Electrostatics in computational protein design

Gadolinium-Based NMR Spin Relaxation Measurements of Near-Surface Electrostatic Potentials of Biomolecules

NMR spectroscopy is an important tool for the measurement of the electrostatic properties of biomolecules. To this point, paramagnetic relaxation enhancements (PREs) of 1H nuclei arising from nitroxide cosolutes in biomolecular solutions have been used to measure effective near-surface electrostatic potentials (ϕENS) of proteins and nucleic acids. Here, we present a gadolinium (Gd)-based NMR method, exploiting Gd chelates with different net charges, for measuring ϕENS values and demonstrate its utility through applications to a number of biomolecular systems. The use of Gd-based cosolutes offers several advantages over nitroxides for ϕENS measurements. First, unlike nitroxide compounds, Gd chelates enable electrostatic potential measurements on oxidation-sensitive proteins that require reducing agents. Second, the large electron spin quantum number of Gd (7/2) results in notably larger PREs for Gd chelates when used at the same concentrations as nitroxide radicals. Thus, it is possible to measure ϕENS values exclusively from + and - charged compounds even for highly charged biomolecules, avoiding the use of neutral cosolutes that, as we further establish here, limits the accuracy of the measured electrostatic potentials. In addition, the smaller concentrations of cosolutes required minimize potential binding to sites on macromolecules. Fourth, the closer proximity of the paramagnetic center and charged groups within Gd chelates, in comparison to the corresponding nitroxide compounds, enables more accurate predictions of ϕENS potentials for cross-validation of the experimental results. The Gd-based method described here, thus, broadens the applicability of studies of biomolecular electrostatics using solution NMR spectroscopy.

Direct measurements of biomolecular electrostatics through experiments

Biomolecular electrostatics has been a subject of computational investigations based on 3D structures. This situation is changing because emerging experimental tools allow us to quantitatively investigate biomolecular electrostatics without any use of structure information. Now, electrostatic potentials around biomolecules can directly be measured for many residues simultaneously by nuclear magnetic resonance (NMR) spectroscopy. This NMR method can be used to study electrostatic aspects of various processes, including macromolecular association and liquid-liquid phase separation. Applications to structurally flexible biomolecules such as intrinsically disordered proteins are particularly useful. The new tools also facilitate examination of theoretical models and methods for biomolecular electrostatics.

Past, present, and future of Lyme disease vaccines: antigen engineering approaches and mechanistic insights

Introduction: Transmitted by ticks, Lyme disease is the most common vector-borne disease in the Northern hemisphere. Despite the geographical expansion of human Lyme disease cases, no effective preventive strategies are currently available. Developing an efficacious and safe vaccine is therefore urgently needed. Efforts have previously been taken to identify vaccine targets in the causative pathogen (Borrelia burgdorferi sensu lato) and arthropod vector (Ixodes spp.). However, progress was impeded due to a lack of consumer confidence caused by the myth of undesired off-target responses, low immune responses, a limited breadth of immune reactivity, as well as by the complexities of the vaccine process development.Area covered: In this review, we summarize the antigen engineering approaches that have been applied to overcome those challenges and the underlying mechanisms that can be exploited to improve both safety and efficacy of future Lyme disease vaccines.Expert opinion: Over the past two decades, several new genetically redesigned Lyme disease vaccine candidates have shown success in both preclinical and clinical settings and built a solid foundation for further development. These studies have greatly informed the protective mechanisms of reducing Lyme disease burdens and ending the endemic of this disease.

Protein Electrostatics Investigated through Paramagnetic NMR for Nonpolar Groups

Experimental validation of theoretical models for protein electrostatics remains rare. Recently, we have developed a paramagnetic NMR-based method for de novo determination of effective near-surface electrostatic potentials, which allows for straightforward examination of electrostatic models for biomolecules. In the current work, we expand this method and demonstrate that effective near-surface electrostatic potentials can readily be determined from ¹H paramagnetic relaxation enhancement (PRE) data for protein C_αH and CH₃ groups. The experimental data were compared with those predicted from the Poisson-Boltzmann theory. The impact of structural dynamics on the effective near-surface electrostatic potentials was also assessed. The agreement between the experimental and theoretical data was particularly good for methyl ¹H nuclei. Compared to the conventional pK_a-based validation, our paramagnetic NMR-based approach can provide a far larger number of experimental data that can directly be used to examine the validity of theoretical electrostatic models for proteins.

De novo protein design by an energy function based on series expansion in distance and orientation dependence

MOTIVATION

Despite many successes, de novo protein design is not yet a solved problem as its success rate remains low. The low success rate is largely because we do not yet have an accurate energy function for describing the solvent-mediated interaction between amino acid residues in a protein chain. Previous studies showed that an energy function based on series expansions with its parameters optimized for side-chain and loop conformations can lead to one of the most accurate methods for side chain (OSCAR) and loop prediction (LEAP). Following the same strategy, we developed an energy function based on series expansions with the parameters optimized in four separate stages (recovering single-residue types without and with orientation dependence, selecting loop decoys and maintaining the composition of amino acids). We tested the energy function for de novo design by using Monte Carlo simulated annealing.

