Electrostatics in computational protein design

Uncovering the properties of energy-weighted conformation space networks with a hydrophobic-hydrophilic model

The conformation spaces generated by short hydrophobic-hydrophilic (HP) lattice chains are mapped to conformation space networks (CSNs). The vertices (nodes) of the network are the conformations and the links are the transitions between them. It has been found that these networks have "small-world" properties without considering the interaction energy of the monomers in the chain, i. e. the hydrophobic or hydrophilic amino acids inside the chain. When the weight based on the interaction energy of the monomers in the chain is added to the CSNs, it is found that the weighted networks show the "scale-free" characteristic. In addition, it reveals that there is a connection between the scale-free property of the weighted CSN and the folding dynamics of the chain by investigating the relationship between the scale-free structure of the weighted CSN and the noted parameter Z score. Moreover, the modular (community) structure of weighted CSNs is also studied. These results are helpful to understand the topological properties of the CSN and the underlying free-energy landscapes.

Solvent accessible surface area approximations for rapid and accurate protein structure prediction

The burial of hydrophobic amino acids in the protein core is a driving force in protein folding. The extent to which an amino acid interacts with the solvent and the protein core is naturally proportional to the surface area exposed to these environments. However, an accurate calculation of the solvent-accessible surface area (SASA), a geometric measure of this exposure, is numerically demanding as it is not pair-wise decomposable. Furthermore, it depends on a full-atom representation of the molecule. This manuscript introduces a series of four SASA approximations of increasing computational complexity and accuracy as well as knowledge-based environment free energy potentials based on these SASA approximations. Their ability to distinguish correctly from incorrectly folded protein models is assessed to balance speed and accuracy for protein structure prediction. We find the newly developed “Neighbor Vector” algorithm provides the most optimal balance of accurate yet rapid exposure measures.

Computational structure-based redesign of enzyme activity

We report a computational, structure-based redesign of the phenylalanine adenylation domain of the nonribosomal peptide synthetase enzyme gramicidin S synthetase A (GrsA-PheA) for a set of noncognate substrates for which the wild-type enzyme has little or virtually no specificity. Experimental validation of a set of top-ranked computationally predicted enzyme mutants shows significant improvement in the specificity for the target substrates. We further present enhancements to the methodology for computational enzyme redesign that are experimentally shown to result in significant additional improvements in the target substrate specificity. The mutant with the highest activity for a noncognate substrate exhibits 1/6 of the wild-type enzyme/wild-type substrate activity, further confirming the feasibility of our computational approach. Our results suggest that structure-based protein design can identify active mutants different from those selected by evolution.

Analysis of oligomeric proteins during unfolding by pH and temperature

During thermal transition and variation of pH, structural properties of 35 proteins and their complexes (bound with substrate and co-factor) were analyzed in detail. During pH alteration, these proteins were shown to have substantial differences in conformations. pH conformers were analyzed in detail. Free energy and other energy parameters were also estimated for these proteins at various pH and temperatures. Detailed structural analysis and binding interfaces of various substrates, inhibitors and cofactor of these proteins were also investigated using docking and molecular dynamic simulation.

Rational stabilization of enzymes by computational redesign of surface charge-charge interactions

Here, we report the application of a computational approach that allows the rational design of enzymes with enhanced thermostability while retaining full enzymatic activity. The approach is based on the optimization of the energy of charge-charge interactions on the protein surface. We experimentally tested the validity of the approach on 2 human enzymes, acylphosphatase (AcPh) and Cdc42 GTPase, that differ in size (98 vs. 198-aa residues, respectively) and tertiary structure. We show that the designed proteins are significantly more stable than the corresponding WT proteins. The increase in stability is not accompanied by significant changes in structure, oligomerization state, or, most importantly, activity of the designed AcPh or Cdc42. This success of the design methodology suggests that it can be universally applied to other enzymes, on its own or in combination with the other strategies based on redesign of the interactions in the protein core.

