Skip to content
2000
Volume 28, Issue 10
  • ISSN: 1386-2073
  • E-ISSN: 1875-5402

Abstract

Accurate identification of protein binding sites is pivotal for understanding molecular interactions and facilitating drug discovery efforts. However, the dynamic nature of protein-ligand interactions presents a formidable challenge, necessitating innovative approaches to bridge the gap between theoretical predictions and experimental realities. This review explores the challenges and recent advancements in protein binding site prediction. Specifically, we highlight the integration of molecular dynamics simulations, machine learning, and deep learning techniques to capture the dynamic and complex nature of protein-ligand interactions. Additionally, we discuss the importance of integrating experimental data, such as structural information and biochemical assays, to enhance prediction accuracy and reliability. By navigating the intersection of classical and the onset of machine learning and deep learning approaches, we aim to provide insights into current state-of-the-art techniques and chart a course for future protein binding site prediction advancements. Ultimately, these efforts could unravel the mysteries of protein-ligand interactions and accelerate drug discovery endeavors.

© 2025 Bentham Science Publishers
Loading

Article metrics loading...

/content/journals/cchts/10.2174/0113862073305298240524050145
2024-06-11
2025-10-11

  • From This Site

    /content/journals/cchts/10.2174/0113862073305298240524050145
    dcterms_title,dcterms_subject,pub_keyword
    -contentType:Contributor -contentType:Concept -contentType:Institution
    10
    5
Loading full text...

Full text loading...

References

  1. LudingtonJ.L. Protein binding site analysis for drug discovery using a computational fragment-based method.Methods Mol. Biol.2015128914515410.1007/978‑1‑4939‑2486‑8_1225709039
     [Google Scholar]
  2. ScheifeRT Protein Binding: What Does it Mean? DICP.198923(7-8)S27S31
     [Google Scholar]
  3. SelvarajC DineshDC RajaramK Macromolecular chemistry: An introduction. In-Silico Approaches to Macromolecular Chemistry.Elsevier202310.1016/B978‑0‑323‑90995‑2.00007‑2
     [Google Scholar]
  4. Barradas-BautistaD. RosellM. PallaraC. Fernández-RecioJ. Structural Prediction of Protein–Protein Interactions by Docking: Application to Biomedical Problems.Advances in Protein Chemistry and Structural Biology.Academic Press Inc.2018203249
     [Google Scholar]
  5. IrisF. GeaM. LampeP.H. DineG. SantamariaP. Integrative biology in the discovery of relevant biomarkers monitoring cognitive disorders pathogenesis and progression.Bio Tribune Magazine200828182310.1007/BF03001641
     [Google Scholar]
  6. HuX. WangK. DongQ. Protein ligand-specific binding residue predictions by an ensemble classifier.BMC Bioinformatics201617147010.1186/s12859‑016‑1348‑327855637
     [Google Scholar]
  7. GuterresH. LeeH.S. ImW. Ligand-Binding-Site Structure Refinement Using Molecular Dynamics with Restraints Derived from Predicted Binding Site Templates.J. Chem. Theory Comput.201915116524653510.1021/acs.jctc.9b0075131557013
     [Google Scholar]
  8. ChristenM. HünenbergerP.H. BakowiesD. BaronR. BürgiR. GeerkeD.P. HeinzT.N. KastenholzM.A. KräutlerV. OostenbrinkC. PeterC. TrzesniakD. van GunsterenW.F. The GROMOS software for biomolecular simulation: GROMOS05.J. Comput. Chem.200526161719175110.1002/jcc.2030316211540
     [Google Scholar]
  9. NeytsE.C. BogaertsA. Combining molecular dynamics with Monte Carlo simulations: implementations and applications.Theor. Chem. Acc.20131322132010.1007/s00214‑012‑1320‑x
     [Google Scholar]
  10. PangY.P. Low-mass molecular dynamics simulation: A simple and generic technique to enhance configurational sampling.Biochem. Biophys. Res. Commun.2014452358859210.1016/j.bbrc.2014.08.11925181342
     [Google Scholar]
  11. Salomon-FerrerR. CaseD.A. WalkerR.C. An overview of the Amber biomolecular simulation package.Wiley Interdiscip. Rev. Comput. Mol. Sci.20133219821010.1002/wcms.1121
     [Google Scholar]
  12. AhsanM.M. LunaS.A. SiddiqueZ. Machine-Learning-Based Disease Diagnosis: A Comprehensive Review.Healthcare2022103541
