Computational methods and tools for binding site recognition between proteins and small molecules: from classical geometrical approaches to modern machine learning strategies
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[1] N. Metropolis. THE BEGINNING of the MONTE CARLO METHOD , 2022 .
[2] Jie Li,et al. PDB-wide collection of binding data: current status of the PDBbind database , 2015, Bioinform..
[3] Chih Lee,et al. PCA-based population structure inference with generic clustering algorithms , 2009, BMC Bioinformatics.
[4] Jeffrey Skolnick,et al. Implications of the small number of distinct ligand binding pockets in proteins for drug discovery, evolution and biochemical function. , 2015, Bioorganic & medicinal chemistry letters.
[5] Vincent Le Guilloux,et al. Fpocket: An open source platform for ligand pocket detection , 2009, BMC Bioinformatics.
[6] Sarita Rajender Potlapally,et al. Structure-based identification of potential novel inhibitors targeting FAM3B (PANDER) causing type 2 diabetes mellitus through virtual screening , 2019, Journal of receptor and signal transduction research.
[7] Shruti Asmita,et al. Review on the Architecture, Algorithm and Fusion Strategies in Ensemble Learning , 2014 .
[8] Penny J. Beuning,et al. Biochemical functional predictions for protein structures of unknown or uncertain function , 2015, Computational and structural biotechnology journal.
[9] Thomas A. Halgren,et al. Identifying and Characterizing Binding Sites and Assessing Druggability , 2009, J. Chem. Inf. Model..
[10] H. Edelsbrunner,et al. Anatomy of protein pockets and cavities: Measurement of binding site geometry and implications for ligand design , 1998, Protein science : a publication of the Protein Society.
[11] T. N. Bhat,et al. The Protein Data Bank , 2000, Nucleic Acids Res..
[12] Yang Zhang,et al. COFACTOR: improved protein function prediction by combining structure, sequence and protein–protein interaction information , 2017, Nucleic Acids Res..
[13] Jeffrey Skolnick,et al. Are predicted protein structures of any value for binding site prediction and virtual ligand screening? , 2013, Current opinion in structural biology.
[14] Qi Wu,et al. COACH-D: improved protein–ligand binding sites prediction with refined ligand-binding poses through molecular docking , 2018, Nucleic Acids Res..
[15] P. Pardalos,et al. An exact algorithm for the maximum clique problem , 1990 .
[16] Gianni De Fabritiis,et al. DeepSite: protein‐binding site predictor using 3D‐convolutional neural networks , 2017, Bioinform..
[17] Hamid D. Ismail,et al. RF-Phos: A Novel General Phosphorylation Site Prediction Tool Based on Random Forest , 2016, BioMed research international.
[18] H. Yamana,et al. SCPSSMpred: A General Sequence-based Method for Ligand-binding Site Prediction , 2013 .
[19] David Hoksza,et al. P2Rank: machine learning based tool for rapid and accurate prediction of ligand binding sites from protein structure , 2018, Journal of Cheminformatics.
[20] Daniel B. Roche,et al. Proteins and Their Interacting Partners: An Introduction to Protein–Ligand Binding Site Prediction Methods , 2015, International journal of molecular sciences.
[21] Liam J. McGuffin,et al. The FunFOLD2 server for the prediction of protein–ligand interactions , 2013, Nucleic Acids Res..
[22] An-Suei Yang,et al. Predicting Ligand Binding Sites on Protein Surfaces by 3-Dimensional Probability Density Distributions of Interacting Atoms , 2016, PloS one.
[23] R. Najmanovich. Evolutionary studies of ligand binding sites in proteins. , 2017, Current opinion in structural biology.
[24] Kentaro Shimizu,et al. Development of a protein–ligand-binding site prediction method based on interaction energy and sequence conservation , 2016, Journal of Structural and Functional Genomics.
[25] R. Laskowski. SURFNET: a program for visualizing molecular surfaces, cavities, and intermolecular interactions. , 1995, Journal of molecular graphics.
