Generalized Born Model Research Articles

Implicit Solvent Model for Million-Atom Atomistic Simulations: Insights into the Organization of 30-nm Chromatin Fiber

Molecular dynamics (MD) simulations based on the implicit solvent generalized Born (GB) models can provide significant computational advantages over the traditional explicit solvent simulations. However, the standard GB becomes prohibitively expensive for all-atom simulations of large structures; the model scales poorly, ∼n2, with the number of solute atoms. Here we combine our recently developed optimal point charge approximation (OPCA) with the hierarchical charge partitioning (HCP) approximation to present an ∼n log n multiscale, yet fully atomistic, GB model (GB-HCPO). The HCP approximation exploits the natural organization of biomolecules (atoms, groups, chains, and complexes) to partition the structure into multiple hierarchical levels of components. OPCA approximates the charge distribution for each of these components by a small number of point charges so that the low order multipole moments of these components are optimally reproduced. The approximate charges are then used for computing electrostatic interactions with distant components, while the full set of atomic charges are used for nearby components. We show that GB-HCPO can deliver up to 2 orders of magnitude speedup compared to the standard GB, with minimal impact on its accuracy. For large structures, GB-HCPO can approach the same nominal speed, as in nanoseconds per day, as the highly optimized explicit-solvent simulation based on particle mesh Ewald (PME). The increase in the nominal simulation speed, relative to the standard GB, coupled with substantially faster sampling of conformational space, relative to the explicit solvent, makes GB-HCPO a suitable candidate for MD simulation of large atomistic systems in implicit solvent. As a practical demonstration, we use GB-HCPO simulation to refine a ∼1.16 million atom structure of 30 nm chromatin fiber (40 nucleosomes). The refined structure suggests important details about spatial organization of the linker DNA and the histone tails in the fiber: (1) the linker DNA fills the core region, allowing the H3 histone tails to interact with the linker DNA, which is consistent with experiment; (2) H3 and H4 tails are found mostly in the core of the structure, closer to the helical axis of the fiber, while H2A and H2B are mostly solvent exposed. Potential functional consequences of these findings are discussed. GB-HCPO is implemented in the open source MD software NAB in Amber 2016.

Evaluation of Generalized Born Models for Large Scale Affinity Prediction of Cyclodextrin Host-Guest Complexes.

Binding affinity prediction with implicit solvent models remains a challenge in virtual screening for drug discovery. In order to assess the predictive power of implicit solvent models in docking techniques with Amber scoring, three generalized Born models (GBHCT, GBOBCI, and GBOBCII) available in Dock 6.7 were utilized, for determining the binding affinity of a large set of β-cyclodextrin complexes with 75 neutral guest molecules. The results were compared to potential of mean force (PMF) free energy calculations with four GB models (GBStill, GBHCT, GBOBCI, and GBOBCII) and to experimental data. Docking results yield similar accuracy to the computationally demanding PMF method with umbrella sampling. Neither docking nor PMF calculations reproduce the experimental binding affinities, however, as indicated by a small Spearman rank order coefficient (∼0.5). The binding energies obtained from GB models were decomposed further into individual contributions of the binding partners and solvent environments and compared to explicit solvent simulations for five complexes allowing for rationalizing the difference between explicit and implicit solvent models. An important observation is that the explicit solvent screens the interaction between host and guest much stronger than GB models. In contrast, the screening in GB models is too strong in solutes, leading to overestimation of short-range interactions and too strong binding. It is difficult to envision a way of overcoming these two opposite effects.

Parallelization and improvements of the generalized born model with a simple sWitching function for modern graphics processors.

