Computer prediction of drug resistance mutations in proteins.

Drug resistance is of increasing concern in the treatment of infectious diseases and cancer. Mutation in drug-interacting disease proteins is one of the primary causes for resistance particularly against anti-infectious drugs. Prediction of resistance mutations in these proteins is valuable both for the molecular dissection of drug resistance mechanisms and for predicting features that guide the design of new agents to counter resistant strains. Several protein structure- and sequence-based computer methods have been explored for mechanistic study and prediction of resistance mutations. These methods and their usefulness are reviewed here.

[1]  Doriano Fabbro,et al.  Prediction of Resistance to Small Molecule FLT3 Inhibitors , 2004, Cancer Research.

[2]  P. Lam,et al.  Molecular basis of HIV-1 protease drug resistance: structural analysis of mutant proteases complexed with cyclic urea inhibitors. , 1997, Biochemistry.

[3]  D. Bentrem,et al.  A mechanism of drug resistance to tamoxifen in breast cancer , 2002, The Journal of Steroid Biochemistry and Molecular Biology.

[4]  S. Macura,et al.  Cation-pi interaction in a folded polypeptide. , 2002, Biopolymers.

[5]  F. Heinz,et al.  Comparison of virtual phenotype and HIV‐SEQ program (Stanford) interpretation for predicting drug resistance of HIV strains , 2002, HIV medicine.

[6]  I. Kuntz.  Structure-Based Strategies for Drug Design and Discovery , 1992, Science.

[7]  M L Lamb,et al.  Monte Carlo calculations on HIV-1 reverse transcriptase complexed with the non-nucleoside inhibitor 8-Cl TIBO: contribution of the L100I and Y181C variants to protein stability and biological activity. , 2000, Protein engineering.

[8]  M. Murcko,et al.  Consensus scoring: A method for obtaining improved hit rates from docking databases of three-dimensional structures into proteins. , 1999, Journal of medicinal chemistry.

[9]  D. Tregouet,et al.  Matrix Metalloproteinase 3 Polymorphism , 2004, Clinical Cancer Research.

[10]  S. Macura,et al.  Cation–π interaction in a folded polypeptide , 2002 .

[11]  P Willett,et al.  Development and validation of a genetic algorithm for flexible docking. , 1997, Journal of molecular biology.

[12]  E. Elonen,et al.  Challenging drug resistance in cancer therapy--review of the First Nordic Conference on Chemoresistance in Cancer Treatment, October 9th and 10th, 1997. , 1998, Acta oncologica.

[13]  Nagarajan Vaidehi,et al.  HierVLS hierarchical docking protocol for virtual ligand screening of large-molecule databases. , 2004, Journal of medicinal chemistry.

[14]  P. Kollman,et al.  Computational study of protein specificity: The molecular basis of HIV-1 protease drug resistance , 2001, Proceedings of the National Academy of Sciences of the United States of America.

[15]  A. Carattoli,et al.  Prediction of Decreased Susceptibility to Penicillin of Neisseria meningitidis Strains by Real-Time PCR , 2003, Journal of Clinical Microbiology.

[16]  Kal Ramnarayan,et al.  Structure‐based phenotyping predicts HIV‐1 protease inhibitor resistance , 2003, Protein science : a publication of the Protein Society.

[17]  E. van Marck,et al.  Population-based validation of dihydrofolate reductase gene mutations for the prediction of sulfadoxine-pyrimethamine resistance in Uganda. , 2003, Transactions of the Royal Society of Tropical Medicine and Hygiene.

[18]  A. Telenti,et al.  Ethambutol resistance in Mycobacterium tuberculosis: critical role of embB mutations , 1997, Antimicrobial agents and chemotherapy.

[19]  Garrett M Morris,et al.  Evolutionary analysis of HIV‐1 protease inhibitors: Methods for design of inhibitors that evade resistance , 2002, Proteins.

[20]  N. Tatsumi,et al.  Method to detect substitutions in the interferon‐sensitivity—Determining region of hepatitis C virus 1b for prediction of response to interferon therapy , 2001, Hepatology.

[21]  広野 修一.  Structure-based drug designのための分子動力学シミュレ-ション (特集 創薬研究とコンピュ-タ-科学(Part 1)) , 1998 .

