dbHDPLS: A database of human disease-related protein-ligand structures

Protein-ligand complexes perform specific functions, most of which are related to human diseases. The database, called as human disease-related protein-ligand structures (dbHDPLS), collected 8833 structures which were extracted from protein data bank (PDB) and other related databases. The database is annotated with comprehensive information involving ligands and drugs, related human diseases and protein-ligand interaction information, with the information of protein structures. The database may be a reliable resource for structure-based drug target discoveries and druggability predictions of protein-ligand binding sites, drug-disease relationships based on protein-ligand complex structures. It can be publicly accessed at the website: http://DeepLearner.ahu.edu.cn/web/dbDPLS/.

[1]  Hans-Jörg Schneider,et al.  Mechanisms of Molecular Recognition : Investigations of Organic Host–Guest Complexes , 1991 .

[2]  María Martín,et al.  UniProt: A hub for protein information , 2015 .

[3]  Lukasz Kurgan,et al.  Finding protein targets for small biologically relevant ligands across fold space using inverse ligand binding predictions. , 2012, Structure.

[4]  M. Sutcliffe,et al.  Protein-ligand interactions: exchange processes and determination of ligand conformation and protein-ligand contacts. , 1994, Methods in enzymology.

[5]  Xiaomin Luo,et al.  PDTD: a web-accessible protein database for drug target identification , 2008, BMC Bioinformatics.

[6]  Tingting Fu,et al.  Therapeutic target database update 2018: enriched resource for facilitating bench-to-clinic research of targeted therapeutics , 2017, Nucleic Acids Res..

[7]  T. N. Bhat,et al.  The Protein Data Bank , 2000, Nucleic Acids Res..

[8]  Lin Gao,et al.  Inferring drug-disease associations based on known protein complexes , 2015, BMC Medical Genomics.

[9]  Ylva Ivarsson,et al.  Affinity and specificity of motif-based protein-protein interactions. , 2019, Current opinion in structural biology.

[10]  Frances M. G. Pearl,et al.  The CATH Domain Structure Database and related resources Gene3D and DHS provide comprehensive domain family information for genome analysis , 2004, Nucleic Acids Res..

[11]  Janet M. Thornton,et al.  PDBsum more: new summaries and analyses of the known 3D structures of proteins and nucleic acids , 2004, Nucleic Acids Res..

[12]  Jinyan Li,et al.  Protein binding hot spots prediction from sequence only by a new ensemble learning method , 2017, Amino Acids.

[13]  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.

[14]  Ruedi Aebersold,et al.  Highlights of the Biology and Disease-driven Human Proteome Project, 2015-2016. , 2016, Journal of proteome research.

[15]  Feng Xu,et al.  Therapeutic target database update 2016: enriched resource for bench to clinical drug target and targeted pathway information , 2015, Nucleic Acids Res..

[16]  M. Schroeder,et al.  Drug repositioning through incomplete bi-cliques in an integrated drug-target-disease network. , 2012, Integrative biology : quantitative biosciences from nano to macro.

[17]  Bernardo Ochoa-Montaño,et al.  Mutations at protein-protein interfaces: Small changes over big surfaces have large impacts on human health. , 2017, Progress in biophysics and molecular biology.

[18]  J. Drews.  Drug discovery: a historical perspective. , 2000, Science.

[19]  P. Imming,et al.  Drugs, their targets and the nature and number of drug targets , 2006, Nature Reviews Drug Discovery.

[20]  Ayesha Obaid,et al.  Mutation-Structure-Function Relationship Based Integrated Strategy Reveals the Potential Impact of Deleterious Missense Mutations in Autophagy Related Proteins on Hepatocellular Carcinoma (HCC): A Comprehensive Informatics Approach , 2017, International journal of molecular sciences.

[21]  Raju S. Bapi,et al.  Protein ligand interaction database (PLID) , 2008, Comput. Biol. Chem..

[22]  Markus Heinonen,et al.  Flex ddG: Rosetta ensemble-based estimation of changes in protein-protein binding affinity upon mutation , 2017, bioRxiv.

[23]  Michal Brylinski,et al.  eFindSite: Enhanced Fingerprint‐Based Virtual Screening Against Predicted Ligand Binding Sites in Protein Models , 2014, Molecular informatics.

[24]  Robert B. Russell,et al.  SuperTarget and Matador: resources for exploring drug-target relationships , 2007, Nucleic Acids Res..

[25]  Scott Barolo,et al.  Three habits of highly effective signaling pathways: principles of transcriptional control by developmental cell signaling. , 2002, Genes & development.

[26]  Michael G. Lerner,et al.  Binding MOAD (Mother Of All Databases) , 2005, Proteins.

[27]  S. Lemeer,et al.  Proteomic tools to study drug function , 2018, Current Opinion in Systems Biology.

[28]  Joanna L. Sharman,et al.  The IUPHAR/BPS Guide to PHARMACOLOGY in 2016: towards curated quantitative interactions between 1300 protein targets and 6000 ligands , 2015, Nucleic Acids Res..

[29]  S. Hanash.  Disease proteomics : Proteomics , 2003 .

[30]  Michal Brylinski,et al.  eFindSite: Improved prediction of ligand binding sites in protein models using meta-threading, machine learning and auxiliary ligands , 2013, Journal of Computer-Aided Molecular Design.

[31]  Yang Zhang,et al.  BioLiP: a semi-manually curated database for biologically relevant ligand–protein interactions , 2012, Nucleic Acids Res..

[32]  Jinyan Li,et al.  Accurate prediction of hot spot residues through physicochemical characteristics of amino acid sequences , 2013, Proteins.

[33]  X. Chen,et al.  TTD: Therapeutic Target Database , 2002, Nucleic Acids Res..

[34]  J. Scheuermann,et al.  Affinity Enhancement of Protein Ligands by Reversible Covalent Modification of Neighboring Lysine Residues. , 2018, Angewandte Chemie.

[35]  John P. Overington,et al.  How many drug targets are there? , 2006, Nature Reviews Drug Discovery.

[36]  S. Jamal,et al.  Drug Discovery: An In Silico Approach , 2018 .

[37]  Lei Xie,et al.  Detecting evolutionary relationships across existing fold space, using sequence order-independent profile–profile alignments , 2008, Proceedings of the National Academy of Sciences.

[38]  Jie Li,et al.  PDB-wide collection of binding data: current status of the PDBbind database , 2015, Bioinform..

[39]  Chen Wang,et al.  PDID: database of molecular-level putative protein-drug interactions in the structural human proteome , 2016, Bioinform..

[40]  Jinyan Li,et al.  A Sequence-Based Dynamic Ensemble Learning System for Protein Ligand-Binding Site Prediction , 2016, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

[41]  Jinyan Li,et al.  dbMPIKT: a database of kinetic and thermodynamic mutant protein interactions , 2018, BMC Bioinformatics.

[42]  V. Šrajer,et al.  Watching proteins function with time-resolved x-ray crystallography , 2017, Journal of physics D: Applied physics.

[43]  J. Fernández-Recio,et al.  Structural Prediction of Protein-Protein Interactions by Docking: Application to Biomedical Problems. , 2018, Advances in protein chemistry and structural biology.

[44]  Joel Dudley,et al.  Exploiting drug-disease relationships for computational drug repositioning , 2011, Briefings Bioinform..

[45]  P. Hajduk,et al.  Predicting protein druggability. , 2005, Drug discovery today.

[46]  Xiangrong Liu,et al.  Machine Learning for Drug-Target Interaction Prediction , 2018, Molecules.