Local structure-based sequence profile database for local and global protein structure predictions

MOTIVATION: A large body of evidence suggests that protein structural information is frequently encoded in local sequences-sequence-structure relationships derived from local structure/sequence analyses could significantly enhance the capacities of protein structure prediction methods. In this paper, the prediction capacity of a database (LSBSP2) that organizes local sequence-structure relationships encoded in local structures with two consecutive secondary structure elements is tested with two computational procedures for protein structure prediction. The goal is twofold: to test the folding hypothesis that local structures are determined by local sequences, and to enhance our capacity in predicting protein structures from their amino acid sequences. RESULTS: The LSBSP2 database contains a large set of sequence profiles derived from exhaustive pair-wise structural alignments for local structures with two consecutive secondary structure elements. One computational procedure makes use of the PSI-BLAST alignment program to predict local structures for testing sequence fragments by matching the testing sequence fragments onto the sequence profiles in the LSBSP2 database. The results show that 54% of the test sequence fragments were predicted with local structures that match closely with their native local structures. The other computational procedure is a filter system that is capable of removing false positives as possible from a set of PSI-BLAST hits. An assessment with a large set of non-redundant protein structures shows that the PSI-BLAST + filter system improves the prediction specificity by up to two-fold over the prediction specificity of the PSI-BLAST program for distantly related protein pairs. Tests with the two computational procedures above demonstrate that local sequence-structure relationships can indeed enhance our capacity in protein structure prediction. The results also indicate that local sequences encoded with strong local structure propensities play an important role in determining the native state folding topology.     

Prediction of protein local structures and folding fragments based on building-block library

In recent years, protein structure prediction using local structure information has made great progress. In this study, a novel and effective method is developed to predict the local structure and the folding fragments of proteins. First, the proteins with known structures are split into fragments. Second, these fragments, represented by dihedrals, are clustered to produce the building blocks (BBs). Third, an efficient machine learning method is used to predict the local structures of proteins from sequence profiles. Finally, a bi-gram model, trained by an iterated algorithm, is introduced to simulate the interactions of these BBs. For test proteins, the building-block lattice is constructed, which contains all the folding fragments of the proteins. The local structures and the optimal fragments are then obtained by the dynamic programming algorithm. The experiment is performed on a subset of the PDB database with sequence identity less than 25%. The results show that the performance of the method is better than the method that uses only sequence information. When multiple paths are returned, the average classification accuracy of local structures is 72.27% and the average prediction accuracy of local structures is 67.72%, which is a significant improvement in comparison with previous studies. The method can predict not only the local structures but also the folding fragments of proteins. This work is helpful for the ab initio protein structure prediction and especially, the understanding of the folding process of proteins.     

De novo protein structure determination using sparse NMR data

We describe a method for generating moderate to high-resolution protein structures using limited NMR data combined with the ab initio protein structure prediction method Rosetta. Peptide fragments are selected from proteins of known structure based on sequence similarity and consistency with chemical shift and NOE data. Models are built from these fragments by minimizing an energy function that favors hydrophobic burial, strand pairing, and satisfaction of NOE constraints. Models generated using this procedure with 1 NOE constraint per residue are in some cases closer to the corresponding X-ray structures than the published NMR solution structures. The method requires only the sparse constraints available during initial stages of NMR structure determination, and thus holds promise for increasing the speed with which protein solution structures can be determined.     

Connecting the protein structure universe by using sparse recurring fragments

The quest to order and classify protein structures has lead to various classification schemes, focusing mostly on hierarchical relationships between structural domains. At the coarsest classification level, such schemes typically identify hundreds of types of fundamental units called folds. As a result, we picture protein structure space as a collection of isolated fold islands. It is obvious, however, that many protein folds share structural and functional commonalities. Locating those commonalities is important for our understanding of protein structure, function, and evolution. Here, we present an alternative view of the protein fold space, based on an interfold similarity measure that is related to the frequency of fragments shared between folds. In this view, protein structures form a complicated, crossconnected network with very interesting topology. We show that interfold similarity based on sequence/structure fragments correlates well with similarities of functions between protein populations in different folds.     

