that will enable many structural, functional, and biological studies of PRMs and SLiMs.
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Affiliation(s)
- Joan Teyra
- The Donnelly CentreUniversity of TorontoTorontoONCanada
| | | | - Shobhit Jain
- The Donnelly CentreUniversity of TorontoTorontoONCanada
- Department of Computer ScienceUniversity of TorontoTorontoONCanada
| | - Mohamed Helmy
- The Donnelly CentreUniversity of TorontoTorontoONCanada
- Present address:
Singapore Institute of Food and Biotechnology Innovation (SIFBI)Agency for ScienceTechnology and Research (A*STAR)Singapore CitySingapore
| | - Raghav Jajodia
- Indian Institute of Engineering Science and TechnologyShibpurIndia
| | - Yogesh Hooda
- The Donnelly CentreUniversity of TorontoTorontoONCanada
- Present address:
MRC Laboratory of Molecular BiologyCambridgeUK
| | - Jun Gu
- The Donnelly CentreUniversity of TorontoTorontoONCanada
| | - Akshay A D’Cruz
- The Walter and Eliza Hall Institute of Medical ResearchParkvilleVic.Australia
| | - Sandra E Nicholson
- The Walter and Eliza Hall Institute of Medical ResearchParkvilleVic.Australia
| | - Jinrong Min
- Structural Genomics ConsortiumUniversity of TorontoTorontoONCanada
- Department of PhysiologyUniversity of TorontoTorontoONCanada
| | - Marius Sudol
- Department of MedicineIcahn School of Medicine at Mount SinaiNew YorkNYUSA
| | - Philip M Kim
- The Donnelly CentreUniversity of TorontoTorontoONCanada
- Department of Computer ScienceUniversity of TorontoTorontoONCanada
- Department of Molecular GeneticsUniversity of TorontoTorontoONCanada
| | - Gary D Bader
- The Donnelly CentreUniversity of TorontoTorontoONCanada
- Department of Computer ScienceUniversity of TorontoTorontoONCanada
- Department of Molecular GeneticsUniversity of TorontoTorontoONCanada
| | - Sachdev S Sidhu
- The Donnelly CentreUniversity of TorontoTorontoONCanada
- Department of Molecular GeneticsUniversity of TorontoTorontoONCanada
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18
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Strokach A, Becerra D, Corbi-Verge C, Perez-Riba A, Kim PM. Fast and Flexible Protein Design Using Deep Graph Neural Networks. Cell Syst 2020; 11:402-411.e4. [DOI: 10.1016/j.cels.2020.08.016] [Citation(s) in RCA: 56] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2020] [Revised: 06/27/2020] [Accepted: 08/26/2020] [Indexed: 11/15/2022]
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19
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Mueller AL, Corbi-Verge C, Giganti DO, Ichikawa DM, Spencer JM, MacRae M, Garton M, Kim PM, Noyes MB. The geometric influence on the Cys2His2 zinc finger domain and functional plasticity. Nucleic Acids Res 2020; 48:6382-6402. [PMID: 32383734 PMCID: PMC7293014 DOI: 10.1093/nar/gkaa291] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2020] [Revised: 04/07/2020] [Accepted: 04/20/2020] [Indexed: 11/25/2022] Open
Abstract
The Cys2His2 zinc finger is the most common DNA-binding domain expanding in metazoans since the fungi human split. A proposed catalyst for this expansion is an arms race to silence transposable elements yet it remains poorly understood how this domain is able to evolve the required specificities. Likewise, models of its DNA binding specificity remain error prone due to a lack of understanding of how adjacent fingers influence each other's binding specificity. Here, we use a synthetic approach to exhaustively investigate binding geometry, one of the dominant influences on adjacent finger function. By screening over 28 billion protein–DNA interactions in various geometric contexts we find the plasticity of the most common natural geometry enables more functional amino acid combinations across all targets. Further, residues that define this geometry are enriched in genomes where zinc fingers are prevalent and specificity transitions would be limited in alternative geometries. Finally, these results demonstrate an exhaustive synthetic screen can produce an accurate model of domain function while providing mechanistic insight that may have assisted in the domains expansion.
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Affiliation(s)
- April L Mueller
- Institute for Systems Genetics, NYU Langone Health, New York, NY 10016, USA.,Department of Biochemistry and Molecular Pharmacology, NYU Langone Health, New York, NY 10016, USA
| | - Carles Corbi-Verge
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario M5S 3E1, Canada
| | - David O Giganti
- Institute for Systems Genetics, NYU Langone Health, New York, NY 10016, USA.,Department of Biochemistry and Molecular Pharmacology, NYU Langone Health, New York, NY 10016, USA
| | - David M Ichikawa
- Institute for Systems Genetics, NYU Langone Health, New York, NY 10016, USA.,Department of Biochemistry and Molecular Pharmacology, NYU Langone Health, New York, NY 10016, USA
| | - Jeffrey M Spencer
- Institute for Systems Genetics, NYU Langone Health, New York, NY 10016, USA.,Department of Biochemistry and Molecular Pharmacology, NYU Langone Health, New York, NY 10016, USA
| | - Mark MacRae
- Institute for Systems Genetics, NYU Langone Health, New York, NY 10016, USA.,Department of Biochemistry and Molecular Pharmacology, NYU Langone Health, New York, NY 10016, USA
| | - Michael Garton
- Institute of Biomaterials and Biomedical Engineering, University of Toronto, Toronto, Ontario M5S 3G9, Canada
| | - Philip M Kim
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario M5S 3E1, Canada.,Department of Molecular Genetics, University of Toronto, Toronto, Ontario M5S3E1, Canada.,Department of Computer Science, University of Toronto, Toronto, Ontario M5S3E1, Canada
| | - Marcus B Noyes
- Institute for Systems Genetics, NYU Langone Health, New York, NY 10016, USA.,Department of Biochemistry and Molecular Pharmacology, NYU Langone Health, New York, NY 10016, USA
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20
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Muscedere J, Afilalo J, Araujo de Carvalho I, Cesari M, Clegg A, Eriksen HE, Evans KR, Heckman G, Hirdes JP, Kim PM, Laffon B, Lynn J, Martin F, Prorok JC, Rockwood K, Rodrigues Mañas L, Rolfson D, Shaw G, Shea B, Sinha S, Theou O, Tugwell P, Valdiglesias V, Vellas B, Veronese N, Wallace LMK, Williamson PR. Moving Towards Common Data Elements and Core Outcome Measures in Frailty Research. J Frailty Aging 2020; 9:14-22. [PMID: 32150209 DOI: 10.14283/jfa.2019.43] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
With aging populations around the world, frailty is becoming more prevalent increasing the need for health systems and social systems to deliver optimal evidence based care. However, in spite of the growing number of frailty publications, high-quality evidence for decision making is often lacking. Inadequate descriptions of the populations enrolled including frailty severity and frailty conceptualization, lack of use of validated frailty assessment tools, utilization of different frailty instruments between studies, and variation in reported outcomes impairs the ability to interpret, generalize and implement the research findings. The utilization of common data elements (CDEs) and core outcome measures (COMs) in clinical trials is increasingly being adopted to address such concerns. To catalyze the development and use of CDEs and COMs for future frailty studies, the Canadian Frailty Network (www.cfn-nce.ca; CFN), a not-for-profit pan-Canadian nationally-funded research network, convened an international group of experts to examine the issue and plan the path forward. The meeting was structured to allow for an examination of current frailty evidence, ability to learn from other COMs and CDEs initiatives, discussions about specific considerations for frailty COMs and CDEs and finally the identification of the necessary steps for a COMs and CDEs consensus initiative going forward. It was agreed at the onset of the meeting that a statement based on the meeting would be published and herein we report the statement.
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Affiliation(s)
- J Muscedere
- John Muscedere, Queen's University and Kingston General Hospital, Canada, E-Mail:
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21
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Valiente PA, Becerra D, Kim PM. A Method to Calculate the Relative Binding Free Energy Differences of α-Helical Stapled Peptides. J Org Chem 2020; 85:1644-1651. [DOI: 10.1021/acs.joc.9b03067] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Affiliation(s)
- Pedro A Valiente
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario M5S 3E2, Canada
| | - David Becerra
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario M5S 3E2, Canada
| | - Philip M Kim
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario M5S 3E2, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, Ontario M5S 3E2, Canada
- Department of Computer Science, University of Toronto, Toronto, Ontario M5S 3E2, Canada
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22
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Savojardo C, Petrosino M, Babbi G, Bovo S, Corbi-Verge C, Casadio R, Fariselli P, Folkman L, Garg A, Karimi M, Katsonis P, Kim PM, Lichtarge O, Martelli PL, Pasquo A, Pal D, Shen Y, Strokach AV, Turina P, Zhou Y, Andreoletti G, Brenner S, Chiaraluce R, Consalvi V, Capriotti E. Evaluating the predictions of the protein stability change upon single amino acid substitutions for the FXN CAGI5 challenge. Hum Mutat 2019; 40:1392-1399. [PMID: 31209948 PMCID: PMC6744327 DOI: 10.1002/humu.23843] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2019] [Revised: 06/02/2019] [Accepted: 06/09/2019] [Indexed: 12/31/2022]
Abstract
Frataxin (FXN) is a highly conserved protein found in prokaryotes and eukaryotes that is required for efficient regulation of cellular iron homeostasis. Experimental evidence associates amino acid substitutions of the FXN to Friedreich Ataxia, a neurodegenerative disorder. Recently, new thermodynamic experiments have been performed to study the impact of somatic variations identified in cancer tissues on protein stability. The Critical Assessment of Genome Interpretation (CAGI) data provider at the University of Rome measured the unfolding free energy of a set of variants (FXN challenge data set) with far-UV circular dichroism and intrinsic fluorescence spectra. These values have been used to calculate the change in unfolding free energy between the variant and wild-type proteins at zero concentration of denaturant ( Δ Δ G H 2 O ) . The FXN challenge data set, composed of eight amino acid substitutions, was used to evaluate the performance of the current computational methods for predicting the Δ Δ G H 2 O value associated with the variants and to classify them as destabilizing and not destabilizing. For the fifth edition of CAGI, six independent research groups from Asia, Australia, Europe, and North America submitted 12 sets of predictions from different approaches. In this paper, we report the results of our assessment and discuss the limitations of the tested algorithms.
