1
|
Wang S, Xie J, Pei J, Lai L. CavityPlus 2022 Update: An Integrated Platform for Comprehensive Protein Cavity Detection and Property Analyses with User-friendly Tools and Cavity Databases. J Mol Biol 2023; 435:168141. [PMID: 37356903 DOI: 10.1016/j.jmb.2023.168141] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2023] [Revised: 04/13/2023] [Accepted: 04/27/2023] [Indexed: 06/27/2023]
Abstract
Ligand binding sites provide essential information for uncovering protein functions and structure-based drug discovery. To facilitate cavity detection and property analysis process, we developed a comprehensive web server, CavityPlus in 2018. CavityPlus applies the CAVITY program to detect potential binding sites in a given protein structure. The CavPharmer, CorrSite, and CovCys tools can then be applied to generate receptor-based pharmacophore models, identify potential allosteric sites, or detect druggable cysteine residues for covalent drug design. While CavityPlus has been widely used, the constantly evolving knowledge and methods make it necessary to improve and extend its functions. This study presents a new version of CavityPlus, CavityPlus 2022 through a series of upgrades. We upgraded the CAVITY tool to greatly speed up cavity detection calculation. We optimized the CavPharmer tool for fast speed and more accurate results. We integrated the newly developed CorrSite2.0 into the CavityPlus 2022 web server for its improved performance of allosteric site prediction. We also added a new CavityMatch module for drug repurposing and protein function studies by searching similar cavities to a given cavity from pre-constructed cavity databases. The new version of CavityPlus is freely available at http://pkumdl.cn:8000/cavityplus/.
Collapse
Affiliation(s)
- Shiwei Wang
- BMLMS, College of Chemistry and Molecular Engineering, Peking University, Beijing, PR China
| | - Juan Xie
- Center for Quantitative Biology, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, PR China
| | - Jianfeng Pei
- Center for Quantitative Biology, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, PR China; Research Unit of Drug Design Method, Chinese Academy of Medical Sciences (2021RU014), Beijing 100871, PR China
| | - Luhua Lai
- BMLMS, College of Chemistry and Molecular Engineering, Peking University, Beijing, PR China; Center for Quantitative Biology, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, PR China; Research Unit of Drug Design Method, Chinese Academy of Medical Sciences (2021RU014), Beijing 100871, PR China.
| |
Collapse
|
2
|
Canner SW, Shanker S, Gray JJ. Structure-based neural network protein-carbohydrate interaction predictions at the residue level. FRONTIERS IN BIOINFORMATICS 2023; 3:1186531. [PMID: 37409346 PMCID: PMC10318439 DOI: 10.3389/fbinf.2023.1186531] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2023] [Accepted: 05/31/2023] [Indexed: 07/07/2023] Open
Abstract
Carbohydrates dynamically and transiently interact with proteins for cell-cell recognition, cellular differentiation, immune response, and many other cellular processes. Despite the molecular importance of these interactions, there are currently few reliable computational tools to predict potential carbohydrate-binding sites on any given protein. Here, we present two deep learning (DL) models named CArbohydrate-Protein interaction Site IdentiFier (CAPSIF) that predicts non-covalent carbohydrate-binding sites on proteins: (1) a 3D-UNet voxel-based neural network model (CAPSIF:V) and (2) an equivariant graph neural network model (CAPSIF:G). While both models outperform previous surrogate methods used for carbohydrate-binding site prediction, CAPSIF:V performs better than CAPSIF:G, achieving test Dice scores of 0.597 and 0.543 and test set Matthews correlation coefficients (MCCs) of 0.599 and 0.538, respectively. We further tested CAPSIF:V on AlphaFold2-predicted protein structures. CAPSIF:V performed equivalently on both experimentally determined structures and AlphaFold2-predicted structures. Finally, we demonstrate how CAPSIF models can be used in conjunction with local glycan-docking protocols, such as GlycanDock, to predict bound protein-carbohydrate structures.
Collapse
Affiliation(s)
- Samuel W. Canner
- Program in Molecular Biophysics, The Johns Hopkins University, Baltimore, MD, United States
| | - Sudhanshu Shanker
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, United States
| | - Jeffrey J. Gray
- Program in Molecular Biophysics, The Johns Hopkins University, Baltimore, MD, United States
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, United States
| |
Collapse
|
3
|
Canner SW, Shanker S, Gray JJ. Structure-Based Neural Network Protein-Carbohydrate Interaction Predictions at the Residue Level. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.03.14.531382. [PMID: 36993750 PMCID: PMC10054975 DOI: 10.1101/2023.03.14.531382] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 04/13/2023]
Abstract
Carbohydrates dynamically and transiently interact with proteins for cell-cell recognition, cellular differentiation, immune response, and many other cellular processes. Despite the molecular importance of these interactions, there are currently few reliable computational tools to predict potential carbohydrate binding sites on any given protein. Here, we present two deep learning models named CArbohydrate-Protein interaction Site IdentiFier (CAPSIF) that predict carbohydrate binding sites on proteins: (1) a 3D-UNet voxel-based neural network model (CAPSIF:V) and (2) an equivariant graph neural network model (CAPSIF:G). While both models outperform previous surrogate methods used for carbohydrate binding site prediction, CAPSIF:V performs better than CAPSIF:G, achieving test Dice scores of 0.597 and 0.543 and test set Matthews correlation coefficients (MCCs) of 0.599 and 0.538, respectively. We further tested CAPSIF:V on AlphaFold2-predicted protein structures. CAPSIF:V performed equivalently on both experimentally determined structures and AlphaFold2 predicted structures. Finally, we demonstrate how CAPSIF models can be used in conjunction with local glycan-docking protocols, such as GlycanDock, to predict bound protein-carbohydrate structures.
