Browse
Submit Data
Databases
API
Help

Dataset Information

24 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

ReFOLD3: refinement of 3D protein models with gradual restraints based on predicted local quality and residue contacts.

ABSTRACT: ReFOLD3 is unique in its application of gradual restraints, calculated from local model quality estimates and contact predictions, which are used to guide the refinement of theoretical 3D protein models towards the native structures. ReFOLD3 achieves improved performance by using an iterative refinement protocol to fix incorrect residue contacts and local errors, including unusual bonds and angles, which are identified in the submitted models by our leading ModFOLD8 model quality assessment method. Following refinement, the likely resulting improvements to the submitted models are recognized by ModFOLD8, which produces both global and local quality estimates. During the CASP14 prediction season (May-Aug 2020), we used the ReFOLD3 protocol to refine hundreds of 3D models, for both the refinement and the main tertiary structure prediction categories. Our group improved the global and local quality scores for numerous starting models in the refinement category, where we ranked in the top 10 according to the official assessment. The ReFOLD3 protocol was also used for the refinement of the SARS-CoV-2 targets as a part of the CASP Commons COVID-19 initiative, and we provided a significant number of the top 10 models. The ReFOLD3 web server is freely available at https://www.reading.ac.uk/bioinf/ReFOLD/.

SUBMITTER: Adiyaman R

PROVIDER: S-EPMC8218204 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Similar Datasets

Protein topology from predicted residue contacts.

Project description:Residue contacts predicted from correlated positions in a multiple sequence alignment are often sparse and uncertain. To some extent, these limitations in the data can be overcome by grouping the contacts by secondary structure elements and enumerating the possible packing arrangements of these elements in a combinatorial manner. Strong interactions appear frequently but inconsistent interactions are down-weighted and missing interactions up-weighted. The resulting improved consistency in the predicted interactions has allowed the method to be successfully applied to proteins up to 200 residues in length which is larger than any structure previously predicted using sequence data alone.

| S-EPMC3324774 | biostudies-literature

Using multi-data hidden Markov models trained on local neighborhoods of protein structure to predict residue-residue contacts.

Project description:MotivationCorrect prediction of residue-residue contacts in proteins that lack good templates with known structure would take ab initio protein structure prediction a large step forward. The lack of correct contacts, and in particular long-range contacts, is considered the main reason why these methods often fail.ResultsWe propose a novel hidden Markov model (HMM)-based method for predicting residue-residue contacts from protein sequences using as training data homologous sequences, predicted secondary structure and a library of local neighborhoods (local descriptors of protein structure). The library consists of recurring structural entities incorporating short-, medium- and long-range interactions and is general enough to reassemble the cores of nearly all proteins in the PDB. The method is tested on an external test set of 606 domains with no significant sequence similarity to the training set as well as 151 domains with SCOP folds not present in the training set. Considering the top 0.2 x L predictions (L = sequence length), our HMMs obtained an accuracy of 22.8% for long-range interactions in new fold targets, and an average accuracy of 28.6% for long-, medium- and short-range contacts. This is a significant performance increase over currently available methods when comparing against results published in the literature.Availabilityhttp://predictioncenter.org/Services/FragHMMent/.

| S-EPMC2677742 | biostudies-literature

Ligand-Binding-Site Structure Refinement Using Molecular Dynamics with Restraints Derived from Predicted Binding Site Templates.

Project description:Accurate modeling of ligand-binding-site structures plays a critical role in structure-based virtual screening. However, the structures of the ligand-binding site in most predicted protein models are generally of low quality and need refinements. In this work, we present a ligand-binding-site structure refinement protocol using molecular dynamics simulation with restraints derived from predicted binding site templates. Our benchmark validation shows great performance for 40 diverse sets of proteins from the Astex list. The ligand-binding sites on modeled protein structures are consistently refined using our method with an average Cα RMSD improvement of 0.90 Å. Comparison of ligand binding modes from ligand docking to initial unrefined and refined structures shows an average of 1.97 Å RMSD improvement in the refined structures. These results demonstrate a promising new method of structure refinement for protein ligand-binding-site structures.

| S-EPMC6884403 | biostudies-literature

Assessing Predicted Contacts for Building Protein Three-Dimensional Models.

