Molecular Modeling in Drug Design

Distal mutation V486M disrupts the catalytic activity of DPP4 by affecting the flap of the propeller domain

Dipeptidyl peptidase-4 (DPP4) plays a crucial role in regulating the bioactivity of glucagon-like peptide-1 (GLP-1) that enhances insulin secretion and pancreatic β-cell proliferation, making it a therapeutic target for type 2 diabetes. Although the crystal structure of DPP4 has been determined, its structure-function mechanism is largely unknown. Here, we examined the biochemical properties of sporadic human DPP4 mutations distal from its catalytic site, among which V486M ablates DPP4 dimerization and causes loss of enzymatic activity. Unbiased molecular dynamics simulations revealed that the distal V486M mutation induces a local conformational collapse in a β-propeller loop (residues 234-260, defined as the flap) and disrupts the dimerization of DPP4. The "open/closed" conformational transitions of the flap whereby capping the active site, are involved in the enzymatic activity of DPP4. Further site-directed mutagenesis guided by theoretical predictions verified the importance of the conformational dynamics of the flap for the enzymatic activity of DPP4. Therefore, the current studies that combined theoretical modeling and experimental identification, provide important insights into the biological function of DPP4 and allow for the evaluation of directed DPP4 genetic mutations before initiating clinical applications and drug development.

Pharmaceutical interest of in-silico approaches

The virtual environment within the computer using software performed on the computer is known as in-silico studies. These drugs designing software play a vital task in discovering new drugs in the field of pharmaceuticals. These designing programs and software are employed in gene sequencing, molecular modeling, and in assessing the three-dimensional structure of the molecule, which can further be used in drug designing and development. Drug development and discovery is not only a powerful, extensive, and an interdisciplinary system but also a very complex and time-consuming method. This book chapter mainly focused on different types of in-silico approaches along with their pharmaceutical applications in numerous diseases.

The future of biomolecular simulation in the pharmaceutical industry: what we can learn from aerodynamics modelling and weather prediction. Part 1. understanding the physical and computational complexity of in silico drug design

The predictive power of simulation has become embedded in the infrastructure of modern economies. Computer-aided design is ubiquitous throughout industry. In aeronautical engineering, built infrastructure and materials manufacturing, simulations are routinely used to compute the performance of potential designs before construction. The ability to predict the behaviour of products is a driver of innovation by reducing the cost barrier to new designs, but also because radically novel ideas can be piloted with relatively little risk. Accurate weather forecasting is essential to guide domestic and military flight paths, and therefore the underpinning simulations are critical enough to have implications for national security. However, in the pharmaceutical and biotechnological industries, the application of computer simulations remains limited by the capabilities of the technology with respect to the complexity of molecular biology and human physiology. Over the last 30 years, molecular-modelling tools have gradually gained a degree of acceptance in the pharmaceutical industry. Drug discovery has begun to benefit from physics-based simulations. While such simulations have great potential for improved molecular design, much scepticism remains about their value. The motivations for such reservations in industry and areas where simulations show promise for efficiency gains in preclinical research are discussed. In this, the first of two complementary papers, the scientific and technical progress that needs to be made to improve the predictive power of biomolecular simulations, and how this might be achieved, is firstly discussed (Part 1). In Part 2, the status of computer simulations in pharma is contrasted with aerodynamics modelling and weather forecasting, and comments are made on the cultural changes needed for equivalent computational technologies to become integrated into life-science industries.

Discovery of new promising USP14 inhibitors: computational evaluation of the thumb-palm pocket

Ubiquitin-specific protease 14 (USP14) is a member of the deubiquitinating enzymes (DUBs) involved in disrupting the ubiquitin-proteasome regulation system, responsible for the degradation of impaired and misfolded proteins, which is an essential mechanism in eukaryotic cells. The involvement of USP14 in cancer progression and neurodegenerative disorders has been reported. Thereof USP14 is a prime therapeutic target; hence, designing efficacious inhibitors against USP14 is central in curbing these conditions. Herein, we relied on structural bioinformatics methods incorporating molecular docking, molecular mechanics generalized born surface area (MM-GBSA), molecular dynamics simulation (MD simulation), and ADME to identify potential allosteric USP14 inhibitors. A library of over 733 compounds from the PubChem repository with >90% match to the IU1 chemical structure was screened in a multi-step framework to attain prospective drug-like inhibitors. Two potential lead compounds (CID 43013232 and CID 112370349) were shown to record better binding affinity compared to IU1, but with subtle difference to IU1-47, a 10-fold potent compound when compared to IU1. The stability of the lead molecules complexed with USP14 was studied via MD simulation. The molecules were found to be stable within the binding site throughout the 50 ns simulation time. Moreover, the protein-ligand interactions across the simulation run time suggest Phe331, Tyr476, and Gln197 as crucial residues for USP14 inhibition. Furthermore, in-silico pharmacological evaluation revealed the lead compounds as pharmacological sound molecules. Overall, the methods deployed in this study revealed two novel candidates that may show selective inhibitory activity against USP14, which could be exploited to produce potent and harmless USP14 inhibitors.Communicated by Ramaswamy H. Sarma.