Collapse
Affiliation(s)
- Samuel W. Canner
- Program in Molecular Biophysics, The Johns Hopkins University, Baltimore, MD, United States of America
| | - Sudhanshu Shanker
- Dept. of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, United States of America
| | - Jeffrey J. Gray
- Program in Molecular Biophysics, The Johns Hopkins University, Baltimore, MD, United States of America
- Dept. of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, United States of America
- Correspondence: Jeffrey J. Gray,
| |
Collapse
|
4
|
Identification of promising multi-targeting inhibitors of obesity from Vernonia amygdalina through computational analysis. Mol Divers 2023; 27:1-25. [PMID: 35179699 DOI: 10.1007/s11030-022-10397-6] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2021] [Accepted: 01/24/2022] [Indexed: 02/08/2023]
Abstract
Vernonia amygdalina, a widely consumed West African food herb, can be a boon in the discovery of safe anti-obesity agents given the extensive reports on its anti-obesity and antidiabetic potentials. The main aim of this study was to screen 78 Vernonia-Derived Phytocompounds (VDPs) against the active site regions of Human Pancreatic Lipase (HPL), Human Pancreatic Amylase and Human Glucosidase (HG) as drug targets associated with obesity in silico. Structure-based virtual screening helped to identify Luteolin 7-O-glucuronoside and Andrographidoid D2 as hit compounds with dual targeting tendency towards the HPL and HG. Analysis of the molecular dynamic simulation trajectory files of the ligand-receptor complexes as computed from the thermodynamic parameters plots showed not only increased flexibility and greater interaction potential of the active site residues of the receptor towards the VDPs as indicated by the root mean square fluctuation but also higher stability as indicated by the root mean square deviation, radius of gyration and number of hydrogen bonds. The cluster analysis further showed that the interactions with important residues were preserved in the dynamic environment. These observations were further verified from Molecular Mechanics Generalized Born Surface Area Analysis, which also showed that residual contributions to the binding free energies were mainly from catalytic residues at the active sites of the enzymes. The hit compounds also feature desirable physicochemical properties and drug-likeness. This study provides in silico evidence for the inhibitory potential of phytochemicals from Vernonia amygdalina against two target enzymes in obesity.
Collapse
|
5
|
Walther D. Specifics of Metabolite-Protein Interactions and Their Computational Analysis and Prediction. Methods Mol Biol 2023; 2554:179-197. [PMID: 36178627 DOI: 10.1007/978-1-0716-2624-5_12] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
Computational approaches to the characterization and prediction of compound-protein interactions have a long research history and are well established, driven primarily by the needs of drug development. While, in principle, many of the computational methods developed in the context of drug development can also be applied directly to the investigation of metabolite-protein interactions, the interactions of metabolites with proteins (enzymes) are characterized by a number of particularities that result from their natural evolutionary origin and their biological and biochemical roles, as well as from a different problem setting when investigating them. In this review, these special aspects will be highlighted and recent research on them and developed computational approaches presented, along with available resources. They concern, among others, binding promiscuity, allostery, the role of posttranslational modifications, molecular steering and crowding effects, and metabolic conversion rate predictions. Recent breakthroughs in the field of protein structure prediction and newly developed machine learning techniques are being discussed as a tremendous opportunity for developing a more detailed molecular understanding of metabolism.
Collapse
Affiliation(s)
- Dirk Walther
- Max Planck Institute of Molecular Plant Physiology, Potsdam, Germany.
| |
Collapse
|
6
|
Piplani S, Winkler D, Honda-Okubo Y, Khanna V, Petrovsky N. In Silico Structure-Based Vaccine Design. Methods Mol Biol 2023; 2673:371-399. [PMID: 37258928 DOI: 10.1007/978-1-0716-3239-0_26] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/02/2023]
Abstract
Structure-based vaccine design (SBVD) is an important technique in computational vaccine design that uses structural information on a targeted protein to design novel vaccine candidates. This increasing ability to rapidly model structural information on proteins and antibodies has provided the scientific community with many new vaccine targets and novel opportunities for future vaccine discovery. This chapter provides a comprehensive overview of the status of in silico SBVD and discusses the current challenges and limitations. Key strategies in the field of SBVD are exemplified by a case study on design of COVID-19 vaccines targeting SARS-CoV-2 spike protein.
Collapse
Affiliation(s)
| | - David Winkler
- School of Pharmacy, University of Nottingham, Nottingham, UK
- Monash Institute of Pharmaceutical Sciences, Monash University, Parkville, VIC, Australia
- Department of Biochemistry and Chemistry, La Trobe Institute for Molecular Science, La Trobe University, Bundoora, VIC, Australia
| | | | | | | |
Collapse
|
7
|
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.
Collapse
|
8
|
Priya S, Tripathi G, Singh DB, Jain P, Kumar A. Machine learning approaches and their applications in drug discovery and design. Chem Biol Drug Des 2022; 100:136-153. [PMID: 35426249 DOI: 10.1111/cbdd.14057] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2022] [Revised: 03/30/2022] [Accepted: 04/10/2022] [Indexed: 01/04/2023]
Abstract
This review is focused on several machine learning approaches used in chemoinformatics. Machine learning approaches provide tools and algorithms to improve drug discovery. Many physicochemical properties of drugs like toxicity, absorption, drug-drug interaction, carcinogenesis, and distribution have been effectively modeled by QSAR techniques. Machine learning is a subset of artificial intelligence, and this technique has shown tremendous potential in the field of drug discovery. Techniques discussed in this review are capable of modeling non-linear datasets, as well as big data of increasing depth and complexity. Various machine learning-based approaches are being used for drug target prediction, modeling the structure of drug target, binding site prediction, ligand-based similarity searching, de novo designing of ligands with desired properties, developing scoring functions for molecular docking, building QSAR model for biological activity prediction, and prediction of pharmacokinetic and pharmacodynamic properties of ligands. In recent years, these predictive tools and models have achieved good accuracy. By the use of more related input data, relevant parameters, and appropriate algorithms, the accuracy of these predictions can be further improved.
Collapse
Affiliation(s)
- Sonal Priya
- Department of Chemistry, T. N. B. College, TMBU, Bhagalpur, India
| | - Garima Tripathi
- Department of Chemistry, T. N. B. College, TMBU, Bhagalpur, India
| | - Dev Bukhsh Singh
- Department of Biotechnology, Siddharth University, Siddharth Nagar, India
| | - Priyanka Jain
- National Institute of Plant Genome Research, New Delhi, India
| | - Abhijeet Kumar
- Department of Chemistry, Mahatma Gandhi Central University, Motihari, India
| |
Collapse
|
9
|
What Makes GPCRs from Different Families Bind to the Same Ligand? Biomolecules 2022; 12:biom12070863. [PMID: 35883418 PMCID: PMC9313020 DOI: 10.3390/biom12070863] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2022] [Revised: 06/09/2022] [Accepted: 06/19/2022] [Indexed: 12/10/2022] Open
Abstract
G protein-coupled receptors (GPCRs) are the largest class of cell-surface receptor proteins with important functions in signal transduction and often serve as therapeutic drug targets. With the rapidly growing public data on three dimensional (3D) structures of GPCRs and GPCR-ligand interactions, computational prediction of GPCR ligand binding becomes a convincing option to high throughput screening and other experimental approaches during the beginning phases of ligand discovery. In this work, we set out to computationally uncover and understand the binding of a single ligand to GPCRs from several different families. Three-dimensional structural comparisons of the GPCRs that bind to the same ligand revealed local 3D structural similarities and often these regions overlap with locations of binding pockets. These pockets were found to be similar (based on backbone geometry and side-chain orientation using APoc), and they correlate positively with electrostatic properties of the pockets. Moreover, the more similar the pockets, the more likely a ligand binding to the pockets will interact with similar residues, have similar conformations, and produce similar binding affinities across the pockets. These findings can be exploited to improve protein function inference, drug repurposing and drug toxicity prediction, and accelerate the development of new drugs.
