Protein Domain Annotations of the SARS-CoV-2 Proteomics as a Blue-Print for Mapping the Features for Drug and Vaccine Designs

SARS-CoV-2 virus, as the causal agent for the COVID-19 pandemic, remains an enigma in the bioinformatics sense. Current efforts in drug and vaccine design in primarily targeting general devised protein domain while overlooking the specific features in the proteomics repertoire. However, the NCBI Conserved Domain Database (CDD) could annotate the specific features that are indispensable for a more advanced drug and vaccine design. In this regard, the annotation efforts were initiated with CDD database, and visualized with the 3D Protein Visualizer of Cn3D. The exsistence of the ATP and ADP binding protein with respected domains were found to be a very potential target for drug design. It is recommended that nucleoside inhibitor that could mimick the ATP molecule could serve as a potential drug lead agains SARS-CoV-2.

CDD/SPARCLE: the conserved domain database in 2020

As NLM’s Conserved Domain Database (CDD) enters its 20th year of operations as a publicly available resource, CDD curation staff continues to develop hierarchical classifications of widely distributed protein domain families, and to record conserved sites associated with molecular function, so that they can be mapped onto user queries in support of hypothesis-driven biomolecular research. CDD offers both an archive of pre-computed domain annotations as well as live search services for both single protein or nucleotide queries and larger sets of protein query sequences. CDD staff has continued to characterize protein families via conserved domain architectures and has built up a significant corpus of curated domain architectures in support of naming bacterial proteins in RefSeq. These architecture definitions are available via SPARCLE, the Subfamily Protein Architecture Labeling Engine. CDD can be accessed at https://www.ncbi.nlm.nih.gov/Structure/cdd/cdd.shtml.

PubMed Text Similarity Model and its application to curation efforts in the Conserved Domain Database

This study proposes a text similarity model to help biocuration efforts of the Conserved Domain Database (CDD). CDD is a curated resource that catalogs annotated multiple sequence alignment models for ancient domains and full-length proteins. These models allow for fast searching and quick identification of conserved motifs in protein sequences via Reverse PSI-BLAST. In addition, CDD curators prepare summaries detailing the function of these conserved domains and specific protein families, based on published peer-reviewed articles. To facilitate information access for database users, it is desirable to specifically identify the referenced articles that support the assertions of curator-composed sentences. Moreover, CDD curators desire an alert system that scans the newly published literature and proposes related articles of relevance to the existing CDD records. Our approach to address these needs is a text similarity method that automatically maps a curator-written statement to candidate sentences extracted from the list of referenced articles, as well as the articles in the PubMed Central database. To evaluate this proposal, we paired CDD description sentences with the top 10 matching sentences from the literature, which were given to curators for review. Through this exercise, we discovered that we were able to map the articles in the reference list to the CDD description statements with an accuracy of 77%. In the dataset that was reviewed by curators, we were able to successfully provide references for 86% of the curator statements. In addition, we suggested new articles for curator review, which were accepted by curators to be added into the reference list at an acceptance rate of 50%. Through this process, we developed a substantial corpus of similar sentences from biomedical articles on protein sequence, structure and function research, which constitute the CDD text similarity corpus. This corpus contains 5159 sentence pairs judged for their similarity on a scale from 1 (low) to 5 (high) doubly annotated by four CDD curators. Curator-assigned similarity scores have a Pearson correlation coefficient of 0.70 and an inter-annotator agreement of 85%. To date, this is the largest biomedical text similarity resource that has been manually judged, evaluated and made publicly available to the community to foster research and development of text similarity algorithms.

Does facial width-to-height ratio predict male offender aggression?

Purpose Based on the previously observed link between greater facial width-to-height ratio (fWHR) and interpersonal aggression in men (see Haselhuhn et al., 2015), the purpose of this paper is to test whether fWHR could differentiate among male offenders as a function of the relative aggressiveness of the crime for which they had been convicted. Design/methodology/approach fWHR measurements (n=550) were computed based on a large subset of male offenders available on a public domain database. Each offender’s index offense and possible confounding variables such as age, ethnicity, and body mass index were also recorded. Findings Multiple analyses yielded no evidence of a relationship between male fWHR and the comparative level of violence of their conviction offense. Originality/value Establishing an empirical basis for probable parameters of an unknown offender’s facial structure could have a considerable practical value for criminal profiling purposes. fWHR – at least as it has been most frequently assessed – does not appear to be a facial parameter that is useful for this purpose, however.