SABBAC: online Structural Alphabet-based protein BackBone reconstruction from Alpha-Carbon trace.
Ontology highlight
ABSTRACT:
SUBMITTER: Maupetit J
PROVIDER: S-EPMC1538914 | biostudies-literature | 2006 Jul
REPOSITORIES: biostudies-literature
Publications
SABBAC: online Structural Alphabet-based protein BackBone reconstruction from Alpha-Carbon trace.
Maupetit Julien J Gautier R R Tufféry Pierre P
Nucleic acids research 20060701 Web Server issue
SABBAC is an on-line service devoted to protein backbone reconstruction from alpha-carbon trace. It is based on the assembly of fragments taken from a library of reduced size, selected from the encoding of the protein trace in a hidden Markov model-derived structural alphabet. The assembly of the fragments is achieved by a greedy algorithm, using an energy-based scoring. Alpha-carbon coordinates remain unaffected. SABBAC simply positions the missing backbone atoms, no further refinement is perfo ...[more]
Similar Datasets
Project description:Libraries of structural prototypes that abstract protein local structures are known as structural alphabets and have proven to be very useful in various aspects of protein structure analyses and predictions. One such library, Protein Blocks, is composed of 16 standard 5-residues long structural prototypes. This form of analyzing proteins involves drafting its structure as a string of Protein Blocks. Predicting the local structure of a protein in terms of protein blocks is the general objective of this work. A new approach, PB-kPRED is proposed towards this aim. It involves (i) organizing the structural knowledge in the form of a database of pentapeptide fragments extracted from all protein structures in the PDB and (ii) applying a knowledge-based algorithm that does not rely on any secondary structure predictions and/or sequence alignment profiles, to scan this database and predict most probable backbone conformations for the protein local structures. Though PB-kPRED uses the structural information from homologues in preference, if available. The predictions were evaluated rigorously on 15,544 query proteins representing a non-redundant subset of the PDB filtered at 30% sequence identity cut-off. We have shown that the kPRED method was able to achieve mean accuracies ranging from 40.8% to 66.3% depending on the availability of homologues. The impact of the different strategies for scanning the database on the prediction was evaluated and is discussed. Our results highlight the usefulness of the method in the context of proteins without any known structural homologues. A scoring function that gives a good estimate of the accuracy of prediction was further developed. This score estimates very well the accuracy of the algorithm (R2 of 0.82). An online version of the tool is provided freely for non-commercial usage at http://www.bo-protscience.fr/kpred/.
| S-EPMC5697859 | biostudies-literature
Project description:An algorithm is proposed for the conversion of a virtual-bond polypeptide chain (connected C alpha atoms) to an all-atom backbone, based on determining the most extensive hydrogen-bond network between the peptide groups of the backbone, while maintaining all of the backbone atoms in energetically feasible conformations. Hydrogen bonding is represented by aligning the peptide-group dipoles. These peptide groups are not contiguous in the amino acid sequence. The first dipoles to be aligned are those that are both sufficiently close in space to be arranged in approximately linear arrays termed dipole paths. The criteria used in the construction of dipole paths are: to assure good alignment of the greatest possible number of dipoles that are close in space; to optimize the electrostatic interactions between the dipoles that belong to different paths close in space; and to avoid locally unfavorable amino acid residue conformations. The equations for dipole alignment are solved separately for each path, and then the remaining single dipoles are aligned optimally with the electrostatic field from the dipoles that belong to the dipole-path network. A least-squares minimizer is used to keep the geometry of the alpha-carbon trace of the resulting backbone close to that of the input virtual-bond chain. This procedure is sufficient to convert the virtual-bond chain to a real chain; in applications to real systems, however, the final structure is obtained by minimizing the total ECEPP/2 (empirical conformational energy program for peptides) energy of the system, starting from the geometry resulting from the solution of the alignment equations. When applied to model alpha-helical and beta-sheet structures, the algorithm, followed by the ECEPP/2 energy minimization, resulted in an energy and backbone geometry characteristic of these alpha-helical and beta-sheet structures. Application to the alpha-carbon trace of the backbone of the crystallographic 5PTI structure of bovine pancreatic trypsin inhibitor, followed by ECEPP/2 energy minimization with C alpha-distance constraints, led to a structure with almost as low energy and root mean square deviation as the ECEPP/2 geometry analog of 5PTI, the best agreement between the crystal and reconstructed backbone being observed for the residues involved in the dipole-path network.
