Prediction of Ligand Activity at Subcellular Location

Understanding subcellular distribution and the mechanism of xenobiotics can help in modulating subcellular dysfunction mediated diseases. Therefore, with improved knowledge of how xenobiotics are distributed across subcellular locations and the mechanism for a specific molecule can play a crucial role in assessing drug efficacy and toxicity. Such knowledge would widen therapeutic windows by allowing specific receptors to be targeted efficiently. Based on datasets that provide information on the subcellular locations of proteins and their ligands, we developed machine learning models for 42 subcellular locations. Such models were trained and validated based on the grid search method and best models based on Cohen’s Kappa scores were selected. With the help of the state-of-the-art supercomputing facilities provided by the Texas Advanced Computing Center(TACC), we were able to develop a suite of more than 22300+ machine learning models. These machine learning models were built using 19 different fingerprints-based features for 42 different subcellular locations using 28 different ML classifiers. The web-application is available on an open portal and can be accessed at https://drugdiscovery.utep.edu/subcell/ by anyone in order to perform high-throughput cheminformatics simulations. All the data and models generated from the project are made available as open-source

A community effort in SARS-CoV-2 drug discovery.

peer reviewedThe COVID-19 pandemic continues to pose a substantial threat to human lives and is likely to do so for years to come. Despite the availability of vaccines, searching for efficient small-molecule drugs that are widely available, including in low- and middle-income countries, is an ongoing challenge. In this work, we report the results of an open science community effort, the "Billion molecules against Covid-19 challenge", to identify small-molecule inhibitors against SARS-CoV-2 or relevant human receptors. Participating teams used a wide variety of computational methods to screen a minimum of 1 billion virtual molecules against 6 protein targets. Overall, 31 teams participated, and they suggested a total of 639,024 molecules, which were subsequently ranked to find 'consensus compounds'. The organizing team coordinated with various contract research organizations (CROs) and collaborating institutions to synthesize and test 878 compounds for biological activity against proteases (Nsp5, Nsp3, TMPRSS2), nucleocapsid N, RdRP (only the Nsp12 domain), and (alpha) spike protein S. Overall, 27 compounds with weak inhibition/binding were experimentally identified by binding-, cleavage-, and/or viral suppression assays and are presented here. Open science approaches such as the one presented here contribute to the knowledge base of future drug discovery efforts in finding better SARS-CoV-2 treatments.R-AGR-3826 - COVID19-14715687-CovScreen (01/06/2020 - 31/01/2021) - GLAAB Enric

Drug design, molecular modelling, and QSAR studies of antimalarial mefloquine and artemisinin derivatives

A common procedure for QSAR analysis consist of data selection (generally sets of homologous series of compounds and their corresponding biological activities), tabulation of trial physicochemical or molecular structural descriptors, followed by a multilinear statistical analysis to derive a statistically valid QSAR correlation of the activity data making use of a subset of the trial descriptors. A final important step is cross-validation to assess the putative predictive (rather than just correlative) capabilities of the derived QSAR model equation. This thesis consists of QSAR model development studies for two data sets. The results presented in this first study will consist of an analysis and criticism of a research paper in which antimalarial activities of a set of aromatic mefloquine derivatives are correlated with calculated atomic charges, using increasingly complex statistical procedures. However, the structures used in the analysis did not actually correspond to the experimental structures. The results of a very successful elementary QSAR study using substituent indicator variables, coupled with two calculated theoretical AM1 parameters for the actual compounds used in the work outlined above are presented. The second QSAR analysis consists of a study of a 74 compound data set of antimalarial artemisinin derivatives. The analysis protocol employs hierarchical molecular structural descriptors, which are correlated with biological activities using multilinear regression analysis. The levels of hierarchical structural descriptors are augmented and tested sequentially to obtain information regarding the lowest levels of descriptors that are crucial for statistically significant rectification of a particular dependent variable property. These concepts are illustrated with biological data comprised of antimalarial activities of Artemisinin derivatives with a wide variety of substituents groups. The first few levels of hierarchical descriptors derived from the molecular structure. The final level consists of quantum mechanical descriptors derived from AM1 calculations using SPARTAN software. The equation defined by the above QSAR procedure may be used to correlate the antimalarial activities of similar type of compounds

Computer aided drug design methods & quantitative structure-activity/property relationships