RESULTS

The method for protein design (OSCAR-Design) is found to be as accurate as OSCAR and LEAP for side-chain and loop prediction, respectively. In de novo design, it can recover native residue types ranging from 38% to 43% depending on test sets, conserve hydrophobic/hydrophilic residues at ∼75%, and yield the overall similarity in amino acid compositions at more than 90%. These performance measures are all statistically significantly better than several protein design programs compared. Moreover, the largest hydrophobic patch areas in designed proteins are near or smaller than those in native proteins. Thus, an energy function based on series expansion can be made useful for protein design.

AVAILABILITY AND IMPLEMENTATION

The Linux executable version is freely available for academic users at http://zhouyq-lab.szbl.ac.cn/resources/.

De novo determination of near-surface electrostatic potentials by NMR

Electrostatic potentials computed from three-dimensional structures of biomolecules by solving the Poisson-Boltzmann equation are widely used in molecular biophysics, structural biology, and medicinal chemistry. Despite the approximate nature of the Poisson-Boltzmann theory, validation of the computed electrostatic potentials around biological macromolecules is rare and methodologically limited. Here, we present a unique and powerful NMR method that allows for straightforward and extensive comparison with electrostatic models for biomolecules and their complexes. This method utilizes paramagnetic relaxation enhancement arising from analogous cationic and anionic cosolutes whose spatial distributions around biological macromolecules reflect electrostatic potentials. We demonstrate that this NMR method enables de novo determination of near-surface electrostatic potentials for individual protein residues without using any structural information. We applied the method to ubiquitin and the Antp homeodomain-DNA complex. The experimental data agreed well with predictions from the Poisson-Boltzmann theory. Thus, our experimental results clearly support the validity of the theory for these systems. However, our experimental study also illuminates certain weaknesses of the Poisson-Boltzmann theory. For example, we found that the theory predicts stronger dependence of near-surface electrostatic potentials on ionic strength than observed in the experiments. Our data also suggest that conformational flexibility or structural uncertainties may cause large errors in theoretical predictions of electrostatic potentials, particularly for highly charged systems. This NMR-based method permits extensive assessment of near-surface electrostatic potentials for various regions around biological macromolecules and thereby may facilitate improvement of the computational approaches for electrostatic potentials.

Yin and Yang in Post-Translational Modifications of Human D-Amino Acid Oxidase

In the central nervous system, the flavoprotein D-amino acid oxidase is responsible for catabolizing D-serine, the main endogenous coagonist of N-methyl-D-aspartate receptor. Dysregulation of D-serine brain levels in humans has been associated with neurodegenerative and psychiatric disorders. This D-amino acid is synthesized by the enzyme serine racemase, starting from the corresponding L-enantiomer, and degraded by both serine racemase (via an elimination reaction) and the flavoenzyme D-amino acid oxidase. To shed light on the role of human D-amino acid oxidase (hDAAO) in D-serine metabolism, the structural/functional relationships of this enzyme have been investigated in depth and several strategies aimed at controlling the enzymatic activity have been identified. Here, we focused on the effect of post-translational modifications: by using a combination of structural analyses, biochemical methods, and cellular studies, we investigated whether hDAAO is subjected to nitrosylation, sulfhydration, and phosphorylation. hDAAO is S-nitrosylated and this negatively affects its activity. In contrast, the hydrogen sulfide donor NaHS seems to alter the enzyme conformation, stabilizing a species with higher affinity for the flavin adenine dinucleotide cofactor and thus positively affecting enzymatic activity. Moreover, hDAAO is phosphorylated in cerebellum; however, the protein kinase involved is still unknown. Taken together, these findings indicate that D-serine levels can be also modulated by post-translational modifications of hDAAO as also known for the D-serine synthetic enzyme serine racemase.

An Iterative Fragment Scheme for the ACKS2 Electronic Polarization Model: Application to Molecular Dimers and Chains

The treatment of electrostatic interactions is a key ingredient in the force field-based simulation of condensed phase systems. Most approaches used fixed, site-specific point charges. Yet, it is now clear that many applications of force fields (FFs) demand more sophisticated treatments, prompting the implementation of charge equilibration methods in polarizable FFs to allow the redistribution of charge within the system. One approach allowing both, charge redistribution and site-specific polarization, while at the same time solving methodological shortcomings of earlier methods, is the first-principles-derived atom-condensed Kohn-Sham density functional theory method approximated to the second order (ACKS2). In this work, we present two fragment approaches to ACKS2, termed f-ACKS2 and a self-consistent version, scf-ACKS2, that treat condensed phase systems as a collection of electronically polarizable molecular fragments. The fragmentation approach to ACKS2 not only leads to a more transferable and less system-specific collection of electronic response parameters but also opens up the method to large condensed phase systems. We validate the accuracies of f-ACKS2 and scf-ACKS2 by comparing polarization energies and induced dipole moments for a number of charged hydrocarbon dimers against DFT reference calculations. Finally, we also apply both fragmented ACKS2 variants to calculate the polarization energy for electron-hole pair separation along a chain of anthracene molecules and find excellent agreement with reference DFT calculations.