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Affiliation(s)
- Castrense Savojardo
- Biocomputing Group, Department of Pharmacy and Biotechnology, University of Bologna, Italy
| | - Maria Petrosino
- Department of Biochemical Sciences “A. Rossi Fanelli”, Sapienza University of Roma, Roma, Italy
| | - Giulia Babbi
- Biocomputing Group, Department of Pharmacy and Biotechnology, University of Bologna, Italy
| | - Samuele Bovo
- Biocomputing Group, Department of Pharmacy and Biotechnology, University of Bologna, Italy
| | - Carles Corbi-Verge
- Donnelly Center for Cellular and Biomolecular Research, University of Toronto, 160 College St, Toronto, ON M5S 3E1, Canada
| | - Rita Casadio
- Biocomputing Group, Department of Pharmacy and Biotechnology, University of Bologna, Italy
- Institute of Biomembranes, Bioenergetics and Molecular Biotechnologies (IBIOM), Italian National Research Council (CNR), Bari, Italy
| | - Piero Fariselli
- Department of Medical Sciences University of Torino, 10126 Torino, Italy
| | - Lukas Folkman
- School of Information and Communication Technology, Griffith University, Parklands Dr, Southport, QLD 4222, Australia
| | - Aditi Garg
- Department of Computational and Data Sciences. Indian Institute of Science, Bengaluru 560 012, India
| | - Mostafa Karimi
- Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX 77840, USA
| | - Panagiotis Katsonis
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas 77030, USA
| | - Philip M. Kim
- Donnelly Center for Cellular and Biomolecular Research, University of Toronto, 160 College St, Toronto, ON M5S 3E1, Canada
- Department of Molecular Genetics, University of Toronto, 1 King’s College Cir, Toronto, ON M5S 1A8, Canada
- Department of Computer Science, University of Toronto, 214 College St, Toronto, ON M5T 3A1, Canada
| | - Olivier Lichtarge
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas 77030, USA
- Department of Biochemistry & Molecular Biology, Baylor College of Medicine, Houston, Texas 77030, USA
- Department of Pharmacology, Baylor College of Medicine, Houston, Texas 77030, USA
- Computational and Integrative Biomedical Research Center, Baylor College of Medicine, Houston, Texas 77030, USA
| | - Pier Luigi Martelli
- Biocomputing Group, Department of Pharmacy and Biotechnology, University of Bologna, Italy
| | - Alessandra Pasquo
- ENEA CR Frascati, Diagnostics and Metrology Laboratory,FSN-TECFIS-DIM, Frascati, Italy
| | - Debnath Pal
- Department of Computational and Data Sciences. Indian Institute of Science, Bengaluru 560 012, India
| | - Yang Shen
- Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX 77840, USA
| | - Alexey V. Strokach
- Department of Computer Science, University of Toronto, 214 College St, Toronto, ON M5T 3A1, Canada
| | - Paola Turina
- Department of Pharmacy and Biotechnology, University of Bologna, 40126 Bologna, Italy
| | - Yaoqi Zhou
- School of Information and Communication Technology, Griffith University, Parklands Dr, Southport, QLD 4222, Australia
- Institute for Glycomics, Griffith University, Parklands Dr, Southport QLD 4222, Australia
| | - Gaia Andreoletti
- Department of Plant and Microbial Biology, University of California, Berkeley, CA 94720, USA
| | - Steven Brenner
- Department of Plant and Microbial Biology, University of California, Berkeley, CA 94720, USA
| | - Roberta Chiaraluce
- Department of Biochemical Sciences “A. Rossi Fanelli”, Sapienza University of Roma, Roma, Italy
| | - Valerio Consalvi
- Department of Biochemical Sciences “A. Rossi Fanelli”, Sapienza University of Roma, Roma, Italy
| | - Emidio Capriotti
- Department of Pharmacy and Biotechnology, University of Bologna, 40126 Bologna, Italy
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23
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Strokach A, Corbi-Verge C, Kim PM. Predicting changes in protein stability caused by mutation using sequence-and structure-based methods in a CAGI5 blind challenge. Hum Mutat 2019; 40:1414-1423. [PMID: 31243847 PMCID: PMC6744338 DOI: 10.1002/humu.23852] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2019] [Revised: 05/16/2019] [Accepted: 06/24/2019] [Indexed: 12/26/2022]
Abstract
Predicting the impact of mutations on proteins remains an important problem. As part of the CAGI5 frataxin challenge, we evaluate the accuracy with which Provean, FoldX, and ELASPIC can predict changes in the Gibbs free energy of a protein using a limited data set of eight mutations. We find that different methods have distinct strengths and limitations, with no method being strictly superior to other methods on all metrics. ELASPIC achieves the highest accuracy while also providing a web interface which simplifies the evaluation and analysis of mutations. FoldX is slightly less accurate than ELASPIC but is easier to run locally, as it does not depend on external tools or datasets. Provean achieves reasonable results while being computational less expensive than the other methods and not requiring a structure of the protein. In addition to methods submitted to the CAGI5 community experiment, and with the aim to inform about other methods with high accuracy, we also evaluate predictions made by Rosetta's ddg_monomer protocol, Rosetta's cartesian_ddg protocol, and thermodynamic integration calculations using Amber package. ELASPIC still achieves the highest accuracy, while Rosetta's catesian_ddg protocol appears to perform best in capturing the overall trend in the data.
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Affiliation(s)
- Alexey Strokach
- Department of Computer Science, University of Toronto, Toronto, Ontario, Canada
| | - Carles Corbi-Verge
- Donnelly Centre for Cellular and Biomolecular Research, Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
| | - Philip M Kim
- Department of Computer Science, University of Toronto, Toronto, Ontario, Canada
- Donnelly Centre for Cellular and Biomolecular Research, Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada
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24
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Ichikawa DM, Corbi-Verge C, Shen MJ, Snider J, Wong V, Stagljar I, Kim PM, Noyes MB. A Multireporter Bacterial 2-Hybrid Assay for the High-Throughput and Dynamic Assay of PDZ Domain-Peptide Interactions. ACS Synth Biol 2019; 8:918-928. [PMID: 30969105 DOI: 10.1021/acssynbio.8b00499] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
The accurate determination of protein-protein interactions has been an important focus of molecular biology toward which much progress has been made due to the continuous development of existing and new technologies. However, current methods can have limitations, including scale and restriction to high affinity interactions, limiting our understanding of a large subset of these interactions. Here, we describe a modified bacterial-hybrid assay that employs combined selectable and scalable reporters that enable the sensitive screening of large peptide libraries followed by the sorting of positive interactions by the level of reporter output. We have applied this tool to characterize a set of human and E. coli PDZ domains. Our results are consistent with prior characterization of these proteins, and the improved sensitivity increases our ability to predict known and novel in vivo binding partners. This approach allows for the recovery of a wide range of affinities with a high throughput method that does not sacrifice the scale of the screen.
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Affiliation(s)
- David M. Ichikawa
- Department of Biochemistry Molecular Pharmacology and Institute for Systems Genetics, NYU Langone Health, New York, New York 10016, United States
| | - Carles Corbi-Verge
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario M5S 3E1, Canada
| | - Michael J. Shen
- Department of Biochemistry Molecular Pharmacology and Institute for Systems Genetics, NYU Langone Health, New York, New York 10016, United States
| | - Jamie Snider
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario M5S 3E1, Canada
| | - Victoria Wong
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario M5S 3E1, Canada
| | - Igor Stagljar
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario M5S 3E1, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, Ontario M5S 3E1, Canada
- Department of Biochemistry, University of Toronto, Toronto, Ontario M5S 3E1, Canada
| | - Philip M. Kim
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario M5S 3E1, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, Ontario M5S 3E1, Canada
- Department of Computer Science, University of Toronto, Toronto, Ontario M5S 3E1, Canada
| | - Marcus B. Noyes
- Department of Biochemistry Molecular Pharmacology and Institute for Systems Genetics, NYU Langone Health, New York, New York 10016, United States
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25
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Garton M, Corbi-Verge C, Hu Y, Nim S, Tarasova N, Sherborne B, Kim PM. Rapid and accurate structure-based therapeutic peptide design using GPU accelerated thermodynamic integration. Proteins 2019; 87:236-244. [PMID: 30520126 DOI: 10.1002/prot.25644] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2018] [Revised: 10/30/2018] [Accepted: 11/29/2018] [Indexed: 11/07/2022]
Abstract
Peptide-based therapeutics are an alternative to small molecule drugs as they offer superior specificity, lower toxicity, and easy synthesis. Here we present an approach that leverages the dramatic performance increase afforded by the recent arrival of GPU accelerated thermodynamic integration (TI). GPU TI facilitates very fast, highly accurate binding affinity optimization of peptides against therapeutic targets. We benchmarked TI predictions using published peptide binding optimization studies. Prediction of mutations involving charged side-chains was found to be less accurate than for non-charged, and use of a more complex 3-step TI protocol was found to boost accuracy in these cases. Using the 3-step protocol for non-charged side-chains either had no effect or was detrimental. We use the benchmarked pipeline to optimize a peptide binding to our recently discovered cancer target: EME1. TI calculations predict beneficial mutations using both canonical and non-canonical amino acids. We validate these predictions using fluorescence polarization and confirm that binding affinity is increased. We further demonstrate that this increase translates to a significant reduction in pancreatic cancer cell viability.
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Affiliation(s)
- Michael Garton
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Canada
| | - Carles Corbi-Verge
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Canada
| | - Yuan Hu
- Merck & Co., Inc., Kenilworth, New Jersey.,Alkermes Inc., Waltham, Massachusetts
| | - Satra Nim
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Canada
| | - Nadya Tarasova
- Cancer and Inflammation Program, Center for Cancer Research, National Cancer Institute-Frederick, Frederick, Maryland
| | | | - Philip M Kim
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Canada.,Department of Molecular Genetics, University of Toronto, Toronto, Canada.,Department of Computer Science, University of Toronto, Toronto, Canada
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26
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Abstract
The function of a protein is largely determined by its three-dimensional structure and its interactions with other proteins. Changes to a protein's amino acid sequence can alter its function by perturbing the energy landscapes of protein folding and binding. Many tools have been developed to predict the energetic effect of amino acid changes, utilizing features describing the sequence of a protein, the structure of a protein, or both. Those tools can have many applications, such as distinguishing between deleterious and benign mutations and designing proteins and peptides with attractive properties. In this chapter, we describe how to use one of such tools, ELASPIC, to predict the effect of mutations on the stability of proteins and the affinity between proteins, in the context of a human protein-protein interaction network. ELASPIC uses a wide range of sequential and structural features to predict the change in the Gibbs free energy for protein folding and protein-protein interactions. It can be used both through a web server and as a stand-alone application. Since ELASPIC was trained using homology models and not crystal structures, it can be applied to a much broader range of proteins than traditional methods. It can leverage precalculated sequence alignments, homology models, and other features, in order to drastically lower the amount of time required to evaluate individual mutations and make tractable the analysis of millions of mutations affecting the majority of proteins in a genome.
