Current Computer - Aided Drug Design - Volume 12, Issue 3, 2016

Background: Synthesis of organic compounds with specific biological activity or physicochemical characteristics needs a thorough analysis of the enumerable data set obtained from literature. Quantitative structure property/activity relationships have made it simple by predicting the structure of the compound with any optimized activity. For that there is a paramount data set of molecular descriptors (MD). This review is a survey on the generation of the molecular descriptors and its probable applications in QSP/AR. Methods: Literatures have been collected from a wide class of research journals, citable web reports, seminar proceedings and books. The MDs were classified according to their generation. The applications of the MDs on the QSP/AR have also been reported in this review. Results: The MDs can be classified into experimental and theoretical types, having a sub classification of the later into structural and quantum chemical descriptors. The structural parameters are derived from molecular graphs or topology of the molecules. Even the pixel of the molecular image can be used as molecular descriptor. In QSPR studies the physicochemical properties include boiling point, heat capacity, density, refractive index, molar volume, surface tension, heat of formation, octanol-water partition coefficient, solubility, chromatographic retention indices etc. Among biological activities toxicity, antimalarial activity, sensory irritant, potencies of local anesthetic, tadpole narcosis, antifungal activity, enzyme inhibiting activity are some important parameters in the QSAR studies. Conclusion: The classification of the MDs is mostly generic in nature. The application of the MDs in QSP/AR also has a generic link. Experimental MDs are more suitable in correlation analysis than the theoretical ones but are more expensive for generation. In advent of sophisticated computational tools and experimental design proliferation of MDs is inevitable, but for a highly optimized MD, studies on generation of MD is an unending process.

Background: Chlamydia pneumoniae (C. pneumoniae) is a pathogen associated with respiratory tract infection of humans and its viable presence in atherosclerotic plaques is also assumed to play significant function in cardiac diseases. Unavailability of effective antimicrobial drugs has implicated the urgent need of some more disease associated vaccines that may provide relief efficiently. Thus, present study has been undertaken to analyse the whole proteome of C. pneumoniae in order to propose bacterial proteins as candidate vaccine for CAD by taking the aid of ‘Reverse Vaccinology’. Methods: Whole proteome of C. pneumoniae was downloaded and redundancy was removed by CD-HIT web server. Similarity search between proteins of C. pneumoniae and Homo sapiens was performed by BLASTP to avoid human similar proteins. Virulent proteins were selected by VirulentPred web server. Sub cellular localization of identified proteins was investigated by LocTree3 and pSORTb servers. Surface accessibility area (SAA) and antigenic epitopes prediction has been undertaken by prediction of protease specificity (POPS) and Ellipro, NetMHCpan 3.0 servers respectively. Functional significance of identified proteins was predicted through 3D model construction followed by the ligand binding site, active site and domain characterization. Results: Three reference proteins RVOM1, RVOM2, RVEC1 were predicted with the crucial role in C. pneumoniae that may be capable of inducing B cell and T cell response. Conclusion: Detailed analysis of these proteins indicated that they may be utilized as the vaccine candidates during the chlamydial infection in near future.

A large number of alignment–free techniques of graphical representation and numerical characterization (GRANCH) of bio-molecular sequences have been proposed in the recent past years, but the relative efficacy of these methods in determining the degree of similarities and dissimilarities of such sequences have not been ascertained. Objective: Our objective is to make an assessment of the relative efficacy of these methods in determining the degree of similarities and dissimilarities of bio-molecular sequences. Method: We have chosen 7 published/communicated methods that represent various classes of GRANCH techniques and computed the descriptors that are expected to characterize similarities and dissimilarities in several sets of gene sequences. We critically appraise the different methods and determine which of these yield non-redundant structural information that could be used to compute different properties of the sequences, and which are correlated enough to one another so that using the simplest representative of the group would suffice. We also do a principal component analysis (PCA) to determine how the variances in the calculated sequence descriptors are explained by the computed principal components (PCs). Results: We found that some of the descriptors are strongly correlated implying a commonality of structural information encoded by them while others are distinctly separate. The PCA results show that the first three PC’s explain >97% of the variances. Conclusion: We found that some mathematical DNA descriptors calculated by a few of these techniques correlate strongly with one another implying a redundancy in the structural information quantified by those descriptors; others are not strongly correlated with one another suggesting that they encode non-redundant sequence information. From this and our PCA results, our recommendation would be to use minimally correlated set of descriptors or orthogonal descriptors like PCs derived from the descriptor set for the characterization of nucleic acid structure and function.

Background: Epidermal Growth Factor Receptor tyrosine kinase (EGFR) is an important anticancer drug target. Series of pyridopyrimidine analogs have been reported as EGFR inhibitors and they inhibit by binding to the ATP binding pocket of the tyrosine kinase domain. Objective: To identify key properties of pyridopyrimidine analogs involved in the inhibition of the EGFR protein tyrosine kinase by developing 2D QSAR model. Methods: Variable selection was performed by least absolute shrinkage and selection operator (LASSO) method and multiple linear regression (MLR) method was applied by using Build QSAR software to develop QSAR model. Model validation was done by Leave One Out method (LOO). Further, based on the bioactive and structural similarity, virtual screening was performed using Pubchem database. Using the developed QSAR model and Molinspiration server, PIC50 values and kinase inhibition activity were predicted for all the virtually screened compounds respectively. Results: The best QSAR model consists of two descriptors namely Basak and MOE type descriptors, and has R2 = 0.8205, F= 57.129 & S = 0.308 and the validation results show significant statistics of R2cv = 0.655, Average standard deviation = 0.416. 140 compounds were obtained from virtual screening and the predicted PIC50 of all these compounds are in the range of 4.73 – 6.78. All the compounds produce positive scores which suggest that the compounds may have good kinase inhibitory profile. Conclusion: This developed model may be useful to predict EGFR inhibition activity (PIC50) for the newly synthesized pyridopyrimidines analogs.

Background: The endogeneous antioxidant mechanism often fails to combat the huge free radical overload necessitating external antioxidant supplementation. Thus identification and definite structural manipulation of the naturally available antioxidant derivatives using in silico methodology help to design new moieties with improved therapeutic potential. Objective: The present work has been performed with the aim to identify the essential molecular fragments that contribute to the antioxidant property of the coumarin derivatives. Method: In this work three separate chemometric methods were utilised to highlight the structural requisites of the coumarin derivatives. Results: The QSAR model thus developed helps to highlight the prime molecular fragments, while the 3D pharmacophore model denotes the features constituting the biological pharmacophore for the coumarin derivatives. Again, the HQSAR contour signifies the relative contribution of the different molecular fragments. Conclusion: In silico techniques thus adapted in the present work highlight a significant paradigm in the process of screening and designing therapeutically active antioxidant moieties.