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The inflammatory chemokine CCL5, which binds the chemokine receptor CCR5 in a two-step mechanism so as to activate signaling pathways in hematopoetic cells, plays an important role in immune surveillance, inflammation, and development as well as in several immune system pathologies. The recently published crystal structure of CCR5 bound to a high-affinity variant of CCL5 lacks the N-terminal segment of the receptor that is post-translationally sulfated and is known to be important for high-affinity binding. Here, we report the NMR solution structure of monomeric CCL5 bound to a synthetic doubly sulfated peptide corresponding to the missing first 27 residues of CCR5. Our structures show that two sulfated tyrosine residues, sY10 and sY14, as well as the unsulfated Y15 form a network of strong interactions with a groove on a surface of CCL5 that is formed from evolutionarily conserved basic and hydrophobic amino acids. We then use our NMR structures, in combination with available crystal data, to create an atomic model of full-length wild-type CCR5:CCL5. Our findings reveal the structural determinants involved in the recognition of CCL5 by the CCR5 N terminus. These findings, together with existing structural data, provide a complete structural framework with which to understand the specificity of receptor:chemokine interactions., Database: Structural data are available in the PDB under the accession number 6FGP., (© 2018 Federation of European Biochemical Societies.)

Motivation: Structural knowledge, extracted from the Protein Data Bank (PDB), underlies numerous potential functions and prediction methods. The PDB, however, is highly biased: many proteins have more than one entry, while entire protein families are represented by a single structure, or even not at all. The standard solution to this problem is to limit the studies to non-redundant subsets of the PDB. While alleviating biases, this solution hides the many-to-many relations between sequences and structures. That is, non-redundant datasets conceal the diversity of sequences that share the same fold and the existence of multiple conformations for the same protein. A particularly disturbing aspect of non-redundant subsets is that they hardly benefit from the rapid pace of protein structure determination, as most newly solved structures fall within existing families., Results: In this study we explore the concept of redundancy-weighted datasets, originally suggested by Miyazawa and Jernigan. Redundancy-weighted datasets include all available structures and associate them (or features thereof) with weights that are inversely proportional to the number of their homologs. Here, we provide the first systematic comparison of redundancy-weighted datasets with non-redundant ones. We test three weighting schemes and show that the distributions of structural features that they produce are smoother (having higher entropy) compared with the distributions inferred from non-redundant datasets. We further show that these smoothed distributions are both more robust and more correct than their non-redundant counterparts. We suggest that the better distributions, inferred using redundancy-weighting, may improve the accuracy of knowledge-based potentials and increase the power of protein structure prediction methods. Consequently, they may enhance model-driven molecular biology., (© The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.)

In eukaryotes, CCT is essential for the correct and efficient folding of many cytosolic proteins, most notably actin and tubulin. Structural studies of CCT have been hindered by the failure of standard crystallographic analysis to resolve its eight different subunit types at low resolutions. Here, we exhaustively assess the R value fit of all possible CCT models to available crystallographic data of the closed and open forms with resolutions of 3.8 Å and 5.5 Å, respectively. This unbiased analysis finds the native subunit arrangements with overwhelming significance. The resulting structures provide independent crystallographic proof of the subunit arrangement of CCT and map major asymmetrical features of the particle onto specific subunits. The actin and tubulin substrates both bind around subunit CCT6, which shows other structural anomalies. CCT is thus clearly partitioned, both functionally and evolutionary, into a substrate-binding side that is opposite to the ATP-hydrolyzing side., (Copyright © 2013 Elsevier Ltd. All rights reserved.)

