The global pandemic crisis, coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has claimed the lives of millions of people across the world. Development and testing of anti-SARS-CoV-2 drugs or vaccines have not turned to be realistic within the timeframe needed to combat this pandemic. Here, we report a comprehensive computational approach to identify the multi-targeted drug molecules against the SARS-CoV-2 proteins, whichare crucially involved in the viral–host interaction, replication of the virus inside the host, disease progression and transmission of coronavirus infection. Virtual screening of 75 FDA-approved potential antiviral drugs against the target proteins, spike (S) glycoprotein, human angiotensin-converting enzyme 2 (hACE2), 3-chymotrypsin-like cysteine protease (3CLpro), cathepsin L (CTSL), nucleocapsid protein, RNA-dependent RNA polymerase (RdRp) and non-structural protein 6 (NSP6), resulted in the selection of seven drugs which preferentially bind to the target proteins. Further, the molecular interactions determined by molecular dynamics simulation revealed that among the 75 drug molecules, catechin can effectively bind to 3CLpro, CTSL, RBD of S protein, NSP6 and nucleocapsid protein. It is more conveniently involved in key molecular interactions, showing binding free energy (ΔGbind) in the range of −5.09 kcal/mol (CTSL) to −26.09 kcal/mol (NSP6). At the binding pocket, catechin is majorly stabilized by the hydrophobic interactions, displays ΔEvdW values: −7.59 to −37.39 kcal/mol. Thus, the structural insights of better binding affinity and favorable molecular interaction of catechin toward multiple target proteins signify that catechin can be potentially explored as a multi-targeted agent against COVID-19.

Background: Profile Hidden Markov Models (HMM) are statistical representations of protein families derived from patterns of sequence conservation in multiple alignments and have been used in identifying remote homologues with considerable success. These conservation patterns arise from fold specific signals, shared across multiple families, and function specific signals unique to the families. The availability of sequences pre-classified according to their function permits the use of negative training sequences to improve the specificity of the HMM, both by optimizing the threshold cutoff and by modifying emission probabilities to minimize the influence of fold-specific signals. A protocol to generate family specific HMMs is described that first constructs a profile HMM from an alignment of the family's sequences and then uses this model to identify sequences belonging to other classes that score above the default threshold (false positives). Ten-fold cross validation is used to optimise the discrimination threshold score for the model. The advent of fast multiple alignment methods enables the use of the profile alignments to align the true and false positive sequences, and the resulting alignments are used to modify the emission probabilities in the original model. Results: The protocol, called HMM-ModE, was validated on a set of sequences belonging to six sub-families of the AGC family of kinases. These sequences have an average sequence similarity of 63% among the group though each sub-group has a different substrate specificity. The optimisation of discrimination threshold, by using negative sequences scored against the model improves specificity in test cases from an average of 21% to 98%. Further discrimination by the HMM after modifying model probabilities using negative training sequences is provided in a few cases, the average specificity rising to 99%. Similar improvements were obtained with a sample of G-Protein coupled receptors sub-classified with respect to their substrate specificity, though the average sequence identity across the sub-families is just 20.6%. The protocol is applied in a high-throughput classification exercise on protein kinases. Conclusion: The protocol has the potential to maximise the contributions of discriminating residues to classify proteins based on their molecular function, using pre-classified positive and negative sequence training data. The high specificity of the method, and increasing availability of pre-classified sequence data holds the potential for its application in sequence annotation.

HMM-ModE is a computational method that generates family specific profile HMMs using negative training sequences. The method optimizes the discrimination threshold using 10 fold cross validation and modifies the emission probabilities of profiles to reduce common fold based signals shared with other sub-families. The protocol depends on the program HMMER for HMM profile building and sequence database searching. The recent release of HMMER3 has improved database search speed by several orders of magnitude, allowing for the large scale deployment of the method in sequence annotation projects. We have rewritten our existing scripts both at the level of parsing the HMM profiles and modifying emission probabilities to upgrade HMM-ModE using HMMER3 that takes advantage of its probabilistic inference with high computational speed. The method is benchmarked and tested on GPCR dataset as an accurate and fast method for functional annotation.

