Browse
Submit Data
Databases
API
Help

Dataset Information

28 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Non-sequential protein structure alignment by conformational space annealing and local refinement.

ABSTRACT: Protein structure alignment is an important tool for studying evolutionary biology and protein modeling. A tool which intensively searches for the globally optimal non-sequential alignments is rarely found. We propose ALIGN-CSA which shows improvement in scores, such as DALI-score, SP-score, SO-score and TM-score over the benchmark set including 286 cases. We performed benchmarking of existing popular alignment scoring functions, where the dependence of the search algorithm was effectively eliminated by using ALIGN-CSA. For the benchmarking, we set the minimum block size to 4 to prevent much fragmented alignments where the biological relevance of small alignment blocks is hard to interpret. With this condition, globally optimal alignments were searched by ALIGN-CSA using the four scoring functions listed above, and TM-score is found to be the most effective in generating alignments with longer match lengths and smaller RMSD values. However, DALI-score is the most effective in generating alignments similar to the manually curated reference alignments, which implies that DALI-score is more biologically relevant score. Due to the high demand on computational resources of ALIGN-CSA, we also propose a relatively fast local refinement method, which can control the minimum block size and whether to allow the reverse alignment. ALIGN-CSA can be used to obtain much improved alignment at the cost of relatively more extensive computation. For faster alignment, we propose a refinement protocol that improves the score of a given alignment obtained by various external tools. All programs are available from http://lee.kias.re.kr.

SUBMITTER: Joung I

PROVIDER: S-EPMC6353097 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Similar Datasets

FlexSnap: flexible non-sequential protein structure alignment.

Project description:BackgroundProteins have evolved subject to energetic selection pressure for stability and flexibility. Structural similarity between proteins that have gone through conformational changes can be captured effectively if flexibility is considered. Topologically unrelated proteins that preserve secondary structure packing interactions can be detected if both flexibility and Sequential permutations are considered. We propose the FlexSnap algorithm for flexible non-topological protein structural alignment.ResultsThe effectiveness of FlexSnap is demonstrated by measuring the agreement of its alignments with manually curated non-sequential structural alignments. FlexSnap showed competitive results against state-of-the-art algorithms, like DALI, SARF2, MultiProt, FlexProt, and FATCAT. Moreover on the DynDom dataset, FlexSnap reported longer alignments with smaller rmsd.ConclusionsWe have introduced FlexSnap, a greedy chaining algorithm that reports both sequential and non-sequential alignments and allows twists (hinges). We assessed the quality of the FlexSnap alignments by measuring its agreements with manually curated non-sequential alignments. On the FlexProt dataset, FlexSnap was competitive to state-of-the-art flexible alignment methods. Moreover, we demonstrated the benefits of introducing hinges by showing significant improvements in the alignments reported by FlexSnap for the structure pairs for which rigid alignment methods reported alignments with either low coverage or large rmsd.AvailabilityAn implementation of the FlexSnap algorithm will be made available online at http://www.cs.rpi.edu/~zaki/software/flexsnap.

| S-EPMC2846951 | biostudies-literature

Local alignment refinement using structural assessment.

Project description:Homology modeling is the most commonly used technique to build a three-dimensional model for a protein sequence. It heavily relies on the quality of the sequence alignment between the protein to model and related proteins with a known three dimensional structure. Alignment quality can be assessed according to the physico-chemical properties of the three dimensional models it produces. In this work, we introduce fifteen predictors designed to evaluate the properties of the models obtained for various alignments. They consist of an energy value obtained from different force fields (CHARMM, ProsaII or ANOLEA) computed on residue selected around misaligned regions. These predictors were evaluated on ten challenging test cases. For each target, all possible ungapped alignments are generated and their corresponding models are computed and evaluated. The best predictor, retrieving the structural alignment for 9 out of 10 test cases, is based on the ANOLEA atomistic mean force potential and takes into account residues around misaligned secondary structure elements. The performance of the other predictors is significantly lower. This work shows that substantial improvement in local alignments can be obtained by careful assessment of the local structure of the resulting models.

| S-EPMC2440426 | biostudies-literature

Recognizing protein-ligand binding sites by global structural alignment and local geometry refinement.