The KoBaMIN web server provides an online interface to a simple, consistent and computationally efficient protein structure refinement protocol based on minimization of a knowledge-based potential of mean force. The server can be used to refine either a single protein structure or an ensemble of proteins starting from their unrefined coordinates in PDB format. The refinement method is particularly fast and accurate due to the underlying knowledge-based potential derived from structures deposited in the PDB; as such, the energy function implicitly includes the effects of solvent and the crystal environment. Our server allows for an optional but recommended step that optimizes stereochemistry using the MESHI software. The KoBaMIN server also allows comparison of the refined structures with a provided reference structure to assess the changes brought about by the refinement protocol. The performance of KoBaMIN has been benchmarked widely on a large set of decoys, all models generated at the seventh worldwide experiments on critical assessment of techniques for protein structure prediction (CASP7) and it was also shown to produce top-ranking predictions in the refinement category at both CASP8 and CASP9, yielding consistently good results across a broad range of model quality values. The web server is fully functional and freely available at http://csb.stanford.edu/kobamin.

Protein structure refinement is an important but unsolved problem; it must be solved if we are to predict biological function that is very sensitive to structural details. Specifically, critical assessment of techniques for protein structure prediction (CASP) shows that the accuracy of predictions in the comparative modeling category is often worse than that of the template on which the homology model is based. Here we describe a refinement protocol that is able to consistently refine submitted predictions for all categories at CASP7. The protocol uses direct energy minimization of the knowledge-based potential of mean force that is based on the interaction statistics of 167 atom types (Summa and Levitt, Proc Natl Acad Sci USA 2007; 104:3177-3182). Our protocol is thus computationally very efficient; it only takes a few minutes of CPU time to run typical protein models (300 residues). We observe an average structural improvement of 1% in GDT_TS, for predictions that have low and medium homology to known PDB structures (Global Distance Test score or GDT_TS between 50 and 80%). We also observe a marked improvement in the stereochemistry of the models. The level of improvement varies amongst the various participants at CASP, but we see large improvements (>10% increase in GDT_TS) even for models predicted by the best performing groups at CASP7. In addition, our protocol consistently improved the best predicted models in the refinement category at CASP7 and CASP8. These improvements in structure and stereochemistry prove the usefulness of our computationally inexpensive, powerful and automatic refinement protocol., (Copyright 2010 Wiley-Liss, Inc.)

Current methods for predicting protein structure depend on two interrelated components: (i) an energy function that should have a low value near the correct structure and (ii) a method for searching through different conformations of the polypeptide chain. Identification of the most efficient search methods is essential if we are to be able to apply such methods broadly and with confidence. In addition, efficient search methods provide a rigorous test of existing energy functions, which are generally knowledge-based and contain different terms added together with arbitrary weights. Here, we test different search methods with one of the most accurate and predictive energy functions, namely Rosetta the knowledge-based force-field from Baker's group [Simons K, Kooperberg C, Huang E, Baker D (1997) J Mol Biol 268:209-225]. We use an implementation of a generalized ensemble search method to scale relevant parts of the energy function. This method, known as Hamiltonian Replica Exchange Monte Carlo, outperforms the original Monte Carlo Simulated Annealing used in the Rosetta package in terms of sampling low-energy states. It also outperforms another widely used generalized ensemble search method known as Temperature Replica Exchange Monte Carlo. Our results reveal clear deficiencies in the low-resolution Rosetta energy function in that the lowest energy structures are not necessarily the most native-like. By using a set of nonnative low-energy structures found by our extensive sampling, we discovered that the long-range and short-range backbone hydrogen-bonding energy terms of the Rosetta energy discriminate between the nonnative and native-like structures significantly better than the low-resolution score used in Rosetta.

We probe the stability and near-native energy landscape of protein fold space using powerful conformational sampling methods together with simple reduced models and statistical potentials. Fold space is represented by a set of 280 protein domains spanning all topological classes and having a wide range of lengths (33-300 residues) amino acid composition and number of secondary structural elements. The degrees of freedom are taken as the loop torsion angles. This choice preserves the native secondary structure but allows the tertiary structure to change. The proteins are represented by three-point per residue, three-dimensional models with statistical potentials derived from a knowledge-based study of known protein structures. When this space is sampled by a combination of parallel tempering and equi-energy Monte Carlo, we find that the three-point model captures the known stability of protein native structures with stable energy basins that are near-native (all alpha: 4.77 A, all beta: 2.93 A, alpha/beta: 3.09 A, alpha+beta: 4.89 A on average and within 6 A for 71.41%, 92.85%, 94.29% and 64.28% for all-alpha, all-beta, alpha/beta and alpha+beta, classes, respectively). Denatured structures also occur and these have interesting structural properties that shed light on the different landscape characteristics of alpha and beta folds. We find that alpha/beta proteins with alternating alpha and beta segments (such as the beta-barrel) are more stable than proteins in other fold classes.

