scholarly journals Drug Resistance Prediction Using Deep Learning Techniques on HIV-1 Sequence Data

Viruses ◽  
2020 ◽  
Vol 12 (5) ◽  
pp. 560
Author(s):  
Margaret C. Steiner ◽  
Keylie M. Gibson ◽  
Keith A. Crandall
Keyword(s):  
Neural Network ◽  
Drug Resistance ◽  
Deep Learning ◽  
Sequence Data ◽  
Classification Performance ◽  
Resistance Mutations ◽  
Evolutionary Mechanisms ◽  
Drug Resistance Prediction ◽  
Resistance Prediction ◽  
Hiv 1

The fast replication rate and lack of repair mechanisms of human immunodeficiency virus (HIV) contribute to its high mutation frequency, with some mutations resulting in the evolution of resistance to antiretroviral therapies (ART). As such, studying HIV drug resistance allows for real-time evaluation of evolutionary mechanisms. Characterizing the biological process of drug resistance is also critically important for sustained effectiveness of ART. Investigating the link between “black box” deep learning methods applied to this problem and evolutionary principles governing drug resistance has been overlooked to date. Here, we utilized publicly available HIV-1 sequence data and drug resistance assay results for 18 ART drugs to evaluate the performance of three architectures (multilayer perceptron, bidirectional recurrent neural network, and convolutional neural network) for drug resistance prediction, jointly with biological analysis. We identified convolutional neural networks as the best performing architecture and displayed a correspondence between the importance of biologically relevant features in the classifier and overall performance. Our results suggest that the high classification performance of deep learning models is indeed dependent on drug resistance mutations (DRMs). These models heavily weighted several features that are not known DRM locations, indicating the utility of model interpretability to address causal relationships in viral genotype-phenotype data.

Algorithm evolution for drug resistance prediction: comparison of systems for HIV-1 genotyping

2015 ◽  
Vol 20 (6) ◽  
pp. 661-665 ◽  
Author(s):  
Sarah Wagner ◽  
◽  
Mario Kurz ◽  
Thomas Klimkait
Keyword(s):  
Drug Resistance ◽  
Drug Resistance Prediction ◽  
Resistance Prediction ◽  
Hiv 1

scholarly journals HIV-1 drug resistance profiling using amino acid sequence space cartography

2021 ◽  
Author(s):  
Karina Pikalyova ◽  
Alexey Orlov ◽  
Arkadii Lin ◽  
Olga Tarasova ◽  
Gilles Marcou ◽  
...  
Keyword(s):  
Drug Resistance ◽  
Computational Methods ◽  
Sequence Data ◽  
High Accuracy ◽  
Generative Topographic Mapping ◽  
Hiv Drug Resistance ◽  
Treatment Regimens ◽  
Depth Analysis ◽  
Drug Resistance Prediction ◽  
Resistance Prediction

Motivation: Human immunodeficiency virus (HIV) drug resistance is a global healthcare issue. The emergence of drug resistance demands treatment adaptation. Computational methods predicting the drug resistance profile from genomic data of HIV isolates are advantageous for monitoring drug resistance in patients. Yet, the currently existing computational methods for drug resistance prediction are either not suitable for complex mutational patterns in emerging HIV strains or lack interpretability of prediction results which is of paramount importance in clinical practice. Hence, to overcome these limitations, new approaches for the HIV drug resistance prediction combining high accuracy and interpretability are required. Results: In this work, a new methodology for the analysis of protein sequence data based on the application of generative topographic mapping was developed and applied for HIV drug resistance profiling. It allowed achieving high accuracy of resistance predictions and intuitive interpretation of prediction results. The developed approach was successfully applied for the prediction of HIV re-sistance towards protease, reverse-transcriptase and integrase inhibitors and in-depth analysis of HIV resistance-inducing mutation patterns. Hence, it can serve as an efficient and interpretable tool to suggest optimal treatment regimens. Availability: https://github.com/karinapikalyova/ISIDASeq

scholarly journals Predominance of positive epistasis among drug resistance-associated mutations in HIV-1 protease

