Prediction of Intrinsic Disorder in Protein Sequences Using the Fast and Accurate flDPnn2 Method
Kui Wang1, Gang Hu1, Lukasz Kurgan2
1NITFID, School of Statistics and Data Science, AAIS, LPMC, and KLMDASR, Nankai University, Tianjin, China.
Abstract:
Many proteins, particularly in eukaryotes, contain intrinsically disordered regions (IDRs) in their sequences. IDRs lack a stable tertiary structure under physiological conditions, yet they are essential for a broad range of cellular functions. They can be efficiently and accurately identified with computational methods. We focus on flDPnn2, which is an improved version of its predecessor, flDPnn. Results from the recently completed third Critical Assessment of Protein Intrinsic Disorder Prediction (CAID3) show that these improvements led to relatively high prediction accuracy and low runtime, with flDPnn2 taking only 15 s to predict a 1,000-residue-long sequence. Comparative evaluation on CAID3 also revealed that flDPnn2 is the fastest among the most accurate disorder predictors. We provide a practical guide for flDPnn2 users, explaining how to collect and interpret results produced by flDPnn2 via its freely available web server at https://biomine.cs.vcu.edu/servers/flDPnn2/. We also include a case study that provides a helpful illustration for these explanations. The flDPnn2 web server supports batch submission of up to 50 FASTA-formatted protein sequences and provides results in multiple formats, facilitating convenient analysis and interpretation. © 2026 Wiley Periodicals LLC. Basic Protocol 1: Using the flDPnn2 web server to predict protein intrinsic disorder.
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