CRAFT: a web-integrated cavity prediction tool based on flow transfer algorithm.

Numerous computational methods, including evolutionary-based, energy-based, and geometrical-based methods, are utilized to identify cavities inside proteins. Cavity information aids protein function annotation, drug design, poly-pharmacology, and allosteric site investigation. This article introduces "flow transfer algorithm" for rapid and effective identification of diverse protein cavities through multidimensional cavity scan. Initially, it identifies delimiter and susceptible tetrahedra to establish boundary regions and provide seed tetrahedra. Seed tetrahedron faces are precisely scanned using the maximum circle radius to transfer seed flow to neighboring tetrahedra. Seed flow continues until terminated by boundaries or forbidden faces, where a face is forbidden if the estimated maximum circle radius is less or equal to the user-defined maximum circle radius. After a seed scanning, tetrahedra involved in the flow are clustered to locate the cavity. The CRAFT web interface integrates this algorithm for protein cavity identification with enhanced user control. It supports proteins with cofactors, hydrogens, and ligands and provides comprehensive features such as 3D visualization, cavity physicochemical properties, percentage contribution graphs, and highlighted residues for each cavity. CRAFT can be accessed through its web interface at http://pitools.niper.ac.in/CRAFT , complemented by the command version available at https://github.com/PGlab-NIPER/CRAFT/ .Scientific contribution: Flow transfer algorithm is a novel geometric approach for accurate and reliable prediction of diverse protein cavities. This algorithm employs a distinct concept involving maximum circle radius within the 3D Delaunay triangulation to address diverse van der Waals radii while existing methods overlook atom specific van der Waals radii or rely on complex weighted geometric techniques.

Target validation and structure-based virtual screening to Discover potential lead molecules against the oncogenic NSD1 histone methyltransferase.

The aim of the study was to validate Nuclear receptor-binding SET Domain NSD1 as a cancer drug target followed by the design of lead molecules against NSD1. TCGA clinical data, molecular expression techniques were used to validate the target and structure-based virtual screening was performed to design hits against NSD1. Clinical data analysis suggests the role of NSD1 in metastasis, prognosis and influence on overall survival in various malignancies. Furthermore, the mRNA and protein expression profile of NSD1 was evaluated in various cell lines. NSD1 was exploited as a target protein for in silico design of inhibitors using two major databases including ZINC15 and ChemDiv by structure-based virtual screening approach. Virtual screening was performed using the pharmacophore hypothesis designed with a protein complex S-adenosyl-l-methionine (SAM) as an endogenous ligand. Subsequently, a combined score was used to distinguish the top 10 compounds from the docking screened compounds having high performance in all four scores (docking score, XP, Gscore, PhaseScreenScore, and MMGBSA delta G Bind). Finally, the top three Zinc compounds were subjected to molecular dynamic simulation. The binding MMGBSA data suggests that ZINC000257261703 and ZINC000012405780 can be taken for in vitro and in vivo studies as they have lesser MMGBSA energy towards the cofactor binding site of NSD1 than the sinefungin. Our data validates NSD1 as a cancer drug target and provides promising structures that can be utilized for further lead optimization and rational drug design to open new gateways in the field of cancer therapeutics. Supplementary Information: The online version contains supplementary material available at 10.1007/s40203-023-00158-0.

Characterization of the enzymatic and multifunctional properties of Acinetobacter baumannii erythrose-4-phosphate dehydrogenase (E4PDH).

Infections due to Acinetobacter baumannii (A. baumannii) are rapidly increasing worldwide and consequently therapeutic options for treatment are limited. The emergence of multi drug resistant (MDR) strains has rendered available antibiotics ineffective, necessitating the urgent discovery of new drugs and drug targets. The vitamin B6 biosynthetic pathway has been considered as a potential antibacterial drug target but it is as yet uncharacterized for A. baumannii. In the current work, we have carried out in silico and biochemical characterization of Erythrose-4-phosphate dehydrogenase (E4PDH) (EC 1.2.1.72). This enzyme catalyzes the first step in the deoxyxylulose-5-phosphate (DXP) dependent Vitamin B6 biosynthetic pathway i.e. the conversion of d-erythrose-4-phosphate (E4P) to 4-Phosphoerythronate. E4PDH also possesses an additional activity whereby it can catalyze the conversion of Glyceraldehyde-3-phosphate (G3P) to 1,3 bisphosphoglycerate (1,3BPG). Our studies have revealed that this enzyme exhibits an alternate moonlighting function as a cell surface receptor for the human iron transport proteins transferrin (Tf) and lactoferrin (Lf). The present work reports the internalization of Tf and consequent iron acquisition as an alternate strategy for iron acquisition. Given its essential role in two crucial pathways i.e. metabolism and iron acquisition, A. baumannii E4PDH may play a vital role in bacterial pathogenesis.

