mCSM-lig: quantifying the effects of mutations on protein-small molecule affinity in genetic disease and emergence of drug resistance

The ability to predict how a mutation affects ligand binding is an essential step in understanding, anticipating and improving the design of new treatments for drug resistance, and in understanding genetic diseases. Here we present mCSM-lig, a structure-guided computational approach for quantifying the effects of single-point missense mutations on affinities of small molecules for proteins. mCSM-lig uses graph-based signatures to represent the wild-type environment of mutations, and small-molecule chemical features and changes in protein stability as evidence to train a predictive model using a representative set of protein-ligand complexes from the Platinum database. We show our method provides a very good correlation with experimental data (up to ρ = 0.67) and is effective in predicting a range of chemotherapeutic, antiviral and antibiotic resistance mutations, providing useful insights for genotypic screening and to guide drug development. mCSM-lig also provides insights into understanding Mendelian disease mutations and as a tool for guiding protein design. mCSM-lig is freely available as a web server at http://structure.bioc.cam.ac.uk/mcsm_lig.

[1]  Douglas E. V. Pires,et al.  Analysis of HGD Gene Mutations in Patients with Alkaptonuria from the United Kingdom: Identification of Novel Mutations. , 2015, JIMD reports.

[2]  Douglas E. V. Pires,et al.  mCSM: predicting the effects of mutations in proteins using graph-based signatures , 2013, Bioinform..

[3]  David T. W. Jones,et al.  Mechismo: predicting the mechanistic impact of mutations and modifications on molecular interactions , 2014, Nucleic acids research.

[4]  François Stricher,et al.  The FoldX web server: an online force field , 2005, Nucleic Acids Res..

[5]  Douglas E. V. Pires,et al.  CSM-lig: a web server for assessing and comparing protein–small molecule affinities , 2016, Nucleic Acids Res..

[6]  A. Skerra,et al.  Small antibody-like proteins with prescribed ligand specificities derived from the lipocalin fold. , 1999, Proceedings of the National Academy of Sciences of the United States of America.

[7]  K. Anderson,et al.  Current Perspectives on HIV-1 Antiretroviral Drug Resistance , 2014, Viruses.

[8]  J. Mccammon,et al.  HIV‐1 protease molecular dynamics of a wild‐type and of the V82F/I84V mutant: Possible contributions to drug resistance and a potential new target site for drugs , 2004, Protein science : a publication of the Protein Society.

[9]  Douglas E. V. Pires,et al.  DUET: a server for predicting effects of mutations on protein stability using an integrated computational approach , 2014, Nucleic Acids Res..

[10]  Jerome Wielens,et al.  Potent hepatitis C inhibitors bind directly to NS5A and reduce its affinity for RNA , 2014, Scientific Reports.

[11]  E. Arnold,et al.  Multifaceted Roles of Crystallography in Modern Drug Discovery , 2015, NATO Science for Peace and Security Series A: Chemistry and Biology.

[12]  Douglas E. V. Pires,et al.  Platinum: a database of experimentally measured effects of mutations on structurally defined protein–ligand complexes , 2014, Nucleic Acids Res..

[13]  R. Cherny,et al.  Regulation of insulin-regulated membrane aminopeptidase activity by its C-terminal domain. , 2011, Biochemistry.

[14]  Akinori Sarai,et al.  ProTherm and ProNIT: thermodynamic databases for proteins and protein–nucleic acid interactions , 2005, Nucleic Acids Res..

[15]  Douglas E. V. Pires,et al.  pkCSM: Predicting Small-Molecule Pharmacokinetic and Toxicity Properties Using Graph-Based Signatures , 2015, Journal of medicinal chemistry.

[16]  Michael W Parker,et al.  Identification and characterization of a new cognitive enhancer based on inhibition of insulin‐regulated aminopeptidase , 2008, FASEB journal : official publication of the Federation of American Societies for Experimental Biology.

[17]  Simona Soverini,et al.  BCR-ABL kinase domain mutation analysis in chronic myeloid leukemia patients treated with tyrosine kinase inhibitors: recommendations from an expert panel on behalf of European LeukemiaNet. , 2011, Blood.

