Protein structure alignment beyond spatial proximity.
Ontology highlight
ABSTRACT:
SUBMITTER: Wang S
PROVIDER: S-EPMC3596798 | biostudies-other | 2013
REPOSITORIES: biostudies-other
Similar Datasets
Project description: Not available
| S-EPMC11344802 | biostudies-literature
Project description:A method was developed to compare protein structures and to combine them into a multiple structure consensus. Previous methods of multiple structure comparison have only concatenated pairwise alignments or produced a consensus structure by averaging coordinate sets. The current method is a fusion of the fast structure comparison program SSAP and the multiple sequence alignment program MULTAL. As in MULTAL, structures are progressively combined, producing intermediate consensus structures that are compared directly to each other and all remaining single structures. This leads to a hierarchic "condensation," continually evaluated in the light of the emerging conserved core regions. Following the SSAP approach, all interatomic vectors were retained with well-conserved regions distinguished by coherent vector bundles (the structural equivalent of a conserved sequence position). Each bundle of vectors is summarized by a resultant, whereas vector coherence is captured in an error term, which is the only distinction between conserved and variable positions. Resultant vectors are used directly in the comparison, which is weighted by their error values, giving greater importance to the matching of conserved positions. The resultant vectors and their errors can also be used directly in molecular modeling. Applications of the method were assessed by the quality of the resulting sequence alignments, phylogenetic tree construction, and databank scanning with the consensus. Visual assessment of the structural superpositions and consensus structure for various well-characterized families confirmed that the consensus had identified a reasonable core.
| S-EPMC2142613 | biostudies-other
Project description:Visualizing the functional interactions of biomolecules such as proteins and nucleic acids is key to understanding cellular life on the molecular scale. Spatial proximity is often used as a proxy for the direct interaction of biomolecules. However, current techniques to visualize spatial proximity are either limited by spatial resolution, dynamic range, or lack of single-molecule sensitivity. Here, we introduce Proximity-PAINT (pPAINT), a variation of the super-resolution microscopy technique DNA-PAINT. pPAINT uses a split-docking-site configuration to detect spatial proximity with high sensitivity, low false-positive rates, and tunable detection distances. We benchmark and optimize pPAINT using designer DNA nanostructures and demonstrate its cellular applicability by visualizing the spatial proximity of alpha- and beta-tubulin in microtubules using super-resolution detection.
| S-EPMC7839522 | biostudies-literature
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
Project description:We have recently developed a flexible protein structure alignment program (FATCAT) that identifies structural similarity, at the same time accounting for flexibility of protein structures. One of the most important applications of a structure alignment method is to aid in functional annotations by identifying similar structures in large structural databases. However, none of the flexible structure alignment methods were applied in this task because of a lack of significance estimation of flexible alignments. In this paper, we developed an estimate of the statistical significance of FATCAT alignment score, allowing us to use it as a database-searching tool. The results reported here show that (1) the distribution of the similarity score of FATCAT alignment between two unrelated protein structures follows the extreme value distribution (EVD), adding one more example to the current collection of EVDs of sequence and structure similarities; (2) introducing flexibility into structure comparison only slightly influences the sensitivity and specificity of identifying similar structures; and (3) the overall performance of FATCAT as a database searching tool is comparable to that of the widely used rigid-body structure comparison programs DALI and CE. Two examples illustrating the advantages of using flexible structure alignments in database searching are also presented. The conformational flexibilities that were detected in the first example may be involved with substrate specificity, and the conformational flexibilities detected in the second example may reflect the evolution of structures by block building.
| S-EPMC2279924 | biostudies-literature
Project description:Microcrystal electron diffraction (MicroED) was first coined and developed in 2013 at the Janelia Research Campus as a new modality in electron cryomicroscopy (cryoEM). Since then, MicroED has not only made important contributions in pushing the resolution limits of cryoEM protein structure characterization but also of peptides, small-organic and inorganic molecules, and natural-products that have resisted structure determination by other methods. This review showcases important recent developments in MicroED, highlighting the importance of the technique in fields of studies beyond protein structure determination where MicroED is beginning to have paradigm shifting roles.
| S-EPMC7321661 | biostudies-literature
Project description:Performance in everyday spatial orientation tasks (e.g., map reading and navigation) has been considered functionally separate from performance on more abstract object-based spatial abilities (e.g., mental rotation and visualization). However, few studies have examined the link between spatial orientation and object-based spatial skills, and even fewer have done so including a wide range of spatial tests. To examine this issue and more generally to test the structure of spatial ability, we used a novel gamified battery to assess six tests of spatial orientation in a virtual environment and examined their association with ten object-based spatial tests, as well as their links to general cognitive ability (g). We further estimated the role of genetic and environmental factors in underlying variation and covariation in these spatial tests. Participants (N = 2660; aged 19-22) were part of the Twins Early Development Study. The six tests of spatial orientation clustered into a single 'Navigation' factor that was 64% heritable. Examining the structure of spatial ability across all 16 tests, three, substantially correlated, factors emerged: Navigation, Object Manipulation, and Visualization. These, in turn, loaded strongly onto a general factor of Spatial Ability, which was highly heritable (84%). A large portion (45%) of this high heritability was independent of g. The results point towards the existence of a common genetic network that supports all spatial abilities.