Two fundamental challenges of simulating biologically relevant systems are the rapid calculation of the energy of solvation and the trajectory length of a given simulation. The Generalized Born model with a Simple sWitching function (GBSW) addresses these issues by using an efficient approximation of Poisson-Boltzmann (PB) theory to calculate each solute atom's free energy of solvation, the gradient of this potential, and the subsequent forces of solvation without the need for explicit solvent molecules. This study presents a parallel refactoring of the original GBSW algorithm and its implementation on newly available, low cost graphics chips with thousands of processing cores. Depending on the system size and nonbonded force cutoffs, the new GBSW algorithm offers speed increases of between one and two orders of magnitude over previous implementations while maintaining similar levels of accuracy. We find that much of the algorithm scales linearly with an increase of system size, which makes this water model cost effective for solvating large systems. Additionally, we utilize our GPU-accelerated GBSW model to fold the model system chignolin, and in doing so we demonstrate that these speed enhancements now make accessible folding studies of peptides and potentially small proteins.

Generalized Born and Explicit Solvent Models for Free Energy Calculations in Organic Solvents: Cyclodextrin Dimerization.

Evaluation of solvation (binding) free energies with implicit solvent models in different dielectric environments for biological simulations as well as high throughput ligand screening remain challenging endeavors. In order to address how well implicit solvent models approximate explicit ones we examined four generalized Born models (GB(Still), GB(HCT), GB(OBC)I, and GB(OBC)II) for determining the dimerization free energy (ΔG(0)) of β-cyclodextrin monomers in 17 implicit solvents with dielectric constants (D) ranging from 5 to 80 and compared the results to previous free energy calculations with explicit solvents ( Zhang et al. J. Phys. Chem. B 2012 , 116 , 12684 - 12693 ). The comparison indicates that neglecting the environmental dependence of Born radii appears acceptable for such calculations involving cyclodextrin and that the GB(Still) and GB(OBC)I models yield a reasonable estimation of ΔG(0), although the details of binding are quite different from explicit solvents. Large discrepancies between implicit and explicit solvent models occur in high-dielectric media with strong hydrogen bond (HB) interruption properties. ΔG(0) with the GB models is shown to correlate strongly to 2(D-1)/(2D+1) (R(2) ∼ 0.90) in line with the Onsager reaction field ( Onsager J. Am. Chem. Soc. 1936 , 58 , 1486 - 1493 ) but to be very sensitive to D (D < 10) as well. Both high-dielectric environments where hydrogen bonds are of interest and low-dielectric media such as protein binding pockets and membrane interiors therefore need to be considered with caution in GB-based calculations. Finally, a literature analysis of Gibbs energy of solvation of small molecules in organic liquids shows that the Onsager relation does not hold for real molecules since the correlation between ΔG(0) and 2(D-1)/(2D+1) is low for most solutes. Interestingly, explicit solvent calculations of the solvation free energy ( Zhang et al. J. Chem. Inf. Model . 2015 , 55 , 1192 - 1201 ) reproduce the weak experimental correlations with 2(D-1)/(2D+1) very well.

Refinement of Generalized Born Implicit Solvation Parameters for Nucleic Acids and Their Complexes with Proteins.

The Generalized Born (GB) implicit solvent model has undergone significant improvements in accuracy for modeling of proteins and small molecules. However, GB still remains a less widely explored option for nucleic acid simulations, in part because fast GB models are often unable to maintain stable nucleic acid structures or they introduce structural bias in proteins, leading to difficulty in application of GB models in simulations of protein-nucleic acid complexes. Recently, GB-neck2 was developed to improve the behavior of protein simulations. In an effort to create a more accurate model for nucleic acids, a similar procedure to the development of GB-neck2 is described here for nucleic acids. The resulting parameter set significantly reduces absolute and relative energy error relative to Poisson-Boltzmann for both nucleic acids and nucleic acid-protein complexes, when compared to its predecessor GB-neck model. This improvement in solvation energy calculation translates to increased structural stability for simulations of DNA and RNA duplexes, quadruplexes, and protein-nucleic acid complexes. The GB-neck2 model also enables successful folding of small DNA and RNA hairpins to near native structures as determined from comparison with experiment. The functional form and all required parameters are provided here and also implemented in the AMBER software.

Computational design of enzyme-ligand binding using a combined energy function and deterministic sequence optimization algorithm.

