1
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Sha W, Gong C, Xiao G, Hou C, Ren J. Interaction-based screening, Monte Carlo Bayesian inference-based de novo design and in vitro verification of adenine-binding peptide. Food Chem 2024; 448:139076. [PMID: 38537545 DOI: 10.1016/j.foodchem.2024.139076] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2023] [Revised: 03/15/2024] [Accepted: 03/18/2024] [Indexed: 04/24/2024]
Abstract
One of the main reasons for hyperuricemia is high purine intake. The primary strategy for treating hyperuricemia is blocking the purine metabolism enzyme. However, by binding the purine bases directly, we suggested a unique therapeutic strategy that might interfere with purine metabolism. There have been numerous reports of extensive interactions between proteins and purine bases. Adenine, constituting numerous protein co-factors, can interact with the adenine-binding motif. Using Bayesian Inference and Markov chain Monte Carlo sampling, we created a novel adenine-binding peptide Ile-Tyr-Val-Thr based on the structure of the adenine-binding motifs. Ile-Tyr-Val-Thr generates a semi-pocket that can clip the adenine within, as demonstrated by docking. Then, using thermodynamic techniques, the interaction between Ile-Tyr-Val-Thr and adenine was confirmed. The KD value is 1.50e-5 (ΔH = -20.2 kJ/mol and ΔG = -27.6 kJ/mol), indicating the high affinity. In brief, the adenine-binding peptide Ile-Tyr-Val-Thr may help lower uric acid level by blocking the absorption of food-derived adenine.
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Affiliation(s)
- Wanqian Sha
- School of Food Science and Engineering, South China University of Technology, Guangzhou 510640, China
| | - Congcong Gong
- School of Food Science and Engineering, South China University of Technology, Guangzhou 510640, China
| | - Ganhong Xiao
- School of Food Science and Engineering, South China University of Technology, Guangzhou 510640, China
| | - Chuanli Hou
- School of Food Science and Engineering, South China University of Technology, Guangzhou 510640, China.
| | - Jiaoyan Ren
- School of Food Science and Engineering, South China University of Technology, Guangzhou 510640, China.
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2
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Thomson TM. On the importance for drug discovery of a transnational Latin American database of natural compound structures. Front Pharmacol 2023; 14:1207559. [PMID: 37426821 PMCID: PMC10324963 DOI: 10.3389/fphar.2023.1207559] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2023] [Accepted: 06/15/2023] [Indexed: 07/11/2023] Open
Affiliation(s)
- Timothy M. Thomson
- Institute for Molecular Biology (IBMB-CSIC), Barcelona, Spain
- CIBER de Enfermedades Hepáticas y Digestivas (CIBERehd), Madrid, Spain
- Universidad Peruana Cayetano Heredia, Lima, Peru
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3
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RPflex: A Coarse-Grained Network Model for RNA Pocket Flexibility Study. Int J Mol Sci 2023; 24:ijms24065497. [PMID: 36982570 PMCID: PMC10058308 DOI: 10.3390/ijms24065497] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2023] [Revised: 03/09/2023] [Accepted: 03/11/2023] [Indexed: 03/18/2023] Open
Abstract
RNA regulates various biological processes, such as gene regulation, RNA splicing, and intracellular signal transduction. RNA’s conformational dynamics play crucial roles in performing its diverse functions. Thus, it is essential to explore the flexibility characteristics of RNA, especially pocket flexibility. Here, we propose a computational approach, RPflex, to analyze pocket flexibility using the coarse-grained network model. We first clustered 3154 pockets into 297 groups by similarity calculation based on the coarse-grained lattice model. Then, we introduced the flexibility score to quantify the flexibility by global pocket features. The results show strong correlations between the flexibility scores and root-mean-square fluctuation (RMSF) values, with Pearson correlation coefficients of 0.60, 0.76, and 0.53 in Testing Sets I–III. Considering both flexibility score and network calculations, the Pearson correlation coefficient was increased to 0.71 in flexible pockets on Testing Set IV. The network calculations reveal that the long-range interaction changes contributed most to flexibility. In addition, the hydrogen bonds in the base–base interactions greatly stabilize the RNA structure, while backbone interactions determine RNA folding. The computational analysis of pocket flexibility could facilitate RNA engineering for biological or medical applications.
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4
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Data-driven analysis and druggability assessment methods to accelerate the identification of novel cancer targets. Comput Struct Biotechnol J 2022; 21:46-57. [PMID: 36514341 PMCID: PMC9732000 DOI: 10.1016/j.csbj.2022.11.042] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2022] [Revised: 11/21/2022] [Accepted: 11/21/2022] [Indexed: 11/27/2022] Open
Abstract
Over the past few decades, drug discovery has greatly improved the outcomes for patients, but several challenges continue to hinder the rapid development of novel drugs. Addressing unmet clinical needs requires the pursuit of drug targets that have a higher likelihood to lead to the development of successful drugs. Here we describe a bioinformatic approach for identifying novel cancer drug targets by performing statistical analysis to ascertain quantitative changes in expression levels between protein-coding genes, as well as co-expression networks to classify these genes into groups. Subsequently, we provide an overview of druggability assessment methodologies to prioritize and select the best targets to pursue.