Project description:Recent successes of contact-guided protein structure prediction methods have revived interest in solving the long-standing problem of ab initio protein structure prediction. With homology modeling failing for many protein sequences that do not have templates, contact-guided structure prediction has shown promise, and consequently, contact prediction has gained a lot of interest recently. Although a few dozen contact prediction tools are already currently available as web servers and downloadables, not enough research has been done towards using existing measures like precision and recall to evaluate these contacts with the goal of building three-dimensional models. Moreover, when we do not have a native structure for a set of predicted contacts, the only analysis we can perform is a simple contact map visualization of the predicted contacts. A wider and more rigorous assessment of the predicted contacts is needed, in order to build tertiary structure models. This chapter discusses instructions and protocols for using tools and applying techniques in order to assess predicted contacts for building three-dimensional models.

| S-EPMC5512107 | biostudies-literature

ModFOLD8: accurate global and local quality estimates for 3D protein models.

Project description:Methods for estimating the quality of 3D models of proteins are vital tools for driving the acceptance and utility of predicted tertiary structures by the wider bioscience community. Here we describe the significant major updates to ModFOLD, which has maintained its position as a leading server for the prediction of global and local quality of 3D protein models, over the past decade (>20 000 unique external users). ModFOLD8 is the latest version of the server, which combines the strengths of multiple pure-single and quasi-single model methods. Improvements have been made to the web server interface and there has been successive increases in prediction accuracy, which were achieved through integration of newly developed scoring methods and advanced deep learning-based residue contact predictions. Each version of the ModFOLD server has been independently blind tested in the biennial CASP experiments, as well as being continuously evaluated via the CAMEO project. In CASP13 and CASP14, the ModFOLD7 and ModFOLD8 variants ranked among the top 10 quality estimation methods according to almost every official analysis. Prior to CASP14, ModFOLD8 was also applied for the evaluation of SARS-CoV-2 protein models as part of CASP Commons 2020 initiative. The ModFOLD8 server is freely available at: https://www.reading.ac.uk/bioinf/ModFOLD/.

| S-EPMC8218196 | biostudies-literature

ReFOLD: a server for the refinement of 3D protein models guided by accurate quality estimates.

Project description:ReFOLD is a novel hybrid refinement server with integrated high performance global and local Accuracy Self Estimates (ASEs). The server attempts to identify and to fix likely errors in user supplied 3D models of proteins via successive rounds of refinement. The server is unique in providing output for multiple alternative refined models in a way that allows users to quickly visualize the key residue locations, which are likely to have been improved. This is important, as global refinement of a full chain model may not always be possible, whereas local regions, or individual domains, can often be much improved. Thus, users may easily compare the specific regions of the alternative refined models in which they are most interested e.g. key interaction sites or domains. ReFOLD was used to generate hundreds of alternative refined models for the CASP12 experiment, boosting our group's performance in the main tertiary structure prediction category. Our successful refinement of initial server models combined with our built-in ASEs were instrumental to our second place ranking on Template Based Modeling (TBM) and Free Modeling (FM)/TBM targets. The ReFOLD server is freely available at: http://www.reading.ac.uk/bioinf/ReFOLD/.

| S-EPMC5570150 | biostudies-literature

Protein tolerance to random circular permutation correlates with thermostability and local energetics of residue-residue contacts.