Collapse
|
10
|
Drug Design by Pharmacophore and Virtual Screening Approach. Pharmaceuticals (Basel) 2022; 15:ph15050646. [PMID: 35631472 PMCID: PMC9145410 DOI: 10.3390/ph15050646] [Citation(s) in RCA: 63] [Impact Index Per Article: 31.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2022] [Revised: 05/18/2022] [Accepted: 05/21/2022] [Indexed: 12/20/2022] Open
Abstract
Computer-aided drug discovery techniques reduce the time and the costs needed to develop novel drugs. Their relevance becomes more and more evident with the needs due to health emergencies as well as to the diffusion of personalized medicine. Pharmacophore approaches represent one of the most interesting tools developed, by defining the molecular functional features needed for the binding of a molecule to a given receptor, and then directing the virtual screening of large collections of compounds for the selection of optimal candidates. Computational tools to create the pharmacophore model and to perform virtual screening are available and generated successful studies. This article describes the procedure of pharmacophore modelling followed by virtual screening, the most used software, possible limitations of the approach, and some applications reported in the literature.
Collapse
|
11
|
Wang W, Zhang Y, Liu D, Zhang H, Wang X, Zhou Y. Prediction of DNA-Binding Protein–Drug-Binding Sites Using Residue Interaction Networks and Sequence Feature. Front Bioeng Biotechnol 2022; 10:822392. [PMID: 35519609 PMCID: PMC9065339 DOI: 10.3389/fbioe.2022.822392] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2021] [Accepted: 03/14/2022] [Indexed: 11/13/2022] Open
Abstract
Identification of protein–ligand binding sites plays a critical role in drug discovery. However, there is still a lack of targeted drug prediction for DNA-binding proteins. This study aims at the binding sites of DNA-binding proteins and drugs, by mining the residue interaction network features, which can describe the local and global structure of amino acids, combined with sequence feature. The predictor of DNA-binding protein–drug-binding sites is built by employing the Extreme Gradient Boosting (XGBoost) model with random under-sampling. We found that the residue interaction network features can better characterize DNA-binding proteins, and the binding sites with high betweenness value and high closeness value are more likely to interact with drugs. The model shows that the residue interaction network features can be used as an important quantitative indicator of drug-binding sites, and this method achieves high predictive performance for the binding sites of DNA-binding protein–drug. This study will help in drug discovery research for DNA-binding proteins.
Collapse
Affiliation(s)
- Wei Wang
- College of Computer and Information Engineering, Henan Normal University, Xinxiang, China
- Key Laboratory of Artificial Intelligence and Personalized Learning in Education of Henan Province, College of Computer and Information Engineering, Henan Normal University, Xinxiang, China
- *Correspondence: Wei Wang, ; Dong Liu, ; Yun Zhou,
| | - Yu Zhang
- College of Computer and Information Engineering, Henan Normal University, Xinxiang, China
| | - Dong Liu
- College of Computer and Information Engineering, Henan Normal University, Xinxiang, China
- Key Laboratory of Artificial Intelligence and Personalized Learning in Education of Henan Province, College of Computer and Information Engineering, Henan Normal University, Xinxiang, China
- *Correspondence: Wei Wang, ; Dong Liu, ; Yun Zhou,
| | - HongJun Zhang
- Computer Science and Technology, Anyang University, Anyang, China
| | - XianFang Wang
- Computer Science and Technology, Henan Institute of Technology, Xinxiang, China
| | - Yun Zhou
- College of Computer and Information Engineering, Henan Normal University, Xinxiang, China
- *Correspondence: Wei Wang, ; Dong Liu, ; Yun Zhou,
| |
Collapse
|
12
|
CAVIAR: a method for automatic cavity detection, description and decomposition into subcavities. J Comput Aided Mol Des 2021; 35:737-750. [PMID: 34050420 DOI: 10.1007/s10822-021-00390-w] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2021] [Accepted: 05/11/2021] [Indexed: 10/21/2022]
Abstract
The accurate description of protein binding sites is essential to the determination of similarity and the application of machine learning methods to relate the binding sites to observed functions. This work describes CAVIAR, a new open source tool for generating descriptors for binding sites, using protein structures in PDB and mmCIF format as well as trajectory frames from molecular dynamics simulations as input. The applicability of CAVIAR descriptors is showcased by computing machine learning predictions of binding site ligandability. The method can also automatically assign subcavities, even in the absence of a bound ligand. The defined subpockets mimic the empirical definitions used in medicinal chemistry projects. It is shown that the experimental binding affinity scales relatively well with the number of subcavities filled by the ligand, with compounds binding to more than three subcavities having nanomolar or better affinities to the target. The CAVIAR descriptors and methods can be used in any machine learning-based investigations of problems involving binding sites, from protein engineering to hit identification. The full software code is available on GitHub and a conda package is hosted on Anaconda cloud.
Collapse
|
13
|
Hendrix SG, Chang KY, Ryu Z, Xie ZR. DeepDISE: DNA Binding Site Prediction Using a Deep Learning Method. Int J Mol Sci 2021; 22:ijms22115510. [PMID: 34073705 PMCID: PMC8197219 DOI: 10.3390/ijms22115510] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2021] [Revised: 04/30/2021] [Accepted: 05/19/2021] [Indexed: 11/18/2022] Open
Abstract
It is essential for future research to develop a new, reliable prediction method of DNA binding sites because DNA binding sites on DNA-binding proteins provide critical clues about protein function and drug discovery. However, the current prediction methods of DNA binding sites have relatively poor accuracy. Using 3D coordinates and the atom-type of surface protein atom as the input, we trained and tested a deep learning model to predict how likely a voxel on the protein surface is to be a DNA-binding site. Based on three different evaluation datasets, the results show that our model not only outperforms several previous methods on two commonly used datasets, but also demonstrates its robust performance to be consistent among the three datasets. The visualized prediction outcomes show that the binding sites are also mostly located in correct regions. We successfully built a deep learning model to predict the DNA binding sites on target proteins. It demonstrates that 3D protein structures plus atom-type information on protein surfaces can be used to predict the potential binding sites on a protein. This approach should be further extended to develop the binding sites of other important biological molecules.
