CoRNeA: A Pipeline to Decrypt the Inter-Protein Interfaces from Amino Acid Sequence Information

Decrypting the interface residues of the protein complexes provides insight into the functions of the proteins and, hence, the overall cellular machinery. Computational methods have been devised in the past to predict the interface residues using amino acid sequence information, but all these methods have been majorly applied to predict for prokaryotic protein complexes. Since the composition and rate of evolution of the primary sequence is different between prokaryotes and eukaryotes, it is important to develop a method specifically for eukaryotic complexes. Here, we report a new hybrid pipeline for predicting the protein-protein interaction interfaces in a pairwise manner from the amino acid sequence information of the interacting proteins. It is based on the framework of Co-evolution, machine learning (Random Forest), and Network Analysis named CoRNeA trained specifically on eukaryotic protein complexes. We use Co-evolution, physicochemical properties, and contact potential as major group of features to train the Random Forest classifier. We also incorporate the intra-contact information of the individual proteins to eliminate false positives from the predictions keeping in mind that the amino acid sequence of a protein also holds information for its own folding and not only the interface propensities. Our prediction on example datasets shows that CoRNeA not only enhances the prediction of true interface residues but also reduces false positive rates significantly

Identification of <i>Plasmodium falciparum</i> apicoplast-targeted tRNA-guanine transglycosylase and its potential inhibitors using comparative genomics, molecular modelling, docking and simulation studies

<div><p>tRNA modifications play an important role in the proper folding of tRNA and thereby determine its functionality as an adaptor molecule. Notwithstanding the centrality of this basic process in translation, a major gap in the genomics of <i>Plasmodium falciparum</i> is unambiguous identification of enzymes catalysing the various tRNA modifications. In this study, tRNA-modifying enzymes of <i>P. falciparum</i> were annotated using homology-based approach. Based on the presence of these identified enzymes, the modifications were compared with those of prokaryotic and eukaryotic organisms. Through sequence comparison and phylogenetic analysis, we have identified <i>P. falciparum</i> apicoplast tRNA-guanine 34 transglycosylase (TGT, EC: 2.4.2.29), which shows evidence of its prokaryotic origin. The docking analysis of the modelled TGT structures revealed that binding of quinazolinone derivatives is more favourable with <i>P. falciparum</i> apicoplast TGT as compared to human TGT. Molecular dynamic simulation and molecular mechanics/generalized Born surface area analysis of the complex confirmed the greater binding affinity of the ligand in the binding pocket of <i>P. falciparum</i> TGT protein. Further, evolutionary patterning analysis identified the amino acids of <i>P. falciparum</i> apicoplast TGT that are under purifying selection pressure and hence can be good inhibitor-targeting sites. Based on these computational studies, we suggest that <i>P. falciparum</i> apicoplast tRNA-guanine 34 transglycosylase can be a promising drug target.</p></div

Analysis &amp; prognosis of sustainable development goals using big data-based approach during COVID-19 pandemic

The world has changed considerably in the previous two decades. Today, people are facing extreme poverty, global warming, and unwanted climate changes. The economic gap between countries is continuously growing. Moreover, with the expanding influence of technology, governance is getting more difficult. To address these issues, the UN announced Sustainable Development Goals (SDGs), also called Global Goals, in 2015. These goals fill in as an overall source of inspiration to annihilate poverty, protect the environment, and guarantee that all individuals live in harmony and thrive by 2030. The 17 SDGs are interconnected in that they recognize that activities in a single region sway result in others and that improvement should adjust to social, monetary, and natural sustainability. The SDGs intend to kill poverty, hunger, AIDS, and gender discrimination against women and girls. The COVID-19 epidemic, on the other hand, has hampered attempts to accomplish the 2030 Agenda for Sustainable Development. As a result, the impact of these SDGs must be thoroughly studied and analyzed. As a result, the purpose of this research is to examine the SDG before and after Covid-19, as well as how they have influenced various national and international markets. The research also assesses the 17 SDGs in each of India's 29 states in depth. Since SDGs have a larger scope, this paper predicts the SDG-9 scores of few countries like UAE, New Zealand, Japan, India, Germany, China, Bhutan, and USA

A hemoprotein with a zinc-mirror heme site ties heme availability to carbon metabolism in cyanobacteria

Heme has a critical role in the chemical framework of the cell as an essential protein cofactor and signaling molecule that controls diverse processes and molecular interactions. Using a phylogenomics-based approach and complementary structural techniques, we identify a family of dimeric hemoproteins comprising a domain of unknown function DUF2470. The heme iron is axially coordinated by two zinc-bound histidine residues, forming a distinct two-fold symmetric zinc-histidine-iron-histidine-zinc site. Together with structure-guided in vitro and in vivo experiments, we further demonstrate the existence of a functional link between heme binding by Dri1 (Domain related to iron 1, formerly ssr1698) and post-translational regulation of succinate dehydrogenase in the cyanobacterium Synechocystis, suggesting an iron-dependent regulatory link between photosynthesis and respiration. Given the ubiquity of proteins containing homologous domains and connections to heme metabolism across eukaryotes and prokaryotes, we propose that DRI (Domain Related to Iron; formerly DUF2470) functions at the molecular level as a heme-dependent regulatory domain