     [Google Scholar]
  13. WójcikowskiM. BallesterP.J. SiedleckiP. Performance of machine-learning scoring functions in structure-based virtual screening.Sci. Rep.2017714671010.1038/srep4671028440302
     [Google Scholar]
  14. CheX. ChaiS. ZhangZ. ZhangL. Prediction of ligand binding sites using improved blind docking method with a Machine Learning-Based scoring function.Chem. Eng. Sci.202226111796210.1016/j.ces.2022.117962
     [Google Scholar]
  15. BonettaR. ValentinoG. Machine learning techniques for protein function prediction.Proteins202088339741310.1002/prot.2583231603244
     [Google Scholar]
  16. DaiT. LiuQ. GaoJ. CaoZ. ZhuR. A new protein-ligand binding sites prediction method based on the integration of protein sequence conservation information.BMC Bioinformatics201112S14Suppl. 14S910.1186/1471‑2105‑12‑S14‑S922373099
     [Google Scholar]
  17. PalmerRA NiwaH X-ray crystallographic studies of protein-ligand interactions.Biochem Soc Trans.200331(Pt 5)97397910.1093/oso/9780199637508.003.0001
     [Google Scholar]
  18. CalaO. GuillièreF. KrimmI. NMR-based analysis of protein–ligand interactions.Anal. Bioanal. Chem.2014406494395610.1007/s00216‑013‑6931‑023591643
     [Google Scholar]
  19. TurnbullA.P. EmsleyP. Studying protein-ligand interactions using X-ray crystallography.Methods Mol. Biol.2013100845747710.1007/978‑1‑62703‑398‑5_1723729263
     [Google Scholar]
  20. HuangB. SchroederM. LIGSITEcsc: Predicting ligand binding sites using the Connolly surface and degree of conservation.BMC Struct. Biol.2006611910.1186/1472‑6807‑6‑1916995956
     [Google Scholar]
  21. LevittDG BanaszakLJ POCKET: A computer graphics method for identifying and displaying protein cavities and their surrounding amino acids.J. Mol. Graph.199210422923410.1016/0263‑7855(92)80074‑N
     [Google Scholar]
  22. LaskowskiRA SURFNET: A program for visualizing molecular surfaces, cavities, and intermolecular interactions.J. Mol. Graph.199513532333010.1016/0263‑7855(95)00073‑9
     [Google Scholar]
  23. HendlichM RippmannF BarnickelG LIGSITE: Automatic and efficient detection of potential small molecule-binding sites in proteins.J. Mol. Graph. Model.1997156359363
     [Google Scholar]
  24. LiangJ. EdelsbrunnerH. WoodwardC. Anatomy of protein pockets and cavities: measurement of binding site geometry and implications for ligand design. Protein Sci.19987918841897
     [Google Scholar]
  25. DundasJ. OuyangZ. TsengJ. BinkowskiA. TurpazY. LiangJ. CASTp: computed atlas of surface topography of proteins with structural and topographical mapping of functionally annotated residues.Nucleic Acids Res.200634Web ServerW116W11810.1093/nar/gkl28216844972
     [Google Scholar]
  26. Le GuillouxV. SchmidtkeP. TufferyP. Fpocket: An open source platform for ligand pocket detection.BMC Bioinformatics200910116810.1186/1471‑2105‑10‑16819486540
     [Google Scholar]
  27. ZhuH. PisabarroM.T. MSPocket: an orientation-independent algorithm for the detection of ligand binding pockets.Bioinformatics201127335135810.1093/bioinformatics/btq67221134896
     [Google Scholar]
  28. LinY YooS SanchezR SiteComp: a server for ligand binding site analysis in protein structures.Bioinformatics.2012 2881172117310.1093/bioinformatics/bts095
     [Google Scholar]
  29. XieZ.R. LiuC.K. HsiaoF.C. YaoA. HwangM.J. LISE: a server using ligand-interacting and site-enriched protein triangles for prediction of ligand-binding sites.Nucleic Acids Res.201341W1W292W29610.1093/nar/gkt30023609546
     [Google Scholar]
  30. ZhuX. XiongY. KiharaD. Large-scale binding ligand prediction by improved patch-based method Patch-Surfer2.0.Bioinformatics201531570771310.1093/bioinformatics/btu72425359888
     [Google Scholar]
  31. LiuY. GrimmM. DaiW. HouM. XiaoZ.X. CaoY. CB-Dock: a web server for cavity detection-guided protein–ligand blind docking.Acta Pharmacol. Sin.202041113814410.1038/s41401‑019‑0228‑631263275
     [Google Scholar]
  32. TripathiA. KelloggG.E. A novel and efficient tool for locating and characterizing protein cavities and binding sites.Proteins201078482584210.1002/prot.2260819847777
     [Google Scholar]