The first part of dissertation consists of development of a QSAR model for 229 mutagenic aromatic amines and a QSPR model of partial molar volumes of amino acids. A common procedure for QSAR analysis consist of data selection (generally sets of homologous series of compounds and their corresponding biological activities), tabulation of trial physicochemical or molecular structural descriptors, followed by a multilinear statistical analysis to derive a statistically valid QSAR correlation of the activity data making use of a subset of the trial descriptors. A final important step is cross-validation to assess the putative predictive (rather than just correlative) capabilities of the derived QSAR model equation. The results of a very successful elementary QSA(/P)R studies using substituent indicator variables, coupled with calculated theoretical parameters for the compounds in the work outlined above are presented. The second part of the dissertation illustrates that betalactoglobulin and human serum albumin can be used as a vehicle to improve the bioavailability of curcumin and it’s derivatives. Curcumin a major component of Indian spice turmeric (Curcuma longa), possesses diverse anti-inflammatory, antitumour and antioxidant properties. Several studies have confirmed that curcumin can reduce the oxidative/nitrosative stress and there by decrease the neuronal attrition. But the bioavailability of curcumin is poor and has raised several concerns regarding limited clinical impact. The aim of this study was to find molecules similar to curcumin which can assist in decreasing nitrosative stress and possess enhanced bioavailability. Here, we examined the use of beta-lactoglobulin as a vehicle to transport molecules to the gut. Curcumin analogs were searched from Zinc database and 6457 compounds were selected for the study. These compounds were docked to betalactoglobulin using Glide to find the best fit ligands. Our findings indicated four compounds that have better binding to betalactoglobulin and efficient NOx (free radical) scavenging activity

An Improved Free Energy Perturbation FEP+ Sampling Protocol for Flexible Ligand-Binding Domains

© 2019, The Author(s). Recent improvements to the free energy perturbation (FEP) calculations, especially FEP+ , established their utility for pharmaceutical lead optimization. Herein, we propose a modified version of the FEP/REST (i.e., replica exchange with solute tempering) sampling protocol, based on detail studies on several targets by probing a large number of perturbations with different sampling schemes. Improved FEP+ binding affinity predictions for regular flexible-loop motions and considerable structural changes can be obtained by extending the prior to REST (pre-REST) sampling time from 0.24 ns/λ to 5 ns/λ and 2 × 10 ns/λ, respectively. With this new protocol, much more precise ∆∆G values of the individual perturbations, including the sign of the transformations and decreased error were obtained. We extended the REST simulations from 5 ns to 8 ns to achieve reasonable free energy convergence. Implementing REST to the entire ligand as opposed to solely the perturbed region, and also some important flexible protein residues (pREST region) in the ligand binding domain (LBD) has considerably improved the FEP+ results in most of the studied cases. Preliminary molecular dynamics (MD) runs were useful for establishing the correct binding mode of the compounds and thus precise alignment for FEP+. Our improved protocol may further increase the FEP+ accuracy

An Improved Free Energy Perturbation FEP+ Sampling Protocol for Flexible Ligand-Binding Domains

Recent improvements to free energy perturbation (FEP) calculations, especiallyFEP+, established their utility for pharmaceutical lead optimization. However, to dateFEP has typically been helpful only when (1) high-quality X-ray data is available and(2) the target protein does not undergo significant conformational changes. Also, alack of systematic studies on determining an adequate sampling time is often one ofthe primary limitations of FEP calculations. Herein, we propose a modified versionof the FEP/REST (i.e., replica exchange with solute tempering) sampling protocol,based on systematic studies on several targets by probing a large number of permutations with different sampling schemes. Improved FEP+ binding affinity predictions for regular flexible-loop (F-loop) motions and considerable structural changes can be obtained by extending the pre-REST sampling time from 0.24 ns to 5 ns/λand 2×10 ns/λ, respectively. We obtained much more precise ∆∆G calculations of the individual perturbations, including the sign of the transformations and less error. We extended the REST simulations from 5 ns to 8 ns to achieve reasonable free energy convergence.Implementing REST to the entire ligand as opposed to solely the perturbed region, and also some important flexible protein residues (pREST region) in ligand binding domain (LBD) , also considerably improved the FEP+ results in most of the studied cases. Preliminary molecular dynamics (MD) runs were useful for establishing the correct binding mode of the compounds and thus precise alignment for FEP+.<br /

