1
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Soleymani F, Paquet E, Viktor H, Michalowski W, Spinello D. Protein-protein interaction prediction with deep learning: A comprehensive review. Comput Struct Biotechnol J 2022; 20:5316-5341. [PMID: 36212542 PMCID: PMC9520216 DOI: 10.1016/j.csbj.2022.08.070] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2022] [Revised: 08/29/2022] [Accepted: 08/30/2022] [Indexed: 11/15/2022] Open
Abstract
Most proteins perform their biological function by interacting with themselves or other molecules. Thus, one may obtain biological insights into protein functions, disease prevalence, and therapy development by identifying protein-protein interactions (PPI). However, finding the interacting and non-interacting protein pairs through experimental approaches is labour-intensive and time-consuming, owing to the variety of proteins. Hence, protein-protein interaction and protein-ligand binding problems have drawn attention in the fields of bioinformatics and computer-aided drug discovery. Deep learning methods paved the way for scientists to predict the 3-D structure of proteins from genomes, predict the functions and attributes of a protein, and modify and design new proteins to provide desired functions. This review focuses on recent deep learning methods applied to problems including predicting protein functions, protein-protein interaction and their sites, protein-ligand binding, and protein design.
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Affiliation(s)
- Farzan Soleymani
- Department of Mechanical Engineering, University of Ottawa, Ottawa, ON, Canada
| | - Eric Paquet
- National Research Council, 1200 Montreal Road, Ottawa, ON K1A 0R6, Canada
| | - Herna Viktor
- School of Electrical Engineering and Computer Science, University of Ottawa, ON, Canada
| | | | - Davide Spinello
- Department of Mechanical Engineering, University of Ottawa, Ottawa, ON, Canada
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2
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Ameerul A, Almasmoum H, Pavanello L, Dominguez C, Sebastiaan Winkler G. Structural model of the human BTG2–PABPC1 complex by combining mutagenesis, NMR chemical shift perturbation data and molecular docking. J Mol Biol 2022; 434:167662. [DOI: 10.1016/j.jmb.2022.167662] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2022] [Revised: 05/20/2022] [Accepted: 05/24/2022] [Indexed: 11/28/2022]
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3
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Quadrini M, Daberdaku S, Ferrari C. Hierarchical representation for PPI sites prediction. BMC Bioinformatics 2022; 23:96. [PMID: 35307006 PMCID: PMC8934516 DOI: 10.1186/s12859-022-04624-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2021] [Accepted: 02/23/2022] [Indexed: 01/06/2023] Open
Abstract
Abstract
Background
Protein–protein interactions have pivotal roles in life processes, and aberrant interactions are associated with various disorders. Interaction site identification is key for understanding disease mechanisms and design new drugs. Effective and efficient computational methods for the PPI prediction are of great value due to the overall cost of experimental methods. Promising results have been obtained using machine learning methods and deep learning techniques, but their effectiveness depends on protein representation and feature selection.
Results
We define a new abstraction of the protein structure, called hierarchical representations, considering and quantifying spatial and sequential neighboring among amino acids. We also investigate the effect of molecular abstractions using the Graph Convolutional Networks technique to classify amino acids as interface and no-interface ones. Our study takes into account three abstractions, hierarchical representations, contact map, and the residue sequence, and considers the eight functional classes of proteins extracted from the Protein–Protein Docking Benchmark 5.0. The performance of our method, evaluated using standard metrics, is compared to the ones obtained with some state-of-the-art protein interface predictors. The analysis of the performance values shows that our method outperforms the considered competitors when the considered molecules are structurally similar.
Conclusions
The hierarchical representation can capture the structural properties that promote the interactions and can be used to represent proteins with unknown structures by codifying only their sequential neighboring. Analyzing the results, we conclude that classes should be arranged according to their architectures rather than functions.
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4
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Chatzigoulas A, Cournia Z. Predicting protein–membrane interfaces of peripheral membrane proteins using ensemble machine learning. Brief Bioinform 2022; 23:6527274. [PMID: 35152294 PMCID: PMC8921665 DOI: 10.1093/bib/bbab518] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2021] [Revised: 10/23/2021] [Accepted: 11/12/2021] [Indexed: 12/13/2022] Open
Abstract
Abstract
Abnormal protein–membrane attachment is involved in deregulated cellular pathways and in disease. Therefore, the possibility to modulate protein–membrane interactions represents a new promising therapeutic strategy for peripheral membrane proteins that have been considered so far undruggable. A major obstacle in this drug design strategy is that the membrane-binding domains of peripheral membrane proteins are usually unknown. The development of fast and efficient algorithms predicting the protein–membrane interface would shed light into the accessibility of membrane–protein interfaces by drug-like molecules. Herein, we describe an ensemble machine learning methodology and algorithm for predicting membrane-penetrating amino acids. We utilize available experimental data from the literature for training 21 machine learning classifiers and meta-classifiers. Evaluation of the best ensemble classifier model accuracy yields a macro-averaged F1 score = 0.92 and a Matthews correlation coefficient = 0.84 for predicting correctly membrane-penetrating amino acids on unknown proteins of a validation set. The python code for predicting protein–membrane interfaces of peripheral membrane proteins is available at https://github.com/zoecournia/DREAMM.
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Affiliation(s)
- Alexios Chatzigoulas
- Biomedical Research Foundation, Academy of Athens, 4 Soranou Ephessiou, 11527 Athens, Greece
- Department of Informatics and Telecommunications, National and Kapodistrian University of Athens, 15784 Athens, Greece
| | - Zoe Cournia
- Biomedical Research Foundation, Academy of Athens, 4 Soranou Ephessiou, 11527 Athens, Greece
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5
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Hot spots-making directed evolution easier. Biotechnol Adv 2022; 56:107926. [DOI: 10.1016/j.biotechadv.2022.107926] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2021] [Revised: 01/04/2022] [Accepted: 02/07/2022] [Indexed: 01/20/2023]
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6
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Souza SA, Held A, Lu WJ, Drouhard B, Avila B, Leyva-Montes R, Hu M, Miller BR, Ng HL. Mechanisms of allosteric and mixed mode aromatase inhibitors. RSC Chem Biol 2021; 2:892-905. [PMID: 34458816 PMCID: PMC8341375 DOI: 10.1039/d1cb00046b] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2021] [Accepted: 03/17/2021] [Indexed: 11/21/2022] Open
Abstract
Aromatase (CYP19) catalyzes the last biosynthetic step of estrogens in mammals and is a primary drug target for hormone-related breast cancer. However, treatment with aromatase inhibitors is often associated with adverse effects and drug resistance. In this study, we used virtual screening targeting a predicted cytochrome P450 reductase binding site on aromatase to discover four novel non-steroidal aromatase inhibitors. The inhibitors have potencies comparable to the noncompetitive tamoxifen metabolite, endoxifen. Our two most potent inhibitors, AR11 and AR13, exhibit both mixed-type and competitive-type inhibition. The cytochrome P450 reductase-CYP19 coupling interface likely acts as a transient binding site. Our modeling shows that our inhibitors bind better at different sites near the catalytic site. Our results predict the location of multiple ligand binding sites on aromatase. The combination of modeling and experimental results supports the important role of the reductase binding interface as a low affinity, promiscuous ligand binding site. Our new inhibitors may be useful as alternative chemical scaffolds that may show different adverse effects profiles than current clinically used aromatase inhibitors.
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Affiliation(s)
- Samson A Souza
- Department of Biochemistry and Molecular Biophysics, Kansas State University Manhattan KS USA
| | - Abby Held
- Department of Chemistry, Truman State University Kirksville MO USA
| | - Wenjie J Lu
- Department of Chemistry, University of Hawai'i at Mānoa Honolulu HI USA
| | - Brendan Drouhard
- Department of Biochemistry and Molecular Biophysics, Kansas State University Manhattan KS USA
| | - Bryant Avila
- Department of Biochemistry and Molecular Biophysics, Kansas State University Manhattan KS USA
| | - Raul Leyva-Montes
- Department of Biochemistry and Molecular Biophysics, Kansas State University Manhattan KS USA
| | - Michelle Hu
- Department of Chemistry, University of Hawai'i at Mānoa Honolulu HI USA
| | - Bill R Miller
- Department of Chemistry, Truman State University Kirksville MO USA
| | - Ho Leung Ng
- Department of Biochemistry and Molecular Biophysics, Kansas State University Manhattan KS USA
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7
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Hashemi ZS, Zarei M, Fath MK, Ganji M, Farahani MS, Afsharnouri F, Pourzardosht N, Khalesi B, Jahangiri A, Rahbar MR, Khalili S. In silico Approaches for the Design and Optimization of Interfering Peptides Against Protein-Protein Interactions. Front Mol Biosci 2021; 8:669431. [PMID: 33996914 PMCID: PMC8113820 DOI: 10.3389/fmolb.2021.669431] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2021] [Accepted: 04/06/2021] [Indexed: 01/01/2023] Open
Abstract
Large contact surfaces of protein-protein interactions (PPIs) remain to be an ongoing issue in the discovery and design of small molecule modulators. Peptides are intrinsically capable of exploring larger surfaces, stable, and bioavailable, and therefore bear a high therapeutic value in the treatment of various diseases, including cancer, infectious diseases, and neurodegenerative diseases. Given these promising properties, a long way has been covered in the field of targeting PPIs via peptide design strategies. In silico tools have recently become an inevitable approach for the design and optimization of these interfering peptides. Various algorithms have been developed to scrutinize the PPI interfaces. Moreover, different databases and software tools have been created to predict the peptide structures and their interactions with target protein complexes. High-throughput screening of large peptide libraries against PPIs; "hotspot" identification; structure-based and off-structure approaches of peptide design; 3D peptide modeling; peptide optimization strategies like cyclization; and peptide binding energy evaluation are among the capabilities of in silico tools. In the present study, the most recent advances in the field of in silico approaches for the design of interfering peptides against PPIs will be reviewed. The future perspective of the field and its advantages and limitations will also be pinpointed.
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Affiliation(s)
- Zahra Sadat Hashemi
- ATMP Department, Breast Cancer Research Center, Motamed Cancer Institute, Academic Center for Education, Culture and Research, Tehran, Iran
| | - Mahboubeh Zarei
- Pharmaceutical Sciences Research Center, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Mohsen Karami Fath
- Department of Cellular and Molecular Biology, Faculty of Biological Sciences, Kharazmi University, Tehran, Iran
| | - Mahmoud Ganji
- Department of Medical Biotechnology, Faculty of Medical Sciences, Tarbiat Modares University, Tehran, Iran
| | - Mahboube Shahrabi Farahani
- Department of Medical Biotechnology, Faculty of Medical Sciences, Tarbiat Modares University, Tehran, Iran
| | - Fatemeh Afsharnouri
- Department of Medical Biotechnology, Faculty of Medical Sciences, Tarbiat Modares University, Tehran, Iran
| | - Navid Pourzardosht
- Cellular and Molecular Research Center, Faculty of Medicine, Guilan University of Medical Sciences, Rasht, Iran
- Department of Biochemistry, Guilan University of Medical Sciences, Rasht, Iran
| | - Bahman Khalesi
- Department of Research and Production of Poultry Viral Vaccine, Razi Vaccine and Serum Research Institute, Agricultural Research Education and Extension Organization, Karaj, Iran
| | - Abolfazl Jahangiri
- Applied Microbiology Research Center, Systems Biology and Poisonings Institute, Baqiyatallah University of Medical Sciences, Tehran, Iran
| | - Mohammad Reza Rahbar
- Pharmaceutical Sciences Research Center, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Saeed Khalili
- Department of Biology Sciences, Shahid Rajaee Teacher Training University, Tehran, Iran
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8
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McCafferty CL, Marcotte EM, Taylor DW. Simplified geometric representations of protein structures identify complementary interaction interfaces. Proteins 2021; 89:348-360. [PMID: 33140424 PMCID: PMC7855953 DOI: 10.1002/prot.26020] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2020] [Revised: 09/22/2020] [Accepted: 10/25/2020] [Indexed: 12/12/2022]
Abstract
Protein-protein interactions are critical to protein function, but three-dimensional (3D) arrangements of interacting proteins have proven hard to predict, even given the identities and 3D structures of the interacting partners. Specifically, identifying the relevant pairwise interaction surfaces remains difficult, often relying on shape complementarity with molecular docking while accounting for molecular motions to optimize rigid 3D translations and rotations. However, such approaches can be computationally expensive, and faster, less accurate approximations may prove useful for large-scale prediction and assembly of 3D structures of multi-protein complexes. We asked if a reduced representation of protein geometry retains enough information about molecular properties to predict pairwise protein interaction interfaces that are tolerant of limited structural rearrangements. Here, we describe a reduced representation of 3D protein accessible surfaces on which molecular properties such as charge, hydrophobicity, and evolutionary rate can be easily mapped, implemented in the MorphProt package. Pairs of surfaces are compared to rapidly assess partner-specific potential surface complementarity. On two available benchmarks of 185 overall known protein complexes, we observe predictions comparable to other structure-based tools at correctly identifying protein interaction surfaces. Furthermore, we examined the effect of molecular motion through normal mode simulation on a benchmark receptor-ligand pair and observed no marked loss of predictive accuracy for distortions of up to 6 Å Cα-RMSD. Thus, a shape reduction of protein surfaces retains considerable information about surface complementarity, offers enhanced speed of comparison relative to more complex geometric representations, and exhibits tolerance to conformational changes.