Enzyme amino-acid sequences at ligand-binding interfaces are evolutionarily optimized for reactions, and the natural conformation of an enzyme-ligand complex must have a low free energy relative to alternative conformations in native-like or non-native sequences. Based on this assumption, a combined energy function was developed for enzyme design and then evaluated by recapitulating native enzyme sequences at ligand-binding interfaces for 10 enzyme-ligand complexes. In this energy function, the electrostatic interaction between polar or charged atoms at buried interfaces is described by an explicitly orientation-dependent hydrogen-bonding potential and a pairwise-decomposable generalized Born model based on the general side chain in the protein design framework. The energy function is augmented with a pairwise surface-area based hydrophobic contribution for nonpolar atom burial. Using this function, on average, 78% of the amino acids at ligand-binding sites were predicted correctly in the minimum-energy sequences, whereas 84% were predicted correctly in the most-similar sequences, which were selected from the top 20 sequences for each enzyme-ligand complex. Hydrogen bonds at the enzyme-ligand binding interfaces in the 10 complexes were usually recovered with the correct geometries. The binding energies calculated using the combined energy function helped to discriminate the active sequences from a pool of alternative sequences that were generated by repeatedly solving a series of mixed-integer linear programming problems for sequence selection with increasing integer cuts.

Empirical estimation of local dielectric constants: Toward atomistic design of collagen mimetic peptides.

One of the key challenges in modeling protein energetics is the treatment of solvent interactions. This is particularly important in the case of peptides, where much of the molecule is highly exposed to solvent due to its small size. In this study, we develop an empirical method for estimating the local dielectric constant based on an additive model of atomic polarizabilities. Calculated values match reported apparent dielectric constants for a series of Staphylococcus aureus nuclease mutants. Calculated constants are used to determine screening effects on Coulombic interactions and to determine solvation contributions based on a modified Generalized Born model. These terms are incorporated into the protein modeling platform protCAD, and benchmarked on a data set of collagen mimetic peptides for which experimentally determined stabilities are available. Computing local dielectric constants using atomistic protein models and the assumption of additive atomic polarizabilities is a rapid and potentially useful method for improving electrostatics and solvation calculations that can be applied in the computational design of peptides.

The Cellobiose Structures with Global Minimum Potential Energy in Vacuum and Water: LMOD Optimization Method

To understand the structure of cellulose, we searched for the global minimum potential energy of cellobiose, a basic structural unit of cellulose, in vacuum and water. We used the Low‐MODe (LMOD) optimization method in the Ambertools1.5 package with a GLYCAM_06 force field. The generalized Born model proposed by Hawkins, Cramer, and Trühlar was used as a solvation model. The global minima in vacuum and water had different conformations. These differences were explained by solvation effects, especially the change in electrostatic interaction upon solvation. The global minimum in vacuum was determined by the strength of hydrogen bonding, which was reduced in water. The conformation, which was subject to electrostatic repulsion in vacuum, became the global minimum in water because the electrostatic repulsion decreased as a result of the attractive interaction with water.

Accuracy assessment of the linear Poisson-Boltzmann equation and reparametrization of the OBC generalized Born model for nucleic acids and nucleic acid-protein complexes.

The generalized Born model in the Onufriev, Bashford, and Case (Onufriev et al., Proteins: Struct Funct Genet 2004, 55, 383) implementation has emerged as one of the best compromises between accuracy and speed of computation. For simulations of nucleic acids, however, a number of issues should be addressed: (1) the generalized Born model is based on a linear model and the linearization of the reference Poisson-Boltmann equation may be questioned for highly charged systems as nucleic acids; (2) although much attention has been given to potentials, solvation forces could be much less sensitive to linearization than the potentials; and (3) the accuracy of the Onufriev-Bashford-Case (OBC) model for nucleic acids depends on fine tuning of parameters. Here, we show that the linearization of the Poisson Boltzmann equation has mild effects on computed forces, and that with optimal choice of the OBC model parameters, solvation forces, essential for molecular dynamics simulations, agree well with those computed using the reference Poisson-Boltzmann model.

