Exploring the binding of HIV-1 integrase inhibitors by comparative residue interaction analysis (CoRIA)

Machine learning-based predictive models for identifying high active compounds against HIV-1 integrase

HIV-integrase is an important drug target because it catalyzes chromosomal integration of proviral DNA towards establishing latent infection. Computer-aided drug design has immensely contributed to identifying and developing novel antiviral drugs. We have developed various machine learning-based predictive models for identifying high activity compounds against HIV-integrase. Multiclass models were built using support vector machine with reasonable accuracy on the test and evaluation sets. The developed models were evaluated by rigorous validation approaches and the best features were selected by Boruta method. As compared to the model developed from all descriptors set, a slight improvement was observed among the selected descriptors. Validated models were further used for virtual screening of potential compounds from ChemBridge library. Of the six high active compounds predicted from selected models, compounds 9103124, 6642917 and 9082952 showed the most reasonable binding-affinity and stable-interaction with HIV-integrase active-site residues Asp64, Glu152 and Asn155. This was in agreement with previous reports on the essentiality of these residues against a wide range of inhibitors. We therefore highlight the rigorosity of validated classification models for accurate prediction and ranking of high active lead drugs against HIV-integrase.

The recent application of 3D-QSAR and docking studies to novel HIV-protease inhibitor drug discovery

INTRODUCTION

Despite the availability of FDA approved inhibitors of HIV protease, numerous efforts are still ongoing to achieve 'near-perfect' drugs devoid of characteristic adverse side effects, toxicities, and mutational resistance. While experimental methods have been plagued with huge consumption of time and resources, there has been an incessant shift towards the use of computational simulations in HIV protease inhibitor drug discovery.

AREAS COVERED

Herein, the authors review the numerous applications of 3D-QSAR modeling methods over recent years relative to the design of new HIV protease inhibitors from a series of experimentally derived compounds. Also, the augmentative contributions of molecular docking are discussed.

EXPERT OPINION

Efforts to optimize 3D QSAR and molecular docking for HIV-1 drug discovery are ongoing, which could further incorporate inhibitor motions at the active site using molecular dynamics parameters. Also, highly predictive machine learning algorithms such as random forest, K-means, decision trees, linear regression, hierarchical clustering, and Bayesian classifiers could be employed.

Recent Developments in 3D QSAR and Molecular Docking Studies of Organic and Nanostructures

The development of quantitative structure–activity relationship (QSAR) methods is going very fast for the last decades. OSAR approach already plays an important role in lead structure optimization, and nowadays, with development of big data approaches and computer power, it can even handle a huge amount of data associated with combinatorial chemistry. One of the recent developments is a three-dimensional QSAR, i.e., 3D QSAR. For the last two decades, 3D-OSAR has already been successfully applied to many datasets, especially of enzyme and receptor ligands. Moreover, quite often 3D QSAR investigations are going together with protein–ligand docking studies and this combination works synergistically. In this review, we outline recent advances in development and applications of 3D QSAR and protein–ligand docking approaches, as well as combined approaches for conventional organic compounds and for nanostructured materials, such as fullerenes and carbon nanotubes.

Other Related Techniques

With the advances in computational resources, there is an increasing urge among the computational researchers to make the in silico approaches fast, convenient, reproducible, acceptable, and sensible ones. Along with the typical two-dimensional (2D) and three-dimensional (3D) quantitative structure–activity relationship (QSAR) methods, approaches like pharmacophore, structure-based docking studies, and combinations of ligand- and structure-based approaches like comparative residue interaction analysis (CoRIA) and comparative binding energy analysis (COMBINE) have gained a significant popularity in the computational drug design process. A pharmacophore can be developed either in a ligand-based method, by superposing a set of active molecules and extracting common chemical features which are vital for their bioactivity; or in a structure-based manner, by probing probable interaction points between the macromolecular target and ligands. The interaction of protein and ligand molecules with each other is one of the interesting studies in modern molecular biology and molecular recognition. This interaction can well be explained with the conceptof a docking study to show how a molecule can bind to another molecule to exert the bioactivity. Docking and pharmacophore are non-QSAR approaches in in silico drug design that can support the QSAR findings. Approaches like CoRIA and COMBINE can use information generated from the ligand–receptor complexes to extract the critical clue concerning the types of significant interaction at the level of both the receptor and the ligand. Employing the abovementioned ligand- and structure-based methodologies and chemical libraries, virtual screening (VS) emerged as an important tool in the quest to develop novel drug compounds. VS serves as an efficient computational tool that integrates structural data with lead optimization as a cost-effective approach to drug discovery.