Collapse
Affiliation(s)
- Samuel Godfrey Hendrix
- Computational Drug Discovery Laboratory, School of Electrical and Computer Engineering, College of Engineering, University of Georgia, Athens, GA 30602, USA; (S.G.H.); (Z.R.)
| | - Kuan Y. Chang
- Department of Computer Science and Engineering, National Taiwan Ocean University, Keelung 202, Taiwan;
| | - Zeezoo Ryu
- Computational Drug Discovery Laboratory, School of Electrical and Computer Engineering, College of Engineering, University of Georgia, Athens, GA 30602, USA; (S.G.H.); (Z.R.)
- Department of Computer Science, Franklin College of Arts and Sciences, University of Georgia, Athens, GA 30602, USA
| | - Zhong-Ru Xie
- Computational Drug Discovery Laboratory, School of Electrical and Computer Engineering, College of Engineering, University of Georgia, Athens, GA 30602, USA; (S.G.H.); (Z.R.)
- Correspondence:
| |
Collapse
|
14
|
Predicting binding sites from unbound versus bound protein structures. Sci Rep 2020; 10:15856. [PMID: 32985584 PMCID: PMC7522209 DOI: 10.1038/s41598-020-72906-7] [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: 04/21/2020] [Accepted: 07/27/2020] [Indexed: 11/30/2022] Open
Abstract
We present the application of seven binding-site prediction algorithms to a meticulously curated dataset of ligand-bound and ligand-free crystal structures for 304 unique protein sequences (2528 crystal structures). We probe the influence of starting protein structures on the results of binding-site prediction, so the dataset contains a minimum of two ligand-bound and two ligand-free structures for each protein. We use this dataset in a brief survey of five geometry-based, one energy-based, and one machine-learning-based methods: Surfnet, Ghecom, LIGSITEcsc, Fpocket, Depth, AutoSite, and Kalasanty. Distributions of the F scores and Matthew’s correlation coefficients for ligand-bound versus ligand-free structure performance show no statistically significant difference in structure type versus performance for most methods. Only Fpocket showed a statistically significant but low magnitude enhancement in performance for holo structures. Lastly, we found that most methods will succeed on some crystal structures and fail on others within the same protein family, despite all structures being relatively high-quality structures with low structural variation. We expected better consistency across varying protein conformations of the same sequence. Interestingly, the success or failure of a given structure cannot be predicted by quality metrics such as resolution, Cruickshank Diffraction Precision index, or unresolved residues. Cryptic sites were also examined.
Collapse
|
15
|
Wu Y, Lou L, Xie ZR. A Pilot Study of All-Computational Drug Design Protocol-From Structure Prediction to Interaction Analysis. Front Chem 2020; 8:81. [PMID: 32117898 PMCID: PMC7028743 DOI: 10.3389/fchem.2020.00081] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2019] [Accepted: 01/24/2020] [Indexed: 11/13/2022] Open
Abstract
Speeding up the drug discovery process is of great significance. To achieve that, high-efficiency methods should be exploited. The conventional wet-bench methods hardly meet the high-speed demand due to time-consuming experiments. Conversely, in silico approaches are much more efficient for drug discovery and design. However, in silico approaches usually serve as a supportive role in research processes. To fully exert the strength of computational methods, we propose a protocol which integrates various in silico approaches, from de novo protein structure prediction to ligand-protein interaction simulation. As a proof of concept, human SK2/calmodulin complex was used as a target for validation. First, we obtained a predicted structure of SK2/calmodulin and predicted binding sites which were consistent with the literature data. Then we investigated the ligand-protein interaction via virtual mutagenesis, flexible docking, and binding affinity calculation. As a result, the binding energies of mutants have similar trends compared with the EC50 values (R = 0.6 for NS309 in V481 mutants). The results indicate that our protocol can be applied to the drug design of structure unknown proteins. Our study also demonstrates that the integration of in silico approaches is feasible and it facilitates the acceleration of new drug discovery.
Collapse
Affiliation(s)
- Yifei Wu
- Computational Drug Discovery Laboratory, School of Electrical and Computer Engineering, College of Engineering, University of Georgia, Athens, GA, United States
| | - Lei Lou
- Computational Drug Discovery Laboratory, School of Electrical and Computer Engineering, College of Engineering, University of Georgia, Athens, GA, United States
| | - Zhong-Ru Xie
- Computational Drug Discovery Laboratory, School of Electrical and Computer Engineering, College of Engineering, University of Georgia, Athens, GA, United States
| |
Collapse
|
16
|
Mishra SK, Kandoi G, Jernigan RL. Coupling dynamics and evolutionary information with structure to identify protein regulatory and functional binding sites. Proteins 2019; 87:850-868. [PMID: 31141211 DOI: 10.1002/prot.25749] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2019] [Accepted: 05/26/2019] [Indexed: 12/25/2022]
Abstract
Binding sites in proteins can be either specifically functional binding sites (active sites) that bind specific substrates with high affinity or regulatory binding sites (allosteric sites), that modulate the activity of functional binding sites through effector molecules. Owing to their significance in determining protein function, the identification of protein functional and regulatory binding sites is widely acknowledged as an important biological problem. In this work, we present a novel binding site prediction method, Active and Regulatory site Prediction (AR-Pred), which supplements protein geometry, evolutionary, and physicochemical features with information about protein dynamics to predict putative active and allosteric site residues. As the intrinsic dynamics of globular proteins plays an essential role in controlling binding events, we find it to be an important feature for the identification of protein binding sites. We train and validate our predictive models on multiple balanced training and validation sets with random forest machine learning and obtain an ensemble of discrete models for each prediction type. Our models for active site prediction yield a median area under the curve (AUC) of 91% and Matthews correlation coefficient (MCC) of 0.68, whereas the less well-defined allosteric sites are predicted at a lower level with a median AUC of 80% and MCC of 0.48. When tested on an independent set of proteins, our models for active site prediction show comparable performance to two existing methods and gains compared to two others, while the allosteric site models show gains when tested against three existing prediction methods. AR-Pred is available as a free downloadable package at https://github.com/sambitmishra0628/AR-PRED_source.