Chemometric Models of Differential Amino Acids at the Navα and Navβ Interface of Mammalian Sodium Channel Isoforms

(1) Background: voltage-gated sodium channels (Na_vs) are integral membrane proteins that allow the sodium ion flux into the excitable cells and initiate the action potential. They comprise an α (Na_vα) subunit that forms the channel pore and are coupled to one or more auxiliary β (Na_vβ) subunits that modulate the gating to a variable extent. (2) Methods: after performing homology in silico modeling for all nine isoforms (Na_v1.1α to Na_v1.9α), the Na_vα and Na_vβ protein-protein interaction (PPI) was analyzed chemometrically based on the primary and secondary structures as well as topological or spatial mapping. (3) Results: our findings reveal a unique isoform-specific correspondence between certain segments of the extracellular loops of the Na_vα subunits. Precisely, loop S5 in domain I forms part of the PPI and assists Na_vβ1 or Na_vβ3 on all nine mammalian isoforms. The implied molecular movements resemble macroscopic springs, all of which explains published voltage sensor effects on sodium channel fast inactivation in gating. (4) Conclusions: currently, the specific functions exerted by the Na_vβ1 or Na_vβ3 subunits on the modulation of Na_vα gating remain unknown. Our work determined functional interaction in the extracellular domains on theoretical grounds and we propose a schematic model of the gating mechanism of fast channel sodium current inactivation by educated guessing.

Effects of tosyl-l-arginine methyl ester (TAME) on the APC/c subunits: An in silico investigation for inhibiting cell cycle

The anaphase-promoting complex/cyclosome (APC/c) is requisite for controlling mitosis, which is activated by Cdh1 and Cdc20 activators. Dysregulation of APC/c is observed in many cancers and is known as a targeted drug particularly in cancer drug resistance. It was shown that tosyl-l-arginine methyl ester (TAME), via mimicking isoleucine-arginine (IR) tail of co-activators, inhibits APC/c functions. However, structure details and interaction of TAME with APC/c are poorly defined. In the current study, a well-established set of computational methods was used to identify the best binding pocket in order to inhibit APC activity. Therefore, the interaction of IR tail and Cbox of co-activators, as well as TAME as an inhibitor, as an inhibitor, with APC3 and APC8 subunits of APC/c were analyzed, regarding structure, molecular docking, molecular dynamics, and free binding energy. The results indicated that TAME bound to APC3 with a higher binding affinity (∼-7.3 kcal/mol) than APC8 (∼-5.7 kcal/mol). Also, the binding free energy value obtained for the APC3-TAME was -22.25 ± 1.12 kcal/mol. According to binding free energies, van der Waals energy was the major favorable contributor to the ligand binding. These results offer that TAME had more affinity to interact with the APC3 subunit, at the IR binding pocket than the APC8 subunit at the Cbox binding pocket. In conclusion, IR binding pocket can serve as an appropriate potential target for TAME as an inhibitor of APC/c.

Knowledge-based entropies improve the identification of native protein structures

Evaluating protein structures requires reliable free energies with good estimates of both potential energies and entropies. Although there are many demonstrated successes from using knowledge-based potential energies, computing entropies of proteins has lagged far behind. Here we take an entirely different approach and evaluate knowledge-based conformational entropies of proteins based on the observed frequencies of contact changes between amino acids in a set of 167 diverse proteins, each of which has two alternative structures. The results show that charged and polar interactions break more often than hydrophobic pairs. This pattern correlates strongly with the average solvent exposure of amino acids in globular proteins, as well as with polarity indices and the sizes of the amino acids. Knowledge-based entropies are derived by using the inverse Boltzmann relationship, in a manner analogous to the way that knowledge-based potentials have been extracted. Including these new knowledge-based entropies almost doubles the performance of knowledge-based potentials in selecting the native protein structures from decoy sets. Beyond the overall energy-entropy compensation, a similar compensation is seen for individual pairs of interacting amino acids. The entropies in this report have immediate applications for 3D structure prediction, protein model assessment, and protein engineering and design.

The Framework of Computational Protein Design

Computational protein design (CPD) has established itself as a leading field in basic and applied science with a strong coupling between the two. Proteins are computationally designed from the level of amino acids to the level of a functional protein complex. Design targets range from increased thermo- (or other) stability to specific requested reactions such as protein-protein binding, enzymatic reactions, or nanotechnology applications. The design scheme may encompass small regions of the proteins or the entire protein. In either case, the design may aim at the side-chains or at the full backbone conformation. Herein, the main framework for the process is outlined highlighting key elements in the CPD iterative cycle. These include the very definition of CPD, the diverse goals of CPD, components of the CPD protocol, methods for searching sequence and structure space, scoring functions, and augmenting the CPD with other optimization tools. Taken together, this chapter aims to introduce the framework of CPD.

