SNB‐PSSM: A spatial neighbor‐based PSSM used for protein–RNA binding site prediction

Abstract

Protein–RNA interactions play essential roles in a wide variety of biological processes. Recognition of RNA‐binding residues on proteins has been a challenging problem. Most of methods utilize the position‐specific scoring matrix (PSSM). It has been found that considering the evolutionary information of sequence neighboring residues can improve the prediction. In this work, we introduce a novel method SNB‐PSSM (spatial neighbor–based PSSM) combined with the structure window scheme where the evolutionary information of spatially neighboring residues is considered. The results show our method consistently outperforms the standard and smoothed PSSM methods. Tested on multiple datasets, this approach shows an encouraging performance compared with RNABindRPlus, BindN+, PPRInt, xypan, Predict_RBP, SpaPF, PRNA, and KYG, although is inferior to RNAProSite, RBscore, and aaRNA. In addition, since our method is not sensitive to protein structure changes, it can be applied well on binding site predictions of modeled structures. Thus, the result also suggests the evolution of binding sites is spatially cooperative. The proposed method as an effective tool of considering evolutionary information can be widely used for the nucleic acid–/protein‐binding site prediction and functional motif finding.

A Deep Learning and XGBoost-Based Method for Predicting Protein-Protein Interaction Sites

“…As shown in Figure 1B , the DeepPPISP proposed by Zeng et al ( Zeng et al, 2020 ) for PPIS prediction had three types of input: position-specific scoring matrix (PSSM), secondary structure, and raw protein sequences. The PSSM is an excellent feature extractor for protein sequences and thus have widely been applied to problems in the field of computational biology, such as predicting protein post-translational modification ( Huang et al, 2013 ; Huang et al, 2014 ; Dehzangi et al, 2017 ), membrane type ( Wang et al, 2019 ), protein-RNA binding site ( Liu et al, 2021 ), and structure ( Guo et al, 2021 ). The quality of PSSM features is closely associated with the underlying multiple sequence alignments.…”

PSSMCOOL: a comprehensive R package for generating evolutionary-based descriptors of protein sequences from PSSM profiles

“…Moreover, the information conveyed through PSSMs is widely used in predicting various attributes of proteins ranging from the prediction of secondary and tertiary structures [ 1 ], protein–protein interactions [ 2 ], accessible surface area [ 3 ], flexibility [ 4 ], binding sites domains [ 5 , 6 ], post-translational modification [ 7 ], protein localization [ 8 ], identifying the binding regions of protein–RNA [ 9 ], and protein–DNA [ 10 ] to the prediction of drug–target interaction [ 11 ]. Figure 2 shows the categorized papers based on their subjects that utilized PSSM-based features.…”