Effect of calcium on electrical energy transfer by microtubules

Microtubules (MTs) are important cytoskeletal superstructures implicated in neuronal morphology and function, which are involved in vesicle trafficking, neurite formation and differentiation and other morphological changes. The structural and functional properties of MTs depend on their high intrinsic charge density and functional regulation by the MT depolymerising properties of changes in Ca(2 + ) concentration. Recently, we reported on remarkable properties of isolated MTs, which behave as biomolecular transistors capable of amplifying electrical signals (Priel et al., Biophys J 90:4639-4643, 2006). Here, we demonstrate that MT-bathing (cytoplasmic) Ca(2 + ) concentrations modulate the electrodynamic properties of MTs. Electrical amplification by MTs was exponentially dependent on the Ca(2 + ) concentration between 10( - 7) and 10( - 2) M. However, the electrical connectivity (coupling) of MTs was optimal at a narrower window of Ca(2 + ) concentrations. We observed that while raising bathing Ca(2 + ) concentration increased electrical amplification by MTs, energy transfer was highest in the presence of ethylene glycol tetraacetic acid (lowest Ca(2 + ) concentration). Our data indicate that Ca(2 + ) is an important modulator of electrical amplification by MTs, supporting the hypothesis that this divalent cation, which adsorbs onto the polymer's surface, plays an important role as a regulator of the electrical properties of MTs. The Ca(2 + )-dependent ability of MTs to modulate and amplify electrical signals may provide a novel means of cell signaling, likely contributing to neuronal function.

Algorithm for backrub motions in protein design

Motivation: The Backrub is a small but kinematically efficient side-chain-coupled local backbone motion frequently observed in atomic-resolution crystal structures of proteins. A backrub shifts the C_α–C_β orientation of a given side-chain by rigid-body dipeptide rotation plus smaller individual rotations of the two peptides, with virtually no change in the rest of the protein. Backrubs can therefore provide a biophysically realistic model of local backbone flexibility for structure-based protein design. Previously, however, backrub motions were applied via manual interactive model-building, so their incorporation into a protein design algorithm (a simultaneous search over mutation and backbone/side-chain conformation space) was infeasible.

Results: We present a combinatorial search algorithm for protein design that incorporates an automated procedure for local backbone flexibility via backrub motions. We further derive a dead-end elimination (DEE)-based criterion for pruning candidate rotamers that, in contrast to previous DEE algorithms, is provably accurate with backrub motions. Our backrub-based algorithm successfully predicts alternate side-chain conformations from ≤0.9 Å resolution structures, confirming the suitability of the automated backrub procedure. Finally, the application of our algorithm to redesign two different proteins is shown to identify a large number of lower-energy conformations and mutation sequences that would have been ignored by a rigid-backbone model.

Availability: Contact authors for source code.

Contact:brd+ismb08@cs.duke.edu

An improved pairwise decomposable finite-difference Poisson-Boltzmann method for computational protein design

Our goal is to develop accurate electrostatic models that can be implemented in current computational protein design protocols. To this end, we improve upon a previously reported pairwise decomposable, finite difference Poisson-Boltzmann (FDPB) model for protein design (Marshall et al., Protein Sci 2005, 14, 1293). The improvement involves placing generic sidechains at positions with unknown amino acid identity and explicitly capturing two-body perturbations to the dielectric environment. We compare the original and improved FDPB methods to standard FDPB calculations in which the dielectric environment is completely determined by protein atoms. The generic sidechain approach yields a two to threefold increase in accuracy per residue or residue pair over the original pairwise FDPB implementation, with no additional computational cost. Distance dependent dielectric and solvent-exclusion models were also compared with standard FDPB energies. The accuracy of the new pairwise FDPB method is shown to be superior to these models, even after reparameterization of the solvent-exclusion model.

