Force Field Validation Using Protein Side Chain Prediction

Jacobson, Matthew P.; Kaminski, George A.; Friesner, Richard A.; Rapp, Chaya S.

doi:10.1021/jp021564n

Cited by 172 publications

(189 citation statements)

References 24 publications

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“…The all-atom OPLS force field was used to describe protein energetics (6), and the solvation free energy was estimated by using an implicit solvent model consisting of the surface-generalized Born model of polar solvation (7) and Levy's nonpolar estimator (8). The sampling of single side-chain conformations was accomplished by using a highly detailed (10°resolution) rotamer library (9). Because conformational states of most side chains were coupled to residues not subject to mutation, the conformations of all side chains within 5 Å of the mutated side chain were optimized at each step as well.…”

Section: Methodsmentioning

confidence: 99%

Computational prediction of native protein ligand-binding and enzyme active site sequences

Chakrabarti

Klibanov

Friesner

2005

Proc. Natl. Acad. Sci. U.S.A.

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Recent studies reveal that the core sequences of many proteins were nearly optimized for stability by natural evolution. Surface residues, by contrast, are not so optimized, presumably because protein function is mediated through surface interactions with other molecules. Here, we sought to determine the extent to which the sequences of protein ligand-binding and enzyme active sites could be predicted by optimization of scoring functions based on protein ligand-binding affinity rather than structural stability. Optimization of binding affinity under constraints on the folding free energy correctly predicted 83% of amino acid residues (94% similar) in the binding sites of two model receptor-ligand complexes, streptavidin-biotin and glucose-binding protein. To explore the applicability of this methodology to enzymes, we applied an identical algorithm to the active sites of diverse enzymes from the peptidase, ␤-gal, and nucleotide synthase families. Although simple optimization of binding affinity reproduced the sequences of some enzyme active sites with high precision, imposition of additional, geometric constraints on side-chain conformations based on the catalytic mechanism was required in other cases. With these modifications, our sequence optimization algorithm correctly predicted 78% of residues from all of the enzymes, with 83% similar to native (90% correct, with 95% similar, excluding residues with high variability in multiple sequence alignments). Furthermore, the conformations of the selected side chains were often correctly predicted within crystallographic error. These findings suggest that simple selection pressures may have played a predominant role in determining the sequences of ligand-binding and active sites in proteins.computational protein design ͉ enzyme design ͉ protein evolution A central goal of theoretical protein design is to understand the properties that define the space of amino acid sequences compatible with a given protein structure (1). Substantial progress has stemmed from the hypothesis that protein stability plays a major role in shaping these properties (2-4). Recent work has focused on the question of whether natural protein sequences have reached equilibrium within sequence space. Remarkably, this question has been answered largely in the affirmative for protein core sequences. Kuhlman and Baker (3) demonstrated, by using an extensive test set of protein backbones drawn from seven fold families, that more than half of core residues were predicted correctly simply by minimizing a folding free energy function in sequence space; moreover, the resulting sequence distributions closely reproduced those observed naturally. However, protein surface residues were not predicted correctly using this approach: Ͻ15% of all predicted surface residues matched the native residues. Jaramillo et al. (4) subsequently carried out a more extensive analysis of the degree of core vs. surface sequence optimization to examine whether the observed discrepancy might be caused by inadequacies in the pote...

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Section: Methodsmentioning

confidence: 99%

Computational prediction of native protein ligand-binding and enzyme active site sequences

Chakrabarti

Klibanov

Friesner

2005

Proc. Natl. Acad. Sci. U.S.A.

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Section: Methodsmentioning

confidence: 99%

“…The sampling of single side-chain conformations was accomplished primarily by using a highly detailed (10-deg resolution) rotamer library constructed from a database of 297 proteins. Computational expense was mitigated by prescreening rotamers by using only hard sphere overlap before energy evaluations (7,8).Enzyme-substrate binding affinities are computed by using an extensively developed semiempirical potential function, GLIDESCORE, that is sensitive to the delicate balance of interactions in enzyme active sites. GLIDESCORE consists of a lipophilic-lipophilic contact term and a hydrogen-bonding term separated into weighted components based on donor and acceptor charge, a Coulomb and van der Waals interaction term that reduces charges and van der Waals interaction energies from their gas-phase values, and a solvation model based on the computational introduction of explicit Abbreviations: TK, thymidine kinase; dT, deoxythymidine; dTMP, dT-5Ј-monophosphate; CK, cytidine kinase; MSA, multiple sequence alignment; rmsd, rms deviation.…”

mentioning

confidence: 99%

Sequence optimization and designability of enzyme active sites

Chakrabarti

Klibanov

Friesner

2005

Proc. Natl. Acad. Sci. U.S.A.

