Recent Trends in In-silico Drug Discovery

A Drug designing is a process in which new leads (potential drugs) are discovered which have therapeutic benefits in diseased condition. With development of various computational tools and availability of databases (having information about 3D structure of various molecules) discovery of drugs became comparatively, a faster process. The two major drug development methods are structure based drug designing and ligand based drug designing. Structure based methods try to make predictions based on three dimensional structure of the target molecules. The major approach of structure based drug designing is Molecular docking, a method based on several sampling algorithms and scoring functions. Docking can be performed in several ways depending upon whether ligand and receptors are rigid or flexible. Hotspot grafting, is another method of drug designing. It is preferred when the structure of a native binding protein and target protein complex is available and the hotspots on the interface are known. In absence of information of three Dimensional structure of target molecule, Ligand based methods are used. Two common methods used in ligand based drug designing are Pharmacophore modelling and QSAR. Pharmacophore modelling explains only essential features of an active ligand whereas QSAR model determines effect of certain property on activity of ligand. Fragment based drug designing is a de novo approach of building new lead compounds using fragments within the active site of the protein. All the candidate leads obtained by various drug designing method need to satisfy ADMET properties for its development as a drug. In-silico ADMET prediction tools have made ADMET profiling an easier and faster process. In this review, various softwares available for drug designing and ADMET property predictions have also been listed

Software for molecular docking: a review

Publshed ArticleMolecular docking methodology explores the behavior of small molecules in the binding site of a target protein. As more protein structures are determined experimentally using X-ray crystallography or nuclear magnetic resonance (NMR) spectroscopy, molecular docking is increasingly used as a tool in drug discovery. Docking against homologymodeled targets also becomes possible for proteins whose structures are not known. With the docking strategies, the druggability of the compounds and their specificity against a particular target can be calculated for further lead optimization processes. Molecular docking programs perform a search algorithm in which the conformation of the ligand is evaluated recursively until the convergence to the minimum energy is reached. Finally, an affinity scoring function, ΔG [U total in kcal/mol], is employed to rank the candidate poses as the sum of the electrostatic and van der Waals energies. The driving forces for these specific interactions in biological systems aim toward complementarities between the shape and electrostatics of the binding site surfaces and the ligand or substrate

Targeting Acetylcholinesterase: Identification of Chemical Leads by High Throughput Screening, Structure Determination and Molecular Modeling

Acetylcholinesterase (AChE) is an essential enzyme that terminates cholinergic transmission by rapid hydrolysis of the neurotransmitter acetylcholine. Compounds inhibiting this enzyme can be used (inter alia) to treat cholinergic deficiencies (e.g. in Alzheimer's disease), but may also act as dangerous toxins (e.g. nerve agents such as sarin). Treatment of nerve agent poisoning involves use of antidotes, small molecules capable of reactivating AChE. We have screened a collection of organic molecules to assess their ability to inhibit the enzymatic activity of AChE, aiming to find lead compounds for further optimization leading to drugs with increased efficacy and/or decreased side effects. 124 inhibitors were discovered, with considerable chemical diversity regarding size, polarity, flexibility and charge distribution. An extensive structure determination campaign resulted in a set of crystal structures of protein-ligand complexes. Overall, the ligands have substantial interactions with the peripheral anionic site of AChE, and the majority form additional interactions with the catalytic site (CAS). Reproduction of the bioactive conformation of six of the ligands using molecular docking simulations required modification of the default parameter settings of the docking software. The results show that docking-assisted structure-based design of AChE inhibitors is challenging and requires crystallographic support to obtain reliable results, at least with currently available software. The complex formed between C5685 and Mus musculus AChE (C5685•mAChE) is a representative structure for the general binding mode of the determined structures. The CAS binding part of C5685 could not be structurally determined due to a disordered electron density map and the developed docking protocol was used to predict the binding modes of this part of the molecule. We believe that chemical modifications of our discovered inhibitors, biochemical and biophysical characterization, crystallography and computational chemistry provide a route to novel AChE inhibitors and reactivators

Identifying prospective inhibitors against LdtMt5 from Mycobacterium tuberculosis as a potential drug target.