Characterization of protein-protein interfaces

We analyze the characteristics of protein-protein interfaces using the largest datasets available from the Protein Data Bank (PDB). We start with a comparison of interfaces with protein cores and non-interface surfaces. The results show that interfaces differ from protein cores and non-interface surfaces in residue composition, sequence entropy, and secondary structure. Since interfaces, protein cores, and non-interface surfaces have different solvent accessibilities, it is important to investigate whether the observed differences are due to the differences in solvent accessibility or differences in functionality. We separate out the effect of solvent accessibility by comparing interfaces with a set of residues having the same solvent accessibility as the interfaces. This strategy reveals residue distribution propensities that are not observable by comparing interfaces with protein cores and non-interface surfaces. Our conclusions are that there are larger numbers of hydrophobic residues, particularly aromatic residues, in interfaces, and the interactions apparently favored in interfaces include the opposite charge pairs and hydrophobic pairs. Surprisingly, Pro-Trp pairs are over represented in interfaces, presumably because of favorable geometries. The analysis is repeated using three datasets having different constraints on sequence similarity and structure quality. Consistent results are obtained across these datasets. We have also investigated separately the characteristics of heteromeric interfaces and homomeric interfaces.

Solvent reaction field potential inside an uncharged globular protein: a bridge between implicit and explicit solvent models?

The solvent reaction field potential of an uncharged protein immersed in simple point charge/extended explicit solvent was computed over a series of molecular dynamics trajectories, in total 1560 ns of simulation time. A finite, positive potential of 13-24 kbTec(-1) (where T=300 K), dependent on the geometry of the solvent-accessible surface, was observed inside the biomolecule. The primary contribution to this potential arose from a layer of positive charge density 1.0 A from the solute surface, on average 0.008 ec/A3, which we found to be the product of a highly ordered first solvation shell. Significant second solvation shell effects, including additional layers of charge density and a slight decrease in the short-range solvent-solvent interaction strength, were also observed. The impact of these findings on implicit solvent models was assessed by running similar explicit solvent simulations on the fully charged protein system. When the energy due to the solvent reaction field in the uncharged system is accounted for, correlation between per-atom electrostatic energies for the explicit solvent model and a simple implicit (Poisson) calculation is 0.97, and correlation between per-atom energies for the explicit solvent model and a previously published, optimized Poisson model is 0.99.

Computationally mapping sequence space to understand evolutionary protein engineering

Evolutionary protein engineering has been dramatically successful, producing a wide variety of new proteins with altered stability, binding affinity, and enzymatic activity. However, the success of such procedures is often unreliable, and the impact of the choice of protein, engineering goal, and evolutionary procedure is not well understood. We have created a framework for understanding aspects of the protein engineering process by computationally mapping regions of feasible sequence space for three small proteins using structure-based design protocols. We then tested the ability of different evolutionary search strategies to explore these sequence spaces. The results point to a non-intuitive relationship between the error-prone PCR mutation rate and the number of rounds of replication. The evolutionary relationships among feasible sequences reveal hub-like sequences that serve as particularly fruitful starting sequences for evolutionary search. Moreover, genetic recombination procedures were examined, and tradeoffs relating sequence diversity and search efficiency were identified. This framework allows us to consider the impact of protein structure on the allowed sequence space and therefore on the challenges that each protein presents to error-prone PCR and genetic recombination procedures.

An object-oriented library for computational protein design

Recent advances in computational protein design have established it as a viable technique for the rational generation of stable protein sequences, novel protein folds, and even enzymatic activity. We present a new and object-oriented library of code, written specifically for protein design applications in C(++), called EGAD Library. The modular fashion in which this library is written allows developers to tailor various energy functions and minimizers for a specific purpose. It also allows for the generation of novel protein design applications with a minimal amount of code investment. It is our hope that this will permit labs that have not considered protein design to apply it to their own systems, thereby increasing its potential as a tool in biology. We also present various uses of EGAD Library: in the development of Interaction Viewer, a PyMOL plug-in for viewing interactions between protein residues; in the repacking of protein cores; and in the prediction of protein-protein complex stabilities.