We describe the derivation and testing of a knowledge-based atomic environment potential for the modeling of protein structural energetics. An analysis of the probabilities of atomic interactions in a dataset of high-resolution protein structures shows that the probabilities of non-bonded inter-atomic contacts are not statistically independent events, and that the multi-body contact frequencies are poorly predicted from pairwise contact potentials. A pseudo-energy function is defined that measures the preferences for protein atoms to be in a given microenvironment defined by the number of contacting atoms in the environment and its atomic composition. This functional form is tested for its ability to recognize native protein structures amongst an ensemble of decoy structures and a detailed relative performance comparison is made with a number of common functions used in protein structure prediction.

To understand the physical and evolutionary determinants of protein folding, we map out the complete organization of thermodynamic and kinetic properties for protein sequences that share the same fold. The exhaustive nature of our study necessitates using simplified models of protein folding. We obtain a stability map and a folding rate map in sequence space. Comparison of the two maps reveals a common organizational principle: optimality decreases more or less uniformly with distance from the optimal sequence in the sequence space. This gives a funnel-shaped optimality surface. Evolutionary dynamics of a sequence population on these two maps reveal how the simple organization of sequence space affects the distributions of stability and folding rate preferred by evolution., (Copyright 2004 Wiley-Liss, Inc.)

We suggest a new approach to the generation of candidate structures (decoys) for ab initio prediction of protein structures. Our method is based on random sampling of conformation space and subsequent local energy minimization. At the core of this approach lies the design of a novel type of energy function. This energy function has local minima with native structure characteristics and wide basins of attraction. The current work presents our motivation for deriving such an energy function and also tests the derived energy function. Our approach is novel in that it takes advantage of the inherently rough energy landscape of proteins, which is generally considered a major obstacle for protein structure prediction. When local minima have wide basins of attraction, the protein's conformation space can be greatly reduced by the convergence of large regions of the space into single points, namely the local minima corresponding to these funnels. We have implemented this concept by an iterative process. The potential is first used to generate decoy sets and then we study these sets of decoys to guide further development of the potential. A key feature of our potential is the use of cooperative multi-body interactions that mimic the role of the entropic and solvent contributions to the free energy. The validity and value of our approach is demonstrated by applying it to 14 diverse, small proteins. We show that, for these proteins, the size of conformation space is considerably reduced by the new energy function. In fact, the reduction is so substantial as to allow efficient conformational sampling. As a result we are able to find a significant number of near-native conformations in random searches performed with limited computational resources.

It is commonly believed that similarities between the sequences of two proteins infer similarities between their structures. Sequence alignments reliably recognize pairs of protein of similar structures provided that the percentage sequence identity between their two sequences is sufficiently high. This distinction, however, is statistically less reliable when the percentage sequence identity is lower than 30% and little is known then about the detailed relationship between the two measures of similarity. Here, we investigate the inverse correlation between structural similarity and sequence similarity on 12 protein structure families. We define the structure similarity between two proteins as the cRMS distance between their structures. The sequence similarity for a pair of proteins is measured as the mean distance between the sequences in the subsets of sequence space compatible with their structures. We obtain an approximation of the sequence space compatible with a protein by designing a collection of protein sequences both stable and specific to the structure of that protein. Using these measures of sequence and structure similarities, we find that structural changes within a protein family are linearly related to changes in sequence similarity.