Electrostatic Similarity Analysis of Human β-Defensin Binding in the Melanocortin System

The β-defensins are a class of small cationic proteins that serve as components of numerous systems in vertebrate biology, including the immune and melanocortin systems. Human β-defensin 3 (HBD3), which is produced in the skin, has been found to bind to melanocortin receptors 1 and 4 through complementary electrostatics, a unique mechanism of ligand-receptor interaction. This finding indicates that electrostatics alone, and not specific amino acid contact points, could be sufficient for function in this ligand-receptor system, and further suggests that other small peptide ligands could interact with these receptors in a similar fashion. Here, we conducted molecular-similarity analyses and functional studies of additional members of the human β-defensin family, examining their potential as ligands of melanocortin-1 receptor, through selection based on their electrostatic similarity to HBD3. Using Poisson-Boltzmann electrostatic calculations and molecular-similarity analysis, we identified members of the human β-defensin family that are both similar and dissimilar to HBD3 in terms of electrostatic potential. Synthesis and functional testing of a subset of these β-defensins showed that peptides with an HBD3-like electrostatic character bound to melanocortin receptors with high affinity, whereas those that were anticorrelated to HBD3 showed no binding affinity. These findings expand on the central role of electrostatics in the control of this ligand-receptor system and further demonstrate the utility of employing molecular-similarity analysis. Additionally, we identified several new potential ligands of melanocortin-1 receptor, which may have implications for our understanding of the role defensins play in melanocortin physiology.

Dissecting Electrostatic Contributions to Folding and Self-Assembly Using Designed Multicomponent Peptide Systems

We investigate formation of nano- to microscale peptide fibers and sheets where assembly requires association of two distinct collagen mimetic peptides (CMPs). The multicomponent nature of these designs allows the decoupling of amino acid contributions to peptide folding versus higher-order assembly. While both arginine and lysine containing CMP sequences can favor triple-helix folding, only arginine promotes rapid supramolecular assembly in each of the three two-component systems examined. Unlike lysine, the polyvalent guanidyl group of arginine is capable of both intra- and intermolecular contacts, promoting assembly. This is consistent with the supramolecular diversity of CMP morphologies observed throughout the literature. It also connects CMP self-assembly with a broad range of biomolecular interaction phenomena, providing general principles for modeling and design.

Predicting stabilizing mutations in proteins using Poisson-Boltzmann based models: study of unfolded state ensemble models and development of a successful binary classifier based on residue interaction energies

In many cases the stability of a protein has to be increased to permit its biotechnological use. Rational methods of protein stabilization based on optimizing electrostatic interactions have provided some fine successful predictions. However, the precise calculation of stabilization energies remains challenging, one reason being that the electrostatic effects on the unfolded state are often neglected. We have explored here the feasibility of incorporating Poisson-Boltzmann model electrostatic calculations performed on representations of the unfolded state as large ensembles of geometrically optimized conformations calculated using the ProtSA server. Using a data set of 80 electrostatic mutations experimentally tested in two-state proteins, the predictive performance of several such models has been compared to that of a simple one that considers an unfolded structure of non-interacting residues. The unfolded ensemble models, while showing correlation between the predicted stabilization values and the experimental ones, are worse than the simple model, suggesting that the ensembles do not capture well the energetics of the unfolded state. A more attainable goal is classifying potential mutations as either stabilizing or non-stabilizing, rather than accurately calculating their stabilization energies. To implement a fast classification method that can assist in selecting stabilizing mutations, we have used a much simpler electrostatic model based only on the native structure and have determined its precision using different stabilizing energy thresholds. The binary classifier developed finds 7 true stabilizing mutants out of every 10 proposed candidates and can be used as a robust tool to propose stabilizing mutations.

Protein:Ligand binding free energies: A stringent test for computational protein design

A computational protein design method is extended to allow Monte Carlo simulations where two ligands are titrated into a protein binding pocket, yielding binding free energy differences. These provide a stringent test of the physical model, including the energy surface and sidechain rotamer definition. As a test, we consider tyrosyl-tRNA synthetase (TyrRS), which has been extensively redesigned experimentally. We consider its specificity for its substrate l-tyrosine (l-Tyr), compared to the analogs d-Tyr, p-acetyl-, and p-azido-phenylalanine (ac-Phe, az-Phe). We simulate l- and d-Tyr binding to TyrRS and six mutants, and compare the structures and binding free energies to a more rigorous "MD/GBSA" procedure: molecular dynamics with explicit solvent for structures and a Generalized Born + Surface Area model for binding free energies. Next, we consider l-Tyr, ac- and az-Phe binding to six other TyrRS variants. The titration results are sensitive to the precise rotamer definition, which involves a short energy minimization for each sidechain pair to help relax bad contacts induced by the discrete rotamer set. However, when designed mutant structures are rescored with a standard GBSA energy model, results agree well with the more rigorous MD/GBSA. As a third test, we redesign three amino acid positions in the substrate coordination sphere, with either l-Tyr or d-Tyr as the ligand. For two, we obtain good agreement with experiment, recovering the wildtype residue when l-Tyr is the ligand and a d-Tyr specific mutant when d-Tyr is the ligand. For the third, we recover His with either ligand, instead of wildtype Gln.

Computational tools for epitope vaccine design and evaluation

Rational approaches will be required to develop universal vaccines for viral pathogens such as human immunodeficiency virus, hepatitis C virus, and influenza, for which empirical approaches have failed. The main objective of a rational vaccine strategy is to design novel immunogens that are capable of inducing long-term protective immunity. In practice, this requires structure-based engineering of the target neutralizing epitopes and a quantitative readout of vaccine-induced immune responses. Therefore, computational tools that can facilitate these two areas have played increasingly important roles in rational vaccine design in recent years. Here we review the computational techniques developed for protein structure prediction and antibody repertoire analysis, and demonstrate how they can be applied to the design and evaluation of epitope vaccines.