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5
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Gu L, Li B, Ming D. A multilayer dynamic perturbation analysis method for predicting ligand-protein interactions. BMC Bioinformatics 2022; 23:456. [PMID: 36324073 PMCID: PMC9628359 DOI: 10.1186/s12859-022-04995-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2022] [Accepted: 10/19/2022] [Indexed: 01/24/2023] Open
Abstract
BACKGROUND Ligand-protein interactions play a key role in defining protein function, and detecting natural ligands for a given protein is thus a very important bioengineering task. In particular, with the rapid development of AI-based structure prediction algorithms, batch structural models with high reliability and accuracy can be obtained at low cost, giving rise to the urgent requirement for the prediction of natural ligands based on protein structures. In recent years, although several structure-based methods have been developed to predict ligand-binding pockets and ligand-binding sites, accurate and rapid methods are still lacking, especially for the prediction of ligand-binding regions and the spatial extension of ligands in the pockets. RESULTS In this paper, we proposed a multilayer dynamics perturbation analysis (MDPA) method for predicting ligand-binding regions based solely on protein structure, which is an extended version of our previously developed fast dynamic perturbation analysis (FDPA) method. In MDPA/FDPA, ligand binding tends to occur in regions that cause large changes in protein conformational dynamics. MDPA, examined using a standard validation dataset of ligand-protein complexes, yielded an averaged ligand-binding site prediction Matthews coefficient of 0.40, with a prediction precision of at least 50% for 71% of the cases. In particular, for 80% of the cases, the predicted ligand-binding region overlaps the natural ligand by at least 50%. The method was also compared with other state-of-the-art structure-based methods. CONCLUSIONS MDPA is a structure-based method to detect ligand-binding regions on protein surface. Our calculations suggested that a range of spaces inside the protein pockets has subtle interactions with the protein, which can significantly impact on the overall dynamics of the protein. This work provides a valuable tool as a starting point upon which further docking and analysis methods can be used for natural ligand detection in protein functional annotation. The source code of MDPA method is freely available at: https://github.com/mingdengming/mdpa .
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Affiliation(s)
- Lin Gu
- grid.412022.70000 0000 9389 5210College of Biotechnology and Pharmaceutical Engineering, Nanjing Tech University, Biotech Building Room B1-404, 30 South Puzhu Road, Jiangbei New District, Nanjing City, 211816 Jiangsu People’s Republic of China
| | - Bin Li
- grid.412022.70000 0000 9389 5210College of Biotechnology and Pharmaceutical Engineering, Nanjing Tech University, Biotech Building Room B1-404, 30 South Puzhu Road, Jiangbei New District, Nanjing City, 211816 Jiangsu People’s Republic of China
| | - Dengming Ming
- grid.412022.70000 0000 9389 5210College of Biotechnology and Pharmaceutical Engineering, Nanjing Tech University, Biotech Building Room B1-404, 30 South Puzhu Road, Jiangbei New District, Nanjing City, 211816 Jiangsu People’s Republic of China
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6
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Akdel M, Pires DEV, Pardo EP, Jänes J, Zalevsky AO, Mészáros B, Bryant P, Good LL, Laskowski RA, Pozzati G, Shenoy A, Zhu W, Kundrotas P, Serra VR, Rodrigues CHM, Dunham AS, Burke D, Borkakoti N, Velankar S, Frost A, Basquin J, Lindorff-Larsen K, Bateman A, Kajava AV, Valencia A, Ovchinnikov S, Durairaj J, Ascher DB, Thornton JM, Davey NE, Stein A, Elofsson A, Croll TI, Beltrao P. A structural biology community assessment of AlphaFold2 applications. Nat Struct Mol Biol 2022; 29:1056-1067. [PMID: 36344848 PMCID: PMC9663297 DOI: 10.1038/s41594-022-00849-w] [Citation(s) in RCA: 223] [Impact Index Per Article: 111.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2021] [Accepted: 09/20/2022] [Indexed: 11/09/2022]
Abstract
Most proteins fold into 3D structures that determine how they function and orchestrate the biological processes of the cell. Recent developments in computational methods for protein structure predictions have reached the accuracy of experimentally determined models. Although this has been independently verified, the implementation of these methods across structural-biology applications remains to be tested. Here, we evaluate the use of AlphaFold2 (AF2) predictions in the study of characteristic structural elements; the impact of missense variants; function and ligand binding site predictions; modeling of interactions; and modeling of experimental structural data. For 11 proteomes, an average of 25% additional residues can be confidently modeled when compared with homology modeling, identifying structural features rarely seen in the Protein Data Bank. AF2-based predictions of protein disorder and complexes surpass dedicated tools, and AF2 models can be used across diverse applications equally well compared with experimentally determined structures, when the confidence metrics are critically considered. In summary, we find that these advances are likely to have a transformative impact in structural biology and broader life-science research.
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Affiliation(s)
- Mehmet Akdel
- Bioinformatics Group, Department of Plant Sciences, Wageningen University and Research, Wageningen, the Netherlands
| | - Douglas E V Pires
- School of Computing and Information Systems, University of Melbourne, Melbourne, Victoria, Australia
| | - Eduard Porta Pardo
- Josep Carreras Leukaemia Research Institute (IJC), Badalona, Spain
- Barcelona Supercomputing Center (BSC), Barcelona, Spain
| | - Jürgen Jänes
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Cambridge, UK
| | - Arthur O Zalevsky
- Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Russian Academy of Sciences, Moscow, Russian Federation
| | | | - Patrick Bryant
- Dep of Biochemistry and Biophysics and Science for Life Laboratory, Solna, Sweden
| | - Lydia L Good
- Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, Copenhagen, Denmark
| | - Roman A Laskowski
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Cambridge, UK
| | - Gabriele Pozzati
- Dep of Biochemistry and Biophysics and Science for Life Laboratory, Solna, Sweden
| | - Aditi Shenoy
- Dep of Biochemistry and Biophysics and Science for Life Laboratory, Solna, Sweden
| | - Wensi Zhu
- Dep of Biochemistry and Biophysics and Science for Life Laboratory, Solna, Sweden
| | - Petras Kundrotas
- Dep of Biochemistry and Biophysics and Science for Life Laboratory, Solna, Sweden
| | | | - Carlos H M Rodrigues
- School of Computing and Information Systems, University of Melbourne, Melbourne, Victoria, Australia
| | - Alistair S Dunham
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Cambridge, UK
| | - David Burke
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Cambridge, UK
| | - Neera Borkakoti
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Cambridge, UK
| | - Sameer Velankar
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Cambridge, UK
| | - Adam Frost
- Department of Biochemistry and Biophysics University of California, San Francisco, CA, USA
| | - Jérôme Basquin
- Department of Structural Cell Biology, Max Planck Institute of Biochemistry, Martinsried, Germany
| | - Kresten Lindorff-Larsen
- Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, Copenhagen, Denmark
| | - Alex Bateman
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Cambridge, UK
| | - Andrey V Kajava
- Université de Montpellier, Centre de Recherche en Biologie Cellulaire de Montpellier (CRBM) CNRS, Montpellier, France
| | | | - Sergey Ovchinnikov
- Faculty of Arts and Sciences, Division of Science, Harvard University, Cambridge, MA, USA.
