FLIPPER: Predicting and Characterizing Linear Interacting Peptides in the Protein Data Bank. Issue 9 (30th April 2021)

• Record Type: Journal Article
• Authors: Monzon, Alexander Miguel; Bonato, Paolo; Necci, Marco; Tosatto, Silvio C.E.; Piovesan, Damiano
• Is Part Of: Journal of molecular biology. Volume 433:Issue 9(2021)
• Publication Date: 2021-04-30
• Subjects: linear interacting peptides -- intrinsic disorder -- protein structure -- binding modes prediction -- machine learning; PDB Protein Data Bank -- LIP Linear Interacting Peptide -- IDP Intrinsically Disordered Protein -- IDR Intrinsically Disordered Region -- RSA Relative Solvent accessibility -- TPR True Positive Rate, or recall, or sensitivity -- TNR True Negative Rate, or specificity -- FPR False Positive Rate -- BAC Balanced Accuracy -- MCC Matthews' Correlation Coefficient -- PPV Positive Predictive Value, or precision; Molecular biology -- Periodicals; Biology -- Periodicals; Biochemistry -- Periodicals; Bacteriology -- Periodicals; Molecular Biology -- Periodicals; Biochemistry -- Periodicals; Biologie moléculaire -- Périodiques; Biologie -- Périodiques; Biochimie -- Périodiques; Moleculaire biologie; Biochemistry; Biology; Molecular biology; Periodicals; 572.805
• DOI: 10.1016/j.jmb.2021.166900
• Languages: English
• ISSNs: 0022-2836

Abstract

Graphical abstract: Highlights: IDPs/IDRs exhibit a wide diversity of binding modes. LIPs are functional regions with specific structural features. FLIPPER is an accurate and fast LIP predictor from PDB structure complexes. Different types of LIPs can be identified depending on the interaction partners. FLIPPER provides the largest high quality data set of LIPs. Abstract: 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.