Modified Poisson–Boltzmann equations for characterizing biomolecular solvation

Methods for computing electrostatic interactions often account implicitly for the solvent, due to the much smaller number of degrees of freedom involved. In the Poisson–Boltzmann (PB) approach the electrostatic potential is obtained by solving the Poisson–Boltzmann equation (PBE), where the solvent region is modeled as a homogeneous medium with a high dielectric constant. PB however is not exempt of problems. It does not take into account for example the sizes of the ions in the atmosphere surrounding the solute, nor does it take into account the inhomogeneous dielectric response of water due to the presence of a highly charged surface. In this paper we review two major modifications of PB that circumvent these problems, namely the size-modified PB (SMPB) equation and the Dipolar Poisson–Boltzmann Langevin (DPBL) model. In SMPB, steric effects between ions are accounted for with a lattice gas model. In DPBL, the solvent region is no longer modeled as a homogeneous dielectric media but rather as an assembly of self-orienting interacting dipoles of variable density. This model results in a dielectric profile that transits smoothly from the solute to the solvent region as well as in a variable solvent density that depends on the charges of the solute. We show successful applications of the DPBL formalism to computing the solvation free energies of isolated ions in water. Further developments of more accurately modified PB models are discussed.

Uncovering the determinants of a highly perturbed tyrosine pKa in the active site of ketosteroid isomerase

Within the idiosyncratic enzyme active-site environment, side chain and ligand pKa values can be profoundly perturbed relative to their values in aqueous solution. Whereas structural inspection of systems has often attributed perturbed pKa values to dominant contributions from placement near charged groups or within hydrophobic pockets, Tyr57 of a Pseudomonas putida ketosteroid isomerase (KSI) mutant, suggested to have a pKa perturbed by nearly 4 units to 6.3, is situated within a solvent-exposed active site devoid of cationic side chains, metal ions, or cofactors. Extensive comparisons among 45 variants with mutations in and around the KSI active site, along with protein semisynthesis, (13)C NMR spectroscopy, absorbance spectroscopy, and X-ray crystallography, was used to unravel the basis for this perturbed Tyr pKa. The results suggest that the origin of large energetic perturbations are more complex than suggested by visual inspection. For example, the introduction of positively charged residues near Tyr57 raises its pKa rather than lowers it; this effect, and part of the increase in the Tyr pKa from the introduction of nearby anionic groups, arises from accompanying active-site structural rearrangements. Other mutations with large effects also cause structural perturbations or appear to displace a structured water molecule that is part of a stabilizing hydrogen-bond network. Our results lead to a model in which three hydrogen bonds are donated to the stabilized ionized Tyr, with these hydrogen-bond donors, two Tyr side chains, and a water molecule positioned by other side chains and by a water-mediated hydrogen-bond network. These results support the notion that large energetic effects are often the consequence of multiple stabilizing interactions rather than a single dominant interaction. Most generally, this work provides a case study for how extensive and comprehensive comparisons via site-directed mutagenesis in a tight feedback loop with structural analysis can greatly facilitate our understanding of enzyme active-site energetics. The extensive data set provided may also be a valuable resource for those wishing to extensively test computational approaches for determining enzymatic pKa values and energetic effects.

The electrostatic response of water to neutral polar solutes: implications for continuum solvent modeling

Continuum solvation models are widely used to estimate the hydration free energies of small molecules and proteins, in applications ranging from drug design to protein engineering, and most such models are based on the approximation of a linear dielectric response by the solvent. We used explicit-water molecular dynamics simulations with the TIP3P water model to probe this linear response approximation in the case of neutral polar molecules, using miniature cucurbituril and cyclodextrin receptors and protein side-chain analogs as model systems. We observe supralinear electrostatic solvent responses, and this nonlinearity is found to result primarily from waters' being drawn closer and closer to the solutes with increased solute-solvent electrostatic interactions; i.e., from solute electrostriction. Dielectric saturation and changes in the water-water hydrogen bonding network, on the other hand, play little role. Thus, accounting for solute electrostriction may be a productive approach to improving the accuracy of continuum solvation models.

Energy functions in de novo protein design: current challenges and future prospects

In the past decade, a concerted effort to successfully capture specific tertiary packing interactions produced specific three-dimensional structures for many de novo designed proteins that are validated by nuclear magnetic resonance and/or X-ray crystallographic techniques. However, the success rate of computational design remains low. In this review, we provide an overview of experimentally validated, de novo designed proteins and compare four available programs, RosettaDesign, EGAD, Liang-Grishin, and RosettaDesign-SR, by assessing designed sequences computationally. Computational assessment includes the recovery of native sequences, the calculation of sizes of hydrophobic patches and total solvent-accessible surface area, and the prediction of structural properties such as intrinsic disorder, secondary structures, and three-dimensional structures. This computational assessment, together with a recent community-wide experiment in assessing scoring functions for interface design, suggests that the next-generation protein-design scoring function will come from the right balance of complementary interaction terms. Such balance may be found when more negative experimental data become available as part of a training set.

