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Background Over the last decades, a vast structural knowledge has been gathered on the HIV-1 protease (PR). Noticeably, most of the studies focused the B-subtype, which has the highest prevalence in developed countries. Accordingly, currently available anti-HIV drugs target this subtype, with considerable benefits for the corresponding patients. However, in developing countries, there is a wide variety of HIV-1 subtypes carrying PR polymorphisms related to reduced drug susceptibility. The non-active site mutation, M36I, is the most frequent polymorphism, and is considered as a non-B subtype marker. Yet, the structural impact of this substitution on the PR structure and on the interaction with natural substrates remains poorly documented. Results Herein, we used molecular dynamics simulations to investigate the role of this polymorphism on the interaction of PR with six of its natural cleavage-sites substrates. Free energy analyses by MMPB/SA calculations showed an affinity decrease of M36I-PR for the majority of its substrates. The only exceptions were the RT-RH, with equivalent affinity, and the RH-IN, for which an increased affinity was found. Furthermore, molecular simulations suggest that, unlike other peptides, RH-IN induced larger structural fluctuations in the wild-type enzyme than in the M36I variant. Conclusions With multiple approaches and analyses we identified structural and dynamical determinants associated with the changes found in the binding affinity of the M36I variant. This mutation influences the flexibility of both PR and its complexed substrate. The observed impact of M36I, suggest that combination with other non-B subtype polymorphisms, could lead to major effects on the interaction with the 12 known cleavage sites, which should impact the virion maturation.

Human Immunodeficiency Virus type 1 protease (HIV-1 PR) is one of the most challenging targets of antiretroviral therapy used in the treatment of AIDS-infected people. The performance of protease inhibitors (PIs) is limited by the development of protease mutations that can promote resistance to the treatment. The current study was carried out using statistics and bioinformatics tools. A series of thirty-three compounds with known enzymatic inhibitory activities against HIV-1 protease was used in this paper to build a mathematical model relating the structure to the biological activity. These compounds were designed by software; their descriptors were computed using various tools, such as Gaussian, Chem3D, ChemSketch and MarvinSketch. Computational methods generated the best model based on its statistical parameters. The model’s applicability domain (AD) was elaborated. Furthermore, one compound has been proposed as efficient against HIV-1 protease with comparable biological activity to the existing ones; this drug candidate was evaluated using ADMET properties and Lipinski’s rule. Molecular Docking performed on Wild Type, and Mutant Type HIV-1 proteases allowed the investigation of the interaction types displayed between the proteases and the ligands, Darunavir (DRV) and the new drug (ND). Molecular dynamics simulation was also used in order to investigate the complexes’ stability allowing a comparative study on the performance of both ligands (DRV & ND). Our study suggested that the new molecule showed comparable results to that of darunavir and maybe used for further experimental studies. Our study may also be used as pipeline to search and design new potential inhibitors of HIV-1 proteases.

The battle against viral drug resistance highlights the need for innovative approaches to replace time-consuming and costly traditional methods. Deep generative models offer automation potential, especially in the fight against Human immunodeficiency virus (HIV), as they can synthesize diverse molecules effectively. In this paper, an application of an LSTM-based deep generative model named “LSTM-ProGen” is proposed to be tailored explicitly for the de novo design of drug candidate molecules that interact with a specific target protein (HIV-1 protease). LSTM-ProGen distinguishes itself by employing a long-short-term memory (LSTM) architecture, to generate novel molecules target specificity against the HIV-1 protease. Following a thorough training process involves fine-tuning LSTM-ProGen on a diverse range of compounds sourced from the ChEMBL database. The model was optimized to meet specific requirements, with multiple iterations to enhance its predictive capabilities and ensure it generates molecules that exhibit favorable target interactions. The training process encompasses an array of performance evaluation metrics, such as drug-likeness properties. Our evaluation includes extensive silico analysis using molecular docking and PCA-based visualization to explore the chemical space that the new molecules cover compared to those in the training set. These evaluations reveal that a subset of 12 de novo molecules generated by LSTM-ProGen exhibit a striking ability to interact with the target protein, rivaling or even surpassing the efficacy of native ligands. Extended versions with further refinement of LSTM-ProGen hold promise as versatile tools for designing efficacious and customized drug candidates tailored to specific targets, thus accelerating drug development and facilitating the discovery of new therapies for various diseases.

Since the first cases of AIDS, reported in 1980, this disease has become chronic over the years, and researchers have been trying to keep it under control. Despite the development and spread of mutate viruses, HIV protease remains an important pharmacological target. In the development of new HIV protease inhibitors, heterocyclic fragments have proven to be of great importance, owing to their rigid core structure, which may fit better into the enzyme’s hydrophobic pockets, and the presence of a heteroatom, which may increase the number of H-bonding interactions at the active site. According to the concept of targeting the protein backbone, different aromatic or non-aromatic heterocyclic moieties have yielded inhibitors with sufficient activity against mutant viruses. This paper provides an overview of HIV protease inhibitors developed over the last fifteen years, with a focus on the presence of heterocycles in their structure, either in the core or on the side chains, which are crucial for their activity. The rationale behind the design of these new inhibitors, as well as the key synthetic steps involved in their preparation, is also described.

