Prediction of HIV drug resistance based on the 3D protein structure: Proposal of molecular field mapping.
Ontology highlight
ABSTRACT:
SUBMITTER: Ota R
PROVIDER: S-EPMC8336827 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:Drug resistance has become a severe challenge for treatment of HIV infections. Mutations accumulate in the HIV genome and make certain drugs ineffective. Prediction of resistance from genotype data is a valuable guide in choice of drugs for effective therapy.In order to improve the computational prediction of resistance from genotype data we have developed a unified encoding of the protein sequence and three-dimensional protein structure of the drug target for classification and regression analysis. The method was tested on genotype-resistance data for mutants of HIV protease and reverse transcriptase. Our graph based sequence-structure approach gives high accuracy with a new sparse dictionary classification method, as well as support vector machine and artificial neural networks classifiers. Cross-validated regression analysis with the sparse dictionary gave excellent correlation between predicted and observed resistance.The approach of encoding the protein structure and sequence as a 210-dimensional vector, based on Delaunay triangulation, has promise as an accurate method for predicting resistance from sequence for drugs inhibiting HIV protease and reverse transcriptase.
| S-EPMC4120140 | biostudies-literature
Project description:BackgroundHIV/AIDS is a serious threat to public health. The emergence of drug resistance mutations diminishes the effectiveness of drug therapy for HIV/AIDS. Developing a computational prediction of drug resistance phenotype will enable efficient and timely selection of the best treatment regimens.ResultsA unified encoding of protein sequence and structure was used as the feature vector for predicting phenotypic resistance from genotype data. Two machine learning algorithms, Random Forest and K-nearest neighbor, were used. The prediction accuracies were examined by five-fold cross-validation on the genotype-phenotype datasets. A supervised machine learning approach for automatic prediction of drug resistance was developed to handle genotype-phenotype datasets of HIV protease (PR) and reverse transcriptase (RT). It predicts the drug resistance phenotype and its relative severity from a query sequence. The accuracy of the classification was higher than 0.973 for eight PR inhibitors and 0.986 for ten RT inhibitors, respectively. The overall cross-validated regression R(2)-values for the severity of drug resistance were 0.772-0.953 for 8 PR inhibitors and 0.773-0.995 for 10 RT inhibitors.ConclusionsMachine learning using a unified encoding of sequence and protein structure as a feature vector provides an accurate prediction of drug resistance from genotype data. A practical webserver for clinicians has been implemented.
| S-EPMC5009519 | biostudies-literature
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.
| S-EPMC6668108 | biostudies-literature
Project description:HIV rapidly evolves drug resistance in response to antiviral drugs used in AIDS therapy. Estimating the specific resistance of a given strain of HIV to individual drugs from sequence data has important benefits for both the therapy of individual patients and the development of novel drugs. We have developed an accurate classification method based on the sparse representation theory, and demonstrate that this method is highly effective with HIV-1 protease. The protease structure is represented using our newly proposed encoding method based on Delaunay triangulation, and combined with the mutated amino acid sequences of known drug-resistant strains to train a machine-learning algorithm both for classification and regression of drug-resistant mutations. An overall cross-validated classification accuracy of 97% is obtained when trained on a publically available data base of approximately 1.5×104 known sequences (Stanford HIV database http://hivdb.stanford.edu/cgi-bin/GenoPhenoDS.cgi). Resistance to four FDA approved drugs is computed and comparisons with other algorithms demonstrate that our method shows significant improvements in classification accuracy.
| S-EPMC4045614 | biostudies-literature
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.
| S-EPMC7202381 | biostudies-literature
Project description: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.
| S-EPMC7290575 | biostudies-literature
Project description:Key messageImproving crop resistance against insect pests is crucial for ensuring future food security. Integrating genomics with modern breeding methods holds enormous potential in dissecting the genetic architecture of this complex trait and accelerating crop improvement. Insect resistance in crops has been a major research objective in several crop improvement programs. However, the use of conventional breeding methods to develop high-yielding cultivars with sustainable and durable insect pest resistance has been largely unsuccessful. The use of molecular markers for identification and deployment of insect resistance quantitative trait loci (QTLs) can fastrack traditional breeding methods. Till date, several QTLs for insect pest resistance have been identified in field-grown crops, and a few of them have been cloned by positional cloning approaches. Genome editing technologies, such as CRISPR/Cas9, are paving the way to tailor insect pest resistance loci for designing crops for the future. Here, we provide an overview of diverse defense mechanisms exerted by plants in response to insect pest attack, and review recent advances in genomics research and genetic improvements for insect pest resistance in major field crops. Finally, we discuss the scope for genomic breeding strategies to develop more durable insect pest resistant crops.
| S-EPMC9729161 | biostudies-literature
Project description:Design of corona virus testing kit is proposed in this paper using silicon based 3D photonic structure through zirconium quantum dot solution at the signal of 412 nm. The principle of measurement depends on the computation of reflectance, absorbance and transmittance of virus based quantum dot solution. In this paper, the reflectance is studied through the analysis of photonic band gap and absorbance is made through its numerical treatment. Further, the numerical investigation shows that the transmitted energy through photonic structure would determine the type of corona virus. For example; if the transmitted energy lies within the visible spectrum the sample would be normal corona virus. However, the sample could be IBV (SARS COV-2) if the transmitted energy would be Infrared.
| S-EPMC7594986 | biostudies-literature
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.
| S-EPMC6339428 | biostudies-literature
Project description:A new method is presented for rapid and accurate large volumetric radiofrequency (RF) field (B(1) (+)) mapping. This method is a modification of the double-angle method (DAM), which accelerates imaging speed and applies 3D acquisition to improve B(1) (+) measurement accuracy. It reduces repetition time and scan time by introducing a catalyzation RF pulse chain at the end of each DAM repetition cycle. The catalyzation pulse chain ensures that, after each TR period, the longitudinal magnetizations reach the same state for both measurements at two flip angles for the DAM so that the long TR requirement (TR >or= 5 T(1)) for complete relaxation of longitudinal magnetization of DAM becomes unnecessary. A multi-echo imaging sequence is additionally incorporated to further improve the efficiency of data acquisition. Simulations demonstrate an excellent flip angle measurement accuracy for catalyzed DAM even with TR << T(1). Phantom and in vivo volunteer studies are presented to demonstrate the catalyzation effect upon B(1)(+) mapping and the application of 3D catalyzed DAM for rapid and accurate large volume RF field mapping.
| S-EPMC2896243 | biostudies-other