MEDUSA: Prediction of Protein Flexibility from Sequence

• Published in: Journal of molecular biology Vol. 433; no. 11; p. 166882
• Main Authors: Vander Meersche, Yann, Cretin, Gabriel, de Brevern, Alexandre G., Gelly, Jean-Christophe, Galochkina, Tatiana
• Format: Journal Article
• Language: English
• Published: England Elsevier Ltd 28.05.2021; Elsevier
• Subjects: B-factor; Bioinformatics; Computer Science; deep learning; prediction tool; protein flexibility; protein structure; protein structure; deep learning; protein flexibility; B-factor; prediction tool
• ISSN: 0022-2836; 1089-8638
• DOI: 10.1016/j.jmb.2021.166882

[Display omitted] •Protein flexibility plays a crucial role in biological function.•MEDUSA is a web-server for prediction of protein flexibility from sequence.•It uses a deep convolutional network to assign flexibility class for each residue.•MEDUSA provides binary, three-class and five-class predictions.•It provides important insights about the protein function mechanism. Information on the protein flexibility is essential to understand crucial molecular mechanisms such as protein stability, interactions with other molecules and protein functions in general. B-factor obtained in the X-ray crystallography experiments is the most common flexibility descriptor available for the majority of the resolved protein structures. Since the gap between the number of the resolved protein structures and available protein sequences is continuously growing, it is important to provide computational tools for protein flexibility prediction from amino acid sequence. In the current study, we report a Deep Learning based protein flexibility prediction tool MEDUSA (https://www.dsimb.inserm.fr/MEDUSA). MEDUSA uses evolutionary information extracted from protein homologous sequences and amino acid physico-chemical properties as input for a convolutional neural network to assign a flexibility class to each protein sequence position. Trained on a non-redundant dataset of X-ray structures, MEDUSA provides flexibility prediction in two, three and five classes. MEDUSA is freely available as a web-server providing a clear visualization of the prediction results as well as a standalone utility (https://github.com/DSIMB/medusa). Analysis of the MEDUSA output allows a user to identify the potentially highly deformable protein regions and general dynamic properties of the protein.