Evaluation of Generalized Born Model Accuracy for Absolute Binding Free Energy Calculations.

Generalized Born (GB) implicit solvent models are widely used in molecular dynamics simulations to evaluate the interactions of biomolecular complexes. The continuum treatment of the solvent results in significant computational savings in comparison to an explicit solvent representation. It is, however, not clear how accurately the GB approach reproduces the absolute free energies of biomolecular binding. On the basis of induced dissociation by means of umbrella sampling simulations, the absolute binding free energies of small proline-rich peptide ligands and a protein receptor were calculated. Comparative simulations according to the same protocol were performed by employing an explicit solvent model and various GB-type implicit solvent models in combination with a nonpolar surface tension term. The peptide ligands differed in a key residue at the peptide-protein interface, including either a nonpolar, a neutral polar, a positively charged, or a negatively charged group. For the peptides with a neutral polar or nonpolar interface residue, very good agreement between the explicit solvent and GB implicit solvent results was found. Deviations in the main separation free energy contributions are smaller than 1 kcal/mol. In contrast, for peptides with a charged interface residue, significant deviations of 2-4 kcal/mol were observed. The results indicate that recent GB models can compete with explicit solvent representations in total binding free energy calculations as long as no charged residues are present at the binding interface.

Transferring the PRIMO Coarse-Grained Force Field to the Membrane Environment: Simulations of Membrane Proteins and Helix-Helix Association.

An extension of the recently developed PRIMO coarse-grained force field to membrane environments, PRIMO-M, is described. The membrane environment is modeled with the heterogeneous dielectric generalized Born (HDGB) methodology that simply replaces the standard generalized Born model in PRIMO without further parametrization. The resulting model was validated by comparing amino acid insertion free energy profiles and application in molecular dynamics simulations of membrane proteins and membrane-interacting peptides. Membrane proteins with 148–661 amino acids show stable root-mean-squared-deviations (RMSD) between 2 and 4 Å for most systems. Transmembrane helical peptides maintain helical shape and exhibit tilt angles in good agreement with experimental or other simulation data. The association of two glycophorin A (GpA) helices was simulated using replica exchange molecular dynamics simulations yielding the correct dimer structure with a crossing angle in agreement with previous studies. Finally, conformational sampling of the influenza fusion peptide also generates structures in agreement with previous studies. Overall, these findings suggest that PRIMO-M can be used to study membrane bound peptides and proteins and validates the transferable nature of the PRIMO coarse-grained force field.

Accuracy of continuum electrostatic calculations based on three common dielectric boundary definitions.

We investigate the influence of three common definitions of the solute/solvent dielectric boundary (DB) on the accuracy of the electrostatic solvation energy ΔGel computed within the Poisson Boltzmann and the generalized Born models of implicit solvation. The test structures include small molecules, peptides and small proteins; explicit solvent ΔGel are used as accuracy reference. For common atomic radii sets BONDI, PARSE (and ZAP9 for small molecules) the use of van der Waals (vdW) DB results, on average, in considerably larger errors in ΔGel than the molecular surface (MS) DB. The optimal probe radius ρw for which the MS DB yields the most accurate ΔGel varies considerably between structure types. The solvent accessible surface (SAS) DB becomes optimal at ρw ~ 0.2 Å (exact value is sensitive to the structure and atomic radii), at which point the average accuracy of ΔGel is comparable to that of the MS-based boundary. The geometric equivalence of SAS to vdW surface based on the same atomic radii uniformly increased by ρw gives the corresponding optimal vdW DB. For small molecules, the optimal vdW DB based on BONDI + 0.2 Å radii can yield ΔGel estimates at least as accurate as those based on the optimal MS DB. Also, in small molecules, pairwise charge-charge interactions computed with the optimal vdW DB are virtually equal to those computed with the MS DB, suggesting that in this case the two boundaries are practically equivalent by the electrostatic energy criteria. In structures other than small molecules, the optimal vdW and MS dielectric boundaries are not equivalent: the respective pairwise electrostatic interactions in the presence of solvent can differ by up to 5 kcal/mol for individual atomic pairs in small proteins, even when the total ΔGel are equal. For small proteins, the average decrease in pairwise electrostatic interactions resulting from the switch from optimal MS to optimal vdW DB definition can be mimicked within the MS DB definition by doubling of the solute dielectric constant. However, the use of the higher interior dielectric does not eliminate the large individual deviations between pairwise interactions computed within the two DB definitions. It is argued that while the MS based definition of the dielectric boundary is more physically correct in some types of practical calculations, the choice is not so clear in some other common scenarios.