Design and synthesis of novel isatin-based derivatives targeting cell cycle checkpoint pathways as potential anticancer agents

In recent years, cell cycle and checkpoint pathways regulation are offering new therapeutic approaches against cancer. Isatin, is a well exploited scaffold in the anticancer domain. Accordingly, the current work describes the design and synthesis of two series of (Z)-3-substituted-2-(((E/Z)-5-substituted-2-oxo-1-substituted-indolin-3-ylidene)hydrazinylidene)-thiazolidin-4-ones, 4(a-s) and (E/Z)-1-substituted-3-(((Z)-3-substituted-4-methylthiazol-2(3H)-ylidene)hydrazineylidene)-5-substituted-indolin-2-ones, 5(a-s). The structures of the synthesized molecules were confirmed by spectral and elemental methods of analyses. Pure diastereomers were further identified with ¹H-¹H-NOESY and confirmed with X-ray crystallography. The target compounds were tested in vitro for their cytotoxicity against three human epithelial cell lines, liver (HepG2), breast (MCF-7), and colon (HT-29) in addition to the diploid human normal cells (WI-38) compared to doxorubicin as a reference drug. Variable cytotoxic effects (IC₅₀ 3.29-100 µmol) were reported on the three cancer cell lines with pronounced selectivity compared to the normal one WI-38. The potency of the most active compounds, 4o, 4s, 5e, 5f, 5l, 5m and 5o (IC₅₀ 3.29-9.92 µmol), in both series associated with the (Z) configurations of N = thiazolidin/ene or one, however, the configuration of the N = isatin moiety seemed to be of no importance to the activity. The tested compounds were grouped for their possible mechanism of action into 4 categories. Compound 4o with no apparent effect on all genes examined. Compounds 4s and 5o affected all genes investigated and seem to have multiple cellular targets; induced the expression of p53 and caspases, and downregulated that of CDK1. Compounds 5l and 5m directly elevated the expression of initiator and effector caspases without going through p53 pathway. Finally, compounds 5e and 5f elevated the expression of p53 and inhibited CDK1. Compounds 4s, 5e, 5f, 5l, 5m, and 5o caused a significant elevation in the activity of cleaved caspase 3 as well. Docking studies on CDK1 revealed that the active molecules bind to the tested enzyme by the same manner of the co-crystallized ligands and the isatin-thiazoldinone/ene scaffold is essential for binding of these molecules.

Computer-Aided Ligand Discovery for Estrogen Receptor Alpha

Breast cancer (BCa) is one of the most predominantly diagnosed cancers in women. Notably, 70% of BCa diagnoses are Estrogen Receptor α positive (ERα+) making it a critical therapeutic target. With that, the two subtypes of ER, ERα and ERβ, have contrasting effects on BCa cells. While ERα promotes cancerous activities, ERβ isoform exhibits inhibitory effects on the same. ER-directed small molecule drug discovery for BCa has provided the FDA approved drugs tamoxifen, toremifene, raloxifene and fulvestrant that all bind to the estrogen binding site of the receptor. These ER-directed inhibitors are non-selective in nature and may eventually induce resistance in BCa cells as well as increase the risk of endometrial cancer development. Thus, there is an urgent need to develop novel drugs with alternative ERα targeting mechanisms that can overcome the limitations of conventional anti-ERα therapies. Several functional sites on ERα, such as Activation Function-2 (AF2), DNA binding domain (DBD), and F-domain, have been recently considered as potential targets in the context of drug research and discovery. In this review, we summarize methods of computer-aided drug design (CADD) that have been employed to analyze and explore potential targetable sites on ERα, discuss recent advancement of ERα inhibitor development, and highlight the potential opportunities and challenges of future ERα-directed drug discovery.

Periodic DFT Calculations-Review of Applications in the Pharmaceutical Sciences

In the introduction to this review the complex chemistry of solid-state pharmaceutical compounds is summarized. It is also explained why the density functional theory (DFT) periodic calculations became recently so popular in studying the solid APIs (active pharmaceutical ingredients). Further, the most popular programs enabling DFT periodic calculations are presented and compared. Subsequently, on the large number of examples, the applications of such calculations in pharmaceutical sciences are discussed. The mentioned topics include, among others, validation of the experimentally obtained crystal structures and crystal structure prediction, insight into crystallization and solvation processes, development of new polymorph synthesis ways, and formulation techniques as well as application of the periodic DFT calculations in the drug analysis.

Investigation of Phospholipase Cγ1 Interaction with SLP76 Using Molecular Modeling Methods for Identifying Novel Inhibitors

The enzyme phospholipase C gamma 1 (PLCγ1) has been identified as a potential drug target of interest for various pathological conditions such as immune disorders, systemic lupus erythematosus, and cancers. Targeting its SH3 domain has been recognized as an efficient pharmacological approach for drug discovery against PLCγ1. Therefore, for the first time, a combination of various biophysical methods has been employed to shed light on the atomistic interactions between PLCγ1 and its known binding partners. Indeed, molecular modeling of PLCγ1 with SLP76 peptide and with previously reported inhibitors (ritonavir, anethole, daunorubicin, diflunisal, and rosiglitazone) facilitated the identification of the common critical residues (Gln805, Arg806, Asp808, Glu809, Asp825, Gly827, and Trp828) as well as the quantification of their interaction through binding energies calculations. These features are in agreement with previous experimental data. Such an in depth biophysical analysis of each complex provides an opportunity to identify new inhibitors through pharmacophore mapping, molecular docking and MD simulations. From such a systematic procedure, a total of seven compounds emerged as promising inhibitors, all characterized by a strong binding with PLCγ1 and a comparable or higher binding affinity to ritonavir (∆G_bind < -25 kcal/mol), one of the most potent inhibitor reported till now.