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Affiliation(s)
- Alexey Strokach
- Department of Computer Science, University of Toronto, Toronto, ON, Canada
- Terrence Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON, Canada
| | - Carles Corbi-Verge
- Terrence Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON, Canada
| | - Joan Teyra
- Terrence Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON, Canada
| | - Philip M Kim
- Department of Computer Science, University of Toronto, Toronto, ON, Canada.
- Terrence Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON, Canada.
- Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada.
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27
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Garton M, Nim S, Stone TA, Wang KE, Deber CM, Kim PM. Method to generate highly stable D-amino acid analogs of bioactive helical peptides using a mirror image of the entire PDB. Proc Natl Acad Sci U S A 2018; 115:1505-1510. [PMID: 29378946 PMCID: PMC5816147 DOI: 10.1073/pnas.1711837115] [Citation(s) in RCA: 78] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023] Open
Abstract
Biologics are a rapidly growing class of therapeutics with many advantages over traditional small molecule drugs. A major obstacle to their development is that proteins and peptides are easily destroyed by proteases and, thus, typically have prohibitively short half-lives in human gut, plasma, and cells. One of the most effective ways to prevent degradation is to engineer analogs from dextrorotary (D)-amino acids, with up to 105-fold improvements in potency reported. We here propose a general peptide-engineering platform that overcomes limitations of previous methods. By creating a mirror image of every structure in the Protein Data Bank (PDB), we generate a database of ∼2.8 million D-peptides. To obtain a D-analog of a given peptide, we search the (D)-PDB for similar configurations of its critical-"hotspot"-residues. As a proof of concept, we apply our method to two peptides that are Food and Drug Administration approved as therapeutics for diabetes and osteoporosis, respectively. We obtain D-analogs that activate the GLP1 and PTH1 receptors with the same efficacy as their natural counterparts and show greatly increased half-life.
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Affiliation(s)
- Michael Garton
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto M5S 3E1, Canada
| | - Satra Nim
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto M5S 3E1, Canada
| | - Tracy A Stone
- Department of Biochemistry, University of Toronto, Toronto M5S 1A8, Canada
- Division of Molecular Medicine, Research Institute, Hospital for Sick Children, Toronto M5G 0A4, Canada
| | - Kyle Ethan Wang
- Department of Molecular Genetics, University of Toronto, Toronto M5S 1A8, Canada
| | - Charles M Deber
- Department of Biochemistry, University of Toronto, Toronto M5S 1A8, Canada
- Division of Molecular Medicine, Research Institute, Hospital for Sick Children, Toronto M5G 0A4, Canada
| | - Philip M Kim
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto M5S 3E1, Canada;
- Department of Molecular Genetics, University of Toronto, Toronto M5S 1A8, Canada
- Department of Computer Science, University of Toronto, Toronto M5S 2E4, Canada
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28
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Park SJ, Song S, Yang GS, Kim PM, Yoon S, Kim JH, Sung J. The Chemical Fluctuation Theorem governing gene expression. Nat Commun 2018; 9:297. [PMID: 29352116 PMCID: PMC5775451 DOI: 10.1038/s41467-017-02737-0] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2017] [Accepted: 12/20/2017] [Indexed: 11/20/2022] Open
Abstract
Gene expression is a complex stochastic process composed of numerous enzymatic reactions with rates coupled to hidden cell-state variables. Despite advances in single-cell technologies, the lack of a theory accurately describing the gene expression process has restricted a robust, quantitative understanding of gene expression variability among cells. Here we present the Chemical Fluctuation Theorem (CFT), providing an accurate relationship between the environment-coupled chemical dynamics of gene expression and gene expression variability. Combined with a general, accurate model of environment-coupled transcription processes, the CFT provides a unified explanation of mRNA variability for various experimental systems. From this analysis, we construct a quantitative model of transcription dynamics enabling analytic predictions for the dependence of mRNA noise on the mRNA lifetime distribution, confirmed against stochastic simulation. This work suggests promising new directions for quantitative investigation into cellular control over biological functions by making complex dynamics of intracellular reactions accessible to rigorous mathematical deductions. A unified framework to understand gene expression noise is still lacking. Here the authors derive a universal theorem relating the biological noise with dynamics of birth and death processes and present a model of transcription dynamics, allowing analytical prediction of the dependence of mRNA noise on mRNA lifetime variability.
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Affiliation(s)
- Seong Jun Park
- Creative Research Initiative Center for Chemical Dynamics in Living Cells, Chung-Ang University, Seoul, 06974, Korea.,Department of Chemistry, Chung-Ang University, Seoul, 06974, Korea.,National Institute of Innovative Functional Imaging, Chung-Ang University, Seoul, 06974, Korea
| | - Sanggeun Song
- Creative Research Initiative Center for Chemical Dynamics in Living Cells, Chung-Ang University, Seoul, 06974, Korea.,Department of Chemistry, Chung-Ang University, Seoul, 06974, Korea.,National Institute of Innovative Functional Imaging, Chung-Ang University, Seoul, 06974, Korea
| | - Gil-Suk Yang
- Creative Research Initiative Center for Chemical Dynamics in Living Cells, Chung-Ang University, Seoul, 06974, Korea
| | - Philip M Kim
- Terrence Donnelly Center for Cellular and Biomolecular Research, Department of Molecular Genetics and Department of Computer Science, University of Toronto, Toronto, M5S 3E1, ON, Canada
| | - Sangwoon Yoon
- Department of Chemistry, Chung-Ang University, Seoul, 06974, Korea.
| | - Ji-Hyun Kim
- Creative Research Initiative Center for Chemical Dynamics in Living Cells, Chung-Ang University, Seoul, 06974, Korea.
| | - Jaeyoung Sung
- Creative Research Initiative Center for Chemical Dynamics in Living Cells, Chung-Ang University, Seoul, 06974, Korea. .,Department of Chemistry, Chung-Ang University, Seoul, 06974, Korea. .,National Institute of Innovative Functional Imaging, Chung-Ang University, Seoul, 06974, Korea.
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29
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Seo MH, Nim S, Jeon J, Kim PM. Large-Scale Interaction Profiling of Protein Domains Through Proteomic Peptide-Phage Display Using Custom Peptidomes. Methods Mol Biol 2018; 1518:213-226. [PMID: 27873209 DOI: 10.1007/978-1-4939-6584-7_14] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Abstract
Protein-protein interactions are essential to cellular functions and signaling pathways. We recently combined bioinformatics and custom oligonucleotide arrays to construct custom-made peptide-phage libraries for screening peptide-protein interactions, an approach we call proteomic peptide-phage display (ProP-PD). In this chapter, we describe protocols for phage display for the identification of natural peptide binders for a given protein. We finally describe deep sequencing for the analysis of the proteomic peptide-phage display.
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Affiliation(s)
- Moon-Hyeong Seo
- Terrence Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON, Canada, M5S 3E1
| | - Satra Nim
- Terrence Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON, Canada, M5S 3E1
| | - Jouhyun Jeon
- Terrence Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON, Canada, M5S 3E1
| | - Philip M Kim
- Terrence Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON, Canada, M5S 3E1. .,Department of Molecular Genetics, University of Toronto, 160 College Street, Room 230, Toronto, ON, Canada, M5S 3E1. .,Department of Computer Science, University of Toronto, 160 College Street, Room 230, Toronto, ON, Canada, M5S 3E1.
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30
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Najafabadi HS, Garton M, Weirauch MT, Mnaimneh S, Yang A, Kim PM, Hughes TR. Non-base-contacting residues enable kaleidoscopic evolution of metazoan C2H2 zinc finger DNA binding. Genome Biol 2017; 18:167. [PMID: 28877740 PMCID: PMC5588721 DOI: 10.1186/s13059-017-1287-y] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2017] [Accepted: 07/14/2017] [Indexed: 02/07/2023] Open
Abstract
Background The C2H2 zinc finger (C2H2-ZF) is the most numerous protein domain in many metazoans, but is not as frequent or diverse in other eukaryotes. The biochemical and evolutionary mechanisms that underlie the diversity of this DNA-binding domain exclusively in metazoans are, however, mostly unknown. Results Here, we show that the C2H2-ZF expansion in metazoans is facilitated by contribution of non-base-contacting residues to DNA binding energy, allowing base-contacting specificity residues to mutate without catastrophic loss of DNA binding. In contrast, C2H2-ZF DNA binding in fungi, plants, and other lineages is constrained by reliance on base-contacting residues for DNA-binding functionality. Reconstructions indicate that virtually every DNA triplet was recognized by at least one C2H2-ZF domain in the common progenitor of placental mammals, but that extant C2H2-ZF domains typically bind different sequences from these ancestral domains, with changes facilitated by non-base-contacting residues. Conclusions Our results suggest that the evolution of C2H2-ZFs in metazoans was expedited by the interaction of non-base-contacting residues with the DNA backbone. We term this phenomenon “kaleidoscopic evolution,” to reflect the diversity of both binding motifs and binding motif transitions and the facilitation of their diversification. Electronic supplementary material The online version of this article (doi:10.1186/s13059-017-1287-y) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Hamed S Najafabadi
- Department of Human Genetics, McGill University, Montreal, QC, Canada. .,McGill University and Genome Quebec Innovation Centre, Montreal, QC, Canada. .,Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON, Canada.
| | - Michael Garton
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON, Canada
| | - Matthew T Weirauch
- Center for Autoimmune Genomics and Etiology, and Divisions of Biomedical Informatics and Developmental Biology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA.,Canadian Institute for Advanced Research, Toronto, ON, Canada
| | - Sanie Mnaimneh
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON, Canada
| | - Ally Yang
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON, Canada
| | - Philip M Kim
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON, Canada.,Department of Computer Science, University of Toronto, Toronto, ON, Canada.,Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada
| | - Timothy R Hughes
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON, Canada. .,Canadian Institute for Advanced Research, Toronto, ON, Canada. .,Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada.
