Discovering novel carbonic anhydrase type IX (CA IX) inhibitors from seven million compounds using virtual screening and in vitro analysis

Carbonic anhydrase type IX (CA IX) enzyme is mostly over expressed in different cancer cell lines and tumor tissues. Potent CA IX inhibitors can be effective for adjusting the pH imbalance in tumor cells. In the present work, we represented the successful application of high throughput virtual screening (HTVS) of large dataset from ZINC database included of ～7 million compounds to discover novel inhibitors of CA IX. HTVS and molecular docking were performed using consequence Glide/standard precision (SP), extra precision (XP) and induced fit docking (IFD) molecular docking protocols. For each compound, docking code calculates a set of low-energy poses and then exhaustively scans the binding pocket of the target with small compounds. Novel CA IX inhibitor candidates were suggested based on molecular modeling studies and a few of them were tested using in vitro analysis. These compounds were determined as good inhibitors against human CA IX target with K_i in the range of 0.85–1.58?μM. In order to predict the pharmaceutical properties of the selected compounds, ADME (absorption, distribution, metabolism and excretion) analysis was also carried out.     

Virtual screening,docking, and dynamics of potential new inhibitors of dihydrofolate reductase from Yersinia pestis

In the present work, we propose to design drugs that target the enzyme dihydrofolate redutase (DHFR) as a means of a novel drug therapy against plague. Potential inhibitors of DHFR from Yersinia pestis (YpDHFR) were selected by virtual screening and subjected to docking, molecular dynamics (MD) simulations, and Poisson–Boltzmann surface area method, in order to evaluate their interactions in the active sites of YpDHFR and human DHFR (HssDHFR). The results suggested selectivity for three compounds that were further used to propose the structures of six new potential selective inhibitors for YpDHFR.     

When mental fatigue maybe characterized by Event Related Potential (P300) during virtual wheelchair navigation

The goal of this study is to investigate the influence of mental fatigue on the event related potential P300 features (maximum pick, minimum amplitude, latency and period) during virtual wheelchair navigation. For this purpose, an experimental environment was set up based on customizable environmental parameters (luminosity, number of obstacles and obstacles velocities). A correlation study between P300 and fatigue ratings was conducted. Finally, the best correlated features supplied three classification algorithms which are MLP (Multi Layer Perceptron), Linear Discriminate Analysis and Support Vector Machine. The results showed that the maximum feature over visual and temporal regions as well as period feature over frontal, fronto-central and visual regions were correlated with mental fatigue levels. In the other hand, minimum amplitude and latency features didn’t show any correlation. Among classification techniques, MLP showed the best performance although the differences between classification techniques are minimal. Those findings can help us in order to design suitable mental fatigue based wheelchair control.     

Identification of selective and reversible LSD1 inhibitors with anti-metastasis activity by high-throughput docking

The overexpression of lysine specific demethylase 1 (LSD1) has been reported in various human tumors. There is increasing interest in targeting LSD1 with small molecules for cancer treatment. A released structure of an LSD1 kinase domain in complex with FAD was used to set up a low-cost high-throughput docking protocol for quick identification of LSD1 inhibitors. The most promising hit L05 was confirmed to be a potent, selective and reversible LSD1 inhibitor and displayed marked inhibition of colorectal cells migration without significant cytotoxicity.     

组织学与胚胎学虚拟仿真实验教学平台的构建

虚拟仿真实验课程建设,是推进现代信息技术与实验教学项目深度融合、拓展实验教学内容广度和深度、延伸实验教学时间和空间、提升实验教学质量和水平的重要举措。开展虚拟仿真实验教学能够强化学生实验基本技能、激发学生求知欲、培养学生创造性、提高学生专业技能,在组织学与胚胎学实验教学中具有重要意义。     

An interactive surgical simulation tool to assess the consequences of a partial glossectomy on a biomechanical model of the tongue

Oral cancer surgery has a negative influence on the quality of life (QOL). As a result of the complex physiology involved in oral functions, estimation of surgical effects on functionality remains difficult. We present a user-friendly biomechanical simulation of tongue surgery, including closure with suturing and scar formation, followed by an automated adaptation of a finite element (FE) model to the shape of the tongue. Different configurations of our FE model were evaluated and compared to a well-established FE model. We showed that the post-operative impairment as predicted by our model was qualitatively comparable to a patient case for five different tongue maneuvers.     