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Affiliation(s)
- Caitlyn L. McCafferty
- Department of Molecular BiosciencesUniversity of Texas at AustinAustinTexasUSA
- Center for Systems and Synthetic BiologyUniversity of Texas at AustinAustinTexasUSA
- Institute for Cellular and Molecular BiologyUniversity of Texas at AustinAustinTexasUSA
| | - Edward M. Marcotte
- Department of Molecular BiosciencesUniversity of Texas at AustinAustinTexasUSA
- Center for Systems and Synthetic BiologyUniversity of Texas at AustinAustinTexasUSA
- Institute for Cellular and Molecular BiologyUniversity of Texas at AustinAustinTexasUSA
| | - David W. Taylor
- Department of Molecular BiosciencesUniversity of Texas at AustinAustinTexasUSA
- Center for Systems and Synthetic BiologyUniversity of Texas at AustinAustinTexasUSA
- Institute for Cellular and Molecular BiologyUniversity of Texas at AustinAustinTexasUSA
- LIVESTRONG Cancer InstitutesDell Medical SchoolAustinTexasUSA
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9
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Das S, Chakrabarti S. Classification and prediction of protein-protein interaction interface using machine learning algorithm. Sci Rep 2021; 11:1761. [PMID: 33469042 PMCID: PMC7815773 DOI: 10.1038/s41598-020-80900-2] [Citation(s) in RCA: 33] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2020] [Accepted: 12/15/2020] [Indexed: 01/29/2023] Open
Abstract
Structural insight of the protein-protein interaction (PPI) interface can provide knowledge about the kinetics, thermodynamics and molecular functions of the complex while elucidating its role in diseases and further enabling it as a potential therapeutic target. However, owing to experimental lag in solving protein-protein complex structures, three-dimensional (3D) knowledge of the PPI interfaces can be gained via computational approaches like molecular docking and post-docking analyses. Despite development of numerous docking tools and techniques, success in identification of native like interfaces based on docking score functions is limited. Hence, we employed an in-depth investigation of the structural features of the interface that might successfully delineate native complexes from non-native ones. We identify interface properties, which show statistically significant difference between native and non-native interfaces belonging to homo and hetero, protein-protein complexes. Utilizing these properties, a support vector machine (SVM) based classification scheme has been implemented to differentiate native and non-native like complexes generated using docking decoys. Benchmarking and comparative analyses suggest very good performance of our SVM classifiers. Further, protein interactions, which are proven via experimental findings but not resolved structurally, were subjected to this approach where 3D-models of the complexes were generated and most likely interfaces were predicted. A web server called Protein Complex Prediction by Interface Properties (PCPIP) is developed to predict whether interface of a given protein-protein dimer complex resembles known protein interfaces. The server is freely available at http://www.hpppi.iicb.res.in/pcpip/ .
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Affiliation(s)
- Subhrangshu Das
- grid.417635.20000 0001 2216 5074Structural Biology and Bioinformatics Division, CSIR-Indian Institute of Chemical Biology, Kolkata, WB India
| | - Saikat Chakrabarti
- grid.417635.20000 0001 2216 5074Structural Biology and Bioinformatics Division, CSIR-Indian Institute of Chemical Biology, Kolkata, WB India
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10
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Slater O, Miller B, Kontoyianni M. Decoding Protein-protein Interactions: An Overview. Curr Top Med Chem 2021; 20:855-882. [PMID: 32101126 DOI: 10.2174/1568026620666200226105312] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2019] [Revised: 11/27/2019] [Accepted: 11/27/2019] [Indexed: 12/24/2022]
Abstract
Drug discovery has focused on the paradigm "one drug, one target" for a long time. However, small molecules can act at multiple macromolecular targets, which serves as the basis for drug repurposing. In an effort to expand the target space, and given advances in X-ray crystallography, protein-protein interactions have become an emerging focus area of drug discovery enterprises. Proteins interact with other biomolecules and it is this intricate network of interactions that determines the behavior of the system and its biological processes. In this review, we briefly discuss networks in disease, followed by computational methods for protein-protein complex prediction. Computational methodologies and techniques employed towards objectives such as protein-protein docking, protein-protein interactions, and interface predictions are described extensively. Docking aims at producing a complex between proteins, while interface predictions identify a subset of residues on one protein that could interact with a partner, and protein-protein interaction sites address whether two proteins interact. In addition, approaches to predict hot spots and binding sites are presented along with a representative example of our internal project on the chemokine CXC receptor 3 B-isoform and predictive modeling with IP10 and PF4.
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Affiliation(s)
- Olivia Slater
- Department of Pharmaceutical Sciences, Southern Illinois University, Edwardsville, IL 62026, United States
| | - Bethany Miller
- Department of Pharmaceutical Sciences, Southern Illinois University, Edwardsville, IL 62026, United States
| | - Maria Kontoyianni
- Department of Pharmaceutical Sciences, Southern Illinois University, Edwardsville, IL 62026, United States
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11
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Khan MAAK, Turjya RR, Islam ABMMK. Computational engineering the binding affinity of Adalimumab monoclonal antibody for designing potential biosimilar candidate. J Mol Graph Model 2021; 102:107774. [DOI: 10.1016/j.jmgm.2020.107774] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2020] [Revised: 08/15/2020] [Accepted: 10/05/2020] [Indexed: 12/26/2022]
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12
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Leveraging a gain-of-function allele of Caenorhabditis elegans paqr-1 to elucidate membrane homeostasis by PAQR proteins. PLoS Genet 2020; 16:e1008975. [PMID: 32750056 PMCID: PMC7428288 DOI: 10.1371/journal.pgen.1008975] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2020] [Revised: 08/14/2020] [Accepted: 07/01/2020] [Indexed: 12/12/2022] Open
Abstract
The C. elegans proteins PAQR-2 (a homolog of the human seven-transmembrane domain AdipoR1 and AdipoR2 proteins) and IGLR-2 (a homolog of the mammalian LRIG proteins characterized by a single transmembrane domain and the presence of immunoglobulin domains and leucine-rich repeats in their extracellular portion) form a complex that protects against plasma membrane rigidification by promoting the expression of fatty acid desaturases and the incorporation of polyunsaturated fatty acids into phospholipids, hence increasing membrane fluidity. In the present study, we leveraged a novel gain-of-function allele of PAQR-1, a PAQR-2 paralog, to carry out structure-function studies. We found that the transmembrane domains of PAQR-2 are responsible for its functional requirement for IGLR-2, that PAQR-1 does not require IGLR-2 but acts via the same pathway as PAQR-2, and that the divergent N-terminal cytoplasmic domains of the PAQR-1 and PAQR-2 proteins serve a regulatory function and may regulate access to the catalytic site of these proteins. We also show that overexpression of human AdipoR1 or AdipoR2 alone is sufficient to confer increased palmitic acid resistance in HEK293 cells, and thus act in a manner analogous to the PAQR-1 gain-of-function allele.
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13
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Chopra K, Burdak B, Sharma K, Kembhavi A, Mande SC, Chauhan R. CoRNeA: A Pipeline to Decrypt the Inter-Protein Interfaces from Amino Acid Sequence Information. Biomolecules 2020; 10:biom10060938. [PMID: 32580303 PMCID: PMC7356028 DOI: 10.3390/biom10060938] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2020] [Revised: 05/26/2020] [Accepted: 05/27/2020] [Indexed: 12/27/2022] Open
Abstract
Decrypting the interface residues of the protein complexes provides insight into the functions of the proteins and, hence, the overall cellular machinery. Computational methods have been devised in the past to predict the interface residues using amino acid sequence information, but all these methods have been majorly applied to predict for prokaryotic protein complexes. Since the composition and rate of evolution of the primary sequence is different between prokaryotes and eukaryotes, it is important to develop a method specifically for eukaryotic complexes. Here, we report a new hybrid pipeline for predicting the protein-protein interaction interfaces in a pairwise manner from the amino acid sequence information of the interacting proteins. It is based on the framework of Co-evolution, machine learning (Random Forest), and Network Analysis named CoRNeA trained specifically on eukaryotic protein complexes. We use Co-evolution, physicochemical properties, and contact potential as major group of features to train the Random Forest classifier. We also incorporate the intra-contact information of the individual proteins to eliminate false positives from the predictions keeping in mind that the amino acid sequence of a protein also holds information for its own folding and not only the interface propensities. Our prediction on example datasets shows that CoRNeA not only enhances the prediction of true interface residues but also reduces false positive rates significantly.
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Affiliation(s)
- Kriti Chopra
- National Centre for Cell Science, Pune 411007, Maharashtra, India; (K.C.); (B.B.)
| | - Bhawna Burdak
- National Centre for Cell Science, Pune 411007, Maharashtra, India; (K.C.); (B.B.)
| | - Kaushal Sharma
- Inter-University Centre for Astronomy and Astrophysics, Pune 411007, Maharashtra, India; (K.S.); (A.K.)
| | - Ajit Kembhavi
- Inter-University Centre for Astronomy and Astrophysics, Pune 411007, Maharashtra, India; (K.S.); (A.K.)
| | - Shekhar C. Mande
- Council of Scientific and Industrial Research (CSIR), New Delhi 110001, India;
| | - Radha Chauhan
- National Centre for Cell Science, Pune 411007, Maharashtra, India; (K.C.); (B.B.)
- Correspondence: ; Tel.: +91-20-25708255
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14
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Sanyanga TA, Tastan Bishop Ö. Structural Characterization of Carbonic Anhydrase VIII and Effects of Missense Single Nucleotide Variations to Protein Structure and Function. Int J Mol Sci 2020; 21:E2764. [PMID: 32316137 PMCID: PMC7215520 DOI: 10.3390/ijms21082764] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2020] [Revised: 04/11/2020] [Accepted: 04/13/2020] [Indexed: 12/13/2022] Open
Abstract
Human carbonic anhydrase 8 (CA-VIII) is an acatalytic isoform of the α -CA family. Though the protein cannot hydrate CO2, CA-VIII is essential for calcium (Ca2+) homeostasis within the body, and achieves this by allosterically inhibiting the binding of inositol 1,4,5-triphosphate (IP3) to the IP3 receptor type 1 (ITPR1) protein. However, the mechanism of interaction of CA-VIII to ITPR1 is not well understood. In addition, functional defects to CA-VIII due to non-synonymous single nucleotide polymorphisms (nsSNVs) result in Ca2+ dysregulation and the development of the phenotypes such as cerebellar ataxia, mental retardation and disequilibrium syndrome 3 (CAMRQ3). The pathogenesis of CAMRQ3 is also not well understood. The structure and function of CA-VIII was characterised, and pathogenesis of CAMRQ3 investigated. Structural and functional characterisation of CA-VIII was conducted through SiteMap and CPORT to identify potential binding site residues. The effects of four pathogenic nsSNVs, S100A, S100P, G162R and R237Q, and two benign S100L and E109D variants on CA-VIII structure and function was then investigated using molecular dynamics (MD) simulations, dynamic cross correlation (DCC) and dynamic residue network (DRN) analysis. SiteMap and CPORT analyses identified 38 unique CA-VIII residues that could potentially bind to ITPR1. MD analysis revealed less conformational sampling within the variant proteins and highlighted potential increases to variant protein rigidity. Dynamic cross correlation (DCC) showed that wild-type (WT) protein residue motion is predominately anti-correlated, with variant proteins showing no correlation to greater residue correlation. DRN revealed variant-associated increases to the accessibility of the N-terminal binding site residues, which could have implications for associations with ITPR1, and further highlighted differences to the mechanism of benign and pathogenic variants. SNV presence is associated with a reduction to the usage of Trp37 in all variants, which has implications for CA-VIII stability. The differences to variant mechanisms can be further investigated to understand pathogenesis of CAMRQ3, enhancing precision medicine-related studies into CA-VIII.