  33. WeiselM. ProschakE. SchneiderG. PocketPicker: analysis of ligand binding-sites with shape descriptors.Chem. Cent. J.200711710.1186/1752‑153X‑1‑717880740
     [Google Scholar]
  34. NayalM. HonigB. On the nature of cavities on protein surfaces: Application to the identification of drug‐binding sites.Proteins200663489290610.1002/prot.2089716477622
     [Google Scholar]
  35. YuJ. ZhouY. TanakaI. YaoM. Roll: A new algorithm for the detection of protein pockets and cavities with a rolling probe sphere.Bioinformatics2010261465210.1093/bioinformatics/btp59919846440
     [Google Scholar]
  36. KalidasY. ChandraN. PocketDepth: A new depth based algorithm for identification of ligand binding sites in proteins.J. Struct. Biol.20081611314210.1016/j.jsb.2007.09.00517949996
     [Google Scholar]
  37. TanK.P. VaradarajanR. MadhusudhanM.S. DEPTH: A web server to compute depth and predict small-molecule binding cavities in proteins.Nucleic Acids Res.201139Web Server issueSuppl. 2W242W24810.1093/nar/gkr35621576233
     [Google Scholar]
  38. VolkamerA. KuhnD. RippmannF. RareyM. DoGSiteScorer: A web server for automatic binding site prediction, analysis and druggability assessment.Bioinformatics201228152074207510.1093/bioinformatics/bts31022628523
     [Google Scholar]
  39. RocheD.B. TetchnerS.J. McGuffinL.J. FunFOLD: an improved automated method for the prediction of ligand binding residues using 3D models of proteins.BMC Bioinformatics201112116010.1186/1471‑2105‑12‑16021575183
     [Google Scholar]
  40. GlaserF. MorrisR.J. NajmanovichR.J. LaskowskiR.A. ThorntonJ.M. A method for localizing ligand binding pockets in protein structures.Proteins200662247948810.1002/prot.2076916304646
     [Google Scholar]
  41. LaurieA.T.R. JacksonR.M. Q-SiteFinder: An energy-based method for the prediction of protein-ligand binding sites.Bioinformatics20052191908191610.1093/bioinformatics/bti31515701681
     [Google Scholar]
  42. HernandezM. GhersiD. SanchezR. SITEHOUND-web: a server for ligand binding site identification in protein structures.Nucleic Acids Res.200937Web ServerW413W41610.1093/nar/gkp28119398430
     [Google Scholar]
  43. NganC.H. HallD.R. ZerbeB. GroveL.E. KozakovD. VajdaS. FTSite: high accuracy detection of ligand binding sites on unbound protein structures.Bioinformatics201228228628710.1093/bioinformatics/btr65122113084
     [Google Scholar]
  44. AmariS. AizawaM. ZhangJ. VISCANA: Visualized cluster analysis of protein - Ligand interaction based on the ab initio fragment molecular orbital method for virtual ligand screening.J. Chem. Inf. Model.20064622123010.1021/ci050262q
     [Google Scholar]
  45. GhersiD. SanchezR. Improving accuracy and efficiency of blind protein‐ligand docking by focusing on predicted binding sites.Proteins200974241742410.1002/prot.2215418636505
     [Google Scholar]
  46. SilbersteinM. DennisS. BrownL.III KortvelyesiT. ClodfelterK. VajdaS. Identification of substrate binding sites in enzymes by computational solvent mapping.J. Mol. Biol.200333251095111310.1016/j.jmb.2003.08.01914499612
     [Google Scholar]
  47. WassM.N. SternbergM.J.E. Prediction of ligand binding sites using homologous structures and conservation at CASP8.Proteins200977S9Suppl. 914715110.1002/prot.2251319626715
     [Google Scholar]
  48. WassM.N. KelleyL.A. SternbergM.J.E. 3DLigandSite: predicting ligand-binding sites using similar structures.Nucleic Acids Res.201038Web Server issueSuppl. 2W469W47310.1093/nar/gkq40620513649
     [Google Scholar]
  49. BrylinskiM SkolnickJ A threading-based method (FINDSITE) for ligand-binding site prediction and functional annotation.Proc Natl Acad Sci.20071051129134
     [Google Scholar]
  50. LopezG. MaiettaP. RodriguezJ.M. ValenciaA. TressM.L. firestar —advances in the prediction of functionally important residues.Nucleic Acids Res.201139Web Server issueSuppl. 2W235W24110.1093/nar/gkr43721672959
     [Google Scholar]
  51. RoyA. KucukuralA. ZhangY. I-TASSER: a unified platform for automated protein structure and function prediction.Nat. Protoc.20105472573810.1038/nprot.2010.520360767
     [Google Scholar]