A method for predicting HIV drug resistance by using genotypes would greatly assist in selecting appropriate combinations of antiviral drugs. Models reported previously have had two major problems: lack of information on the 3D protein structure and processing of incomplete sequencing data in the modeling procedure. We propose obtaining the 3D structural information of viral proteins by using homology modeling and molecular field mapping, instead of just their primary amino acid sequences. The molecular field potential parameters reflect the physicochemical characteristics associated with the 3D structure of the proteins. We also introduce the Bayesian conditional mutual information theory to estimate the probabilities of occurrence of all possible protein candidates from an incomplete sequencing sample. This approach allows for the effective use of uncertain information for the modeling process. We applied these data analysis techniques to the HIV-1 protease inhibitor dataset and developed drug resistance prediction models with reasonable performance.

The AutoDock family of software has been widely used in protein-ligand docking research. This study compares AutoDock 4 and AutoDock Vina in the context of virtual screening by using these programs to select compounds active against HIV protease. Both programs were used to rank the members of two chemical libraries, each containing experimentally verified binders to HIV protease. In the case of the NCI Diversity Set II, both AutoDock 4 and Vina were able to select active compounds significantly better than random (AUC = 0.69 and 0.68, respectively; p<0.001). The binding energy predictions were highly correlated in this case, with r = 0.63 and iota = 0.82. For a set of larger, more flexible compounds from the Directory of Universal Decoys, the binding energy predictions were not correlated, and only Vina was able to rank compounds significantly better than random. In ranking smaller molecules with few rotatable bonds, AutoDock 4 and Vina were equally capable, though both exhibited a size-related bias in scoring. However, as Vina executes more quickly and is able to more accurately rank larger molecules, researchers should look to it first when undertaking a virtual screen.

Heat capacity is one of the most important thermodynamic quantities in protein biochemistry. Upon the binding of small molecules, a change in the heat capacity of proteins is generally observed, and this is often used in drug discovery. However, few computational works dedicated to the study of these phenomena are available in the literature. Here, a simple computational method for determining the change in heat capacity upon the binding of small ligands has been evaluated. The method is based on the accurate calibration of the solvent’s thermal properties in the simulation conditions used in order to simply subtract its contribution to calculate the variations in the heat capacity of the system of interest. Using HIV protease as a model system, for which numerous experimental thermodynamic data are available, estimates of the change in heat capacity upon binding were obtained, which were similar to those observed experimentally. Furthermore, the predicted variations in heat capacity appear to be able to discriminate between molecules that behave as effective inhibitors of the enzyme and molecules that are able to bind the enzyme but not inhibit it. The results obtained suggest that this computational approach could be a useful aid in the in silico screening of new ligands for targets of interest.

Despite significant advancements with combination anti-retroviral agents, eradicating human immunodeficiency virus (HIV) remains a challenge due to adverse effects, adherence issues, and emerging viral resistance to existing therapies. This underscores the urgent need for safer, more effective drugs to combat resistant strains and advance acquired immunodeficiency syndrome (AIDS) therapeutics. Eight triterpene esters ( 1 – 8 ) were identified from Uncaria rhynchophylla hooks. These compounds exhibited potent inhibition of HIV-1 protease (PR), one of the essential enzymes in the virus’s life cycle, with 3β-hydroxy-27- p-Z- coumaroyloxyurs-12-en-28-oic acid ( 8 ) showing the most potent inhibitory activity. Structure–activity relationship (SAR) analysis highlighted the importance of the ursane moiety, cis configuration, and p -coumaroyloxy group for inhibitory activity. In silico docking result of triterpene ester 8 elucidated conventional hydrogen bonding with specific amino acid residues—Asp29B, Lys45B, and Asn25A—interacting with the aromatic hydroxyl group at position 7′ and the carboxylic acid at position 28. Additionally, these interactions occur via π–anion and π–alkyl and alkyl hydrophobic interactions, which are responsible for the compound’s mode of action. These molecular docking studies strongly confirmed an excellent SAR. The study suggests that triterpene esters from U. rhynchophylla could represent a new class of potent HIV-1 PR inhibitors with less toxicity, suitable for combination antiretroviral therapy for AIDS.

The present work reports the cost-effective, high yielding and environmentally acceptable preparation of unsymmetrical ureas from thiocarbamate salts using sodium percarbonate as an oxidant. Efficacy of the unsymmetrical ureas as potential human immune deficiency virus (HIV-1) protease inhibitors has been evaluated via in silico approach. The results revealed interactions of the urea compounds at the active site of the enzyme with favorable binding affinities causing possible mutations hindering the functioning of the enzyme. Further computational assessment of IC50 using known references satisfactorily authenticated the inhibitory action of the selected compounds against HIV-1 protease. Added to the easy synthesis of the ureas following an environmentally benign protocol, this work may be a valuable addition to the ongoing search for drugs with better efficacy profiles and reduced toxicity against HIV.

Protein–protein interactions play a central role in cellular function. Improving the understanding of complex formation has many practical applications, including the rational design of new therapeutic agents and the mechanisms governing signal transduction networks. The generally large, flat, and relatively featureless binding sites of protein complexes pose many challenges for drug design. Fragment docking and direct coupling analysis are used in an integrated computational method to estimate druggable protein–protein interfaces. (i) This method explores the binding of fragment-sized molecular probes on the protein surface using a molecular docking-based screen. (ii) The energetically favorable binding sites of the probes, called hot spots, are spatially clustered to map out candidate binding sites on the protein surface. (iii) A coevolution-based interface interaction score is used to discriminate between different candidate binding sites, yielding potential interfacial targets for therapeutic drug design. This approach is validated for important, well-studied disease-related proteins with known pharmaceutical targets, and also identifies targets that have yet to be studied. Moreover, therapeutic agents are proposed by chemically connecting the fragments that are strongly bound to the hot spots.