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31
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Abstract
Protein design remains an important problem in computational structural biology. Current computational protein design methods largely use physics-based methods, which make use of information from a single protein structure. This is despite the fact that multiple structures of many protein folds are now readily available in the PDB. While ensemble protein design methods can use multiple protein structures, they treat each structure independently. Here, we introduce a flexible backbone strategy, FlexiBaL-GP, which learns global protein backbone movements directly from multiple protein structures. FlexiBaL-GP uses the machine learning method of Gaussian Process Latent Variable Models to learn a lower dimensional representation of the protein coordinates that best represent backbone movements. These learned backbone movements are used to explore alternative protein backbones, while engineering a protein within a parallel tempered MCMC framework. Using the human ubiquitin-USP21 complex as a model we demonstrate that our design strategy outperforms current strategies for the interface design task of identifying tight binding ubiquitin variants for USP21.
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Affiliation(s)
- Mark G. F. Sun
- Department of Computer Science, University of Toronto, Toronto, Canada
- Terrence Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Canada
| | - Philip M. Kim
- Department of Computer Science, University of Toronto, Toronto, Canada
- Terrence Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, Canada
- Banting and Best Department of Medical Research, University of Toronto, Toronto, Canada
- * E-mail:
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32
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Davey NE, Seo MH, Yadav VK, Jeon J, Nim S, Krystkowiak I, Blikstad C, Dong D, Markova N, Kim PM, Ivarsson Y. Discovery of short linear motif-mediated interactions through phage display of intrinsically disordered regions of the human proteome. FEBS J 2017; 284:485-498. [PMID: 28002650 DOI: 10.1111/febs.13995] [Citation(s) in RCA: 65] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2016] [Revised: 12/04/2016] [Accepted: 12/19/2016] [Indexed: 12/29/2022]
Abstract
The intrinsically disordered regions of eukaryotic proteomes are enriched in short linear motifs (SLiMs), which are of crucial relevance for cellular signaling and protein regulation; many mediate interactions by providing binding sites for peptide-binding domains. The vast majority of SLiMs remain to be discovered highlighting the need for experimental methods for their large-scale identification. We present a novel proteomic peptide phage display (ProP-PD) library that displays peptides representing the disordered regions of the human proteome, allowing direct large-scale interrogation of most potential binding SLiMs in the proteome. The performance of the ProP-PD library was validated through selections against SLiM-binding bait domains with distinct folds and binding preferences. The vast majority of identified binding peptides contained sequences that matched the known SLiM-binding specificities of the bait proteins. For SHANK1 PDZ, we establish a novel consensus TxF motif for its non-C-terminal ligands. The binding peptides mostly represented novel target proteins, however, several previously validated protein-protein interactions (PPIs) were also discovered. We determined the affinities between the VHS domain of GGA1 and three identified ligands to 40-130 μm through isothermal titration calorimetry, and confirmed interactions through coimmunoprecipitation using full-length proteins. Taken together, we outline a general pipeline for the design and construction of ProP-PD libraries and the analysis of ProP-PD-derived, SLiM-based PPIs. We demonstrated the methods potential to identify low affinity motif-mediated interactions for modular domains with distinct binding preferences. The approach is a highly useful complement to the current toolbox of methods for PPI discovery.
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Affiliation(s)
- Norman E Davey
- Conway Institute of Biomolecular and Biomedical Sciences, University College Dublin, Ireland
| | - Moon-Hyeong Seo
- Terrence Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Canada
| | | | - Jouhyun Jeon
- Terrence Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Canada
| | - Satra Nim
- Terrence Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Canada
| | - Izabella Krystkowiak
- Conway Institute of Biomolecular and Biomedical Sciences, University College Dublin, Ireland
| | | | - Debbie Dong
- Terrence Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Canada
| | | | - Philip M Kim
- Terrence Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Canada.,Department of Molecular Genetics and Department of Computer Science, University of Toronto, Canada
| | - Ylva Ivarsson
- Department of Chemistry - BMC, Uppsala University, Sweden
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33
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Abstract
Protein-protein interactions are fundamental for virtually all functions of the cell. A large fraction of these interactions involve short peptide motifs, and there has been increased interest in targeting them using peptide-based therapeutics. Peptides benefit from being specific, relatively safe, and easy to produce. They are also easy to modify using chemical synthesis and molecular biology techniques. However, significant challenges remain regarding the use of peptides as therapeutic agents. Identification of peptide motifs is difficult, and peptides typically display low cell permeability and sensitivity to enzymatic degradation. In this review, we outline the principal high-throughput methodologies for motif discovery and describe current methods for overcoming pharmacokinetic and bioavailability limitations.
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Affiliation(s)
- Carles Corbi-Verge
- Terrence Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario M5S 3E1, Canada; , , ,
| | - Michael Garton
- Terrence Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario M5S 3E1, Canada; , , ,
| | - Satra Nim
- Terrence Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario M5S 3E1, Canada; , , ,
| | - Philip M Kim
- Terrence Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario M5S 3E1, Canada; , , , .,Department of Molecular Genetics, University of Toronto, Toronto, Ontario M5S 3E1, Canada.,Department of Computer Science, University of Toronto, Toronto, Ontario M5S 3E1, Canada
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34
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Sun MGF, Seo MH, Nim S, Corbi-Verge C, Kim PM. Protein engineering by highly parallel screening of computationally designed variants. Sci Adv 2016; 2:e1600692. [PMID: 27453948 PMCID: PMC4956399 DOI: 10.1126/sciadv.1600692] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/31/2016] [Accepted: 06/23/2016] [Indexed: 06/06/2023]
Abstract
Current combinatorial selection strategies for protein engineering have been successful at generating binders against a range of targets; however, the combinatorial nature of the libraries and their vast undersampling of sequence space inherently limit these methods due to the difficulty in finely controlling protein properties of the engineered region. Meanwhile, great advances in computational protein design that can address these issues have largely been underutilized. We describe an integrated approach that computationally designs thousands of individual protein binders for high-throughput synthesis and selection to engineer high-affinity binders. We show that a computationally designed library enriches for tight-binding variants by many orders of magnitude as compared to conventional randomization strategies. We thus demonstrate the feasibility of our approach in a proof-of-concept study and successfully obtain low-nanomolar binders using in vitro and in vivo selection systems.
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Affiliation(s)
- Mark G. F. Sun
- Department of Computer Science, University of Toronto, Toronto, Ontario M5S 3E1, Canada
- Terrence Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario M5S 3E1, Canada
| | - Moon-Hyeong Seo
- Terrence Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario M5S 3E1, Canada
| | - Satra Nim
- Terrence Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario M5S 3E1, Canada
| | - Carles Corbi-Verge
- Terrence Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario M5S 3E1, Canada
| | - Philip M. Kim
- Department of Computer Science, University of Toronto, Toronto, Ontario M5S 3E1, Canada
- Terrence Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario M5S 3E1, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, Ontario M5S 3E1, Canada
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35
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Jeon J, Arnold R, Singh F, Teyra J, Braun T, Kim PM. PAT: predictor for structured units and its application for the optimization of target molecules for the generation of synthetic antibodies. BMC Bioinformatics 2016; 17:150. [PMID: 27039071 PMCID: PMC4818438 DOI: 10.1186/s12859-016-1001-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2016] [Accepted: 03/23/2016] [Indexed: 11/22/2022] Open
Abstract
Background The identification of structured units in a protein sequence is an important first step for most biochemical studies. Importantly for this study, the identification of stable structured region is a crucial first step to generate novel synthetic antibodies. While many approaches to find domains or predict structured regions exist, important limitations remain, such as the optimization of domain boundaries and the lack of identification of non-domain structured units. Moreover, no integrated tool exists to find and optimize structural domains within protein sequences. Results Here, we describe a new tool, PAT (http://www.kimlab.org/software/pat) that can efficiently identify both domains (with optimized boundaries) and non-domain putative structured units. PAT automatically analyzes various structural properties, evaluates the folding stability, and reports possible structural domains in a given protein sequence. For reliability evaluation of PAT, we applied PAT to identify antibody target molecules based on the notion that soluble and well-defined protein secondary and tertiary structures are appropriate target molecules for synthetic antibodies. Conclusion PAT is an efficient and sensitive tool to identify structured units. A performance analysis shows that PAT can characterize structurally well-defined regions in a given sequence and outperforms other efforts to define reliable boundaries of domains. Specially, PAT successfully identifies experimentally confirmed target molecules for antibody generation. PAT also offers the pre-calculated results of 20,210 human proteins to accelerate common queries. PAT can therefore help to investigate large-scale structured domains and improve the success rate for synthetic antibody generation. Electronic supplementary material The online version of this article (doi:10.1186/s12859-016-1001-1) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Jouhyun Jeon
- Terrence Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, M5S 3E1, ON, Canada
| | - Roland Arnold
- Terrence Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, M5S 3E1, ON, Canada
| | - Fateh Singh
- Terrence Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, M5S 3E1, ON, Canada
| | - Joan Teyra
- Terrence Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, M5S 3E1, ON, Canada
| | - Tatjana Braun
- Terrence Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, M5S 3E1, ON, Canada
| | - Philip M Kim
- Terrence Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, M5S 3E1, ON, Canada. .,Department of Molecular Genetics, University of Toronto, Toronto, M5S 3E1, ON, Canada. .,Department of Computer Science, University of Toronto, Toronto, M5S 3E1, ON, Canada.