| | | | - David B Ascher
- School of Chemistry and Molecular Biology, University of Queensland, Brisbane, Queensland, Australia.
| | - Janet M Thornton
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Cambridge, UK.
| | | | - Amelie Stein
- Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, Copenhagen, Denmark.
| | - Arne Elofsson
- Dep of Biochemistry and Biophysics and Science for Life Laboratory, Solna, Sweden.
| | - Tristan I Croll
- Cambridge Institute for Medical Research, Department of Haematology, The University of Cambridge, Cambridge, UK.
| | - Pedro Beltrao
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Cambridge, UK.
- Institute of Molecular Systems Biology, ETH Zürich, Zürich, Switzerland.
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7
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Eguida M, Rognan D. Estimating the Similarity between Protein Pockets. Int J Mol Sci 2022; 23:12462. [PMID: 36293316 PMCID: PMC9604425 DOI: 10.3390/ijms232012462] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2022] [Revised: 10/15/2022] [Accepted: 10/16/2022] [Indexed: 10/28/2023] Open
Abstract
With the exponential increase in publicly available protein structures, the comparison of protein binding sites naturally emerged as a scientific topic to explain observations or generate hypotheses for ligand design, notably to predict ligand selectivity for on- and off-targets, explain polypharmacology, and design target-focused libraries. The current review summarizes the state-of-the-art computational methods applied to pocket detection and comparison as well as structural druggability estimates. The major strengths and weaknesses of current pocket descriptors, alignment methods, and similarity search algorithms are presented. Lastly, an exhaustive survey of both retrospective and prospective applications in diverse medicinal chemistry scenarios illustrates the capability of the existing methods and the hurdle that still needs to be overcome for more accurate predictions.
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Affiliation(s)
| | - Didier Rognan
- Laboratoire d’Innovation Thérapeutique, UMR7200 CNRS-Université de Strasbourg, 67400 Illkirch, France
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8
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Eguida M, Schmitt-Valencia C, Hibert M, Villa P, Rognan D. Target-Focused Library Design by Pocket-Applied Computer Vision and Fragment Deep Generative Linking. J Med Chem 2022; 65:13771-13783. [PMID: 36256484 DOI: 10.1021/acs.jmedchem.2c00931] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
We here describe a computational approach (POEM: Pocket Oriented Elaboration of Molecules) to drive the generation of target-focused libraries while taking advantage of all publicly available structural information on protein-ligand complexes. A collection of 31 384 PDB-derived images with key shapes and pharmacophoric properties, describing fragment-bound microenvironments, is first aligned to the query target cavity by a computer vision method. The fragments of the most similar PDB subpockets are then directly positioned in the query cavity using the corresponding image transformation matrices. Lastly, suitable connectable atoms of oriented fragment pairs are linked by a deep generative model to yield fully connected molecules. POEM was applied to generate a library of 1.5 million potential cyclin-dependent kinase 8 inhibitors. By synthesizing and testing as few as 43 compounds, a few nanomolar inhibitors were quickly obtained with limited resources in just two iterative cycles.
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Affiliation(s)
- Merveille Eguida
- Laboratoire d'Innovation Thérapeutique, UMR7200 CNRS-Université de Strasbourg, F-67400Illkirch, France
| | - Christel Schmitt-Valencia
- Plateforme de Chimie Biologique Intégrative de Strasbourg, UAR3286 CNRS-Université de Strasbourg, Institut du Médicament de Strasbourg, F-67400Illkirch, France
| | - Marcel Hibert
- Laboratoire d'Innovation Thérapeutique, UMR7200 CNRS-Université de Strasbourg, F-67400Illkirch, France
| | - Pascal Villa
- Plateforme de Chimie Biologique Intégrative de Strasbourg, UAR3286 CNRS-Université de Strasbourg, Institut du Médicament de Strasbourg, F-67400Illkirch, France
| | - Didier Rognan
- Laboratoire d'Innovation Thérapeutique, UMR7200 CNRS-Université de Strasbourg, F-67400Illkirch, France
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9
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Sim J, Kwon S, Seok C. HProteome-BSite: predicted binding sites and ligands in human 3D proteome. Nucleic Acids Res 2022; 51:D403-D408. [PMID: 36243970 PMCID: PMC9825455 DOI: 10.1093/nar/gkac873] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2022] [Revised: 09/20/2022] [Accepted: 09/29/2022] [Indexed: 01/29/2023] Open
Abstract
Atomic-level knowledge of protein-ligand interactions allows a detailed understanding of protein functions and provides critical clues to discovering molecules regulating the functions. While recent innovative deep learning methods for protein structure prediction dramatically increased the structural coverage of the human proteome, molecular interactions remain largely unknown. A new database, HProteome-BSite, provides predictions of binding sites and ligands in the enlarged 3D human proteome. The model structures for human proteins from the AlphaFold Protein Structure Database were processed to structural domains of high confidence to maximize the coverage and reliability of interaction prediction. For ligand binding site prediction, an updated version of a template-based method GalaxySite was used. A high-level performance of the updated GalaxySite was confirmed. HProteome-BSite covers 80.74% of the UniProt entries in the AlphaFold human 3D proteome. Predicted binding sites and binding poses of potential ligands are provided for effective applications to further functional studies and drug discovery. The HProteome-BSite database is available at https://galaxy.seoklab.org/hproteome-bsite/database and is free and open to all users.