Combinatorial peptide library screening for discovery of diverse α-glucosidase inhibitors using molecular dynamics simulations and binary QSAR models

Human α-glucosidase is an enzyme involved in the catalytic cleavage of the glucoside bond and involved in numerous functionalities of the organism, as well as in the insurgence of diabetes mellitus 2 and obesity. Thus, developing chemicals that inhibit this enzyme is a promising approach for the treatment of several pathologies. Small peptides such as di- and tri-peptides may be in natural organism as well as in the GI tract in high concentration, coming from the digestive process of meat, wheat and milk proteins. In this work, we reported the first tentative hierarchical structure-based virtual screening of peptides for human α-glucosidase. The goal of this work is to discover novel and diverse lead compounds that my act as inhibitors of α-glucosidase such as small peptides by performing a computer aided virtual screening and to find novel scaffolds for further development. Thus, in order to select novel candidates with original structure we performed molecular dynamics (MD) simulations among the 12 top-ranked peptides taking as comparison the MD simulations performed on crystallographic inhibitor acarbose. The compounds with the lower RMSD variability during the MD, were reserved for in vitro biological assay. The selected 4 promising structures were prepared on solid phase peptide synthesis and used for the inhibitory assay, among them compound 2 showed good inhibitory activity, which validated our method as an original strategy to discover novel peptide inhibitors. Moreover, pharmacokinetic profile predictions of these 4 peptides were also carried out with binary QSAR models using MetaCore/MetaDrug applications.     

In silico discovery of potential drug molecules to improve the treatment of isoniazid-resistant Mycobacterium tuberculosis

The emergence of multidrug-resistant Mycobacterium tuberculosis (M.tb) has become one of the major hurdles in the treatment of tuberculosis (TB). Drug-resistant M.tb has evolved with various strategies to avoid killing by the anti-tubercular drugs. Thus, there is a rising need to develop effective anti-TB drugs to improve the treatment of these strains. Traditional drug design approach has earned little success due to time and the cost involved in the process of development of anti-infective drugs. Numerous reports have demonstrated that several mutations in the drug target sites cause emergence of drug-resistant M.tb strains. In this study, we performed computational mutational analysis of M.tb inhA, fabD, and ahpC genes, which are the primary targets for first-line isoniazid (INH) drug. In silico virtual drug screening was performed to identify the potent drugs from a ChEMBL compound library to improve the treatment of INH-resistant M.tb. Further, these compounds were analyzed for their binding efficiency against active drug binding cavity of M.tb wild-type and mutant InhA, FabD and AhpC proteins. The drug efficacy of predicted lead compounds was verified by molecular docking using M.tb wild-type and mutant InhA, FabD and AhpC protein template models. Different in silico and pharmacophore analysis predicted three potent lead compounds with better drug-like properties against both M.tb wild-type and mutant InhA, FabD, and AhpC proteins as compared to INH drug, and thus may be considered as effective drugs for the treatment of INH-resistant M.tb strains. We hypothesize that this work may accelerate drug discovery process for the treatment of drug-resistant TB.

Communicated by Ramaswamy H. Sarma     

A novel identification approach for discovery of 5-HydroxyTriptamine 2A antagonists: combination of 2D/3D similarity screening,molecular docking and molecular dynamics

5-HydroxyTriptamine 2A antagonists are potential targets for treatment of various cerebrovascular and cardiovascular disorders. In this study, we have developed and performed a unique screening pipeline for filtering ZINC database compounds on the basis of similarities to known antagonists to determine novel small molecule antagonists of 5-HydroxyTriptamine 2A. The screening pipeline is based on 2D similarity, 3D dissimilarity and a combination of 2D/3D similarity. The shortlisted compounds were docked to a 5-HydroxyTriptamine 2A homology-based model, and complexes with low binding energies (287 complexes) were selected for molecular dynamics (MD) simulations in a lipid bilayer. The MD simulations of the shortlisted compounds in complex with 5-HydroxyTriptamine 2A confirmed the stability of the complexes and revealed novel interaction insights. The receptor residues S239, N343, S242, S159, Y370 and D155 predominantly participate in hydrogen bonding. π–π stacking is observed in F339, F340, F234, W151 and W336, whereas hydrophobic interactions are observed amongst V156, F339, F234, V362, V366, F340, V235, I152 and W151. The known and potential antagonists shortlisted by us have similar overlapping molecular interaction patterns. The 287 potential 5-HydroxyTriptamine 2A antagonists may be experimentally verified.