DeepKa Web Server: High-Throughput Protein p Ka Prediction

Zhitao Cai; Hao Peng; Shuo Sun; Jiahao He; Fangfang Luo; Yandong Huang

doi:10.1021/acs.jcim.3c02013

DeepKa Web Server: High-Throughput Protein p K_a Prediction

J Chem Inf Model. 2024 Apr 22;64(8):2933-2940. doi: 10.1021/acs.jcim.3c02013. Epub 2024 Mar 26.

Authors

Zhitao Cai¹, Hao Peng², Shuo Sun¹, Jiahao He¹, Fangfang Luo¹, Yandong Huang¹

Affiliations

¹ College of Computer Engineering, Jimei University, Xiamen 361021, China.
² National Pilot School of Software, Yunnan University, Kunming 650504, China.

PMID: 38530291
DOI: 10.1021/acs.jcim.3c02013

Abstract

DeepKa is a deep-learning-based protein pK_a predictor proposed in our previous work. In this study, a web server was developed that enables online protein pK_a prediction driven by DeepKa. The web server provides a user-friendly interface where a single step of entering a valid PDB code or uploading a PDB format file is required to submit a job. Two case studies have been attached in order to explain how pK_a's calculated by the web server could be utilized by users. Finally, combining the web server with post processing as described in case studies, this work suggests a quick workflow of investigating the relationship between protein structure and function that are pH dependent. The web server of DeepKa is freely available at http://www.computbiophys.com/DeepKa/main.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Databases, Protein
Deep Learning
Hydrogen-Ion Concentration
Internet*
Protein Conformation
Protein Kinases / chemistry
Protein Kinases / metabolism
Software*
User-Computer Interface

Substances

Protein Kinases