| S-EPMC2142257 | biostudies-literature
Project description:Proteins with insignificant sequence and overall structure similarity may still share locally conserved contiguous structural segments; i.e. structural/3D motifs. Most methods for finding 3D motifs require a known motif to search for other similar structures or functionally/structurally crucial residues. Here, without requiring a query motif or essential residues, a fully automated method for discovering 3D motifs of various sizes across protein families with different folds based on a 16-letter structural alphabet is presented. It was applied to structurally non-redundant proteins bound to DNA, RNA, obligate/non-obligate proteins as well as free DNA-binding proteins (DBPs) and proteins with known structures but unknown function. Its usefulness was illustrated by analyzing the 3D motifs found in DBPs. A non-specific motif was found with a 'corner' architecture that confers a stable scaffold and enables diverse interactions, making it suitable for binding not only DNA but also RNA and proteins. Furthermore, DNA-specific motifs present 'only' in DBPs were discovered. The motifs found can provide useful guidelines in detecting binding sites and computational protein redesign.
| S-EPMC2919736 | biostudies-literature
Project description:BACKGROUND: In a number of protein-protein complexes, the 3D structures of bound and unbound partners significantly differ, supporting the induced fit hypothesis for protein-protein binding. RESULTS: In this study, we explore the induced fit modifications on a set of 124 proteins available in both bound and unbound forms, in terms of local structure. The local structure is described thanks to a structural alphabet of 27 structural letters that allows a detailed description of the backbone. Using a control set to distinguish induced fit from experimental error and natural protein flexibility, we show that the fraction of structural letters modified upon binding is significantly greater than in the control set (36% versus 28%). This proportion is even greater in the interface regions (41%). Interface regions preferentially involve coils. Our analysis further reveals that some structural letters in coil are not favored in the interface. We show that certain structural letters in coil are particularly subject to modifications at the interface, and that the severity of structural change also varies. These information are used to derive a structural letter substitution matrix that summarizes the local structural changes observed in our data set. We also illustrate the usefulness of our approach to identify common binding motifs in unrelated proteins. CONCLUSION: Our study provides qualitative information about induced fit. These results could be of help for flexible docking.
| S-EPMC2315654 | biostudies-literature
Project description:Here, we systematically decompose the known protein structural universe into its basic elements, which we dub tertiary structural motifs (TERMs). A TERM is a compact backbone fragment that captures the secondary, tertiary, and quaternary environments around a given residue, comprising one or more disjoint segments (three on average). We seek the set of universal TERMs that capture all structure in the Protein Data Bank (PDB), finding remarkable degeneracy. Only ?600 TERMs are sufficient to describe 50% of the PDB at sub-Angstrom resolution. However, more rare geometries also exist, and the overall structural coverage grows logarithmically with the number of TERMs. We go on to show that universal TERMs provide an effective mapping between sequence and structure. We demonstrate that TERM-based statistics alone are sufficient to recapitulate close-to-native sequences given either NMR or X-ray backbones. Furthermore, sequence variability predicted from TERM data agrees closely with evolutionary variation. Finally, locations of TERMs in protein chains can be predicted from sequence alone based on sequence signatures emergent from TERM instances in the PDB. For multisegment motifs, this method identifies spatially adjacent fragments that are not contiguous in sequence-a major bottleneck in structure prediction. Although all TERMs recur in diverse proteins, some appear specialized for certain functions, such as interface formation, metal coordination, or even water binding. Structural biology has benefited greatly from previously observed degeneracies in structure. The decomposition of the known structural universe into a finite set of compact TERMs offers exciting opportunities toward better understanding, design, and prediction of protein structure.
| S-EPMC5127300 | biostudies-literature
Project description:SA-Search is a web tool that can be used to mine for protein structures and extract structural similarities. It is based on a hidden Markov model derived Structural Alphabet (SA) that allows the compression of three-dimensional (3D) protein conformations into a one-dimensional (1D) representation using a limited number of prototype conformations. Using such a representation, classical methods developed for amino acid sequences can be employed. Currently, SA-Search permits the performance of fast 3D similarity searches such as the extraction of exact words using a suffix tree approach, and the search for fuzzy words viewed as a simple 1D sequence alignment problem. SA-Search is available at http://bioserv.rpbs.jussieu.fr/cgi-bin/SA-Search.
| S-EPMC441605 | biostudies-literature
Project description:We present a novel protein structure database search tool, 3D-BLAST, that is useful for analyzing novel structures and can return a ranked list of alignments. This tool has the features of BLAST (for example, robust statistical basis, and effective and reliable search capabilities) and employs a kappa-alpha (kappa, alpha) plot derived structural alphabet and a new substitution matrix. 3D-BLAST searches more than 12,000 protein structures in 1.2 s and yields good results in zones with low sequence similarity.
