Ionizable residues play key roles in many biological phenomena including protein folding, enzyme catalysis and binding. We present PKAD, a database of experimentally measured pKas of protein residues reported in the literature or taken from existing databases. The database contains pKa data for 1350 residues in 157 wild-type proteins and for 232 residues in 45 mutant proteins. Most of these values are for Asp, Glu, His and Lys amino acids. The database is available as downloadable file as well as a web server ( http://compbio.clemson.edu/pkad ). The PKAD database can be used as a benchmarking source for development and improvement of pKa’s prediction methods. The web server provides additional information taken from the corresponding structures and amino acid sequences, which allows for easy search and grouping of the experimental pKas according to various biophysical characteristics, amino acid type and others.

Supplementary data are available at Bioinformatics online.

The DelPhiPKa is a widely used and unique approach to compute pKa’s of ionizable groups that does not require molecular surface to be defined. Instead, it uses smooth Gaussian-based dielectric function to treat computational space via Poisson-Boltzmann equation (PBE). Here we report an expansion of DelPhiPKa functionality to enable inclusion of salt in the modeling protocol. The method considers the salt mobile ions in solvent phase without defining solute-solvent boundary. Instead, the ions are penalized to enter solute interior via a desolvation penalty term in the Boltzmann factor in the framework of PBE. Hence, the concentration of ions near to protein is balanced by the desolvation penalty and electrostatic interactions. The study reveals that correlation between experimental and calculated pKa’s is improved significantly by taking into consideration the presence of salt. Furthermore, it is demonstrated that DelphiPKa reproduces the salt sensitivity of experimentally measured pKa’s. Another new development of DelPhiPKa allows for computing the pKa’s of polar residues such as cysteine, serine, threonine and tyrosine. With this regard, DelPhiPKa is benchmarked against experimentally measured cysteine and tyrosine pKa’s and for cysteine it is shown to outperform other existing methods (DelPhiPKa RMSD of 1.73 versus RMSD between 2.40 and 4.72 obtained by other existing pKa prediction methods).

Supplementary data are available at Bioinformatics online.

The permeation of small molecules through membranes can presently be observed in conventional (i.e., non-enhanced) molecular dynamics simulations. This contribution focuses on three important aspects of such calculations. (1) The advantages and disadvantages of calculating permeability by direct counting of transition events versus Bayesian analysis based on the inhomogeneous solubility diffusion model. (2) A new Python/Cþþ tool that speeds up a previous implementation of the Bayesian analysis by two orders of magnitude and allows permeabilities to be extracted in a matter of seconds from a previously generated trajectory. (3) Simulated and permeabilities of water, oxygen, and ethanol through various homogeneous bilayers. The results fall short of the experimental values, clearly demonstrating the requirement for accurate polarizable force fields. There have been numerous methodologies developed over the years to address the time scale problem associated with molecular dynamics (MD) simulations of complex biological systems. In recent years, Markov State Models (MSM's) have gained prominence in computing long-time dynamics from a pool of short MD simulations. However, the predicted dynamics is prone to errors since MSM attempts to model a non-Markovian jump process by a Markov chain. In this work, we propose a new approach, dynamically corrected kinetic Monte Carlo (DC-KMC), that propagates a complex system from state to state with arbitrary accuracy on both short and long-time scales, irrespective of the definition of the metastable state boundaries, and irrespective of the basis set on which the states are defined. This method builds on concepts introduced in accelerated MD approaches and multistate dynamical corrections to transition state theory. We demonstrate the robustness of our approach by reproducing the folding dynamics of villin headpiece and the conformational transitions of membrane associated Kras-4B protein. An automatic, multi-scale, and three-dimensional (3D) summary of local configurations of the dynamics of proteins can help to discover and describe the relationships between different parts of proteins across spatial scales, including the overall conformation and 3D configurations of side chains and domains. These discoveries can improve understanding of the function and allosteric mechanism of proteins. Current methods are unable to effectively summarize 3D shapes or dynamics of local configurations across multiple spatial scales. We propose Frequent Substructure Clustering (FSC) and Subconformational Hierarchical Hidden Markov Model (SHHMM) to fill this gap. FSC of the C b atoms of the GB3 protein identifies six clusters of co-occurring local configurations. The clusters localize at different regions, contribute to the overall conformation, and form two anti-correlating groups. The results suggest FSC could describe dynamical relationships between different parts of proteins through 3D descriptions of the frequently occurring local configurations at different spatial resolutions. SHHMM consi...