Additivity in the analysis and design of HIV protease inhibitors

Evaluation of novel HIV-1 protease inhibitors with DRV-resistance by utilizing 3D-QSAR molecular docking and molecular dynamics simulation

Molecular dynamics simulations were performed to explore the interaction mode of DRV derivatives binding to target proteins and to identify new potential HIV-1 PR inhibitors with stronger activity.

Biphenyl substituted lysine derivatives as recognition elements for the matrix metalloproteinases MMP-2 and MMP-9

Matrix metalloproteinases (MMPs) are an important factor in cancer progression and metastasis, especially gelatinases MMP-2 and MMP-9. A simple methodology for their detection and monitoring is highly desirable. Molecular probes have been very widely and successfully applied to study the activity of MMPs in cellular processes in vitro. We thus synthesized a small compound library of MMP-2 and MMP-9 binding probes based on drug molecules and endowed with free amine groups for the functionalization of transducer surfaces. In this study, we combined experimental results obtained by a kinetic fluorogenic peptide substrate cleavage assay with molecular modeling studies in order to assess the ability of the probe to bind to their target enzymes. The synthesized biphenyl substituted lysine derivatives showed IC₅₀-values in the low nanomolar concentration range against MMP-2 (ligands 3a-d: 3 nM to 8 µM, ligands 4a-d: 45 nM to 350 µM) and low micromolar range against MMP-9 (ligands 3a-d: 350 nM to 60 µM, ligands 4a-d: 5 µM to 600 µM), with a selectivity up to more than 160-fold for MMP-2. The experimental results correlated well with molecular modelling with FleXAID and X-score functions. We showed that in our compound series, the side chain remained far away from the S1' cavity and the ligand for all the docked minima. Ligands 4a-d with their free amine group on the side chain may thus be bound to transducer surfaces for the fabrication of sensors, while retaining their activity against their target enzymes.

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.

Design and synthesis of potent HIV-1 protease inhibitors with (S)-tetrahydrofuran-tertiary amine-acetamide as P2-ligand: Structure-activity studies and biological evaluation

The design, synthesis, and SAR study of a new series of HIV-1 protease inhibitors incorporating stereochemically defined tetrahydrofuran-tertiary amine-acetamide P2-ligand are described. Various substituent effects on the tertiary amine P2-ligand and phenylsulfonamide P2'-ligand were investigated to maximize the ligand-binding site interactions in the protease active site. Most of inhibitors displayed low nanomolar to subnanomolar inhibitory potency. Inhibitor 20e containing N-(S-tetrahydrofuran)-N-(2-methoxyethyl)acetamide as P2-ligand along with 4-methoxylphenylsulfonamide as P2'-ligand displayed the most potent enzyme inhibitory activity (IC₅₀ = 0.35 nM) and remarkably low cytotoxicity (CC₅₀ = 305 μM).

Role and analysis of monocyte subsets in cardiovascular disease. Joint consensus document of the European Society of Cardiology (ESC) Working Groups "Atherosclerosis & Vascular Biology" and "Thrombosis"

Monocytes as cells of the innate immunity are prominently involved in the development of atherosclerotic lesions. The heterogeneity of blood monocytes has widely been acknowledged by accumulating experimental and clinical data suggesting a differential, subset-specific contribution of the corresponding subpopulations to the pathology of cardiovascular and other diseases. This document re-evaluates current nomenclature and summarises key findings on monocyte subset biology to propose a consensus statement about phenotype, separation and quantification of the individual subsets.

Synthesis and biological evaluation of novel HIV-1 protease inhibitors using tertiary amine as P2-ligands

A series of tertiary amine derivatives exhibiting potent HIV-1 protease inhibiting properties were identified. These novel inhibitors were designed based on the structure of Darunavir with modification on the P2 and P2' position. This effort led to discovery of 35e and 38e, which exhibited excellent HIV-1 protease inhibition with IC50 values of 15 nM and 64 nM, respectively.

