Automated method to differentiate between native and mirror protein models obtained from contact maps
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ABSTRACT:
SUBMITTER: Monika Kurczynska
PROVIDER: S-BSST81 | biostudies-other |
REPOSITORIES: biostudies-other
Similar Datasets
Project description:Mirror protein structures are often considered as artifacts in modeling protein structures. However, they may soon become a new branch of biochemistry. Moreover, methods of protein structure reconstruction, based on their residue-residue contact maps, need methodology to differentiate between models of native and mirror orientation, especially regarding the reconstructed backbones. We analyzed 130 500 structural protein models obtained from contact maps of 1 305 SCOP domains belonging to all 7 structural classes. On average, the same numbers of native and mirror models were obtained among 100 models generated for each domain. Since their structural features are often not sufficient for differentiating between the two types of model orientations, we proposed to apply various energy terms (ETs) from PyRosetta to separate native and mirror models. To automate the procedure for differentiating these models, the k-means clustering algorithm was applied. Using total energy did not allow to obtain appropriate clusters-the accuracy of the clustering for class A (all helices) was no more than 0.52. Therefore, we tested a series of different k-means clusterings based on various combinations of ETs. Finally, applying two most differentiating ETs for each class allowed to obtain satisfying results. To unify the method for differentiating between native and mirror models, independent of their structural class, the two best ETs for each class were considered. Finally, the k-means clustering algorithm used three common ETs: probability of amino acid assuming certain values of dihedral angles Φ and Ψ, Ramachandran preferences and Coulomb interactions. The accuracies of clustering with these ETs were in the range between 0.68 and 0.76, with sensitivity and selectivity in the range between 0.68 and 0.87, depending on the structural class. The method can be applied to all fully-automated tools for protein structure reconstruction based on contact maps, especially those analyzing big sets of models.
| S-EPMC5963800 | biostudies-literature
Project description:Reconstructing protein structure based on contact maps leads to two types of models: properly oriented models and mirror models. This is due to the fact that contact maps do not include information on protein chirality. Therefore, both types of model orientations share the same contact map and are geometrically allowed. In this work, we verified the hypothesis that some of the energy terms calculated by PyRosetta could be useful to distinguish between properly oriented and mirror models. We studied 440 models of all-alpha protein domains reconstructed manually from their contact maps, where 50% of the models were properly oriented and 50% had mirror orientation. We showed that dihedral angles and energy terms, based on the probability of specific geometrical arrangement of the residues, differed significantly for properly oriented and mirror models.
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Project description:MOTIVATION:Molecular interaction maps have emerged as a meaningful way of representing biological mechanisms in a comprehensive and systematic manner. However, their static nature provides limited insights to the emerging behaviour of the described biological system under different conditions. Computational modelling provides the means to study dynamic properties through in silico simulations and perturbations. We aim to bridge the gap between static and dynamic representations of biological systems with CaSQ, a software tool that infers Boolean rules based on the topology and semantics of molecular interaction maps built with CellDesigner. RESULTS:We developed CaSQ by defining conversion rules and logical formulas for inferred Boolean models according to the topology and the annotations of the starting molecular interaction maps. We used CaSQ to produce executable files of existing molecular maps that differ in size, complexity and the use of Systems Biology Graphical Notation (SBGN) standards. We also compared, where possible, the manually built logical models corresponding to a molecular map to the ones inferred by CaSQ. The tool is able to process large and complex maps built with CellDesigner (either following SBGN standards or not) and produce Boolean models in a standard output format, Systems Biology Marked Up Language-qualitative (SBML-qual), that can be further analyzed using popular modelling tools. References, annotations and layout of the CellDesigner molecular map are retained in the obtained model, facilitating interoperability and model reusability. AVAILABILITY AND IMPLEMENTATION:The present tool is available online: https://lifeware.inria.fr/?soliman/post/casq/ and distributed as a Python package under the GNU GPLv3 license. The code can be accessed here: https://gitlab.inria.fr/soliman/casq. SUPPLEMENTARY INFORMATION:Supplementary data are available at Bioinformatics online.
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Project description:While the high-dimensional biological data have provided unprecedented data resources for the identification of biomarkers, consensus is still lacking on how to best analyze them. The recently developed Gaussian mirror (GM) and Model-X (MX) knockoff-based methods have much related model assumptions, which makes them appealing for the detection of new biomarkers. However, there are no guidelines for their practical use. In this research, we systematically compared the performance of MX-based and GM methods, where the impacts of the distribution of explanatory variables, their relatedness and the signal-to-noise ratio were evaluated. MX with knockoff generated using the second-order approximates (MX-SO) has the best performance as compared to other MX-based methods. MX-SO and GM have similar levels of power and computational speed under most of the simulations, but GM is more robust in the control of false discovery rate (FDR). In particular, MX-SO can only control the FDR well when there are weak correlations among explanatory variables and the sample size is at least moderate. On the contrary, GM can have the desired FDR as long as explanatory variables are not highly correlated. We further used GM and MX-based methods to detect biomarkers that are associated with the Alzheimer's disease-related PET-imaging trait and the Parkinson's disease-related T-tau of cerebrospinal fluid. We found that MX-based and GM methods are both powerful for the analysis of big biological data. Although genes selected from MX-based methods are more similar as compared to those from the GM method, both MX-based and GM methods can identify the well-known disease-associated genes for each disease. While MX-based methods can have a slightly higher power than that of the GM method, it is less robust, especially for data with small sample sizes, unknown distributions, and high correlations.