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MESH Headings
- Binding Sites
- Biomarkers, Tumor/chemistry
- Biomarkers, Tumor/genetics
- Biomarkers, Tumor/metabolism
- Cerebellar Ataxia/genetics
- Cerebellar Ataxia/pathology
- Databases, Genetic
- Humans
- Inositol 1,4,5-Trisphosphate Receptors/chemistry
- Inositol 1,4,5-Trisphosphate Receptors/metabolism
- Intellectual Disability/genetics
- Intellectual Disability/pathology
- Molecular Dynamics Simulation
- Mutation, Missense
- Polymorphism, Single Nucleotide
- Protein Binding
- Protein Interaction Maps
- Protein Stability
- Protein Structure, Tertiary
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Affiliation(s)
| | - Özlem Tastan Bishop
- Research Unit in Bioinformatics (RUBi), Department of Biochemistry and Microbiology, Rhodes University, Grahamstown 6140, South Africa;
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15
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Barreto CAV, Baptista SJ, Preto AJ, Matos-Filipe P, Mourão J, Melo R, Moreira I. Prediction and targeting of GPCR oligomer interfaces. PROGRESS IN MOLECULAR BIOLOGY AND TRANSLATIONAL SCIENCE 2020; 169:105-149. [PMID: 31952684 DOI: 10.1016/bs.pmbts.2019.11.007] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
GPCR oligomerization has emerged as a hot topic in the GPCR field in the last years. Receptors that are part of these oligomers can influence each other's function, although it is not yet entirely understood how these interactions work. The existence of such a highly complex network of interactions between GPCRs generates the possibility of alternative targets for new therapeutic approaches. However, challenges still exist in the characterization of these complexes, especially at the interface level. Different experimental approaches, such as FRET or BRET, are usually combined to study GPCR oligomer interactions. Computational methods have been applied as a useful tool for retrieving information from GPCR sequences and the few X-ray-resolved oligomeric structures that are accessible, as well as for predicting new and trustworthy GPCR oligomeric interfaces. Machine-learning (ML) approaches have recently helped with some hindrances of other methods. By joining and evaluating multiple structure-, sequence- and co-evolution-based features on the same algorithm, it is possible to dilute the issues of particular structures and residues that arise from the experimental methodology into all-encompassing algorithms capable of accurately predict GPCR-GPCR interfaces. All these methods used as a single or a combined approach provide useful information about GPCR oligomerization and its role in GPCR function and dynamics. Altogether, we present experimental, computational and machine-learning methods used to study oligomers interfaces, as well as strategies that have been used to target these dynamic complexes.
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Affiliation(s)
- Carlos A V Barreto
- Center for Neuroscience and Cell Biology, University of Coimbra, Coimbra, Portugal
| | - Salete J Baptista
- Center for Neuroscience and Cell Biology, University of Coimbra, Coimbra, Portugal; Centro de Ciências e Tecnologias Nucleares, Instituto Superior Técnico, Universidade de Lisboa, CTN, LRS, Portugal
| | - António José Preto
- Center for Neuroscience and Cell Biology, University of Coimbra, Coimbra, Portugal
| | - Pedro Matos-Filipe
- Center for Neuroscience and Cell Biology, University of Coimbra, Coimbra, Portugal
| | - Joana Mourão
- Center for Neuroscience and Cell Biology, University of Coimbra, Coimbra, Portugal; Institute for Interdisciplinary Research, University of Coimbra, Coimbra, Portugal
| | - Rita Melo
- Center for Neuroscience and Cell Biology, University of Coimbra, Coimbra, Portugal; Centro de Ciências e Tecnologias Nucleares, Instituto Superior Técnico, Universidade de Lisboa, CTN, LRS, Portugal
| | - Irina Moreira
- Center for Neuroscience and Cell Biology, University of Coimbra, Coimbra, Portugal; Science and Technology Faculty, University of Coimbra, Coimbra, Portugal.
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16
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Hughes SA, Wang F, Wang S, Kreutzberger MAB, Osinski T, Orlova A, Wall JS, Zuo X, Egelman EH, Conticello VP. Ambidextrous helical nanotubes from self-assembly of designed helical hairpin motifs. Proc Natl Acad Sci U S A 2019; 116:14456-14464. [PMID: 31262809 PMCID: PMC6642399 DOI: 10.1073/pnas.1903910116] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
Tandem repeat proteins exhibit native designability and represent potentially useful scaffolds for the construction of synthetic biomimetic assemblies. We have designed 2 synthetic peptides, HEAT_R1 and LRV_M3Δ1, based on the consensus sequences of single repeats of thermophilic HEAT (PBS_HEAT) and Leucine-Rich Variant (LRV) structural motifs, respectively. Self-assembly of the peptides afforded high-aspect ratio helical nanotubes. Cryo-electron microscopy with direct electron detection was employed to analyze the structures of the solvated filaments. The 3D reconstructions from the cryo-EM maps led to atomic models for the HEAT_R1 and LRV_M3Δ1 filaments at resolutions of 6.0 and 4.4 Å, respectively. Surprisingly, despite sequence similarity at the lateral packing interface, HEAT_R1 and LRV_M3Δ1 filaments adopt the opposite helical hand and differ significantly in helical geometry, while retaining a local conformation similar to previously characterized repeat proteins of the same class. The differences in the 2 filaments could be rationalized on the basis of differences in cohesive interactions at the lateral and axial interfaces. These structural data reinforce previous observations regarding the structural plasticity of helical protein assemblies and the need for high-resolution structural analysis. Despite these observations, the native designability of tandem repeat proteins offers the opportunity to engineer novel helical nanotubes. Moreover, the resultant nanotubes have independently addressable and chemically distinguishable interior and exterior surfaces that would facilitate applications in selective recognition, transport, and release.
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Affiliation(s)
| | - Fengbin Wang
- Department of Biochemistry and Molecular Genetics, University of Virginia, Charlottesville, VA 22908
| | - Shengyuan Wang
- Department of Chemistry, Emory University, Atlanta, GA 30322
| | - Mark A B Kreutzberger
- Department of Biochemistry and Molecular Genetics, University of Virginia, Charlottesville, VA 22908
| | - Tomasz Osinski
- Department of Biochemistry and Molecular Genetics, University of Virginia, Charlottesville, VA 22908
| | - Albina Orlova
- Department of Biochemistry and Molecular Genetics, University of Virginia, Charlottesville, VA 22908
| | - Joseph S Wall
- Department of Biology, Brookhaven National Laboratory, Upton, NY 11973
| | - Xiaobing Zuo
- X-Ray Science Division, Argonne National Laboratory, Argonne, IL 60439
| | - Edward H Egelman
- Department of Biochemistry and Molecular Genetics, University of Virginia, Charlottesville, VA 22908
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17
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Sun P, Guo S, Sun J, Tan L, Lu C, Ma Z. Advances in In-silico B-cell Epitope Prediction. Curr Top Med Chem 2019; 19:105-115. [PMID: 30499399 DOI: 10.2174/1568026619666181130111827] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2018] [Revised: 07/27/2018] [Accepted: 08/09/2018] [Indexed: 01/25/2023]
Abstract
Identification of B-cell epitopes in target antigens is one of the most crucial steps for epitopebased vaccine development, immunodiagnostic tests, antibody production, and disease diagnosis and therapy. Experimental methods for B-cell epitope mapping are time consuming, costly and labor intensive; in the meantime, various in-silico methods are proposed to predict both linear and conformational B-cell epitopes. The accurate identification of B-cell epitopes presents major challenges for immunoinformaticians. In this paper, we have comprehensively reviewed in-silico methods for B-cell epitope identification. The aim of this review is to stimulate the development of better tools which could improve the identification of B-cell epitopes, and further for the development of therapeutic antibodies and diagnostic tools.
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Affiliation(s)
- Pingping Sun
- School of Information Science and Technology, Northeast Normal University, Changchun 130117, China.,Key Laboratory of Intelligent Information Processing of Jilin University, Northeast Normal University, Changchun 130117, China.,Institute of Computational Biology, Northeast Normal University, Changchun 130117, China
| | - Sijia Guo
- School of Information Science and Technology, Northeast Normal University, Changchun 130117, China.,Key Laboratory of Intelligent Information Processing of Jilin University, Northeast Normal University, Changchun 130117, China.,Institute of Computational Biology, Northeast Normal University, Changchun 130117, China
| | - Jiahang Sun
- School of Information Science and Technology, Northeast Normal University, Changchun 130117, China.,Key Laboratory of Intelligent Information Processing of Jilin University, Northeast Normal University, Changchun 130117, China.,Institute of Computational Biology, Northeast Normal University, Changchun 130117, China
| | - Liming Tan
- School of Information Science and Technology, Northeast Normal University, Changchun 130117, China.,Key Laboratory of Intelligent Information Processing of Jilin University, Northeast Normal University, Changchun 130117, China.,Institute of Computational Biology, Northeast Normal University, Changchun 130117, China
| | - Chang Lu
- School of Information Science and Technology, Northeast Normal University, Changchun 130117, China.,Key Laboratory of Intelligent Information Processing of Jilin University, Northeast Normal University, Changchun 130117, China.,Institute of Computational Biology, Northeast Normal University, Changchun 130117, China
| | - Zhiqiang Ma
- School of Information Science and Technology, Northeast Normal University, Changchun 130117, China.,Key Laboratory of Intelligent Information Processing of Jilin University, Northeast Normal University, Changchun 130117, China.,Institute of Computational Biology, Northeast Normal University, Changchun 130117, China
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18
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Bevacizumab Antibody Affinity Maturation to Improve Ovarian Cancer Immunotherapy: In Silico Approach. Int J Pept Res Ther 2018. [DOI: 10.1007/s10989-018-9787-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
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19
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Lazar T, Guharoy M, Schad E, Tompa P. Unique Physicochemical Patterns of Residues in Protein–Protein Interfaces. J Chem Inf Model 2018; 58:2164-2173. [DOI: 10.1021/acs.jcim.8b00270] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/02/2023]
Affiliation(s)
- Tamas Lazar
- VIB-VUB Center for Structural Biology, Vlaams Instituut voor Biotechnologie, Pleinlaan 2, 1050 Brussels, Belgium
- Structural Biology Brussels, Vrije Universiteit Brussel, Pleinlaan 2, 1050 Brussels, Belgium
| | - Mainak Guharoy
- VIB-VUB Center for Structural Biology, Vlaams Instituut voor Biotechnologie, Pleinlaan 2, 1050 Brussels, Belgium
- Structural Biology Brussels, Vrije Universiteit Brussel, Pleinlaan 2, 1050 Brussels, Belgium
| | - Eva Schad
- Institute of Enzymology, Research Centre for Natural Sciences, Hungarian Academy of Sciences, Magyar tudosok korutja 2, 1117 Budapest, Hungary
| | - Peter Tompa
- VIB-VUB Center for Structural Biology, Vlaams Instituut voor Biotechnologie, Pleinlaan 2, 1050 Brussels, Belgium
- Structural Biology Brussels, Vrije Universiteit Brussel, Pleinlaan 2, 1050 Brussels, Belgium
- Institute of Enzymology, Research Centre for Natural Sciences, Hungarian Academy of Sciences, Magyar tudosok korutja 2, 1117 Budapest, Hungary
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20
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Macalino SJY, Basith S, Clavio NAB, Chang H, Kang S, Choi S. Evolution of In Silico Strategies for Protein-Protein Interaction Drug Discovery. Molecules 2018; 23:E1963. [PMID: 30082644 PMCID: PMC6222862 DOI: 10.3390/molecules23081963] [Citation(s) in RCA: 57] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2018] [Revised: 08/03/2018] [Accepted: 08/04/2018] [Indexed: 12/14/2022] Open
Abstract
The advent of advanced molecular modeling software, big data analytics, and high-speed processing units has led to the exponential evolution of modern drug discovery and better insights into complex biological processes and disease networks. This has progressively steered current research interests to understanding protein-protein interaction (PPI) systems that are related to a number of relevant diseases, such as cancer, neurological illnesses, metabolic disorders, etc. However, targeting PPIs are challenging due to their "undruggable" binding interfaces. In this review, we focus on the current obstacles that impede PPI drug discovery, and how recent discoveries and advances in in silico approaches can alleviate these barriers to expedite the search for potential leads, as shown in several exemplary studies. We will also discuss about currently available information on PPI compounds and systems, along with their usefulness in molecular modeling. Finally, we conclude by presenting the limits of in silico application in drug discovery and offer a perspective in the field of computer-aided PPI drug discovery.