Raltegravir flexibility and its impact on recognition by the HIV-1 IN targets

HIV-1 IN is a pertinent target for the development of AIDS chemotherapy. The first IN-specific inhibitor approved for the treatment of HIV/AIDS, RAL, was designed to block the ST reaction. We characterized the structural and conformational features of RAL and its recognition by putative HIV-1 targets - the unbound IN, the vDNA, and the IN•vDNA complex - mimicking the IN states over the integration process. RAL binding to the targets was studied by performing an extensive sampling of the inhibitor conformational landscape and by using four different docking algorithms: Glide, Autodock, VINA, and SurFlex. The obtained data evidenced that: (i) a large binding pocket delineated by the active site and an extended loop in the unbound IN accommodates RAL in distinct conformational states all lacking specific interactions with the target; (ii) a well-defined cavity formed by the active site, the vDNA, and the shortened loop in the IN•vDNA complex provide a more optimized inhibitor binding site in which RAL chelates Mg(2+) cations; (iii) a specific recognition between RAL and the unpaired cytosine of the processed DNA is governed by a pair of strong H-bonds similar to those observed in DNA base pair G-C. The identified RAL pose at the cleaved vDNA shed light on a putative step of RAL inhibition mechanism. This modeling study indicates that the inhibition process may include as a first step RAL recognition by the processed vDNA bound to a transient intermediate IN state, and thus provides a potentially promising route to the design of IN inhibitors with improved affinity and selectivity.

3D-QSAR studies of quinoline ring derivatives as HIV-1 integrase inhibitors

In the process of HIV-1 virus replication, integrase plays a quite important role. Integrase inhibitors of quinoline ring derivatives were analysed by the Comparative Molecular Field Analysis (CoMFA), Comparative Molecular Similarity Induces Analysis (CoMSIA) and Topomer CoMFA methods. Firstly, 77 compounds were selected to form the training and test sets. Secondly, predictive models were constructed with the CoMFA, CoMSIA and Topomer CoMFA methods. The CoMFA model yielded the best model with q (2) of 0.76 and [Formula: see text] of 0.99, the CoMSIA model has q (2 )= 0.70 and [Formula: see text] of 0.99, while the Topomer CoMFA model has q (2) of 0.66 and [Formula: see text] of 0.97. These results provide a helpful contribution to the design of novel highly active HIV-1 integrase inhibitors.

Identification of new checkpoint kinase-1 (Chk1) inhibitors by docking, 3D-QSAR, and pharmacophore-modeling methods

Inhibition of checkpoint kinase-1 (Chk1) by small molecules is of great therapeutic interest in the field of oncology and for understanding cell-cycle regulations. This paper presents a model with elements from docking, pharmacophore mapping, the 3D-QSAR approaches CoMFA, CoMSIA and CoRIA, and virtual screening to identify novel hits against Chk1. Docking, 3D-QSAR (CoRIA, CoMFA and CoMSIA), and pharmacophore studies delineate crucial site points on the Chk1 inhibitors, which can be modified to improve activity. The docking analysis showed residues in the proximity of the ligands that are involved in ligand–receptor interactions, whereas CoRIA models were able to derive the magnitude of these interactions that impact the activity. The ligand-based 3D-QSAR methods (CoMFA and CoMSIA) highlight key areas on the molecules that are beneficial and (or) detrimental for activity. The docking studies and 3D-QSAR models are in excellent agreement in terms of binding-site interactions. The pharmacophore hypotheses validated using sensitivity, selectivity, and specificity parameters is a four-point model, characterized by a hydrogen-bond acceptor (A), hydrogen-bond donor (D), and two hydrophobes (H). This map was used to screen a database of 2.7 million druglike compounds, which were pruned to a small set of potential inhibitors by CoRIA, CoMFA, and CoMSIA models with predicted activity in the range of 8.5–10.5 log units.

Computer tools in the discovery of HIV-1 integrase inhibitors

Computer-aided drug design (CADD) methodologies have made great advances and contributed significantly to the discovery and/or optimization of many clinically used drugs in recent years. CADD tools have likewise been applied to the discovery of inhibitors of HIV-1 integrase, a difficult and worthwhile target for the development of efficient anti-HIV drugs. This article reviews the application of CADD tools, including pharmacophore search, quantitative structure-activity relationships, model building of integrase complexed with viral DNA and quantum-chemical studies in the discovery of HIV-1 integrase inhibitors. Different structurally diverse integrase inhibitors have been identified by, or with significant help from, various CADD tools.

Raltegravir: molecular basis of its mechanism of action

Integration of the HIV-1 viral DNA generated by reverse transcription of the RNA genome into the host cell chromosomes is a key step of viral replication, catalyzed by the viral integrase. In October 2007, the first integrase inhibitor, raltegravir, was approved for clinical use under the name of Isentress™. The results of the various clinical trials that have evaluated raltegravir have been very encouraging with regard to the immunological and virological efficacy and tolerance. However, as observed for other anti-retrovirals, specific resistance mutations have been identified in patients failing to respond to treatment with raltegravir. Although knowledge of the integrase structural biology remains fragmentary, the structures and modeling data available might provide relevant clues on the origin of the emergence of these resistance mutations. In this review, we describe the mechanism of action of this drug and the main data relating to its use in vivo, together with recent structural data important to our understanding of the origin of viral resistance.