Dataset Information

Occupation of a thermoresistant-scaffold (?Rep) at SP1-NC cleavage site disturbs the function of HIV-1 protease.

ABSTRACT: HIV-1 nucleocapsid (NC) becomes an attractive target for the development of novel anti-HIV-1 agents. Discovering of non-antibody scaffolds that disrupt the function of NC will be a potential aspect for disturbing viral maturation process. Correspondingly, we explored the specific binding site of the thermoresistant-scaffold protein, ?Rep9A8 which formerly demonstrated the inhibitory effect on HIV-1 replication. The portion of Gag, CA21-SP1-NC has been used as a template for designing nine overlapping peptides (P4-P12). The P9 peptide showed the strongest binding activity followed by P8 and P12 respectively. The amino acid sequences on those peptides resemble the N-terminal domain of the NC proximity to the SP1-NC initial cleavage site and across the conserved CCHC zinc finger 1 (ZF1) of NC. The interaction KD between ?Rep9A8 with its target was 224.9 ± 57.4 nM. Consequently, ?Rep9A8 demonstrated the interference of the HIV-1 protease function by hindering a protease cleavage site. The released NC product from CA21-SP1-NC was diminished. The present study provided an additional information of ?Rep9A8 function in interfering of viral maturation processes resulting in the decremental efficiency of viral infectivity.

SUBMITTER: Hadpech S

PROVIDER: S-EPMC7313444 | biostudies-literature | 2020 Jun

REPOSITORIES: biostudies-literature

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Occupation of a thermoresistant-scaffold (αRep) at SP1-NC cleavage site disturbs the function of HIV-1 protease.

Hadpech Sudarat S Peerakam Nichakan N Chupradit Koollawat K Tayapiwatana Chatchai C

Bioscience reports 20200601 6

HIV-1 nucleocapsid (NC) becomes an attractive target for the development of novel anti-HIV-1 agents. Discovering of non-antibody scaffolds that disrupt the function of NC will be a potential aspect for disturbing viral maturation process. Correspondingly, we explored the specific binding site of the thermoresistant-scaffold protein, αRep9A8 which formerly demonstrated the inhibitory effect on HIV-1 replication. The portion of Gag, CA21-SP1-NC has been used as a template for designing nine overla ...[more]

PMID: 32519747

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Project description:BackgroundPredicting type-1 Human Immunodeficiency Virus (HIV-1) protease cleavage site in protein molecules and determining its specificity is an important task which has attracted considerable attention in the research community. Achievements in this area are expected to result in effective drug design (especially for HIV-1 protease inhibitors) against this life-threatening virus. However, some drawbacks (like the shortage of the available training data and the high dimensionality of the feature space) turn this task into a difficult classification problem. Thus, various machine learning techniques, and specifically several classification methods have been proposed in order to increase the accuracy of the classification model. In addition, for several classification problems, which are characterized by having few samples and many features, selecting the most relevant features is a major factor for increasing classification accuracy.ResultsWe propose for HIV-1 data a consistency-based feature selection approach in conjunction with recursive feature elimination of support vector machines (SVMs). We used various classifiers for evaluating the results obtained from the feature selection process. We further demonstrated the effectiveness of our proposed method by comparing it with a state-of-the-art feature selection method applied on HIV-1 data, and we evaluated the reported results based on attributes which have been selected from different combinations.ConclusionApplying feature selection on training data before realizing the classification task seems to be a reasonable data-mining process when working with types of data similar to HIV-1. On HIV-1 data, some feature selection or extraction operations in conjunction with different classifiers have been tested and noteworthy outcomes have been reported. These facts motivate for the work presented in this paper.Software availabilityThe software is available at http://ozyer.etu.edu.tr/c-fs-svm.rar. The software can be downloaded at esnag.etu.edu.tr/software/hiv_cleavage_site_prediction.rar; you will find a readme file which explains how to set the software in order to work.

Project description:BackgroundIn most parts of the world, especially in underdeveloped countries, acquired immunodeficiency syndrome (AIDS) still remains a major cause of death, disability, and unfavorable economic outcomes. This has necessitated intensive research to develop effective therapeutic agents for the treatment of human immunodeficiency virus (HIV) infection, which is responsible for AIDS. Peptide cleavage by HIV-1 protease is an essential step in the replication of HIV-1. Thus, correct and timely prediction of the cleavage site of HIV-1 protease can significantly speed up and optimize the drug discovery process of novel HIV-1 protease inhibitors. In this work, we built and compared the performance of selected machine learning models for the prediction of HIV-1 protease cleavage site utilizing a hybrid of octapeptide sequence information comprising bond composition, amino acid binary profile (AABP), and physicochemical properties as numerical descriptors serving as input variables for some selected machine learning algorithms. Our work differs from antecedent studies exploring the same subject in the combination of octapeptide descriptors and method used. Instead of using various subsets of the dataset for training and testing the models, we combined the dataset, applied a 3-way data split, and then used a "stratified" 10-fold cross-validation technique alongside the testing set to evaluate the models.ResultsAmong the 8 models evaluated in the "stratified" 10-fold CV experiment, logistic regression, multi-layer perceptron classifier, linear discriminant analysis, gradient boosting classifier, Naive Bayes classifier, and decision tree classifier with AUC, F-score, and B. Acc. scores in the ranges of 0.91-0.96, 0.81-0.88, and 80.1-86.4%, respectively, have the closest predictive performance to the state-of-the-art model (AUC 0.96, F-score 0.80 and B. Acc. ~ 80.0%). Whereas, the perceptron classifier and the K-nearest neighbors had statistically lower performance (AUC 0.77-0.82, F-score 0.53-0.69, and B. Acc. 60.0-68.5%) at p < 0.05. On the other hand, logistic regression, and multi-layer perceptron classifier (AUC of 0.97, F-score > 0.89, and B. Acc. > 90.0%) had the best performance on further evaluation on the testing set, though linear discriminant analysis, gradient boosting classifier, and Naive Bayes classifier equally performed well (AUC > 0.94, F-score > 0.87, and B. Acc. > 86.0%).ConclusionsLogistic regression and multi-layer perceptron classifiers have comparable predictive performances to the state-of-the-art model when octapeptide sequence descriptors consisting of AABP, bond composition and standard physicochemical properties are used as input variables. In our future work, we hope to develop a standalone software for HIV-1 protease cleavage site prediction utilizing the linear regression algorithm and the aforementioned octapeptide sequence descriptors.

Dataset Information

Occupation of a thermoresistant-scaffold (?Rep) at SP1-NC cleavage site disturbs the function of HIV-1 protease.

Publications

Occupation of a thermoresistant-scaffold (αRep) at SP1-NC cleavage site disturbs the function of HIV-1 protease.

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