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Affiliation(s)
- Stephani Joy Y Macalino
- College of Pharmacy and Graduate School of Pharmaceutical Sciences, Ewha Womans University, Seoul 03760, Korea.
| | - Shaherin Basith
- College of Pharmacy and Graduate School of Pharmaceutical Sciences, Ewha Womans University, Seoul 03760, Korea.
| | - Nina Abigail B Clavio
- College of Pharmacy and Graduate School of Pharmaceutical Sciences, Ewha Womans University, Seoul 03760, Korea.
| | - Hyerim Chang
- College of Pharmacy and Graduate School of Pharmaceutical Sciences, Ewha Womans University, Seoul 03760, Korea.
| | - Soosung Kang
- College of Pharmacy and Graduate School of Pharmaceutical Sciences, Ewha Womans University, Seoul 03760, Korea.
| | - Sun Choi
- College of Pharmacy and Graduate School of Pharmaceutical Sciences, Ewha Womans University, Seoul 03760, Korea.
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21
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In silico prediction of active site and in vitro DNase and RNase activities of Helicoverpa-inducible pathogenesis related-4 protein from Cicer arietinum. Int J Biol Macromol 2018. [DOI: 10.1016/j.ijbiomac.2018.03.027] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
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22
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Daberdaku S, Ferrari C. Exploring the potential of 3D Zernike descriptors and SVM for protein-protein interface prediction. BMC Bioinformatics 2018; 19:35. [PMID: 29409446 PMCID: PMC5802066 DOI: 10.1186/s12859-018-2043-3] [Citation(s) in RCA: 27] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2017] [Accepted: 01/24/2018] [Indexed: 12/22/2022] Open
Abstract
Background The correct determination of protein–protein interaction interfaces is important for understanding disease mechanisms and for rational drug design. To date, several computational methods for the prediction of protein interfaces have been developed, but the interface prediction problem is still not fully understood. Experimental evidence suggests that the location of binding sites is imprinted in the protein structure, but there are major differences among the interfaces of the various protein types: the characterising properties can vary a lot depending on the interaction type and function. The selection of an optimal set of features characterising the protein interface and the development of an effective method to represent and capture the complex protein recognition patterns are of paramount importance for this task. Results In this work we investigate the potential of a novel local surface descriptor based on 3D Zernike moments for the interface prediction task. Descriptors invariant to roto-translations are extracted from circular patches of the protein surface enriched with physico-chemical properties from the HQI8 amino acid index set, and are used as samples for a binary classification problem. Support Vector Machines are used as a classifier to distinguish interface local surface patches from non-interface ones. The proposed method was validated on 16 classes of proteins extracted from the Protein–Protein Docking Benchmark 5.0 and compared to other state-of-the-art protein interface predictors (SPPIDER, PrISE and NPS-HomPPI). Conclusions The 3D Zernike descriptors are able to capture the similarity among patterns of physico-chemical and biochemical properties mapped on the protein surface arising from the various spatial arrangements of the underlying residues, and their usage can be easily extended to other sets of amino acid properties. The results suggest that the choice of a proper set of features characterising the protein interface is crucial for the interface prediction task, and that optimality strongly depends on the class of proteins whose interface we want to characterise. We postulate that different protein classes should be treated separately and that it is necessary to identify an optimal set of features for each protein class. Electronic supplementary material The online version of this article (10.1186/s12859-018-2043-3) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Sebastian Daberdaku
- Department of Information Engineering, University of Padova, via Gradenigo 6/A, Padova, 35131, Italy.
| | - Carlo Ferrari
- Department of Information Engineering, University of Padova, via Gradenigo 6/A, Padova, 35131, Italy
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23
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Sefid F, Payandeh Z, Azamirad G, Abdolhamidi R, Rasooli I. In Silico Engineering Towards Enhancement of Bap–VHH Monoclonal Antibody Binding Affinity. Int J Pept Res Ther 2018. [DOI: 10.1007/s10989-017-9670-9] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
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24
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Meyer MJ, Beltrán JF, Liang S, Fragoza R, Rumack A, Liang J, Wei X, Yu H. Interactome INSIDER: a structural interactome browser for genomic studies. Nat Methods 2018; 15:107-114. [PMID: 29355848 PMCID: PMC6026581 DOI: 10.1038/nmeth.4540] [Citation(s) in RCA: 93] [Impact Index Per Article: 15.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2017] [Accepted: 10/22/2017] [Indexed: 02/07/2023]
Abstract
We present Interactome INSIDER, a tool to link genomic variant information with
structural protein-protein interactomes. Underlying this tool is the application of
machine learning to predict protein interaction interfaces for 185,957 protein
interactions with previously unresolved interfaces, in human and 7 model organisms,
including the entire experimentally determined human binary interactome. Predicted
interfaces exhibit similar functional properties as known interfaces, including enrichment
for disease mutations and recurrent cancer mutations. Through 2,164 de
novo mutagenesis experiments, we show that mutations of predicted and known
interface residues disrupt interactions at a similar rate, and much more frequently than
mutations outside of predicted interfaces. To spur functional genomic studies, Interactome
INSIDER (http://interactomeinsider.yulab.org) enables users to identify whether
variants or disease mutations are enriched in known and predicted interaction interfaces
at various resolutions. Users may explore known population variants, disease mutations,
and somatic cancer mutations, or upload their own set of mutations for this purpose.
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Affiliation(s)
- Michael J Meyer
- Department of Biological Statistics and Computational Biology, Cornell University, Ithaca, New York, USA.,Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, New York, USA.,Tri-Institutional Training Program in Computational Biology and Medicine, New York, New York, USA
| | - Juan Felipe Beltrán
- Department of Biological Statistics and Computational Biology, Cornell University, Ithaca, New York, USA.,Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, New York, USA
| | - Siqi Liang
- Department of Biological Statistics and Computational Biology, Cornell University, Ithaca, New York, USA.,Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, New York, USA
| | - Robert Fragoza
- Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, New York, USA.,Department of Molecular Biology and Genetics, Cornell University, Ithaca, New York, USA
| | - Aaron Rumack
- Department of Biological Statistics and Computational Biology, Cornell University, Ithaca, New York, USA.,Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, New York, USA
| | - Jin Liang
- Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, New York, USA
| | - Xiaomu Wei
- Department of Biological Statistics and Computational Biology, Cornell University, Ithaca, New York, USA.,Department of Medicine, Weill Cornell College of Medicine, New York, New York, USA
| | - Haiyuan Yu
- Department of Biological Statistics and Computational Biology, Cornell University, Ithaca, New York, USA.,Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, New York, USA
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25
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Payandeh Z, Rajabibazl M, Mortazavi Y, Rahimpour A, Taromchi AH. Ofatumumab Monoclonal Antibody Affinity Maturation Through in silico Modeling. IRANIAN BIOMEDICAL JOURNAL 2017; 22:180-92. [PMID: 28992681 PMCID: PMC5889503 DOI: 10.22034/ibj.22.3.180] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
Abstract
Background: Ofatumumab, an anti-CD20 mAb, was approved in 2009 for the treatment of chronic lymphocytic leukemia. This mAb acts through immune-mediated mechanisms, in particular complement-dependent cytotoxicity and antibody-dependent cellular cytotoxicity by natural killer cells as well as antibody-dependent phagocytosis by macrophages. Apoptosis induction is another mechanism of this antibody. Computational docking is the method of predicting the conformation of an antibody-antigen from its separated elements. Validation of the designed antibodies is carried out by docking tools. Increased affinity enhances the biological action of the antibody, which in turn improves the therapeutic effects. Furthermore, the increased antibody affinity can reduce the therapeutic dose of the antibody, resulting in lower toxicity and handling cost. Methods: Considering the importance of this issue, using in silico analysis such as docking and molecular dynamics, we aimed to find the important amino acids of the Ofatumumab antibody and then replaced these amino acids with others to improve antibody-binding affinity. Finally, we examined the binding affinity of antibody variants to antigen. Results: Our findings showed that variant 3 mutations have improved the characteristics of antibody binding compared to normal Ofatumumab antibodies. Conclusion: The designed anti-CD20 antibodies showed potentiality for improved affinity in comparison to commercial Ofatumumab.
