Interface property responsible for effective interactions of protean segments: Intrinsically disordered regions that undergo disorder-to-order transitions upon binding

CLIP: accurate prediction of disordered linear interacting peptides from protein sequences using co-evolutionary information

One of key features of intrinsically disordered regions (IDRs) is facilitation of protein-protein and protein-nucleic acids interactions. These disordered binding regions include molecular recognition features (MoRFs), short linear motifs (SLiMs) and longer binding domains. Vast majority of current predictors of disordered binding regions target MoRFs, with a handful of methods that predict SLiMs and disordered protein-binding domains. A new and broader class of disordered binding regions, linear interacting peptides (LIPs), was introduced recently and applied in the MobiDB resource. LIPs are segments in protein sequences that undergo disorder-to-order transition upon binding to a protein or a nucleic acid, and they cover MoRFs, SLiMs and disordered protein-binding domains. Although current predictors of MoRFs and disordered protein-binding regions could be used to identify some LIPs, there are no dedicated sequence-based predictors of LIPs. To this end, we introduce CLIP, a new predictor of LIPs that utilizes robust logistic regression model to combine three complementary types of inputs: co-evolutionary information derived from multiple sequence alignments, physicochemical profiles and disorder predictions. Ablation analysis suggests that the co-evolutionary information is particularly useful for this prediction and that combining the three inputs provides substantial improvements when compared to using these inputs individually. Comparative empirical assessments using low-similarity test datasets reveal that CLIP secures area under receiver operating characteristic curve (AUC) of 0.8 and substantially improves over the results produced by the closest current tools that predict MoRFs and disordered protein-binding regions. The webserver of CLIP is freely available at http://biomine.cs.vcu.edu/servers/CLIP/ and the standalone code can be downloaded from http://yanglab.qd.sdu.edu.cn/download/CLIP/.

FLIPPER: Predicting and Characterizing Linear Interacting Peptides in the Protein Data Bank

A large fraction of peptides or protein regions are disordered in isolation and fold upon binding. These regions, also called MoRFs, SLiMs or LIPs, are often associated with signaling and regulation processes. However, despite their importance, only a limited number of examples are available in public databases and their automatic detection at the proteome level is problematic. Here we present FLIPPER, an automatic method for the detection of structurally linear sub-regions or peptides that interact with another chain in a protein complex. FLIPPER is a random forest classification that takes the protein structure as input and provides the propensity of each amino acid to be part of a LIP region. Models are built taking into consideration structural features such as intra- and inter-chain contacts, secondary structure, solvent accessibility in both bound and unbound state, structural linearity and chain length. FLIPPER is accurate when evaluated on non-redundant independent datasets, 99% precision and 99% sensitivity on PixelDB-25 and 87% precision and 88% sensitivity on DIBS-25. Finally, we used FLIPPER to process the entire Protein Data Bank and identified different classes of LIPs based on different binding modes and partner molecules. We provide a detailed description of these LIP categories and show that a large fraction of these regions are not detected by disorder predictors. All FLIPPER predictions are integrated in the MobiDB 4.0 database.