Additivity-based design of the strongest possible turkey ovomucoid third domain inhibitors for porcine pancreatic elastase (PPE) and Streptomyces griseus protease B (SGPB)

We describe here successful designs of strong inhibitors for porcine pancreatic elastase (PPE) and Streptomyces griseus protease B (SGPB). For each enzyme two inhibitor variants were designed. In one, the reactive site residue (position 18) was retained and the best residues were substituted at contact positions 13, 14, and 15. In the other variant the best residues were substituted at all contact positions except the reactive site where a Gly was substituted. The four designed variants were: for PPE, T(13)E(14)Y(15)-OMTKY3 and T(13)E(14)Y(15)G(18)M(21)P(32)V(36)-OMTKY3, and for SGPB, S(13)D(14)Y(15)-OMTKY3 and S(13)D(14)Y(15)G(18)I(19)K(21)-OMTKY3. The free energies of association (ΔG(0)) of expressed variants have been measured with the proteases for which they were designed as well as with five other serine proteases and the results are discussed.

A combined 3D-QSAR and docking studies for the In-silico prediction of HIV-protease inhibitors

BACKGROUND

Tremendous research from last twenty years has been pursued to cure human life against HIV virus. A large number of HIV protease inhibitors are in clinical trials but still it is an interesting target for researchers due to the viral ability to get mutated. Mutated viral strains led the drug ineffective but still used to increase the life span of HIV patients.

RESULTS

In the present work, 3D-QSAR and docking studies were performed on a series of Danuravir derivatives, the most potent HIV- protease inhibitor known so far. Combined study of 3D-QSAR was applied for Danuravir derivatives using ligand-based and receptor-based protocols and generated models were compared. The results were in good agreement with the experimental results. Additionally, docking analysis of most active 32 and least active 46 compounds into wild type and mutated protein structures further verified our results. The 3D-QSAR and docking results revealed that compound 32 bind efficiently to the wild and mutated protein whereas, sufficient interactions were lost in compound 46.

CONCLUSION

The combination of two computational techniques would helped to make a clear decision that compound 32 with well inhibitory activity bind more efficiently within the binding pocket even in case of mutant virus whereas compound 46 lost its interactions on mutation and marked as least active compound of the series. This is all due to the presence or absence of substituents on core structure, evaluated by 3D-QSAR studies. This set of information could be used to design highly potent drug candidates for both wild and mutated form of viruses.

Characterization of small molecule binding. I. Accurate identification of strong inhibitors in virtual screening

Accurately ranking docking poses remains a great challenge in computer-aided drug design. In this study, we present an integrated approach called MIEC-SVM that combines structure modeling and statistical learning to characterize protein-ligand binding based on the complex structure generated from docking. Using the HIV-1 protease as a model system, we showed that MIEC-SVM can successfully rank the docking poses and consistently outperformed the state-of-art scoring functions when the true positives only account for 1% or 0.5% of all the compounds under consideration. More excitingly, we found that MIEC-SVM can achieve a significant enrichment in virtual screening even when trained on a set of known inhibitors as small as 50, especially when enhanced by a model average approach. Given these features of MIEC-SVM, we believe it provides a powerful tool for searching for and designing new drugs.

Structure-based methods for predicting target mutation-induced drug resistance and rational drug design to overcome the problem

Drug resistance has become one of the biggest challenges in drug discovery and/or development and has attracted great research interests worldwide. During the past decade, computational strategies have been developed to predict target mutation-induced drug resistance. Meanwhile, various molecular design strategies, including targeting protein backbone, targeting highly conserved residues and dual/multiple targeting, have been used to design novel inhibitors for combating the drug resistance. In this article we review recent advances in development of computational methods for target mutation-induced drug resistance prediction and strategies for rational design of novel inhibitors that could be effective against the possible drug-resistant mutants of the target.

Site-Specific Fragment Identification Guided by Single-Step Free Energy Perturbation Calculations