Collapse
Affiliation(s)
- Sambit K Mishra
- Bioinformatics and Computational Biology Program, Iowa State University, Ames, Iowa.,Roy J. Carver Department of Biochemistry, Biophysics and Molecular Biology, Iowa State University, Ames, Iowa
| | - Gaurav Kandoi
- Bioinformatics and Computational Biology Program, Iowa State University, Ames, Iowa.,Department of Electrical and Computer Engineering, Iowa State University, Ames, Iowa
| | - Robert L Jernigan
- Bioinformatics and Computational Biology Program, Iowa State University, Ames, Iowa.,Roy J. Carver Department of Biochemistry, Biophysics and Molecular Biology, Iowa State University, Ames, Iowa
| |
Collapse
|
17
|
Protein structure and computational drug discovery. Biochem Soc Trans 2018; 46:1367-1379. [DOI: 10.1042/bst20180202] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2018] [Revised: 08/08/2018] [Accepted: 08/13/2018] [Indexed: 12/12/2022]
Abstract
The first protein structures revealed a complex web of weak interactions stabilising the three-dimensional shape of the molecule. Small molecule ligands were then found to exploit these same weak binding events to modulate protein function or act as substrates in enzymatic reactions. As the understanding of ligand–protein binding grew, it became possible to firstly predict how and where a particular small molecule might interact with a protein, and then to identify putative ligands for a specific protein site. Computer-aided drug discovery, based on the structure of target proteins, is now a well-established technique that has produced several marketed drugs. We present here an overview of the various methodologies being used for structure-based computer-aided drug discovery and comment on possible future developments in the field.
Collapse
|
18
|
Rachman MM, Barril X, Hubbard RE. Predicting how drug molecules bind to their protein targets. Curr Opin Pharmacol 2018; 42:34-39. [PMID: 30041063 DOI: 10.1016/j.coph.2018.07.001] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2018] [Accepted: 07/01/2018] [Indexed: 01/27/2023]
Abstract
There have been substantial advances in the application of molecular modelling and simulation to drug discovery in recent years, as massive increases in computer power are coupled with continued development in the underlying methods and understanding of how to apply them. Here, we survey recent advances in one particular area-predicting how a known ligand binds to a particular protein. We focus on the four contributing classes of calculation: predicting where a binding site is on a protein; characterizing where chemical functional groups will bind to that site; molecular docking to generate a binding mode for a ligand and dynamics simulations to refine that pose and allow for protein conformation change. Examples of successful application are provided for each class.
Collapse
Affiliation(s)
- Moira M Rachman
- Facultat de Farmàcia and Institut de Biomedicina, Universitat de Barcelona, Av. Joan XXIII, 27-31, 08028 Barcelona, Spain
| | - Xavier Barril
- Facultat de Farmàcia and Institut de Biomedicina, Universitat de Barcelona, Av. Joan XXIII, 27-31, 08028 Barcelona, Spain; Catalan Institution for Research and Advanced Studies (ICREA), Passeig Lluís Companys 23, 08010 Barcelona, Spain
| | - Roderick E Hubbard
- YSBL, University of York, Heslington, York YO10 5DD, UK; Vernalis (R&D) Ltd, Granta Park, Abington, Cambridge CB21 6GB, UK.
| |
Collapse
|
19
|
Tripathi H, Khan F. Identification of potential inhibitors against nuclear Dam1 complex subunit Ask1 of Candida albicans using virtual screening and MD simulations. Comput Biol Chem 2018; 72:33-44. [DOI: 10.1016/j.compbiolchem.2017.12.013] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2016] [Revised: 11/22/2017] [Accepted: 12/30/2017] [Indexed: 11/29/2022]
|
20
|
Zheng M, Zhao J, Cui C, Fu Z, Li X, Liu X, Ding X, Tan X, Li F, Luo X, Chen K, Jiang H. Computational chemical biology and drug design: Facilitating protein structure, function, and modulation studies. Med Res Rev 2018; 38:914-950. [DOI: 10.1002/med.21483] [Citation(s) in RCA: 27] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2017] [Revised: 12/13/2017] [Accepted: 12/15/2017] [Indexed: 12/12/2022]
Affiliation(s)
- Mingyue Zheng
- State Key Laboratory of Drug Research, Drug Discovery and Design Center, Shanghai Institute of Materia Medica; Chinese Academy of Sciences; Shanghai China
| | - Jihui Zhao
- State Key Laboratory of Drug Research, Drug Discovery and Design Center, Shanghai Institute of Materia Medica; Chinese Academy of Sciences; Shanghai China
| | - Chen Cui
- State Key Laboratory of Drug Research, Drug Discovery and Design Center, Shanghai Institute of Materia Medica; Chinese Academy of Sciences; Shanghai China
| | - Zunyun Fu
- State Key Laboratory of Drug Research, Drug Discovery and Design Center, Shanghai Institute of Materia Medica; Chinese Academy of Sciences; Shanghai China
| | - Xutong Li
- State Key Laboratory of Drug Research, Drug Discovery and Design Center, Shanghai Institute of Materia Medica; Chinese Academy of Sciences; Shanghai China
| | - Xiaohong Liu
- State Key Laboratory of Drug Research, Drug Discovery and Design Center, Shanghai Institute of Materia Medica; Chinese Academy of Sciences; Shanghai China
- School of Life Science and Technology; ShanghaiTech University; Shanghai China
| | - Xiaoyu Ding
- State Key Laboratory of Drug Research, Drug Discovery and Design Center, Shanghai Institute of Materia Medica; Chinese Academy of Sciences; Shanghai China
| | - Xiaoqin Tan
- State Key Laboratory of Drug Research, Drug Discovery and Design Center, Shanghai Institute of Materia Medica; Chinese Academy of Sciences; Shanghai China
| | - Fei Li
- State Key Laboratory of Drug Research, Drug Discovery and Design Center, Shanghai Institute of Materia Medica; Chinese Academy of Sciences; Shanghai China
- Department of Chemistry, College of Sciences; Shanghai University; Shanghai China
| | - Xiaomin Luo
- State Key Laboratory of Drug Research, Drug Discovery and Design Center, Shanghai Institute of Materia Medica; Chinese Academy of Sciences; Shanghai China
| | - Kaixian Chen
- State Key Laboratory of Drug Research, Drug Discovery and Design Center, Shanghai Institute of Materia Medica; Chinese Academy of Sciences; Shanghai China
- School of Life Science and Technology; ShanghaiTech University; Shanghai China
| | - Hualiang Jiang
- State Key Laboratory of Drug Research, Drug Discovery and Design Center, Shanghai Institute of Materia Medica; Chinese Academy of Sciences; Shanghai China
| |
Collapse
|
21
|
Caumes G, Borrel A, Abi Hussein H, Camproux AC, Regad L. Investigating the Importance of the Pocket-estimation Method in Pocket-based Approaches: An Illustration Using Pocket-ligand Classification. Mol Inform 2017; 36. [PMID: 28452177 DOI: 10.1002/minf.201700025] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2017] [Accepted: 04/06/2017] [Indexed: 11/12/2022]
Abstract
Small molecules interact with their protein target on surface cavities known as binding pockets. Pocket-based approaches are very useful in all of the phases of drug design. Their first step is estimating the binding pocket based on protein structure. The available pocket-estimation methods produce different pockets for the same target. The aim of this work is to investigate the effects of different pocket-estimation methods on the results of pocket-based approaches. We focused on the effect of three pocket-estimation methods on a pocket-ligand (PL) classification. This pocket-based approach is useful for understanding the correspondence between the pocket and ligand spaces and to develop pharmacological profiling models. We found pocket-estimation methods yield different binding pockets in terms of boundaries and properties. These differences are responsible for the variation in the PL classification results that can have an impact on the detected correspondence between pocket and ligand profiles. Thus, we highlighted the importance of the pocket-estimation method choice in pocket-based approaches.