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Affiliation(s)
- Jiho Sim
- Department of Chemistry, Seoul National University, Seoul 08826, Republic of Korea
| | - Sohee Kwon
- Department of Chemistry, Seoul National University, Seoul 08826, Republic of Korea,Galux Inc, Gwanak-gu, Seoul 08738, Republic of Korea
| | - Chaok Seok
- To whom correspondence should be addressed. Tel: +82 2 880 9197; Fax: +82 2 889 1568;
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10
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Sankar S, Chandran Sakthivel N, Chandra N. Fast Local Alignment of Protein Pockets (FLAPP): A System-Compiled Program for Large-Scale Binding Site Alignment. J Chem Inf Model 2022; 62:4810-4819. [PMID: 36122166 DOI: 10.1021/acs.jcim.2c00967] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Protein function is a direct consequence of its sequence, structure, and the arrangement at the binding site. Bioinformatics using sequence analysis is typically used to gain a first insight into protein function. Protein structures, on the other hand, provide a higher resolution platform into understanding functions. As the protein structural information is increasingly becoming available through experimental structure determination and through advances in computational methods for structure prediction, the opportunity to utilize these data is also increasing. Structural analysis of small molecule ligand binding sites in particular provides a direct and more accurate window to infer protein function. However, it remains a poorly utilized resource due to the huge computational cost of existing methods that make large-scale structural comparisons of binding sites prohibitive. Here, we present an algorithm called FLAPP that produces very rapid atomic level alignments. By combining clique matching in graphs and the power of modern CPU architectures, FLAPP aligns a typical pair of binding sites at ∼12.5 ms using a single CPU core, ∼1 ms using 12 cores on a standard desktop machine, and performs a PDB-wide scan in 1-2 min. We perform rigorous validation of the algorithm at multiple levels of complexity and show that FLAPP provides accurate alignments. We also present a case study involving vitamin B12 binding sites to showcase the usefulness of FLAPP for performing an exhaustive alignment-based PDB-wide scan. We expect that this tool will be invaluable to the scientific community to quickly align millions of site pairs on a normal desktop machine to gain insights into protein function and drug discovery for drug target and off-target identification and polypharmacology.
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Affiliation(s)
- Santhosh Sankar
- Department of Biochemistry, Indian Institute of Science, Bangalore 560012, Karnataka, India
| | | | - Nagasuma Chandra
- Department of Biochemistry, Indian Institute of Science, Bangalore 560012, Karnataka, India.,BioSystems Science and Engineering, Indian Institute of Science, Bangalore 560012, Karnataka, India
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11
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A pocket-based 3D molecule generative model fueled by experimental electron density. Sci Rep 2022; 12:15100. [PMID: 36068257 PMCID: PMC9448726 DOI: 10.1038/s41598-022-19363-6] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2022] [Accepted: 08/29/2022] [Indexed: 11/08/2022] Open
Abstract
We report for the first time the use of experimental electron density (ED) as training data for the generation of drug-like three-dimensional molecules based on the structure of a target protein pocket. Similar to a structural biologist building molecules based on their ED, our model functions with two main components: a generative adversarial network (GAN) to generate the ligand ED in the input pocket and an ED interpretation module for molecule generation. The model was tested on three targets: a kinase (hematopoietic progenitor kinase 1), protease (SARS-CoV-2 main protease), and nuclear receptor (vitamin D receptor), and evaluated with a reference dataset composed of over 8000 compounds that have their activities reported in the literature. The evaluation considered the chemical validity, chemical space distribution-based diversity, and similarity with reference active compounds concerning the molecular structure and pocket-binding mode. Our model can generate molecules with similar structures to classical active compounds and novel compounds sharing similar binding modes with active compounds, making it a promising tool for library generation supporting high-throughput virtual screening. The ligand ED generated can also be used to support fragment-based drug design. Our model is available as an online service to academic users via https://edmg.stonewise.cn/#/create .
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12
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CavitySpace: A Database of Potential Ligand Binding Sites in the Human Proteome. Biomolecules 2022; 12:biom12070967. [PMID: 35883523 PMCID: PMC9312471 DOI: 10.3390/biom12070967] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2022] [Revised: 07/07/2022] [Accepted: 07/08/2022] [Indexed: 02/01/2023] Open
Abstract
Location and properties of ligand binding sites provide important information to uncover protein functions and to direct structure-based drug design approaches. However, as binding site detection depends on the three-dimensional (3D) structural data of proteins, functional analysis based on protein ligand binding sites is formidable for proteins without structural information. Recent developments in protein structure prediction and the 3D structures built by AlphaFold provide an unprecedented opportunity for analyzing ligand binding sites in human proteins. Here, we constructed the CavitySpace database, the first pocket library for all the proteins in the human proteome, using a widely-applied ligand binding site detection program CAVITY. Our analysis showed that known ligand binding sites could be well recovered. We grouped the predicted binding sites according to their similarity which can be used in protein function prediction and drug repurposing studies. Novel binding sites in highly reliable predicted structure regions provide new opportunities for drug discovery. Our CavitySpace is freely available and provides a valuable tool for drug discovery and protein function studies.
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13
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Ruan C, Zhou J, Li Z, Li K, Fang Z, Zhang X, Ye M. Proteome-Wide Deconvolution of Drug Targets and Binding Sites by Lysine Reactivity Profiling. Anal Chem 2022; 94:3352-3359. [PMID: 35147412 DOI: 10.1021/acs.analchem.1c05455] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Recently, numerous efforts have been devoted to identifying drug targets and binding sites in complex proteomes, which is of great importance in modern drug discovery. In this study, we developed a robust lysine reactivity profiling method to systematically study drug-binding targets and binding sites at the proteome level. This method is based on the principle that binding of a drug to a specific region of target proteins will change the reactivity of lysine residues that are located at this region, and these changes can be detected with an enrichable and lysine reactive probe. Coupled with data-independent acquisition (DIA), the known target proteins and corresponding binding sites were successfully revealed from K562 cell lysates for three model drugs: geldanamycin, staurosporine, and dasatinib. In addition, the drug-induced conformational changes of certain targets were also revealed by our method during the screening of staurosporine. The screening sensitivity of our method revealed from the screening of stuarosporine and dasatinib was comparable with that of thermal proteome profiling (TPP) or machine learning-based limited proteolysis (LiP-Quant). Overall, 21 and 4 kinase targets, including adenosine 5'-triphosphate (ATP)-binding targets, were identified for staurosporine and dasatinib in K562 cell lysates, respectively. We found that target proteins identified by TPP, LiP-Quant, and our method were complementary, emphasizing that the development of new methods that probe different properties of proteins is of great importance in drug target deconvolution. We also envision further applications of our method in proteome-wide probing multiple events that involve lysine reactivity changes.