Treatment of charge singularities in implicit solvent models

This paper presents a novel method for solving the Poisson-Boltzmann (PB) equation based on a rigorous treatment of geometric singularities of the dielectric interface and a Green's function formulation of charge singularities. Geometric singularities, such as cusps and self-intersecting surfaces, in the dielectric interfaces are bottleneck in developing highly accurate PB solvers. Based on an advanced mathematical technique, the matched interface and boundary (MIB) method, we have recently developed a PB solver by rigorously enforcing the flux continuity conditions at the solvent-molecule interface where geometric singularities may occur. The resulting PB solver, denoted as MIBPB-II, is able to deliver second order accuracy for the molecular surfaces of proteins. However, when the mesh size approaches half of the van der Waals radius, the MIBPB-II cannot maintain its accuracy because the grid points that carry the interface information overlap with those that carry distributed singular charges. In the present Green's function formalism, the charge singularities are transformed into interface flux jump conditions, which are treated on an equal footing as the geometric singularities in our MIB framework. The resulting method, denoted as MIBPB-III, is able to provide highly accurate electrostatic potentials at a mesh as coarse as 1.2 A for proteins. Consequently, at a given level of accuracy, the MIBPB-III is about three times faster than the APBS, a recent multigrid PB solver. The MIBPB-III has been extensively validated by using analytically solvable problems, molecular surfaces of polyatomic systems, and 24 proteins. It provides reliable benchmark numerical solutions for the PB equation.

Evaluation of the influence of the internal aqueous solvent structure on electrostatic interactions at the protein-solvent interface by nonlocal continuum electrostatic approach

The dielectric properties of the polar solvent on the protein-solvent interface at small intercharge distances are still poorly explored. To deconvolute this problem and to evaluate the pair-wise electrostatic interaction (PEI) energies of the point charges located at the protein-solvent interface we used a nonlocal (NL) electrostatic approach along with a static NL dielectric response function of water. The influence of the aqueous solvent microstructure (determined by a strong nonelectrostatic correlation effect between water dipoles within the orientational Debye polarization mode) on electrostatic interactions at the interface was studied in our work. It was shown that the PEI energies can be significantly higher than the energies evaluated by the classical (local) consideration, treating water molecules as belonging to the bulk solvent with a high dielectric constant. Our analysis points to the existence of a rather extended, effective low-dielectric interfacial water shell on the protein surface. The main dielectric properties of this shell (effective thickness together with distance- and orientation-dependent dielectric permittivity function) were evaluated. The dramatic role of this shell was demonstrated when estimating the protein association rate constants.

Polarizable Atomic Multipole Solutes in a Generalized Kirkwood Continuum

The generalized Born (GB) model of continuum electrostatics is an analytic approximation to the Poisson equation useful for predicting the electrostatic component of the solvation free energy for solutes ranging in size from small organic molecules to large macromolecular complexes. This work presents a new continuum electrostatics model based on Kirkwood's analytic result for the electrostatic component of the solvation free energy for a solute with arbitrary charge distribution. Unlike GB, which is limited to monopoles, our generalized Kirkwood (GK) model can treat solute electrostatics represented by any combination of permanent and induced atomic multipole moments of arbitrary degree. Here we apply the GK model to the newly developed Atomic Multipole Optimized Energetics for Biomolecular Applications (AMOEBA) force field, which includes permanent atomic multipoles through the quadrupole and treats polarization via induced dipoles. A derivation of the GK gradient is presented, which enables energy minimization or molecular dynamics of an AMOEBA solute within a GK continuum. For a series of 55 proteins, GK electrostatic solvation free energies are compared to the Polarizable Multipole Poisson-Boltzmann (PMPB) model and yield a mean unsigned relative difference of 0.9%. Additionally, the reaction field of GK compares well to that of the PMPB model, as shown by a mean unsigned relative difference of 2.7% in predicting the total solvated dipole moment for each protein in this test set. The CPU time needed for GK relative to vacuum AMOEBA calculations is approximately a factor of 3, making it suitable for applications that require significant sampling of configuration space.

