Take and Give: Protein Structure Analysis and Prediction with Statistical Scoring Functions
A special issue of International Journal of Molecular Sciences (ISSN 1422-0067). This special issue belongs to the section "Molecular Informatics".
Deadline for manuscript submissions: closed (30 November 2020) | Viewed by 20486
Special Issue Editors
Special Issue Information
Dear Colleagues,
The PDB database provides more than 150,000 entries for biological macromolecular structures. The vast majority of the entries comprise proteins. Thus, we can resort to a large dataset which encodes information about sequence–structure–function relationships. Many bioinformatics approaches take advantage of this information and utilize it for a wealth of basic biological, biochemical, and biophysical problems. A well-established key approach is the statistical analysis of experimentally resolved structures for the subsequent derivation of statistical scoring functions (SSFs, also referred to as statistical energy functions, knowledge-based potentials or mean force potentials). Such SSFs are employed in numerous bioinformatics methods, e.g., for the assessment of experimentally determined structures or in the prediction of 3D protein structures, protein–protein interactions, protein–ligand interactions, protein stability, and many more. Methods may either utilize SSFs alone or combine them with physics-based force fields and employ different optimization or machine learning techniques.
The aim of this Special Issue is to focus on some of the most recent and interesting developments in SSF-based bioinformatics methods and their application in the analysis and prediction of biological macromolecular structures.
Prof. Dr. Peter Lackner
Prof. Dr. Markus Wiederstein
Guest Editors
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