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Protein design algorithms predict viable resistance to an experimental antifolate.

ABSTRACT: Methods to accurately predict potential drug target mutations in response to early-stage leads could drive the design of more resilient first generation drug candidates. In this study, a structure-based protein design algorithm (K* in the OSPREY suite) was used to prospectively identify single-nucleotide polymorphisms that confer resistance to an experimental inhibitor effective against dihydrofolate reductase (DHFR) from Staphylococcus aureus. Four of the top-ranked mutations in DHFR were found to be catalytically competent and resistant to the inhibitor. Selection of resistant bacteria in vitro reveals that two of the predicted mutations arise in the background of a compensatory mutation. Using enzyme kinetics, microbiology, and crystal structures of the complexes, we determined the fitness of the mutant enzymes and strains, the structural basis of resistance, and the compensatory relationship of the mutations. To our knowledge, this work illustrates the first application of protein design algorithms to prospectively predict viable resistance mutations that arise in bacteria under antibiotic pressure.

SUBMITTER: Reeve SM

PROVIDER: S-EPMC4311853 | biostudies-literature | 2015 Jan

REPOSITORIES: biostudies-literature

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Protein design algorithms predict viable resistance to an experimental antifolate.

Reeve Stephanie M SM Gainza Pablo P Frey Kathleen M KM Georgiev Ivelin I Donald Bruce R BR Anderson Amy C AC

Proceedings of the National Academy of Sciences of the United States of America 20141231 3

Methods to accurately predict potential drug target mutations in response to early-stage leads could drive the design of more resilient first generation drug candidates. In this study, a structure-based protein design algorithm (K* in the OSPREY suite) was used to prospectively identify single-nucleotide polymorphisms that confer resistance to an experimental inhibitor effective against dihydrofolate reductase (DHFR) from Staphylococcus aureus. Four of the top-ranked mutations in DHFR were found ...[more]

PMID: 25552560

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Project description:BACKGROUND:Artesunate plus sulphadoxine-pyrimethamine (AS+SP) is now first-line treatment for Plasmodium falciparum infection in several south Asian countries, including Afghanistan. Molecular studies provide a sensitive means to investigate the current state of drug susceptibility to the SP component, and can also provide information on the likely efficacy of other potential forms of artemisinin-combination therapy. METHODS:During the years 2007 to 2010, 120 blood spots from patients with P. falciparum malaria were obtained in four provinces of Afghanistan. PCR-based methods were used to detect drug-resistance mutations in dhfr, dhps, pfcrt and pfmdr1, as well as to determine copy number of pfmdr1. RESULTS:The majority (95.5%) of infections had a double mutation in the dhfr gene (C59R, S108N); no mutations at dhfr positions 16, 51 or 164 were seen. Most isolates were wild type across the dhps gene, but five isolates from the provinces of Kunar and Nangarhar in eastern Afghanistan had the triple mutation A437G / K540E / A581G; all five cases were successfully treated with three receiving AS+SP and two receiving dihydroartemisinin-piperaquine. All isolates showed the pfcrt SVNMT chloroquine resistance haplotype. Five of 79 isolates had the pfmdr1 N86Y mutation, while 52 had pfmdr1 Y184F; positions 1034, 1042 and 1246 were wild type in all isolates. The pfmdr1 gene was not amplified in any sample. CONCLUSIONS:This study indicates that shortly after the adoption of AS+SP as first-line treatment in Afghanistan, most parasites had a double mutation haplotype in dhfr, and a small number of isolates from eastern Afghanistan harboured a triple mutation haplotype in dhps. The impact of these mutations on the efficacy of AS+SP remains to be assessed in significant numbers of patients, but these results are clearly concerning since they suggest a higher degree of SP resistance than previously detected. Further focused molecular and clinical studies in this region are urgently required.

Project description:BACKGROUND: Gene function annotations, which are associations between a gene and a term of a controlled vocabulary describing gene functional features, are of paramount importance in modern biology. Datasets of these annotations, such as the ones provided by the Gene Ontology Consortium, are used to design novel biological experiments and interpret their results. Despite their importance, these sources of information have some known issues. They are incomplete, since biological knowledge is far from being definitive and it rapidly evolves, and some erroneous annotations may be present. Since the curation process of novel annotations is a costly procedure, both in economical and time terms, computational tools that can reliably predict likely annotations, and thus quicken the discovery of new gene annotations, are very useful. METHODS: We used a set of computational algorithms and weighting schemes to infer novel gene annotations from a set of known ones. We used the latent semantic analysis approach, implementing two popular algorithms (Latent Semantic Indexing and Probabilistic Latent Semantic Analysis) and propose a novel method, the Semantic IMproved Latent Semantic Analysis, which adds a clustering step on the set of considered genes. Furthermore, we propose the improvement of these algorithms by weighting the annotations in the input set. RESULTS: We tested our methods and their weighted variants on the Gene Ontology annotation sets of three model organism genes (Bos taurus, Danio rerio and Drosophila melanogaster ). The methods showed their ability in predicting novel gene annotations and the weighting procedures demonstrated to lead to a valuable improvement, although the obtained results vary according to the dimension of the input annotation set and the considered algorithm. CONCLUSIONS: Out of the three considered methods, the Semantic IMproved Latent Semantic Analysis is the one that provides better results. In particular, when coupled with a proper weighting policy, it is able to predict a significant number of novel annotations, demonstrating to actually be a helpful tool in supporting scientists in the curation process of gene functional annotations.

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Protein design algorithms predict viable resistance to an experimental antifolate.

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Protein design algorithms predict viable resistance to an experimental antifolate.

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