PKAD: a database of experimentally measured pKa values of ionizable groups in proteins

Pahari, Swagata; Sun, Lexuan; Alexov, Emil

doi:10.1093/database/baz024

Cited by 133 publications

(160 citation statements)

References 34 publications

Supporting

Mentioning

151

Contrasting

Unclassified

Order By: Relevance

“…45 All experimental reference pKa were drawn from the PKAD database. 46 The pKa values used ultimately come from Bartik et al 47 and Webb et al 48 for HEWL, from Forman-Kay et al 49 and Qin et al 50 for HTRX, and Wang et al 51 for HMCK. The pKa predictions from our simulations measure error and deviations to the average of these datasets for each residue with more than one reported value.…”

Section: Theory and Methodsmentioning

confidence: 99%

Titr-DMD – A Rapid, Coarse-Grained Quasi-All-Atom Constant pH Molecular Dynamics Framework

Reilley

Alexandrova²,

Wang³

et al. 2021

Preprint

View full text Add to dashboard Cite

The pH dependence of enzyme fold stability and catalytic activity is a fundamentally dynamic, structural property which is difficult to study. Computational methods, particularly constant pH molecular dynamics (CpHMD), are the best situated tools for this. However, these often struggle with affordable sampling of sufficiently long timescales, accuracy of pKa prediction, and verification of the structures they generate. We introduce Titr-DMD, an affordable CpHMD method with a protonation state sampler that can be systematically improved, to circumvent these issues. We benchmark the method on a set of proteins with experimentally attested pKa and on the pH triggered conformational change in a staphylococcal nuclease mutant, a rare experimental study of such behavior. Our results show Titr-DMD to be an effective method to study pH coupled protein dynamics. <br>

show abstract

Section: Theory and Methodsmentioning

confidence: 99%

Titr-DMD – A Rapid, Coarse-Grained Quasi-All-Atom Constant pH Molecular Dynamics Framework

Reilley

Alexandrova²,

Wang³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…PKAD is a database of experimentally measured pKa values of titratable residues in proteins. The database [39] is available as downloadable file and accessible as a web server at http://compbio.clemson.edu/pkad. PKAD database can be used as a test set for development of new pKa's prediction methods and improvement of existing algorithms.…”

Section: Pkadmentioning

confidence: 99%

Modeling electrostatics in molecular biology: A tutorial of DelPhi and associated resources [Article v1.0]

Panday¹,

Shashikala²,

Koirala³

et al. 2019

LiveCoMS

Self Cite

View full text Add to dashboard Cite

Electrostatics play an indispensable role in practically any process in molecular biology. Indeed, at distances larger than several Angstroms, all other forces are negligibly small and electrostatic force dominates. However, modeling electrostatics in molecular biology is a complicated task due to presence of water phase, mobile ions and irregularly shaped inhomogeneous biological macromolecules. A particular approach to calculating electrostatics in such systems is to apply the Poisson-Boltzmann equation (PBE). Here, we provide a tutorial for the popular DelPhi package that solves PBE using a finite-difference method and delivers the electrostatic potential distribution throughout the modeling box. The tutorial comes with a detailed description of different tasks that DelPhi can handle, an assessment of the accuracy against cases with analytical solutions and recommendations about DelPhi usage. Furthermore, since electrostatics is a key component of virtually any modeling in molecular biology, we have created many additional resources utilizing DelPhi to model various biology relevant quantities. Tutorials for these resources are also provided along with examples of their usage.

show abstract

“…In particular, it includes the hydration free energies of molecules database; 12 the heat of formation (or standard enthalpy of formation) of small molecules using quantum mechanics calculations with the Perdew-Burke-Ernzerhof hybrid functional (PBE0). ; 5,20 pKa values experimentally calculated for different amino acids in different proteins; [14][15][16][17] the experimental changes on the Gibbs free energies of the mutant proteins. 18,19 The clients are the personal computers (PCs), where the users with a login account will be able to access the first layer of the provided web-based services from the main computer, namely the server.…”