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36
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Na H, Laver JD, Jeon J, Singh F, Ancevicius K, Fan Y, Cao WX, Nie K, Yang Z, Luo H, Wang M, Rissland O, Westwood JT, Kim PM, Smibert CA, Lipshitz HD, Sidhu SS. A high-throughput pipeline for the production of synthetic antibodies for analysis of ribonucleoprotein complexes. RNA 2016; 22:636-655. [PMID: 26847261 PMCID: PMC4793217 DOI: 10.1261/rna.055186.115] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/06/2015] [Accepted: 12/12/2015] [Indexed: 06/05/2023]
Abstract
Post-transcriptional regulation of mRNAs plays an essential role in the control of gene expression. mRNAs are regulated in ribonucleoprotein (RNP) complexes by RNA-binding proteins (RBPs) along with associated protein and noncoding RNA (ncRNA) cofactors. A global understanding of post-transcriptional control in any cell type requires identification of the components of all of its RNP complexes. We have previously shown that these complexes can be purified by immunoprecipitation using anti-RBP synthetic antibodies produced by phage display. To develop the large number of synthetic antibodies required for a global analysis of RNP complex composition, we have established a pipeline that combines (i) a computationally aided strategy for design of antigens located outside of annotated domains, (ii) high-throughput antigen expression and purification in Escherichia coli, and (iii) high-throughput antibody selection and screening. Using this pipeline, we have produced 279 antibodies against 61 different protein components of Drosophila melanogaster RNPs. Together with those produced in our low-throughput efforts, we have a panel of 311 antibodies for 67 RNP complex proteins. Tests of a subset of our antibodies demonstrated that 89% immunoprecipitate their endogenous target from embryo lysate. This panel of antibodies will serve as a resource for global studies of RNP complexes in Drosophila. Furthermore, our high-throughput pipeline permits efficient production of synthetic antibodies against any large set of proteins.
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Affiliation(s)
- Hong Na
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario, Canada M5S 3E1
| | - John D Laver
- Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada M5S 1A8
| | - Jouhyun Jeon
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario, Canada M5S 3E1
| | - Fateh Singh
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario, Canada M5S 3E1
| | - Kristin Ancevicius
- Department of Biology, University of Toronto at Mississauga, Mississauga, Ontario, Canada L5L 1C6 Department of Cell and Systems Biology, University of Toronto at Mississauga, Mississauga, Ontario, Canada L5L 1C6
| | - Yujie Fan
- Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada M5S 1A8
| | - Wen Xi Cao
- Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada M5S 1A8
| | - Kun Nie
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario, Canada M5S 3E1 Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada M5S 1A8
| | - Zhenglin Yang
- Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada M5S 1A8
| | - Hua Luo
- Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada M5S 1A8
| | - Miranda Wang
- Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada M5S 1A8 Research Institute, Hospital for Sick Children, Toronto, Ontario, Canada M5G 0A4
| | - Olivia Rissland
- Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada M5S 1A8 Research Institute, Hospital for Sick Children, Toronto, Ontario, Canada M5G 0A4
| | - J Timothy Westwood
- Department of Biology, University of Toronto at Mississauga, Mississauga, Ontario, Canada L5L 1C6 Department of Cell and Systems Biology, University of Toronto at Mississauga, Mississauga, Ontario, Canada L5L 1C6
| | - Philip M Kim
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario, Canada M5S 3E1 Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada M5S 1A8 Department of Computer Science, University of Toronto, Toronto, Ontario, Canada M5S 1A8
| | - Craig A Smibert
- Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada M5S 1A8 Department of Biochemistry, University of Toronto, Toronto, Ontario, Canada M5S 1A8
| | - Howard D Lipshitz
- Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada M5S 1A8
| | - Sachdev S Sidhu
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario, Canada M5S 3E1 Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada M5S 1A8
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37
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Abstract
Protein-protein interactions (PPI) are involved in virtually every cellular process and thus represent an attractive target for therapeutic interventions. A significant number of protein interactions are frequently formed between globular domains and short linear peptide motifs (DMI). Targeting these DMIs has proven challenging and classical approaches to inhibiting such interactions with small molecules have had limited success. However, recent new approaches have led to the discovery of potent inhibitors, some of them, such as Obatoclax, ABT-199, AEG-40826 and SAH-p53-8 are likely to become approved drugs. These novel inhibitors belong to a wide range of different molecule classes, ranging from small molecules to peptidomimetics and biologicals. This article reviews the main reasons for limited success in targeting PPIs, discusses how successful approaches overcome these obstacles to discovery promising inhibitors for human protein double minute 2 (HDM2), B-cell lymphoma 2 (Bcl-2), X-linked inhibitor of apoptosis protein (XIAP), and provides a summary of the promising approaches currently in development that indicate the future potential of PPI inhibitors in drug discovery.
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Affiliation(s)
- Carles Corbi-Verge
- Terrence Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON, M5S 3E1, Canada.
| | - Philip M Kim
- Terrence Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON, M5S 3E1, Canada.
- Department of Molecular Genetics, University of Toronto, Toronto, ON, M5S 3E1, Canada.
- Department of Computer Science, University of Toronto, Toronto, ON, M5S 3E1, Canada.
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38
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Nim S, Jeon J, Corbi-Verge C, Seo MH, Ivarsson Y, Moffat J, Tarasova N, Kim PM. Pooled screening for antiproliferative inhibitors of protein-protein interactions. Nat Chem Biol 2016; 12:275-81. [PMID: 26900867 PMCID: PMC5756068 DOI: 10.1038/nchembio.2026] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2015] [Accepted: 01/04/2016] [Indexed: 11/30/2022]
Abstract
Protein-protein interactions (PPIs) are emerging as a promising new class of drug targets. Here, we present a novel high-throughput approach to screen inhibitors of PPIs in cells. We designed a library of 50,000 human peptide binding motifs and used a pooled lentiviral system to express them intracellularly and screen for their effects on cell proliferation. We thereby identified inhibitors that drastically reduced the viability of a pancreas cancer line (RWP1) while leaving a control line virtually unaffected. We identified their target interactions computationally, and validated a subset in experiments. We also discovered their potential mechanisms of action including apoptosis and cell cycle arrest. Finally, we confirmed that synthetic lipopeptide versions of our inhibitors have similarly specific and dosage dependent effects on cancer cell growth. Our screen reveals new drug targets and peptide drug leads and it provides a rich dataset covering phenotypes for inhibition of thousands of interactions.
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Affiliation(s)
- Satra Nim
- Terrence Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario, Canada
| | - Jouhyun Jeon
- Terrence Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario, Canada
| | - Carles Corbi-Verge
- Terrence Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario, Canada
| | - Moon-Hyeong Seo
- Terrence Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario, Canada
| | - Ylva Ivarsson
- Department of Chemistry, Biomedical Center (BMC), Uppsala University, Uppsala, Sweden
| | - Jason Moffat
- Terrence Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario, Canada.,Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada.,Canadian Institute for Advanced Research, Toronto, Ontario, Canada
| | - Nadya Tarasova
- Cancer and Inflammation Program, Center for Cancer Research, National Cancer Institute-Frederick, Frederick, Maryland, USA
| | - Philip M Kim
- Terrence Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario, Canada.,Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada.,Department of Computer Science, University of Toronto, Toronto, Ontario, Canada
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39
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Witvliet DK, Strokach A, Giraldo-Forero AF, Teyra J, Colak R, Kim PM. ELASPIC web-server: proteome-wide structure-based prediction of mutation effects on protein stability and binding affinity. Bioinformatics 2016; 32:1589-91. [DOI: 10.1093/bioinformatics/btw031] [Citation(s) in RCA: 41] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2015] [Accepted: 01/17/2016] [Indexed: 11/15/2022] Open
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40
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Colak R, Kim T, Kazan H, Oh Y, Cruz M, Valladares-Salgado A, Peralta J, Escobedo J, Parra EJ, Kim PM, Goldenberg A. JBASE: Joint Bayesian Analysis of Subphenotypes and Epistasis. Bioinformatics 2016; 32:203-10. [PMID: 26411870 PMCID: PMC4708100 DOI: 10.1093/bioinformatics/btv504] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2014] [Revised: 08/02/2015] [Accepted: 08/24/2015] [Indexed: 01/22/2023] Open
Abstract
MOTIVATION Rapid advances in genotyping and genome-wide association studies have enabled the discovery of many new genotype-phenotype associations at the resolution of individual markers. However, these associations explain only a small proportion of theoretically estimated heritability of most diseases. In this work, we propose an integrative mixture model called JBASE: joint Bayesian analysis of subphenotypes and epistasis. JBASE explores two major reasons of missing heritability: interactions between genetic variants, a phenomenon known as epistasis and phenotypic heterogeneity, addressed via subphenotyping. RESULTS Our extensive simulations in a wide range of scenarios repeatedly demonstrate that JBASE can identify true underlying subphenotypes, including their associated variants and their interactions, with high precision. In the presence of phenotypic heterogeneity, JBASE has higher Power and lower Type 1 Error than five state-of-the-art approaches. We applied our method to a sample of individuals from Mexico with Type 2 diabetes and discovered two novel epistatic modules, including two loci each, that define two subphenotypes characterized by differences in body mass index and waist-to-hip ratio. We successfully replicated these subphenotypes and epistatic modules in an independent dataset from Mexico genotyped with a different platform. AVAILABILITY AND IMPLEMENTATION JBASE is implemented in C++, supported on Linux and is available at http://www.cs.toronto.edu/∼goldenberg/JBASE/jbase.tar.gz. The genotype data underlying this study are available upon approval by the ethics review board of the Medical Centre Siglo XXI. Please contact Dr Miguel Cruz at mcruzl@yahoo.com for assistance with the application. CONTACT anna.goldenberg@utoronto.ca SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Recep Colak
- Department of Computer Science, University of Toronto, M5S 2E4, Toronto, ON, Canada, Donnelly Centre for Cellular & Biomolecular Research, University of Toronto, M5S 3E1, Toronto, ON, Canada
| | - TaeHyung Kim
- Department of Computer Science, University of Toronto, M5S 2E4, Toronto, ON, Canada, Department of Computer Engineering, Antalya International University, 07190, Antalya, Turkey
| | - Hilal Kazan
- Department of Computer Engineering, Antalya International University, 07190, Antalya, Turkey
| | - Yoomi Oh
- Donnelly Centre for Cellular & Biomolecular Research, University of Toronto, M5S 3E1, Toronto, ON, Canada, Department of Molecular Genetics, University of Toronto, M5S 1A8, Toronto, ON, Canada
| | - Miguel Cruz
- Unidad de Investigación Médica en Bioquímica, Hospital de Especialidades, IMSS, 06720, Mexico City, Mexico
| | - Adan Valladares-Salgado
- Unidad de Investigación Médica en Bioquímica, Hospital de Especialidades, IMSS, 06720, Mexico City, Mexico
| | - Jesus Peralta
- Unidad de Investigación Médica en Bioquímica, Hospital de Especialidades, IMSS, 06720, Mexico City, Mexico
| | - Jorge Escobedo
- Unidad de Investigación en Epidemiología Clínica, Instituto Mexicano del Seguro Social, Mexico City, Mexico
| | - Esteban J Parra
- Department of Anthropology, University of Toronto, L5L 1C6, Mississauga, ON, Canada
| | - Philip M Kim
- Donnelly Centre for Cellular & Biomolecular Research, University of Toronto, M5S 3E1, Toronto, ON, Canada, Department of Molecular Genetics, University of Toronto, M5S 1A8, Toronto, ON, Canada, Genetics and Genome Biology, Hospital for Sick Children, M5G 0A4, Toronto, ON, Canada and Banting and Best Department of Medical Research, University of Toronto, M5G 1L6, Toronto, ON, Canada
| | - Anna Goldenberg
- Department of Computer Science, University of Toronto, M5S 2E4, Toronto, ON, Canada, Genetics and Genome Biology, Hospital for Sick Children, M5G 0A4, Toronto, ON, Canada and
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Garrido-Urbani S, Garg P, Ghossoub R, Arnold R, Lembo F, Sundell GN, Kim PM, Lopez M, Zimmermann P, Sidhu SS, Ivarsson Y. Proteomic peptide phage display uncovers novel interactions of the PDZ1-2 supramodule of syntenin. FEBS Lett 2016; 590:3-12. [PMID: 26787460 PMCID: PMC4819696 DOI: 10.1002/1873-3468.12037] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2015] [Accepted: 11/29/2015] [Indexed: 11/10/2022]
Abstract
Syntenin has crucial roles in cell adhesion, cell migration and synaptic transmission. Its closely linked postsynaptic density-95, discs large 1, zonula occludens-1 (PDZ) domains typically interact with C-terminal ligands. We profile syntenin PDZ1-2 through proteomic peptide phage display (ProP-PD) using a library that displays C-terminal regions of the human proteome. The protein recognizes a broad range of peptides, with a preference for hydrophobic motifs and has a tendency to recognize cryptic internal ligands. We validate the interaction with nectin-1 through orthogonal assays. The study demonstrates the power of ProP-PD as a complementary approach to uncover interactions of potential biological relevance.
