DeepKa Web Server: High-Throughput Protein p<i>K</i><sub>a</sub> Prediction

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

doi:10.1021/acs.jcim.3c02013

J. Chem. Inf. Model.

2024

DOI: 10.1021/acs.jcim.3c02013

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DeepKa Web Server: High-Throughput Protein pK_a Prediction

Zhitao Cai,

Hao Peng,

Shuo Sun

et al.

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 po… Show more

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Cited by 2 publications

References 47 publications

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KaMLs for Predicting Protein pK_aValues and Ionization States: Are Trees All You Need?

Shen,

Kortzak,

Ambrozak

et al. 2024

Preprint

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Despite its relevance for understanding biology and computer-aided drug discovery, accurate prediction of protein ionization states remains a formidable challenge. Physics-based approaches struggle to capture the small, competing contributions in the complex protein environment, while machine learning (ML) is hampered by scarcity of experimental data. Here we developed the pKa ML (KaML) models based on decision trees and graph attention networks (GATs), exploiting physicochemical features and a new experimental pKa database (PKAD-3) enriched with highly shifted pKa's. KaML-CBtree significantly outperforms the current state of the art in predicting pKa values and ionization states across all six titratable amino acids, notably achieving accurate predictions for deprotonated cysteines and lysines - a blind spot in previous models. The superior performance of KaMLs is achieved in part through several innovations, including separate treatment of acid and base, utilization of pKa shifts as training targets, data augmentation using AlphaFold structures, and model pre-training on a theoretical pKa database. A meta-feature analysis reveals why the lightweight tree model outperforms the more complex deep learning GAT. We release an end-to-end pKa predictor based on KaML-CBtree and the new database PKD-3, enabling applications and laying groundwork for further advances in protein electrostatics research.

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KaMLs for Predicting Protein pK_aValues and Ionization States: Are Trees All You Need?

Shen,

Kortzak,

Ambrozak

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

Accurate Predictions of Molecular Properties of Proteins via Graph Neural Networks and Transfer Learning

Wozniak,

Janson,

Feig

2024

Preprint

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Machine learning has emerged as a promising approach for predicting molecular properties of proteins, as it addresses limitations of experimental and traditional computational methods. Here, we introduce GSnet, a graph neural network (GNN) trained to predict physicochemical and geometric properties including solvation free energies, diffusion constants, and hydrodynamic radii, based on three-dimensional protein structures. By leveraging transfer learning, pre-trained GSnet embeddings were adapted to predict solvent-accessible surface area (SASA) and residue-specific pKavalues, achieving high accuracy and generalizability. Notably, GSnet outperformed existing protein embeddings for SASA prediction, and a locally charge-aware variant, aLCnet, approached the accuracy of simulation-based and empirical methods for pKaprediction. Our GNN framework demonstrated robustness across diverse datasets, including intrinsically disordered peptides, and scalability for high-throughput applications. These results highlight the potential of GNN-based embeddings and transfer learning to advance protein structure analysis, providing a foundation for integrating predictive models into proteome-wide studies and structural biology pipelines.

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DeepKa Web Server: High-Throughput Protein pK_a Prediction

Cited by 2 publications

References 47 publications

KaMLs for Predicting Protein pK_aValues and Ionization States: Are Trees All You Need?

KaMLs for Predicting Protein pK_aValues and Ionization States: Are Trees All You Need?

Accurate Predictions of Molecular Properties of Proteins via Graph Neural Networks and Transfer Learning

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Product

Resources

About

DeepKa Web Server: High-Throughput Protein pKa Prediction

Cited by 2 publications

References 47 publications

KaMLs for Predicting Protein pKaValues and Ionization States: Are Trees All You Need?

KaMLs for Predicting Protein pKaValues and Ionization States: Are Trees All You Need?

Accurate Predictions of Molecular Properties of Proteins via Graph Neural Networks and Transfer Learning

Contact Info

Product

Resources

About

DeepKa Web Server: High-Throughput Protein pK_a Prediction

KaMLs for Predicting Protein pK_aValues and Ionization States: Are Trees All You Need?

KaMLs for Predicting Protein pK_aValues and Ionization States: Are Trees All You Need?