Ad interim recommendations for diagnosing SARS-CoV-2 infection by the IFCC SARS-CoV-2 variants working group.

This document, endorsed by the IFCC Working Group on SARS-CoV-2 Variants, aims to update previous indications for diagnosing acute SARS-CoV-2 infection, taking into consideration the evidence that has emerged after the origin and spread of new lineages and sub-lineages of the virus characterized by mutated genetics and altered biochemical, biological and clinical characteristics. These indications encompass the use of different diagnostic strategies in specific clinical settings, such as high risk of SARS-CoV-2 infection (symptomatic patients), low risk of SARS-CoV-2 infection (asymptomatic subjects) at hospital admission/contact tracing, testing in asymptomatic subjects, in epidemiologic surveys and/or population screening, along with tentative indications for identification of new lineages and/or sub-lineages of SARS-CoV-2.

The non-enzymatic RAS effector RASSF7 inhibits oncogenic c-Myc function.

c-Myc is a proto-oncogene controlling expression of multiple genes involved in cell growth and differentiation. Although the functional role of c-Myc as a transcriptional regulator has been intensively studied, targeting this protein in cancer remains a challenge. Here, we report a trimodal regulation of c-Myc function by the Ras effector, Ras-association domain family member 7 (RASSF7), a nonenzymatic protein modulating protein-protein interactions to regulate cell proliferation. Using HEK293T and HeLa cell lines, we provide evidence that RASSF7 destabilizes the c-Myc protein by promoting Cullin4B-mediated polyubiquitination and degradation. Furthermore, RASSF7 competed with MYC-associated factor X (MAX) in the formation of a heterodimeric complex with c-Myc and attenuated its occupancy on target gene promoters to regulate transcription. Consequently, RASSF7 inhibited c-Myc-mediated oncogenic transformation, and an inverse correlation between the expression levels of the RASSF7 and c-Myc genes was evident in human cancers. Furthermore, we found that RASSF7 interacts with c-Myc via its RA and leucine zipper (LZ) domains and LZ domain peptide is sufficient to inhibit c-Myc function, suggesting that this peptide might be used to target oncogenic c-Myc. These results unveil that RASSF7 and c-Myc are functionally linked in the control of tumorigenesis and open up potential therapeutic avenues for targeting the "undruggable" c-Myc protein in a subset of human cancers.

APBioNet's annual International Conference on Bioinformatics (InCoB) returns to India in 2018.

InCoB, one of the largest annual bioinformatics conferences in the Asia-Pacific region since its launch in 2002, returned to New Delhi, India after 12 years, with a conference attendance of 314 delegates. The 2018 conference had sessions on Big Data and Algorithms, Next Generation Sequencing and Omics Science, Structure, Function and Interactions, Disease and Drug Discovery and Plant and Agricultural Bioinformatics. The conference also featured an industry track as well as panel discussions on Women in Bioinformatics and Democratization vs. Quality control in academic publishing. Asia Pacific Bioinformatics Interaction & Networking Society (APbians) was launched as an APBionet Special Interest Group. Of the 52 oral presentations made, 22 were accepted in supplemental issues of BMC Bioinformatics, BMC Genomics or BMC Medical Genomics and are briefly reviewed here. Next year's InCoB will be held in Jakarta, Indonesia from September 10-12, 2019.

Importance of Inter-residue Contacts for Understanding Protein Folding and Unfolding Rates, Remote Homology, and Drug Design.

Inter-residue interactions in protein structures provide valuable insights into protein folding and stability. Understanding these interactions can be helpful in many crucial applications, including rational design of therapeutic small molecules and biologics, locating functional protein sites, and predicting protein-protein and protein-ligand interactions. The process of developing machine learning models incorporating inter-residue interactions has been improved recently. This review highlights the theoretical models incorporating inter-residue interactions in predicting folding and unfolding rates of proteins. Utilizing contact maps to depict inter-residue interactions aids researchers in developing computer models for detecting remote homologs and interface residues within protein-protein complexes which, in turn, enhances our knowledge of the relationship between sequence and structure of proteins. Further, the application of contact maps derived from inter-residue interactions is highlighted in the field of drug discovery. Overall, this review presents an extensive assessment of the significant models that use inter-residue interactions to investigate folding rates, unfolding rates, remote homology, and drug development, providing potential future advancements in constructing efficient computational models in structural biology.

Prediction of disease-associated mutations in the transmembrane regions of proteins with known 3D structure.

Being able to assess the phenotypic effects of mutations is a much required capability in precision medicine. However, most of the currently available structure-based methods actually predict stability changes caused by mutations rather than their pathogenic potential. There are also no dedicated methods to predict damaging mutations specifically in transmembrane proteins. In this study we developed and applied a machine-learning approach to discriminate between disease-associated and benign point mutations in the transmembrane regions of proteins with known 3D structure. The method, called BorodaTM (BOosted RegressiOn trees for Disease-Associated mutations in TransMembrane proteins), was trained on sequence-, structure-, and energy-derived descriptors. When compared with the state-of-the-art methods, BorodaTM is superior in classifying point mutations in transmembrane regions. Using BorodaTM we have conducted a large-scale mutation analysis in the transmembrane regions of human proteins with known 3D structures. For each protein we generated structural models for all point mutations by replacing each residue to 19 possible residue types. We classified ~1.8 millions point mutations as benign or diseased-associated and made all predictions available as a Web-server at https://www.iitm.ac.in/bioinfo/MutHTP/boroda.php.

