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Open AccessArticle

TMP-SSurface: A Deep Learning-Based Predictor for Surface Accessibility of Transmembrane Protein Residues

by Chang Lu 1,2, Zhe Liu 1,2, Bowen Kan 1,2, Yingli Gong 1,2, Zhiqiang Ma 1,2,3,* and Han Wang 1,2,3,*
1
School of Information Science and Technology, Northeast Normal University, Changchun 130117, China
2
Institute of Computational Biology, Northeast Normal University, Changchun 130117, China
3
Department of Computer Science, College of Humanities & Sciences of Northeast Normal University, Changchun 130117, China
*
Authors to whom correspondence should be addressed.
Crystals 2019, 9(12), 640; https://doi.org/10.3390/cryst9120640
Received: 4 November 2019 / Revised: 29 November 2019 / Accepted: 29 November 2019 / Published: 1 December 2019
(This article belongs to the Special Issue Protein Crystallography)
Transmembrane proteins (TMPs) play vital and diverse roles in many biological processes, such as molecular transportation and immune response. Like other proteins, many major interactions with other molecules happen in TMPs’ surface area, which is important for function annotation and drug discovery. Under the condition that the structure of TMP is hard to derive from experiment and prediction, it is a practical way to predict the TMP residues’ surface area, measured by the relative accessible surface area (rASA), based on computational methods. In this study, we presented a novel deep learning-based predictor TMP-SSurface for both alpha-helical and beta-barrel transmembrane proteins (α-TMP and β-TMP), where convolutional neural network (CNN), inception blocks, and CapsuleNet were combined to construct a network framework, simply accepting one-hot code and position-specific score matrix (PSSM) of protein fragment as inputs. TMP-SSurface was tested against an independent dataset achieving appreciable performance with 0.584 Pearson correlation coefficients (CC) value. As the first TMP’s rASA predictor utilizing the deep neural network, our method provided a referenceable sample for the community, as well as a practical step to discover the interaction sites of TMPs based on their sequence.
Keywords: transmembrane protein; surface accessibility; deep learning transmembrane protein; surface accessibility; deep learning
MDPI and ACS Style

Lu, C.; Liu, Z.; Kan, B.; Gong, Y.; Ma, Z.; Wang, H. TMP-SSurface: A Deep Learning-Based Predictor for Surface Accessibility of Transmembrane Protein Residues. Crystals 2019, 9, 640.

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