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Affiliation(s)
- Zahra Payandeh
- Department of Medical Biotechnology and Nanotechnology, Faculty of Medicine, Zanjan University of Medical Sciences, Zanjan, Iran
| | - Masoumeh Rajabibazl
- Department of Clinical Biochemistry, Faculty of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran.,School of Advanced Technologies in Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Yousef Mortazavi
- Department of Medical Biotechnology and Nanotechnology, Faculty of Medicine, Zanjan University of Medical Sciences, Zanjan, Iran.,Cancer Gene Therapy Research Center, Faculty of Medicine, Zanjan University of Medical Sciences, Zanjan, Iran
| | - Azam Rahimpour
- School of Advanced Technologies in Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Amir Hossein Taromchi
- Department of Medical Biotechnology and Nanotechnology, Faculty of Medicine, Zanjan University of Medical Sciences, Zanjan, Iran
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26
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Northey TC, Barešić A, Martin ACR. IntPred: a structure-based predictor of protein-protein interaction sites. Bioinformatics 2017; 34:223-229. [PMID: 28968673 PMCID: PMC5860208 DOI: 10.1093/bioinformatics/btx585] [Citation(s) in RCA: 41] [Impact Index Per Article: 5.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2016] [Revised: 08/21/2017] [Accepted: 09/15/2017] [Indexed: 11/17/2022] Open
Abstract
Motivation Protein–protein interactions are vital for protein function with the average protein having between three and ten interacting partners. Knowledge of precise protein–protein interfaces comes from crystal structures deposited in the Protein Data Bank (PDB), but only 50% of structures in the PDB are complexes. There is therefore a need to predict protein–protein interfaces in silico and various methods for this purpose. Here we explore the use of a predictor based on structural features and which exploits random forest machine learning, comparing its performance with a number of popular established methods. Results On an independent test set of obligate and transient complexes, our IntPred predictor performs well (MCC = 0.370, ACC = 0.811, SPEC = 0.916, SENS = 0.411) and compares favourably with other methods. Overall, IntPred ranks second of six methods tested with SPPIDER having slightly better overall performance (MCC = 0.410, ACC = 0.759, SPEC = 0.783, SENS = 0.676), but considerably worse specificity than IntPred. As with SPPIDER, using an independent test set of obligate complexes enhanced performance (MCC = 0.381) while performance is somewhat reduced on a dataset of transient complexes (MCC = 0.303). The trade-off between sensitivity and specificity compared with SPPIDER suggests that the choice of the appropriate tool is application-dependent. Availability and implementation IntPred is implemented in Perl and may be downloaded for local use or run via a web server at www.bioinf.org.uk/intpred/. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Thomas C Northey
- Institute of Structural and Molecular Biology, Division of Biosciences, University College London, London, UK
| | - Anja Barešić
- Institute of Structural and Molecular Biology, Division of Biosciences, University College London, London, UK
| | - Andrew C R Martin
- Institute of Structural and Molecular Biology, Division of Biosciences, University College London, London, UK
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27
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Mutations at protein-protein interfaces: Small changes over big surfaces have large impacts on human health. PROGRESS IN BIOPHYSICS AND MOLECULAR BIOLOGY 2017; 128:3-13. [DOI: 10.1016/j.pbiomolbio.2016.10.002] [Citation(s) in RCA: 107] [Impact Index Per Article: 15.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/30/2016] [Revised: 10/15/2016] [Accepted: 10/19/2016] [Indexed: 12/22/2022]
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28
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Biswas R, Ghosh S, Bagchi A. A structural perspective on the interactions of TRAF6 and Basigin during the onset of melanoma: A molecular dynamics simulation study. J Mol Recognit 2017; 30. [PMID: 28612997 DOI: 10.1002/jmr.2643] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2017] [Revised: 04/19/2017] [Accepted: 05/10/2017] [Indexed: 12/12/2022]
Abstract
Metastatic melanoma is the most fatal type of skin cancer. The roles of matrix metalloproteinases (MMPs) have well been established in the onset of melanoma. Basigin (BSG) belongs to the immunoglobulin superfamily and is critical for induction of extracellular MMPs during the onset of various cancers including melanoma. Tumor necrosis factor receptor-associated factor 6 (TRAF6) is an E3-ligase that interacts with BSG and mediates its membrane localization, which leads to MMP expression in melanoma cells. This makes TRAF6 a potential therapeutic target in melanoma. We here conducted protein-protein interaction studies on TRAF6 and BSG to get molecular level insights of the reactions. The structure of human BSG was constructed by protein threading. Molecular-docking method was applied to develop the TRAF6-BSG complex. The refined docked complex was further optimized by molecular dynamics simulations. Results from binding free energy, surface properties, and electrostatic interaction analysis indicate that Lys340 and Glu417 of TRAF6 play as the anchor residues in the protein interaction interface. The current study will be helpful in designing specific modulators of TRAF6 to control melanoma metastasis.
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Affiliation(s)
- Ria Biswas
- Department of Biochemistry and Biophysics, University of Kalyani, Kalyani, Nadia, India
| | - Semanti Ghosh
- Department of Biochemistry and Biophysics, University of Kalyani, Kalyani, Nadia, India
| | - Angshuman Bagchi
- Department of Biochemistry and Biophysics, University of Kalyani, Kalyani, Nadia, India
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29
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Structural analysis and insight into Zika virus NS5 mediated interferon inhibition. INFECTION GENETICS AND EVOLUTION 2017; 51:143-152. [PMID: 28365387 DOI: 10.1016/j.meegid.2017.03.027] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/02/2017] [Revised: 03/03/2017] [Accepted: 03/25/2017] [Indexed: 11/20/2022]
Abstract
The Zika virus outbreak in 2015-2016 is the largest of its kind for which WHO declared a Public Health Emergency of International Concerns. No FDA approved drug is available for the treatment of the viral infection. The interaction of flavivirus NS5 protein with SIAH2 ubiquitin ligase has been previously known. NS5 of Zika virus has been implicated in the degradation of STAT2 protein, which activates interferon-stimulated antiviral activity. Based on our proposition that NS5 utilizes SIAH2-mediated proteasomal degradation of STAT2, an in-silico study was carried out to characterize the protein-protein interactions between NS5, SIAH2 and STAT2 proteins. The aim of our study was to identify the amino acid residues of NS5 involved in IFN antagonism as well as to find the association between NS5, SIAH2 and STAT2 to predict the interaction pattern of these proteins. Analysis proposed that NS5 recruits SIAH2 for the ubiquitination-dependent degradation of STAT2. NS5 residues involved in interaction with SIAH2 and/or STAT2 were found to be mostly conserved across related flaviviruses. These are novel findings regarding the Zika virus and require confirmation through experimental approaches.
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30
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Murakami Y, Tripathi LP, Prathipati P, Mizuguchi K. Network analysis and in silico prediction of protein-protein interactions with applications in drug discovery. Curr Opin Struct Biol 2017; 44:134-142. [PMID: 28364585 DOI: 10.1016/j.sbi.2017.02.005] [Citation(s) in RCA: 61] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2016] [Revised: 02/05/2017] [Accepted: 02/23/2017] [Indexed: 11/29/2022]
Abstract
Protein-protein interactions (PPIs) are vital to maintaining cellular homeostasis. Several PPI dysregulations have been implicated in the etiology of various diseases and hence PPIs have emerged as promising targets for drug discovery. Surface residues and hotspot residues at the interface of PPIs form the core regions, which play a key role in modulating cellular processes such as signal transduction and are used as starting points for drug design. In this review, we briefly discuss how PPI networks (PPINs) inferred from experimentally characterized PPI data have been utilized for knowledge discovery and how in silico approaches to PPI characterization can contribute to PPIN-based biological research. Next, we describe the principles of in silico PPI prediction and survey the existing PPI and PPI site prediction servers that are useful for drug discovery. Finally, we discuss the potential of in silico PPI prediction in drug discovery.
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Affiliation(s)
- Yoichi Murakami
- National Institutes of Biomedical Innovation, Health and Nutrition, 7-6-8 Saito Asagi, Ibaraki, Osaka 567-0085, Japan.
| | - Lokesh P Tripathi
- National Institutes of Biomedical Innovation, Health and Nutrition, 7-6-8 Saito Asagi, Ibaraki, Osaka 567-0085, Japan.
| | - Philip Prathipati
- National Institutes of Biomedical Innovation, Health and Nutrition, 7-6-8 Saito Asagi, Ibaraki, Osaka 567-0085, Japan
| | - Kenji Mizuguchi
- National Institutes of Biomedical Innovation, Health and Nutrition, 7-6-8 Saito Asagi, Ibaraki, Osaka 567-0085, Japan.
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31
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Computational Approaches for Predicting Binding Partners, Interface Residues, and Binding Affinity of Protein-Protein Complexes. Methods Mol Biol 2017; 1484:237-253. [PMID: 27787830 DOI: 10.1007/978-1-4939-6406-2_16] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
Studying protein-protein interactions leads to a better understanding of the underlying principles of several biological pathways. Cost and labor-intensive experimental techniques suggest the need for computational methods to complement them. Several such state-of-the-art methods have been reported for analyzing diverse aspects such as predicting binding partners, interface residues, and binding affinity for protein-protein complexes with reliable performance. However, there are specific drawbacks for different methods that indicate the need for their improvement. This review highlights various available computational algorithms for analyzing diverse aspects of protein-protein interactions and endorses the necessity for developing new robust methods for gaining deep insights about protein-protein interactions.
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Bai F, Morcos F, Cheng RR, Jiang H, Onuchic JN. Elucidating the druggable interface of protein-protein interactions using fragment docking and coevolutionary analysis. Proc Natl Acad Sci U S A 2016; 113:E8051-E8058. [PMID: 27911825 PMCID: PMC5167203 DOI: 10.1073/pnas.1615932113] [Citation(s) in RCA: 53] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022] Open
Abstract
Protein-protein interactions play a central role in cellular function. Improving the understanding of complex formation has many practical applications, including the rational design of new therapeutic agents and the mechanisms governing signal transduction networks. The generally large, flat, and relatively featureless binding sites of protein complexes pose many challenges for drug design. Fragment docking and direct coupling analysis are used in an integrated computational method to estimate druggable protein-protein interfaces. (i) This method explores the binding of fragment-sized molecular probes on the protein surface using a molecular docking-based screen. (ii) The energetically favorable binding sites of the probes, called hot spots, are spatially clustered to map out candidate binding sites on the protein surface. (iii) A coevolution-based interface interaction score is used to discriminate between different candidate binding sites, yielding potential interfacial targets for therapeutic drug design. This approach is validated for important, well-studied disease-related proteins with known pharmaceutical targets, and also identifies targets that have yet to be studied. Moreover, therapeutic agents are proposed by chemically connecting the fragments that are strongly bound to the hot spots.
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Affiliation(s)
- Fang Bai
- Center for Theoretical Biological Physics, Rice University, Houston, TX 77005
| | - Faruck Morcos
- Department of Biological Sciences, University of Texas at Dallas, Dallas, TX 75080
- Department of Bioengineering, University of Texas at Dallas, Dallas, TX 75080
- Center for Systems Biology, University of Texas at Dallas, Dallas, TX 75080
| | - Ryan R Cheng
- Center for Theoretical Biological Physics, Rice University, Houston, TX 77005
| | - Hualiang Jiang
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China;
| | - José N Onuchic
- Center for Theoretical Biological Physics, Rice University, Houston, TX 77005;
- Department of Physics and Astronomy, Rice University, Houston, TX 77005
- Department of Chemistry, Rice University, Houston, TX 77005
- Department of Biosciences, Rice University, Houston, TX 77005
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Goldberg AB, Cho E, Miller CJ, Lou HJ, Turk BE. Identification of a Substrate-selective Exosite within the Metalloproteinase Anthrax Lethal Factor. J Biol Chem 2016; 292:814-825. [PMID: 27909054 DOI: 10.1074/jbc.m116.761734] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2016] [Revised: 11/23/2016] [Indexed: 01/02/2023] Open
Abstract
The metalloproteinase anthrax lethal factor (LF) is secreted by Bacillus anthracis to promote disease virulence through disruption of host signaling pathways. LF is a highly specific protease, exclusively cleaving mitogen-activated protein kinase kinases (MKKs) and rodent NLRP1B (NACHT leucine-rich repeat and pyrin domain-containing protein 1B). How LF achieves such restricted substrate specificity is not understood. Previous studies have suggested the existence of an exosite interaction between LF and MKKs that promotes cleavage efficiency and specificity. Through a combination of in silico prediction and site-directed mutagenesis, we have mapped an exosite to a non-catalytic region of LF. Mutations within this site selectively impair proteolysis of full-length MKKs yet have no impact on cleavage of short peptide substrates. Although this region appears important for cleaving all LF protein substrates, we found that mutation of specific residues within the exosite differentially affects MKK and NLRP1B cleavage in vitro and in cultured cells. One residue in particular, Trp-271, is essential for cleavage of MKK3, MKK4, and MKK6 but dispensable for targeting of MEK1, MEK2, and NLRP1B. Analysis of chimeric substrates suggests that this residue interacts with the MKK catalytic domain. We found that LF-W271A blocked ERK phosphorylation and growth in a melanoma cell line, suggesting that it may provide a highly selective inhibitor of MEK1/2 for use as a cancer therapeutic. These findings provide insight into how a bacterial toxin functions to specifically impair host signaling pathways and suggest a general strategy for mapping protease exosite interactions.