[26] Akira Saito,et al. Recent advances in functional region prediction by using structural and evolutionary information – Remaining problems and future extensions , 2013, Computational and structural biotechnology journal.
[27] Barry Honig,et al. Structure-based prediction of ligand–protein interactions on a genome-wide scale , 2017, Proceedings of the National Academy of Sciences.
[28] Nikos Kyrpides,et al. The Genomes On Line Database (GOLD) v.2: a monitor of genome projects worldwide , 2005, Nucleic Acids Res..
[29] P. Jayaprakash,et al. Design of novel PhMTNA inhibitors, targeting neurological disorder through homology modeling, molecular docking, and dynamics approaches , 2019, Journal of receptor and signal transduction research.
[30] Rushi Longadge,et al. Class Imbalance Problem in Data Mining Review , 2013, ArXiv.
[31] Prudence Mutowo-Meullenet,et al. The GOA database: Gene Ontology annotation updates for 2015 , 2014, Nucleic Acids Res..
[32] M. Schroeder,et al. LIGSITEcsc: predicting ligand binding sites using the Connolly surface and degree of conservation , 2006, BMC Structural Biology.
[33] T. Kawabata. Detection of multiscale pockets on protein surfaces using mathematical morphology , 2010, Proteins.
[34] Liam J. McGuffin,et al. FunFOLD: an improved automated method for the prediction of ligand binding residues using 3D models of proteins , 2011, BMC Bioinformatics.
[35] Daniel R. Caffrey,et al. Structure-based maximal affinity model predicts small-molecule druggability , 2007, Nature Biotechnology.
[36] Shoshana J. Wodak,et al. LigASite—a database of biologically relevant binding sites in proteins with known apo-structures , 2007, Nucleic Acids Res..
[37] G. Schneider,et al. PocketPicker: analysis of ligand binding-sites with shape descriptors , 2007, Chemistry Central Journal.
[38] Kenji Mizuguchi,et al. Network analysis and in silico prediction of protein-protein interactions with applications in drug discovery. , 2017, Current opinion in structural biology.
[39] Yang Zhang,et al. Protein-ligand binding site recognition using complementary binding-specific substructure comparison and sequence profile alignment , 2013, Bioinform..
[40] Torsten Schwede,et al. Assessment of ligand binding site predictions in CASP10 , 2014, Proteins.
[41] J. Skolnick,et al. A threading-based method (FINDSITE) for ligand-binding site prediction and functional annotation , 2008, Proceedings of the National Academy of Sciences.
[42] Liam J. McGuffin,et al. FunFOLDQA: A Quality Assessment Tool for Protein-Ligand Binding Site Residue Predictions , 2012, PloS one.
[43] Hongyi Zhou,et al. FINDSITEcomb: A Threading/Structure-Based, Proteomic-Scale Virtual Ligand Screening Approach , 2013, J. Chem. Inf. Model..
[44] Tatiana Tatusova,et al. NCBI Reference Sequence (RefSeq): a curated non-redundant sequence database of genomes, transcripts and proteins , 2004, Nucleic Acids Res..
[45] B. Honig,et al. Toward a “Structural BLAST”: Using structural relationships to infer function , 2013, Protein science : a publication of the Protein Society.
[46] Richard M. Jackson,et al. Q-SiteFinder: an energy-based method for the prediction of protein-ligand binding sites , 2005, Bioinform..
[47] G. Klebe,et al. A new method to detect related function among proteins independent of sequence and fold homology. , 2002, Journal of molecular biology.
[48] Maxim Totrov,et al. Ligand binding site superposition and comparison based on Atomic Property Fields: identification of distant homologues, convergent evolution and PDB-wide clustering of binding sites , 2011, BMC Bioinformatics.
[49] Subrayal M. Reddy,et al. Towards Rational Design of Selective Molecularly Imprinted Polymers (MIPs) for Proteins: Computational and Experimental Studies of Acrylamide-Based Polymers for Myoglobin. , 2019, The journal of physical chemistry. B.