  52. OhM. JooK. LeeJ. Protein‐binding site prediction based on three‐dimensional protein modeling.Proteins200977S9Suppl. 915215610.1002/prot.2257219768678
     [Google Scholar]
  53. KoncJ. JanežičD. ProBiS algorithm for detection of structurally similar protein binding sites by local structural alignment.Bioinformatics20102691160116810.1093/bioinformatics/btq10020305268
     [Google Scholar]
  54. TroitzschK.G. MohringM. LotzmannU. Proceedings26th European Conference on Modelling and Simulation2012
     [Google Scholar]
  55. TuH. ShiT. Ligand binding site similarity identification based on chemical and geometric similarity.Protein J.201332537338510.1007/s10930‑013‑9494‑123700221
     [Google Scholar]
  56. XieL. BourneP.E. A robust and efficient algorithm for the shape description of protein structures and its application in predicting ligand binding sites.BMC Bioinformatics20078S4Suppl. 4S910.1186/1471‑2105‑8‑S4‑S917570152
     [Google Scholar]
  57. HassanS.A. GraciaL. VasudevanG. SteinbachP.J. Computer Simulation of Protein-Ligand Interactions.Protein-Ligand Interactions: Methods and Applications. Ulrich NienhausG. Totowa, NJHumana Press200545149210.1385/1‑59259‑912‑5:451
     [Google Scholar]
  58. ZhaoJ. CaoY. ZhangL. Exploring the computational methods for protein-ligand binding site prediction.Comput. Struct. Biotechnol. J.20201841742610.1016/j.csbj.2020.02.00832140203
     [Google Scholar]
  59. YasudaI. EndoK. YamamotoE. HiranoY. YasuokaK. Differences in ligand-induced protein dynamics extracted from an unsupervised deep learning approach correlate with protein–ligand binding affinities.Commun. Biol.20225148110.1038/s42003‑022‑03416‑735589949
     [Google Scholar]
  60. RizziV. BonatiL. AnsariN. ParrinelloM. The role of water in host-guest interaction.Nat. Commun.20211219310.1038/s41467‑020‑20310‑033397926
     [Google Scholar]
  61. MahmoudA.H. MastersM.R. YangY. LillM.A. Elucidating the multiple roles of hydration for accurate protein-ligand binding prediction via deep learning.Commun. Chem.2020311910.1038/s42004‑020‑0261‑x36703428
     [Google Scholar]
  62. CrippenG.M. BradleyM.P. RichardsonW.W. Why are binding-site models more complicated than molecules?Perspect. Drug Discov. Des.19931232132810.1007/BF02174532
     [Google Scholar]
  63. ChenC.T. PengH.P. JianJ.W. TsaiK.C. ChangJ.Y. YangE.W. ChenJ.B. HoS.Y. HsuW.L. YangA.S. Protein-protein interaction site predictions with three-dimensional probability distributions of interacting atoms on protein surfaces.PLoS One201276e3770610.1371/journal.pone.003770622701576
     [Google Scholar]
  64. SchlessingerA. RostB. Protein flexibility and rigidity predicted from sequence.Proteins200561111512610.1002/prot.2058716080156
     [Google Scholar]
  65. KhanS. FarooqU. KurnikovaM. Protein stability and dynamics influenced by ligands in extremophilic complexes – a molecular dynamics investigation.Mol. Biosyst.20171391874188710.1039/C7MB00210F28737816
     [Google Scholar]
  66. ChikhaleH.U. JoshiP.P. NerkarA.G. SawantS.D. Molecular mechanics and dynamics.Annals. Pharm. Pharmaceut. Sci.201341-22635
     [Google Scholar]
  67. Basics of Molecular Dynamics SimulationAvailable from: https://dasher.wustl.edu/chem430/lectures/lecture-05.pdf
  68. HollingsworthS.A. DrorR.O. Molecular dynamics simulation for all.Neuron20189961129114310.1016/j.neuron.2018.08.01130236283
     [Google Scholar]
  69. AllenMP. The molecular dynamics simulation process.Available from: https://www.ks.uiuc.edu/Training/Workshop/Atlanta/lectures/day1/Day1b_MD_intro.pdf 1987
  70. YuS. PengD. ZhuW. LiaoB. WangP. YangD. WuF. Hybrid_DBP: Prediction of DNA-binding proteins using hybrid features and convolutional neural networks.Front. Pharmacol.202213103175910.3389/fphar.2022.103175936299898
     [Google Scholar]
  71. AlghamedyF BopaiahJ JonesD Incorporating Protein Dynamics Through Ensemble Docking in Machine Learning Models to Predict Drug Binding.AMIA Jt. Summits. Transl. Sci. Proc.20172634
     [Google Scholar]
  72. HeckG.S. PintroV.O. PereiraR.R. de ÁvilaM.B. LevinN.M.B. de AzevedoW.F. Supervised Machine Learning Methods Applied to Predict Ligand- Binding Affinity.Curr. Med. Chem.201724232459247010.2174/092986732466617062309250328641555
