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Expansion and Additional Validation of PKA17: A Fast Real-Time and Web-Based p $K_{a}$ Predictor

John P. Cvitkovic

Department of Chemistry and Biochemistry, Worcester Polytechnic Institute, Worcester, MA 01609, USA

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Connor D. Pauplis

Department of Chemistry and Biochemistry, Worcester Polytechnic Institute, Worcester, MA 01609, USA

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Phoebe C. Carney

Department of Chemistry and Biochemistry, Worcester Polytechnic Institute, Worcester, MA 01609, USA

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, and

George A. Kaminski

Department of Chemistry and Biochemistry, Worcester Polytechnic Institute, Worcester, MA 01609, USA

E-mail Address: gkaminski@wpi.edu

Corresponding author.

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https://doi.org/10.1142/S273741652042003XCited by:2 (Source: Crossref)

This article is part of the issue:

Special Issue: Computational Chemistry Methods to Predict pKa’s of Ionizable Groups in Proteins, RNAs, DNAs and Small Molecules

Abstract

We have further improved and validated PKA17, our fast software for predicting p $K_{a}$ values of protein residues. The methodology employs coarse-grained lattice-based model of proteins. It was previously demonstrated to perform ca. an order of magnitude faster than such successful and widely used frameworks as PROPKA without losing accuracy of the calculations. In this paper, we report the following improvements: (i) We have expanded our training and testing sets of protein residues by 128%, from 442 to 1009 cases; (ii) we have added and parameterized PKA17’s capability to predict acidity constants of cysteine residues that are important in many biomedical applications, including but not limited to binding of such transition metal ions as copper(I) and platinum(II); (iii) we have carried out the comparison of accuracy of predicted Asp and Glu p $K_{a}$ values not only between PKA17 and PROPKA, but also with DelPhiPKa and H $+ +$ . The computational speed of PKA17 remains the highest of all the methods used in our studies, and the accuracy of PKA17 is somewhat inferior only to those of such more sophisticated methods as Multi-Conformation Continuum Electrostatic (MCCE) ones. For instance, the average unsigned deviations of predicted p $K_{a}$ values from the experiment for 416 Glu residues were found to be 0.706, 0.766, 0.867, and 0.520pH units when obtained with PROPKA, DelPhiPKa, H $+ +$ , and PKA17, respectively (0.487pH units with PKA17 after refitting). The average unsigned errors for cysteine p $K_{a}$ values calculated with PROPKA, DelPhiPKa, H $+ +$ , and PKA17 were 3.50, 2.06, 3.17, and 1.26pH units. PKA17 has also performed well in assessing the cysteine acidity constants of the CXXC motif of CopZ protein involved in binding of copper(I) metal ions. Our results demonstrate that the PKA17 methodology and current parameters are accurate and robust, and its computational speed makes it possible to be employed in large-scale p $K_{a}$ screening calculations and in constant-pH protein dynamics simulations. The resulting PKA17 software has been deployed online at http://kaminski.wpi.edu/PKA17/pka_calc.html.

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References

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Vol. 20, No. 02

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Received 29 March 2020

Accepted 28 June 2020

Published: 21 August 2020

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System Upgrade on Tue, May 28th, 2024 at 2am (EDT)

Expansion and Additional Validation of PKA17: A Fast Real-Time and Web-Based p $K_{a}$ Predictor

Abstract

References

TOPTMH: TOPOLOGY PREDICTOR FOR TRANSMEMBRANE α-HELICES

THE RAMACHANDRAN PLOT AND PROTEIN STRUCTURE VALIDATION

PKAD-2: New Entries and Expansion of Functionalities of the Database of Experimentally Measured pKa’s of Proteins

EXPLORING THE ROLE OF C–H … π INTERACTIONS ON THE STRUCTURAL STABILITY OF ANTIMICROBIAL PEPTIDES

THE PROTEIN-FOLDING NUCLEUS: FROM SIMPLE MODELS TO REAL PROTEINS

Computing Protein pKas Using the TABI Poisson–Boltzmann Solver

Titration of Adenine in a GA Mismatch with Grand Canonical Simulations

Quantum-Chemical ab initio Study of Side Chain pKa of Linear and Cyclic Lysine Dipeptides

System Upgrade on Tue, May 28th, 2024 at 2am (EDT)

Expansion and Additional Validation of PKA17: A Fast Real-Time and Web-Based pKa<math altimg="eq-00001.gif" display="inline"><msub><mrow><mi mathvariant="bold-italic">K</mi></mrow><mrow><mstyle><mtext mathvariant="bold">a</mtext></mstyle></mrow></msub></math> Predictor

Abstract

Recommended

TOPTMH: TOPOLOGY PREDICTOR FOR TRANSMEMBRANE α-HELICES

THE RAMACHANDRAN PLOT AND PROTEIN STRUCTURE VALIDATION

PKAD-2: New Entries and Expansion of Functionalities of the Database of Experimentally Measured pKa’s of Proteins

EXPLORING THE ROLE OF C–H … π INTERACTIONS ON THE STRUCTURAL STABILITY OF ANTIMICROBIAL PEPTIDES

THE PROTEIN-FOLDING NUCLEUS: FROM SIMPLE MODELS TO REAL PROTEINS

Computing Protein pKas Using the TABI Poisson–Boltzmann Solver

Titration of Adenine in a GA Mismatch with Grand Canonical Simulations

Quantum-Chemical ab initio Study of Side Chain pKa of Linear and Cyclic Lysine Dipeptides

Expansion and Additional Validation of PKA17: A Fast Real-Time and Web-Based p $K_{a}$ Predictor