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Affiliation(s)
- Chengfei Ruan
- CAS Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences (CAS), Dalian 116023, China.,University of Chinese Academy of Sciences, Beijing 100049, China
| | - Jiahua Zhou
- CAS Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences (CAS), Dalian 116023, China.,University of Chinese Academy of Sciences, Beijing 100049, China
| | - Zhouxian Li
- CAS Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences (CAS), Dalian 116023, China.,Shanghai Key Laboratory of Functional Materials Chemistry, Department of Chemistry and Molecular Engineering, East China University of Science and Technology, Shanghai 200237, China
| | - Kejia Li
- CAS Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences (CAS), Dalian 116023, China.,University of Chinese Academy of Sciences, Beijing 100049, China
| | - Zheng Fang
- CAS Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences (CAS), Dalian 116023, China.,University of Chinese Academy of Sciences, Beijing 100049, China
| | - Xiaolei Zhang
- CAS Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences (CAS), Dalian 116023, China
| | - Mingliang Ye
- CAS Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences (CAS), Dalian 116023, China
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14
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Sankar S, Chandra N. SiteMotif: A graph-based algorithm for deriving structural motifs in Protein Ligand binding sites. PLoS Comput Biol 2022; 18:e1009901. [PMID: 35202398 PMCID: PMC8903255 DOI: 10.1371/journal.pcbi.1009901] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2021] [Revised: 03/08/2022] [Accepted: 02/07/2022] [Indexed: 12/03/2022] Open
Abstract
Studying similarities in protein molecules has become a fundamental activity in much of biology and biomedical research, for which methods such as multiple sequence alignments are widely used. Most methods available for such comparisons cater to studying proteins which have clearly recognizable evolutionary relationships but not to proteins that recognize the same or similar ligands but do not share similarities in their sequence or structural folds. In many cases, proteins in the latter class share structural similarities only in their binding sites. While several algorithms are available for comparing binding sites, there are none for deriving structural motifs of the binding sites, independent of the whole proteins. We report the development of SiteMotif, a new algorithm that compares binding sites from multiple proteins and derives sequence-order independent structural site motifs. We have tested the algorithm at multiple levels of complexity and demonstrate its performance in different scenarios. We have benchmarked against 3 current methods available for binding site comparison and demonstrate superior performance of our algorithm. We show that SiteMotif identifies new structural motifs of spatially conserved residues in proteins, even when there is no sequence or fold-level similarity. We expect SiteMotif to be useful for deriving key mechanistic insights into the mode of ligand interaction, predict the ligand type that a protein can bind and improve the sensitivity of functional annotation. A large number of biological functions are orchestrated by proteins. The function of proteins is governed by its structure and its interacting ligand. However, it is known that not all residues are involved in ligand recognition. More specifically, residues that are located within 4.5 Å of ligand atoms are considered to be ’binding sites’. Here, we have developed an algorithm called SiteMotif that efficiently aligns multiple binding sites into a common frame. This process enables us to derive conservation among the binding site residues in a sequence order independent manner. The algorithm was validated extensively across five different levels and measured binding site similarities in each of them. Previous research has found multiple instances where different proteins have comparable binding sites and hence perform the same function. We present the ability of our method to detect such scenarios. Finally, As a use case, we applied SiteMotif to a set of glutathione binding proteins and derived a site based sequence motif characteristic of all glutathione binding proteins.
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Affiliation(s)
- Santhosh Sankar
- Department of Biochemistry, Indian Institute of Science, Bangalore, Karnataka, India
| | - Nagasuma Chandra
- Department of Biochemistry, Indian Institute of Science, Bangalore, Karnataka, India
- BioSystems Science and Engineering, Indian Institute of Science, Bangalore, Karnataka, India
- * E-mail:
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15
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Zha J, Li M, Kong R, Lu S, Zhang J. Explaining and Predicting Allostery with Allosteric Database and Modern Analytical Techniques. J Mol Biol 2022; 434:167481. [DOI: 10.1016/j.jmb.2022.167481] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2021] [Revised: 01/25/2022] [Accepted: 01/31/2022] [Indexed: 12/17/2022]
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16
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Basciu A, Callea L, Motta S, Bonvin AM, Bonati L, Vargiu AV. No dance, no partner! A tale of receptor flexibility in docking and virtual screening. VIRTUAL SCREENING AND DRUG DOCKING 2022. [DOI: 10.1016/bs.armc.2022.08.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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17
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Eguida M, Rognan D. Unexpected similarity between HIV-1 reverse transcriptase and tumor necrosis factor binding sites revealed by computer vision. J Cheminform 2021; 13:90. [PMID: 34814950 PMCID: PMC8609734 DOI: 10.1186/s13321-021-00567-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2021] [Accepted: 11/06/2021] [Indexed: 11/10/2022] Open
Abstract
Rationalizing the identification of hidden similarities across the repertoire of druggable protein cavities remains a major hurdle to a true proteome-wide structure-based discovery of novel drug candidates. We recently described a new computational approach (ProCare), inspired by numerical image processing, to identify local similarities in fragment-based subpockets. During the validation of the method, we unexpectedly identified a possible similarity in the binding pockets of two unrelated targets, human tumor necrosis factor alpha (TNF-α) and HIV-1 reverse transcriptase (HIV-1 RT). Microscale thermophoresis experiments confirmed the ProCare prediction as two of the three tested and FDA-approved HIV-1 RT inhibitors indeed bind to soluble human TNF-α trimer. Interestingly, the herein disclosed similarity could be revealed neither by state-of-the-art binding sites comparison methods nor by ligand-based pairwise similarity searches, suggesting that the point cloud registration approach implemented in ProCare, is uniquely suited to identify local and unobvious similarities among totally unrelated targets.