Collapse
Affiliation(s)
- Géraldine Caumes
- Molécules thérapeutiques In silico (MTi), INSERM UMR-S973, University Paris Diderot, 35 rue Hélène Brion, 75013, Paris Cedex, France.,IMPMC, UMR 7590, Equipe de Géobiologie, Université Pierre et Marie Curie, 4 place Jussieu, 75252, Paris Cedex, France
| | - Alexandre Borrel
- Molécules thérapeutiques In silico (MTi), INSERM UMR-S973, University Paris Diderot, 35 rue Hélène Brion, 75013, Paris Cedex, France.,Division of Pharmaceutical Chemistry and Technology, Faculty of Pharmacy, University of Helsinki, P.O. Box 56, FI-00014, Helsinki, Finland
| | - Hiba Abi Hussein
- Molécules thérapeutiques In silico (MTi), INSERM UMR-S973, University Paris Diderot, 35 rue Hélène Brion, 75013, Paris Cedex, France
| | - Anne-Claude Camproux
- Molécules thérapeutiques In silico (MTi), INSERM UMR-S973, University Paris Diderot, 35 rue Hélène Brion, 75013, Paris Cedex, France
| | - Leslie Regad
- Molécules thérapeutiques In silico (MTi), INSERM UMR-S973, University Paris Diderot, 35 rue Hélène Brion, 75013, Paris Cedex, France
| |
Collapse
|
22
|
Hu X, Wang K, Dong Q. Protein ligand-specific binding residue predictions by an ensemble classifier. BMC Bioinformatics 2016; 17:470. [PMID: 27855637 PMCID: PMC5114821 DOI: 10.1186/s12859-016-1348-3] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2016] [Accepted: 11/10/2016] [Indexed: 11/22/2022] Open
Abstract
BACKGROUND Prediction of ligand binding sites is important to elucidate protein functions and is helpful for drug design. Although much progress has been made, many challenges still need to be addressed. Prediction methods need to be carefully developed to account for chemical and structural differences between ligands. RESULTS In this study, we present ligand-specific methods to predict the binding sites of protein-ligand interactions. First, a sequence-based method is proposed that only extracts features from protein sequence information, including evolutionary conservation scores and predicted structure properties. An improved AdaBoost algorithm is applied to address the serious imbalance problem between the binding and non-binding residues. Then, a combined method is proposed that combines the current template-free method and four other well-established template-based methods. The above two methods predict the ligand binding sites along the sequences using a ligand-specific strategy that contains metal ions, acid radical ions, nucleotides and ferroheme. Testing on a well-established dataset showed that the proposed sequence-based method outperformed the profile-based method by 4-19% in terms of the Matthews correlation coefficient on different ligands. The combined method outperformed each of the individual methods, with an improvement in the average Matthews correlation coefficients of 5.55% over all ligands. The results also show that the ligand-specific methods significantly outperform the general-purpose methods, which confirms the necessity of developing elaborate ligand-specific methods for ligand binding site prediction. CONCLUSIONS Two efficient ligand-specific binding site predictors are presented. The standalone package is freely available for academic usage at http://dase.ecnu.edu.cn/qwdong/TargetCom/TargetCom_standalone.tar.gz or request upon the corresponding author.
Collapse
Affiliation(s)
- Xiuzhen Hu
- College of Sciences, Inner Mongolia University of Technology, Hohhot, 010051 People’s Republic of China
| | - Kai Wang
- College of Animal Science and Technology, Jilin Agricultural University, Changchun, 130118 People’s Republic of China
| | - Qiwen Dong
- Institute for Data Science and Engineering, East China Normal University, Shanghai, 200062 People’s Republic of China
- Key Laboratory of Network Oriented Intelligent Computation, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, Guangdong 518055 People’s Republic of China
- Present Address: School of Computer Science and Software Engineering, East China Normal University, #3663, North Zhongshan RD, Shanghai, 200062 China
| |
Collapse
|
23
|
Uzunova VV, Quareshy M, Del Genio CI, Napier RM. Tomographic docking suggests the mechanism of auxin receptor TIR1 selectivity. Open Biol 2016; 6:160139. [PMID: 27805904 PMCID: PMC5090058 DOI: 10.1098/rsob.160139] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2016] [Accepted: 09/22/2016] [Indexed: 01/14/2023] Open
Abstract
We study the binding of plant hormone IAA on its receptor TIR1, introducing a novel computational method that we call tomographic docking and that accounts for interactions occurring along the depth of the binding pocket. Our results suggest that selectivity is related to constraints that potential ligands encounter on their way from the surface of the protein to their final position at the pocket bottom. Tomographic docking helps develop specific hypotheses about ligand binding, distinguishing binders from non-binders, and suggests that binding is a three-step mechanism, consisting of engagement with a niche in the back wall of the pocket, interaction with a molecular filter which allows or precludes further descent of ligands, and binding on the pocket base. Only molecules that are able to descend the pocket and bind at its base allow the co-receptor IAA7 to bind on the complex, thus behaving as active auxins. Analysing the interactions at different depths, our new method helps in identifying critical residues that constitute preferred future study targets and in the quest for safe and effective herbicides. Also, it has the potential to extend the utility of docking from ligand searches to the study of processes contributing to selectivity.