[50] Mona Singh,et al. Predicting Protein Ligand Binding Sites by Combining Evolutionary Sequence Conservation and 3D Structure , 2009, PLoS Comput. Biol..
[51] Andreas Windemuth,et al. Structural coverage of the proteome for pharmaceutical applications. , 2017, Drug discovery today.
[52] Daniel B. Roche,et al. Automated tertiary structure prediction with accurate local model quality assessment using the intfold‐ts method , 2011, Proteins.
[53] Andras Fiser,et al. Trends in structural coverage of the protein universe and the impact of the Protein Structure Initiative , 2014, Proceedings of the National Academy of Sciences.
[54] Yanlin Zhao,et al. The Beginning of the rpoB Gene in Addition to the Rifampin Resistance Determination Region Might Be Needed for Identifying Rifampin/Rifabutin Cross-Resistance in Multidrug-Resistant Mycobacterium tuberculosis Isolates from Southern China , 2011, Journal of Clinical Microbiology.
[55] Yang Zhang,et al. BioLiP: a semi-manually curated database for biologically relevant ligand–protein interactions , 2012, Nucleic Acids Res..
[56] R. Loisy,et al. Sur la forme des courbes [voir pdf] , 1951 .
[57] Gisele L. Pappa,et al. GASS: identifying enzyme active sites with genetic algorithms , 2015, Bioinform..
[58] Torsten Schwede,et al. Assessment of ligand‐binding residue predictions in CASP9 , 2011, Proteins.
[59] Hans-Peter Kriegel,et al. A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise , 1996, KDD.
[60] José Ignacio Garzón,et al. Template-based prediction of protein function. , 2015, Current opinion in structural biology.
[61] Mona Singh,et al. Predicting functionally important residues from sequence conservation , 2007, Bioinform..
[62] I. Bahar,et al. Coupling between catalytic site and collective dynamics: a requirement for mechanochemical activity of enzymes. , 2005, Structure.
[63] Fabio Polticelli,et al. LIBRA: LIgand Binding site Recognition Application , 2015, Bioinform..
[64] Janet M. Thornton,et al. The Catalytic Site Atlas 2.0: cataloging catalytic sites and residues identified in enzymes , 2013, Nucleic Acids Res..
[65] Xin Gao,et al. LigandRFs: random forest ensemble to identify ligand-binding residues from sequence information alone , 2014, BMC Bioinformatics.
[66] Alasdair T. R. Laurie,et al. Methods for the prediction of protein-ligand binding sites for structure-based drug design and virtual ligand screening. , 2006, Current protein & peptide science.
[67] B. Honig,et al. Structure-based prediction of protein-protein interactions on a genome-wide scale , 2012, Nature.
[68] Didier Rognan,et al. sc-PDB: a 3D-database of ligandable binding sites—10 years on , 2014, Nucleic Acids Res..
[69] David S. Goodsell,et al. AutoDock4 and AutoDockTools4: Automated docking with selective receptor flexibility , 2009, J. Comput. Chem..
[70] Shaojie Qiao,et al. ENSEMBLE-CNN: Predicting DNA Binding Sites in Protein Sequences by an Ensemble Deep Learning Method , 2018, ICIC.
[71] B KC Dukka,et al. Structure-based Methods for Computational Protein Functional Site Prediction , 2013, Computational and structural biotechnology journal.
[72] Fabio Polticelli,et al. Protein-ligand binding site detection as an alternative route to molecular docking and drug repurposing , 2018, Bio Algorithms Med Syst..
[73] Liam J. McGuffin,et al. IntFOLD: an integrated server for modelling protein structures and functions from amino acid sequences , 2015, Nucleic Acids Res..
[74] Diego Garrido-Martín,et al. Effect of the sequence data deluge on the performance of methods for detecting protein functional residues , 2018, BMC Bioinformatics.
[75] Fabio Polticelli,et al. ASSIST: a fast versatile local structural comparison tool , 2014, Bioinform..
