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 belonging to cytochrome C oxidase subunit-II like transmembrane domains. Apart from the web-server, a standalone program is also available on GitHub (https://github.com/vam-sin/deepcys).
Subject(s)
Cysteine/chemistry , Deep Learning , Disulfides/chemistry , Electron Transport Complex IV/chemistry , Protein Processing, Post-Translational , Software , Amino Acid Sequence , Cations, Divalent/chemistry , Cations, Divalent/metabolism , Cysteine/metabolism , Disulfides/metabolism , Electron Transport Complex IV/metabolism , Glutathione/chemistry , Glutathione/metabolism , Models, Molecular , Nitroso Compounds/chemistry , Nitroso Compounds/metabolism , Protein Domains , Protein Structure, Secondary , Structure-Activity Relationship , Sulfides/chemistry , Sulfides/metabolism , Sulfinic Acids/chemistry , Sulfinic Acids/metabolism , Sulfonic Acids/chemistry , Sulfonic Acids/metabolismABSTRACT
INTRODUCTION: Hepatitis C virus (HCV) displays high genetic diversity, characterized by regional variations in the prevalence of genotype posing challenges to the development of vaccines and definitive treatment. Very few reports exist on the distribution and frequency change of HCV genotypes in India. In the present retrospective study, we aimed to understand the distribution pattern of HCV genotypes and viral load among HCV-infected patients attending the Asian Institute of Gastroenterology, Hyderabad, India, a tertiary care hospital. METHODS: Patients referred to the Hepatology Department from January 2009 to December 2015 were screened for this study. Eight hundred and sixty-two chronic HCV patients were included in this study. Genotyping was performed using type-specific probe-based hybridization assay and viral load was estimated by real-time polymerase chain reaction. RESULTS: Out of 862 patients, genotype 1 was detected predominantly in 392 (45.5%), followed by genotype 3 in 344 (39.9%) patients; genotypes 4, 6, and 2 were detected in 115 (13.3%), 8 (0.9%), and 3 (0.3%) patients, respectively. The number of patients having genotype 1 increased in frequency while genotype 3 became less from the year 2009 to 2015. Patients having genotype 1 had significantly (p < 0.0001) higher viral load compared with the patients infected with other genotypes. CONCLUSION: Our study results demonstrate a change in HCV genotypic distribution pattern from genotypes 3 to 1 during the span of 7 years in patients referred to our hospital. In the light of the reported difference in the pathogenic potential of various HCV genotypes, detection of HCV genotype appears to be still essential for better patient management.