| S-EPMC7331750 | biostudies-literature
Project description:BackgroundThere is currently no way to verify the quality of a multiple sequence alignment that is independent of the assumptions used to build it. Sequence alignments are typically evaluated by a number of established criteria: sequence conservation, the number of aligned residues, the frequency of gaps, and the probable correct gap placement. Covariation analysis is used to find putatively important residue pairs in a sequence alignment. Different alignments of the same protein family give different results demonstrating that covariation depends on the quality of the sequence alignment. We thus hypothesized that current criteria are insufficient to build alignments for use with covariation analyses.Methodology/principal findingsWe show that current criteria are insufficient to build alignments for use with covariation analyses as systematic sequence alignment errors are present even in hand-curated structure-based alignment datasets like those from the Conserved Domain Database. We show that current non-parametric covariation statistics are sensitive to sequence misalignments and that this sensitivity can be used to identify systematic alignment errors. We demonstrate that removing alignment errors due to 1) improper structure alignment, 2) the presence of paralogous sequences, and 3) partial or otherwise erroneous sequences, improves contact prediction by covariation analysis. Finally we describe two non-parametric covariation statistics that are less sensitive to sequence alignment errors than those described previously in the literature.Conclusions/significanceProtein alignments with errors lead to false positive and false negative conclusions (incorrect assignment of covariation and conservation, respectively). Covariation analysis can provide a verification step, independent of traditional criteria, to identify systematic misalignments in protein alignments. Two non-parametric statistics are shown to be somewhat insensitive to misalignment errors, providing increased confidence in contact prediction when analyzing alignments with erroneous regions because of an emphasis on they emphasize pairwise covariation over group covariation.
| S-EPMC2893159 | biostudies-literature
Project description:MotivationMost protein-structure superimposition tools consider only Cartesian coordinates. Yet, much of biology happens on the surface of proteins, which is why proteins with shared ancestry and similar function often have comparable surface shapes. Superposition of proteins based on surface shape can enable comparison of highly divergent proteins, identify convergent evolution and enable detailed comparison of surface features and binding sites.ResultsWe present ZEAL, an interactive tool to superpose global and local protein structures based on their shape resemblance using 3D (Zernike-Canterakis) functions to represent the molecular surface. In a benchmark study of structures with the same fold, we show that ZEAL outperforms two other methods for shape-based superposition. In addition, alignments from ZEAL were of comparable quality to the coordinate-based superpositions provided by TM-align. For comparisons of proteins with limited sequence and backbone-fold similarity, where coordinate-based methods typically fail, ZEAL can often find alignments with substantial surface-shape correspondence. In combination with shape-based matching, ZEAL can be used as a general tool to study relationships between shape and protein function. We identify several categories of protein functions where global shape similarity is significantly more likely than expected by random chance, when comparing proteins with little similarity on the fold level. In particular, we find that global surface shape similarity is particular common among DNA binding proteins.Availability and implementationZEAL can be used online at https://andrelab.org/zeal or as a standalone program with command line or graphical user interface. Source files and installers are available at https://github.com/Andre-lab/ZEAL.Supplementary informationSupplementary data are available at Bioinformatics online.
| S-EPMC10262298 | biostudies-literature
Project description:BackgroundProtein-structure alignment is a fundamental tool to study protein function, evolution and model building. In the last decade several methods for structure alignment were introduced, but most of them ignore that structurally similar proteins can share the same spatial arrangement of secondary structure elements (SSE) but differ in the underlying polypeptide chain connectivity (non-sequential SSE connectivity).ResultsWe perform protein-structure alignment using a two-level hierarchical approach implemented in the program GANGSTA. On the first level, pair contacts and relative orientations between SSEs (i.e. alpha-helices and beta-strands) are maximized with a genetic algorithm (GA). On the second level residue pair contacts from the best SSE alignments are optimized. We have tested the method on visually optimized structure alignments of protein pairs (pairwise mode) and for database scans. For a given protein structure, our method is able to detect significant structural similarity of functionally important folds with non-sequential SSE connectivity. The performance for structure alignments with strictly sequential SSE connectivity is comparable to that of other structure alignment methods.ConclusionAs demonstrated for several applications, GANGSTA finds meaningful protein-structure alignments independent of the SSE connectivity. GANGSTA is able to detect structural similarity of protein folds that are assigned to different superfamilies but nevertheless possess similar structures and perform related functions, even if these proteins differ in SSE connectivity.
| S-EPMC1683948 | biostudies-literature