     [Google Scholar]
  73. EllingsonS.R. DavisB. AllenJ. Machine learning and ligand binding predictions: A review of data, methods, and obstacles.Biochim. Biophys. Acta, Gen. Subj.20201864612954510.1016/j.bbagen.2020.12954532057823
     [Google Scholar]
  74. KrivákR. HokszaD. P2Rank: machine learning based tool for rapid and accurate prediction of ligand binding sites from protein structure.J. Cheminform.20181013910.1186/s13321‑018‑0285‑830109435
     [Google Scholar]
  75. MoenE. BannonD. KudoT. GrafW. CovertM. Van ValenD. Deep learning for cellular image analysis.Nat. Methods201916121233124610.1038/s41592‑019‑0403‑131133758
     [Google Scholar]
  76. DengW BrenemanC EmbrechtsMJ Predicting protein-ligand binding affinities using novel geometrical descriptors and machine-learning methods.J. Chem. Inf. Comput. Sci.2004442699703
     [Google Scholar]
  77. LangleyP. The changing science of machine learning.Mach. Learn.201182327527910.1007/s10994‑011‑5242‑y
     [Google Scholar]
  78. LangleyP. SimonH.A. Cognitive skills and their acquisition.Lawrence Erlbaum Associates1981
     [Google Scholar]
  79. VieiraS. Lopez PinayaW.H. MechelliA. Introduction to machine learning.Machine Learning: Methods and Applications to Brain Disorders.Elsevier2019120
     [Google Scholar]
  80. MitchellTM Machine Learning.McGraw-Hill1997
     [Google Scholar]
  81. SantanaC.A. IzidoroS.C. de Melo-MinardiR.C. TyzackJ.D. RibeiroA.J.M. PiresD.E.V. ThorntonJ.M. de A SilveiraS. GRaSP-web: a machine learning strategy to predict binding sites based on residue neighborhood graphs.Nucleic Acids Res.202250W1W392W39710.1093/nar/gkac32335524575
     [Google Scholar]
  82. JendeleL. KrivakR. SkodaP. NovotnyM. HokszaD. PrankWeb: a web server for ligand binding site prediction and visualization.Nucleic Acids Res.201947W1W345W34910.1093/nar/gkz42431114880
     [Google Scholar]
  83. ZhangJ. KurganL. SCRIBER: Accurate and partner type-specific prediction of protein-binding residues from protein sequences.Bioinformatics.Oxford University Press2019i343i353
     [Google Scholar]
  84. GuoT. ShiY. SunZ. A novel statistical ligand-binding site predictor: application to ATP-binding sites.Protein Eng. Des. Sel.2005182657010.1093/protein/gzi00615799998
     [Google Scholar]
  85. ZhouS. HeD. GuW. WuZ. AbbasG. HongQ. BoothC. Design and Evaluation of Operational Scheduling Approaches for HCNG Penetrated Integrated Energy System.IEEE Access20197877928780710.1109/ACCESS.2019.2925197
     [Google Scholar]
  86. DurrantJ.D. McCammonJ.A. NNScore: a neural-network-based scoring function for the characterization of protein-ligand complexes.J. Chem. Inf. Model.201050101865187110.1021/ci100244v20845954
     [Google Scholar]
  87. ChenP. HuangJ.Z. GaoX. LigandRFs: random forest ensemble to identify ligand-binding residues from sequence information alone.BMC Bioinformatics201415S15Suppl. 15S410.1186/1471‑2105‑15‑S15‑S425474163
     [Google Scholar]
  88. KauffmanC. KarypisG. LIBRUS: combined machine learning and homology information for sequence-based ligand-binding residue prediction.Bioinformatics200925233099310710.1093/bioinformatics/btp56119786483
     [Google Scholar]
  89. LuW. ZhangJ-X. TechnologiesG. DynamicBind: Predicting ligand-specific protein-ligand complex structure with a deep equivariant generative model.Nat. Commun.202410.21203/rs.3.rs‑3225151/v1
     [Google Scholar]
  90. MengEC ShoichetBK Kuntz ID Automated Docking with Grid-Based Energy Evaluation.J. Comput. Chem.134399540
     [Google Scholar]
  91. RahaK. PetersM.B. WangB. YuN. WollacottA.M. WesterhoffL.M. MerzK.M.Jr The role of quantum mechanics in structure-based drug design.Drug Discov. Today20071217-1872573110.1016/j.drudis.2007.07.00617826685
     [Google Scholar]
  92. JorgensenW.L. ChandrasekharJ. MaduraJ.D. ImpeyR.W. KleinM.L. Comparison of simple potential functions for simulating liquid water.J. Chem. Phys.198379292693510.1063/1.445869
     [Google Scholar]
  93. SennH.M. ThielW. QM/MM methods for biomolecular systems.Angew. Chem. Int. Ed.20094871198122910.1002/anie.20080201919173328
     [Google Scholar]