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Affiliation(s)
- Sarah Garrido-Urbani
- Inserm U1068, Centre de Recherche en Cancérologie de Marseille, Institut Paoli-Calmettes, Aix-Marseille University, Centre National de la Recherche Scientifique UMR7258, Marseille, France
| | - Pankaj Garg
- Terrence Donnelly Centre for Cellular and Biomolecular Research, Toronto, Ontario, Canada.,Banting and Best Department of Medical Research and Department of Molecular Genetics, University of Toronto, Ontario, Canada
| | - Rania Ghossoub
- Inserm U1068, Centre de Recherche en Cancérologie de Marseille, Institut Paoli-Calmettes, Aix-Marseille University, Centre National de la Recherche Scientifique UMR7258, Marseille, France
| | - Roland Arnold
- Terrence Donnelly Centre for Cellular and Biomolecular Research, Toronto, Ontario, Canada
| | - Frédérique Lembo
- Inserm U1068, Centre de Recherche en Cancérologie de Marseille, Institut Paoli-Calmettes, Aix-Marseille University, Centre National de la Recherche Scientifique UMR7258, Marseille, France
| | | | - Philip M Kim
- Terrence Donnelly Centre for Cellular and Biomolecular Research, Toronto, Ontario, Canada
| | - Marc Lopez
- Inserm U1068, Centre de Recherche en Cancérologie de Marseille, Institut Paoli-Calmettes, Aix-Marseille University, Centre National de la Recherche Scientifique UMR7258, Marseille, France
| | - Pascale Zimmermann
- Inserm U1068, Centre de Recherche en Cancérologie de Marseille, Institut Paoli-Calmettes, Aix-Marseille University, Centre National de la Recherche Scientifique UMR7258, Marseille, France.,Department of Human Genetics, KU Leuven, Belgium
| | - Sachdev S Sidhu
- Terrence Donnelly Centre for Cellular and Biomolecular Research, Toronto, Ontario, Canada.,Banting and Best Department of Medical Research and Department of Molecular Genetics, University of Toronto, Ontario, Canada
| | - Ylva Ivarsson
- Department of Chemistry - BMC, Uppsala University, Sweden
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42
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Garton M, Najafabadi HS, Schmitges FW, Radovani E, Hughes TR, Kim PM. A structural approach reveals how neighbouring C2H2 zinc fingers influence DNA binding specificity. Nucleic Acids Res 2015; 43:9147-57. [PMID: 26384429 PMCID: PMC4627083 DOI: 10.1093/nar/gkv919] [Citation(s) in RCA: 34] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2015] [Accepted: 09/05/2015] [Indexed: 12/28/2022] Open
Abstract
Development of an accurate protein–DNA recognition code that can predict DNA specificity from protein sequence is a central problem in biology. C2H2 zinc fingers constitute by far the largest family of DNA binding domains and their binding specificity has been studied intensively. However, despite decades of research, accurate prediction of DNA specificity remains elusive. A major obstacle is thought to be the inability of current methods to account for the influence of neighbouring domains. Here we show that this problem can be addressed using a structural approach: we build structural models for all C2H2-ZF–DNA complexes with known binding motifs and find six distinct binding modes. Each mode changes the orientation of specificity residues with respect to the DNA, thereby modulating base preference. Most importantly, the structural analysis shows that residues at the domain interface strongly and predictably influence the binding mode, and hence specificity. Accounting for predicted binding mode significantly improves prediction accuracy of predicted motifs. This new insight into the fundamental behaviour of C2H2-ZFs has implications for both improving the prediction of natural zinc finger-binding sites, and for prioritizing further experiments to complete the code. It also provides a new design feature for zinc finger engineering.
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Affiliation(s)
- Michael Garton
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto M5S 3E1, Canada
| | - Hamed S Najafabadi
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto M5S 3E1, Canada
| | - Frank W Schmitges
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto M5S 3E1, Canada
| | - Ernest Radovani
- Department of Molecular Genetics, University of Toronto, Toronto M5S 1A8, Canada
| | - Timothy R Hughes
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto M5S 3E1, Canada Department of Molecular Genetics, University of Toronto, Toronto M5S 1A8, Canada
| | - Philip M Kim
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto M5S 3E1, Canada Department of Molecular Genetics, University of Toronto, Toronto M5S 1A8, Canada Department of Computer Science, University of Toronto, Toronto M5S 2E4, Canada
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43
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Hao Y, Colak R, Teyra J, Corbi-Verge C, Ignatchenko A, Hahne H, Wilhelm M, Kuster B, Braun P, Kaida D, Kislinger T, Kim PM. Semi-supervised Learning Predicts Approximately One Third of the Alternative Splicing Isoforms as Functional Proteins. Cell Rep 2015. [PMID: 26146086 DOI: 10.1016/j.celrep.2015.06.031:183] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
Abstract
Alternative splicing acts on transcripts from almost all human multi-exon genes. Notwithstanding its ubiquity, fundamental ramifications of splicing on protein expression remain unresolved. The number and identity of spliced transcripts that form stably folded proteins remain the sources of considerable debate, due largely to low coverage of experimental methods and the resulting absence of negative data. We circumvent this issue by developing a semi-supervised learning algorithm, positive unlabeled learning for splicing elucidation (PULSE; http://www.kimlab.org/software/pulse), which uses 48 features spanning various categories. We validated its accuracy on sets of bona fide protein isoforms and directly on mass spectrometry (MS) spectra for an overall AU-ROC of 0.85. We predict that around 32% of "exon skipping" alternative splicing events produce stable proteins, suggesting that the process engenders a significant number of previously uncharacterized proteins. We also provide insights into the distribution of positive isoforms in various functional classes and into the structural effects of alternative splicing.
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Affiliation(s)
- Yanqi Hao
- Terrence Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON M5S 1AS, Canada; Department of Computer Science, University of Toronto, Toronto, ON M5S 3G4, Canada
| | - Recep Colak
- Terrence Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON M5S 1AS, Canada; Department of Computer Science, University of Toronto, Toronto, ON M5S 3G4, Canada
| | - Joan Teyra
- Terrence Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON M5S 1AS, Canada
| | - Carles Corbi-Verge
- Terrence Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON M5S 1AS, Canada
| | - Alexander Ignatchenko
- Department of Medical Biophysics, University of Toronto, Toronto, ON M5G 1L7, Canada
| | - Hannes Hahne
- Chair for Proteomics and Bioanalytics, TU Muenchen, Freising 85354, Germany
| | - Mathias Wilhelm
- Chair for Proteomics and Bioanalytics, TU Muenchen, Freising 85354, Germany
| | - Bernhard Kuster
- Chair for Proteomics and Bioanalytics, TU Muenchen, Freising 85354, Germany; German Cancer Consortium (DKTK), Munich, Germany; German Cancer Research Center (DKFZ), Heidelberg, Germany; Center for Integrated Protein Science Munich, Munich, Germany; Bavarian Biomolecular Mass Spectrometry Center, Technische Universität München, Freising, Germany
| | - Pascal Braun
- Lehrstuhl fuer Systembiologie der Pflanzen, TU Muenchen, Munich, Germany
| | - Daisuke Kaida
- Frontier Research Core for Life Sciences, University of Toyama, Toyama 930-8555, Japan
| | - Thomas Kislinger
- Department of Medical Biophysics, University of Toronto, Toronto, ON M5G 1L7, Canada; Princess Margaret Cancer Center, University Health Network, Toronto, ON M5T 2M9, Canada
| | - Philip M Kim
- Terrence Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON M5S 1AS, Canada; Department of Computer Science, University of Toronto, Toronto, ON M5S 3G4, Canada; Department of Molecular Genetics, University of Toronto, Toronto, ON M5S 1AS, Canada.
