A Two-Layer SVM Ensemble-Classifier to Predict Interface Residue Pairs of Protein Trimers

Abstract

Study of interface residue pairs is important for understanding the interactions between monomers inside a trimer protein–protein complex. We developed a two-layer support vector machine (SVM) ensemble-classifier that considers physicochemical and geometric properties of amino acids and the influence of surrounding amino acids. Different descriptors and different combinations may give different prediction results. We propose feature combination engineering based on correlation coefficients and F-values. The accuracy of our method is 65.38% in independent test set, indicating biological significance. Our predictions are consistent with the experimental results. It shows the effectiveness and reliability of our method to predict interface residue pairs of protein trimers.

Prediction of the tetramer protein complex interaction based on CNN and SVM

“…The PDB ID of these 111 protein tetramers is shown in Supplementary Table S1 . If the contact area between any two atoms from two residues of two chains is bigger than zero, we call these two residues an interface residue pair ( Lyu and Gong, 2020 ). The contact area between two atoms is calculated by Qcontacts software.…”

“…In protein P three-dimensional structure, different amino acids have different geometric properties. These geometric properties, such as Accessible Surface Area (ASA), Relative solvent Accessible Surface Area (RASA), Exterior Contact Area (ECA), Interior Contact Area (ICA), and Exterior Void Area (EVA), play important roles in multibody protein complex interactions ( Wang et al, 2017 ; Yang and Gong, 2018 ; Liu and Gong, 2019 ; Zhao and Gong, 2019 ; Lyu and Gong, 2020 ; Sun and Gong, 2020 ). In this paper, we consider using the above five geometric properties to predict the tetramer protein complex interaction.…”

“…In this paper, we consider using the above five geometric properties to predict the tetramer protein complex interaction. References ( Liu and Gong, 2019 ; Lyu and Gong, 2020 ) and ( Zhao and Gong, 2019 ) introduce the five geometric properties and their computing tools in detail. Where is the abbreviation of amino acid name.…”

“…Martin et al predicted residue contact in protein-protein interaction by message passing ( Weigt et al, 2009 ). We also developed a two-layer support vector machine ensemble classifier to predict trimer protein complex interface residue pairs ( Lyu and Gong, 2020 ). There are many other methods, see references ( Kamisetty et al, 2013 ; Fu et al, 2014 ; Michel et al, 2014 ; He et al, 2017 ; Li et al, 2019 ; Zhang et al, 2020 ; Li et al, 2021a ; Li et al, 2021b ; Humphreys et al, 2021 ; Jumper et al, 2021 ; Mylonas et al, 2021 ; Knutson et al, 2022 ).…”

Modelling and Prediction of Monthly Global Irradiation Using Different Prediction Models

“…Support vector machines can also be used in different fields such as Bioinformatics to identify single-nucleotide polymorphisms [16], to predict dihedral angle regions [17] or to forecast interface residue pairs of protein trimers [18].…”