Prediction of partial molar volumes of amino acids and small peptides: Counting atoms versus topological indices

Experimental data of partial molar volumes of amino acids and small peptides were compiled from several publications and enabled us to perform a predicative analysis based on quantitative structure-property relationships (QSPR). Based on the simplest level of the descriptors, the new method has high accuracy and was found to be more reliable when compared to the latter QSPR method based on topological indexes. Incorporation of isoelectric pH and 3-D solvent-accessible surface area parameters increased the predictability of the equation to a small extent. Cross-validation studies show that this method is successful in predicting the partial molar volumes of other noncoded amino acids, dipeptides, and diketopiperazine derivatives. This method is the beginning of new studies for larger peptides and proteins. It also can be suggested to be used for molecules that contain the same type of atoms as an amino acid. © 2010 American Chemical Society

An Improved Free Energy Perturbation FEP+ Sampling Protocol for Flexible Ligand-Binding Domains

Recent improvements to free energy perturbation (FEP) calculations, especiallyFEP+, established their utility for pharmaceutical lead optimization. However, to dateFEP has typically been helpful only when (1) high-quality X-ray data is available and(2) the target protein does not undergo significant conformational changes. Also, alack of systematic studies on determining an adequate sampling time is often one ofthe primary limitations of FEP calculations. Herein, we propose a modified versionof the FEP/REST (i.e., replica exchange with solute tempering) sampling protocol,based on systematic studies on several targets by probing a large number of permutations with different sampling schemes. Improved FEP+ binding affinity predictions for regular flexible-loop (F-loop) motions and considerable structural changes can be obtained by extending the pre-REST sampling time from 0.24 ns to 5 ns/λand 2×10 ns/λ, respectively. We obtained much more precise ∆∆G calculations of the individual perturbations, including the sign of the transformations and less error. We extended the REST simulations from 5 ns to 8 ns to achieve reasonable free energy convergence.Implementing REST to the entire ligand as opposed to solely the perturbed region, and also some important flexible protein residues (pREST region) in ligand binding domain (LBD) , also considerably improved the FEP+ results in most of the studied cases. Preliminary molecular dynamics (MD) runs were useful for establishing the correct binding mode of the compounds and thus precise alignment for FEP+.<br

Looking Back, Looking Forward at Halogen Bonding in Drug Discovery

Halogen bonding has emerged at the forefront of advances in improving ligand: receptor interactions. In particular the newfound ability of this extant non-covalent-bonding phenomena has revolutionized computational approaches to drug discovery while simultaneously reenergizing synthetic approaches to the field. Here we survey, via examples of classical applications involving halogen atoms in pharmaceutical compounds and their biological hosts, the unique advantages that halogen atoms offer as both Lewis acids and Lewis bases

Withaferin-A suppress AKT induced tumor growth in colorectal cancer cells

The oncogenic activation of AKT gene has emerged as a key determinant of the aggressiveness of colorectal cancer (CRC); hence, research has focused on targeting AKT signaling for the treatment of advanced stages of CRC. In this study, we explored the anti-tumorigenic effects of withaferin A (WA) on CRC cells overexpressing AKT in preclinical (in vitro and in vivo) models. Our results indicated that WA, a natural compound, resulted in significant inhibition of AKT activity and led to the inhibition of cell proliferation, migration and invasion by downregulating the epithelial to mesenchymal transition (EMT) markers in CRC cells overexpressing AKT. The oral administration of WA significantly suppressed AKT-induced aggressive tumor growth in a xenograft model. Molecular analysis revealed that the decreased expression of AKT and its downstream pro-survival signaling molecules may be responsible for tumor inhibition. Further, significant inhibition of some important EMT markers, i.e., Snail, Slug, β-catenin and vimentin, was observed in WA-treated human CRC cells overexpressing AKT. Significant inhibition of micro-vessel formation and the length of vessels were evident in WA-treated tumors, which correlated with a low expression of the angiogenic marker RETIC. In conclusion, the present study emphasizes the crucial role of AKT activation in inducing cell proliferation, angiogenesis and EMT in CRC cells and suggests that WA may overcome AKT-induced cell proliferation and tumor growth in CRC