Background: We thoroughly analyse the results of 40 blind predictions for which an experimental answer was made available at the fourth meeting on the critical assessment of protein structure methods (CASP4). Using our comparative modelling and fold recognition methodologies, we made 29 predictions for targets that had sequence identities ranging from 50% to 10% to the nearest related protein with known structure. Using our ab initio methodologies, we made eleven predictions for targets that had no detectable sequence relationships., Results: For 23 of these proteins, we produced models ranging from 1.0 to 6.0 A root mean square deviation (RMSD) for the Calpha atoms between the model and the corresponding experimental structure for all or large parts of the protein, with model accuracies scaling fairly linearly with respect to sequence identity (i.e., the higher the sequence identity, the better the prediction). We produced nine models with accuracies ranging from 4.0 to 6.0 A Calpha RMSD for 60-100 residue proteins (or large fragments of a protein), with a prediction accuracy of 4.0 A Calpha RMSD for residues 1-80 for T110/rbfa., Conclusions: The areas of protein structure prediction that work well, and areas that need improvement, are discernable by examining how our methods have performed over the past four CASP experiments. These results have implications for modelling the structure of all tractable proteins encoded by the genome of an organism.

Is there value in constructing side chains while searching protein conformational space during an ab initio simulation? If so, what is the most computationally efficient method for constructing these side chains? To answer these questions, four published approaches were used to construct side chain conformations on a range of near-native main chains generated by ab initio protein structure prediction methods. The accuracy of these approaches was compared with a naive approach that selects the most frequently observed rotamer for a given amino acid to construct side chains. An all-atom conditional probability discriminatory function is useful at selecting conformations with overall low all-atom root mean square deviation (r.m.s.d.) and the discrimination improves on sets that are closer to the native conformation. In addition, the naive approach performs as well as more sophisticated methods in terms of the percentage of chi(1) angles built accurately and the all-atom r. m.s.d., between the native and near-native conformations. The results suggest that the naive method would be extremely useful for fast and efficient side chain construction on vast numbers of conformations for ab initio prediction of protein structure.

The ASTRAL compendium provides several databases and tools to aid in the analysis of protein structures, particularly through the use of their sequences. The SPACI scores included in the system summarize the overall characteristics of a protein structure. A structural alignments database indicates residue equivalencies in superimposed protein domain structures. The PDB sequence-map files provide a linkage between the amino acid sequence of the molecule studied (SEQRES records in a database entry) and the sequence of the atoms experimentally observed in the structure (ATOM records). These maps are combined with information in the SCOPdatabase to provide sequences of protein domains. Selected subsets of the domain database, with varying degrees of similarity measured in several different ways, are also available. ASTRALmay be accessed at http://astral.stanford.edu/

Eighteen low and medium resolution empirical energy functions were tested for their ability to distinguish correct from incorrect folds from three test sets of decoy protein conformations. The energy functions included 13 pairwise potentials of mean force, covering a wide range of functional forms and methods of parameterization, four potentials that attempt to detect properly formed hydrophobic cores, and one environment-based potential. the first of the three test sets consists of large ensembles of plausible conformations for eight small proteins, all of which have correct native secondary structure and are reasonably compact. The second is the set of all subconformations in a database of known protein structures applied to the sequences in that database (ungapped threading). The third is a set of ensembles of 1000 conformations each for seven small proteins taken from molecular dynamics simulations at 298 K and 498 K. Our results show that there are functions effective for each challenge set; moreover, success in one test is no guarantee of success in another. We examine the factors that seem to be important for accurate discrimination of correct structures in each of the test sets, and note that extremely simple functions are often as effective as more complex functions.

Analysis of CASP2 protein threading results shows that the success rate of structure predictions varies widely among prediction targets. We set "critical" thresholds in fold recognition specificity and threading model accuracy at the points where "incorrect" CASP2 predictions just outnumber "correct" predictions. Using these thresholds we find that correct predictions were made for all of those targets and for only those targets where more than 50% of target residues may be superimposed on previously known structures. Three-fourths of these correct predictions were furthermore made for targets with greater than 12% residue identity in structural alignment, where characteristic sequence motifs are also present. Based on these observations we suggest that the sustained performance of threading methods is best characterized by counting the numbers of correct predictions for targets of increasing "difficulty." We suggest that target difficulty may be assigned, once the true structure of the target is known, according to the fraction of residues superimposable onto previously known structures and the fraction of identical residues in those structural alignments.