Restricted N-glycan Conformational Space in the PDB and Its Implication in Glycan Structure Modeling

Understanding glycan structure and dynamics is central to understanding protein-carbohydrate recognition and its role in protein-protein interactions. Given the difficulties in obtaining the glycan''s crystal structure in glycoconjugates due to its flexibility and heterogeneity, computational modeling could play an important role in providing glycosylated protein structure models. To address if glycan structures available in the PDB can be used as templates or fragments for glycan modeling, we present a survey of the N-glycan structures of 35 different sequences in the PDB. Our statistical analysis shows that the N-glycan structures found on homologous glycoproteins are significantly conserved compared to the random background, suggesting that N-glycan chains can be confidently modeled with template glycan structures whose parent glycoproteins share sequence similarity. On the other hand, N-glycan structures found on non-homologous glycoproteins do not show significant global structural similarity. Nonetheless, the internal substructures of these N-glycans, particularly, the substructures that are closer to the protein, show significantly similar structures, suggesting that such substructures can be used as fragments in glycan modeling. Increased interactions with protein might be responsible for the restricted conformational space of N-glycan chains. Our results suggest that structure prediction/modeling of N-glycans of glycoconjugates using structure database could be effective and different modeling approaches would be needed depending on the availability of template structures.     

Protein sequence randomness and sequence/structure correlations.

We investigated protein sequence/structure correlation by constructing a space of protein sequences, based on methods developed previously for constructing a space of protein structures. The space is constructed by using a representation of the amino acids as vectors of 10 property factors that encode almost all of their physical properties. Each sequence is represented by a distribution of overlapping sequence fragments. A distance between any two sequences can be calculated. By attaching a weight to each factor, intersequence distances can be varied. We optimize the correlation between corresponding distances in the sequence and structure spaces. The optimal correlation between the sequence and structure spaces is significantly better than that which results from correlating randomly generated sequences, having the overall composition of the data base, with the structure space. However, sets of randomly generated sequences, each of which approximates the composition of the real sequence it replaces, produce correlations with the structure space that are as good as that observed for the actual protein sequences. A connection is proposed with previous studies of the protein folding code. It is shown that the most important property factors for the correlation of the sequence and structure spaces are related to helix/bend preference, side chain bulk, and beta-structure preference.     

Estimating the significance of sequence order in protein secondary structure and prediction

MOTIVATION: How critical is the sequence order information in predicting protein secondary structure segments? We tried to get a rough insight on it from a theoretical approach using both a prediction algorithm and structural fragments from Protein Databank (PDB). RESULTS: Using reverse protein sequences and PDB structural fragments, we theoretically estimated the significance of the order for protein secondary structure and prediction. On average: (1) 79% of protein sequence segments resulted in the same prediction in both normal and reverse directions, which indicated a relatively high conservation of secondary structure propensity in the reverse direction; (2) the reversed sequence prediction alone performed less accurately than the normal forward sequence prediction, but comparably high (2% difference); (3) the commonly predicted regions showed a slightly higher prediction accuracy (4%) than the normal sequences prediction; and (4) structural fragments which have counterparts in reverse direction in the same protein showed a comparable degree of secondary structure conservation (73% identity with reversed structures on average for pentamers). CONTACT: jong@biosophy.org; dietmann@ebi.ac.uk; heger@ebi.ac.uk; holm@ebi.ac.uk     

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.     