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Affiliation(s)
- Allison B Goldberg
- From the Department of Pharmacology, Yale University School of Medicine, New Haven, Connecticut 06520
| | - Eunice Cho
- From the Department of Pharmacology, Yale University School of Medicine, New Haven, Connecticut 06520
| | - Chad J Miller
- From the Department of Pharmacology, Yale University School of Medicine, New Haven, Connecticut 06520
| | - Hua Jane Lou
- From the Department of Pharmacology, Yale University School of Medicine, New Haven, Connecticut 06520
| | - Benjamin E Turk
- From the Department of Pharmacology, Yale University School of Medicine, New Haven, Connecticut 06520
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Esmaielbeiki R, Krawczyk K, Knapp B, Nebel JC, Deane CM. Progress and challenges in predicting protein interfaces. Brief Bioinform 2016; 17:117-31. [PMID: 25971595 PMCID: PMC4719070 DOI: 10.1093/bib/bbv027] [Citation(s) in RCA: 100] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2015] [Revised: 03/18/2015] [Indexed: 12/31/2022] Open
Abstract
The majority of biological processes are mediated via protein-protein interactions. Determination of residues participating in such interactions improves our understanding of molecular mechanisms and facilitates the development of therapeutics. Experimental approaches to identifying interacting residues, such as mutagenesis, are costly and time-consuming and thus, computational methods for this purpose could streamline conventional pipelines. Here we review the field of computational protein interface prediction. We make a distinction between methods which address proteins in general and those targeted at antibodies, owing to the radically different binding mechanism of antibodies. We organize the multitude of currently available methods hierarchically based on required input and prediction principles to provide an overview of the field.
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Xue LC, Dobbs D, Bonvin AMJJ, Honavar V. Computational prediction of protein interfaces: A review of data driven methods. FEBS Lett 2015; 589:3516-26. [PMID: 26460190 PMCID: PMC4655202 DOI: 10.1016/j.febslet.2015.10.003] [Citation(s) in RCA: 101] [Impact Index Per Article: 11.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2015] [Revised: 10/01/2015] [Accepted: 10/02/2015] [Indexed: 01/06/2023]
Abstract
Reliably pinpointing which specific amino acid residues form the interface(s) between a protein and its binding partner(s) is critical for understanding the structural and physicochemical determinants of protein recognition and binding affinity, and has wide applications in modeling and validating protein interactions predicted by high-throughput methods, in engineering proteins, and in prioritizing drug targets. Here, we review the basic concepts, principles and recent advances in computational approaches to the analysis and prediction of protein-protein interfaces. We point out caveats for objectively evaluating interface predictors, and discuss various applications of data-driven interface predictors for improving energy model-driven protein-protein docking. Finally, we stress the importance of exploiting binding partner information in reliably predicting interfaces and highlight recent advances in this emerging direction.
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Affiliation(s)
- Li C Xue
- Faculty of Science - Chemistry, Bijvoet Center for Biomolecular Research, Utrecht Univ., Utrecht 3584 CH, The Netherlands.
| | - Drena Dobbs
- Department of Genetics, Development & Cell Biology, Iowa State Univ., Ames, IA 50011, USA; Bioinformatics & Computational Biology Program, Iowa State Univ., Ames, IA 50011, USA
| | - Alexandre M J J Bonvin
- Faculty of Science - Chemistry, Bijvoet Center for Biomolecular Research, Utrecht Univ., Utrecht 3584 CH, The Netherlands
| | - Vasant Honavar
- College of Information Sciences & Technology, Pennsylvania State Univ., University Park, PA 16802, USA; Genomics & Bioinformatics Program, Pennsylvania State Univ., University Park, PA 16802, USA; Neuroscience Program, Pennsylvania State Univ., University Park, PA 16802, USA; The Huck Institutes of the Life Sciences, Pennsylvania State Univ., University Park, PA 16802, USA; Center for Big Data Analytics & Discovery Informatics, Pennsylvania State Univ., University Park, PA 16802, USA; Institute for Cyberscience, Pennsylvania State Univ., University Park, PA 16802, USA
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Survey of Natural Language Processing Techniques in Bioinformatics. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2015; 2015:674296. [PMID: 26525745 PMCID: PMC4615216 DOI: 10.1155/2015/674296] [Citation(s) in RCA: 32] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/10/2015] [Revised: 06/12/2015] [Accepted: 06/21/2015] [Indexed: 01/02/2023]
Abstract
Informatics methods, such as text mining and natural language processing, are always involved in bioinformatics research. In this study, we discuss text mining and natural language processing methods in bioinformatics from two perspectives. First, we aim to search for knowledge on biology, retrieve references using text mining methods, and reconstruct databases. For example, protein-protein interactions and gene-disease relationship can be mined from PubMed. Then, we analyze the applications of text mining and natural language processing techniques in bioinformatics, including predicting protein structure and function, detecting noncoding RNA. Finally, numerous methods and applications, as well as their contributions to bioinformatics, are discussed for future use by text mining and natural language processing researchers.
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Sudha G, Naveenkumar N, Srinivasan N. Evolutionary and structural analyses of heterodimeric proteins composed of subunits with same fold. Proteins 2015; 83:1766-86. [PMID: 26148218 DOI: 10.1002/prot.24849] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2015] [Revised: 05/30/2015] [Accepted: 06/21/2015] [Indexed: 11/10/2022]
Abstract
Heterodimeric proteins with homologous subunits of same fold are involved in various biological processes. The objective of this study is to understand the evolution of structural and functional features of such heterodimers. Using a non-redundant dataset of 70 such heterodimers of known 3D structure and an independent dataset of 173 heterodimers from yeast, we note that the mean sequence identity between interacting homologous subunits is only 23-24% suggesting that, generally, highly diverged paralogues assemble to form such a heterodimer. We also note that the functional roles of interacting subunits/domains are generally quite different. This suggests that, though the interacting subunits/domains are homologous, the high evolutionary divergence characterize their high functional divergence which contributes to a gross function for the heterodimer considered as a whole. The inverse relationship between sequence identity and RMSD of interacting homologues in heterodimers is not followed. We also addressed the question of formation of homodimers of the subunits of heterodimers by generating models of fictitious homodimers on the basis of the 3D structures of the heterodimers. Interaction energies associated with these homodimers suggests that, in overwhelming majority of the cases, such homodimers are unlikely to be stable. Majority of the homologues of heterodimers of known structures form heterodimers (51.8%) and a small proportion (14.6%) form homodimers. Comparison of 3D structures of heterodimers with homologous homodimers suggests that interfacial nature of residues is not well conserved. In over 90% of the cases we note that the interacting subunits of heterodimers are co-localized in the cell.
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Affiliation(s)
- Govindarajan Sudha
- Molecular Biophysics Unit, Indian Institute of Science, Bangalore, Karnataka, 560012, India
| | - Nagarajan Naveenkumar
- National Centre for Biological Sciences, Tata Institute of Fundamental Research, Bangalore, Karnataka, 560065, India.,Bharathidasan University, Tiruchirappalli, Tamil Nadu, 620024, India
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Afzal M, Khurshid S, Khalid R, Paracha RZ, Khan IH, Akhtar MW. Fusion of selected regions of mycobacterial antigens for enhancing sensitivity in serodiagnosis of tuberculosis. J Microbiol Methods 2015; 115:104-11. [PMID: 26068786 DOI: 10.1016/j.mimet.2015.06.003] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2015] [Revised: 06/04/2015] [Accepted: 06/06/2015] [Indexed: 11/27/2022]
Abstract
Serodiagnosis of tuberculosis requires detection of antibodies against multiple antigens of Mycobacterium tuberculosis, because antibody profiles differ among the patients. Using fusion proteins with epitopes from two or more antigens would facilitate in the detection of multiple antibodies. Fusion constructs tn1FbpC1-tnPstS1 and tn2FbpC1-tnPstS1 were produced by linking truncated regions of variable lengths from FbpC1 to the N-terminus of the truncated PstS1. Similarly a truncated fragment of HSP was linked to the N-terminus of a truncated fragment from FbpC1 to produce tnHSP-tn1FbpC1. ELISA analysis of the plasma samples of TB patients against tn2FbpC1-tnPstS1 showed 72.2% sensitivity which is nearly the same as the expected combined value for the two individual antigens. However, the sensitivity of tn1FbpC1-tnPstS1 was lowered to 60%. tnHSP-tn1FbpC1 showed 67.7% sensitivity which is slightly less than the expected combined value for the two individual antigens, but still significantly higher than that of each of the individual antigen. Data for secondary structure analysis by CD spectrometry was in reasonable agreement with the X-ray crystallographic data of the native proteins and the predicted structure of the fusion proteins. Comparative molecular modeling suggests that the epitopes of the constituent proteins are better exposed in tn2FbpC1-tnPstS1 as compared to those in tn1FbpC1-tnPstS1. Therefore, removal of the N-terminal non-epitopic region of FbpC1 from 34-96 amino acids seems to have unmasked at least some of the epitopes, resulting in greater sensitivity. The high level of sensitivity of tn2FbpC1-tnPstS1 and tnHSP-tn1FbpC1, not reported before, shows that these fusion proteins have great potential for use in serodiagnosis of tuberculosis.
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Affiliation(s)
- Madeeha Afzal
- School of Biological Sciences, University of the Punjab, Lahore 54590, Pakistan.
| | - Sana Khurshid
- School of Biological Sciences, University of the Punjab, Lahore 54590, Pakistan.
| | - Ruqyya Khalid
- School of Biological Sciences, University of the Punjab, Lahore 54590, Pakistan.
| | - Rehan Zafar Paracha
- Atta-Ur-Rahman School of Applied Biosciences, National University of Sciences and Technology, Islamabad 44000, Pakistan.
| | - Imran H Khan
- Department of Pathology and Laboratory Medicine, University of California, Davis 95616, USA.
| | - M Waheed Akhtar
- School of Biological Sciences, University of the Punjab, Lahore 54590, Pakistan.
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Maheshwari S, Brylinski M. Predicting protein interface residues using easily accessible on-line resources. Brief Bioinform 2015; 16:1025-34. [PMID: 25797794 DOI: 10.1093/bib/bbv009] [Citation(s) in RCA: 35] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2014] [Indexed: 01/20/2023] Open
Abstract
It has been more than a decade since the completion of the Human Genome Project that provided us with a complete list of human proteins. The next obvious task is to figure out how various parts interact with each other. On that account, we review 10 methods for protein interface prediction, which are freely available as web servers. In addition, we comparatively evaluate their performance on a common data set comprising different quality target structures. We find that using experimental structures and high-quality homology models, structure-based methods outperform those using only protein sequences, with global template-based approaches providing the best performance. For moderate-quality models, sequence-based methods often perform better than those structure-based techniques that rely on fine atomic details. We note that post-processing protocols implemented in several methods quantitatively improve the results only for experimental structures, suggesting that these procedures should be tuned up for computer-generated models. Finally, we anticipate that advanced meta-prediction protocols are likely to enhance interface residue prediction. Notwithstanding further improvements, easily accessible web servers already provide the scientific community with convenient resources for the identification of protein-protein interaction sites.
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Wiech EM, Cheng HP, Singh SM. Molecular modeling and computational analyses suggests that the Sinorhizobium meliloti periplasmic regulator protein ExoR adopts a superhelical fold and is controlled by a unique mechanism of proteolysis. Protein Sci 2015; 24:319-27. [PMID: 25492513 PMCID: PMC4353358 DOI: 10.1002/pro.2616] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2014] [Revised: 11/26/2014] [Accepted: 12/01/2014] [Indexed: 12/12/2022]
Abstract
The Sinorhizobium meliloti periplasmic ExoR protein and the ExoS/ChvI two-component system form a regulatory mechanism that directly controls the transformation of free-living to host-invading cells. In the absence of crystal structures, understanding the molecular mechanism of interaction between ExoR and the ExoS sensor, which is believed to drive the key regulatory step in the invasion process, remains a major challenge. In this study, we present a theoretical structural model of the active form of ExoR protein, ExoRm , generated using computational methods. Our model suggests that ExoR possesses a super-helical fold comprising 12 α-helices forming six Sel1-like repeats, including two that were unidentified in previous studies. This fold is highly conducive to mediating protein-protein interactions and this is corroborated by the identification of putative protein binding sites on the surface of the ExoRm protein. Our studies reveal two novel insights: (a) an extended conformation of the third Sel1-like repeat that might be important for ExoR regulatory function and (b) a buried proteolytic site that implies a unique proteolytic mechanism. This study provides new and interesting insights into the structure of S. meliloti ExoR, lays the groundwork for elaborating the molecular mechanism of ExoRm cleavage, ExoRm -ExoS interactions, and studies of ExoR homologs in other bacterial host interactions.