  94. KulikH.J. Large-scale QM/MM free energy simulations of enzyme catalysis reveal the influence of charge transfer.Phys. Chem. Chem. Phys.20182031206502066010.1039/C8CP03871F30059109
     [Google Scholar]
  95. ChaskarP. ZoeteV. RöhrigU.F. On-the-Fly QM/MM Docking with Attracting Cavities.J. Chem. Inf. Model.2017571738410.1021/acs.jcim.6b0040627983849
     [Google Scholar]
  96. EldridgeMD MurrayCW AutonTR Empirical scoring functions: I. The development of a fast empirical scoring function to estimate the binding affinity of ligands in receptor complexes.J. Comput. Aided. Molec. Design199711425445
     [Google Scholar]
  97. MurrayCW AutonTR EldridgeMD Empirical scoring functions. II. The testing of an empirical scoring function for the prediction of ligand-receptor binding affinities and the use of Bayesian regression to improve the quality of the model.J. Comput. Aided. Mol. Des.1998125503519
     [Google Scholar]
  98. FriesnerR.A. MurphyR.B. RepaskyM.P. FryeL.L. GreenwoodJ.R. HalgrenT.A. SanschagrinP.C. MainzD.T. Extra precision glide: docking and scoring incorporating a model of hydrophobic enclosure for protein-ligand complexes.J. Med. Chem.200649216177619610.1021/jm051256o17034125
     [Google Scholar]
  99. ZhengZ. MerzK.M.Jr Ligand Identification Scoring Algorithm (LISA).J. Chem. Inf. Model.20115161296130610.1021/ci200066521561101
     [Google Scholar]
  100. WangR. LuY. WangS. Comparative evaluation of 11 scoring functions for molecular docking.J. Med. Chem.200346122287230310.1021/jm020378312773034
     [Google Scholar]
  101. VerdonkM.L. BerdiniV. HartshornM.J. MooijW.T.M. MurrayC.W. TaylorR.D. WatsonP. Virtual screening using protein-ligand docking: Avoiding artificial enrichment.J. Chem. Inf. Comput. Sci.200444379380610.1021/ci034289q15154744
     [Google Scholar]
  102. FerraraP. GohlkeH. PriceD.J. KlebeG. BrooksC.L.III Assessing scoring functions for protein-ligand interactions.J. Med. Chem.200447123032304710.1021/jm030489h15163185
     [Google Scholar]
  103. McGovernS.L. ShoichetB.K. Information decay in molecular docking screens against holo, apo, and modeled conformations of enzymes.J. Med. Chem.200346142895290710.1021/jm030033012825931
     [Google Scholar]
  104. BordognaA. PandiniA. BonatiL. Predicting the accuracy of protein–ligand docking on homology models.J. Comput. Chem.2011321819810.1002/jcc.2160120607693
     [Google Scholar]
  105. EricksonJ.A. JalaieM. RobertsonD.H. LewisR.A. ViethM. Lessons in molecular recognition: The effects of ligand and protein flexibility on molecular docking accuracy.J. Med. Chem.2004471455510.1021/jm030209y14695819
     [Google Scholar]
  106. JonesG WillettP GlenRC Development and validation of a genetic algorithm for flexible docking.JMB19972673727748
     [Google Scholar]
  107. MacchiagodenaM. PagliaiM. ProcacciP. Characterization of the non-covalent interaction between the PF-07321332 inhibitor and the SARS-CoV-2 main protease.J. Mol. Graph. Model.202211010804210.1016/j.jmgm.2021.10804234653812
     [Google Scholar]
  108. FriesnerR.A. BanksJ.L. MurphyR.B. HalgrenT.A. KlicicJ.J. MainzD.T. RepaskyM.P. KnollE.H. ShelleyM. PerryJ.K. ShawD.E. FrancisP. ShenkinP.S. Glide: a new approach for rapid, accurate docking and scoring. 1. Method and assessment of docking accuracy.J. Med. Chem.20044771739174910.1021/jm030643015027865
     [Google Scholar]
  109. MorrisG.M. GoodsellD.S. HallidayR.S. Automated Docking Using a Lamarckian Genetic Algorithm and an Empirical Binding Free Energy Function.J. Comput. Chem.16391916391662
     [Google Scholar]
  110. TrottO. OlsonA.J. AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading.J. Comput. Chem.201031245546110.1002/jcc.21334
     [Google Scholar]
  111. SipplMJ Calculation of conformational ensembles from potentials of mean force. An approach to the knowledge-based prediction of local structures in globular proteins.J. Mol. Biol.19902134859883
     [Google Scholar]
  112. MueggeI. MartinY.C. A general and fast scoring function for protein-ligand interactions: A simplified potential approach.J. Med. Chem.199942579180410.1021/jm980536j10072678
     [Google Scholar]