Rationalization and design of the complementarity determining region sequences in an antibody-antigen recognition interface

Protein-protein interactions are critical determinants in biological systems. Engineered proteins binding to specific areas on protein surfaces could lead to therapeutics or diagnostics for treating diseases in humans. But designing epitope-specific protein-protein interactions with computational atomistic interaction free energy remains a difficult challenge. Here we show that, with the antibody-VEGF (vascular endothelial growth factor) interaction as a model system, the experimentally observed amino acid preferences in the antibody-antigen interface can be rationalized with 3-dimensional distributions of interacting atoms derived from the database of protein structures. Machine learning models established on the rationalization can be generalized to design amino acid preferences in antibody-antigen interfaces, for which the experimental validations are tractable with current high throughput synthetic antibody display technologies. Leave-one-out cross validation on the benchmark system yielded the accuracy, precision, recall (sensitivity) and specificity of the overall binary predictions to be 0.69, 0.45, 0.63, and 0.71 respectively, and the overall Matthews correlation coefficient of the 20 amino acid types in the 24 interface CDR positions was 0.312. The structure-based computational antibody design methodology was further tested with other antibodies binding to VEGF. The results indicate that the methodology could provide alternatives to the current antibody technologies based on animal immune systems in engineering therapeutic and diagnostic antibodies against predetermined antigen epitopes.

Assessment of Atomic Charge Models for Gas-Phase Computations on Polypeptides

The concept of the atomic charge is extensively used to model the electrostatic properties of proteins. Atomic charges are not only the basis for the electrostatic energy term in biomolecular force fields but are also derived from quantum mechanical computations on protein fragments to get more insight into their electronic structure. Unfortunately there are many atomic charge schemes which lead to significantly different results, and it is not trivial to determine which scheme is most suitable for biomolecular studies. Therefore, we present an extensive methodological benchmark using a selection of atomic charge schemes [Mulliken, natural, restrained electrostatic potential, Hirshfeld-I, electronegativity equalization method (EEM), and split-charge equilibration (SQE)] applied to two sets of penta-alanine conformers. Our analysis clearly shows that Hirshfeld-I charges offer the best compromise between transferability (robustness with respect to conformational changes) and the ability to reproduce electrostatic properties of the penta-alanine. The benchmark also considers two charge equilibration models (EEM and SQE), which both clearly fail to describe the locally charged moieties in the zwitterionic form of penta-alanine. This issue is analyzed in detail because charge equilibration models are computationally much more attractive than the Hirshfeld-I scheme. Based on the latter analysis, a straightforward extension of the SQE model is proposed, SQE+Q(0), that is suitable to describe biological systems bearing many locally charged functional groups.

Adapting Poisson-Boltzmann to the self-consistent mean field theory: application to protein side-chain modeling

We present an extension of the self-consistent mean field theory for protein side-chain modeling in which solvation effects are included based on the Poisson-Boltzmann (PB) theory. In this approach, the protein is represented with multiple copies of its side chains. Each copy is assigned a weight that is refined iteratively based on the mean field energy generated by the rest of the protein, until self-consistency is reached. At each cycle, the variational free energy of the multi-copy system is computed; this free energy includes the internal energy of the protein that accounts for vdW and electrostatics interactions and a solvation free energy term that is computed using the PB equation. The method converges in only a few cycles and takes only minutes of central processing unit time on a commodity personal computer. The predicted conformation of each residue is then set to be its copy with the highest weight after convergence. We have tested this method on a database of hundred highly refined NMR structures to circumvent the problems of crystal packing inherent to x-ray structures. The use of the PB-derived solvation free energy significantly improves prediction accuracy for surface side chains. For example, the prediction accuracies for χ(1) for surface cysteine, serine, and threonine residues improve from 68%, 35%, and 43% to 80%, 53%, and 57%, respectively. A comparison with other side-chain prediction algorithms demonstrates that our approach is consistently better in predicting the conformations of exposed side chains.

Mathematical analysis of the boundary-integral based electrostatics estimation approximation for molecular solvation: exact results for spherical inclusions

We analyze the mathematically rigorous BIBEE (boundary-integral based electrostatics estimation) approximation of the mixed-dielectric continuum model of molecular electrostatics, using the analytically solvable case of a spherical solute containing an arbitrary charge distribution. Our analysis, which builds on Kirkwood's solution using spherical harmonics, clarifies important aspects of the approximation and its relationship to generalized Born models. First, our results suggest a new perspective for analyzing fast electrostatic models: the separation of variables between material properties (the dielectric constants) and geometry (the solute dielectric boundary and charge distribution). Second, we find that the eigenfunctions of the reaction-potential operator are exactly preserved in the BIBEE model for the sphere, which supports the use of this approximation for analyzing charge-charge interactions in molecular binding. Third, a comparison of BIBEE to the recent GBε theory suggests a modified BIBEE model capable of predicting electrostatic solvation free energies to within 4% of a full numerical Poisson calculation. This modified model leads to a projection-framework understanding of BIBEE and suggests opportunities for future improvements.