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Affiliation(s)
- Eliza M Wiech
- Department of Biology, The Graduate Center of the City University of New YorkNew York, New York, 10016
- Department of Biology, Brooklyn College, The City University of New YorkBrooklyn, New York, 11210
| | - Hai-Ping Cheng
- Department of Biology, The Graduate Center of the City University of New YorkNew York, New York, 10016
- Biological Sciences Department, Lehman College, The City University of New YorkBronx, New York, 10468
| | - Shaneen M Singh
- Department of Biology, The Graduate Center of the City University of New YorkNew York, New York, 10016
- Department of Biology, Brooklyn College, The City University of New YorkBrooklyn, New York, 11210
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Aumentado-Armstrong TT, Istrate B, Murgita RA. Algorithmic approaches to protein-protein interaction site prediction. Algorithms Mol Biol 2015; 10:7. [PMID: 25713596 PMCID: PMC4338852 DOI: 10.1186/s13015-015-0033-9] [Citation(s) in RCA: 44] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2014] [Accepted: 01/07/2015] [Indexed: 12/19/2022] Open
Abstract
Interaction sites on protein surfaces mediate virtually all biological activities, and their identification holds promise for disease treatment and drug design. Novel algorithmic approaches for the prediction of these sites have been produced at a rapid rate, and the field has seen significant advancement over the past decade. However, the most current methods have not yet been reviewed in a systematic and comprehensive fashion. Herein, we describe the intricacies of the biological theory, datasets, and features required for modern protein-protein interaction site (PPIS) prediction, and present an integrative analysis of the state-of-the-art algorithms and their performance. First, the major sources of data used by predictors are reviewed, including training sets, evaluation sets, and methods for their procurement. Then, the features employed and their importance in the biological characterization of PPISs are explored. This is followed by a discussion of the methodologies adopted in contemporary prediction programs, as well as their relative performance on the datasets most recently used for evaluation. In addition, the potential utility that PPIS identification holds for rational drug design, hotspot prediction, and computational molecular docking is described. Finally, an analysis of the most promising areas for future development of the field is presented.
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Kufareva I, Lenoir M, Dancea F, Sridhar P, Raush E, Bissig C, Gruenberg J, Abagyan R, Overduin M. Discovery of novel membrane binding structures and functions. Biochem Cell Biol 2014; 92:555-63. [PMID: 25394204 DOI: 10.1139/bcb-2014-0074] [Citation(s) in RCA: 38] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
Abstract
The function of a protein is determined by its intrinsic activity in the context of its subcellular distribution. Membranes localize proteins within cellular compartments and govern their specific activities. Discovering such membrane-protein interactions is important for understanding biological mechanisms and could uncover novel sites for therapeutic intervention. We present a method for detecting membrane interactive proteins and their exposed residues that insert into lipid bilayers. Although the development process involved analysis of how C1b, C2, ENTH, FYVE, Gla, pleckstrin homology (PH), and PX domains bind membranes, the resulting membrane optimal docking area (MODA) method yields predictions for a given protein of known three-dimensional structures without referring to canonical membrane-targeting modules. This approach was tested on the Arf1 GTPase, ATF2 acetyltransferase, von Willebrand factor A3 domain, and Neisseria gonorrhoeae MsrB protein and further refined with membrane interactive and non-interactive FAPP1 and PKD1 pleckstrin homology domains, respectively. Furthermore we demonstrate how this tool can be used to discover unprecedented membrane binding functions as illustrated by the Bro1 domain of Alix, which was revealed to recognize lysobisphosphatidic acid (LBPA). Validation of novel membrane-protein interactions relies on other techniques such as nuclear magnetic resonance spectroscopy (NMR), which was used here to map the sites of micelle interaction. Together this indicates that genome-wide identification of known and novel membrane interactive proteins and sites is now feasible and provides a new tool for functional annotation of the proteome.
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Affiliation(s)
- Irina Kufareva
- a Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093, USA
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Dong Z, Wang K, Dang TKL, Gültas M, Welter M, Wierschin T, Stanke M, Waack S. CRF-based models of protein surfaces improve protein-protein interaction site predictions. BMC Bioinformatics 2014; 15:277. [PMID: 25124108 PMCID: PMC4150965 DOI: 10.1186/1471-2105-15-277] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2013] [Accepted: 08/01/2014] [Indexed: 11/13/2022] Open
Abstract
Background The identification of protein-protein interaction sites is a computationally challenging task and important for understanding the biology of protein complexes. There is a rich literature in this field. A broad class of approaches assign to each candidate residue a real-valued score that measures how likely it is that the residue belongs to the interface. The prediction is obtained by thresholding this score. Some probabilistic models classify the residues on the basis of the posterior probabilities. In this paper, we introduce pairwise conditional random fields (pCRFs) in which edges are not restricted to the backbone as in the case of linear-chain CRFs utilized by Li et al. (2007). In fact, any 3D-neighborhood relation can be modeled. On grounds of a generalized Viterbi inference algorithm and a piecewise training process for pCRFs, we demonstrate how to utilize pCRFs to enhance a given residue-wise score-based protein-protein interface predictor on the surface of the protein under study. The features of the pCRF are solely based on the interface predictions scores of the predictor the performance of which shall be improved. Results We performed three sets of experiments with synthetic scores assigned to the surface residues of proteins taken from the data set PlaneDimers compiled by Zellner et al. (2011), from the list published by Keskin et al. (2004) and from the very recent data set due to Cukuroglu et al. (2014). That way we demonstrated that our pCRF-based enhancer is effective given the interface residue score distribution and the non-interface residue score are unimodal. Moreover, the pCRF-based enhancer is also successfully applicable, if the distributions are only unimodal over a certain sub-domain. The improvement is then restricted to that domain. Thus we were able to improve the prediction of the PresCont server devised by Zellner et al. (2011) on PlaneDimers. Conclusions Our results strongly suggest that pCRFs form a methodological framework to improve residue-wise score-based protein-protein interface predictors given the scores are appropriately distributed. A prototypical implementation of our method is accessible at http://ppicrf.informatik.uni-goettingen.de/index.html.
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Affiliation(s)
| | | | | | | | | | | | | | - Stephan Waack
- Institute of Computer Science, University of Göttingen, Goldschmidtstr, 7, 37077 Göttingen, Germany.
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Lenoir M, Sugawara M, Kaur J, Ball LJ, Overduin M. Structural insights into the activation of the RhoA GTPase by the lymphoid blast crisis (Lbc) oncoprotein. J Biol Chem 2014; 289:23992-4004. [PMID: 24993829 PMCID: PMC4156082 DOI: 10.1074/jbc.m114.561787] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
The small GTPase RhoA promotes deregulated signaling upon interaction with lymphoid blast crisis (Lbc), the oncogenic form of A-kinase anchoring protein 13 (AKAP13). The onco-Lbc protein is a hyperactive Rho-specific guanine nucleotide exchange factor (GEF), but its structural mechanism has not been reported despite its involvement in cardiac hypertrophy and cancer causation. The pleckstrin homology (PH) domain of Lbc is located at the C-terminal end of the protein and is shown here to specifically recognize activated RhoA rather than lipids. The isolated dbl homology (DH) domain can function as an independent activator with an enhanced activity. However, the DH domain normally does not act as a solitary Lbc interface with RhoA-GDP. Instead it is negatively controlled by the PH domain. In particular, the DH helical bundle is coupled to the structurally dependent PH domain through a helical linker, which reduces its activity. Together the two domains form a rigid scaffold in solution as evidenced by small angle x-ray scattering and 1H,13C,15N-based NMR spectroscopy. The two domains assume a “chair” shape with its back possessing independent GEF activity and the PH domain providing a broad seat for RhoA-GTP docking rather than membrane recognition. This provides structural and dynamical insights into how DH and PH domains work together in solution to support regulated RhoA activity. Mutational analysis supports the bifunctional PH domain mediation of DH-RhoA interactions and explains why the tandem domain is required for controlled GEF signaling.
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Affiliation(s)
- Marc Lenoir
- From the School of Cancer Sciences, University of Birmingham, Birmingham B15 2TT, United Kingdom
| | - Masae Sugawara
- From the School of Cancer Sciences, University of Birmingham, Birmingham B15 2TT, United Kingdom
| | - Jaswant Kaur
- From the School of Cancer Sciences, University of Birmingham, Birmingham B15 2TT, United Kingdom
| | - Linda J Ball
- Structural Genomics Consortium, University of Oxford, Oxford OX3 7DQ, United Kingdom, and The Leibniz Institute of Molecular Pharmacology, Campus Buch, 13125 Berlin, Germany
| | - Michael Overduin
- From the School of Cancer Sciences, University of Birmingham, Birmingham B15 2TT, United Kingdom,
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Paracha RZ, Ali A, Ahmad J, Hussain R, Niazi U, Muhammad SA. Structural evaluation of BTK and PKCδ mediated phosphorylation of MAL at positions Tyr86 and Tyr106. Comput Biol Chem 2014; 51:22-35. [PMID: 24840642 DOI: 10.1016/j.compbiolchem.2014.04.001] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2014] [Revised: 04/03/2014] [Accepted: 04/07/2014] [Indexed: 01/02/2023]
Abstract
A number of diseases including sepsis, rheumatoid arthritis, diabetes, cardiovascular diseases and hyperinflammatory immune disorders have been associated with Toll like receptor (TLR) 2 and TLR4. Endogenous adaptor protein known as MyD88 adapter-like protein (MAL) bind exclusively to the cytosolic portions of TLR2 and TLR4 to initiate downstream signalling. Brutons tyrosine kinase (BTK) and protein kinase C delta (PKCδ) have been implicated to phosphorylate MAL and activate it to initiate downstream signalling. BTK has been associated with phosphorylation at positions Tyr86 and Tyr106, necessary for the activation of MAL but definite residual target of PKCδ in MAL is still to be explored. To produce a better understanding of the functional domains involved in the formation of MAL-kinase complexes, computer-aided studies were used to characterize the protein-protein interactions (PPIs) of phosphorylated BTK and PKCδ with MAL. Docking and physicochemical studies indicated that BTK was involved in close contact with Tyr86 and Tyr106 of MAL whereas PKCδ may phosphorylate Tyr106 only. Moreover, the electrostatics charge distribution of binding interfaces of BTK and PKCδ were distinct but compatible with respective regions of MAL. Our results implicate that position of Tyr86 is specifically phosphorylated by BTK whereas Tyr106 can be phosphorylated by competitive action of both BTK and PKCδ. Additionally, the residues of MAL which are necessary for interaction with TLR2, TLR4, MyD88 and SOCS-1 also play their roles in maintaining interaction with kinases and can be targeted in future to reduce TLR2 and TLR4 induced pathological responses.