Drug repurposing for Alzheimer's disease: in silico and in vitro investigation of FDA-approved drugs as acetylcholinesterase inhibitors.

Alzheimer's disease (AD) is one of the most familiar multifactorial and complex neurodegenerative disorders characterized by loss of cholinergic neurons in the brain. The various attempts for drug development to treat AD have been hampered by largely unsuccessful clinical trials in the last two decades. Developing a new drug from scratch takes enormous amounts of time, effort and money, mainly due to several barriers in the therapeutic drug development process. Drug repurposing strategy resuscitates this slow drug discovery process by finding new uses and clinical indications for existing drugs. This study is focused on the cholinergic hypothesis, a well-established target of the clinically available drugs in the market for the treatment of AD. The computational virtual screening (VS) led to the identification of thiazolidinedione (TZD, antidiabetic) and aminoquinoline (antimalarial) class of drugs as acetylcholinesterase (AChE) inhibitors. Intriguingly, rosiglitazone (RGZ) and hydroxychloroquine (HCQ) were found to be mild-to-moderate inhibitors of hAChE (human acetylcholinesterase) in our enzyme inhibition studies which are complementary to our computational studies. On the basis of our computational and experimental studies, it can be suggested that the beneficial effect of RGZ and HCQ in AD patients reported in the literature may partly be due to their AChE inhibitory property. The VS also led to the identification of antifungal drugs miconazole and oxiconazole as potential AChE inhibitors. The molecular dynamics (MD) simulation of the potential hits belonging to TZD, aminoquinoline and azoles class were also carried out. The MD simulations studies revealed detailed computational insights related to molecular interactions and protein-ligand stability for selected hits.

Mechanistic studies on the drug metabolism and toxicity originating from cytochromes P450.

Cytochromes P450 are oxidizing enzymes; a few families of cytochromes P450 are implicated in drug metabolism. These enzymatic reactions involve many processes including (i) prodrug to drug conversion, (ii) easy excretion of drug, (iii) generation of reactive metabolites, many of which cause toxicity. In this review, the fundamental biochemical mechanisms associated with the conversion of drugs into the useful or toxic metabolites have been discussed. The mechanisms can be established with the help of many experimental methods like mass spectral analysis, NMR and in vitro analysis etc. Computational methods provide detailed atomic level information, which is generally not available from experimental studies. Thus, the in silico efforts in elucidating the molecular mechanisms are complementary to the known experimental methods and are often clearer (especially in providing 3D information about the metabolites and their reactions). Quantum chemical methods and molecular docking become especially very useful. This review includes five case studies, which explain how the atomic level details were obtained to explore the reaction mechanisms of drug metabolism by cytochromes P450.

Structure-Based Virtual Screening to Discover Potential Lead Molecules for the SARS-CoV-2 Main Protease.

The COVID-19 disease is caused by a new strain of the coronavirus family (SARS-CoV-2), and it has affected at present millions of people all over the world. The indispensable role of the main protease (Mpro) in viral replication and gene expression makes this enzyme an attractive drug target. Therefore, inhibition of SARS-CoV-2 Mpro as a proposition to halt virus ingression is being pursued by scientists globally. Here we carried out a study with two objectives: the first being to perform comparative protein sequence and 3D structural analysis to understand the effect of 12 point mutations on the active site. Among these, two mutations, viz., Ser46 and Phe134, were found to cause a significant change at the active sites of SARS-CoV-2. The Ser46 mutation present at the entrance of the S5 subpocket of SARS-CoV-2 increases the contribution of other two hydrophilic residues, while the Phe134 mutation, present in the catalytic cysteine loop, can cause an increase in catalytic efficiency of Mpro by facilitating fast proton transfer from the Cys145 to His41 residue. It was observed that active site remained conserved among Mpro of both SARS-CoVs, except at the entrance of the S5 subpocket, suggesting sustenance of substrate specificity. The second objective was to screen the inhibitory effects of three different data sets (natural products, coronaviruses main protease inhibitors, and FDA-approved drugs) using a structure-based virtual screening approach. A total of 73 hits had a combo score >2.0. Eight different structural scaffold classes were identified, such as one/two tetrahydropyran ring(s), dipeptide/tripeptide/oligopeptide, large (approximately 20 atoms) cyclic peptide, and miscellaneous. The screened hits showed key interactions with subpockets of the active site. Further, molecular dynamics studies of selected screened compounds confirmed their perfect fitting into the subpockets of the active site. This study suggests promising structures that can fit into the SARS-CoV-2 Mpro active site and also offers direction for further lead optimization and rational drug design.