Project description:Proteins perform functions through interacting with other molecules. However, structural details for most of the protein-ligand interactions are unknown. We present a comparative approach (COFACTOR) to recognize functional sites of protein-ligand interactions using low-resolution protein structural models, based on a global-to-local sequence and structural comparison algorithm. COFACTOR was tested on 501 proteins, which harbor 582 natural and drug-like ligand molecules. Starting from I-TASSER structure predictions, the method successfully identifies ligand-binding pocket locations for 65% of apo receptors with an average distance error 2 Å. The average precision of binding-residue assignments is 46% and 137% higher than that by FINDSITE and ConCavity. In CASP9, COFACTOR achieved a binding-site prediction precision 72% and Matthews correlation coefficient 0.69 for 31 blind test proteins, which was significantly higher than all other participating methods. These data demonstrate the power of structure-based approaches to protein-ligand interaction predictions applicable for genome-wide structural and functional annotations.

| S-EPMC3372652 | biostudies-literature

A unified approach to sequential and non-sequential structure alignment of proteins, RNAs, and DNAs.

Project description:Many distantly related structure pairs exhibit structural similarities that can only be fully captured by a non-sequential alignment program. We present US-align2, a unified protocol for both sequential and non-sequential alignment of proteins and nucleic acids. On manually curated reference alignments for protein structural pairs with non-sequential relations, US-align2 achieves ≥13% higher agreement with reference alignments than existing sequential and non-sequential alignment methods. Non-sequential alignments also enabled US-align2 to have higher sensitivities in detecting RNA pairs from the same family with sequence identities <40%, obtaining ≥9% higher area under the receiver operating characteristic curve than third-party programs. The unique ability of US-align2 to parse both proteins and nucleic acids allows the method to detect protein-RNA and protein-DNA mimicries. Additionally, US-align2 performs full and semi-non-sequential alignments with at least 48% and 14% faster speed than existing programs for the same tasks, making it particularly useful for large-scale structural similarity detection.

| S-EPMC9557024 | biostudies-literature

Local Protein Structure Refinement via Molecular Dynamics Simulations with locPREFMD.

Project description:A method for the local refinement of protein structures that targets improvements in local stereochemistry while preserving the overall fold is presented. The method uses force field-based minimization and sampling via molecular dynamics simulations with a modified force field to bring bonds, angles, and torsion angles into an acceptable range for high-resolution protein structures. The method is implemented in the locPREFMD web server and was tested on computational models submitted to CASP11. Using MolProbity scores as the main assessment criterion, the locPREFMD method significantly improves the stereochemical quality of given input models close to the quality expected for experimental structures while maintaining the C? coordinates of the initial model.

| S-EPMC4962792 | biostudies-literature

Assessment and refinement of eukaryotic gene structure prediction with gene-structure-aware multiple protein sequence alignment.

Project description:BackgroundAccurate computational identification of eukaryotic gene organization is a long-standing problem. Despite the fundamental importance of precise annotation of genes encoded in newly sequenced genomes, the accuracy of predicted gene structures has not been critically evaluated, mostly due to the scarcity of proper assessment methods.ResultsWe present a gene-structure-aware multiple sequence alignment method for gene prediction using amino acid sequences translated from homologous genes from many genomes. The approach provides rich information concerning the reliability of each predicted gene structure. We have also devised an iterative method that attempts to improve the structures of suspiciously predicted genes based on a spliced alignment algorithm using consensus sequences or reliable homologs as templates. Application of our methods to cytochrome P450 and ribosomal proteins from 47 plant genomes indicated that 50?~?60 % of the annotated gene structures are likely to contain some defects. Whereas more than half of the defect-containing genes may be intrinsically broken, i.e. they are pseudogenes or gene fragments, located in unfinished sequencing areas, or corresponding to non-productive isoforms, the defects found in a majority of the remaining gene candidates can be remedied by our iterative refinement method.ConclusionsRefinement of eukaryotic gene structures mediated by gene-structure-aware multiple protein sequence alignment is a useful strategy to dramatically improve the overall prediction quality of a set of homologous genes. Our method will be applicable to various families of protein-coding genes if their domain structures are evolutionarily stable. It is also feasible to apply our method to gene families from all kingdoms of life, not just plants.

| S-EPMC4065584 | biostudies-literature

Conformational disorder of proteins assessed by real-space molecular dynamics refinement.

