Studying the relationship between sequences and their corresponding three-dimensional structure assists structural biologists in solving the protein-folding problem. Despite several experimental and in-silico approaches, still understanding or decoding the three-dimensional structures from the sequence remains a mystery. In such cases, the accuracy of the structure prediction plays an indispensable role. To address this issue, an updated web server (CSSP-2.0) has been created to improve the accuracy of our previous version of CSSP by deploying the existing algorithms. It uses input as probabilities and predicts the consensus for the secondary structure as a highly accurate three-state Q3 (helix, strand, and coil). This prediction is achieved using six recent top-performing methods: MUFOLD-SS, RaptorX, PSSpred v4, PSIPRED, JPred v4, and Porter 5.0. CSSP-2.0 validation includes datasets involving various protein classes from the PDB, CullPDB, and AlphaFold databases. Our results indicate a significant improvement in the accuracy of the consensus Q3 prediction. Using CSSP-2.0, crystallographers can sort out the stable regular secondary structures from the entire complex structure, which would aid in inferring the functional annotation of hypothetical proteins. The web server is freely available at https://bioserver3.physics.iisc.ac.in/cgi-bin/cssp-2/.

OBJECTIVE: The 2022 outbreak of the monkeypox virus already involves, by April 2023, 110 countries with 86,956 confirmed cases and 119 deaths. Understanding an emerging disease on a molecular level is essential to study infection processes and eventually guide drug discovery at an early stage. To support this, we provide the so far most comprehensive structural proteome of the monkeypox virus, which includes 210 structural models, each computed with three state-of-the-art structure prediction methods. Instead of building on a single-genome sequence, we generated our models from a consensus of 3,713 high-quality genome sequences sampled from patients within 1 year of the outbreak. Therefore, we present an average structural proteome of the currently isolated viruses, including mutational analyses with a special focus on drug-binding sites. Continuing dynamic mutation monitoring within the structural proteome presented here is essential to timely predict possible physiological changes in the evolving virus.

The identification of RNA secondary structure has been an important tool for the characterization of nucleic acids. Computational structure prediction has been an effective approach toward this end, but improvement of established methods is often slow and reliant on redundant methodology. Here we present a novel consensus scoring approach, created to incorporate inputs from an array of established methods with the goal of producing outputs that contain mutual structures from these programs. This method is implemented in RNAdemocracy, a python program capable of competing with existing methods. This ensemble approach was limited by commonalities in established methods like parameter sourcing, which may lead to agreement error, an unavoidable outcome due to the limit of available RNA structure datasets. The modular construction of RNAdemocracy allows for its easy upgrading and customization to suit user\'s needs. RNAdemocracy, while capable of accurate predictions, is best suited to guide users to regions of the sequence space that exhibit agreement instead of a totally reliant predictor of structure. It is also capable of grading predictions for potential accuracy by providing a percentage of consensus between contributing methods in the final structure.