DeepCys: structure-based multiple cysteine function prediction method
trained on deep neural network: case study on Domains of Unknown
Functions (DUFs) belonging to COX2 family
Abstract
Cysteine (Cys) is the most reactive amino acid participating in a wide
range of biological functions. In-silico predictions complement the
experiments to meet the need of functional characterization. Multiple
Cys function prediction algorithm is scarce, in contrast to specific
function prediction algorithms. Here we present a deep neural
network-based multiple Cys function prediction, available on web-server
(DeepCys) (https://deepcys.herokuapp.com/). DeepCys model was trained
and tested on two independent datasets curated from protein crystal
structures. This prediction method requires three inputs, namely, PDB
identifier (ID), chain ID and residue ID for a given Cys and outputs the
probabilities of four cysteine functions, namely, disulphide,
metal-binding, thioether and sulphenylation and predicts the most
probable Cys function. The algorithm exploits the local and global
protein properties, like, sequence and secondary structure motifs,
buried fractions, microenvironments and protein/enzyme class. DeepCys
outperformed most of the multiple and specific Cys function algorithms.
This method can predict maximum number of cysteine functions. Moreover,
for the first time, explicitly predicts thioether function. This tool
was used to elucidate the cysteine functions on domains of unknown
functions (DUFs) belonging to cytochrome C oxidase subunit-II (COX2)
like transmembrane domains. Apart from the web-server, a standalone
program is also available on GitHub (https://github.com/vam-sin/deepcys)