Treatment of geometric singularities in implicit solvent models

Geometric singularities, such as cusps and self-intersecting surfaces, are major obstacles to the accuracy, convergence, and stability of the numerical solution of the Poisson-Boltzmann (PB) equation. In earlier work, an interface technique based PB solver was developed using the matched interface and boundary (MIB) method, which explicitly enforces the flux jump condition at the solvent-solute interfaces and leads to highly accurate biomolecular electrostatics in continuum electric environments. However, such a PB solver, denoted as MIBPB-I, cannot maintain the designed second order convergence whenever there are geometric singularities, such as cusps and self-intersecting surfaces. Moreover, the matrix of the MIBPB-I is not optimally symmetrical, resulting in the convergence difficulty. The present work presents a new interface method based PB solver, denoted as MIBPB-II, to address the aforementioned problems. The present MIBPB-II solver is systematical and robust in treating geometric singularities and delivers second order convergence for arbitrarily complex molecular surfaces of proteins. A new procedure is introduced to make the MIBPB-II matrix optimally symmetrical and diagonally dominant. The MIBPB-II solver is extensively validated by the molecular surfaces of few-atom systems and a set of 24 proteins. Converged electrostatic potentials and solvation free energies are obtained at a coarse grid spacing of 0.5 A and are considerably more accurate than those obtained by the PBEQ and the APBS at finer grid spacings.

Optimization of electrostatic interactions in protein-protein complexes

In this article, we present a statistical analysis of the electrostatic properties of 298 protein-protein complexes and 356 domain-domain structures extracted from the previously developed database of protein complexes (ProtCom, http://www.ces.clemson.edu/compbio/protcom). For each structure in the dataset we calculated the total electrostatic energy of the binding and its two components, Coulombic and reaction field energy. It was found that in a vast majority of the cases (>90%), the total electrostatic component of the binding energy was unfavorable. At the same time, the Coulombic component of the binding energy was found to favor the complex formation while the reaction field component of the binding energy opposed the binding. It was also demonstrated that the components in a wild-type (WT) structure are optimized/anti-optimized with respect to the corresponding distributions, arising from random shuffling of the charged side chains. The degree of this optimization was assessed through the Z-score of WT energy in respect to the random distribution. It was found that the Z-scores of Coulombic interactions peak at a considerably negative value for all 654 cases considered while the Z-score of the reaction field energy varied among different types of complexes. All these findings indicate that the Coulombic interactions within WT protein-protein complexes are optimized to favor the complex formation while the total electrostatic energy predominantly opposes the binding. This observation was used to discriminate WT structures among sets of structural decoys and showed that the electrostatic component of the binding energy is not a good discriminator of the WT; while, Coulombic or reaction field energies perform better depending upon the decoy set used.

Progress in computational protein design

Current progress in computational structure-based protein design is reviewed in the areas of methodology and applications. Foundational advances include new potential functions, more efficient ways of computing energetics, flexible treatments of solvent, and useful energy function approximations, as well as ensemble-based approaches to scoring designs for inclusion of entropic effects, improvements to guaranteed and to stochastic search techniques, and methods to design combinatorial libraries for screening and selection. Applications include new approaches and successes in the design of specificity for protein folding, binding, and catalysis, in the redesign of proteins for enhanced binding affinity, and in the application of design technology to study and alter enzyme catalysis. Computational protein design continues to mature and advance.