Introducing Charge Hydration Asymmetry into the Generalized Born Model.

The effect of charge hydration asymmetry (CHA)—non-invariance of solvation free energy upon solute charge inversion—is missing from the standard linear response continuum electrostatics. The proposed charge hydration asymmetric–generalized Born (CHA–GB) approximation introduces this effect into the popular generalized Born (GB) model. The CHA is added to the GB equation via an analytical correction that quantifies the specific propensity of CHA of a given water model; the latter is determined by the charge distribution within the water model. Significant variations in CHA seen in explicit water (TIP3P, TIP4P-Ew, and TIP5P-E) free energy calculations on charge-inverted “molecular bracelets” are closely reproduced by CHA–GB, with the accuracy similar to models such as SEA and 3D-RISM that go beyond the linear response. Compared against reference explicit (TIP3P) electrostatic solvation free energies, CHA–GB shows about a 40% improvement in accuracy over the canonical GB, tested on a diverse set of 248 rigid small neutral molecules (root mean square error, rmse = 0.88 kcal/mol for CHA–GB vs 1.24 kcal/mol for GB) and 48 conformations of amino acid analogs (rmse = 0.81 kcal/mol vs 1.26 kcal/mol). CHA–GB employs a novel definition of the dielectric boundary that does not subsume the CHA effects into the intrinsic atomic radii. The strategy leads to finding a new set of intrinsic atomic radii optimized for CHA–GB; these radii show physically meaningful variation with the atom type, in contrast to the radii set optimized for GB. Compared to several popular radii sets used with the original GB model, the new radii set shows better transferability between different classes of molecules.

A Computational Method Including Protein Flexibility to Estimate Affinities with Small Ligands

Computational methods are used to generate protein-ligand complex structures and predict their binding affinities. Usually protein flexibility is not fully considered, and the energy functions used to estimate binding affinities are poor. This work investigated how implicit ligand and solvation theories as well as the linear response approximation may be combined to better describe the effect of protein flexibility on ligand binding affinities.T4 lysozyme mutants, HIV-1 reverse transcriptase (HIVRT) and human FK506 binding protein 12 (FKBP) were chosen as model systems. An adaptive energy function based on the linear interaction energy approximation was parametrized and used to estimate partial affinities. Parameters were adapted according to ligand and protein surface polarities. Proteins were represented as an approximate conformational ensemble derived from molecular dynamics simulations. Interaction energies were obtained using the OPLS-AA force field with modified partial charges for ligands. A generalized Born model was used for implicit solvation.The parametrized energy function resulted in average deviations between experimental and calculated affinities of 1.0 kcal/mol and a correlation coefficient R2=0.8 for a test set of complexes with known binding sites. Discrimination of false-positive poses was also substantial. Then, approximations to the implicit ligand theory were proposed in order to obtain total binding affinities by combining interaction energies calculated for ligand complexes with the protein conformational ensembles. Several configurations contribute with the same weight for the FKBP protein. But, for lysozyme and HIVRT proteins, total affinities are dominated by one configuration. These results suggest that a faithful representation of protein conformational flexibility and an adequate statistical treatment based on implicit theories may be used to rapidly estimate reliable binding affinities.