Collapse
Affiliation(s)
- Veselina V Uzunova
- School of Life Sciences, University of Warwick, Gibbet Hill Road, Coventry CV4 7AL, UK
| | - Mussa Quareshy
- School of Life Sciences, University of Warwick, Gibbet Hill Road, Coventry CV4 7AL, UK
| | - Charo I Del Genio
- School of Life Sciences, University of Warwick, Gibbet Hill Road, Coventry CV4 7AL, UK
| | - Richard M Napier
- School of Life Sciences, University of Warwick, Gibbet Hill Road, Coventry CV4 7AL, UK
| |
Collapse
|
24
|
Jian JW, Elumalai P, Pitti T, Wu CY, Tsai KC, Chang JY, Peng HP, Yang AS. Predicting Ligand Binding Sites on Protein Surfaces by 3-Dimensional Probability Density Distributions of Interacting Atoms. PLoS One 2016; 11:e0160315. [PMID: 27513851 PMCID: PMC4981321 DOI: 10.1371/journal.pone.0160315] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2015] [Accepted: 07/18/2016] [Indexed: 11/18/2022] Open
Abstract
Predicting ligand binding sites (LBSs) on protein structures, which are obtained either from experimental or computational methods, is a useful first step in functional annotation or structure-based drug design for the protein structures. In this work, the structure-based machine learning algorithm ISMBLab-LIG was developed to predict LBSs on protein surfaces with input attributes derived from the three-dimensional probability density maps of interacting atoms, which were reconstructed on the query protein surfaces and were relatively insensitive to local conformational variations of the tentative ligand binding sites. The prediction accuracy of the ISMBLab-LIG predictors is comparable to that of the best LBS predictors benchmarked on several well-established testing datasets. More importantly, the ISMBLab-LIG algorithm has substantial tolerance to the prediction uncertainties of computationally derived protein structure models. As such, the method is particularly useful for predicting LBSs not only on experimental protein structures without known LBS templates in the database but also on computationally predicted model protein structures with structural uncertainties in the tentative ligand binding sites.
Collapse
Affiliation(s)
- Jhih-Wei Jian
- Genomics Research Center, Academia Sinica, Taipei, Taiwan 115
- Institute of Biomedical Informatics, National Yang-Ming University, Taipei, Taiwan 11221
- Bioinformatics Program, Taiwan International Graduate Program, Institute of Information Science, Academia Sinica, Taipei, Taiwan 115
| | | | - Thejkiran Pitti
- Genomics Research Center, Academia Sinica, Taipei, Taiwan 115
- Bioinformatics Program, Taiwan International Graduate Program, Institute of Information Science, Academia Sinica, Taipei, Taiwan 115
- Institute of Bioinformatics and Structural Biology, National Tsing Hua University, Hsinchu, Taiwan 30013
| | - Chih Yuan Wu
- Genomics Research Center, Academia Sinica, Taipei, Taiwan 115
| | - Keng-Chang Tsai
- Genomics Research Center, Academia Sinica, Taipei, Taiwan 115
| | - Jeng-Yih Chang
- Genomics Research Center, Academia Sinica, Taipei, Taiwan 115
| | - Hung-Pin Peng
- Genomics Research Center, Academia Sinica, Taipei, Taiwan 115
| | - An-Suei Yang
- Genomics Research Center, Academia Sinica, Taipei, Taiwan 115
- * E-mail:
| |
Collapse
|
25
|
Villoutreix B. Combining bioinformatics, chemoinformatics and experimental approaches to design chemical probes: Applications in the field of blood coagulation. ANNALES PHARMACEUTIQUES FRANÇAISES 2016; 74:253-66. [DOI: 10.1016/j.pharma.2016.03.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2016] [Revised: 03/21/2016] [Accepted: 03/21/2016] [Indexed: 11/08/2022]
|
26
|
Bendl J, Stourac J, Sebestova E, Vavra O, Musil M, Brezovsky J, Damborsky J. HotSpot Wizard 2.0: automated design of site-specific mutations and smart libraries in protein engineering. Nucleic Acids Res 2016; 44:W479-87. [PMID: 27174934 PMCID: PMC4987947 DOI: 10.1093/nar/gkw416] [Citation(s) in RCA: 62] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2016] [Accepted: 05/03/2016] [Indexed: 01/13/2023] Open
Abstract
HotSpot Wizard 2.0 is a web server for automated identification of hot spots and design of smart libraries for engineering proteins' stability, catalytic activity, substrate specificity and enantioselectivity. The server integrates sequence, structural and evolutionary information obtained from 3 databases and 20 computational tools. Users are guided through the processes of selecting hot spots using four different protein engineering strategies and optimizing the resulting library's size by narrowing down a set of substitutions at individual randomized positions. The only required input is a query protein structure. The results of the calculations are mapped onto the protein's structure and visualized with a JSmol applet. HotSpot Wizard lists annotated residues suitable for mutagenesis and can automatically design appropriate codons for each implemented strategy. Overall, HotSpot Wizard provides comprehensive annotations of protein structures and assists protein engineers with the rational design of site-specific mutations and focused libraries. It is freely available at http://loschmidt.chemi.muni.cz/hotspotwizard.
Collapse
Affiliation(s)
- Jaroslav Bendl
- Loschmidt Laboratories, Department of Experimental Biology and Research Centre for Toxic Compounds in the Environment RECETOX, Masaryk University, 625 00 Brno, Czech Republic Department of Information Systems, Faculty of Information Technology, Brno University of Technology, 612 66 Brno, Czech Republic International Clinical Research Center, St. Anne's University Hospital Brno, Brno, Czech Republic
| | - Jan Stourac
- Loschmidt Laboratories, Department of Experimental Biology and Research Centre for Toxic Compounds in the Environment RECETOX, Masaryk University, 625 00 Brno, Czech Republic
| | - Eva Sebestova
- Loschmidt Laboratories, Department of Experimental Biology and Research Centre for Toxic Compounds in the Environment RECETOX, Masaryk University, 625 00 Brno, Czech Republic
| | - Ondrej Vavra
- Loschmidt Laboratories, Department of Experimental Biology and Research Centre for Toxic Compounds in the Environment RECETOX, Masaryk University, 625 00 Brno, Czech Republic
| | - Milos Musil
- Loschmidt Laboratories, Department of Experimental Biology and Research Centre for Toxic Compounds in the Environment RECETOX, Masaryk University, 625 00 Brno, Czech Republic Department of Information Systems, Faculty of Information Technology, Brno University of Technology, 612 66 Brno, Czech Republic
| | - Jan Brezovsky
- Loschmidt Laboratories, Department of Experimental Biology and Research Centre for Toxic Compounds in the Environment RECETOX, Masaryk University, 625 00 Brno, Czech Republic International Clinical Research Center, St. Anne's University Hospital Brno, Brno, Czech Republic
| | - Jiri Damborsky
- Loschmidt Laboratories, Department of Experimental Biology and Research Centre for Toxic Compounds in the Environment RECETOX, Masaryk University, 625 00 Brno, Czech Republic International Clinical Research Center, St. Anne's University Hospital Brno, Brno, Czech Republic
| |
Collapse
|
27
|
Ehrt C, Brinkjost T, Koch O. Impact of Binding Site Comparisons on Medicinal Chemistry and Rational Molecular Design. J Med Chem 2016; 59:4121-51. [PMID: 27046190 DOI: 10.1021/acs.jmedchem.6b00078] [Citation(s) in RCA: 66] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
Modern rational drug design not only deals with the search for ligands binding to interesting and promising validated targets but also aims to identify the function and ligands of yet uncharacterized proteins having impact on different diseases. Additionally, it contributes to the design of inhibitors with distinct selectivity patterns and the prediction of possible off-target effects. The identification of similarities between binding sites of various proteins is a useful approach to cope with those challenges. The main scope of this perspective is to describe applications of different protein binding site comparison approaches to outline their applicability and impact on molecular design. The article deals with various substantial application domains and provides some outstanding examples to show how various binding site comparison methods can be applied to promote in silico drug design workflows. In addition, we will also briefly introduce the fundamental principles of different protein binding site comparison methods.