[76] Romano T. Kroemer,et al. Large-Scale Comparison of Four Binding Site Detection Algorithms , 2010, J. Chem. Inf. Model..
[77] Gisele L. Pappa,et al. GASS-WEB: a web server for identifying enzyme active sites based on genetic algorithms , 2017, Nucleic Acids Res..
[78] Yang Zhang,et al. COFACTOR: an accurate comparative algorithm for structure-based protein function annotation , 2012, Nucleic Acids Res..
[79] Mark N. Wass,et al. Convergent evolution of enzyme active sites is not a rare phenomenon. , 2007, Journal of molecular biology.
[80] M Hendlich,et al. LIGSITE: automatic and efficient detection of potential small molecule-binding sites in proteins. , 1997, Journal of molecular graphics & modelling.
[81] Fabio Polticelli,et al. LIBRA-WA: a web application for ligand binding site detection and protein function recognition , 2018, Bioinform..
[82] Joaquim A. Jorge,et al. Multi-GPU-based detection of protein cavities using critical points , 2017, Future Gener. Comput. Syst..
[83] Bingding Huang,et al. MetaPocket: a meta approach to improve protein ligand binding site prediction. , 2009, Omics : a journal of integrative biology.
[84] J. Skolnick,et al. TM-align: a protein structure alignment algorithm based on the TM-score , 2005, Nucleic acids research.
[85] Janet M. Thornton,et al. ProFunc: a server for predicting protein function from 3D structure , 2005, Nucleic Acids Res..
[86] C. Orengo,et al. One fold with many functions: the evolutionary relationships between TIM barrel families based on their sequences, structures and functions. , 2002, Journal of molecular biology.
[87] Yong Zhou,et al. Roll: a new algorithm for the detection of protein pockets and cavities with a rolling probe sphere , 2010, Bioinform..
[88] Dachuan Zhang,et al. MMDB and VAST+: tracking structural similarities between macromolecular complexes , 2013, Nucleic Acids Res..
[89] Sukanta Mondal,et al. Ensemble Architecture for Prediction of Enzyme‐ligand Binding Residues Using Evolutionary Information , 2017, Molecular informatics.
[90] Matthew J. O’Meara,et al. The Recognition of Identical Ligands by Unrelated Proteins. , 2015, ACS chemical biology.
[91] Hiroyuki Ogata,et al. AAindex: Amino Acid Index Database , 1999, Nucleic Acids Res..
[92] Jun Hu,et al. Designing Template-Free Predictor for Targeting Protein-Ligand Binding Sites with Classifier Ensemble and Spatial Clustering , 2013, IEEE/ACM Transactions on Computational Biology and Bioinformatics.
[93] Kevin Skadron,et al. Scalable parallel programming , 2008, 2008 IEEE Hot Chips 20 Symposium (HCS).
[94] Michel F. Sanner,et al. AutoSite: an automated approach for pseudo-ligands prediction - from ligand-binding sites identification to predicting key ligand atoms , 2016, Bioinform..
[95] O. Stegle,et al. Deep learning for computational biology , 2016, Molecular systems biology.
[96] A. Elofsson,et al. Structure is three to ten times more conserved than sequence—A study of structural response in protein cores , 2009, Proteins.
[97] Oliver Koch,et al. A benchmark driven guide to binding site comparison: An exhaustive evaluation using tailor-made data sets (ProSPECCTs) , 2018, PLoS Comput. Biol..
[98] David P. Dobkin,et al. The quickhull algorithm for convex hulls , 1996, TOMS.
[99] Paul N. Mortenson,et al. Diverse, high-quality test set for the validation of protein-ligand docking performance. , 2007, Journal of medicinal chemistry.
[100] Barry Honig,et al. GRASP2: visualization, surface properties, and electrostatics of macromolecular structures and sequences. , 2003, Methods in enzymology.
[101] Pieter F. W. Stouten,et al. Fast prediction and visualization of protein binding pockets with PASS , 2000, J. Comput. Aided Mol. Des..