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Project description:Setup and optimisation of a high throughput pipeline for ChIPseq. The protocol used is ChIP carried out using the Agilent Bravo robot with subsequent Ilumina sequencing library preparation.This data is part of a pre-publication release. For information on the proper use of pre-publication data shared by the Wellcome Trust Sanger Institute (including details of any publication moratoria), please see http://www.sanger.ac.uk/datasharing/
2013-02-12 | E-ERAD-77 | biostudies-arrayexpress
Project description:PURPOSE:To evaluate whether automated assessment of beta zone parapapillary atrophy (?PPA) area can differentiate between glaucomatous and healthy eyes of varying axial lengths (AL). DESIGN:Cross-sectional study. METHODS:?PPA was automatically identified in glaucoma and healthy eyes with enhanced-depth imaging optical coherence tomography (OCT) optic nerve head (ONH) radial B-scans. Associations with AL and the presence of glaucoma were assessed. Manually delineated ?PPA on individual OCT ONH B-scans of 35 eyes from the Diagnostic Innovations in Glaucoma Study served to validate the automated method. RESULTS:One hundred fifty-three glaucoma eyes (mean ± standard deviation) (visual field mean deviation, -5.0 ± 6.4 dB and mean AL, 25.1 ± 1.1 mm) and 73 healthy eyes (visual field mean deviation, 0.1 ± 1.4 dB and mean AL, 24.1 ± 1.1 mm) were included. In multivariable analysis, larger ?PPA area was significantly associated with a diagnosis of glaucoma after controlling for age, central corneal thickness, and AL. Moreover, in multivariable analysis, the odds of having glaucoma were doubled for each 0.2 mm2 larger ?PPA area. The age- and AL-adjusted area under the receiver operating characteristic curve (95% confidence interval) of ?PPA area for differentiating between glaucoma and healthy eyes was 0.75 (0.68-0.81). Agreement for the location of the Bruch membrane opening and the location of retinal pigment epithelium tips was stronger between the automated technique and each individual observer than it was between the 2 observers. CONCLUSIONS:Larger ?PPA area, as determined by automated OCT assessment, is significantly associated with a diagnosis of glaucoma, even after adjusting for age and AL, and may aid in differentiating healthy from glaucomatous eyes.
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Project description:In view of the increasing interest both in inhibitors of protein-protein interactions and in protein drugs themselves, analysis of the three-dimensional structure of protein-protein complexes is assuming greater relevance in drug design. In the many cases where an experimental structure is not available, protein-protein docking becomes the method of choice for predicting the arrangement of the complex. However, reliably scoring protein-protein docking poses is still an unsolved problem. As a consequence, the screening of many docking models is usually required in the analysis step, to possibly single out the correct ones. Here, making use of exemplary cases, we review our recently introduced methods for the analysis of protein complex structures and for the scoring of protein docking poses, based on the use of inter-residue contacts and their visualization in inter-molecular contact maps. We also show that the ensemble of tools we developed can be used in the context of rational drug design targeting protein-protein interactions.
| S-EPMC6332208 | biostudies-literature
Project description:We report a fully automated procedure for the optimization and interpretation of reconstructions from cryo-electron microscopy (cryo-EM) data, available in Phenix as phenix.map_to_model. We applied our approach to 476 datasets with resolution of 4.5 Å or better, including reconstructions of 47 ribosomes and 32 other protein-RNA complexes. The median fraction of residues in the deposited structures reproduced automatically was 71% for reconstructions determined at resolutions of 3 Å or better and 47% for those at resolutions worse than 3 Å.
| S-EPMC6214191 | biostudies-literature
Project description:MotivationThe three-dimensional structure of genomes makes it possible for genomic regions not adjacent in the primary sequence to be spatially proximal. These DNA contacts have been found to be related to various molecular activities. Previous methods for analyzing DNA contact maps obtained from Hi-C experiments have largely focused on studying individual interactions, forming spatial clusters composed of contiguous blocks of genomic locations, or classifying these clusters into general categories based on some global properties of the contact maps.ResultsHere, we describe a novel computational method that can flexibly identify small clusters of spatially proximal genomic regions based on their local contact patterns. Using simulated data that highly resemble Hi-C data obtained from real genome structures, we demonstrate that our method identifies spatial clusters that are more compact than methods previously used for clustering genomic regions based on DNA contact maps. The clusters identified by our method enable us to confirm functionally related genomic regions previously reported to be spatially proximal in different species. We further show that each genomic region can be assigned a numeric affinity value that indicates its degree of participation in each local cluster, and these affinity values correlate quantitatively with DNase I hypersensitivity, gene expression, super enhancer activities and replication timing in a cell type specific manner. We also show that these cluster affinity values can precisely define boundaries of reported topologically associating domains, and further define local sub-domains within each domain.Availability and implementationThe source code of BNMF and tutorials on how to use the software to extract local clusters from contact maps are available at http://yiplab.cse.cuhk.edu.hk/bnmf/Contactkevinyip@cse.cuhk.edu.hkSupplementary informationSupplementary data are available at Bioinformatics online.
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Project description:While the role of 5-methylcytosine has been well studied, the biological role of 5-hydroxymethylcytosine still remains unclear due to the limited methods available for single-base detection of 5-hydroxymethylcytosine (5hmC). Here we present Mirror bisulfite sequencing for the detection of 5hmC at a single CpG site by synthesizing a DNA strand to mirror the parental strand. This semi-conservative duplex is sequentially treated with β-glucosyltransferase and M.SssI methylase. A glucosyl-5hmCpG in the parental strand inhibits methylation of the mirroring CpG site, and after bisulfite conversion, a thymine in the mirroring strand indicates a 5hmCpG site in the parental strand whereas a cytosine indicates a non-5hmC site. Using this method, the 5hmC levels of various human tissues and paired tumor liver tissues were mapped genome-wide.
2018-05-22 | GSE69272 | GEO