Masters Degree. University of KwaZulu-Natal, Durban.Tuberculosis (TB) caused by the bacterium, Mycobacterium tuberculosis (M.tb) has resulted in an unprecedented number of deaths over centuries. L,D-transpeptidase enzymes are known to play a crucial role in the biosynthesis of the cell wall, which confers resistance to most antibiotics. These enzymes catalyze the 3→3 peptidoglycan cross-links of the M.tb cell wall. Specific β-lactam antibiotics (carbapenems) have been reported to inhibit cell wall polymerization of M.tb and they inactivate L,D-transpeptidases through acylation. L,Dtranspeptidase 5 (LdtMt5) is a unique paralog and a vital protein in maintaining integrity of the cell wall specifically in peptidoglycan metabolism therefore making it an important protein target. Carbapenems inhibit LdtMt2, but do not show reasonable inhibitory activities against LdtMt5. We therefore sought to perform virtual screening in order to acquire potential inhibitors against LdtMt5 and to investigate the affinity and to calculate the binding free energies between LdtMt5 and potential inhibitors. Furthermore, we sought to investigate the nature of the transition state involved in the catalytic reaction mechanism; to determine the activation free energies of the mechanism using ONIOM through the thermodynamics and energetics of the reaction path and lastly to express, purify and perform inhibition studies on LdtMt5. A total of 12766 compounds were computationally screened from the ZINC database to identify potential leads against LdtMt5. Docking was performed using two different software programs. Molecular dynamics (MD) simulations were subsequently performed on compounds obtained through virtual screening. Density functional theory (DFT) calculations were then carried out to understand the catalytic mechanism of LdtMt5 with respect to β-lactam derivatives using a hybrid ONIOM quantum mechanics/molecular mechanics (QM/MM) method. LdtMt5 complexes with six selected β-lactam compounds were evaluated. Finally, a lyophilised pET28a-LdtMt5 was used to transform E. coli strain BL21 (DE3) and SDS-PAGE was used to verify the purity, molecular weight and protein profile determination. Finally, an in vitro binding thermodynamics analysis using isothermal titration calorimetry (ITC) was later on performed on a single compound (the strongest binder) from the final set, in a bid to further validate the calculated binding energy values. A number of compounds from four different antimicrobial classes (n = 98) were obtained from the virtual screening and those with docking scores ranging from -7.2 to -9.9 kcal mol-1 were considered for MD analysis (n = 37). A final set of 10 compounds which exhibited the greatest affinity, from four antibiotic classes was selected and Molecular Mechanics/Generalized Born iii Surface Area (MM-GBSA) binding free energies (ΔGbind) from the set were characterised. The calculated binding free energies ranged from -30.68 to -48.52 kcal mol-1 . The β-lactam class of compounds demonstrated the highest ΔGbind and also the greatest number of potential inhibitors. The DFT activation energies (∆G # ) obtained for the acylation of LdtMt5 by the six selected β-lactams were calculated as 13.67, 20.90, 22.88, 24.29, 27.86 and 28.26 kcal mol-1 . The ∆G# results from the 6-membered ring transition state (TS) revealed that all selected six βlactams were thermodynamically more favourable than previously calculated activation energy values for imipenem and meropenem complexed with LdtMt5. The results are also comparable to those observed for LdtMt2, however for compound 1 the values are considerably lower than those obtained for meropenem and imipenem in complex with LdtMt2, thus suggesting in theory that compound 1 is a more potent inhibitor of LdtMt5. We also report the successful expression and and purification of LdtMt5, however the molecule selected for the in vitro inhibition study gave a poor result. On further review, we concluded that the main cause of this outcome was due to the relatively low insolubility of the compound. The outcome of this study provides insight into the design of potential novel leads for LdtMt5. Our screening obtained ten novel compounds from four different antimicrobial classes. We suggest that further in vitro binding thermodynamics analysis of the novel compounds from the four classes, including the carbapenems be performed to evaluate inhibition of these compounds on LdtMt5. If the experimental observations suggest binding affinity to the protein, catalytic mechanistic studies can be undertaken. These results will also be used to verify or modify our computational model

In silico data mining of large-scale databases for the virtual screening of human interleukin-2 inhibitors

