DisProt: intrinsic protein disorder annotation in 2020

The Database of Protein Disorder (DisProt, URL: https://disprot.org) provides manually curated annotations of intrinsically disordered proteins from the literature. Here we report recent developments with DisProt (version 8), including the doubling of protein entries, a new disorder ontology, improvements of the annotation format and a completely new website. The website includes a redesigned graphical interface, a better search engine, a clearer API for programmatic access and a new annotation interface that integrates text mining technologies. The new entry format provides a greater flexibility, simplifies maintenance and allows the capture of more information from the literature. The new disorder ontology has been formalized and made interoperable by adopting the OWL format, as well as its structure and term definitions have been improved. The new annotation interface has made the curation process faster and more effective. We recently showed that new DisProt annotations can be effectively used to train and validate disorder predictors. We believe the growth of DisProt will accelerate, contributing to the improvement of function and disorder predictors and therefore to illuminate the ‘dark’ proteome

Critical assessment of protein intrinsic disorder prediction

Abstract: Intrinsically disordered proteins, defying the traditional protein structure–function paradigm, are a challenge to study experimentally. Because a large part of our knowledge rests on computational predictions, it is crucial that their accuracy is high. The Critical Assessment of protein Intrinsic Disorder prediction (CAID) experiment was established as a community-based blind test to determine the state of the art in prediction of intrinsically disordered regions and the subset of residues involved in binding. A total of 43 methods were evaluated on a dataset of 646 proteins from DisProt. The best methods use deep learning techniques and notably outperform physicochemical methods. The top disorder predictor has Fmax = 0.483 on the full dataset and Fmax = 0.792 following filtering out of bona fide structured regions. Disordered binding regions remain hard to predict, with Fmax = 0.231. Interestingly, computing times among methods can vary by up to four orders of magnitude

Physioland - A serious game for rehabilitation of patients with neurological diseases

Current society has observed an increasing number of victims of neurological disease, with reduced mobility, leading to a necessity to perform physical therapy to optimize their quality of life. This action results in physiotherapeutic programs filled with repetitive exercises, often fastidious, that lead to the demotivation of patients and consequent poor adherence and withdrawal. As a result of the technological evolution, new tools such as serious games are emerging, so their use in the field of physical therapy can modify the way patients face their treatments, promoting their motivation. Thus, we have developed a serious game based on image processing techniques to motivate and monitor patients with neurological diseases in their physical therapy practice.This work has been supported by COMPETE: POCI-01-0145-FEDER-007043 and FCT - "Fundação para a Ciencia e Tecnologia" within the Project Scope: UID/CEC/00319/2013 and also by FCT - "Fundação para a Ciencia e Tecnologia" within the Project Scope: SFRH/BD/74852/2010