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Affiliation(s)
- Merveille Eguida
- Laboratoire d'Innovation Thérapeutique, UMR 7200 CNRS, Université de Strasbourg, 67400, Illkirch, France
| | - Didier Rognan
- Laboratoire d'Innovation Thérapeutique, UMR 7200 CNRS, Université de Strasbourg, 67400, Illkirch, France.
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18
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Pereira JM, Vieira M, Santos SM. Step-by-step design of proteins for small molecule interaction: A review on recent milestones. Protein Sci 2021; 30:1502-1520. [PMID: 33934427 PMCID: PMC8284594 DOI: 10.1002/pro.4098] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2021] [Revised: 04/21/2021] [Accepted: 04/23/2021] [Indexed: 01/01/2023]
Abstract
Protein design is the field of synthetic biology that aims at developing de novo custom-made proteins and peptides for specific applications. Despite exploring an ambitious goal, recent computational advances in both hardware and software technologies have paved the way to high-throughput screening and detailed design of novel folds and improved functionalities. Modern advances in the field of protein design for small molecule targeting are described in this review, organized in a step-by-step fashion: from the conception of a new or upgraded active binding site, to scaffold design, sequence optimization, and experimental expression of the custom protein. In each step, contemporary examples are described, and state-of-the-art software is briefly explored.
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Affiliation(s)
- José M. Pereira
- CICECO & Departamento de QuímicaUniversidade de AveiroAveiroPortugal
| | - Maria Vieira
- CICECO & Departamento de QuímicaUniversidade de AveiroAveiroPortugal
| | - Sérgio M. Santos
- CICECO & Departamento de QuímicaUniversidade de AveiroAveiroPortugal
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19
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Qi J, Rader C. Redirecting cytotoxic T cells with chemically programmed antibodies. Bioorg Med Chem 2020; 28:115834. [PMID: 33166926 DOI: 10.1016/j.bmc.2020.115834] [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: 09/11/2020] [Revised: 10/20/2020] [Accepted: 10/24/2020] [Indexed: 11/30/2022]
Abstract
T-cell engaging bispecific antibodies (T-biAbs) mediate potent and selective cytotoxicity by combining specificities for target and effector cells in one molecule. Chemically programmed T-biAbs (cp-T-biAbs) are precisely assembled compositions of (i) small molecules that govern cancer cell surface targeting with high affinity and specificity and (ii) antibodies that recruit and activate T cells and equip the small molecule with confined biodistribution and longer circulatory half-life. Conceptually similar to cp-T-biAbs, switchable chimeric antigen receptor T cells (sCAR-Ts) can also be put under the control of small molecules by using a chemically programmed antibody as a bispecific adaptor molecule. As such, cp-T-biAbs and cp-sCAR-Ts can endow small molecules with the power of cancer immunotherapy. We here review the concept of chemically programmed antibodies for recruiting and activating T cells as a promising strategy for broadening the utility of small molecules in cancer therapy.
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Affiliation(s)
- Junpeng Qi
- Department of Immunology and Microbiology, The Scripps Research Institute, Jupiter, FL 33458, USA.
| | - Christoph Rader
- Department of Immunology and Microbiology, The Scripps Research Institute, Jupiter, FL 33458, USA.
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20
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Liu X, Lu S, Song K, Shen Q, Ni D, Li Q, He X, Zhang H, Wang Q, Chen Y, Li X, Wu J, Sheng C, Chen G, Liu Y, Lu X, Zhang J. Unraveling allosteric landscapes of allosterome with ASD. Nucleic Acids Res 2020; 48:D394-D401. [PMID: 31665428 PMCID: PMC7145546 DOI: 10.1093/nar/gkz958] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2019] [Revised: 09/30/2019] [Accepted: 10/10/2019] [Indexed: 12/17/2022] Open
Abstract
Allosteric regulation is one of the most direct and efficient ways to fine-tune protein function; it is induced by the binding of a ligand at an allosteric site that is topographically distinct from an orthosteric site. The Allosteric Database (ASD, available online at http://mdl.shsmu.edu.cn/ASD) was developed ten years ago to provide comprehensive information related to allosteric regulation. In recent years, allosteric regulation has received great attention in biological research, bioengineering, and drug discovery, leading to the emergence of entire allosteric landscapes as allosteromes. To facilitate research from the perspective of the allosterome, in ASD 2019, novel features were curated as follows: (i) >10 000 potential allosteric sites of human proteins were deposited for allosteric drug discovery; (ii) 7 human allosterome maps, including protease and ion channel maps, were built to reveal allosteric evolution within families; (iii) 1312 somatic missense mutations at allosteric sites were collected from patient samples from 33 cancer types and (iv) 1493 pharmacophores extracted from allosteric sites were provided for modulator screening. Over the past ten years, the ASD has become a central resource for studying allosteric regulation and will play more important roles in both target identification and allosteric drug discovery in the future.