| S-EPMC1868941 | biostudies-literature
Project description:Protein Blocks (PBs) comprise a structural alphabet of 16 protein fragments, each 5 Calpha long. They make it possible to approximate and correctly predict local protein three-dimensional (3D) structures. We have selected the 72 most frequent sequences of five PBs, which we call Structural Words (SWs). Analysis of four different protein data banks shows that SWs cover 92% of the amino acids in them and provide a good structural approximation for residues (i.e., sequences) 9 Calpha long. We present most of them in a simple network that describes 90% of the overall residues and, interestingly, includes more than 80% of the amino acids present in coils. Analysis of the network shows the specificity and quality of the 3D descriptions as well as a new type of relation between local folds and amino acid distribution. The results show that the 3D structure of these protein data banks can be easily described by a combination of subgraphs included in the network. Finally, a Bayesian probabilistic approach improved the prediction rate by 4%.
| S-EPMC2373739 | biostudies-literature
Project description:The HIV-2 protease (PR2) is a homodimer of 99 residues with asymmetric assembly and binding various ligands. We propose an exhaustive study of the local structural asymmetry between the two monomers of all available PR2 structures complexed with various inhibitors using a structural alphabet approach. On average, PR2 exhibits asymmetry in 31% of its positions-i.e., exhibiting different backbone local conformations in the two monomers. This asymmetry was observed all along its structure, particularly in the elbow and flap regions. We first differentiated structural asymmetry conserved in most PR2 structures from the one specific to some PR2. Then, we explored the origin of the detected asymmetry in PR2. We localized asymmetry that could be induced by PR2's flexibility, allowing transition from the semi-open to closed conformations and the asymmetry potentially induced by ligand binding. This latter could be important for the PR2's adaptation to diverse ligands. Our results highlighted some differences between asymmetry of PR2 bound to darunavir and amprenavir that could explain their differences of affinity. This knowledge is critical for a better description of PR2's recognition and adaptation to various ligands and for a better understanding of the resistance of PR2 to most PR2 inhibitors, a major antiretroviral class.
| S-EPMC5768731 | biostudies-literature
Project description:BACKGROUND: Protein loops encompass 50% of protein residues in available three-dimensional structures. These regions are often involved in protein functions, e.g. binding site, catalytic pocket... However, the description of protein loops with conventional tools is an uneasy task. Regular secondary structures, helices and strands, have been widely studied whereas loops, because they are highly variable in terms of sequence and structure, are difficult to analyze. Due to data sparsity, long loops have rarely been systematically studied. RESULTS: We developed a simple and accurate method that allows the description and analysis of the structures of short and long loops using structural motifs without restriction on loop length. This method is based on the structural alphabet HMM-SA. HMM-SA allows the simplification of a three-dimensional protein structure into a one-dimensional string of states, where each state is a four-residue prototype fragment, called structural letter. The difficult task of the structural grouping of huge data sets is thus easily accomplished by handling structural letter strings as in conventional protein sequence analysis. We systematically extracted all seven-residue fragments in a bank of 93000 protein loops and grouped them according to the structural-letter sequence, named structural word. This approach permits a systematic analysis of loops of all sizes since we consider the structural motifs of seven residues rather than complete loops. We focused the analysis on highly recurrent words of loops (observed more than 30 times). Our study reveals that 73% of loop-lengths are covered by only 3310 highly recurrent structural words out of 28274 observed words). These structural words have low structural variability (mean RMSd of 0.85 A). As expected, half of these motifs display a flanking-region preference but interestingly, two thirds are shared by short (less than 12 residues) and long loops. Moreover, half of recurrent motifs exhibit a significant level of amino-acid conservation with at least four significant positions and 87% of long loops contain at least one such word. We complement our analysis with the detection of statistically over-represented patterns of structural letters as in conventional DNA sequence analysis. About 30% (930) of structural words are over-represented, and cover about 40% of loop lengths. Interestingly, these words exhibit lower structural variability and higher sequential specificity, suggesting structural or functional constraints. CONCLUSIONS: We developed a method to systematically decompose and study protein loops using recurrent structural motifs. This method is based on the structural alphabet HMM-SA and not on structural alignment and geometrical parameters. We extracted meaningful structural motifs that are found in both short and long loops. To our knowledge, it is the first time that pattern mining helps to increase the signal-to-noise ratio in protein loops. This finding helps to better describe protein loops and might permit to decrease the complexity of long-loop analysis. Detailed results are available at http://www.mti.univ-paris-diderot.fr/publication/supplementary/2009/ACCLoop/.
| S-EPMC2833150 | biostudies-literature