HIVprotI: an integrated web based platform for prediction and design of HIV proteins inhibitors

Abstract

A number of anti-retroviral drugs are being used for treating Human Immunodeficiency Virus (HIV) infection. Due to emergence of drug resistant strains, there is a constant quest to discover more effective anti-HIV compounds. In this endeavor, computational tools have proven useful in accelerating drug discovery. Although methods were published to design a class of compounds against a specific HIV protein, but an integrated web server for the same is lacking. Therefore, we have developed support vector machine based regression models using experimentally validated data from ChEMBL repository. Quantitative structure activity relationship based features were selected for predicting inhibition activity of a compound against HIV proteins namely protease (PR), reverse transcriptase (RT) and integrase (IN). The models presented a maximum Pearson correlation coefficient of 0.78, 0.76, 0.74 and 0.76, 0.68, 0.72 during tenfold cross-validation on IC50 and percent inhibition datasets of PR, RT, IN respectively. These models performed equally well on the independent datasets. Chemical space mapping, applicability domain analyses and other statistical tests further support robustness of the predictive models. Currently, we have identified a number of chemical descriptors that are imperative in predicting the compound inhibition potential. HIVprotI platform (http://bioinfo.imtech.res.in/manojk/hivproti) would be useful in virtual screening of inhibitors as well as designing of new molecules against the important HIV proteins for therapeutics development.Electronic supplementary materialThe online version of this article (10.1186/s13321-018-0266-y) contains supplementary material, which is available to authorized users.

LigTMap: Ligand and Structure-Based Target Identification and Activity Prediction for Small Molecular Compounds

“…On the other hand, the HIVprotI server successfully returned prediction results for nine compounds against the reverse transcriptase target. However, poor correlation of 0.56 was obtained, which was significantly lower than the originally reported correlation of 0.76 (Qureshi et al, 2018). Figure S5 We compared the binding modes predicted by PSOVina2 in LigTMap to the previously reported binding modes (Pribut et al, 2019).…”

“…Furthermore, for comparison, we also tested the new compounds using two recently released online servers for anti-HIV biological activity prediction, namely, AntiHIV-Pred (Stolbov et al, 2019) and HIVprotI (Qureshi et al, 2018). Their methods employed large-scale experimental data extracted from the ChEMBL database and their prediction models are ligand-based and HIV protein-specific.…”

Anti-Nipah: A QSAR Based Prediction Method to Identify the Inhibitors Against Nipah Virus

“…It become more important, especially for the viruses against whom no treatment modality is available. Although, the limited antiviral prediction algorihtms are available for the prediction of compounds against viral infections, which includes the AVCpred and HIVprotI [15,16]. The AVCpred is an antiviral compound prediction server, especially for viruses HIV, HCV, HBV, HHV and also, include a general prediction tool for 26 viruses.…”

“…Thus, there is a need for a computational tool that can identify the unexplored putative inhibitor against NiV. We have previously developed the antiviral prediction servers mainly for Zika virus, Human immunodeficiency virus (HIV), Hepatitis B virus (HBV) and Hepatitis C virus (HCV) [15][16][17]. However, in the present study, we have collected the overall anti-nipah inhibitors available in the literature and developed first quantitative structure-activity relation (QSAR) based prediction algorithm using support vector machine learning for the identification of anti-NiV compounds along the data visualization modules.…”

Identifying potential entry inhibitors for emerging Nipah virus by molecular docking and chemical-protein interaction network

“…Our group has already developed therapeutic and antiviral resources (AVPdb (Qureshi, Thakur, Tandon, & Kumar, 2014) and ZikaVR (Gupta et al, 2016)), and various methods to predict antiviral peptides (AVPpred (Thakur, Qureshi, & Kumar, 2012) and AVP-IC 50 Pred (Qureshi, Tandon, & Kumar, 2015)), small molecules (AVCpred (Qureshi, Kaur, & Kumar, 2017) and HIVprotI (Qureshi, Rajput, Kaur, & Kumar, 2018)) and siRNAs (VIRsiRNApred (Qureshi, Thakur, & Kumar, 2013)). To further extend the scope of these already developed approaches, we further implemented an integrative structure-and network-based approach for identification of potential small molecule inhibitors against NiV, since there is an immediate need of small molecule inhibitors against emergent NiV.…”