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Prediction of HIV drug resistance based on the 3D protein structure: Proposal of molecular field mapping.

ABSTRACT: A method for predicting HIV drug resistance by using genotypes would greatly assist in selecting appropriate combinations of antiviral drugs. Models reported previously have had two major problems: lack of information on the 3D protein structure and processing of incomplete sequencing data in the modeling procedure. We propose obtaining the 3D structural information of viral proteins by using homology modeling and molecular field mapping, instead of just their primary amino acid sequences. The molecular field potential parameters reflect the physicochemical characteristics associated with the 3D structure of the proteins. We also introduce the Bayesian conditional mutual information theory to estimate the probabilities of occurrence of all possible protein candidates from an incomplete sequencing sample. This approach allows for the effective use of uncertain information for the modeling process. We applied these data analysis techniques to the HIV-1 protease inhibitor dataset and developed drug resistance prediction models with reasonable performance.

SUBMITTER: Ota R

PROVIDER: S-EPMC8336827 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature

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Prediction of HIV drug resistance based on the 3D protein structure: Proposal of molecular field mapping.

Ota Ryosaku R So Kanako K Tsuda Masahiro M Higuchi Yuriko Y Yamashita Fumiyoshi F

PloS one 20210804 8

A method for predicting HIV drug resistance by using genotypes would greatly assist in selecting appropriate combinations of antiviral drugs. Models reported previously have had two major problems: lack of information on the 3D protein structure and processing of incomplete sequencing data in the modeling procedure. We propose obtaining the 3D structural information of viral proteins by using homology modeling and molecular field mapping, instead of just their primary amino acid sequences. The m ...[more]

PMID: 34347839

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Project description:BackgroundAntiretroviral drugs are a very effective therapy against HIV infection. However, the high mutation rate of HIV permits the emergence of variants that can be resistant to the drug treatment. Predicting drug resistance to previously unobserved variants is therefore very important for an optimum medical treatment. In this paper, we propose the use of weighted categorical kernel functions to predict drug resistance from virus sequence data. These kernel functions are very simple to implement and are able to take into account HIV data particularities, such as allele mixtures, and to weigh the different importance of each protein residue, as it is known that not all positions contribute equally to the resistance.ResultsWe analyzed 21 drugs of four classes: protease inhibitors (PI), integrase inhibitors (INI), nucleoside reverse transcriptase inhibitors (NRTI) and non-nucleoside reverse transcriptase inhibitors (NNRTI). We compared two categorical kernel functions, Overlap and Jaccard, against two well-known noncategorical kernel functions (Linear and RBF) and Random Forest (RF). Weighted versions of these kernels were also considered, where the weights were obtained from the RF decrease in node impurity. The Jaccard kernel was the best method, either in its weighted or unweighted form, for 20 out of the 21 drugs.ConclusionsResults show that kernels that take into account both the categorical nature of the data and the presence of mixtures consistently result in the best prediction model. The advantage of including weights depended on the protein targeted by the drug. In the case of reverse transcriptase, weights based in the relative importance of each position clearly increased the prediction performance, while the improvement in the protease was much smaller. This seems to be related to the distribution of weights, as measured by the Gini index. All methods described, together with documentation and examples, are freely available at https://bitbucket.org/elies_ramon/catkern.

Project description:In silico methodologies have opened new avenues of research to understanding and predicting drug resistance, a pressing health issue that keeps rising at alarming pace. Sequence-based interpretation systems are routinely applied in clinical context in an attempt to predict mutation-based drug resistance and thus aid the choice of the most adequate antibiotic and antiviral therapy. An important limitation of approaches based on genotypic data exclusively is that mutations are not considered in the context of the three-dimensional (3D) structure of the target. Structure-based in silico methodologies are inherently more suitable to interpreting and predicting the impact of mutations on target-drug interactions, at the cost of higher computational and time demands when compared with sequence-based approaches. Herein, we present a fast, computationally inexpensive, sequence-to-structure-based approach to drug resistance prediction, which makes use of 3D protein structures encoded by input target sequences to draw binding-site comparisons with susceptible templates. Rather than performing atom-by-atom comparisons between input target and template structures, our workflow generates and compares Molecular Interaction Fields (MIFs) that map the areas of energetically favorable interactions between several chemical probe types and the target binding site. Quantitative, pairwise dissimilarity measurements between the target and the template binding sites are thus produced. The method is particularly suited to understanding changes to the 3D structure and the physicochemical environment introduced by mutations into the target binding site. Furthermore, the workflow relies exclusively on freeware, making it accessible to anyone. Using four datasets of known HIV-1 protease sequences as a case-study, we show that our approach is capable of correctly classifying resistant and susceptible sequences given as input. Guided by ROC curve analyses, we fined-tuned a dissimilarity threshold of classification that results in remarkable discriminatory performance (accuracy ≈ ROC AUC ≈ 0.99), illustrating the high potential of sequence-to-structure-, MIF-based approaches in the context of drug resistance prediction. We discuss the complementarity of the proposed methodology to existing prediction algorithms based on genotypic data. The present work represents a new step toward a more comprehensive and structurally-informed interpretation of the impact of genetic variability on the response to HIV-1 therapies.

Project description:BackgroundPrior to this project, only a handful of online visualizations existed for exploring the published literature on molecular markers of antimalarial drug resistance, and none specifically for the markers associated with Plasmodium falciparum resistance to the partner drugs in artemisinin-based combination therapy (ACT). Molecular information is collected in studies with different designs, using a variety of molecular methodologies and data analysis strategies, making it difficult to compare across studies. The purpose of this project was to develop a free online tool, which visualizes the widely published data on molecular markers of antimalarial drug resistance, starting with the two genes pfcrt and pfmdr-1, associated with resistance to the three most common partner drugs; amodiaquine, lumefantrine and mefloquine.MethodsA literature review was conducted, and a standardized method was used to extract data from publications, and critical decisions on visualization were made. A global geospatial database was developed of specific pfmdr1 and pfcrt single nucleotide polymorphisms and pfmdr1 copy number variation. An informatics framework was developed that allowed flexibility in development of the tool over time and efficient adaptation to different source data.ResultsThe database discussed in this paper has pfmdr1 and pfcrt marker prevalence information, from 579 geographic sites in 76 different countries, including results from over 86,000 samples from 456 articles published January 2001-May 2017. The ACT Partner Drugs Molecular Surveyor was launched by the WorldWide Antimalarial Resistance Network (WWARN) in March 2015 and it has attracted over 3000 unique visitors since then. Presented here is a demonstration of how the Surveyor database can be explored to monitor local, temporal changes in the prevalence of molecular markers. Here publications up to May 2017 were included, however the online ACT partner drug Molecular Surveyor is continuously updated with new data and relevant markers.ConclusionsThe WWARN ACT Partner Drugs Molecular Surveyor summarizes data on resistance markers in the pfmdr1 and pfcrt genes. The database is fully accessible, providing users with a rich resource to explore and analyze, and thus utilize a centralized, standardized database for different purposes. This open-source software framework can be adapted to other data, as demonstrated by the subsequent launch of the Artemisinin Molecular Surveyor and the Vivax Surveyor.

Dataset Information

Prediction of HIV drug resistance based on the 3D protein structure: Proposal of molecular field mapping.

Publications

Prediction of HIV drug resistance based on the 3D protein structure: Proposal of molecular field mapping.

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