The Relative Distance Prediction of Transmembrane Protein Surface Residue Based on Improved Residual Networks

Qiufen Chen, Yuanzhao Guo, Jiuhong Jiang, Jing Qu, Li Zhang () and Han Wang ()
Additional contact information
Qiufen Chen: School of Computer Science and Engineering, Changchun University of Technology, Changchun 130012, China
Yuanzhao Guo: School of Information Science and Technology, Institute of Computational Biology, Northeast Normal University, Changchun 130024, China
Jiuhong Jiang: School of Information Science and Technology, Institute of Computational Biology, Northeast Normal University, Changchun 130024, China
Jing Qu: School of Information Science and Technology, Institute of Computational Biology, Northeast Normal University, Changchun 130024, China
Li Zhang: School of Computer Science and Engineering, Changchun University of Technology, Changchun 130012, China
Han Wang: School of Information Science and Technology, Institute of Computational Biology, Northeast Normal University, Changchun 130024, China

Mathematics, 2023, vol. 11, issue 3, 1-16

Abstract: (1) Background: Transmembrane proteins (TMPs) act as gateways connecting the intra- and extra-biomembrane environments, exchanging material and signals crossing the biofilm. Relevant evidence shows that corresponding interactions mostly happen on the TMPs’ surface. Therefore, knowledge of the relative distance among surface residues is critically helpful in discovering the potential local structural characters and setting the foundation for the protein’s interaction with other molecules. However, the prediction of fine-grained distances among residues with sequences remains challenging; (2) Methods: In this study, we proposed a deep-learning method called TMP-SurResD, which capitalized on the combination of the Residual Block (RB) and Squeeze-and-Excitation (SE) for simultaneously predicting the relative distance of functional surface residues based on sequences’ information; (3) Results: The comprehensive evaluation demonstrated that TMP-SurResD could successfully capture the relative distance between residues, with a Pearson Correlation Coefficient ( PCC ) of 0.7105 and 0.6999 on the validation and independent sets, respectively. In addition, TMP-SurResD outperformed other methods when applied to TMPs surface residue contact prediction, and the maximum Matthews Correlation Coefficient (MCC) reached 0.602 by setting a threshold to the predicted distance of 10; (4) Conclusions: TMP-SurResD can serve as a useful tool in supporting a sequence-based local structural feature construction and exploring the function and biological mechanisms of structure determination in TMPs, which can thus significantly facilitate the research direction of molecular drug action, target design, and disease treatment.

Keywords: transmembrane protein; distances among residues; co-evolution; residual network (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2227-7390/11/3/642/pdf (application/pdf)
https://www.mdpi.com/2227-7390/11/3/642/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:11:y:2023:i:3:p:642-:d:1048101

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().