Dynameomics: Data‐driven methods and models for utilizing large‐scale protein structure repositories for improving fragment‐based loop prediction

Protein function is intimately linked to protein structure and dynamics yet experimentally determined structures frequently omit regions within a protein due to indeterminate data, which is often due protein dynamics. We propose that atomistic molecular dynamics simulations provide a diverse sampling of biologically relevant structures for these missing segments (and beyond) to improve structural modeling and structure prediction. Here we make use of the Dynameomics data warehouse, which contains simulations of representatives of essentially all known protein folds. We developed novel computational methods to efficiently identify, rank and retrieve small peptide structures, or fragments, from this database. We also created a novel data model to analyze and compare large repositories of structural data, such as contained within the Protein Data Bank and the Dynameomics data warehouse. Our evaluation compares these structural repositories for improving loop predictions and analyzes the utility of our methods and models. Using a standard set of loop structures, containing 510 loops, 30 for each loop length from 4 to 20 residues, we find that the inclusion of Dynameomics structures in fragment‐based methods improves the quality of the loop predictions without being dependent on sequence homology. Depending on loop length, ～25–75% of the best predictions came from the Dynameomics set, resulting in lower main chain root‐mean‐square deviations for all fragment lengths using the combined fragment library. We also provide specific cases where Dynameomics fragments provide better predictions for NMR loop structures than fragments from crystal structures. Online access to these fragment libraries is available at http://www.dynameomics.org/fragments .     

Statistical analysis and molecular dynamics simulations of ambivalent <Emphasis Type="Italic">α</Emphasis> -helices

Background  

Analysis of known protein structures reveals that identical sequence fragments in proteins can adopt different secondary structure conformations. The extent of this conformational diversity is influenced by various factors like the intrinsic sequence propensity, sequence context and other environmental factors such as pH, site directed mutations or alteration of the binding ligands. Understanding the mechanism by which the environment affects the structural ambivalence of these peptides has potential implications for protein design and reliable local structure prediction algorithms. Identification of the structurally ambivalent sequence fragments and determining the rules which dictate their conformational preferences play an important role in understanding the conformational changes observed in misfolding diseases. However, a systematic classification of their intrinsic sequence patterns or a statistical analysis of their properties and sequence context in relation to the origin of their structural diversity have largely remained unexplored.     

Crystal structure of a 123 amino acids dimerization domain of Drosophila Caprin

Cytoplasmic activation/proliferation-associated protein (Caprin) proteins assume diverse functions in many important biological processes, including synaptic plasticity, stress response, innate immune response, and cellular proliferation. The Caprin family members are characterized by the presence of a highly conserved homologous region (HR1) at the N-terminus and arginine-glycine-rich (RGG) boxes at the C-terminus. We had previously determined the crystal structures of human Caprin-1 and Caprin-2 fragments corresponding to the C-terminal 2/3 of HR1. Both fragments adopt homodimeric structures. Based on sequence conservation, we speculated that all Caprin proteins should have similar homodimeric structures. Here we report the crystal structure of a fragment (residues 187-309) of Drosophila melanogaster Caprin (dCaprin). The dCaprin fragment adopts an all α-helical fold which self-associates to form a homodimer. The overall dCaprin homodimeric structure is similar to the Caprin-1 and Caprin-2 homodimeric structures. Most of the amino acids residues mediating homodimerization in the three structures are conserved among all Caprin family members. These structural and sequence data suggest that homodimerization through a conserved dimerization domain is a common structural feature of the Caprin protein family. The dimeric structures may also be involved in interaction with Caprin partners. Dimer formation creates a V-shape concave surface that may serve as a protein binding groove. The concave surfaces in Caprin-1, Caprin-2, and dCaprin should have different and specific binding partners due to the large difference in electrostatic potentials. We propose the existence of a multi-functional domain in Caprin proteins, which not only mediate homodimerization but also involve in interaction with specific Caprin partners.     