Electrostatics in proteins and protein-ligand complexes

Accurate computational methods for predicting electrostatic energies are of major importance for our understanding of protein energetics in general for computer-aided drug design as well as for the design of novel biocatalysts and protein therapeutics. Electrostatic energies are of particular importance in such applications as virtual screening, drug design and protein-protein docking due to the high charge density of protein ligands and small-molecule drugs, and the frequent protonation state changes observed when drugs bind to their protein targets. Therefore, the development of a reliable and fast algorithm for the evaluation of electrostatic free energies, as an important contributor to the overall protein energy function, has been the focus for many scientists over the past three decades. In this review we describe the current state-of-the-art in modeling electrostatic effects in proteins and protein-ligand complexes. We focus mainly on the merits and drawbacks of the continuum methodology, and speculate on future directions in refining algorithms for calculating electrostatic energies in proteins using experimental data.

Computational protein design with a generalized Born solvent model: application to Asparaginyl-tRNA synthetase

Computational Protein Design (CPD) is a promising method for high throughput protein and ligand mutagenesis. Recently, we developed a CPD method that used a polar-hydrogen energy function for protein interactions and a Coulomb/Accessible Surface Area (CASA) model for solvent effects. We applied this method to engineer aspartyl-adenylate (AspAMP) specificity into Asparaginyl-tRNA synthetase (AsnRS), whose substrate is asparaginyl-adenylate (AsnAMP). Here, we implement a more accurate function, with an all-atom energy for protein interactions and a residue-pairwise generalized Born model for solvent effects. As a first test, we compute aminoacid affinities for several point mutants of Aspartyl-tRNA synthetase (AspRS) and Tyrosyl-tRNA synthetase and stability changes for three helical peptides and compare with experiment. As a second test, we readdress the problem of AsnRS aminoacid engineering. We compare three design criteria, which optimize the folding free-energy, the absolute AspAMP affinity, and the relative (AspAMP-AsnAMP) affinity. The sequences and conformations are improved with respect to our previous, polar-hydrogen/CASA study: For several designed complexes, the AspAMP carboxylate forms three interactions with a conserved arginine and a designed lysine, as in the active site of the AspRS:AspAMP complex. The conformations and interactions are well maintained in molecular dynamics simulations and the sequences have an inverted specificity, favoring AspAMP over AsnAMP. The method is not fully successful, since experimental measurements with the seven most promising sequences show that they do not catalyze at a detectable level the adenylation of Asp (or Asn) with ATP. This may be due to weak AspAMP binding and/or disruption of transition-state stabilization.

Insights into intramolecular Trp and His side-chain orientation and stereospecific π interactions surrounding metal centers: an investigation using protein metal-site mimicry in solution

Metal-binding scaffolds incorporating a Trp/His-paired epitope are instrumental in giving novel insights into the physicochemical basis of functional and mechanistic versatility conferred by the Trp-His interplay at a metal site. Herein, by coupling biometal site mimicry and (1)H and (13)C NMR spectroscopy experiments, modular constructs EDTA-(L-Trp, L-His) (EWH; EDTA=ethylenediamino tetraacetic acid) and DTPA-(L-Trp, L-His) (DWH; DTPA=diethylenetriamino pentaacetic acid) were employed to dissect the static and transient physicochemical properties of hydrophobic/hydrophilic aromatic interactive modes surrounding biometal centers. The binding feature and identities of the stoichiometric metal-bound complexes in solution were investigated by using (1)H and (13)C NMR spectroscopy, which facilitated a cross-validation of the carboxylate, amide oxygen, and tertiary amino groups as the primary ligands and indole as the secondary ligand, with the imidazole (Im) N3 nitrogen being weakly bound to metals such as Ca(2+) owing to a multivalency effect. Surrounding the metal centers, the stereospecific orientation of aromatic rings in the diastereoisomerism is interpreted with the Ca(2+)-EWH complex. With respect to perturbed Trp side-chain rotamer heterogeneity, drastically restricted Trp side-chain flexibility and thus a dynamically constrained rotamer interconversion due to π interactions is evident from the site-selective (13)C NMR spectroscopic signal broadening of the Trp indolyl C3 atom. Furthermore, effects of Trp side-chain fluctuation on indole/Im orientation were the subject of a 2D NMR spectroscopy study by using the Ca(2+)-bound state; a C-H2(indolyl)/C-H5(Im(+)) connectivity observed in the NOESY spectra captured direct evidence that the N-H1 of the Ca(2+)-Im(+) unit interacted with the pyrrole ring of the indole unit in Ca(2+)-bound EWH but not in DWH, which is assignable to a moderately static, anomalous, T-shaped, interplanar π(+)-π stacking alignment. Nevertheless, a comparative (13)C NMR spectroscopy study of the two homologous scaffolds revealed that the overall response of the indole unit arises predominantly from global attractions between the indole ring and the entire positively charged first coordination sphere. The study thus demonstrates the coordination-sphere/geometry dependence of the Trp/His side-chain interplay, and established that π interactions allow (13)C NMR spectroscopy to offer a new window for investigating Trp rotamer heterogeneity near metal-binding centers.