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Affiliation(s)
- Xinyi Liu
- State Key Laboratory of Oncogenes and Related Genes, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200127, China.,Medicinal Bioinformatics Center, Shanghai Jiao Tong University School of Medicine (SJTU-SM), Shanghai 200025, China
| | - Shaoyong Lu
- State Key Laboratory of Oncogenes and Related Genes, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200127, China.,Medicinal Bioinformatics Center, Shanghai Jiao Tong University School of Medicine (SJTU-SM), Shanghai 200025, China
| | - Kun Song
- State Key Laboratory of Oncogenes and Related Genes, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200127, China.,Medicinal Bioinformatics Center, Shanghai Jiao Tong University School of Medicine (SJTU-SM), Shanghai 200025, China
| | - Qiancheng Shen
- State Key Laboratory of Oncogenes and Related Genes, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200127, China.,Medicinal Bioinformatics Center, Shanghai Jiao Tong University School of Medicine (SJTU-SM), Shanghai 200025, China.,Department of Assisted Reproduction, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine (SJTU-SM), Shanghai 200011, China
| | - Duan Ni
- State Key Laboratory of Oncogenes and Related Genes, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200127, China
| | - Qian Li
- Medicinal Bioinformatics Center, Shanghai Jiao Tong University School of Medicine (SJTU-SM), Shanghai 200025, China.,Department of Assisted Reproduction, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine (SJTU-SM), Shanghai 200011, China
| | - Xinheng He
- State Key Laboratory of Oncogenes and Related Genes, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200127, China.,Medicinal Bioinformatics Center, Shanghai Jiao Tong University School of Medicine (SJTU-SM), Shanghai 200025, China
| | - Hao Zhang
- Medicinal Bioinformatics Center, Shanghai Jiao Tong University School of Medicine (SJTU-SM), Shanghai 200025, China
| | - Qi Wang
- China National Pharmaceutical Industry Information Center, Shanghai, 200040, China
| | - Yingyi Chen
- State Key Laboratory of Oncogenes and Related Genes, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200127, China.,Medicinal Bioinformatics Center, Shanghai Jiao Tong University School of Medicine (SJTU-SM), Shanghai 200025, China
| | - Xinyi Li
- State Key Laboratory of Oncogenes and Related Genes, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200127, China.,Medicinal Bioinformatics Center, Shanghai Jiao Tong University School of Medicine (SJTU-SM), Shanghai 200025, China
| | - Jing Wu
- Medicinal Bioinformatics Center, Shanghai Jiao Tong University School of Medicine (SJTU-SM), Shanghai 200025, China.,Department of Assisted Reproduction, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine (SJTU-SM), Shanghai 200011, China
| | - Chunquan Sheng
- School of Pharmacy, Second Military Medical University, Shanghai, 200433, China.,School of Pharmaceutical Sciences, Zhengzhou University, Zhengzhou 450001, China
| | - Guoqiang Chen
- State Key Laboratory of Oncogenes and Related Genes, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200127, China
| | - Yaqin Liu
- State Key Laboratory of Oncogenes and Related Genes, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200127, China
| | - Xuefeng Lu
- Department of Assisted Reproduction, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine (SJTU-SM), Shanghai 200011, China
| | - Jian Zhang
- State Key Laboratory of Oncogenes and Related Genes, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200127, China.,Medicinal Bioinformatics Center, Shanghai Jiao Tong University School of Medicine (SJTU-SM), Shanghai 200025, China.,School of Pharmaceutical Sciences, Zhengzhou University, Zhengzhou 450001, China
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22
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Qi J, Hymel D, Nelson CG, Burke TR, Rader C. Conventional and Chemically Programmed Asymmetric Bispecific Antibodies Targeting Folate Receptor 1. Front Immunol 2019; 10:1994. [PMID: 31497024 PMCID: PMC6712926 DOI: 10.3389/fimmu.2019.01994] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2019] [Accepted: 08/07/2019] [Indexed: 12/19/2022] Open
Abstract
T-cell engaging bispecific antibodies (biAbs) can mediate potent and specific tumor cell eradication in liquid cancers. Substantial effort has been invested in expanding this concept to solid cancers. To explore their utility in the treatment of ovarian cancer, we built a set of asymmetric biAbs in IgG1-like format that bind CD3 on T cells with a conventional scFv arm and folate receptor 1 (FOLR1) on ovarian cancer cells with a conventional or a chemically programmed Fab arm. For avidity engineering, we also built an asymmetric biAb format with a tandem Fab arm. We show that both conventional and chemically programmed CD3 × FOLR1 biAbs exert specific in vitro and in vivo cytotoxicity toward FOLR1-expressing ovarian cancer cells by recruiting and activating T cells. While the conventional T-cell engaging biAb was curative in an aggressive mouse model of human ovarian cancer, the potency of the chemically programmed biAb was significantly boosted by avidity engineering. Both conventional and chemically programmed CD3 × FOLR1 biAbs warrant further investigation for ovarian cancer immunotherapy.
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Affiliation(s)
- Junpeng Qi
- Department of Immunology and Microbiology, The Scripps Research Institute, Jupiter, FL, United States
| | - David Hymel
- Chemical Biology Laboratory, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Frederick, MD, United States
| | - Christopher G Nelson
- Chemical Biology Laboratory, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Frederick, MD, United States
| | - Terrence R Burke
- Chemical Biology Laboratory, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Frederick, MD, United States
| | - Christoph Rader
- Department of Immunology and Microbiology, The Scripps Research Institute, Jupiter, FL, United States
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Abstract
INTRODUCTION The success of binding site comparisons in drug discovery is based on the recognized fact that many different proteins have similar binding sites. Indeed, binding site comparisons have found many uses in drug development and have the potential to dramatically cut the cost and shorten the time necessary for the development of new drugs. Areas covered: The authors review recent methods for comparing protein binding sites and their use in drug repurposing and polypharmacology. They examine emerging fields including the use of binding site comparisons in precision medicine, the prediction of structured water molecules, the search for targets of natural compounds, and their application in the development of protein-based drugs by loop modeling and for comparison of RNA binding sites. Expert opinion: Binding site comparisons have produced many interesting results in drug development, but relatively little work has been done on protein-protein interaction sites, which are particularly relevant in view of the success of biological drugs. Growth of protein loop modeling for modulating biological drugs is anticipated. The fusion of currently distinct methods for the comparison of RNA and protein binding sites into a single comprehensive approach could allow the search for new selective ribosomal antibiotics and initiate pharmaceutical research into other nucleoproteins.
