Tutorial: a guide for the selection of fast and accurate computational tools for the prediction of intrinsic disorder in proteins

Taxonomy-specific assessment of intrinsic disorder predictions at residue and region levels in higher eukaryotes, protists, archaea, bacteria and viruses

Intrinsic disorder predictors were evaluated in several studies including the two large CAID experiments. However, these studies are biased towards eukaryotic proteins and focus primarily on the residue-level predictions. We provide first-of-its-kind assessment that comprehensively covers the taxonomy and evaluates predictions at the residue and disordered region levels. We curate a benchmark dataset that uniformly covers eukaryotic, archaeal, bacterial, and viral proteins. We find that predictive performance differs substantially across taxonomy, where viruses are predicted most accurately, followed by protists and higher eukaryotes, while bacterial and archaeal proteins suffer lower levels of accuracy. These trends are consistent across predictors. We also find that current tools, except for flDPnn, struggle with reproducing native distributions of the numbers and sizes of the disordered regions. Moreover, analysis of two variants of disorder predictions derived from the AlphaFold2 predicted structures reveals that they produce accurate residue-level propensities for archaea, bacteria and protists. However, they underperform for higher eukaryotes and generally struggle to accurately identify disordered regions. Our results motivate development of new predictors that target bacteria and archaea and which produce accurate results at both residue and region levels. We also stress the need to include the region-level assessments in future assessments.

flDPnn2: Accurate and Fast Predictor of Intrinsic Disorder in Proteins

Prediction of the intrinsic disorder in protein sequences is an active research area, with well over 100 predictors that were released to date. These efforts are motivated by the functional importance and high levels of abundance of intrinsic disorder, combined with relatively low amounts of experimental annotations. The disorder predictors are periodically evaluated by independent assessors in the Critical Assessment of protein Intrinsic Disorder prediction (CAID) experiments. The recently completed CAID2 experiment assessed close to 40 state-of-the-art methods demonstrating that some of them produce accurate results. In particular, flDPnn2 method, which is the successor of flDPnn that performed well in the CAID1 experiment, secured the overall most accurate results on the Disorder-NOX dataset in CAID2. flDPnn2 implements a number of improvements when compared to its predecessor including changes to the inputs, increased size of the deep network model that we retrained on a larger training set, and addition of an alignment module. Using results from CAID2, we show that flDPnn2 produces accurate predictions very quickly, modestly improving over the accuracy of flDPnn and reducing the runtime by half, to about 27 s per protein. flDPnn2 is freely available as a convenient web server at http://biomine.cs.vcu.edu/servers/flDPnn2/.

Methodological approaches to studying phase separation and HIV-1 replication: Current and future perspectives

This article reviews tried-and-tested methodologies that have been employed in the first studies on phase separating properties of structural, RNA-binding and catalytic proteins of HIV-1. These are described here to stimulate interest for any who may want to initiate similar studies on virus-mediated liquid-liquid phase separation. Such studies serve to better understand the life cycle and pathogenesis of viruses and open the door to new therapeutics.

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. IUPred is rooted in a simple biophysical model and uses a limited number of parameters largely derived on globular protein structures only. This enabled an incredibly fast and robust prediction method, however, its limitations have also become apparent in light of recent breakthrough methods using deep learning techniques. Here, we present AIUPred, a novel version of IUPred which incorporates deep learning techniques into the energy estimation framework. It achieves improved performance while keeping the robustness of the original method. Based on the evaluation of recent benchmark datasets, AIUPred scored amongst the top three single sequence based methods. With a new web server we offer fast and reliable visual analysis for users as well as options to analyze whole genomes in mere seconds with the downloadable package. AIUPred is available at https://aiupred.elte.hu.

How intrinsically disordered proteins order plant gene silencing

Intrinsically disordered proteins (IDPs) and proteins with intrinsically disordered regions (IDRs) possess low sequence complexity of amino acids and display non-globular tertiary structures. They can act as scaffolds, form regulatory hubs, or trigger biomolecular condensation to control diverse aspects of biology. Emerging evidence has recently implicated critical roles of IDPs and IDR-contained proteins in nuclear transcription and cytoplasmic post-transcriptional processes, among other molecular functions. We here summarize the concepts and organizing principles of IDPs. We then illustrate recent progress in understanding the roles of key IDPs in machineries that regulate transcriptional and post-transcriptional gene silencing (PTGS) in plants, aiming at highlighting new modes of action of IDPs in controlling biological processes.

Mesophiles vs. Thermophiles: Untangling the Hot Mess of Intrinsically Disordered Proteins and Growth Temperature of Bacteria

The dynamic structures and varying functions of intrinsically disordered proteins (IDPs) have made them fascinating subjects in molecular biology. Investigating IDP abundance in different bacterial species is crucial for understanding adaptive strategies in diverse environments. Notably, thermophilic bacteria have lower IDP abundance than mesophiles, and a negative correlation with optimal growth temperature (OGT) has been observed. However, the factors driving these trends are yet to be fully understood. We examined the types of IDPs present in both mesophiles and thermophiles alongside those unique to just mesophiles. The shared group of IDPs exhibits similar disorder levels in the two groups of species, suggesting that certain IDPs unique to mesophiles may contribute to the observed decrease in IDP abundance as OGT increases. Subsequently, we used quasi-independent contrasts to explore the relationship between OGT and IDP abundance evolution. Interestingly, we found no significant relationship between OGT and IDP abundance contrasts, suggesting that the evolution of lower IDP abundance in thermophiles may not be solely linked to OGT. This study provides a foundation for future research into the intricate relationship between IDP evolution and environmental adaptation. Our findings support further research on the adaptive significance of intrinsic disorder in bacterial species.

Availability of web servers significantly boosts citations rates of bioinformatics methods for protein function and disorder prediction

Motivation

Development of bioinformatics methods is a long, complex and resource-hungry process. Hundreds of these tools were released. While some methods are highly cited and used, many suffer relatively low citation rates. We empirically analyze a large collection of recently released methods in three diverse protein function and disorder prediction areas to identify key factors that contribute to increased citations.

Results

We show that provision of a working web server significantly boosts citation rates. On average, methods with working web servers generate three times as many citations compared to tools that are available as only source code, have no code and no server, or are no longer available. This observation holds consistently across different research areas and publication years. We also find that differences in predictive performance are unlikely to impact citation rates. Overall, our empirical results suggest that a relatively low-cost investment into the provision and long-term support of web servers would substantially increase the impact of bioinformatics tools.