Section: (Macro)molecular Feature Descriptionmentioning

confidence: 99%

“…experimental pKa values of inozable groups in 192 proteins both wild type (153 proteins) and mutated (39 proteins). [14][15][16][17] The third database has 2693 experimental values of the Gibbs free energy changes in 14 mutant proteins. 18,19 The last database has 7101 quantum mechanics heat of formation calculations 5 (and the references therein), the so-called QM7, which is a subset of the so-called GDB13 molecules, optimized at the quantum mechanics level with the Perdew-Burke-Ernzerhof hybrid functional (PBE0).…”

Section: Datasetsmentioning

confidence: 99%

“…The database of 642 small organic molecules with known experimental hydration free energies, 12 which wellstudied in Ref. ; 13 the database of 1475 experimental pKa values of ionizable groups in 192 proteins (including 153 wild-type proteins and 39 mutant proteins); [14][15][16][17] the database of 2693 mutants in 14 proteins with given values of experimental values of changes in the Gibbs free energy; 18,19 and a database of 7101 quantum mechanics heat of formation calculations with the Perdew-Burke-Ernzerhof hybrid functional (PBE0). 5,20 All the data are prepared and optimized in advance using the AMBER force field 21 in CHARMM macromolecular computer simulation program.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Automation of (Macro)molecular Properties Using a Bootstrapping Swarm Artificial Neural Network Method: Databases for Machine Learning

Rahmani

Kamberaj

2019

Preprint

View full text Add to dashboard Cite

In this study, we employed a novel method for prediction of (macro)molecular properties using a swarm artificial neural network method as a machine learning approach. In this method, a (macro)molecular structure is represented by a so-called description vector, which then is the input in a so-called bootstrapping swarm artificial neural network (BSANN) for training the neural network. In this study, we aim to develop an efficient approach for performing the training of an artificial neural network using either experimental or quantum mechanics data. In particular, we aim to create different user-friendly online accessible databases of well-selected experimental (or quantum mechanics) results that can be used as proof of the concepts. Furthermore, with the optimized artificial neural network using the training data served as input for BSANN, we can predict properties and their statistical errors of new molecules using the plugins provided from that web-service. There are four databases accessible using the web-based service. That includes a database of 642 small organic molecules with known experimental hydration free energies, the database of 1475 experimental pKa values of ionizable groups in 192 proteins, the database of 2693 mutants in 14 proteins with given values of experimental values of changes in the Gibbs free energy, and a database of 7101 quantum mechanics heat of formation calculations. All the data are prepared and optimized in advance using the AMBER force field in CHARMM macromolecular computer simulation program. The BSANN is code for performing the optimization and prediction written in Python computer programming language. The descriptor vectors of the small molecules are based on the Coulomb matrix and sum over bonds properties, and for the macromolecular systems, they take into account the chemical-physical fingerprints of the region in the vicinity of each amino acid.Recently, neural network method has seen a broad range of applications in molecular modeling. 1 In Ref., 2 a hierarchical interacting particle neural network approach is introduced using quantum models to predict molecular properties. In this approach, different hierarchical regularization terms have been introduced to improve the convergence of the optimized parameters. While in Ref., 3 the machine learning like-potentials are used to predict molecular properties, such as enthalpies or potential energies. The degree to which the general features included in characterizing the chemical space of molecules to improve the predictions of these models is also discussed in Refs. 4,5 Tuckerman and co-workers 6 used a stochastic neural network technique to fit high-dimensional free energy surfaces characterized by reduced subspace of collective coordinates. While very recently 7 a comparison study has been

show abstract

PKAD: a database of experimentally measured pKa values of ionizable groups in proteins

Cited by 133 publications

References 34 publications

Titr-DMD – A Rapid, Coarse-Grained Quasi-All-Atom Constant pH Molecular Dynamics Framework

Titr-DMD – A Rapid, Coarse-Grained Quasi-All-Atom Constant pH Molecular Dynamics Framework

Modeling electrostatics in molecular biology: A tutorial of DelPhi and associated resources [Article v1.0]

Automation of (Macro)molecular Properties Using a Bootstrapping Swarm Artificial Neural Network Method: Databases for Machine Learning

Contact Info

Product

Resources

About