Dataset Information

Estimation of the breadth of CD4bs targeting HIV antibodies by molecular modeling and machine learning.

ABSTRACT: HIV is a highly mutable virus for which all attempts to develop a vaccine have been unsuccessful. Nevertheless, few long-infected patients develop antibodies, called broadly neutralizing antibodies (bnAbs), that have a high breadth and can neutralize multiple variants of the virus. This suggests that a universal HIV vaccine should be possible. A measure of the efficacy of a HIV vaccine is the neutralization breadth of the antibodies it generates. The breadth is defined as the fraction of viruses in the Seaman panel that are neutralized by the antibody. Experimentally the neutralization ability is measured as the half maximal inhibitory concentration of the antibody (IC50). To avoid such time-consuming experimental measurements, we developed a computational approach to estimate the IC50 and use it to determine the antibody breadth. Given that no direct method exists for calculating IC50 values, we resort to a combination of atomistic modeling and machine learning. For each antibody/virus complex, an all-atoms model is built using the amino acid sequence and a known structure of a related complex. Then a series of descriptors are derived from the atomistic models, and these are used to train a Multi-Layer Perceptron (an Artificial Neural Network) to predict the value of the IC50 (by regression), or if the antibody binds or not to the virus (by classification). The neural networks are trained by use of experimental IC50 values collected in the CATNAP database. The computed breadths obtained by regression and classification are reported and the importance of having some related information in the data set for obtaining accurate predictions is analyzed. This approach is expected to prove useful for the design of HIV bnAbs, where the computation of the potency must be accompanied by a computation of the breadth, and for evaluating the efficiency of potential vaccination schemes developed through modeling and simulation.

SUBMITTER: Conti S

PROVIDER: S-EPMC6457539 | biostudies-literature | 2019 Apr

REPOSITORIES: biostudies-literature

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Estimation of the breadth of CD4bs targeting HIV antibodies by molecular modeling and machine learning.

Conti Simone S Karplus Martin M

PLoS computational biology 20190410 4

HIV is a highly mutable virus for which all attempts to develop a vaccine have been unsuccessful. Nevertheless, few long-infected patients develop antibodies, called broadly neutralizing antibodies (bnAbs), that have a high breadth and can neutralize multiple variants of the virus. This suggests that a universal HIV vaccine should be possible. A measure of the efficacy of a HIV vaccine is the neutralization breadth of the antibodies it generates. The breadth is defined as the fraction of viruses ...[more]

PMID: 30970017

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Project description:UnlabelledThe isolation of broadly neutralizing HIV-1 monoclonal antibodies (MAbs) to distinct epitopes on the viral envelope glycoprotein (Env) provides the potential to use combinations of MAbs for prevention and treatment of HIV-1 infection. Since many of these MAbs have been isolated in the last few years, the potency and breadth of MAb combinations have not been well characterized. In two parallel experiments, we examined the in vitro neutralizing activities of double-, triple-, and quadruple-MAb combinations targeting four distinct epitopes, including the CD4-binding site, the V1V2-glycan region, the V3-glycan supersite, and the gp41 membrane-proximal external region (MPER), using a panel of 125 Env-pseudotyped viruses. All MAb combinations showed substantially improved neutralization breadth compared to the corresponding single MAbs, while the neutralization potency of individual MAbs was maintained. At a 50% inhibitory concentration (IC50) cutoff of 1 μg/ml per antibody, double-MAb combinations neutralized 89 to 98% of viruses, and triple combinations neutralized 98 to 100%. Overall, the improvement of neutralization breadth was closely predicted by an additive-effect model and explained by complementary neutralization profiles of antibodies recognizing distinct epitopes. Subtle but consistent favorable interactions were observed in some MAb combinations, whereas less favorable interactions were observed on a small subset of viruses that are highly sensitive to V3-glycan MAbs. These data demonstrate favorable in vitro combinations of broadly neutralizing HIV-1 MAbs and suggest that such combinations could have utility for HIV-1 prevention and treatment.ImportanceOver the last 5 years, numerous broadly reactive HIV-1-neutralizing MAbs have been isolated from B cells of HIV-1-infected donors. Each of these MAbs binds to one of the major vulnerable sites (epitopes) on the surface of the viral envelope glycoprotein. Since antibodies to distinct viral epitopes could theoretically act together to provide greater potency and breadth of virus neutralization, we tested physical mixtures of double, triple, and quadruple combinations of neutralizing MAbs targeting four major epitopes on HIV-1 Env. When tested together, antibody combinations showed substantially improved neutralization breadth compared to single MAbs. This improvement could be explained by the complementary neutralization profiles of individual MAbs. We further demonstrated that each antibody maintained its full neutralization potency when used in combination with other MAbs. These data provide a rationale for clinical use of antibody-based combinations for HIV-1 prevention and therapy.

Project description:Background:Hospital readmission prediction in pediatric hospitals has received little attention. Studies have focused on the readmission frequency analysis stratified by disease and demographic/geographic characteristics but there are no predictive modeling approaches, which may be useful to identify preventable readmissions that constitute a major portion of the cost attributed to readmissions. Objective:To assess the all-cause readmission predictive performance achieved by machine learning techniques in the emergency department of a pediatric hospital in Santiago, Chile. Materials:An all-cause admissions dataset has been collected along six consecutive years in a pediatric hospital in Santiago, Chile. The variables collected are the same used for the determination of the child's treatment administrative cost. Methods:Retrospective predictive analysis of 30-day readmission was formulated as a binary classification problem. We report classification results achieved with various model building approaches after data curation and preprocessing for correction of class imbalance. We compute repeated cross-validation (RCV) with decreasing number of folders to assess performance and sensitivity to effect of imbalance in the test set and training set size. Results:Increase in recall due to SMOTE class imbalance correction is large and statistically significant. The Naive Bayes (NB) approach achieves the best AUC (0.65); however the shallow multilayer perceptron has the best PPV and f-score (5.6 and 10.2, resp.). The NB and support vector machines (SVM) give comparable results if we consider AUC, PPV, and f-score ranking for all RCV experiments. High recall of deep multilayer perceptron is due to high false positive ratio. There is no detectable effect of the number of folds in the RCV on the predictive performance of the algorithms. Conclusions:We recommend the use of Naive Bayes (NB) with Gaussian distribution model as the most robust modeling approach for pediatric readmission prediction, achieving the best results across all training dataset sizes. The results show that the approach could be applied to detect preventable readmissions.

Dataset Information

Estimation of the breadth of CD4bs targeting HIV antibodies by molecular modeling and machine learning.

Publications

Estimation of the breadth of CD4bs targeting HIV antibodies by molecular modeling and machine learning.

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