A Comparative Study to Explore the Effect of Different Compounds in Immune Proteins of Human Beings Against Tuberculosis: An In-silico Approach

Maximal Information Coefficient-Based Testing to Identify Epistasis in Case-Control Association Studies

Interactions between genetic variants (epistasis) are ubiquitous in the model system and can significantly affect evolutionary adaptation, genetic mapping, and precision medical efforts. In this paper, we proposed a method for epistasis detection, called EpiMIC (epistasis detection through a maximal information coefficient (MIC)). MIC is a promising bivariate dependence measure explicitly designed for rapidly exploring various function types equally and for interpreting and comparing them on the same scale. Most epistasis detection approaches make assumptions about the form of the association between genetic variants, resulting in limited statistical performance. Based on the notion that if two SNPs do not interact, their joint distribution in all samples and in only cases should not be substantially different. We developed a statistic that utilizes the difference of MIC as a signal of epistasis and combined it with a permutation resampling strategy to estimate the empirical distribution of our statistic. Results of simulation and real-world data set showed that EpiMIC outperformed previous approaches for identifying epistasis at varying degrees of heredity.

In silico study reveals binding potential of rotenone at multiple sites of pulmonary surfactant proteins: A matter of concern

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Inhalation of rotenone exposes lung surfactant proteins (SP) to this pesticide.

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SP-A and SP-D provides protection from microbial infection.

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SP-B and SP-C maintain structure and respiratory function of lungs.

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Rotenone has potential to bind SPs at multiple sites.

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Such binding can subvert functions of SPs & may invite respiratory ailments.

Rotenone is a broad-spectrum pesticide employed in various agricultural practices all over the world. Human beings are exposed to this chemical through oral, nasal, and dermal routes. Inhalation of rotenone exposes bio-molecular components of lungs to this chemical. Biophysical activity of lungs is precisely regulated by pulmonary surfactant to facilitate gaseous exchange. Surfactant proteins (SPs) are the fundamental components of pulmonary surfactant. SPs like SP-A and SP-D have antimicrobial activities providing a crucial first line of defense against infections in lungs whereas SP-B and SP-C are mainly involved in respiratory cycle and reduction of surface tension at air–water interface. In this study, molecular docking analysis using AutoDock Vina has been conducted to investigate binding potential of rotenone with the four SPs. Results indicate that, rotenone can bind with carbohydrate recognition domain (CRD) of SP-A, N-, and C- terminal peptide of SP-B, SP-C, and CRD of SP-D at multiples sites via several interaction mediators such as H bonds, C–H bonds, alkyl bonds, pi-pi stacked, Van der Waals interaction, and other. Such interactions of rotenone with SPs can disrupt biophysical and anti-microbial functions of SPs in lungs that may invite respiratory ailments and pathogenic infections.

Effects of carbamate pesticides intermediates on Escherichia coli membrane architecture: An in vitro and in silico approach

Methyl isocyanate (MIC), a low molecular weight synthetic aliphatic compound, having an isocyanate group (−NCO), has industrial application. In this study, the effects of methyl isocyanate and its mechanism on outer membrane protein of Escherichia coli were observed using experimental and computational methods. In vitro exposure of N-succinimidyl N-methylcarbamate (NSNM) a synthetic analogue of MIC on E. coli to a final concentration of 2 mM was found to affect the growth curve pattern and changes in cell morphology. Molecular docking studies of MIC and NSNM with E. coli outer membrane protein (OmpW, OmpX, OmpF OmpA), and periplasmic domain (PAL) were performed. The in-silico results revealed that outer membrane protein OmpF showed the highest negative binding energy, i.e. ΔG −4.11 kcal/mole and ΔG −3.19 kcal/mole by NSNM and MIC as compared to other proteins. Our study concludes that methyl isocyanate retains lethal toxicity which leads to cell death due to the membrane protein damage of E. coli membrane.

Amino Acid Reduction Can Help to Improve the Identification of Antimicrobial Peptides and Their Functional Activities

Antimicrobial peptides (AMPs) are considered as potential substitutes of antibiotics in the field of new anti-infective drug design. There have been several machine learning algorithms and web servers in identifying AMPs and their functional activities. However, there is still room for improvement in prediction algorithms and feature extraction methods. The reduced amino acid (RAA) alphabet effectively solved the problems of simplifying protein complexity and recognizing the structure conservative region. This article goes into details about evaluating the performances of more than 5,000 amino acid reduced descriptors generated from 74 types of amino acid reduced alphabet in the first stage and the second stage to construct an excellent two-stage classifier, Identification of Antimicrobial Peptides by Reduced Amino Acid Cluster (iAMP-RAAC), for identifying AMPs and their functional activities, respectively. The results show that the first stage AMP classifier is able to achieve the accuracy of 97.21 and 97.11% for the training data set and independent test dataset. In the second stage, our classifier still shows good performance. At least three of the four metrics, sensitivity (SN), specificity (SP), accuracy (ACC), and Matthews correlation coefficient (MCC), exceed the calculation results in the literature. Further, the ANOVA with incremental feature selection (IFS) is used for feature selection to further improve prediction performance. The prediction performance is further improved after the feature selection of each stage. At last, a user-friendly web server, iAMP-RAAC, is established at http://bioinfor.imu.edu. cn/iampraac.