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

Qureshi et al. J Cheminform (2018) 10:12 https://doi.org/10.1186/s13321-018-0266-y Open Access RESEARCH ARTICLE HIVprotI: an integrated web based platform for prediction and design of HIV proteins inhibitors Abid Qureshi, Akanksha Rajput, Gazaldeep Kaur and Manoj Kumar* 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. Keywords: HIV, Reverse transcriptase, Protease, Integrase, Inhibitors, QSAR, Algorithm, Web server Background Human Immunodeficiency Virus (HIV) is one of the reasons for human death and suffering worldwide. It causes Acquired Immunodeficiency Syndrome (AIDS) in which gradual breakdown of the immune system allows critical opportunistic diseases to flourish [1]. As per the UNAIDS report, around 78 million people have become infected with HIV and 35 million people have died of AIDS-related illnesses since the start of the epidemic. In 2015 alone there were about 36.9 million people living with HIV of which 1.1 million died (http://www.unaids. org/en/resources/campaigns/HowAIDSchangedeverything/factsheet). Due to the high genetic variability and *Correspondence: Bioinformatics Centre, Institute of Microbial Technology, Council of Scientific and Industrial Research, Sector 39A, Chandigarh 160036, India mutation rate of HIV, vaccines are not available to curb the HIV infection [2]. Researchers have put a considerable focus on HIV therapy and a lot of compounds have been tested against this pathogen [3, 4]. However, a few antiretroviral drugs have been able to slow the disease progression. These drugs blocked the function of proteins implicated in certain stages of the HIV life-cycle [5]. Different HIV enzymes are needed for the development of the retrovirus including reverse transcriptase (RT), protease (PR) and integrase (IN) [6]. RT creates complementary DNA from an RNA template which can integrate into the host genome and its inhibitors are widely used as antiretroviral drugs [7]. For example, the first anti-HIV drug zidovudine or azidothymidine (a nucleoside analog) was approved by the Food and Drug Administration (FDA) in 1987. It inhibits HIV reverse transcriptase, hence thwarting viral © The Author(s) 2018. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/ publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. Qureshi et al. J Cheminform (2018) 10:12 Page 2 of 15 replication [8]. PR slices the newly synthesized polyproteins at the relevant positions to form the mature protein apparatus and is a major drug-target for treatment of HIV [9]. In 1995, saquinavir (invirase) became the first approved protease inhibitor. It blocks the enzyme’s active site, thus restricting the processing of HIV poly-proteins [10, 11]. The IN enzyme enables the virus to integrate its genetic material into the DNA of the host cell for a longterm infection. Compounds that inhibit the IN enzyme have demonstrated potent anti-HIV activity [12]. For example, raltegravir (isentress), the first integrase inhibitor was approved by FDA in 2007 [13]. Presently about 30 antiretroviral drugs are prescribed for the clinical treatment of AIDS [14]. An improved knowledge of the structure and function viral proteins has led antiviral drug developers to design better antivirals to treat HIV infections [15]. To conserve capital and time for finding novel drugs, scientists have extensively used different computational approaches to scan virtual compound libraries prior to the wet lab experiments [16]. The preferred targeted region should be off-target free and conserved across many strains of a virus for broad activity. Once the target is chosen, candidate antivirals can be selected by predicting the potential inhibitor using bioinformatics approaches [17, 18]. Amongst the diverse methods, quantitative structure activity relationship (QSAR) is being regularly used [19– 22]. In QSAR, associations involving chemical descriptors and activity are employed to envisage the properties of other compounds [23]. The chemical descriptors present the structural information of a compound as numerical values [24]. Virtual screening employing QSAR is a valuable bioinformatics approach which helps to identify and devise of new antiviral drugs [25]. Several attempts have been made for predicting specific types of compounds against different HIV proteins (discussed later). Nevertheless, till date there no web server/software, which can regressively estimate the IC50/percentage inhibition activity of diverse types of, compounds against different HIV proteins. To accommodate this requirement, we created HIVprotI, a web based algorithm for prediction and design of protein specific anti-HIV compounds. In this approach, we employed experimentally validated inhibitors against RT, PR, IN (with IC50/percentage inhibition) from ChEMBL [26]. We calculated molecular descriptors and performed feature selection to pick the best performing descriptors, which were employed to build support vector machine (SVM) based QSAR models for the prediction of inhibit (...truncated)