  113. MitchellJ.B.O. LaskowskiR.A. AlexA. BLEEP-Potential of Mean Force Describing ProteinLigand Interactions: I. Generating Potential Keywords: The potential of mean force; knowledge-based potential; atomistic representation; protein-ligand interactions; computer-aided drug design.J. Comput. Chem.1999201111771185
     [Google Scholar]
  114. ZhangL. ZhengC. LiT. XingL. ZengH. LiT. YangH. CaoJ. ChenB. ZhouZ. Building Up a Robust Risk Mathematical Platform to Predict Colorectal Cancer.Complexity2017201711410.1155/2017/8917258
     [Google Scholar]
  115. BrylinskiM. Nonlinear scoring functions for similarity-based ligand docking and binding affinity prediction.J. Chem. Inf. Model.201353113097311210.1021/ci400510e24171431
     [Google Scholar]
  116. KinningsS.L. LiuN. TongeP.J. JacksonR.M. XieL. BourneP.E. A machine learning-based method to improve docking scoring functions and its application to drug repurposing.J. Chem. Inf. Model.201151240841910.1021/ci100369f21291174
     [Google Scholar]
  117. RagozaM. HochuliJ. IdroboE. SunseriJ. KoesD.R. Protein–Ligand Scoring with Convolutional Neural Networks.J. Chem. Inf. Model.201757494295710.1021/acs.jcim.6b0074028368587
     [Google Scholar]
  118. GomesJ RamsundarB FeinbergEN PandeVS. Atomic Convolutional Networks for Predicting Protein-Ligand Binding Affinity. arXiv:1703.10603, 2017.
     [Google Scholar]
  119. WallachI DzambaM HeifetsA AtomNet: A Deep Convolutional Neural Network for Bioactivity Prediction in Structure-based Drug DiscoveryarXiv:1510.02855v1, 2015.
     [Google Scholar]
  120. BengioY CourvilleA VincentP 2012Representation Learning: A Review and New Perspectives arXiv:1206.5538, 2014.
     [Google Scholar]
  121. WeiningerD SMILES, a chemical language and information system. 1. Introduction to methodology and encoding rules. J. Chem. Inf. Comput. Sci.19882813136
     [Google Scholar]
  122. WangL. WangY. ChangQ. Feature selection methods for big data bioinformatics: A survey from the search perspective.Methods2016111213110.1016/j.ymeth.2016.08.01427592382
     [Google Scholar]
  123. Fernandez-LozanoC. CuiñasR.F. SeoaneJ.A. Fernández-BlancoE. DoradoJ. MunteanuC.R. Classification of signaling proteins based on molecular star graph descriptors using Machine Learning models.J. Theor. Biol.2015384505810.1016/j.jtbi.2015.07.03826297890
     [Google Scholar]
  124. TangH. ZhaoY.W. ZouP. ZhangC.M. ChenR. HuangP. LinH. HBPred: a tool to identify growth hormone-binding proteins.Int. J. Biol. Sci.201814895796410.7150/ijbs.2417429989085
     [Google Scholar]
  125. LouW. WangX. ChenF. ChenY. JiangB. ZhangH. Sequence based prediction of DNA-binding proteins based on hybrid feature selection using random forest and Gaussian naïve Bayes.PLoS One201491e8670310.1371/journal.pone.008670324475169
     [Google Scholar]
  126. AladeI.O. ZhangY. XuX. Modeling and prediction of lattice parameters of binary spinel compounds (AM 2 X 4 ) using support vector regression with Bayesian optimization.New J. Chem.20214534152551526610.1039/D1NJ01523K
     [Google Scholar]
  127. JinB. XuX. Forecasting wholesale prices of yellow corn through the Gaussian process regression.Neural Comput. Appl.202436158693871010.1007/s00521‑024‑09531‑2
     [Google Scholar]
  128. Barrio-HernandezI. YeoJ. JänesJ. MirditaM. GilchristC.L.M. WeinT. VaradiM. VelankarS. BeltraoP. SteineggerM. Clustering predicted structures at the scale of the known protein universe.Nature2023622798363764510.1038/s41586‑023‑06510‑w37704730
     [Google Scholar]
  129. AlipanahiB. DelongA. WeirauchM.T. FreyB.J. Predicting the sequence specificities of DNA- and RNA-binding proteins by deep learning.Nat. Biotechnol.201533883183810.1038/nbt.330026213851
     [Google Scholar]
  130. JiménezJ. ŠkaličM. Martínez-RosellG. De FabritiisG. K DEEP: Protein–Ligand Absolute Binding Affinity Prediction via 3D-Convolutional Neural Networks.J. Chem. Inf. Model.201858228729610.1021/acs.jcim.7b0065029309725
     [Google Scholar]
  131. BlasseC. SaalfeldS. EtournayR. SagnerA. EatonS. MyersE.W. PreMosa: extracting 2D surfaces from 3D microscopy mosaics.Bioinformatics201733162563256910.1093/bioinformatics/btx19528383656
