Exploration of Mycobacterium tuberculosis structural proteome: An in-silico approach

Prediction of COMT Inhibitors Using Machine Learning and Molecular Dynamics Methods

Catechol O-methyltransferase (COMT) plays a vital role in deactivating neurotransmitters like dopamine, norepinephrine, etc., by methylating those compounds. However, the deactivation of an excess amount of neurotransmitters leads to serious mental ailments such as Parkinson's disease. Molecules that bind inside the enzyme's active site inhibit this methylation mechanism by methylating themselves, termed COMT inhibitors. Our study is focused on designing these inhibitors by various machine learning methods. First, we have developed a classification model with experimentally available COMT inhibitors, which helped us generate a new data set of small inhibitor-like molecules. Then, to predict the activity of the new molecules, we have applied regression techniques such as Random Forest, AdaBoost, gradient boosting, and support vector machines. Each of the regression models yielded an R² value > 70% for both training and test data sets. Finally, to validate our models, 200 ns long molecular dynamics (MD) simulations of the two known inhibitors with known IC₅₀ values and the resultant inhibitors were performed inside the binding pockets to check their stability within. The free energy barrier of the methyl transfer from S-adenosyl-l-methionine (SAM) to each inhibitor was determined by combining steered molecular dynamics (SMD) and umbrella sampling using the quantum mechanics/molecular mechanics (QM/MM) method.

Application of ensemble pharmacophore-based virtual screening to the discovery of novel antimitotic tubulin inhibitors

Tubulin is a well-validated target for herbicides, fungicides, anti-parasitic, and anti-tumor drugs. Many of the non-cancer tubulin drugs bind to its colchicine site but no colchicine-site anticancer drug is available. The colchicine site is composed of three interconnected sub-pockets that fit their ligands and modify others' preference, making the design of molecular hybrids (that bind to more than one sub-pocket) a difficult task. Taking advantage of the more than eighty published X-ray structures of tubulin in complex with ligands bound to the colchicine site, we generated an ensemble of pharmacophore representations that flexibly sample the interactional space between the ligands and target. We searched the ZINC database for scaffolds able to fit several of the subpockets, such as tetrazoles, sulfonamides and diarylmethanes, selected roughly ~8000 compounds with favorable predicted properties. A Flexi-pharma virtual screening, based on ensemble pharmacophore, was performed by two different methodologies. Combining the scaffolds that best fit the ensemble pharmacophore-representation, we designed a new family of ligands, resulting in a novel tubulin modulator. We synthesized tetrazole 5 and tested it as a tubulin inhibitor in vitro. In good agreement with the design principles, it demonstrated micromolar activity against in vitro tubulin polymerization and nanomolar anti-proliferative effect against human epithelioid carcinoma HeLa cells through microtubule disruption, as shown by immunofluorescence confocal microscopy. The integrative methodology succedes in the design of new scaffolds for flexible proteins with structural coupling between pockets, thus expanding the way in which computational methods can be used as significant tools in the drug design process.

Identification of the natural compound inhibitors against Plasmodium falciparum plasmepsin-II via common feature based screening and molecular dynamics simulations

Malaria is counted amongst the deadly disease caused by Plasmodium falciparum. Recently, plasmepsin-II enzyme has gained much importance as an attractive drug target for the exploration of antimalarials. Therefore, the common feature pharmacophore models were generated from the crystallized complexes of the plasmepsin-II proteome. These models were subjected to a series of validation procedures, i.e. test set and Güner Henry studies to enlist the representative models. The selected representative hypotheses incorporating the most essential chemical features (common ZHHA) were screened against the natural product database to retrieve the potential candidates. To ensure the selection of the drug-like candidates, prior to screening, filtering steps (Drug-likeness and ADMET filters) were employed on the selected database. To study the interaction pattern of the candidates within the protein, these molecules were advanced to the molecular docking studies. Subsequently, based on the selected cut-off criteria obtained via redocking of the reference (4Z22), 15 compounds showed higher docking score (> -16.05 kcal/mol), and displayed the presence of hydrogen bonding with the crucial amino acids, i.e. Asp34 and Asp214. Further, the stability of the docked molecules was scrutinized via molecular dynamics simulations, and the results were compared with the reference compound 4Z22. All the docked compounds showed stable dynamics behaviour. Thus, in the present contribution, the combination of screening and stability procedures resulted in the identification of 15 hits that can serve as a new chemical space in the designing of the novel antimalarials.Communicated by Ramaswamy H. Sarma.

In silico tools to study molecular targets of neglected diseases: inhibition of TcSir2rp3, an epigenetic enzyme of Trypanosoma cruzi

There is a growing interest to study and address neglected tropical diseases (NTD). To this end, in silico methods can serve as the bridge that connects academy and industry, encouraging the development of future treatments against these diseases. This chapter discusses current challenges in the development of new therapies, available computational methods and successful cases in computer-aided design with particular focus on human trypanosomiasis. Novel targets are also discussed. As a case study, we identify amentoflavone as a potential inhibitor of TcSir2rp3 (sirtuine) from Trypanosoma cruzi (20.03 μM) with a workflow that integrates chemoinformatic approaches, molecular modeling, and theoretical affinity calculations, as well as in vitro assays.

Identification of natural compound inhibitors against PfDXR: A hybrid structure-based molecular modeling approach and molecular dynamics simulation studies

In the present contribution, multicomplex-based pharmacophore studies were carried out on the structural proteome of Plasmodium falciparum 1-deoxy-D-xylulose-5-phosphate reductoisomerase. Among the constructed models, a representative model with complementary features, accountable for the inhibition was used as a primary filter for the screening of database molecules. Auxiliary evaluations of the screened molecules were performed via drug-likeness and molecular docking studies. Subsequently, the stability of the docked inhibitors was envisioned by molecular dynamics simulations, principle component analysis, and molecular mechanics-Poisson-Boltzmann surface area-based free binding energy calculations. The stability assessment of the hits was done by comparing with the reference (beta-substituted fosmidomycin analog, LC5) to prioritize more potent candidates. All the complexes showed stable dynamic behavior while three of them displayed higher binding free energy compared with the reference. The work resulted in the identification of the compounds with diverse scaffolds, which could be used as initial leads for the design of novel PfDXR inhibitors.