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Affiliation(s)
- Janez Konc
- a Theory Department , National Institute of Chemistry , Ljubljana , Slovenia.,b Faculty of Pharmacy , University of Ljubljana , Ljubljana , Slovenia.,c Faculty of Mathematics , Natural Sciences and Information Technologies, University of Primorska , Koper , Slovenia.,d Faculty of Chemistry and Chemical Technology , University of Maribor , Maribor , Slovenia
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Lu S, Shen Q, Zhang J. Allosteric Methods and Their Applications: Facilitating the Discovery of Allosteric Drugs and the Investigation of Allosteric Mechanisms. Acc Chem Res 2019; 52:492-500. [PMID: 30688063 DOI: 10.1021/acs.accounts.8b00570] [Citation(s) in RCA: 114] [Impact Index Per Article: 22.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
Allostery, or allosteric regulation, is the phenomenon in which protein functional activity is altered by the binding of an effector at an allosteric site that is topographically distinct from the orthosteric, active site. As one of the most direct and efficient ways to regulate protein function, allostery has played a fundamental role in innumerable biological processes of all living organisms, including enzyme catalysis, signal transduction, cell metabolism, and gene transcription. It is thus considered as "the second secret of life". The abnormality of allosteric communication networks between allosteric and orthosteric sites is associated with the pathogenesis of human diseases. Allosteric modulators, by attaching to structurally diverse allosteric sites, offer the potential for differential selectivity and improved safety compared with orthosteric drugs that bind to conserved orthosteric sites. Harnessing allostery has thus been regarded as a novel strategy for drug discovery. Despite much progress having been made in the repertoire of allostery since the turn of the millennium, the identification of allosteric drugs for therapeutic targets and the elucidation of allosteric mechanisms still present substantial challenges. These challenges are derived from the difficulties in the identification of allosteric sites and mutations, the assessment of allosteric protein-modulator interactions, the screening of allosteric modulators, and the elucidation of allosteric mechanisms in biological systems. To address these issues, we have developed a panel of allosteric services for specific allosteric applications over the past decade, including (i) the creation of the Allosteric Database, with the aim of providing comprehensive allosteric information such as allosteric proteins, modulators, sites, pathways, etc., (ii) the construction of the ASBench benchmark of high-quality allosteric sites for the development of computational methods for predicting allosteric sites, (iii) the development of Allosite and AllositePro for the prediction of the location of allosteric sites in proteins, (iv) the development of the Alloscore scoring function for the evaluation of allosteric protein-modulator interactions, (v) the development of Allosterome for evolutionary analysis of query allosteric sites/modulators within the human proteome, (vi) the development of AlloDriver for the prediction of allosteric mutagenesis, and (vii) the development of AlloFinder for the virtual screening of allosteric modulators and the investigation of allosteric mechanisms. Importantly, we have validated computationally predicted allosteric sites, mutations, and modulators in the real cases of sirtuin 6, casein kinase 2α, phosphodiesterase 10A, and signal transduction and activation of transcription 3. Furthermore, our developed allosteric methods have been widely exploited by other users around the world for allosteric research. Therefore, these allosteric services are expected to expedite the discovery of allosteric drugs and the investigation of allosteric mechanisms.
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Affiliation(s)
- Shaoyong Lu
- Department of Pathophysiology, Key Laboratory of Cell Differentiation and Apoptosis of Chinese Ministry of Education, Shanghai Jiao Tong University, School of Medicine, Shanghai 200025, China
| | - Qiancheng Shen
- Medicinal Bioinformatics Center, Shanghai Jiao Tong University, School of Medicine, Shanghai 200025, China
| | - Jian Zhang
- Department of Pathophysiology, Key Laboratory of Cell Differentiation and Apoptosis of Chinese Ministry of Education, Shanghai Jiao Tong University, School of Medicine, Shanghai 200025, China
- Medicinal Bioinformatics Center, Shanghai Jiao Tong University, School of Medicine, Shanghai 200025, China
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25
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Krivák R, Hoksza D. P2Rank: machine learning based tool for rapid and accurate prediction of ligand binding sites from protein structure. J Cheminform 2018; 10:39. [PMID: 30109435 PMCID: PMC6091426 DOI: 10.1186/s13321-018-0285-8] [Citation(s) in RCA: 177] [Impact Index Per Article: 29.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2017] [Accepted: 06/29/2018] [Indexed: 01/29/2023] Open
Abstract
Background Ligand binding site prediction from protein structure has many applications related to elucidation of protein function and structure based drug discovery. It often represents only one step of many in complex computational drug design efforts. Although many methods have been published to date, only few of them are suitable for use in automated pipelines or for processing large datasets.
These use cases require stability and speed, which disqualifies many of the recently introduced tools that are either template based or available only as web servers. Results We present P2Rank, a stand-alone template-free tool for prediction of ligand binding sites based on machine learning. It is based on prediction of ligandability of local chemical neighbourhoods that are centered on points placed on the solvent accessible surface of a protein.
We show that P2Rank outperforms several existing tools, which include two widely used stand-alone tools (Fpocket, SiteHound), a comprehensive consensus based tool (MetaPocket 2.0), and a recent deep learning based method (DeepSite). P2Rank belongs to the fastest available tools (requires under 1 s for prediction on one protein), with additional advantage of multi-threaded implementation. Conclusions P2Rank is a new open source software package for ligand binding site prediction from protein structure. It is available as a user-friendly stand-alone command line program and a Java library. P2Rank has a lightweight installation and does not depend on other bioinformatics tools or large structural or sequence databases. Thanks to its speed and ability to make fully automated predictions, it is particularly well suited for processing large datasets or as a component of scalable structural bioinformatics pipelines. Electronic supplementary material The online version of this article (10.1186/s13321-018-0285-8) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Radoslav Krivák
- Department of Software Engineering, Charles University, Prague, Czech Republic.
| | - David Hoksza
- Department of Software Engineering, Charles University, Prague, Czech Republic.
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