MOTIVATION: In silico functional annotation of proteins is crucial to narrowing the sequencing-accelerated gap in our understanding of protein activities. Numerous function annotation methods exist, and their ranks have been growing, particularly so with the recent deep learning-based developments. However, it is unclear if these tools are truly predictive. As we are not aware of any methods that can identify new terms in functional ontologies, we ask if they can, at least, identify molecular functions of proteins that are non-homologous to or far-removed from known protein families. RESULTS: Here, we explore the potential and limitations of the existing methods in predicting the molecular functions of thousands of such proteins. Lacking the "ground truth" functional annotations, we transformed the assessment of function prediction into evaluation of functional similarity of protein pairs that likely share function but are unlike any of the currently functionally annotated sequences. Notably, our approach transcends the limitations of functional annotation vocabularies, providing a means to assess different-ontology annotation methods. We find that most existing methods are limited to identifying functional similarity of homologous sequences and fail to predict the function of proteins lacking reference. Curiously, despite their seemingly unlimited by-homology scope, deep learning methods also have trouble capturing the functional signal encoded in protein sequence. We believe that our work will inspire the development of a new generation of methods that push boundaries and promote exploration and discovery in the molecular function domain. AVAILABILITY AND IMPLEMENTATION: The data underlying this article are available at https://doi.org/10.6084/m9.figshare.c.6737127.v3. The code used to compute siblings is available openly at https://bitbucket.org/bromberglab/siblings-detector/.

Briefings in Bioinformatics, bbab240, doi: https://doi.org/10.1093/bib/bbab240. At the end of the article, the name of Yutaka Saito was corrected to be Puneet Rawat.

SUMMARY: We have developed a database, Ab-CoV, which contains manually curated experimental interaction profiles of 1780 coronavirus-related neutralizing antibodies. It contains more than 3200 datapoints on half maximal inhibitory concentration (IC50), half maximal effective concentration (EC50) and binding affinity (KD). Each data with experimentally known three-dimensional structures are complemented with predicted change in stability and affinity of all possible point mutations of interface residues. Ab-CoV also includes information on epitopes and paratopes, structural features of viral proteins, sequentially similar therapeutic antibodies and Collier de Perles plots. It has the feasibility for structure visualization and options to search, display and download the data. AVAILABILITY AND IMPLEMENTATION: Ab-CoV database is freely available at https://web.iitm.ac.in/bioinfo2/ab-cov/home. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Several prediction algorithms and tools have been developed in the last two decades to predict protein and peptide aggregation. These in silico tools aid to predict the aggregation propensity and amyloidogenicity as well as the identification of aggregation-prone regions. Despite the immense interest in the field, it is of prime importance to systematically compare these algorithms for their performance. In this review, we have provided a rigorous performance analysis of nine prediction tools using a variety of assessments. The assessments were carried out on several non-redundant datasets ranging from hexapeptides to protein sequences as well as amyloidogenic antibody light chains to soluble protein sequences. Our analysis reveals the robustness of the current prediction tools and the scope for improvement in their predictive performances. Insights gained from this work provide critical guidance to the scientific community on advantages and limitations of different aggregation prediction methods and make informed decisions about their research needs.

Summary A wireless sensor network (WSN) can give a coordinated and minimal effort checking and control answer for such keen emotionally supportive systems. In this paper, we have discussed about the SEP in comparison with LEACH for smart home applications. Smart home scenario of an industry is taken into consideration in our research. The normal sink node is represented in the name of link node that connects the multiple sectors in the smart home scenario. In addition, it will act as a cluster head for the entire smart home setup. Moreover, the present research work proposes the plan of a low energy and high throughput and various levelled cluster‐based directing convention for smart home sensor organize application. Proposed protocol performs dynamic cluster head choice in light of various node qualities like node enduring energy, remoteness of the node from the link (sink), and average distance of the node from various neighbor nodes. Cluster head appropriation is even and brings about upgraded organization lifetime. Sensor nodes process the sense information utilizing nearby on‐board processors and exclude the repetitive information from transmission. In the final section, the energy level and the through put can be compared with the LEACH for getting a better understanding. The simulation results of the proposed technique clarify the energy consumed and the energy remaining in the stream. The energy will be measured in Joules. Moreover, the dead node will be tracked and intimated through our proposed work. The packet delivery and the delayed process of each and every node will be discussed in the proposed simulation result. The energy level remained will be calculated in Joules measurement. The SEP level will be more when compared with LEACH protocol. The energy level will be improved in case of SEP as 5, 10, 15, and 20 J/ ms.

MOTIVATION: Protein aggregation is a major unsolved problem in biochemistry with implications for several human diseases, biotechnology and biomaterial sciences. A majority of sequence-structural properties known for their mechanistic roles in protein aggregation do not correlate well with the aggregation kinetics. This limits the practical utility of predictive algorithms. RESULTS: We analyzed experimental data on 183 unique single point mutations that lead to change in aggregation rates for 23 polypeptides and proteins. Our initial mathematical model obtained a correlation coefficient of 0.43 between predicted and experimental change in aggregation rate upon mutation (P-value <0.0001). However, when the dataset was classified based on protein length and conformation at the mutation sites, the average correlation coefficient almost doubled to 0.82 (range: 0.74-0.87; P-value <0.0001). We observed that distinct sequence and structure-based properties determine protein aggregation kinetics in each class. In conclusion, the protein aggregation kinetics are impacted by local factors and not by global ones, such as overall three-dimensional protein fold, or mechanistic factors such as the presence of aggregation-prone regions. AVAILABILITY AND IMPLEMENTATION: The web server is available at http://www.iitm.ac.in/bioinfo/aggrerate-pred/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.