Polarizable atomic multipole solutes in a Poisson-Boltzmann continuum

Modeling the change in the electrostatics of organic molecules upon moving from vacuum into solvent, due to polarization, has long been an interesting problem. In vacuum, experimental values for the dipole moments and polarizabilities of small, rigid molecules are known to high accuracy; however, it has generally been difficult to determine these quantities for a polar molecule in water. A theoretical approach introduced by Onsager [J. Am. Chem. Soc. 58, 1486 (1936)] used vacuum properties of small molecules, including polarizability, dipole moment, and size, to predict experimentally known permittivities of neat liquids via the Poisson equation. Since this important advance in understanding the condensed phase, a large number of computational methods have been developed to study solutes embedded in a continuum via numerical solutions to the Poisson-Boltzmann equation. Only recently have the classical force fields used for studying biomolecules begun to include explicit polarization in their functional forms. Here the authors describe the theory underlying a newly developed polarizable multipole Poisson-Boltzmann (PMPB) continuum electrostatics model, which builds on the atomic multipole optimized energetics for biomolecular applications (AMOEBA) force field. As an application of the PMPB methodology, results are presented for several small folded proteins studied by molecular dynamics in explicit water as well as embedded in the PMPB continuum. The dipole moment of each protein increased on average by a factor of 1.27 in explicit AMOEBA water and 1.26 in continuum solvent. The essentially identical electrostatic response in both models suggests that PMPB electrostatics offers an efficient alternative to sampling explicit solvent molecules for a variety of interesting applications, including binding energies, conformational analysis, and pK(a) prediction. Introduction of 150 mM salt lowered the electrostatic solvation energy between 2 and 13 kcalmole, depending on the formal charge of the protein, but had only a small influence on dipole moments.

Nonnatural protein-protein interaction-pair design by key residues grafting

Protein-protein interface design is one of the most exciting fields in protein science; however, designing nonnatural protein-protein interaction pairs remains difficult. In this article we report a de novo design of a nonnatural protein-protein interaction pair by scanning the Protein Data Bank for suitable scaffold proteins that can be used for grafting key interaction residues and can form stable complexes with the target protein after additional mutations. Using our design algorithm, an unrelated protein, rat PLCdelta(1)-PH (pleckstrin homology domain of phospholipase C-delta1), was successfully designed to bind the human erythropoietin receptor (EPOR) after grafting the key interaction residues of human erythropoietin binding to EPOR. The designed mutants of rat PLCdelta(1)-PH were expressed and purified to test their binding affinities with EPOR. A designed triple mutation of PLCdelta(1)-PH (ERPH1) was found to bind EPOR with high affinity (K(D) of 24 nM and an IC(50) of 5.7 microM) both in vitro and in a cell-based assay, respectively, although the WT PLCdelta(1)-PH did not show any detectable binding under the assay conditions. The in vitro binding affinities of the PLCdelta(1)-PH mutants correlate qualitatively to the computational binding affinities, validating the design and the protein-protein interaction model. The successful practice of finding a proper protein scaffold and making it bind with EPOR demonstrates a prospective application in protein engineering targeting protein-protein interfaces.

Combinatorial methods for small-molecule placement in computational enzyme design

The incorporation of small-molecule transition state structures into protein design calculations poses special challenges because of the need to represent the added translational, rotational, and conformational freedoms within an already difficult optimization problem. Successful approaches to computational enzyme design have focused on catalytic side-chain contacts to guide placement of small molecules in active sites. We describe a process for modeling small molecules in enzyme design calculations that extends previously described methods, allowing favorable small-molecule positions and conformations to be explored simultaneously with sequence optimization. Because all current computational enzyme design methods rely heavily on sampling of possible active site geometries from discrete conformational states, we tested the effects of discretization parameters on calculation results. Rotational and translational step sizes as well as side-chain library types were varied in a series of computational tests designed to identify native-like binding contacts in three natural systems. We find that conformational parameters, especially the type of rotamer library used, significantly affect the ability of design calculations to recover native binding-site geometries. We describe the construction and use of a crystallographic conformer library and find that it more reliably captures active-site geometries than traditional rotamer libraries in the systems tested.

A Consensus Data Mining secondary structure prediction by combining GOR V and Fragment Database Mining