The in-silico Site Identification by Ligand Competitive Saturation (SILCS) approach identifies the binding sites of representative chemical entities on the entire protein surface, information that can be applied for computational fragment-based drug design. In this study, we report an efficient computational protocol that uses sampling of the protein-fragment conformational space obtained from the SILCS simulations and performs single step free energy perturbation (SSFEP) calculations to identify site-specific favorable chemical modifications of benzene involving substitutions of ring hydrogens with individual non-hydrogen atoms. The SSFEP method is able to capture the experimental trends in relative hydration free energies of benzene analogues and for two datasets of experimental relative binding free energies of congeneric series of ligands of the proteins α-thrombin and P38 MAP kinase. The approach includes a protocol in which data obtained from SILCS simulations of the proteins is first analyzed to identify favorable benzene binding sites following which an ensemble of benzene-protein conformations for that site is obtained. The SSFEP protocol applied to that ensemble results in good reproduction of experimental free energies of the α-thrombin ligands, but not for P38 MAP kinase ligands. Comparison with results from a P38 full-ligand simulation and analysis of conformations reveals the reason for the poor agreement being the connectivity with the remainder of the ligand, a limitation inherent in fragment-based methods. Since the SSFEP approach can identify favorable benzene modifications as well as identify the most favorable fragment conformations, the obtained information can be of value for fragment linking or structure-based optimization.

Multi-target QSAR modelling in the analysis and design of HIV-HCV co-inhibitors: an in-silico study

Background

HIV and HCV infections have become the leading global public-health threats. Even more remarkable, HIV-HCV co-infection is rapidly emerging as a major cause of morbidity and mortality throughout the world, due to the common rapid mutation characteristics of the two viruses as well as their similar complex influence to immunology system. Although considerable progresses have been made on the study of the infection of HIV and HCV respectively, few researches have been conducted on the investigation of the molecular mechanism of their co-infection and designing of the multi-target co-inhibitors for the two viruses simultaneously.

Results

In our study, a multi-target Quantitative Structure-Activity Relationship (QSAR) study of the inhibitors for HIV-HCV co-infection were addressed with an in-silico machine learning technique, i.e. multi-task learning, to help to guide the co-inhibitor design. Firstly, an integrated dataset with 3 HIV inhibitor subsets targeted on protease, integrase and reverse transcriptase respectively, together with another 6 subsets of 2 HCV inhibitors targeted on NS3 serine protease and NS5B polymerase respectively were compiled. Secondly, an efficient multi-target QSAR modelling of HIV-HCV co-inhibitors was performed by applying an accelerated gradient method based multi-task learning on the whole 9 datasets. Furthermore, by solving the L-1-infinity regularized optimization, the Drug-like index features for compound description were ranked according to their joint importance in multi-target QSAR modelling of HIV and HCV. Finally, a drug structure-activity simulation for investigating the relationships between compound structures and binding affinities was presented based on our multiple target analysis, which is then providing several novel clues for the design of multi-target HIV-HCV co-inhibitors with increasing likelihood of successful therapies on HIV, HCV and HIV-HCV co-infection.

Conclusions

The framework presented in our study provided an efficient way to identify and design inhibitors that simultaneously and selectively bind to multiple targets from multiple viruses with high affinity, and will definitely shed new lights on the future work of inhibitor synthesis for multi-target HIV, HCV, and HIV-HCV co-infection treatments.

De novo drug design

Computer-assisted molecular design supports drug discovery by suggesting novel chemotypes and compound modifications for lead structure optimization. While the aspect of synthetic feasibility of the automatically designed compounds has been neglected for a long time, we are currently witnessing an increased interest in this topic. Here, we review state-of-the-art software for de novo drug design with a special emphasis on fragment-based techniques that generate druglike, synthetically accessible compounds. The importance of scoring functions that can be used to predict compound reactivity and potency is highlighted, and several promising solutions are discussed. Recent practical validation studies are presented that have already demonstrated that rule-based fragment assembly can result in novel synthesizable compounds with druglike properties and a desired biological activity.

Therapeutic strategies underpinning the development of novel techniques for the treatment of HIV infection

The HIV replication cycle offers multiple targets for chemotherapeutic intervention, including the viral exterior envelope glycoprotein, gp120; viral co-receptors CXCR4 and CCR5; transmembrane glycoprotein, gp41; integrase; reverse transcriptase; protease and so on. Most currently used anti-HIV drugs are reverse transcriptase inhibitors or protease inhibitors. The expanding application of simulation to drug design combined with experimental techniques have developed a large amount of novel inhibitors that interact specifically with targets besides transcriptase and protease. This review presents details of the anti-HIV inhibitors discovered with computer-aided approaches and provides an overview of the recent five-year achievements in the treatment of HIV infection and the application of computational methods to current drug design.