[22]  B. Larder,et al.  Enhanced prediction of lopinavir resistance from genotype by use of artificial neural networks. , 2003, The Journal of infectious diseases.

[24]  J. Fantini,et al.  Mutation Patterns of the Reverse Transcriptase and Protease Genes in Human Immunodeficiency Virus Type 1-Infected Patients Undergoing Combination Therapy: Survey of 787 Sequences , 1999, Journal of Clinical Microbiology.

[25]  P. V. van Helden,et al.  Prediction of Drug Resistance in M. tuberculosis: Molecular Mechanisms, Tools, and Applications , 2002, IUBMB life.

[26]  J. Fantini,et al.  Resistance of HIV-1 to multiple antiretroviral drugs in France: a 6-year survey (1997–2002) based on an analysis of over 7000 genotypes , 2003, AIDS.

[27]  P. Gangadharam,et al.  Contribution of rpoB Mutations to Development of Rifamycin Cross-Resistance in Mycobacterium tuberculosis , 1998, Antimicrobial Agents and Chemotherapy.

[28]  T. Anderson.  Mapping drug resistance genes in Plasmodium falciparum by genome-wide association. , 2004, Current drug targets. Infectious disorders.

[29]  Leslie A Kuhn,et al.  Modeling correlated main‐chain motions in proteins for flexible molecular recognition , 2004, Proteins.

[30]  D. Beveridge,et al.  Exploratory studies of ab initio protein structure prediction: Multiple copy simulated annealing, AMBER energy functions, and a generalized born/solvent accessibility solvation model , 2002, Proteins.

[31]  Giorgio Palù,et al.  Comparative evaluation of three computerized algorithms for prediction of antiretroviral susceptibility from HIV type 1 genotype. , 2004, The Journal of antimicrobial chemotherapy.

[32]  J. Sacchettini,et al.  Modification of the NADH of the isoniazid target (InhA) from Mycobacterium tuberculosis. , 1998, Science.

[33]  D. Persing,et al.  Direct genotypic detection of Mycobacterium tuberculosis rifampin resistance in clinical specimens by using single-tube heminested PCR , 1995, Journal of clinical microbiology.

[34]  Peter Norvig,et al.  Artificial Intelligence: A Modern Approach , 1995 .

[35]  Liu Hh.  Antibiotic resistance in bacteria. A current and future problem. , 1999 .

[36]  Evon M. O. Abu-Taieh,et al.  Comparative study , 2003, BMJ : British Medical Journal.

[37]  Zheng Rong Yang,et al.  Characterizing proteolytic cleavage site activity using bio-basis function neural networks , 2003, Bioinform..

[38]  Yunqian Ma,et al.  Practical selection of SVM parameters and noise estimation for SVM regression , 2004, Neural Networks.

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

[40]  Jianping Ding,et al.  Targeting HIV reverse transcriptase for anti-AIDS drug design: structural and biological considerations for chemotherapeutic strategies. , 1996, Drug design and discovery.

[41]  I. Durand-zaleski,et al.  HIV-1 drug resistance genotyping. A review of clinical and economic issues. , 2000, PharmacoEconomics.

[42]  H. Liu.  Antibiotic resistance in bacteria. A current and future problem. , 1999, Advances in experimental medicine and biology.

[43]  Y. Z. Chen,et al.  Can an optimization/scoring procedure in ligand-protein docking be employed to probe drug-resistant mutations in proteins? , 2001, Journal of molecular graphics & modelling.

[44]  Bryan Chan,et al.  Human immunodeficiency virus reverse transcriptase and protease sequence database , 2003, Nucleic Acids Res..

[45]  Thomas Lengauer,et al.  Diversity and complexity of HIV-1 drug resistance: A bioinformatics approach to predicting phenotype from genotype , 2002, Proceedings of the National Academy of Sciences of the United States of America.

[46]  G. S. Johnson,et al.  An Information-Intensive Approach to the Molecular Pharmacology of Cancer , 1997, Science.

[47]  P. Harrigan,et al.  2004: which HIV-1 drug resistance mutations are common in clinical practice? , 2004, AIDS reviews.