Interleukin-2 (IL-2) is involved in the activation and differentiation of T-helper cells. Uncontrolled activated T cells play a key role in the pathophysiology by stimulating inflammation and autoimmune diseases like arthritis, psoriasis and Crohn’s disease. T cells activation can be suppressed either by preventing IL-2 production or blocking the IL-2 interaction with its receptor. Hence, IL-2 is now emerging as a target for novel therapeutic approaches in several autoimmune disorders. This study was carried out to set up an effective virtual screening (VS) pipeline for IL-2. Four docking/scoring approaches (FRED, MOE, GOLD and Surflex-Dock) were compared in the re-docking process to test their performance in producing correct binding modes of IL-2 inhibitors. Surflex-Dock and FRED were the best in predicting the native pose in its top-ranking position. Shapegauss and CGO scoring functions identified the known inhibitors of IL-2 in top 1, 5 and 10 % of library and differentiated binders from non-binders efficiently with average AUC of > 0.9 and > 0.7, resp. The applied docking protocol served as a basis for the VS of a large database that will lead to the identification of more active compounds against IL-2

Scoring docking conformations using predicted protein interfaces

BACKGROUND: Since proteins function by interacting with other molecules, analysis of protein-protein interactions is essential for comprehending biological processes. Whereas understanding of atomic interactions within a complex is especially useful for drug design, limitations of experimental techniques have restricted their practical use. Despite progress in docking predictions, there is still room for improvement. In this study, we contribute to this topic by proposing T-PioDock, a framework for detection of a native-like docked complex 3D structure. T-PioDock supports the identification of near-native conformations from 3D models that docking software produced by scoring those models using binding interfaces predicted by the interface predictor, Template based Protein Interface Prediction (T-PIP). RESULTS: First, exhaustive evaluation of interface predictors demonstrates that T-PIP, whose predictions are customised to target complexity, is a state-of-the-art method. Second, comparative study between T-PioDock and other state-of-the-art scoring methods establishes T-PioDock as the best performing approach. Moreover, there is good correlation between T-PioDock performance and quality of docking models, which suggests that progress in docking will lead to even better results at recognising near-native conformations. CONCLUSION: Accurate identification of near-native conformations remains a challenging task. Although availability of 3D complexes will benefit from template-based methods such as T-PioDock, we have identified specific limitations which need to be addressed. First, docking software are still not able to produce native like models for every target. Second, current interface predictors do not explicitly consider pairwise residue interactions between proteins and their interacting partners which leaves ambiguity when assessing quality of complex conformations

STUDY OF MAO-B INHIBITOR ANALOUGES FOR PARKINSON’S DISEASE THROUGH CADD APPROACHES

New derivatives are designed as target directed MAO-B Inhibitors for medical care of the patients for neurodegenerative disorder. Molecular design and estimated pharmacokinetic properties have been evaluated by using Inventus v 1.1 software. The binding mode of the proposed compounds with target protein i.e. 1S2Q was evaluated and the resulting data from docking studies explained that newly designed derivatives have high and better affinity towards target protein. Based on these properties, the binding affinities are used for speeding up drug discovery process by eliminating less potent compounds from synthesis. Keywords: MAO-B, Inventus, Target protein, Neurodegenerative, Docking

Protein-protein interaction networks: unraveling the wiring of molecular machines within the cell

This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License.Mapping and understanding of the protein interaction networks with their key modules and hubs can provide deeper insights into the molecular machinery underlying complex phenotypes. In this article, we present the basic characteristics and definitions of protein networks, starting with a distinction of the different types of associations between proteins. We focus the review on protein-protein interactions (PPIs), a subset of associations defined as physical contacts between proteins that occur by selective molecular docking in a particular biological context. We present such definition as opposed to other types of protein associations derived from regulatory, genetic, structural or functional relations. To determine PPIs, a variety of binary and co-complex methods exist; however, not all the technologies provide the same information and data quality. A way of increasing confidence in a given protein interaction is to integrate orthogonal experimental evidences. The use of several complementary methods testing each single interaction assesses the accuracy of PPI data and tries to minimize the occurrence of false interactions. Following this approach there have been important efforts to unify primary databases of experimentally proven PPIs into integrated databases. These meta-databases provide a measure of the confidence of interactions based on the number of experimental proofs that report them. As a conclusion, we can state that integrated information allows the building of more reliable interaction networks. Identification of communities, cliques, modules and hubs by analysing the topological parameters and graph properties of the protein networks allows the discovery of central/critical nodes, which are candidates to regulate cellular flux and dynamics.This work was supported by the Consejo Superior de Investigaciones Cientificas (CSIC) [project iLINK0398]; the Spanish Government (ISCiii) [project PS09/00843]; and the European Commission [project FP7-HEALTH-2007-223411].Peer Reviewe