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.

Maintaining wild type protein-protein interactions is essential for the normal function of cell and any mutation that alter their characteristics can cause disease. Therefore, the ability to correctly and quickly predict the effect of amino acid mutations is crucial for understanding disease effects and to be able to carry out genome-wide studies. Here, we report a new development of the SAAMBE method, SAAMBE-3D, which is a machine learning-based approach, resulting in accurate predictions and is extremely fast. It achieves the Pearson correlation coefficient ranging from 0.78 to 0.82 depending on the training protocol in benchmarking five-fold validation test against the SKEMPI v2.0 database and outperforms currently existing algorithms on various blind-tests. Furthermore, optimized and tested via five-fold cross-validation on the Cornell University dataset, the SAAMBE-3D achieves AUC of 1.0 and 0.96 on a homo and hereto-dimer test datasets. Another important feature of SAAMBE-3D is that it is very fast, it takes less than a fraction of a second to complete a prediction. SAAMBE-3D is available as a web server and as well as a stand-alone code, the last one being another important feature allowing other researchers to directly download the code and run it on their local computer. Combined all together, SAAMBE-3D is an accurate and fast software applicable for genome-wide studies to assess the effect of amino acid mutations on protein-protein interactions. The webserver and the stand-alone codes (SAAMBE-3D for predicting the change of binding free energy and SAAMBE-3D-DN for predicting if the mutation is disruptive or non-disruptive) are available.

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).

Electrostatic potential, energies, and forces affect virtually any process in molecular biology, however, computing these quantities is a difficult task due to irregularly shaped macromolecules and the presence of water. Here, we report a new edition of the popular software package DelPhi along with describing its functionalities. The new DelPhi is a C++ object‐oriented package supporting various levels of multiprocessing and memory distribution. It is demonstrated that multiprocessing results in significant improvement of computational time. Furthermore, for computations requiring large grid size (large macromolecular assemblages), the approach of memory distribution is shown to reduce the requirement of RAM and thus permitting large‐scale modeling to be done on Linux clusters with moderate architecture. The new release comes with new features, whose functionalities and applications are described as well. © 2019 The Authors. Journal of Computational Chemistry published by Wiley Periodicals, Inc.

Motivation Vast majority of human genetic disorders are associated with mutations that affect protein-protein interactions by altering wild type binding affinity. Therefore, it is extremely important to assess the effect of mutations on protein-protein binding free energy to assist the development of therapeutic solutions. Currently the most popular approaches use structural information to deliver the predictions, which precludes them to be applicable on genome-scale investigations. Indeed, with the progress of genomic sequencing, researchers are frequently dealing with assessing effect of mutations for which there is no structure available. Results Here we report a Gradient Boosting Decision Tree (GBDT) machine learning algorithm, the SAAMBE-SEQ, which is completely sequence-based and does not require structural information at all. SAAMBE-SEQ utilizes 80 features representing evolutionary information, sequence-based features and change of physical properties upon mutation at the mutation site. The approach is shown to achieve Pearson correlation coefficient (PCC) of 0.83 in 5-fold cross validation in a benchmarking test against experimentally determined binding free energy change (ΔΔG). Further a blind test (no-STRUC) is compiled collecting experimental ΔΔG upon mutation for protein complexes for which structure is not available and used to benchmark SAAMBE-SEQ resulting in PCC in the range of 0.37 to 0.46. The accuracy of SAAMBE-SEQ method is found to be either better or comparable to most advanced structure-based methods. SAAMBE-SEQ is very fast, available as webserver and stand-alone code, and indeed utilizes only sequence information, and thus it is applicable for genome-scale investigations to study the effect of mutations on protein-protein interactions. Availability SAAMBE-SEQ is available at http://compbio.clemson.edu/saambe_webserver/indexSEQ.php#started. Supplementary information Supplementary data are available at Bioinformatics online.

Phosphoric acid doped polybenzimidazole is promising electrolyte membranes for high temperature (100 °C and above) fuel cells. Proton conduction is governed by the amount of phosphoric acid content in the polymer membrane. In this present work, we perform molecular dynamics simulations on phosphoric acid doped 2-phenyl-1H,1'H-5,5'-bibenzo[d]imidazole (monomer unit of polybenzimidazole) to characterize the structural and dynamical properties at varying phosphoric acid content and temperature. From the structural analysis, we have predicted the arrangement of the phosphoric acids, formation of H-bonds in the system, and the contribution of different atoms toward H-bonding. We have also examined the stacking of 2-phenyl-1H,1'H-5,5'-bibenzo[d]imidazole molecules and how their arrangement changes with the increasing amount of PA in the system with the help of cluster analysis. From the molecular dynamics simulation conducted at different temperatures and phosphoric acid doping level, we have predicted the diffusion of phosphoric acid and monomer. As a dynamic quantity, we have also calculated ring flipping of the imidazole ring of the monomer.

While Lewis-acid zeolites, such as Sn-Beta, catalyze glucose isomerization in an alcoholic medium, mesoporous Sn-SPP catalyzes both glucose isomerization to fructose and fructose etherification (formally ketalization) to ethyl fructoside, enabling fructose yields in excess of the glucose/fructose equilibrium. Using periodic density functional theory calculations and force-field-based Monte Carlo simulations, the ketalization reaction mechanism and adsorption behavior were examined. The silanols on the Sn-SPP mesopore surface facilitate the ketalization reaction through hydrogen bonding interactions at the transition state, only possible via a Sn–O–Si−OH moiety, present in Sn-SPP but not in Sn-Beta. Fructose ketalization is favored over glucose acetalization due to differences in stability of the oxonium intermediates, which are stabilized by the Sn-SPP active site. The open site of hydrophobic Sn-Beta cannot perform these reactions because its active site does not contain an adjacent silanol of the right geometry. In addition to the more favorable activation barrier of the catalytic process, the adsorption at the catalytic site in Sn-SPP is also found to be more favorable than for Sn-Beta, in spite of competitive adsorption between fructose and ethanol in the ethanol-saturated zeolites.

Our group has implemented a smooth Gaussian-based dielectric function in DelPhi (J. Chem. Theory Comput. 2013, 9 (4), 2126–2136) which models the solute as an object with inhomogeneous dielectric permittivity and provides a smooth transition of dielectric permittivity from surface-bound water to bulk solvent. Although it is well-understood that the protein hydrophobic core is less polarizable than the hydrophilic protein surface, less attention is paid to the polarizability of water molecules inside the solute and on its surface. Here, we apply explicit water simulations to study the behavior of water molecules buried inside a protein and on the surface of that protein and contrast it with the behavior of the bulk water. We selected a protein that is experimentally shown to have five cavities, most of which are occupied by water molecules. We demonstrate through molecular dynamics (MD) simulations that the behavior of water in the cavity is drastically different from that in the bulk. These observations were made by comparing the mean residence times, dipole orientation relaxation times, and average dipole moment fluctuations. We also show that the bulk region has a nonuniform distribution of these tempo-spatial properties. From the perspective of continuum electrostatics, we argue that the dielectric “constant” in water-filled cavities of proteins and the space close to the molecular surface should differ from that assigned to the bulk water. This provides support for the Gaussian-based smooth dielectric model for solving electrostatics in the Poisson–Boltzmann equation framework. Furthermore, we demonstrate that using a well-parametrized Gaussian-based model with a single energy-minimized configuration of a protein can also reproduce its ensemble-averaged polar solvation energy. Thus, we argue that the Gaussian-based smooth dielectric model not only captures accurate physics but also provides an efficient way of computing ensemble-averaged quantities.