[48]  F. Mégraud,et al.  Accurate Prediction of Macrolide Resistance in Helicobacter pylori by a PCR Line Probe Assay for Detection of Mutations in the 23S rRNA Gene: Multicenter Validation Study , 2001, Antimicrobial Agents and Chemotherapy.

[49]  Robert T. Schultz,et al.  Nonlinear Estimation and Modeling of fMRI Data Using Spatio-temporal Support Vector Regression , 2003, IPMI.

[50]  Thomas Lengauer,et al.  Geno2pheno: estimating phenotypic drug resistance from HIV-1 genotypes , 2003, Nucleic Acids Res..

[51]  Irene T Weber,et al.  Molecular dynamics simulations of 14 HIV protease mutants in complexes with indinavir , 2004, Journal of molecular modeling.

[52]  V. Stepanshina,et al.  [Molecular mechanisms of drug resistance of Mycobacterium tuberculosis]. , 1999, Antibiotiki i khimioterapiia = Antibiotics and chemoterapy [sic].

[53]  Luhua Lai,et al.  SCORE: A New Empirical Method for Estimating the Binding Affinity of a Protein-Ligand Complex , 1998 .

[54]  F. Sussman,et al.  Solvation effects are responsible for the reduced inhibitor affinity of some HIV‐1 PR mutants , 1997, Protein science : a publication of the Protein Society.

[55]  Leszek Rutkowski,et al.  Generalized regression neural networks in time-varying environment , 2004, IEEE Transactions on Neural Networks.

[56]  Thomas Lengauer,et al.  A fast flexible docking method using an incremental construction algorithm. , 1996, Journal of molecular biology.

[57]  Y. Martin,et al.  A general and fast scoring function for protein-ligand interactions: a simplified potential approach. , 1999, Journal of medicinal chemistry.

[58]  T. Blundell.  Structure-based drug design. , 1996, Nature.

[59]  S. Rick,et al.  Molecular mechanisms of resistance: Free energy calculations of mutation effects on inhibitor binding to HIV‐1 protease , 1998, Protein science : a publication of the Protein Society.

[60]  I. Kuntz,et al.  Flexible ligand docking: A multistep strategy approach , 1999, Proteins.

[61]  B. Schmidt,et al.  Prediction of Abacavir Resistance from Genotypic Data: Impact of Zidovudine and Lamivudine Resistance In Vitro and In Vivo , 2002, Antimicrobial Agents and Chemotherapy.

[62]  Christian E Elger,et al.  A novel mechanism underlying drug resistance in chronic epilepsy , 2003, Annals of neurology.

[63]  A. D. McLachlan,et al.  Solvation energy in protein folding and binding , 1986, Nature.

[64]  Ursula Rothlisberger,et al.  Drug resistance in HIV‐1 protease: Flexibility‐assisted mechanism of compensatory mutations , 2002, Protein science : a publication of the Protein Society.

[65]  I. Adagu,et al.  Correlation of in vivo‐resistance to chloroquine and allelic polymorphisms in Plasmodium falciparum isolates from Uganda , 2000, Tropical medicine & international health : TM & IH.

[66]  I. Weber,et al.  Molecular mechanics analysis of drug-resistant mutants of HIV protease. , 1999, Protein engineering.

[67]  P. Borst.  Genetic mechanisms of drug resistance. A review. , 1991, Acta oncologica.

[68]  J. Ross Quinlan,et al.  Induction of Decision Trees , 1986, Machine Learning.

[69]  J. Erickson,et al.  Structural mechanisms of HIV drug resistance. , 1996, Annual review of pharmacology and toxicology.

[70]  Sorin Draghici,et al.  Predicting HIV drug resistance with neural networks , 2003, Bioinform..

[71]  P. Kiepiela,et al.  Genomic mutations in the katG, inhA and aphC genes are useful for the prediction of isoniazid resistance in Mycobacterium tuberculosis isolates from Kwazulu Natal, South Africa. , 2000, Tubercle and lung disease : the official journal of the International Union against Tuberculosis and Lung Disease.

[72]  S. Miertus,et al.  Computational studies of the resistance patterns of mutant HIV-1 aspartic proteases towards ABT-538 (ritonavir) and design of new derivatives. , 2002, Journal of molecular graphics & modelling.