Project description:Adenylate kinase (AK) orthologs with a range of thermostabilities were subjected to random circular permutation, and deep mutational scanning was used to evaluate where new protein termini were nondisruptive to activity. The fraction of circularly permuted variants that retained function in each library correlated with AK thermostability. In addition, analysis of the positional tolerance to new termini, which increase local conformational flexibility, showed that bonds were either functionally sensitive to cleavage across all homologs, differentially sensitive, or uniformly tolerant. The mobile AMP-binding domain, which displays the highest calculated contact energies, presented the greatest tolerance to new termini across all AKs. In contrast, retention of function in the lid and core domains was more dependent upon AK melting temperature. These results show that family permutation profiling identifies primary structure that has been selected by evolution for dynamics that are critical to activity within an enzyme family. These findings also illustrate how deep mutational scanning can be applied to protein homologs in parallel to differentiate how topology, stability, and local energetics govern mutational tolerance.

| S-EPMC7462040 | biostudies-literature

Evaluation of the stereochemical quality of predicted RNA 3D models in the RNA-Puzzles submissions.

Project description:In silico prediction is a well-established approach to derive a general shape of an RNA molecule based on its sequence or secondary structure. This paper reports an analysis of the stereochemical quality of the RNA three-dimensional models predicted using dedicated computer programs. The stereochemistry of 1052 RNA 3D structures, including 1030 models predicted by fully automated and human-guided approaches within 22 RNA-Puzzles challenges and reference structures, is analyzed. The evaluation is based on standards of RNA stereochemistry that the Protein Data Bank requires from deposited experimental structures. Deviations from standard bond lengths and angles, planarity, or chirality are quantified. A reduction in the number of such deviations should help in the improvement of RNA 3D structure modeling approaches.

| S-EPMC8906551 | biostudies-literature

Using inferred residue contacts to distinguish between correct and incorrect protein models.

Project description:MotivationThe de novo prediction of 3D protein structure is enjoying a period of dramatic improvements. Often, a remaining difficulty is to select the model closest to the true structure from a group of low-energy candidates. To what extent can inter-residue contact predictions from multiple sequence alignments, information which is orthogonal to that used in most structure prediction algorithms, be used to identify those models most similar to the native protein structure?ResultsWe present a Bayesian inference procedure to identify residue pairs that are spatially proximal in a protein structure. The method takes as input a multiple sequence alignment, and outputs an accurate posterior probability of proximity for each residue pair. We exploit a recent metagenomic sequencing project to create large, diverse and informative multiple sequence alignments for a test set of 1656 known protein structures. The method infers spatially proximal residue pairs in this test set with good accuracy: top-ranked predictions achieve an average accuracy of 38% (for an average 21-fold improvement over random predictions) in cross-validation tests. Notably, the accuracy of predicted 3D models generated by a range of structure prediction algorithms strongly correlates with how well the models satisfy probable residue contacts inferred via our method. This correlation allows for confident rejection of incorrect structural models.AvailabilityAn implementation of the method is freely available at http://www.doe-mbi.ucla.edu/services.

| S-EPMC2638260 | biostudies-literature

ModFOLD6: an accurate web server for the global and local quality estimation of 3D protein models.

Project description:Methods that reliably estimate the likely similarity between the predicted and native structures of proteins have become essential for driving the acceptance and adoption of three-dimensional protein models by life scientists. ModFOLD6 is the latest version of our leading resource for Estimates of Model Accuracy (EMA), which uses a pioneering hybrid quasi-single model approach. The ModFOLD6 server integrates scores from three pure-single model methods and three quasi-single model methods using a neural network to estimate local quality scores. Additionally, the server provides three options for producing global score estimates, depending on the requirements of the user: (i) ModFOLD6_rank, which is optimized for ranking/selection, (ii) ModFOLD6_cor, which is optimized for correlations of predicted and observed scores and (iii) ModFOLD6 global for balanced performance. The ModFOLD6 methods rank among the top few for EMA, according to independent blind testing by the CASP12 assessors. The ModFOLD6 server is also continuously automatically evaluated as part of the CAMEO project, where significant performance gains have been observed compared to our previous server and other publicly available servers. The ModFOLD6 server is freely available at: http://www.reading.ac.uk/bioinf/ModFOLD/.

| S-EPMC5570241 | 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