The major aim of tertiary structure prediction is to obtain protein models with the highest possible accuracy. Fold recognition, homology modeling, and de novo prediction methods typically use predicted secondary structures as input, and all of these methods may significantly benefit from more accurate secondary structure predictions. Although there are many different secondary structure prediction methods available in the literature, their cross-validated prediction accuracy is generally <80%. In order to increase the prediction accuracy, we developed a novel hybrid algorithm called Consensus Data Mining (CDM) that combines our two previous successful methods: (1) Fragment Database Mining (FDM), which exploits the Protein Data Bank structures, and (2) GOR V, which is based on information theory, Bayesian statistics, and multiple sequence alignments (MSA). In CDM, the target sequence is dissected into smaller fragments that are compared with fragments obtained from related sequences in the PDB. For fragments with a sequence identity above a certain sequence identity threshold, the FDM method is applied for the prediction. The remainder of the fragments are predicted by GOR V. The results of the CDM are provided as a function of the upper sequence identities of aligned fragments and the sequence identity threshold. We observe that the value 50% is the optimum sequence identity threshold, and that the accuracy of the CDM method measured by Q(3) ranges from 67.5% to 93.2%, depending on the availability of known structural fragments with sufficiently high sequence identity. As the Protein Data Bank grows, it is anticipated that this consensus method will improve because it will rely more upon the structural fragments.

A Monte Carlo sampling method of amino acid sequences adaptable to given main-chain atoms in the proteins

We have developed a computational method of protein design to detect amino acid sequences that are adaptable to given main-chain coordinates of a protein. In this method, the selection of amino acid types employs a Metropolis Monte Carlo method with a scoring function in conjunction with the approximation of free energies computed from 3D structures. To compute the scoring function, a side-chain prediction using another Metropolis Monte Carlo method was performed to select structurally suitable side-chain conformations from a side-chain library. In total, two layers of Monte Carlo procedures were performed, first to select amino acid types (1st layer Monte Carlo) and then to predict side-chain conformations (2nd layers Monte Carlo). We applied this method to sequence design for the entire sequence on the SH3 domain, Protein G, and BPTI. The predicted sequences were similar to those of the wild-type proteins. We compared the results of the predictions with and without the 2nd layer Monte Carlo method. The results revealed that the two-layer Monte Carlo method produced better sequence similarity to the wild-type proteins than the one-layer method. Finally, we applied this method to neuraminidase of influenza virus. The results were consistent with the sequences identified from the isolated viruses.

Ligand interactions with lactose repressor protein and the repressor-operator complex: the effects of ionization and oligomerization on binding

Specific interactions between proteins and ligands that modify their functions are crucial in biology. Here, we examine sugars that bind the lactose repressor protein (LacI) and modify repressor affinity for operator DNA using isothermal titration calorimetry and equilibrium DNA binding experiments. High affinity binding of the commonly-used inducer isopropyl-beta,D-thiogalactoside is strongly driven by enthalpic forces, whereas inducer 2-phenylethyl-beta,D-galactoside has weaker affinity with low enthalpic contributions. Perturbing the dimer interface with either pH or oligomeric state shows that weak inducer binding is sensitive to changes in this distant region. Effects of the neutral compound o-nitrophenyl-beta,D-galactoside are sensitive to oligomerization, and at elevated pH this compound converts to an anti-inducer ligand with slightly enhanced enthalpic contributions to the binding energy. Anti-inducer o-nitrophenyl-beta,D-fucoside exhibits slightly enhanced affinity and increased enthalpic contributions at elevated pH. Collectively, these results both demonstrate the range of energetic consequences that occur with LacI binding to structurally-similar ligands and expand our insight into the link between effector binding and structural changes at the subunit interface.

Electrostatics calculations: latest methodological advances

Electrostatics plays a major role in the stabilization and function of biomolecules; as such, it remains a major focus of theoretical and computational studies of macromolecules. Electrostatic interactions are long range, and strongly dependent on the solvent and ions surrounding the biomolecule under study. During the past year, progress has been reported in the treatment of electrostatics in explicit and implicit solvent models. Interesting new developments of explicit solvent models include more efficient Ewald summation methods, as well as alternative approaches based on reaction field theory, periodic images and Euler summations. Implicit solvent models remain divided into those that solve the Poisson-Boltzmann equation numerically and those based on the generalized Born formalism. Both approaches are now included in molecular dynamics simulations and their accuracies may be assessed by direct comparison against experimental data. It is worth mentioning the recent development of web interfaces that facilitate access to and usage of existing tools for computing electrostatic interactions.