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We recently found that many residues in enzyme active sites can be computationally predicted by the optimization of scoring functions based on substrate binding affinity, subject to constraints on the geometry of catalytic residues and protein stability. Here, we explore the generality of this surprising observation. First, the impact of hydrogen-bonding networks necessary for catalysis on the accuracy of sequence optimization is assessed; incorporation of these networks, where relevant, into the set of catalytic constraints is found to be essential. Next, the impact of multiple substrate selectivity on sequence optimization is probed by carrying out independent calculations for complexes of deoxyribonucleoside kinases with various cognate ligands, revealing how simultaneous selection pressures determined active-site sequences of these enzymes. Including previous calculations on simpler enzymes, computational sequence optimization correctly predicts 76% of all active-site residues tested (86% correct, with 93% similar, for naturally conserved residues). In these studies, the ligand is fixed in its native conformation. To assess the applicability of these methods to de novo active-site design, the effect of small ligand motions around the native pose is also examined. Robustness of sequence accuracy for topologically similar poses is demonstrated for selected kinases, but not for a model peptidase. Based on these observations, we introduce the notion of the designability of an enzyme active site, a metric that may be used to guide the search for protein scaffolds suitable for the introduction of de novo activity for a desired chemical reaction.S ince it was first observed that all known natural proteins adopt one of only Ϸ1,000 distinct protein folds (1), researchers working in the field of computational protein design have endeavored to understand the basis of this biophysical phenomenon and apply the resultant knowledge to the construction of unnatural proteins. Motivated by the observation that certain folds are associated with more natural sequences than others, the principle of fold designability (the number of unique sequences compatible with the backbone structure of the fold) was introduced (1). To place the problem on a mathematical foundation, an analogy has been drawn between sequence space and phase space in statistical physics, where the backbone structure represents a macroscopic state of the system and distinct sequences are microscopic states whose occupation of the macroscopic state is associated with an energy, the folding free energy of the protein. In this language, a fold's designability is correlated with its sequence entropy (2).Recently, we explored the extension of the concept of equilibrium in sequence space from protein cores to the functional surface residues of proteins, specifically ligand binding sites and enzyme active sites (3). The findings of this work strongly suggested that the enzyme-substrate binding affinity, subject to constraints on the total protein energy and conformati...

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“…All nonbonded interactions between pairs of atoms within 20 Å of each other were calculated. The OPLS torsional energy parameters were recently refined by using high-level quantum chemical calculations 26 and validated by using protein sidechain prediction 24 ; the updated parameters are used here. The solvation free energy was estimated by using an implicit solvent model consisting of the Surface Generalized Born (SGB) model of polar solvation 28 and a nonpolar estimator developed by Gallicchio and coworkers.…”

Section: Details Of Energy Functionmentioning

confidence: 99%

A hierarchical approach to all‐atom protein loop prediction

et al. 2004

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The application of all-atom force fields (and explicit or implicit solvent models) to protein homology-modeling tasks such as side-chain and loop prediction remains challenging both because of the expense of the individual energy calculations and because of the difficulty of sampling the rugged all-atom energy surface. Here we address this challenge for the problem of loop prediction through the development of numerous new algorithms, with an emphasis on multiscale and hierarchical techniques. As a first step in evaluating the performance of our loop prediction algorithm, we have applied it to the problem of reconstructing loops in native structures; we also explicitly include crystal packing to provide a fair comparison with crystal structures. In brief, large numbers of loops are generated by using a dihedral angle-based buildup procedure followed by iterative cycles of clustering, side-chain optimization, and complete energy minimization of selected loop structures. We evaluate this method by using the largest test set yet used for validation of a loop prediction method, with a total of 833 loops ranging from 4 to 12 residues in length. Average/median backbone rootmean-square deviations (RMSDs) to the native structures (superimposing the body of the protein, not the loop itself) are 0.42/0.24 Å for 5 residue loops, 1.00/0.44 Å for 8 residue loops, and 2.47/1.83 Å for 11 residue loops. Median RMSDs are substantially lower than the averages because of a small number of outliers; the causes of these failures are examined in some detail, and many can be attributed to errors in assignment of protonation states of titratable residues, omission of ligands from the simulation, and, in a few cases, probable errors in the experimentally determined structures. When these obvious problems in the data sets are filtered out, average RMSDs to the native structures improve to 0.43 Å for 5 residue loops, 0.84 Å for 8 residue loops, and 1.63 Å for 11 residue loops. In the vast majority of cases, the method locates energy minima that are lower than or equal to that of the minimized native loop, thus indicating that sampling rarely limits prediction accuracy. The overall results are, to our knowledge, the best reported to date, and we attribute this success to the combination of an accurate all-atom energy function, efficient methods for loop buildup and side-chain optimization, and, especially for the longer loops, the hierarchical refinement protocol.

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Force Field Validation Using Protein Side Chain Prediction

Cited by 172 publications

References 24 publications

Computational prediction of native protein ligand-binding and enzyme active site sequences

Computational prediction of native protein ligand-binding and enzyme active site sequences

Sequence optimization and designability of enzyme active sites

A hierarchical approach to all‐atom protein loop prediction

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