2019 ◽  
Author(s):  
Tian-hao Zhang ◽  
Lei Dai ◽  
John P. Barton ◽  
Yushen Du ◽  
Yuxiang Tan ◽  
...  
Keyword(s):  
Drug Resistance ◽  
Fitness Landscape ◽  
Sequence Data ◽  
Fitness Cost ◽  
Resistance Mutations ◽  
Human T Cells ◽  
Multiple Cycles ◽  
Replication Fitness ◽  
Hiv 1

AbstractDrug-resistant mutations often have deleterious impacts on replication fitness, posing a fitness cost that can only be overcome by compensatory mutations. However, the role of fitness cost in the evolution of drug resistance has often been overlooked in clinical studies or in vitro selection experiments, as these observations only capture the outcome of drug selection. In this study, we systematically profile the fitness landscape of resistance-associated sites in HIV-1 protease using deep mutational scanning. We construct a mutant library covering combinations of mutations at 11 sites in HIV-1 protease, all of which are associated with resistance to protease inhibitors in clinic. Using deep sequencing, we quantify the fitness of thousands of HIV-1 protease mutants after multiple cycles of replication in human T cells. Although the majority of resistance-associated mutations have deleterious effects on viral replication, we find that epistasis among resistance-associated mutations is predominantly positive. Furthermore, our fitness data are consistent with genetic interactions inferred directly from HIV sequence data of patients. Fitness valleys formed by strong positive epistasis reduce the likelihood of reversal of drug resistance mutations. Overall, our results support the view that strong compensatory effects are involved in the emergence of clinically observed resistance mutations and provide insights to understanding fitness barriers in the evolution and reversion of drug resistance.

scholarly journals Evaluation of whole-genome sequence data analysis approaches for short- and long-read sequencing of Mycobacterium tuberculosis

2021 ◽  
Vol 7 (11) ◽  
Author(s):  
Nilay Peker ◽  
Leonard Schuele ◽  
Nienke Kok ◽  
Miguel Terrazos ◽  
Stefan M. Neuenschwander ◽  
...  
Keyword(s):  
Drug Resistance ◽  
Mycobacterium Tuberculosis ◽  
Core Genome ◽  
Sequence Data ◽  
Complete Agreement ◽  
Whole Genome ◽  
First Line ◽  
Long Read ◽  
Drug Resistance Prediction ◽  
Resistance Prediction

Whole-genome sequencing (WGS) of Mycobacterium tuberculosis (MTB) isolates can be used to get an accurate diagnosis, to guide clinical decision making, to control tuberculosis (TB) and for outbreak investigations. We evaluated the performance of long-read (LR) and/or short-read (SR) sequencing for anti-TB drug-resistance prediction using the TBProfiler and Mykrobe tools, the fraction of genome recovery, assembly accuracies and the robustness of two typing approaches based on core-genome SNP (cgSNP) typing and core-genome multi-locus sequence typing (cgMLST). Most of the discrepancies between phenotypic drug-susceptibility testing (DST) and drug-resistance prediction were observed for the first-line drugs rifampicin, isoniazid, pyrazinamide and ethambutol, mainly with LR sequence data. Resistance prediction to second-line drugs made by both TBProfiler and Mykrobe tools with SR- and LR-sequence data were in complete agreement with phenotypic DST except for one isolate. The SR assemblies were more accurate than the LR assemblies, having significantly (P<0.05) fewer indels and mismatches per 100 kbp. However, the hybrid and LR assemblies had slightly higher genome fractions. For LR assemblies, Canu followed by Racon, and Medaka polishing was the most accurate approach. The cgSNP approach, based on either reads or assemblies, was more robust than the cgMLST approach, especially for LR sequence data. In conclusion, anti-TB drug-resistance prediction, particularly with only LR sequence data, remains challenging, especially for first-line drugs. In addition, SR assemblies appear more accurate than LR ones, and reproducible phylogeny can be achieved using cgSNP approaches.

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