Malaria is a major health problem in the tropical and subtropical world. In India, 95% of the population resides in malaria endemic regions and it is major public health problem in most parts of the country. The present work has developed malaria maps by integrating socio-economic, epidemiology and geographical dimensions of three eastern districts of Uttar Pradesh, India. The area has been studied in each dimension separately, and later integrated to find a list of vulnerable pockets/villages, called as malarial hotspots. The study has been done at village level. Seasonal variation of malaria, comparison of epidemiology indices and progress of the medical facility were studied. Ten independent geographical information system (GIS) maps of socio-economic aspects (population, child population, literacy, and work force participation), epidemiology (annual parasitic index (API) and slides collected and examined) and geographical features (settlement, forest cover, water bodies, rainfall, relative humidity, and temperature) were drawn and studied. These maps were overlaid based on computed weight matrix to find malarial hotspot. It was found that the studied dimensions were inter-weaving factors for malaria epidemic and closely affected malaria situations as evidenced from the obtained correlation matrix. The regions with water logging, high rainfall and proximity to forest, along with poor socio-economic conditions, are primarily hotspot regions. The work is presented through a series of GIS maps, tables, figures and graphs. A total of 2,054 out of 8,973 villages studied were found to be malarial hotspots and consequently suggestions were made to the concerned government malaria offices. With developing technology, information tools such as GIS, have captured almost every field of scientific research especially of vector-borne diseases, such as malaria. Malarial mapping enables easy update of information and effortless accessibility of geo-referenced data to policy makers to produce cost-effective measures for malaria control in endemic regions.

Background: Theoretical proteome analysis, generated by plotting theoretical isoelectric points (pI) against molecular masses of all proteins encoded by the genome show a multimodal distribution for pI. This multimodal distribution is an effect of allowed combinations of the charged amino acids, and not due to evolutionary causes. The variation in this distribution can be correlated to the organisms ecological niche. Contributions to this variation maybe mapped to individual proteins by studying the variation in pI of orthologs across microorganism genomes. Results: The distribution of ortholog pI values showed trimodal distributions for all prokaryotic genomes analyzed, similar to whole proteome plots. Pairwise analysis of pI variation show that a few COGs are conserved within, but most vary between, the acidic and basic regions of the distribution, while molecular mass is more highly conserved. At the level of functional grouping of orthologs, five groups vary significantly from the population of orthologs, which is attributed to either conservation at the level of sequences or a bias for either positively or negatively charged residues contributing to the function. Individual COGs conserved in both the acidic and basic regions of the trimodal distribution are identified, and orthologs that best represent the variation in levels of the acidic and basic regions are listed. Conclusion: The analysis of pI distribution by using orthologs provides a basis for resolution of theoretical proteome comparison at the level of individual proteins. Orthologs identified that significantly vary between the major acidic and basic regions maybe used as representative of the variation of the entire proteome.

TAR DNA-binding protein 43 (TDP-43) inclusions have been found in Amyotrophic lateral sclerosis (ALS) and several other neurodegenerative diseases. Many studies suggest the involvement of RNA recognition motifs (RRMs) in TDP-43 proteinopathy. To elucidate the structural stability and the unfolding dynamics of RRMs, we have carried out atomistic molecular dynamics simulations at two different temperatures (300 and 500 K). The simulations results indicate that there are distinct structural differences in the unfolding pathway between the two domains and RRM1 unfolds faster than RRM2 in accordance with the lower thermal stability found experimentally. The unfolding behaviors of secondary structures showed that the α-helix was more stable than β-sheet and structural rearrangements of β-sheets results in formation of additional α-helices. At higher temperature, RRM1 exhibit increased overall flexibility and unfolding than RRM2. The temperature-dependent free energy landscapes consist of multiple metastable states stabilized by non-native contacts and hydrogen bonds in RRM2, thus rendering the RRM2 more prone to misfolding. The structural rearrangements of RRM2 could lead to aberrant protein–protein interactions that may account for enhanced aggregation and toxicity of TDP-43. Our analysis, thus identify the structural and thermodynamic characteristics of the RRMs of TDP-43, which will serve to uncover molecular mechanisms and driving forces in TDP-43 misfolding and aggregation.