Backbone flexibility controls the activity and specificity of a protein-protein interface: specificity in snake venom metalloproteases

Protein-protein interfaces have crucial functions in many biological processes. The large interaction areas of such interfaces show complex interaction motifs. Even more challenging is the understanding of (multi)specificity in protein-protein binding. Many proteins can bind several partners to mediate their function. A perfect paradigm to study such multispecific protein-protein interfaces are snake venom metalloproteases (SVMPs). Inherently, they bind to a variety of basement membrane proteins of capillaries, hydrolyze them, and induce profuse bleeding. However, despite having a high sequence homology, some SVMPs show a strong hemorrhagic activity, while others are (almost) inactive. We present computer simulations indicating that the activity to induce hemorrhage, and thus the capability to bind the potential reaction partners, is related to the backbone flexibility in a certain surface region. A subtle interplay between flexibility and rigidity of two loops seems to be the prerequisite for the proteins to carry out their damaging function. Presumably, a significant alteration in the backbone dynamics makes the difference between SVMPs that induce hemorrhage and the inactive ones.

Improving computational protein design by using structure-derived sequence profile

Designing a protein sequence that will fold into a predefined structure is of both practical and fundamental interest. Many successful, computational designs in the last decade resulted from improved understanding of hydrophobic and polar interactions between side chains of amino acid residues in stabilizing protein tertiary structures. However, the coupling between main-chain backbone structure and local sequence has yet to be fully addressed. Here, we attempt to account for such coupling by using a sequence profile derived from the sequences of five residue fragments in a fragment library that are structurally matched to the five-residue segments contained in a target structure. We further introduced a term to reduce low complexity regions of designed sequences. These two terms together with optimized reference states for amino-acid residues were implemented in the RosettaDesign program. The new method, called RosettaDesign-SR, makes a 12% increase (from 34 to 46%) in fraction of proteins whose designed sequences are more than 35% identical to wild-type sequences. Meanwhile, it reduces 8% (from 22% to 14%) to the number of designed sequences that are not homologous to any known protein sequences according to psi-blast. More importantly, the sequences designed by RosettaDesign-SR have 2-3% more polar residues at the surface and core regions of proteins and these surface and core polar residues have about 4% higher sequence identity to wild-type sequences than by RosettaDesign. Thus, the proteins designed by RosettaDesign-SR should be less likely to aggregate and more likely to have unique structures due to more specific polar interactions.

Conformational diversity and computational enzyme design

The application of computational protein design methods to the design of enzyme active sites offers potential routes to new catalysts and new reaction specificities. Computational design methods have typically treated the protein backbone as a rigid structure for the sake of computational tractability. However, this fixed-backbone approximation introduces its own special challenges for enzyme design and it contrasts with an emerging picture of natural enzymes as dynamic ensembles with multiple conformations and motions throughout a reaction cycle. This review considers the impact of conformational variation and dynamics on computational enzyme design and it highlights new approaches to addressing protein conformational diversity in enzyme design including recent advances in multi-state design, backbone flexibility, and computational library design.

Combined use of experimental and computational screens to characterize protein stability

One of the primary goals of protein design is to engineer proteins with improved stability. Protein stability is a key issue for chemical, biotechnology and pharmaceutical industries. The development of robust proteins/enzymes with the ability to withstand the potentially harsh conditions of industrial operations is of high importance. A number of strategies are currently being employed to achieve this goal. Two particular approaches, (i) directed evolution and (ii) computational protein design, are quite powerful yet have only recently been combined or applied and analyzed in parallel. In directed evolution, libraries of variants are searched experimentally for clones possessing the desired properties. With computational methods, protein design algorithms are utilized to perform in silico screening for stable protein sequences. Here, we used gene libraries of an unstable variant of streptococcal protein G (Gbeta1) and an in vivo screening method to identify stabilized variants. Many variants with notably increased thermal stabilities were isolated and characterized. Concomitantly, computational techniques and protein design algorithms were used to perform in silico screening of the same destabilized variant of Gbeta1. The combined use, and critical analysis, of these methods promises to advance the field of protein design.

Effect of the ordered interfacial water layer in protein complex formation: A nonlocal electrostatic approach

Using a nonlocal electrostatic approach that incorporates the short-range structure of the contacting media, we evaluated the electrostatic contribution to the energy of the complex formation of two model proteins. In this study, we have demonstrated that the existence of an ordered interfacial water layer at the protein-solvent interface reduces the charging energy of the proteins in the aqueous solvent, and consequently increases the electrostatic contribution to the protein binding (change in free energy upon the complex formation of two proteins). This is in contrast with the finding of the continuum electrostatic model, which suggests that electrostatic interactions are not strong enough to compensate for the unfavorable desolvation effects.

Computer-aided design of functional protein interactions

Predictive methods for the computational design of proteins search for amino acid sequences adopting desired structures that perform specific functions. Typically, design of 'function' is formulated as engineering new and altered binding activities into proteins. Progress in the design of functional protein-protein interactions is directed toward engineering proteins to precisely control biological processes by specifically recognizing desired interaction partners while avoiding competitors. The field is aiming for strategies to harness recent advances in high-resolution computational modeling-particularly those exploiting protein conformational variability-to engineer new functions and incorporate many functional requirements simultaneously.