Modeling of protein core mutations using sidechain packing can forecast their effects on stability. We have assessed the structural basis of this approach, by evaluating the accuracy of our 1991 model of a three-site mutant of lambda repressor (V36L/M40L/V47I), against the recently reported crystal structure. The three mutated residues matched the crystal structure to within 0.89A (1.11A for sidechain atoms), giving fairly accurate sidechain placement and packing (81-99th percentile rank in coordinate accuracy). However, the model used different sidechain torsional angles than seen in the crystal structure at residues 36 and 40, apparently to compensate for the backbone shifts present in the actual mutant structure, but not accounted for in our modeling method. To understand the structural basis of stability across a set of lambda repressor core mutants, we have analyzed the mutant models, revealing several simple packing effects: V36I, predicted to be stabilized by filling a hydrophobic cavity; M40V, destabilized by a steric clash with the unusual structural demands of a helix-turn transition. These effects illustrate how mutant stability can often be understood directly from scrutiny of wildtype structure. Simply adding the calculated energies of neighboring point mutations predicts the stability effect of the combined mutant relatively well, with little apparent cooperativity, yielding simple rules for each site's amino acid preferences. Our treatment of core packing indicates that it can permit a large fraction of sequences to fit the native fold, as observed experimentally, far more than indicated by rotamer hard-sphere models.

With a simple lattice model and sequence design algorithm, we can design sequences to fit arbitrary compact globular structures. We judged the success of the design algorithm by performing exhaustive conformational searches to determine if a designed sequence's lowest energy conformation matched the target for which it was designed. Designed sequences tend to be much better optimized for their targets than a natural sequence is optimized for its lowest energy model conformation. We examined the effect of varying the number of available amino acid types on the success of the design method. It was more difficult but not impossible to successfully design discriminating sequences using fewer amino acid types.

We show how a basic pairwise alignment procedure can be improved to more accurately align conserved structural regions, by using variable, position-dependent gap penalties that depend on secondary structure and by taking the consensus of a number of suboptimal alignments. These improvements, which are novel for structural alignment, are direct analogs of what is possible with normal sequences alignment. They are feasible for us since our basic structural alignment procedure, unlike others, is so similar to normal sequence alignment. We further present preliminary results that show how our procedure can be generalized to produce a multiple alignment of a family of structures. Our approach is based on finding a "median" structure from doing all possible pairwise alignments and then aligning everything to it.

The prediction of protein structure depends on the quality of the models used. In this paper, we examine the relationship between the complexity and accuracy of representation of various models of protein alpha-carbon backbone structure. First, we develop an efficient algorithm for the near optimal fitting of arbitrary lattice and off-lattice models of polypeptide chains to their true X-ray structures. Using this, we show that the relationship between the complexity of a model, taken as the number of possible conformational states per residue, and the simplest measure of accuracy, the root-mean-square deviation from the X-ray structure, is approximately (Accuracy) varies; is directly proportional to (Complexity)-1/2. This relationship is insensitive to the particularities of individual models, i.e. lattice and off-lattice models of the same complexity tend to have similar average root-mean-square deviations, and this also implies that improvements in model accuracy with increasing complexity are very small. However, other measures of model accuracy, such as the preservation of X-ray residue-residue contacts and the alpha-helix, do distinguish among models. In addition, we show that low complexity models, which take into account the uneven distribution of residue conformations in real proteins, can represent X-ray structures as accurately as more complex models, which do not: a selected 6-state model can represent protein structures almost as accurately (1.7 A root-mean-square) as a 17-state lattice model (1.6 A root-mean-square). Finally, we use a novel optimization procedure to generate eight 4-state models, which fit native proteins to an average of 2.4 A, and preserve 85% of native residue-residue contacts. We discuss the implications of these findings for protein folding and the prediction of protein conformation.

