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Computational prediction of DNA-binding residues (DBRs) and the RNA-binding residues (RBRs) in protein sequences is an active area of research, with about 90 predictors and 20 that were published over the last two years. The new predictors rely on sophisticated deep neural networks and protein language models, produce accurate predictions, and are conveniently available as code and/or web servers. However, we identified shortage of tools that predict these interactions in intrinsically disordered regions and tools capable of predicting residues that interact with specific RNA and DNA types. Moreover, cross-predictions between RBRs and DBRs should be quantified and minimized to ensure that future tools accurately differentiate between these two distinct types of nucleic acids. 

Abstract Dozens of impactful methods that predict intrinsically disordered regions (IDRs) in protein sequences that interact with proteins and/or nucleic acids were developed. Their training and assessment rely on the IDR‐level binding annotations, while the equivalent structure‐trained methods predict more granular annotations of binding amino acids (AA). We compiled a new benchmark dataset that annotates binding AA in IDRs and applied it to complete a first‐of‐its‐kind assessment of predictions of the disordered binding residues. We evaluated a representative collection of 14 methods, used several hundred low‐similarity test proteins, and focused on the challenging task of differentiating these binding residues from other disordered AA and considering ligand type‐specific predictions (protein–protein vs. protein–nucleic acid interactions). We found that current methods struggle to accurately predict binding IDRs among disordered residues; however, better‐than‐random tools predict disordered binding residues significantly better than binding IDRs. We identified at least one relatively accurate tool for predicting disordered protein‐binding and disordered nucleic acid‐binding AA. Analysis of cross‐predictions between interactions with protein and nucleic acids revealed that most methods are ligand‐type‐agnostic. Only two predictors of the nucleic acid‐binding IDRs and two predictors of the protein‐binding IDRs can be considered as ligand‐type‐specific. We also discussed several potential future directions that would move this field forward by producing more accurate methods that target the prediction of binding residues, reduce cross‐predictions, and cover a broader range of ligand types. 

Abstract Computational prediction of nucleic acid-binding residues in protein sequences is an active field of research, with over 80 methods that were released in the past 2 decades. We identify and discuss 87 sequence-based predictors that include dozens of recently published methods that are surveyed for the first time. We overview historical progress and examine multiple practical issues that include availability and impact of predictors, key features of their predictive models, and important aspects related to their training and assessment. We observe that the past decade has brought increased use of deep neural networks and protein language models, which contributed to substantial gains in the predictive performance. We also highlight advancements in vital and challenging issues that include cross-predictions between deoxyribonucleic acid (DNA)-binding and ribonucleic acid (RNA)-binding residues and targeting the two distinct sources of binding annotations, structure-based versus intrinsic disorder-based. The methods trained on the structure-annotated interactions tend to perform poorly on the disorder-annotated binding and vice versa, with only a few methods that target and perform well across both annotation types. The cross-predictions are a significant problem, with some predictors of DNA-binding or RNA-binding residues indiscriminately predicting interactions with both nucleic acid types. Moreover, we show that methods with web servers are cited substantially more than tools without implementation or with no longer working implementations, motivating the development and long-term maintenance of the web servers. We close by discussing future research directions that aim to drive further progress in this area. 

Abstract Current predictors of DNA-binding residues (DBRs) from protein sequences belong to two distinct groups, those trained on binding annotations extracted from structured protein-DNA complexes (structure-trained) vs. intrinsically disordered proteins (disorder-trained). We complete the first empirical analysis of predictive performance across the structure- and disorder-annotated proteins for a representative collection of ten predictors. Majority of the structure-trained tools perform well on the structure-annotated proteins while doing relatively poorly on the disorder-annotated proteins, and vice versa. Several methods make accurate predictions for the structure-annotated proteins or the disorder-annotated proteins, but none performs highly accurately for both annotation types. Moreover, most predictors make excessive cross-predictions for the disorder-annotated proteins, where residues that interact with non-DNA ligand types are predicted as DBRs. Motivated by these results, we design, validate and deploy an innovative meta-model, hybridDBRpred, that uses deep transformer network to combine predictions generated by three best current predictors. HybridDBRpred provides accurate predictions and low levels of cross-predictions across the two annotation types, and is statistically more accurate than each of the ten tools and baseline meta-predictors that rely on averaging and logistic regression. We deploy hybridDBRpred as a convenient web server at http://biomine.cs.vcu.edu/servers/hybridDBRpred/ and provide the corresponding source code at https://github.com/jianzhang-xynu/hybridDBRpred. 

Abstract Intrinsic disorder in proteins is relatively abundant in nature and essential for a broad spectrum of cellular functions. While disorder can be accurately predicted from protein sequences, as it was empirically demonstrated in recent community-organized assessments, it is rather challenging to collect and compile a comprehensive prediction that covers multiple disorder functions. To this end, we introduce the DEPICTER2 (DisorderEd PredictIon CenTER) webserver that offers convenient access to a curated collection of fast and accurate disorder and disorder function predictors. This server includes a state-of-the-art disorder predictor, flDPnn, and five modern methods that cover all currently predictable disorder functions: disordered linkers and protein, peptide, DNA, RNA and lipid binding. DEPICTER2 allows selection of any combination of the six methods, batch predictions of up to 25 proteins per request and provides interactive visualization of the resulting predictions. The webserver is freely available at http://biomine.cs.vcu.edu/servers/DEPICTER2/