Modularity of Protein Folds as a Tool for Template-Free Modeling of Structures

Predicting the three-dimensional structure of proteins from their amino acid sequences remains a challenging problem in molecular biology. While the current structural coverage of proteins is almost exclusively provided by template-based techniques, the modeling of the rest of the protein sequences increasingly require template-free methods. However, template-free modeling methods are much less reliable and are usually applicable for smaller proteins, leaving much space for improvement. We present here a novel computational method that uses a library of supersecondary structure fragments, known as Smotifs, to model protein structures. The library of Smotifs has saturated over time, providing a theoretical foundation for efficient modeling. The method relies on weak sequence signals from remotely related protein structures to create a library of Smotif fragments specific to the target protein sequence. This Smotif library is exploited in a fragment assembly protocol to sample decoys, which are assessed by a composite scoring function. Since the Smotif fragments are larger in size compared to the ones used in other fragment-based methods, the proposed modeling algorithm, SmotifTF, can employ an exhaustive sampling during decoy assembly. SmotifTF successfully predicts the overall fold of the target proteins in about 50% of the test cases and performs competitively when compared to other state of the art prediction methods, especially when sequence signal to remote homologs is diminishing. Smotif-based modeling is complementary to current prediction methods and provides a promising direction in addressing the structure prediction problem, especially when targeting larger proteins for modeling.     

A novel approach to fold recognition using sequence-derived properties from sets of structurally similar local fragments of proteins

Comparative modeling methods can consistently produce reliable structural models for protein sequences with more than 25% sequence identity to proteins with known structure. However, there is a good chance that also sequences with lower sequence identity have their structural components represented in structural databases. To this end, we present a novel fragment-based method using sets of structurally similar local fragments of proteins. The approach differs from other fragment-based methods that use only single backbone fragments. Instead, we use a library of groups containing sets of sequence fragments with geometrically similar local structures and extract sequence related properties to assign these specific geometrical conformations to target sequences. We test the ability of the approach to recognize correct SCOP folds for 273 sequences from the 49 most popular folds. 49% of these sequences have the correct fold as their top prediction, while 82% have the correct fold in one of the top five predictions. Moreover, the approach shows no performance reduction on a subset of sequence targets with less than 10% sequence identity to any protein used to build the library.     

Detecting Protein Candidate Fragments Using a Structural Alphabet Profile Comparison Approach

Predicting accurate fragments from sequence has recently become a critical step for protein structure modeling, as protein fragment assembly techniques are presently among the most efficient approaches for de novo prediction. A key step in these approaches is, given the sequence of a protein to model, the identification of relevant fragments - candidate fragments - from a collection of the available 3D structures. These fragments can then be assembled to produce a model of the complete structure of the protein of interest. The search for candidate fragments is classically achieved by considering local sequence similarity using profile comparison, or threading approaches. In the present study, we introduce a new profile comparison approach that, instead of using amino acid profiles, is based on the use of predicted structural alphabet profiles, where structural alphabet profiles contain information related to the 3D local shapes associated with the sequences. We show that structural alphabet profile-profile comparison can be used efficiently to retrieve accurate structural fragments, and we introduce a fully new protocol for the detection of candidate fragments. It identifies fragments specific of each position of the sequence and of size varying between 6 and 27 amino-acids. We find it outperforms present state of the art approaches in terms (i) of the accuracy of the fragments identified, (ii) the rate of true positives identified, while having a high coverage score. We illustrate the relevance of the approach on complete target sets of the two previous Critical Assessment of Techniques for Protein Structure Prediction (CASP) rounds 9 and 10. A web server for the approach is freely available at http://bioserv.rpbs.univ-paris-diderot.fr/SAFrag.     

Quantitative organization of the known protein x-ray structures. I. Methods and short-length-scale results

