SAAMBE-SEQ: a sequence-based method for predicting mutation effect on protein-protein binding affinity.

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 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 0.37-0.46. 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 AND IMPLEMENTATION: SAAMBE-SEQ is available at http://compbio.clemson.edu/saambe_webserver/indexSEQ.php#started. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Capturing the Effects of Explicit Waters in Implicit Electrostatics Modeling: Qualitative Justification of Gaussian-Based Dielectric Models in DelPhi.

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.

SAAMBE-3D: Predicting Effect of Mutations on Protein-Protein Interactions.

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.

DelPhi Suite: New Developments and Review of Functionalities.

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.

DelPhiPKa: Including salt in the calculations and enabling polar residues to titrate.

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 the 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 vs RMSD between 2.40 and 4.72 obtained by other existing pKa prediction methods).

Mechanism of the formation of microphase separated water clusters in a water-mediated physical network of perfluoropolyether tetraol.

Perfluoropolyether tetraol (PFPE tetraol) possesses a hydrophobic perfluoropolyether chain in the backbone and two hydroxyl groups at each chain terminal, which facilitates the formation of hydrogen bonds with water molecules resulting in the formation an extended physical network. About 3 wt% water was required for the formation of the microphase separated physical network of PFPE tetraol. The mechanism responsible for the microphase separation of water clusters in the physical network was studied using a combination of techniques such as NMR spectroscopy, molecular dynamics (MD) simulations and DSC. MD simulation studies provided evidence for the formation of clusters in the PFPE tetraol physical network and the size of these clusters increased gradually with an increase in the extent of hydration. Both MD simulations and NMR spectroscopy studies revealed that these clusters position themselves away from the hydrophobic backbone or vice versa. The presence of intra- and inter-chain aggregation possibility among hydrophilic groups was evident. DSC results demonstrated the presence of tightly and loosely bound water molecules to the terminal hydroxyl groups of PFPE tetraol through hydrogen bonding. The data from all the three techniques established the formation of a physical network driven by hydrogen bonding between the hydrophilic end groups of PFPE tetraol and water molecules. The flexible nature of the PFPE tetraol backbone and its low solubility parameter favour clustering of water molecules at the terminal groups and result in the formation of a gel.

Proton transport mechanism of imidazole, triazole and phosphoric acid mixtures from ab initio molecular dynamics simulations.

We performed first principles molecular dynamics simulations to elucidate the mechanism and role of 1,2,3-triazole in proton transport while it is mixed with phosphoric acid (PA) and a phosphoric acid imidazole mixture. PA doped imidazole based polymer acts as an efficient polyelectrolyte membrane for fuel cells. The conductivity of this membrane increases when triazole is added to the system. For the first time we performed ab initio molecular dynamics simulations of complex mixtures of PA, imidazole and triazole. We have quantitatively estimated the structural diffusion and vehicular motion of protons. We found that upon the addition of triazole in PA and the PA imidazole mixture, the structural diffusion of protons increases significantly. The mechanism of proton transport is different when triazole is added to the mixture. We have also identified two different paths for structural diffusion (constructive and non-constructive) that contribute to long and short range proton transport.

Evidence and characterization of dynamic heterogeneity in binary mixtures of phosphoric acid and benzimidazole.

We report here anomalous diffusions of components in mixtures of monomer of polybenzimidazole, i.e., 2-phenyl-1H,1'H-5,5'-bibenzo[d]imidazole (BI) and phosphoric acid (PA) from molecular dynamics simulations. We have observed initial drop and further increase in self-diffusion constant for both monomer molecule (BI) and PA with gradual increase in PA concentration. The origin of such anomalous diffusion is identified in this work, which happens to be the presence of dynamic heterogeneity in each component of the binary mixture. We characterized microscopic picture of dynamical heterogeneity by finding correlation between dynamical heterogeneity and structural arrangement among the components of the binary system. Different types of H-bonding arrangements in the BI-PA systems at different concentration of PA are observed. The stability of the H-bonded network consisting of different types of H-bonds between BI and PA in the system has been studied by calculating the lifetime of various H-bonds. The results indicate that there are fast and slow moving PA molecules in the mixtures because of coexistence of different types of hydrogen bonds among the components of the mixture.

Ethanol and Water Adsorption in Conventional and Hierarchical All-Silica MFI Zeolites.

Hierarchical zeolites containing both micro- (<2 nm) and mesopores (2-50 nm) have gained increasing attention in recent years because they combine the intrinsic properties of conventional zeolites with enhanced mass transport rates due to the presence of mesopores. The structure of the hierarchical self-pillared pentasil (SPP) zeolite is of interest because all-silica SPP consists of orthogonally intergrown single-unit-cell MFI nanosheets and contains hydrophilic surface silanol groups on the mesopore surface while its micropores are nominally hydrophobic. Therefore, the distribution of adsorbed polar molecules, like water and ethanol, in the meso- and micropores is of fundamental interest. Here, molecular simulation and experiment are used to investigate the adsorption of water and ethanol on SPP. Vapor-phase single-component adsorption shows that water occupies preferentially the mesopore corner and surface regions of the SPP material at lower pressures (P/P 0 < 0.5) while loading in the mesopore interior dominates adsorption at higher pressures. In contrast, ethanol does not exhibit a marked preference for micro- or mesopores at low pressures. Liquid-phase adsorption from binary water-ethanol mixtures demonstrates a 2 orders of magnitude lower ethanol/water selectivity for the SPP material compared to bulk MFI. For very dilute aqueous solutions of ethanol, the ethanol molecules are mostly adsorbed inside the SPP micropore region due to stronger dispersion interactions and the competition from water for the surface silanols. At high ethanol concentrations (C EtOH > 700 g L-1), the SPP material becomes selective for water over ethanol.

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

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.

Characterization of the structures and dynamics of phosphoric acid doped benzimidazole mixtures: a molecular dynamics study.

Benzimidazole-based polymer membranes like poly(2,5-benzimidazole) (ABPBI) doped with phosphoric acid (PA) are electrolytes that exhibit high proton conductivity in fuel cells at elevated temperatures. The benzimidazole (BI) moiety is an important constituent of these membranes, so the present work was performed in order to achieve a molecular understanding of the BI-PA interactions in the presence of varying levels of the PA dopant, using classical molecular dynamics (MD) simulations. The various hydrogen-bonding interactions, as characterized based on structural properties and hydrogen-bond lifetime calculations, show that both BI and PA molecules exhibit dual proton-acceptor/donor functionality. An examination of diffusion coefficients showed that the diffusion of BI decreases with increasing PA uptake, whereas the diffusion of PA slightly increases. The hydrogen-bond lifetime calculations pointed to the existence of competitive hydrogen bonding between various sites in BI and PA.

Molecular dynamics simulation of phosphoric acid doped monomer of polybenzimidazole: a potential component polymer electrolyte membrane of fuel cell.

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.