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Affiliation(s)
- Rehan Zafar Paracha
- Atta-Ur-Rahman School of Applied Biosciences (ASAB), National University of Sciences and Technology (NUST), Islamabad 44000, Pakistan
| | - Amjad Ali
- Atta-Ur-Rahman School of Applied Biosciences (ASAB), National University of Sciences and Technology (NUST), Islamabad 44000, Pakistan.
| | - Jamil Ahmad
- Research Center for Modeling and Simulation (RCMS), National University of Sciences and Technology (NUST), Islamabad 44000, Pakistan.
| | - Riaz Hussain
- Shifa College of Pharmaceutical Sciences, Shifa Tameer-e-Millat University, Islamabad 44000, Pakistan
| | - Umar Niazi
- IBERS, Aberystwyth University, Edward Llwyd Building, Penglais Campus, Aberystwyth, Ceredigion, Wales SY23 3FG, UK
| | - Syed Aun Muhammad
- Department of Pharmacy, COMSATS Institute of Information Technology Abbottabad, 22060, Pakistan
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Zhao CL, Mahboobi SH, Moussavi-Baygi R, Mofrad MRK. The interaction of CRM1 and the nuclear pore protein Tpr. PLoS One 2014; 9:e93709. [PMID: 24722547 PMCID: PMC3983112 DOI: 10.1371/journal.pone.0093709] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2013] [Accepted: 03/05/2014] [Indexed: 12/20/2022] Open
Abstract
While much has been devoted to the study of transport mechanisms through the nuclear pore complex (NPC), the specifics of interactions and binding between export transport receptors and the NPC periphery have remained elusive. Recent work has demonstrated a binding interaction between the exportin CRM1 and the unstructured carboxylic tail of Tpr, on the nuclear basket. Strong evidence suggests that this interaction is vital to the functions of CRM1. Using molecular dynamics simulations and a newly refined method for determining binding regions, we have identified nine candidate binding sites on CRM1 for C-Tpr. These include two adjacent to RanGTP--from which one is blocked in the absence of RanGTP--and three next to the binding region of the cargo Snurportin. We report two additional interaction sites between C-Tpr and Snurportin, suggesting a possible role for Tpr import into the nucleus. Using bioinformatics tools we have conducted conservation analysis and functional residue prediction investigations to identify which parts of the obtained binding sites are inherently more important and should be highlighted. Also, a novel measure based on the ratio of available solvent accessible surface (RASAS) is proposed for monitoring the ligand/receptor binding process.
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Affiliation(s)
- Charles L. Zhao
- Molecular Cell Biomechanics Laboratory, Departments of Bioengineering and Mechanical Engineering, University of California, Berkeley, California, United States of America
| | - Seyed Hanif Mahboobi
- Molecular Cell Biomechanics Laboratory, Departments of Bioengineering and Mechanical Engineering, University of California, Berkeley, California, United States of America
| | - Ruhollah Moussavi-Baygi
- Molecular Cell Biomechanics Laboratory, Departments of Bioengineering and Mechanical Engineering, University of California, Berkeley, California, United States of America
| | - Mohammad R. K. Mofrad
- Molecular Cell Biomechanics Laboratory, Departments of Bioengineering and Mechanical Engineering, University of California, Berkeley, California, United States of America
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Bendell CJ, Liu S, Aumentado-Armstrong T, Istrate B, Cernek PT, Khan S, Picioreanu S, Zhao M, Murgita RA. Transient protein-protein interface prediction: datasets, features, algorithms, and the RAD-T predictor. BMC Bioinformatics 2014; 15:82. [PMID: 24661439 PMCID: PMC4021185 DOI: 10.1186/1471-2105-15-82] [Citation(s) in RCA: 36] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2013] [Accepted: 02/14/2014] [Indexed: 11/14/2022] Open
Abstract
Background Transient protein-protein interactions (PPIs), which underly most biological processes, are a prime target for therapeutic development. Immense progress has been made towards computational prediction of PPIs using methods such as protein docking and sequence analysis. However, docking generally requires high resolution structures of both of the binding partners and sequence analysis requires that a significant number of recurrent patterns exist for the identification of a potential binding site. Researchers have turned to machine learning to overcome some of the other methods’ restrictions by generalising interface sites with sets of descriptive features. Best practices for dataset generation, features, and learning algorithms have not yet been identified or agreed upon, and an analysis of the overall efficacy of machine learning based PPI predictors is due, in order to highlight potential areas for improvement. Results The presence of unknown interaction sites as a result of limited knowledge about protein interactions in the testing set dramatically reduces prediction accuracy. Greater accuracy in labelling the data by enforcing higher interface site rates per domain resulted in an average 44% improvement across multiple machine learning algorithms. A set of 10 biologically unrelated proteins that were consistently predicted on with high accuracy emerged through our analysis. We identify seven features with the most predictive power over multiple datasets and machine learning algorithms. Through our analysis, we created a new predictor, RAD-T, that outperforms existing non-structurally specializing machine learning protein interface predictors, with an average 59% increase in MCC score on a dataset with a high number of interactions. Conclusion Current methods of evaluating machine-learning based PPI predictors tend to undervalue their performance, which may be artificially decreased by the presence of un-identified interaction sites. Changes to predictors’ training sets will be integral to the future progress of interface prediction by machine learning methods. We reveal the need for a larger test set of well studied proteins or domain-specific scoring algorithms to compensate for poor interaction site identification on proteins in general.
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Affiliation(s)
| | | | | | | | | | | | | | | | - Robert A Murgita
- Department of Microbiology and Immunology, McGill, Montreal, CA.
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Haines AS, Dong X, Song Z, Farmer R, Williams C, Hothersall J, Płoskoń E, Wattana-Amorn P, Stephens ER, Yamada E, Gurney R, Takebayashi Y, Masschelein J, Cox RJ, Lavigne R, Willis CL, Simpson TJ, Crosby J, Winn PJ, Thomas CM, Crump MP. A conserved motif flags acyl carrier proteins for β-branching in polyketide synthesis. Nat Chem Biol 2013; 9:685-692. [PMID: 24056399 PMCID: PMC4658705 DOI: 10.1038/nchembio.1342] [Citation(s) in RCA: 52] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2013] [Accepted: 08/22/2013] [Indexed: 11/14/2022]
Abstract
Type I PKSs often utilise programmed β-branching, via enzymes of an “HMG-CoA synthase (HCS) cassette”, to incorporate various side chains at the second carbon from the terminal carboxylic acid of growing polyketide backbones. We identified a strong sequence motif in Acyl Carrier Proteins (ACPs) where β-branching is known. Substituting ACPs confirmed a correlation of ACP type with β-branching specificity. While these ACPs often occur in tandem, NMR analysis of tandem β-branching ACPs indicated no ACP-ACP synergistic effects and revealed that the conserved sequence motif forms an internal core rather than an exposed patch. Modelling and mutagenesis identified ACP Helix III as a probable anchor point of the ACP-HCS complex whose position is determined by the core. Mutating the core affects ACP functionality while ACP-HCS interface substitutions modulate system specificity. Our method for predicting β-carbon branching expands the potential for engineering novel polyketides and lays a basis for determining specificity rules.
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Affiliation(s)
- Anthony S Haines
- School of Biosciences, University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK
| | - Xu Dong
- School of Chemistry, Cantock's Close, Clifton, Bristol, BS8 1TS, UK
| | - Zhongshu Song
- School of Chemistry, Cantock's Close, Clifton, Bristol, BS8 1TS, UK
| | - Rohit Farmer
- School of Biosciences, University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK
| | | | - Joanne Hothersall
- School of Biosciences, University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK
| | - Eliza Płoskoń
- School of Chemistry, Cantock's Close, Clifton, Bristol, BS8 1TS, UK
| | | | - Elton R Stephens
- School of Biosciences, University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK
| | - Erika Yamada
- School of Biosciences, University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK
| | - Rachel Gurney
- School of Biosciences, University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK
| | - Yuiko Takebayashi
- School of Biosciences, University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK
| | - Joleen Masschelein
- Division of Gene Technology, KU Leuven, Kasteelpark Arenberg 21 - box 2462, 3001 Heverlee, Belgium
| | - Russell J Cox
- School of Chemistry, Cantock's Close, Clifton, Bristol, BS8 1TS, UK
| | - Rob Lavigne
- Division of Gene Technology, KU Leuven, Kasteelpark Arenberg 21 - box 2462, 3001 Heverlee, Belgium
| | | | - Thomas J Simpson
- School of Chemistry, Cantock's Close, Clifton, Bristol, BS8 1TS, UK
| | - John Crosby
- School of Chemistry, Cantock's Close, Clifton, Bristol, BS8 1TS, UK
| | - Peter J Winn
- School of Biosciences, University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK
| | - Christopher M Thomas
- School of Biosciences, University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK
| | - Matthew P Crump
- School of Chemistry, Cantock's Close, Clifton, Bristol, BS8 1TS, UK
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49
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Hwang H, Vreven T, Weng Z. Binding interface prediction by combining protein-protein docking results. Proteins 2013; 82:57-66. [PMID: 23836482 DOI: 10.1002/prot.24354] [Citation(s) in RCA: 30] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2013] [Revised: 06/05/2013] [Accepted: 06/17/2013] [Indexed: 11/10/2022]
Abstract
We developed a method called residue contact frequency (RCF), which uses the complex structures generated by the protein-protein docking algorithm ZDOCK to predict interface residues. Unlike interface prediction algorithms that are based on monomers alone, RCF is binding partner specific. We evaluated the performance of RCF using the area under the precision-recall (PR) curve (AUC) on a large protein docking Benchmark. RCF (AUC = 0.44) performed as well as meta-PPISP (AUC = 0.43), which is one of the best monomer-based interface prediction methods. In addition, we test a support vector machine (SVM) to combine RCF with meta-PPISP and another monomer-based interface prediction algorithm Evolutionary Trace to further improve the performance. We found that the SVM that combined RCF and meta-PPISP achieved the best performance (AUC = 0.47). We used RCF to predict the binding interfaces of proteins that can bind to multiple partners and RCF was able to correctly predict interface residues that are unique for the respective binding partners. Furthermore, we found that residues that contributed greatly to binding affinity (hotspot residues) had significantly higher RCF than other residues.
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Affiliation(s)
- Howook Hwang
- Program in Bioinformatics and Integrative Biology, University of Massachusetts Medical School, Worcester, Massachusetts, 01605
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50
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Sun P, Ju H, Liu Z, Ning Q, Zhang J, Zhao X, Huang Y, Ma Z, Li Y. Bioinformatics resources and tools for conformational B-cell epitope prediction. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2013; 2013:943636. [PMID: 23970944 PMCID: PMC3736542 DOI: 10.1155/2013/943636] [Citation(s) in RCA: 61] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 03/19/2013] [Revised: 05/22/2013] [Accepted: 06/01/2013] [Indexed: 11/22/2022]
Abstract
Identification of epitopes which invoke strong humoral responses is an essential issue in the field of immunology. Localizing epitopes by experimental methods is expensive in terms of time, cost, and effort; therefore, computational methods feature for its low cost and high speed was employed to predict B-cell epitopes. In this paper, we review the recent advance of bioinformatics resources and tools in conformational B-cell epitope prediction, including databases, algorithms, web servers, and their applications in solving problems in related areas. To stimulate the development of better tools, some promising directions are also extensively discussed.
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Affiliation(s)
- Pingping Sun
- School of Computer Science and Information Technology, Northeast Normal University, Changchun 130117, China
- National Engineering Laboratory for Druggable Gene and Protein Screening, Northeast Normal University, Changchun 130024, China
| | - Haixu Ju
- School of Computer Science and Information Technology, Northeast Normal University, Changchun 130117, China
- Key Laboratory of Intelligent Information Processing of Jilin Universities, Northeast Normal University, Changchun 130117, China
| | - Zhenbang Liu
- School of Computer Science and Information Technology, Northeast Normal University, Changchun 130117, China
- Key Laboratory of Intelligent Information Processing of Jilin Universities, Northeast Normal University, Changchun 130117, China
| | - Qiao Ning
- School of Computer Science and Information Technology, Northeast Normal University, Changchun 130117, China
| | - Jian Zhang
- School of Computer Science and Information Technology, Northeast Normal University, Changchun 130117, China
- Key Laboratory of Intelligent Information Processing of Jilin Universities, Northeast Normal University, Changchun 130117, China
| | - Xiaowei Zhao
- School of Computer Science and Information Technology, Northeast Normal University, Changchun 130117, China
- Key Laboratory of Intelligent Information Processing of Jilin Universities, Northeast Normal University, Changchun 130117, China
| | - Yanxin Huang
- National Engineering Laboratory for Druggable Gene and Protein Screening, Northeast Normal University, Changchun 130024, China
| | - Zhiqiang Ma
- School of Computer Science and Information Technology, Northeast Normal University, Changchun 130117, China
- Key Laboratory of Intelligent Information Processing of Jilin Universities, Northeast Normal University, Changchun 130117, China
| | - Yuxin Li
- National Engineering Laboratory for Druggable Gene and Protein Screening, Northeast Normal University, Changchun 130024, China
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