HIV-1 protease function and structure studies with the simplicial neighborhood analysis of protein packing method

Environmentally benign synthesis of unsymmetrical ureas and their evaluation as potential HIV-1 protease inhibitors via a computational approach

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.

Prediction of HIV-1 protease resistance using genotypic, phenotypic, and molecular information with artificial neural networks

Drug resistance is a primary barrier to effective treatments of HIV/AIDS. Calculating quantitative relations between genotype and phenotype observations for each inhibitor with cell-based assays requires time and money-consuming experiments. Machine learning models are good options for tackling these problems by generalizing the available data with suitable linear or nonlinear mappings. The main aim of this study is to construct drug isolate fold (DIF) change-based artificial neural network (ANN) models for estimating the resistance potential of molecules inhibiting the HIV-1 protease (PR) enzyme. Throughout the study, seven of eight protease inhibitors (PIs) have been included in the training set and the remaining ones in the test set. We have obtained 11,803 genotype-phenotype data points for eight PIs from Stanford HIV drug resistance database. Using the leave-one-out (LVO) procedure, eight ANN models have been produced to measure the learning capacity of models from the descriptors of the inhibitors. Mean R² value of eight ANN models for unseen inhibitors is 0.716, and the 95% confidence interval (CI) is [0.592-0.840]. Predicting the fold change resistance for hundreds of isolates allowed a robust comparison of drug pairs. These eight models have predicted the drug resistance tendencies of each inhibitor pair with the mean 2D correlation coefficient of 0.933 and 95% CI [0.930-0.938]. A classification problem has been created to predict the ordered relationship of the PIs, and the mean accuracy, sensitivity, specificity, and Matthews correlation coefficient (MCC) values are calculated as 0.954, 0.791, 0.791, and 0.688, respectively. Furthermore, we have created an external test dataset consisting of 51 unique known HIV-1 PR inhibitors and 87 genotype-phenotype relations. Our developed ANN model has accuracy and area under the curve (AUC) values of 0.749 and 0.818 to predict the ordered relationships of molecules on the same strain for the external dataset. The currently derived ANN models can accurately predict the drug resistance tendencies of PI pairs. This observation could help test new inhibitors with various isolates.

Extracellular binding of indinavir to matrix metalloproteinase-2 and the alpha-7-nicotinic acetylcholine receptor: implications for use in cancer treatment

Introduction

Results from recent studies have suggested a role for protease inhibitors in altering mechanisms involved in the initiation and proliferation of cancer cells. One such inhibitor, indinavir, may act as an anti-cancer agent by modulating the alpha-7-nicotinic acetylcholine receptor, which is a pro-carcinogenic protein that has been researched in conjunction with nicotine in lung cancer development. In our study, we compare indinavir's binding affinity towards α7-nAchR and MMP-2, another promoter of malignancy, to determine what extracellular effects the drug has before being internalized to inhibit HIV-1 protease.

Methods

A computer program, PyRx, was used to compare indinavir's binding affinity with digital models for α7-nAchR, MMP-2 and HIV-1 protease, which were then compared to the results of in vitro binding assays for these targets.

Results

PyRx testing predicted the highest binding affinity values for indinavir to MMP-2 (mean = 8.77 kcal/mol, S.D. = 0.29), followed by the α7-nAchR (mean = 8.53 kcal/mol, S.D. = 0.15) and HIV-1 protease (mean = 7.5 kcal/mol, S.D. = 0.44). In vitro, indinavir's mean percent inhibition of control values were 103.2 for HIV-1 protease, 5.3 for MMP-2, and 7.7 for the α7-nAchR.

Conclusions

Binding affinity values for indinavir to MMP-2 and α7-nAchR were not significantly different. Using PyRx to predict affinity compared with in vitro testing did not yield comparable results. However, indinavir was shown to slightly inhibit both α7-nAchR and MMP-2, which may have ramifications in the drug's delivery to the intracellularly located HIV-1 protease.

Application of Machine Leaning in Drug Discovery and Development

Machine learning techniques have been widely used in drug discovery and development, particularly in the areas of cheminformatics, bioinformatics and other types of pharmaceutical research. It has been demonstrated they are suitable for large high dimensional data, and the models built with these methods can be used for robust external predictions. However, various problems and challenges still exist, and new approaches are in great need. In this Chapter, the authors will review the current development of machine learning techniques, and especially focus on several machine learning techniques they developed as well as their application to model building, lead discovery via virtual screening, integration with molecular docking, and prediction of off-target properties. The authors will suggest some potential different avenues to unify different disciplines, such as cheminformatics, bioinformatics and systems biology, for the purpose of developing integrated in silico drug discovery and development approaches.

Computer-aided drug discovery and development

Computer-aided approaches have been widely used in pharmaceutical research to improve the efficiency of the drug discovery and development pipeline. To identify and design small molecules as clinically effective therapeutics, various computational methods have been evaluated as promising strategies, depending on the purpose and systems of interest. Both ligand and structure-based drug design approaches are powerful technologies, which can be applied to virtual screening for lead identification and optimization. Here, we review the progress in this field and summarize the application of some new technologies we developed. These state-of-the-art tools have been used for the discovery and development of active agents for various diseases, in particular for cancer therapies. The described protocols are appropriate for all drug discovery stages, but expertise is still needed to perform the studies based on the targets of interest.

Another look at the conditions for the extraction of protein knowledge-based potentials

Protein knowledge-based potentials are effective free energies obtained from databases of known protein structures. They are used to parameterize coarse-grained protein models in many folding simulation and structure prediction methods. Two common approaches are used in the derivation of knowledge-based potentials. One assumes that the energy parameters optimize the native structure stability. The other assumes that interaction events are related to their energies according to the Boltzmann distribution, and that they are distributed independently of other events, that is, the quasi-chemical approximation. Here, these assumptions are systematically tested by extracting contact energies from artificial databases of lattice proteins with predefined pairwise contact energies. Databases of protein sequences are designed to either satisfy the Boltzmann distribution at high or low temperatures, or to simultaneously optimize the native stability and folding kinetics. It is found that the quasi-chemical approximation, with the ideal reference state, accurately reproduce the true energies for high temperature Boltzmann distributed sequences (weakly interacting residues), but less accurately at low temperatures, where the sequences correspond to energy minima and the residues are strongly interacting. To overcome this problem, an iterative procedure for Boltzmann distributed sequences is introduced, which accounts for interacting residue correlations and eliminates the need for the quasi-chemical approximation. In this case, the energies are accurately reproduced at any ensemble temperature. However, when the database of sequences designed for optimal stability and kinetics is used, the energy correlation is less than optimal using either method, exhibiting random and systematic deviations from linearity. Therefore, the assumption that native structures are maximally stable or that sequences are determined according to the Boltzmann distribution seems to be inadequate for obtaining accurate energies. The limited number of sequences in the database and the inhomogeneous concentration of amino acids from one structure to another do not seem to be major obstacles for improving the quality of the extracted pairwise energies, with the exception of repulsive interactions.