Collapse
Affiliation(s)
- Christiane Ehrt
- Faculty of Chemistry and Chemical Biology, TU Dortmund University , Otto-Hahn-Straße 6, 44227 Dortmund, Germany
| | - Tobias Brinkjost
- Faculty of Chemistry and Chemical Biology, TU Dortmund University , Otto-Hahn-Straße 6, 44227 Dortmund, Germany.,Department of Computer Science, TU Dortmund University , Otto-Hahn-Straße 14, 44224 Dortmund, Germany
| | - Oliver Koch
- Faculty of Chemistry and Chemical Biology, TU Dortmund University , Otto-Hahn-Straße 6, 44227 Dortmund, Germany
| |
Collapse
|
28
|
Kuenemann MA, Sperandio O, Labbé CM, Lagorce D, Miteva MA, Villoutreix BO. In silico design of low molecular weight protein-protein interaction inhibitors: Overall concept and recent advances. PROGRESS IN BIOPHYSICS AND MOLECULAR BIOLOGY 2015; 119:20-32. [PMID: 25748546 DOI: 10.1016/j.pbiomolbio.2015.02.006] [Citation(s) in RCA: 39] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/22/2014] [Revised: 02/18/2015] [Accepted: 02/24/2015] [Indexed: 12/22/2022]
Abstract
Protein-protein interactions (PPIs) are carrying out diverse functions in living systems and are playing a major role in the health and disease states. Low molecular weight (LMW) "drug-like" inhibitors of PPIs would be very valuable not only to enhance our understanding over physiological processes but also for drug discovery endeavors. However, PPIs were deemed intractable by LMW chemicals during many years. But today, with the new experimental and in silico technologies that have been developed, about 50 PPIs have already been inhibited by LMW molecules. Here, we first focus on general concepts about protein-protein interactions, present a consensual view about ligandable pockets at the protein interfaces and the possibilities of using fast and cost effective structure-based virtual screening methods to identify PPI hits. We then discuss the design of compound collections dedicated to PPIs. Recent financial analyses of the field suggest that LMW PPI modulators could be gaining momentum over biologics in the coming years supporting further research in this area.
Collapse
Affiliation(s)
- Mélaine A Kuenemann
- Université Paris Diderot, Sorbonne Paris Cité, UMRS 973 Inserm, Paris 75013, France; Inserm, U973, Paris 75013, France
| | - Olivier Sperandio
- Université Paris Diderot, Sorbonne Paris Cité, UMRS 973 Inserm, Paris 75013, France; Inserm, U973, Paris 75013, France; CDithem, Faculté de Pharmacie, 1 rue du Prof Laguesse, 59000 Lille, France
| | - Céline M Labbé
- Université Paris Diderot, Sorbonne Paris Cité, UMRS 973 Inserm, Paris 75013, France; Inserm, U973, Paris 75013, France; CDithem, Faculté de Pharmacie, 1 rue du Prof Laguesse, 59000 Lille, France
| | - David Lagorce
- Université Paris Diderot, Sorbonne Paris Cité, UMRS 973 Inserm, Paris 75013, France; Inserm, U973, Paris 75013, France
| | - Maria A Miteva
- Université Paris Diderot, Sorbonne Paris Cité, UMRS 973 Inserm, Paris 75013, France; Inserm, U973, Paris 75013, France
| | - Bruno O Villoutreix
- Université Paris Diderot, Sorbonne Paris Cité, UMRS 973 Inserm, Paris 75013, France; Inserm, U973, Paris 75013, France; CDithem, Faculté de Pharmacie, 1 rue du Prof Laguesse, 59000 Lille, France.
| |
Collapse
|
29
|
Development of a machine learning method to predict membrane protein-ligand binding residues using basic sequence information. Adv Bioinformatics 2015; 2015:843030. [PMID: 25802517 PMCID: PMC4329842 DOI: 10.1155/2015/843030] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2014] [Revised: 01/07/2015] [Accepted: 01/08/2015] [Indexed: 12/26/2022] Open
Abstract
Locating ligand binding sites and finding the functionally important residues from protein sequences as well as structures became one of the challenges in understanding their function. Hence a Naïve Bayes classifier has been trained to predict whether a given amino acid residue in membrane protein sequence is a ligand binding residue or not using only sequence based information. The input to the classifier consists of the features of the target residue and two sequence neighbors on each side of the target residue. The classifier is trained and evaluated on a nonredundant set of 42 sequences (chains with at least one transmembrane domain) from 31 alpha-helical membrane proteins. The classifier achieves an overall accuracy of 70.7% with 72.5% specificity and 61.1% sensitivity in identifying ligand binding residues from sequence. The classifier performs better when the sequence is encoded by psi-blast generated PSSM profiles. Assessment of the predictions in the context of three-dimensional structures of proteins reveals the effectiveness of this method in identifying ligand binding sites from sequence information. In 83.3% (35 out of 42) of the proteins, the classifier identifies the ligand binding sites by correctly recognizing more than half of the binding residues. This will be useful to protein engineers in exploiting potential residues for functional assessment.
Collapse
|