     [Google Scholar]
  132. GruberA. DurhamA.M. HuynhC. del PortilloH.A. Defining and searching for structural motifs using DeepView/Swiss-PdbViewer.BMC Bioinformatics2008
     [Google Scholar]
  133. GuptaA. DeepPocket: Ligand Binding Site Detection and Segmentation using 3D Convolutional Neural Networks.10.26434/chemrxiv.14611146
  134. MylonasSK AxenopoulosA DarasP DeepSurf: A surface-based deep learning approach for the prediction of ligand binding sites on proteins.Bioinformatics.202037121681169010.1093/bioinformatics/btab009
     [Google Scholar]
  135. ÖztürkH. ÖzgürA. OzkirimliE. DeepDTA: Deep drug-target binding affinity prediction. Bioinformatics.Oxford University Press2018i821i829
     [Google Scholar]
  136. JiménezJ. DoerrS. Martínez-RosellG. RoseA.S. De FabritiisG. DeepSite: Protein-binding site predictor using 3D-convolutional neural networks.Bioinformatics201733193036304210.1093/bioinformatics/btx35028575181
     [Google Scholar]
  137. Stepniewska-DziubinskaM.M. ZielenkiewiczP. SiedleckiP. Improving detection of protein-ligand binding sites with 3D segmentation.Sci. Rep.2020101503510.1038/s41598‑020‑61860‑z32193447
     [Google Scholar]
  138. ZhangC. ZhengW. MortuzaS.M. LiY. ZhangY. DeepMSA: constructing deep multiple sequence alignment to improve contact prediction and fold-recognition for distant-homology proteins.Bioinformatics20203672105211210.1093/bioinformatics/btz86331738385
     [Google Scholar]
  139. YouZ.H. ChanK.C.C. HuP. Predicting protein-protein interactions from primary protein sequences using a novel multi-scale local feature representation scheme and the random forest.PLoS One2015105e012581110.1371/journal.pone.012581125946106
     [Google Scholar]
  140. XiaCQ PanX ShenH Protein-ligand binding residue prediction enhancement through hybrid deep heterogeneous sequence and structure data learning.Bioinformatics2020363018302710.1093/bioinformatics/btaa11032091580
     [Google Scholar]
  141. KandelJ. TayaraH. ChongK.T. PUResNet: prediction of protein-ligand binding sites using deep residual neural network.J. Cheminform.20211316510.1186/s13321‑021‑00547‑734496970
     [Google Scholar]
  142. XuX. ZhangY. Price forecasts of ten steel products using Gaussian process regressions.Eng. Appl. Artif. Intell.202312610687010.1016/j.engappai.2023.106870
     [Google Scholar]
  143. XuX. ZhangY. Corn cash price forecasting with neural networks.Comput. Electron. Agric.202118410612010.1016/j.compag.2021.106120
     [Google Scholar]
  144. KhanS.H. TayaraH. ChongK.T. ProB-Site: Protein Binding Site Prediction Using Local Features.Cells20221113211710.3390/cells1113211735805201
     [Google Scholar]
  145. WangM. CangZ. WeiG.W. A topology-based network tree for the prediction of protein–protein binding affinity changes following mutation.Nat. Mach. Intell.20202211612310.1038/s42256‑020‑0149‑634170981
     [Google Scholar]
  146. ChengT. LiQ. ZhouZ. WangY. BryantS.H. Structure-based virtual screening for drug discovery: A problem-centric review.AAPS J.201214113314110.1208/s12248‑012‑9322‑022281989
     [Google Scholar]
  147. MaD.L. ChanD.S.H. LeungC.H. Drug repositioning by structure-based virtual screening.Chem. Soc. Rev.20134252130214110.1039/c2cs35357a23288298
     [Google Scholar]
  148. KhamisM.A. GomaaW. AhmedW.F. Machine learning in computational docking.Artif. Intell. Med.201563313515210.1016/j.artmed.2015.02.00225724101
     [Google Scholar]
/content/journals/cchts/10.2174/0113862073305298240524050145
Loading
Machine Learning in Enhancing Protein Binding Sites Predictions - What Has Changed Since Then?
Comb Chem High Throughput Screen 28, 1640 (2025); https://doi.org/10.2174/0113862073305298240524050145
/content/journals/cchts/10.2174/0113862073305298240524050145
/content/journals/cchts/10.2174/0113862073305298240524050145
Loading

Data & Media loading...


  • Article Type:     Review Article
Keyword(s): deep learning; drug discovery; machine learning; molecular biology; protein binding sites; Protein-ligand binding site prediction

Most Cited Most Cited RSS feed

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error
Please enter a valid_number test