Journal of Chemical Information and Modeling · 2024 · 22 citations · 40 references
Life SciencesBiochemistryBioinformaticsComputational BiologyProtein Structure PredictionStructural BioinformaticsDeep LearningProtein StructureProtein Structure ModelingPh-dependent ActivationPh-dependent RegulationPost-processingPost-processing PipelinesPost-processing Workflow
DeepKa is a deep-learning-based protein p K a predictor proposed in our previous work. In this study, a web server was developed that enables online protein p K 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 p K 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 .
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