[18]  T L Blundell,et al.  Prediction of the stability of protein mutants based on structural environment-dependent amino acid substitution and propensity tables. , 1997, Protein engineering.

[19]  Michael W Parker,et al.  Identification of modulating residues defining the catalytic cleft of insulin-regulated aminopeptidase. , 2008, Biochemistry and cell biology = Biochimie et biologie cellulaire.

[20]  Michael W Parker,et al.  Development of cognitive enhancers based on inhibition of insulin-regulated aminopeptidase , 2008, BMC Neuroscience.

[21]  D. Gibbons,et al.  Molecular dynamics reveal BCR-ABL1 polymutants as a unique mechanism of resistance to PAN-BCR-ABL1 kinase inhibitor therapy , 2014, Proceedings of the National Academy of Sciences.

[22]  Douglas E. V. Pires,et al.  Mycobacterium tuberculosis whole genome sequencing and protein structure modelling provides insights into anti-tuberculosis drug resistance , 2016, BMC Medicine.

[23]  P. Shannon,et al.  Exome sequencing identifies the cause of a Mendelian disorder , 2009, Nature Genetics.

[24]  Douglas E. V. Pires,et al.  mCSM-AB: a web server for predicting antibody–antigen affinity changes upon mutation with graph-based signatures , 2016, Nucleic Acids Res..

[25]  Douglas E. V. Pires,et al.  Germline Mutations in the CDKN2B Tumor Suppressor Gene Predispose to Renal Cell Carcinoma. , 2015, Cancer discovery.

[26]  Wagner Meira,et al.  PDBest: a user-friendly platform for manipulating and enhancing protein structures , 2015, Bioinform..

[27]  Christopher K. I. Williams,et al.  Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning) , 2005 .

[28]  Wagner Meira,et al.  Cutoff Scanning Matrix (CSM): structural classification and function prediction by protein inter-residue distance patterns , 2011, BMC Genomics.

[29]  F. Baquero,et al.  Tackling antibiotic resistance: the environmental framework , 2015, Nature Reviews Microbiology.

[30]  Mallur S. Madhusudhan,et al.  Depth: a web server to compute depth, cavity sizes, detect potential small-molecule ligand-binding cavities and predict the pKa of ionizable residues in proteins , 2013, Nucleic Acids Res..

[31]  R. Lamb,et al.  Molecular dynamics simulation directed rational design of inhibitors targeting drug-resistant mutants of influenza A virus M2. , 2011, Journal of the American Chemical Society.

[32]  Kenny Q. Ye,et al.  An integrated map of genetic variation from 1,092 human genomes , 2012, Nature.

[33]  Douglas E. V. Pires,et al.  In silico functional dissection of saturation mutagenesis: Interpreting the relationship between phenotypes and changes in protein stability, interactions and activity , 2016, Scientific Reports.

[34]  Juan Fernández-Recio,et al.  SKEMPI: a Structural Kinetic and Energetic database of Mutant Protein Interactions and its use in empirical models , 2012, Bioinform..

[35]  Rational engineering of a fluorescein-binding anticalin for improved ligand affinity , 2005, Biological chemistry.

[36]  Guy Boivin,et al.  Impact of Neuraminidase Mutations Conferring Influenza Resistance to Neuraminidase Inhibitors in the N1 and N2 Genetic Backgrounds , 2006, Antiviral therapy.

[37]  Tom L. Blundell,et al.  Flexibility and small pockets at protein–protein interfaces: New insights into druggability , 2015, Progress in biophysics and molecular biology.

[38]  Piero Fariselli,et al.  I-Mutant2.0: predicting stability changes upon mutation from the protein sequence or structure , 2005, Nucleic Acids Res..

[39]  Marianne Rooman,et al.  BeAtMuSiC: prediction of changes in protein–protein binding affinity on mutations , 2013, Nucleic Acids Res..

[40]  Ludevit Kadasi,et al.  Twelve novel HGD gene variants identified in 99 alkaptonuria patients: focus on ‘black bone disease’ in Italy , 2015, European Journal of Human Genetics.

[41]  Philippe Bogaerts,et al.  Fast and accurate predictions of protein stability changes upon mutations using statistical potentials and neural networks: PoPMuSiC-2.0 , 2009, Bioinform..