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44
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Hao Y, Colak R, Teyra J, Corbi-Verge C, Ignatchenko A, Hahne H, Wilhelm M, Kuster B, Braun P, Kaida D, Kislinger T, Kim PM. Semi-supervised Learning Predicts Approximately One Third of the Alternative Splicing Isoforms as Functional Proteins. Cell Rep 2015; 12:183-9. [PMID: 26146086 DOI: 10.1016/j.celrep.2015.06.031] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2014] [Revised: 02/18/2015] [Accepted: 06/09/2015] [Indexed: 12/30/2022] Open
Abstract
Alternative splicing acts on transcripts from almost all human multi-exon genes. Notwithstanding its ubiquity, fundamental ramifications of splicing on protein expression remain unresolved. The number and identity of spliced transcripts that form stably folded proteins remain the sources of considerable debate, due largely to low coverage of experimental methods and the resulting absence of negative data. We circumvent this issue by developing a semi-supervised learning algorithm, positive unlabeled learning for splicing elucidation (PULSE; http://www.kimlab.org/software/pulse), which uses 48 features spanning various categories. We validated its accuracy on sets of bona fide protein isoforms and directly on mass spectrometry (MS) spectra for an overall AU-ROC of 0.85. We predict that around 32% of "exon skipping" alternative splicing events produce stable proteins, suggesting that the process engenders a significant number of previously uncharacterized proteins. We also provide insights into the distribution of positive isoforms in various functional classes and into the structural effects of alternative splicing.
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Affiliation(s)
- Yanqi Hao
- Terrence Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON M5S 1AS, Canada; Department of Computer Science, University of Toronto, Toronto, ON M5S 3G4, Canada
| | - Recep Colak
- Terrence Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON M5S 1AS, Canada; Department of Computer Science, University of Toronto, Toronto, ON M5S 3G4, Canada
| | - Joan Teyra
- Terrence Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON M5S 1AS, Canada
| | - Carles Corbi-Verge
- Terrence Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON M5S 1AS, Canada
| | - Alexander Ignatchenko
- Department of Medical Biophysics, University of Toronto, Toronto, ON M5G 1L7, Canada
| | - Hannes Hahne
- Chair for Proteomics and Bioanalytics, TU Muenchen, Freising 85354, Germany
| | - Mathias Wilhelm
- Chair for Proteomics and Bioanalytics, TU Muenchen, Freising 85354, Germany
| | - Bernhard Kuster
- Chair for Proteomics and Bioanalytics, TU Muenchen, Freising 85354, Germany; German Cancer Consortium (DKTK), Munich, Germany; German Cancer Research Center (DKFZ), Heidelberg, Germany; Center for Integrated Protein Science Munich, Munich, Germany; Bavarian Biomolecular Mass Spectrometry Center, Technische Universität München, Freising, Germany
| | - Pascal Braun
- Lehrstuhl fuer Systembiologie der Pflanzen, TU Muenchen, Munich, Germany
| | - Daisuke Kaida
- Frontier Research Core for Life Sciences, University of Toyama, Toyama 930-8555, Japan
| | - Thomas Kislinger
- Department of Medical Biophysics, University of Toronto, Toronto, ON M5G 1L7, Canada; Princess Margaret Cancer Center, University Health Network, Toronto, ON M5T 2M9, Canada
| | - Philip M Kim
- Terrence Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON M5S 1AS, Canada; Department of Computer Science, University of Toronto, Toronto, ON M5S 3G4, Canada; Department of Molecular Genetics, University of Toronto, Toronto, ON M5S 1AS, Canada.
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45
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Najafabadi HS, Mnaimneh S, Schmitges FW, Garton M, Lam KN, Yang A, Albu M, Weirauch MT, Radovani E, Kim PM, Greenblatt J, Frey BJ, Hughes TR. C2H2 zinc finger proteins greatly expand the human regulatory lexicon. Nat Biotechnol 2015; 33:555-62. [PMID: 25690854 DOI: 10.1038/nbt.3128] [Citation(s) in RCA: 224] [Impact Index Per Article: 24.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2014] [Accepted: 12/15/2014] [Indexed: 12/11/2022]
Abstract
Cys2-His2 zinc finger (C2H2-ZF) proteins represent the largest class of putative human transcription factors. However, for most C2H2-ZF proteins it is unknown whether they even bind DNA or, if they do, to which sequences. Here, by combining data from a modified bacterial one-hybrid system with protein-binding microarray and chromatin immunoprecipitation analyses, we show that natural C2H2-ZFs encoded in the human genome bind DNA both in vitro and in vivo, and we infer the DNA recognition code using DNA-binding data for thousands of natural C2H2-ZF domains. In vivo binding data are generally consistent with our recognition code and indicate that C2H2-ZF proteins recognize more motifs than all other human transcription factors combined. We provide direct evidence that most KRAB-containing C2H2-ZF proteins bind specific endogenous retroelements (EREs), ranging from currently active to ancient families. The majority of C2H2-ZF proteins, including KRAB proteins, also show widespread binding to regulatory regions, indicating that the human genome contains an extensive and largely unstudied adaptive C2H2-ZF regulatory network that targets a diverse range of genes and pathways.
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Affiliation(s)
- Hamed S Najafabadi
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario, Canada
| | - Sanie Mnaimneh
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario, Canada
| | - Frank W Schmitges
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario, Canada
| | - Michael Garton
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario, Canada
| | - Kathy N Lam
- Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada
| | - Ally Yang
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario, Canada
| | - Mihai Albu
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario, Canada
| | - Matthew T Weirauch
- 1] Center for Autoimmune Genomics and Etiology (CAGE) and Divisions of Biomedical Informatics and Developmental Biology, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, USA. [2] Canadian Institutes for Advanced Research, Toronto, Ontario, Canada
| | - Ernest Radovani
- Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada
| | - Philip M Kim
- 1] Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario, Canada. [2] Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada. [3] Department of Computer Science, University of Toronto, Toronto, Ontario, Canada
| | - Jack Greenblatt
- 1] Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario, Canada. [2] Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada
| | - Brendan J Frey
- 1] Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario, Canada. [2] Canadian Institutes for Advanced Research, Toronto, Ontario, Canada. [3] Department of Computer Science, University of Toronto, Toronto, Ontario, Canada. [4] Department of Electrical and Computer Engineering, University of Toronto, Toronto, Ontario, Canada
| | - Timothy R Hughes
- 1] Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario, Canada. [2] Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada. [3] Canadian Institutes for Advanced Research, Toronto, Ontario, Canada
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46
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Berliner N, Teyra J, Çolak R, Garcia Lopez S, Kim PM. Combining structural modeling with ensemble machine learning to accurately predict protein fold stability and binding affinity effects upon mutation. PLoS One 2014; 9:e107353. [PMID: 25243403 PMCID: PMC4170975 DOI: 10.1371/journal.pone.0107353] [Citation(s) in RCA: 61] [Impact Index Per Article: 6.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2014] [Accepted: 07/21/2014] [Indexed: 12/04/2022] Open
Abstract
Advances in sequencing have led to a rapid accumulation of mutations, some of which are associated with diseases. However, to draw mechanistic conclusions, a biochemical understanding of these mutations is necessary. For coding mutations, accurate prediction of significant changes in either the stability of proteins or their affinity to their binding partners is required. Traditional methods have used semi-empirical force fields, while newer methods employ machine learning of sequence and structural features. Here, we show how combining both of these approaches leads to a marked boost in accuracy. We introduce ELASPIC, a novel ensemble machine learning approach that is able to predict stability effects upon mutation in both, domain cores and domain-domain interfaces. We combine semi-empirical energy terms, sequence conservation, and a wide variety of molecular details with a Stochastic Gradient Boosting of Decision Trees (SGB-DT) algorithm. The accuracy of our predictions surpasses existing methods by a considerable margin, achieving correlation coefficients of 0.77 for stability, and 0.75 for affinity predictions. Notably, we integrated homology modeling to enable proteome-wide prediction and show that accurate prediction on modeled structures is possible. Lastly, ELASPIC showed significant differences between various types of disease-associated mutations, as well as between disease and common neutral mutations. Unlike pure sequence-based prediction methods that try to predict phenotypic effects of mutations, our predictions unravel the molecular details governing the protein instability, and help us better understand the molecular causes of diseases.