We present a low resolution lattice model for which we can exhaustively generate all possible compact backbone conformations for small proteins. Using simple structural and energetic criteria, for a variety of proteins, we can select for lattice structures that have significant similarities with their known native structures. Our energetic parameters are based on pairwise amino acid contact frequencies in a database of experimentally determined structures. A key step in our method involves the threading of a sequence onto every lattice model, such that a locally optimal pattern of tertiary interactions is formed. We evaluate our results against statistics collected for structures covering all of conformational space, and against statistics collected for permuted sequences. Despite the low resolution of the model, our low energy structures contain many native features. These results indicate that the overall pattern of hydrophobicity of a sequence significantly constrains the range of folds that sequence is likely to adopt.

Segment match modeling uses a data base of highly refined known protein X-ray structures to build an unknown target structure from its amino acid sequence and the atomic coordinates of a few of its atoms (generally only the C alpha atoms). The target structure is first broken into a set of short segments. The data base is then searched for matching segments, which are fitted onto the framework of the target structure. Three criteria are used for choosing a matching data base segment: amino acid sequence similarity, conformational similarity (atomic co-ordinates), and compatibility with the target structure (van der Waals' interactions). The new method works surprisingly well: for eight test proteins ranging in size from 46 to 323 residues, the all-atom root-mean-square deviation of the modeled structures is between 0.93 A and 1.73 A (the average is 1.26 A). Deviations of this magnitude are comparable with those found for protein co-ordinates before and after refinement against X-ray data or for co-ordinates of the same protein in different crystal packings. These results are insensitive to errors in the C alpha positions or to missing C alpha atoms: accurate models can be built with C alpha errors of up to 1 A or by using only half the C alpha atoms. The fit to the X-ray structures is improved significantly by building several independent models based on different random choices and then averaging co-ordinates; this novel concept has general implications for other modeling tasks. The segment match modeling method is fully automatic, yields a complete set of atomic co-ordinates without any human intervention and is efficient (14 s/residue on the Silicon Graphics 4D/25 Personal Iris workstation.

The prediction of the folded structure of a protein from its sequence has proven to be a very difficult computational problem. We have developed an exceptionally simple representation of a polypeptide chain, with which we can enumerate all possible backbone conformations of small proteins. A protein is represented by a self-avoiding path of connected vertices on a tetrahedral lattice, with several amino acid residues assigned to each lattice vertex. For five small structurally dissimilar proteins, we find that we can separate native-like structures from the vast majority of non-native folds by using only simple structural and energetic criteria. This method demonstrates significant generality and predictive power without requiring foreknowledge of any native structural details.

A simple diagrammatic representation has been used to show the arrangement of alpha helices and beta sheets in 31 globular proteins, which are classified into four clearly separated classes. The observed arrangements are significantly non-random in that pieces of secondary structure adjacent in sequence along the polypeptide chain are also often in contact in three dimensions.

Simple models are presented that describe the rules for almost all the packing that occurs between and among alpha-helices and pleated sheets. These packing rules, together with the primary and secondary structures, are the major determinants of the three-dimensional structure of proteins.

In a previous paper [Levitt, M., and Greer, J. (1977), J. Mol. Biol. 114, 181--239], an objective compilation of the secondary-structure regions in more than 50 different globular proteins was produced automatically. In the present paper, these assignments of secondary structure are analyzed to give the frequency of occurrence of the 20 naturally occurring amino acids in alpha helix, beta sheet, and reverse-turn secondary structure. Nineteen of these amino acids have a weak but statistically signficant preference for only on type of secondary structure. These preferences correlate well with the chemical structure of the particular amino acids giving a more objective classification of the conformational properties of amino acids than available before.

A new and very simple representation of protein conformations has been used together with energy minimisation and thermalisation to simulate protein folding. Under certain conditions, the method succeeds in "renaturing" bovine pancreatic trypsin inhibitor from an open-chain conformation into a folded conformation close to that of the native molecule.