FUpred: Detecting protein domains through deep-learning based contact map prediction.

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Abstract

Protein domains are subunits that can fold and function independently. Correct domain boundary assignment is thus a critical step towards accurate protein structure and function analyses. There is, however, no efficient algorithm available for accurate domain prediction from sequence. The problem is particularly challenging for proteins with discontinuous domains, which consist of domain segments that are separated along the sequence.
We developed a new algorithm, FUpred, which predicts protein domain boundaries utilizing contact maps created by deep residual neural networks coupled with co-evolutionary precision matrices. The core idea of the algorithm is to retrieve domain boundary locations by maximizing the number of intra-domain contacts, while minimizing the number of inter-domain contacts from the contact maps. FUpred was tested on a large-scale dataset consisting of 2,549 proteins and generated correct single- and multi-domain classifications with an MCC of 0.799, which was 19.1% (or 5.3%) higher than the best machine learning (or threading) based method. For proteins with discontinuous domains, the DBD (domain boundary detection) and NDO (normalized domain overlapping) scores of FUpred were 0.788 and 0.521, respectively, which were 17.3% and 23.8% higher than the best control method. The results demonstrate a new avenue to accurately detect domain composition from sequence alone, especially for discontinuous, multi-domain proteins.
https://zhanglab.ccmb.med.umich.edu/FUpred.
Supplementary data are available at Bioinformatics online.
© The Author(s) (2020). Published by Oxford University Press. All rights reserved. For Permissions, please email: [email protected].