Assessment of Nonpolar Terms in Implicit Solvent Models to Estimate Small Molecule Hydration Free Energies

Solvation effects are of great importance to describe many chemical and biological processes, rendering their understanding an important goal of biophysical research. Here we investigate implicit solvent models, which are desired for applications in which an explicit solvent representation is too demanding from a computational perspective. The estimation of hydration free energies for small organic molecules presents a common test case for all solvent models. Unfortunately, a survey of common implicit solvent models showed that their estimates are not as accurate as estimates based on explicit water models, and further improvement of the nonpolar term in these models has been suggested as a possible solution for this problem.[1] We have optimized model parameters for three different nonpolar terms in combination with a generalized Born model to estimate experimental hydration free energies for a large set of small neutral organic molecules. Our results show that a nonpolar term with atom type depended surface tension coefficients delivers the most accurate estimates for a defined set of atom types, yielding a root mean square error of 0.99 kcal/mol and a squared Pearson correlation coefficient of 0.900. For explicit TIP3P water calculations based on the same molecule set, the corresponding values reported by Mobley et al. are 1.26 kcal/mol and 0.888.[2] Our study provides a thorough overview of the capabilities of these three nonpolar terms. We anticipate that the general conclusions drawn from the analysis of our results will help to improve other existing implicit solvent models.[1] J.L. Knight, C.L. Brooks III, J. Comput. Chem., 2011.[2] D.L. Mobley, C.I. Bayly, M.D. Cooper, M.R. Shirts, K.A. Dill, J Chem Theory Comput, 2009.

PRIMO-M: An Extension of the Coarse-Grained Force Field Primo to the Membrane Environment

CG models for biomolecules and lipids are widely established but often suffer from a lack of transferability between different systems and to different environments. Furthermore, the combination with atomistic force fields in hybrid AA/CG multiscale models is often challenging. To alleviate these problems, we have previously developed the PRIMO coarse-grained force field. Here, the extension of PRIMO to membrane environments, PRIMO-M, is presented. The membrane environment is modeled implicitly with the heterogeneous dielectric generalized Born methodology that simply replaces the standard generalized Born model in PRIMO without further parameterization. The resulting model was initially validated by reproducing amino acid insertion free energy profiles. Membrane proteins with 148-661 amino acids show stable root-mean-squared-deviation between 2 and 4 A for most systems. PRIMO-M was able to predict tilt angles of several transmembrane helical peptides that are in good agreement with experimental or other simulation data. The association of two glycophorin A helices was simulated using replica exchange molecular dynamics simulations yielding the correct dimer structure with a crossing angle in agreement with previous studies. Finally, the conformational sampling of influenza fusion peptide also generates structures in agreement with previous studies. Overall, these findings suggest that PRIMO-M can be used to study membrane bound peptides and proteins and validates the transferable nature of the PRIMO coarse-grained force field.

VERIFICATION OF THE GENERALIZED BORN MODEL AT SHORT DISTANCES

The generalized Born (GB) model, one of the implicit solvent models, is widely applied in molecular dynamics (MD) simulations as a simple description of the solvation effect. In the GB model, an empirical function called the Still's formula, with the algorithmic simplicity, is utilized to calculate the solvation energy due to the polarization, termed as ΔGpol. Applications of the GB model have exhibited reasonable accuracy and high computational efficiency. However, there is still room for improvements. Most of the attempts to improve the GB model focus on optimizing effective Born radii. Contrarily, limited researches have been performed to improve the feasibility of the Still's formula. In this paper, analytical methods was applied to investigate the validity of the Still's formula at short distance. Taking advantage of the toroidal coordinates and Mehler–Fock transform, the analytical solutions of the GB model at short distances was derived explicitly for the first time. Additionally, the solvation energy was numerically computed using proper algorithms based on the analytical solutions and compared with ΔGpolcalculated in the GB model. With the analysis on the deficiencies of the Still's formula at short distances, potential methods to improve the validity of the GB model were discussed.