Forging New Scaffolds from Old: Combining Scaffold Hopping and Hierarchical Virtual Screening for Identifying Novel Bcl-2 Inhibitors.

BACKGROUND: Though virtual screening methods have proven to be potent in various instances, the technique is practically incomplete to quench the need of drug discovery process. Thus, the quest for novel designing approaches and chemotypes for improved efficacy of lead compounds has been intensified and logistic approaches such as scaffold hopping and hierarchical virtual screening methods were evolved. Till now, in all the previous attempts these two approaches were applied separately. OBJECTIVE: In the current work, we made a novel attempt in terms of blending scaffold hopping and hierarchical virtual screening. The prime objective is to assess the hybrid method for its efficacy in identifying active lead molecules for emerging PPI target Bcl-2 (B-cell Lymphoma 2). METHODS: We designed novel scaffolds from the reported cores and screened a set of 8270 compounds using both scaffold hopping and hierarchical virtual screening for Bcl-2 protein. Also, we enumerated the libraries using clustering, PAINS filtering, physicochemical characterization and SAR matching. RESULTS: We generated a focused library of compounds towards Bcl-2 interface, screened the 8270 compounds and identified top hits for seven families upon fine filtering with PAINS algorithm, features, SAR mapping, synthetic accessibility and similarity search. Our approach retrieved a set of 50 lead compounds. CONCLUSION: Finding rational approach meeting the needs of drug discovery process for PPI targets is the need of the hour which can be fulfilled by an extended scaffold hopping approach resulting in focused PPI targeting by providing novel leads with better potency.

Identification of DNA-binding proteins using support vector machines and evolutionary profiles.

BACKGROUND: Identification of DNA-binding proteins is one of the major challenges in the field of genome annotation, as these proteins play a crucial role in gene-regulation. In this paper, we developed various SVM modules for predicting DNA-binding domains and proteins. All models were trained and tested on multiple datasets of non-redundant proteins. RESULTS: SVM models have been developed on DNAaset, which consists of 1153 DNA-binding and equal number of non DNA-binding proteins, and achieved the maximum accuracy of 72.42% and 71.59% using amino acid and dipeptide compositions, respectively. The performance of SVM model improved from 72.42% to 74.22%, when evolutionary information in form of PSSM profiles was used as input instead of amino acid composition. In addition, SVM models have been developed on DNAset, which consists of 146 DNA-binding and 250 non-binding chains/domains, and achieved the maximum accuracy of 79.80% and 86.62% using amino acid composition and PSSM profiles. The SVM models developed in this study perform better than existing methods on a blind dataset. CONCLUSION: A highly accurate method has been developed for predicting DNA-binding proteins using SVM and PSSM profiles. This is the first study in which evolutionary information in form of PSSM profiles has been used successfully for predicting DNA-binding proteins. A web-server DNAbinder has been developed for identifying DNA-binding proteins and domains from query amino acid sequences http://www.imtech.res.in/raghava/dnabinder/.

In silico identification of essential proteins in Corynebacterium pseudotuberculosis based on protein-protein interaction networks.

BACKGROUND: Corynebacterium pseudotuberculosis (Cp) is a gram-positive bacterium that is classified into equi and ovis serovars. The serovar ovis is the etiological agent of caseous lymphadenitis, a chronic infection affecting sheep and goats, causing economic losses due to carcass condemnation and decreased production of meat, wool, and milk. Current diagnosis or treatment protocols are not fully effective and, thus, require further research of Cp pathogenesis. RESULTS: Here, we mapped known protein-protein interactions (PPI) from various species to nine Cp strains to reconstruct parts of the potential Cp interactome and to identify potentially essential proteins serving as putative drug targets. On average, we predict 16,669 interactions for each of the nine strains (with 15,495 interactions shared among all strains). An in silico sanity check suggests that the potential networks were not formed by spurious interactions but have a strong biological bias. With the inferred Cp networks we identify 181 essential proteins, among which 41 are non-host homologous. CONCLUSIONS: The list of candidate interactions of the Cp strains lay the basis for developing novel hypotheses and designing according wet-lab studies. The non-host homologous essential proteins are attractive targets for therapeutic and diagnostic proposes. They allow for searching of small molecule inhibitors of binding interactions enabling modern drug discovery. Overall, the predicted Cp PPI networks form a valuable and versatile tool for researchers interested in Corynebacterium pseudotuberculosis.

ASAView: database and tool for solvent accessibility representation in proteins.

BACKGROUND: Accessible surface area (ASA) or solvent accessibility of amino acids in a protein has important implications. Knowledge of surface residues helps in locating potential candidates of active sites. Therefore, a method to quickly see the surface residues in a two dimensional model would help to immediately understand the population of amino acid residues on the surface and in the inner core of the proteins. RESULTS: ASAView is an algorithm, an application and a database of schematic representations of solvent accessibility of amino acid residues within proteins. A characteristic two-dimensional spiral plot of solvent accessibility provides a convenient graphical view of residues in terms of their exposed surface areas. In addition, sequential plots in the form of bar charts are also provided. Online plots of the proteins included in the entire Protein Data Bank (PDB), are provided for the entire protein as well as their chains separately. CONCLUSIONS: These graphical plots of solvent accessibility are likely to provide a quick view of the overall topological distribution of residues in proteins. Chain-wise computation of solvent accessibility is also provided.