Project description:Motion is critical to the function of many proteins, but much more difficult to study than structure. Due to lack of easy alternatives, although there are inherent limitations, there have been several prior attempts to extract some information from the Bragg scattering in conventional diffraction patterns. Bragg diffraction reflects only a small proportion of a protein's motion and disorder, so fitted values likely underestimate reality. However, this work shows that the fitted estimates should be even smaller, because current methods of refinement over-fit the Bragg diffraction, leading to a component of the disorder that is not based on any experimental data, and could be characterized as a guess. Real-space refinement is less susceptible than other methods, but its application depends on the availability of very accurate experimental phases. A future challenge will be the collection of such data without resort to cryo-techniques, so that a physiologically relevant understanding can be achieved.

| S-EPMC1301337 | biostudies-other

Multistart simulated annealing refinement of the crystal structure of the 70S ribosome.

Project description:A macromolecular X-ray crystal structure is usually represented as a single static model with a single set of temperature factors representing a simple approximation of motion and disorder of the structure. Multiconformer representations of small proteins have been shown to better describe anisotropic motion and disorder and improve the quality of their electron density maps. Here, we apply multistart simulated annealing crystallographic refinement to a 70S ribosome-RF1 translation termination complex that was recently solved at 3.2 A resolution. The analysis improves the interpretability of the electron density map of this 2.5-MDa ribonucleoprotein complex and provides insights into its structural dynamics. We also used multistart refinement and conventional Fourier difference maps to address a recent study in which cross-crystal averaging between two crystal forms of the 70S ribosome was used to evaluate reported differences between two ribosome crystal structures solved at 2.8 and 3.7 A resolution. Our analysis suggests that results obtained from cross-crystal averaging are inherently biased toward the higher-resolution dataset.

| S-EPMC2775320 | biostudies-literature

Local RNA structure alignment with incomplete sequence.

Project description:MotivationAccuracy of automated structural RNA alignment is improved by using models that consider not only primary sequence but also secondary structure information. However, current RNA structural alignment approaches tend to perform poorly on incomplete sequence fragments, such as single reads from metagenomic environmental surveys, because nucleotides that are expected to be base paired are missing.ResultsWe present a local RNA structural alignment algorithm, trCYK, for aligning and scoring incomplete sequences under a model using primary sequence conservation and secondary structure information when possible. The trCYK algorithm improves alignment accuracy and coverage of sequence fragments of structural RNAs in simulated metagenomic shotgun datasets.AvailabilityThe source code for Infernal 1.0, which includes trCYK, is available at http://infernal.janelia.org.

| S-EPMC2677745 | biostudies-literature

Generating reservoir conformations for replica exchange through the use of the conformational space annealing method.

Project description:Temperature replica exchange molecular dynamics (T-REM) has been successfully used to improve the conformational search for model peptides and small proteins. However, for larger and more complicated systems, the use of T-REM is computationally intensive since the complexity of the free energy landscape and number of replicas required increase with system size. Achieving convergence of systems with slow transition kinetics is often difficult. Several methods have been proposed to overcome the size and convergence speed issues of standard T-REM. One of these is the Reservoir Replica Exchange Method (R-REM), in which the conformational search and temperature equilibration are separated by exchanging with a pre-existing reservoir of structures. This approach allows the integration of computationally efficient search algorithms with replica exchange. The Conformational Space Annealing (CSA) method has been shown to be able to determine the global energy minimum of proteins efficiently and has been used in structure prediction successfully. CSA uses a genetic algorithm to generate a diverse set of conformations to determine the minimum energy structure. We combine these methods by using conformations generated by the CSA method to build a reservoir. R-REM is then used to seed the top replica with the structures from the reservoir; fast convergence at every temperature is observed. The efficiency of this method is then demonstrated with model peptides and small proteins, and significant improvement of efficiency is observed while maintaining the overall shape of the free energy landscape.

| S-EPMC3621806 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data