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Affiliation(s)
- Niklas Berliner
- Terrence Donnelly Centre for Cellular and Biomolecular Research (CCBR), University of Toronto, Toronto, Ontario, Canada
| | - Joan Teyra
- Terrence Donnelly Centre for Cellular and Biomolecular Research (CCBR), University of Toronto, Toronto, Ontario, Canada
| | - Recep Çolak
- Terrence Donnelly Centre for Cellular and Biomolecular Research (CCBR), University of Toronto, Toronto, Ontario, Canada
- Department of Computer Science, University of Toronto, Toronto, Ontario, Canada
| | - Sebastian Garcia Lopez
- Terrence Donnelly Centre for Cellular and Biomolecular Research (CCBR), University of Toronto, Toronto, Ontario, Canada
- Universidad Nacional de Colombia, Manizales, Colombia
| | - Philip M. Kim
- Terrence Donnelly Centre for Cellular and Biomolecular Research (CCBR), University of Toronto, Toronto, Ontario, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada
- Department of Computer Science, University of Toronto, Toronto, Ontario, Canada
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47
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van der Lee R, Buljan M, Lang B, Weatheritt RJ, Daughdrill GW, Dunker AK, Fuxreiter M, Gough J, Gsponer J, Jones D, Kim PM, Kriwacki R, Oldfield CJ, Pappu RV, Tompa P, Uversky VN, Wright P, Babu MM. Classification of intrinsically disordered regions and proteins. Chem Rev 2014; 114:6589-631. [PMID: 24773235 PMCID: PMC4095912 DOI: 10.1021/cr400525m] [Citation(s) in RCA: 1366] [Impact Index Per Article: 136.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2013] [Indexed: 12/11/2022]
Affiliation(s)
- Robin van der Lee
- MRC
Laboratory of Molecular Biology, Francis Crick Avenue, Cambridge CB2 0QH, United Kingdom
- Centre
for Molecular and Biomolecular Informatics, Radboud University Medical Centre, 6500 HB Nijmegen, The
Netherlands
| | - Marija Buljan
- MRC
Laboratory of Molecular Biology, Francis Crick Avenue, Cambridge CB2 0QH, United Kingdom
| | - Benjamin Lang
- MRC
Laboratory of Molecular Biology, Francis Crick Avenue, Cambridge CB2 0QH, United Kingdom
| | - Robert J. Weatheritt
- MRC
Laboratory of Molecular Biology, Francis Crick Avenue, Cambridge CB2 0QH, United Kingdom
| | - Gary W. Daughdrill
- Department
of Cell Biology, Microbiology, and Molecular Biology, University of South Florida, 3720 Spectrum Boulevard, Suite 321, Tampa, Florida 33612, United States
| | - A. Keith Dunker
- Department
of Biochemistry and Molecular Biology, Indiana
University School of Medicine, Indianapolis, Indiana 46202, United States
| | - Monika Fuxreiter
- MTA-DE
Momentum Laboratory of Protein Dynamics, Department of Biochemistry
and Molecular Biology, University of Debrecen, H-4032 Debrecen, Nagyerdei krt 98, Hungary
| | - Julian Gough
- Department
of Computer Science, University of Bristol, The Merchant Venturers Building, Bristol BS8 1UB, United Kingdom
| | - Joerg Gsponer
- Department
of Biochemistry and Molecular Biology, Centre for High-Throughput
Biology, University of British Columbia, Vancouver, British Columbia V6T 1Z4, Canada
| | - David
T. Jones
- Bioinformatics
Group, Department of Computer Science, University
College London, London, WC1E 6BT, United Kingdom
| | - Philip M. Kim
- Terrence Donnelly Centre for Cellular and Biomolecular Research, Department of Molecular
Genetics, and Department of Computer Science, University
of Toronto, Toronto, Ontario M5S 3E1, Canada
| | - Richard
W. Kriwacki
- Department
of Structural Biology, St. Jude Children’s
Research Hospital, Memphis, Tennessee 38105, United States
| | - Christopher J. Oldfield
- Department
of Biochemistry and Molecular Biology, Indiana
University School of Medicine, Indianapolis, Indiana 46202, United States
| | - Rohit V. Pappu
- Department
of Biomedical Engineering and Center for Biological Systems Engineering, Washington University in St. Louis, St. Louis, Missouri 63130, United States
| | - Peter Tompa
- VIB Department
of Structural Biology, Vrije Universiteit
Brussel, Brussels, Belgium
- Institute
of Enzymology, Research Centre for Natural Sciences, Hungarian Academy of Sciences, Budapest, Hungary
| | - Vladimir N. Uversky
- Department
of Molecular Medicine and USF Health Byrd Alzheimer’s Research
Institute, Morsani College of Medicine, University of South Florida, Tampa, Florida 33612, United States
- Institute for Biological Instrumentation,
Russian Academy of Sciences, Pushchino,
Moscow Region, Russia
| | - Peter
E. Wright
- Department
of Integrative Structural and Computational Biology and Skaggs Institute
of Chemical Biology, The Scripps Research
Institute, 10550 North
Torrey Pines Road, La Jolla, California 92037, United States
| | - M. Madan Babu
- MRC
Laboratory of Molecular Biology, Francis Crick Avenue, Cambridge CB2 0QH, United Kingdom
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48
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Abu-Odeh M, Bar-Mag T, Huang H, Kim T, Salah Z, Abdeen SK, Sudol M, Reichmann D, Sidhu S, Kim PM, Aqeilan RI. Characterizing WW domain interactions of tumor suppressor WWOX reveals its association with multiprotein networks. J Biol Chem 2014; 289:8865-80. [PMID: 24550385 DOI: 10.1074/jbc.m113.506790] [Citation(s) in RCA: 68] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022] Open
Abstract
WW domains are small modules present in regulatory and signaling proteins that mediate specific protein-protein interactions. The WW domain-containing oxidoreductase (WWOX) encodes a 46-kDa tumor suppressor that contains two N-terminal WW domains and a central short-chain dehydrogenase/reductase domain. Based on its ligand recognition motifs, the WW domain family is classified into four groups. The largest one, to which WWOX belongs, recognizes ligands with a PPXY motif. To pursue the functional properties of the WW domains of WWOX, we employed mass spectrometry and phage display experiments to identify putative WWOX-interacting partners. Our analysis revealed that the first WW (WW1) domain of WWOX is the main functional interacting domain. Furthermore, our study uncovered well known and new PPXY-WW1-interacting partners and shed light on novel LPXY-WW1-interacting partners of WWOX. Many of these proteins are components of multiprotein complexes involved in molecular processes, including transcription, RNA processing, tight junction, and metabolism. By utilizing GST pull-down and immunoprecipitation assays, we validated that WWOX is a substrate of the E3 ubiquitin ligase ITCH, which contains two LPXY motifs. We found that ITCH mediates Lys-63-linked polyubiquitination of WWOX, leading to its nuclear localization and increased cell death. Our data suggest that the WW1 domain of WWOX provides a versatile platform that links WWOX with individual proteins associated with physiologically important networks.
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Affiliation(s)
- Mohammad Abu-Odeh
- From the Lautenberg Center for Immunology and Cancer Research, IMRIC, Hebrew University-Hadassah Medical School, Jerusalem, Israel 91120
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49
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Jeon J, Nim S, Teyra J, Datti A, Wrana JL, Sidhu SS, Moffat J, Kim PM. A systematic approach to identify novel cancer drug targets using machine learning, inhibitor design and high-throughput screening. Genome Med 2014; 6:57. [PMID: 25165489 PMCID: PMC4143549 DOI: 10.1186/s13073-014-0057-7] [Citation(s) in RCA: 69] [Impact Index Per Article: 6.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2014] [Accepted: 07/18/2014] [Indexed: 12/14/2022] Open
Abstract
We present an integrated approach that predicts and validates novel anti-cancer drug targets. We first built a classifier that integrates a variety of genomic and systematic datasets to prioritize drug targets specific for breast, pancreatic and ovarian cancer. We then devised strategies to inhibit these anti-cancer drug targets and selected a set of targets that are amenable to inhibition by small molecules, antibodies and synthetic peptides. We validated the predicted drug targets by showing strong anti-proliferative effects of both synthetic peptide and small molecule inhibitors against our predicted targets.
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Affiliation(s)
- Jouhyun Jeon
- Terrence Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON M5S 3E1 Canada
| | - Satra Nim
- Terrence Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON M5S 3E1 Canada
| | - Joan Teyra
- Terrence Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON M5S 3E1 Canada
| | - Alessandro Datti
- Center for Systems Biology, Samuel Lunenfeld Research Institute, Mount Sinai Hospital, University of Toronto, Toronto, ON M5S 3E1 Canada
- Department of Agricultural, Food and Environmental Sciences, University of Perugia, Perugia, 06100 Italy
| | - Jeffrey L Wrana
- Center for Systems Biology, Samuel Lunenfeld Research Institute, Mount Sinai Hospital, University of Toronto, Toronto, ON M5S 3E1 Canada
| | - Sachdev S Sidhu
- Terrence Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON M5S 3E1 Canada
- Department of Molecular Genetics, University of Toronto, Toronto, ON M5S 3E1 Canada
| | - Jason Moffat
- Terrence Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON M5S 3E1 Canada
- Department of Molecular Genetics, University of Toronto, Toronto, ON M5S 3E1 Canada
| | - Philip M Kim
- Terrence Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON M5S 3E1 Canada
- Department of Molecular Genetics, University of Toronto, Toronto, ON M5S 3E1 Canada
- Department of Computer Science, University of Toronto, Toronto, ON M5S 3E1 Canada
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50
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Chen C, Ha BH, Thévenin AF, Lou HJ, Zhang R, Yip KY, Peterson JR, Gerstein M, Kim PM, Filippakopoulos P, Knapp S, Boggon TJ, Turk BE. Identification of a major determinant for serine-threonine kinase phosphoacceptor specificity. Mol Cell 2013; 53:140-7. [PMID: 24374310 PMCID: PMC3898841 DOI: 10.1016/j.molcel.2013.11.013] [Citation(s) in RCA: 77] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2013] [Revised: 10/24/2013] [Accepted: 11/18/2013] [Indexed: 01/06/2023]
Abstract
Eukaryotic protein kinases are generally classified as being either tyrosine or serine-threonine specific. Though not evident from inspection of their primary sequences, many serine-threonine kinases display a significant preference for serine or threonine as the phosphoacceptor residue. Here we show that a residue located in the kinase activation segment, which we term the “DFG+1” residue, acts as a major determinant for serine-threonine phosphorylation site specificity. Mutation of this residue was sufficient to switch the phosphorylation site preference for multiple kinases, including the serine-specific kinase PAK4 and the threonine-specific kinase MST4. Kinetic analysis of peptide substrate phosphorylation and crystal structures of PAK4-peptide complexes suggested that phosphoacceptor residue preference is not mediated by stronger binding of the favored substrate. Rather, favored kinase-phosphoacceptor combinations likely promote a conformation optimal for catalysis. Understanding the rules governing kinase phosphoacceptor preference allows kinases to be classified as serine or threonine specific based on their sequence. A single active site residue can determine kinase phosphoacceptor specificity Favored and disfavored substrates promote distinct kinase-bound conformations A simple rule predicts kinase phosphoacceptor preference from its DFG+1 residue
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Affiliation(s)
- Catherine Chen
- Department of Pharmacology, Yale University School of Medicine, New Haven, CT 06520, USA
| | - Byung Hak Ha
- Department of Pharmacology, Yale University School of Medicine, New Haven, CT 06520, USA
| | - Anastasia F Thévenin
- Department of Pharmacology, Yale University School of Medicine, New Haven, CT 06520, USA
| | - Hua Jane Lou
- Department of Pharmacology, Yale University School of Medicine, New Haven, CT 06520, USA
| | - Rong Zhang
- Department of Pharmacology, Yale University School of Medicine, New Haven, CT 06520, USA
| | - Kevin Y Yip
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA; Department of Computer Science and Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong
| | - Jeffrey R Peterson
- Division of Basic Science, Fox Chase Cancer Center, Philadelphia, PA 19111, USA
| | - Mark Gerstein
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA
| | - Philip M Kim
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON M5S 3E1, Canada
| | - Panagis Filippakopoulos
- Oxford University, Nuffield Department of Clinical Medicine, Target Discovery Institute (TDI) and Structural Genomics Consortium (SGC), Oxford OX3 7FZ, UK; Ludwig Institute for Cancer Research, Old Road Campus Research Building, Oxford OX3 7DQ, UK
| | - Stefan Knapp
- Oxford University, Nuffield Department of Clinical Medicine, Target Discovery Institute (TDI) and Structural Genomics Consortium (SGC), Oxford OX3 7FZ, UK
| | - Titus J Boggon
- Department of Pharmacology, Yale University School of Medicine, New Haven, CT 06520, USA
| | - Benjamin E Turk
- Department of Pharmacology, Yale University School of Medicine, New Haven, CT 06520, USA.
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