계산화학적 방법을 통한 β-D-glucopyranose 구조 연구

본 연구에서는 <TEX>${\beta}$</TEX>-D-glucopyranose 분자의 hydroxymethyl group의 두 torsion angle의 변화에 따른 포텐셜 에너지의 변화를 진공 상태와 implicit water 상태에서 연구하였으며 이를 통해 Solvation Energy가 구조에 미치는 영향에 대해서 알아보았다. 계산에 사용한 프로그램은 AMBER package였으며, force field는 GLYCAM_06을 사용하였다. Solvation model은 Hawkins, Cramer, Truhlar 등이 제안한 generalized Born model을 사용하였다. 계산 결과, methyl hydroxyl group 내의 hydroxyl group이 고리구조의 hydroxyl group과 강한 수소결합이 가능한 영역에서 많은 변화가 일어났다. 이를 통해 solvation effect로 인해서 수소 결합의 중요성이 감소했다는 결론을 내렸다. In this study, we have investigated potential energy of <TEX>${\beta}$</TEX>-D-glucopyranose in vacuum and implicit water condition. By Comparing two conditions we find that how solvation energy influence <TEX>${\beta}$</TEX>-D-glucopyranose structure. We use AMBER package program and GLYCAM_06 force field. Solvation model was used for the generalized Born model with Hawkins, Cramer, Truhlar has been proposed. We conclude that difference of contour map of two conditions is caused by solvation effect by reducing hydrogen bonding interaction.

Monte carlo simulations of proteins at constant pH with generalized born solvent, flexible sidechains, and an effective dielectric boundary

Titratable residues determine the acid/base behavior of proteins, strongly influencing their function; in addition, proton binding is a valuable reporter on electrostatic interactions. We describe a method for pK(a) calculations, using constant-pH Monte Carlo (MC) simulations to explore the space of sidechain conformations and protonation states, with an efficient and accurate generalized Born model (GB) for the solvent effects. To overcome the many-body dependency of the GB model, we use a "Native Environment" approximation, whose accuracy is shown to be good. It allows the precalculation and storage of interactions between all sidechain pairs, a strategy borrowed from computational protein design, which makes the MC simulations themselves very fast. The method is tested for 12 proteins and 167 titratable sidechains. It gives an rms error of 1.1 pH units, similar to the trivial "Null" model. The only adjustable parameter is the protein dielectric constant. The best accuracy is achieved for values between 4 and 8, a range that is physically plausible for a protein interior. For sidechains with large pKa shifts, ≥2, the rms error is 1.6, compared to 2.5 with the Null model and 1.5 with the empirical PROPKA method.

Computational Alanine Scanning Mutagenesis-An Improved Methodological Approach for Protein-DNA Complexes.

Proteins and protein-based complexes are the basis of many key systems in nature and have been the subject of intense research in the last decades, in an attempt to acquire comprehensive knowledge of reactions that take place in nature. Computational Alanine Scanning Mutagenesis approaches have been extensively used in the study of protein interfaces and in the determination of the most important residues for complex formation, the Hot-spots. However, as it is usually applied to the study of protein-protein interfaces, we tried to modify and apply it to the study of protein-DNA interfaces, which are also crucial in nature but have not been the subject of as much research. In this work, we carry out MD simulations of seven protein-DNA complexes and tested the influence of the variation of different parameters on the determination of the binding free energy terms (ΔΔGbinding) of 78 mutations: solvent representation, internal dielectric constant, Linear and Nonlinear Poisson-Boltzmann equation, Generalized Born model, simulation time, number of structures analyzed, number of MD trajectories, force field used, and energetic terms involved. Overall, this new approach gave an average error of 1.55 kcal/mol, and P, R, F1, accuracy, and specificity values of 0.78, 0.50, 0.61, 0.77, and 0.92, respectively. This improved computational alanine scanning mutagenesis approach may serve as a tool to explore the behavior of this important class of complexes.