Deep learning for intrinsically disordered proteins: From improved predictions to deciphering conformational ensembles.

Intrinsically disordered proteins (IDPs) lack a stable three-dimensional structure under physiological conditions, challenging traditional structure-based prediction methods. This review explores how modern deep learning approaches, which have revolutionized structure prediction for globular proteins, have impacted protein disorder predictions. We highlight the role of community-driven efforts in curating data and assessing state-of-the-art, which have been crucial in advancing the field.

ViralPrimer: a web server to monitor viral nucleic acid amplification tests' primer efficiency during pandemics, with emphasis on SARS-CoV-2 and Mpox.

Summary: Accurate pathogen identification is crucial during outbreaks, especially with the emergence of new variants requiring frequent primer updates. However, resources for maintaining up-to-date verification of primer sequences are often limited, which poses challenges for reliable diagnosis and hinders potential monitoring efforts based on genome sequencing. To address this, we introduce ViralPrimer, a web server facilitating primer design, SARS-CoV-2 and Mpox variant monitoring, and adaptation to future threats.

AIUPred: combining energy estimation with deep learning for the enhanced prediction of protein disorder.

Intrinsically disordered proteins and protein regions (IDPs/IDRs) carry out important biological functions without relying on a single well-defined conformation. As these proteins are a challenge to study experimentally, computational methods play important roles in their characterization. One of the commonly used tools is the IUPred web server which provides prediction of disordered regions and their binding sites.

Determination of the most significant rubber components influencing the hardness of natural rubber (NR) using various statistical methods.

Manufacturers use a large number of components in the production of modern rubber products. The selection of the constituents of the rubber recipe is primarily determined by the purpose of use. The different fields of applications of rubbers require the presence of appropriate mechanical properties.

The Origin of Discrepancies between Predictions and Annotations in Intrinsically Disordered Proteins.

Disorder prediction methods that can discriminate between ordered and disordered regions have contributed fundamentally to our understanding of the properties and prevalence of intrinsically disordered proteins (IDPs) in proteomes as well as their functional roles. However, a recent large-scale assessment of the performance of these methods indicated that there is still room for further improvements, necessitating novel approaches to understand the strengths and weaknesses of individual methods. In this study, we compared two methods, IUPred and disorder prediction, based on the pLDDT scores derived from AlphaFold2 (AF2) models.