We address herein the problem of delineating the relationships between the known protein structures. In order to study this problem, methods have been developed to represent arbitrarily sized fragments of biopolymer backbone, and to compare distributions of such fragments. These methods are applied to a classification of 123 structures representing the entire set of known x-ray structures. The resulting data are analyzed (on the four-C alpha length scale) to determine both the large-scale organization of the set of known structures (i.e., the relationships between large groups of structures, each comprised of proteins that are structurally related) and its local structure (i.e., the quantitative degree of similarity between any two specific structures). It is shown that the set of structures forms a continuum of structural types, ranging from all-helical to all-sheet/barrel proteins. It is further demonstrated that the density of protein structures is not uniform across this continuum, but rather that structures cluster in certain regions, separated by regions of lower population. The properties of the various regions of the structural space are determined. The existence is demonstrated of strong quantitative correlations between the contents of different types of four-C alpha fragments within protein structures, which imply significant constraints on the types of architecture that can occur in proteins. Analysis of the distribution of structures demonstrates some hitherto unsuspected similarities and suggests that, in some circumstances, neither structural similarity nor sequence homology may be necessary conditions for evolutionary relationship between proteins. It is also suggested that these unsuspected similarities may imply similar folding mechanisms for structures of apparently different global architecture. Cases are also noted in which apparently similar structures may fold by different mechanisms. The connection between structure and dynamic properties is discussed, and a possible role of dynamics in the evolution of protein structures is suggested. The sensitivity of the methods presented herein to anomalies of structure refinement is demonstrated. It is suggested that the present results provide a framework for analyzing experimental results on structural similarity obtained using vibrational circular dichroism spectra, which are sensitive to local backbone structure.     

A permissive secondary structure-guided superposition tool for clustering of protein fragments toward protein structure prediction via fragment assembly

MOTIVATION: Secondary-Structure Guided Superposition tool (SSGS) is a permissive secondary structure-based algorithm for matching of protein structures and in particular their fragments. The algorithm was developed towards protein structure prediction via fragment assembly. RESULTS: In a fragment-based structural prediction scheme, a protein sequence is cut into building blocks (BBs). The BBs are assembled to predict their relative 3D arrangement. Finally, the assemblies are refined. To implement this prediction scheme, a clustered structural library representing sequence patterns for protein fragments is essential. To create a library, BBs generated by cutting proteins from the PDB are compared and structurally similar BBs are clustered. To allow structural comparison and clustering of the BBs, which are often relatively short with flexible loops, we have devised SSGS. SSGS maintains high similarity between cluster members and is highly efficient. When it comes to comparing BBs for clustering purposes, the algorithm obtains better results than other, non-secondary structure guided protein superimposition algorithms.     

Structural alphabets derived from attractors in conformational space

Background  

The hierarchical and partially redundant nature of protein structures justifies the definition of frequently occurring conformations of short fragments as 'states'. Collections of selected representatives for these states define Structural Alphabets, describing the most typical local conformations within protein structures. These alphabets form a bridge between the string-oriented methods of sequence analysis and the coordinate-oriented methods of protein structure analysis.     

MSDmotif: exploring protein sites and motifs

Background  

Protein structures have conserved features – motifs, which have a sufficient influence on the protein function. These motifs can be found in sequence as well as in 3D space. Understanding of these fragments is essential for 3D structure prediction, modelling and drug-design. The Protein Data Bank (PDB) is the source of this information however present search tools have limited 3D options to integrate protein sequence with its 3D structure.     

Backbone solution structures of proteins using residual dipolar couplings: application to a novel structural genomics target

Structural genomics (or proteomics) activities are critically dependent on the availability of high-throughput structure determination methodology. Development of such methodology has been a particular challenge for NMR based structure determination because of the demands for isotopic labeling of proteins and the requirements for very long data acquisition times. We present here a methodology that gains efficiency from a focus on determination of backbone structures of proteins as opposed to full structures with all sidechains in place. This focus is appropriate given the presumption that many protein structures in the future will be built using computational methods that start from representative fold family structures and replace as many as 70% of the sidechains in the course of structure determination. The methodology we present is based primarily on residual dipolar couplings (RDCs), readily accessible NMR observables that constrain the orientation of backbone fragments irrespective of separation in space. A new software tool is described for the assembly of backbone fragments under RDC constraints and an application to a structural genomics target is presented. The target is an 8.7 kDa protein from Pyrococcus furiosus, PF1061, that was previously not well annotated, and had a nearest structurally characterized neighbor with only 33% sequence identity. The structure produced shows structural similarity to this sequence homologue, but also shows similarity to other proteins, which suggests a functional role in sulfur transfer. Given the backbone structure and a possible functional link this should be an ideal target for development of modeling methods.