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Literature DB >> 22691493

Genomics-aided structure prediction.

Joanna I Sułkowska¹, Faruck Morcos, Martin Weigt, Terence Hwa, José N Onuchic.

Abstract

We introduce a theoretical framework that exploits the ever-increasing genomic sequence information for protein structure prediction. Structure-based models are modified to incorporate constraints by a large number of non-local contacts estimated from direct coupling analysis (DCA) of co-evolving genomic sequences. A simple hybrid method, called DCA-fold, integrating DCA contacts with an accurate knowledge of local information (e.g., the local secondary structure) is sufficient to fold proteins in the range of 1-3 Å resolution.

Entities: Chemical

Mesh：

Substances：
Proteins

Year: 2012 PMID： 22691493 PMCID： PMC3387073 DOI： 10.1073/pnas.1207864109

Source DB: PubMed Journal: Proc Natl Acad Sci U S A ISSN： 0027-8424 Impact factor: 11.205

35 in total

1. A neural network based predictor of residue contacts in proteins.

Authors: P Fariselli; R Casadio
Journal: Protein Eng Date: 1999-01

2. Prediction of contact maps with neural networks and correlated mutations.

Authors: P Fariselli; O Olmea; A Valencia; R Casadio
Journal: Protein Eng Date: 2001-11

3. Predictions without templates: new folds, secondary structure, and contacts in CASP5.

Authors: Patrick Aloy; Alexander Stark; Caroline Hadley; Robert B Russell
Journal: Proteins Date: 2003

4. TOUCHSTONE II: a new approach to ab initio protein structure prediction.

Authors: Yang Zhang; Andrzej Kolinski; Jeffrey Skolnick
Journal: Biophys J Date: 2003-08 Impact factor: 4.033

5. Automated structure prediction of weakly homologous proteins on a genomic scale.

Authors: Yang Zhang; Jeffrey Skolnick
Journal: Proc Natl Acad Sci U S A Date: 2004-05-04 Impact factor: 11.205

6. Direct-coupling analysis of residue coevolution captures native contacts across many protein families.

Authors: Faruck Morcos; Andrea Pagnani; Bryan Lunt; Arianna Bertolino; Debora S Marks; Chris Sander; Riccardo Zecchina; José N Onuchic; Terence Hwa; Martin Weigt
Journal: Proc Natl Acad Sci U S A Date: 2011-11-21 Impact factor: 11.205

7. Robustness and generalization of structure-based models for protein folding and function.

Authors: Heiko Lammert; Alexander Schug; José N Onuchic
Journal: Proteins Date: 2009-12

8. Critical assessment of methods of protein structure prediction - Round VIII.

Authors: John Moult; Krzysztof Fidelis; Andriy Kryshtafovych; Burkhard Rost; Anna Tramontano
Journal: Proteins Date: 2009

9. Canonical dynamics: Equilibrium phase-space distributions.

Authors:
Journal: Phys Rev A Gen Phys Date: 1985-03

10. Neighbor-dependent Ramachandran probability distributions of amino acids developed from a hierarchical Dirichlet process model.

Authors: Daniel Ting; Guoli Wang; Maxim Shapovalov; Rajib Mitra; Michael I Jordan; Roland L Dunbrack
Journal: PLoS Comput Biol Date: 2010-04-29 Impact factor: 4.475

87 in total

1. From residue coevolution to protein conformational ensembles and functional dynamics.

Authors: Ludovico Sutto; Simone Marsili; Alfonso Valencia; Francesco Luigi Gervasio
Journal: Proc Natl Acad Sci U S A Date: 2015-10-20 Impact factor: 11.205

Review 2. Interpreting functional effects of coding variants: challenges in proteome-scale prediction, annotation and assessment.

Authors: Khader Shameer; Lokesh P Tripathi; Krishna R Kalari; Joel T Dudley; Ramanathan Sowdhamini
Journal: Brief Bioinform Date: 2015-10-22 Impact factor: 11.622

Genomics-aided structure prediction.

1. A neural network based predictor of residue contacts in proteins.

2. Prediction of contact maps with neural networks and correlated mutations.

3. Predictions without templates: new folds, secondary structure, and contacts in CASP5.

4. TOUCHSTONE II: a new approach to ab initio protein structure prediction.

5. Automated structure prediction of weakly homologous proteins on a genomic scale.

6. Direct-coupling analysis of residue coevolution captures native contacts across many protein families.

7. Robustness and generalization of structure-based models for protein folding and function.

8. Critical assessment of methods of protein structure prediction - Round VIII.

9. Canonical dynamics: Equilibrium phase-space distributions.

10. Neighbor-dependent Ramachandran probability distributions of amino acids developed from a hierarchical Dirichlet process model.

1. From residue coevolution to protein conformational ensembles and functional dynamics.

Review 2. Interpreting functional effects of coding variants: challenges in proteome-scale prediction, annotation and assessment.

Review 3. The functional importance of co-evolving residues in proteins.

4. Assessing the utility of coevolution-based residue-residue contact predictions in a sequence- and structure-rich era.

5. Coevolutionary signals across protein lineages help capture multiple protein conformations.

Review 6. Reads meet rotamers: structural biology in the age of deep sequencing.

7. Interaction specificity of clustered protocadherins inferred from sequence covariation and structural analysis.

8. Influence of multiple-sequence-alignment depth on Potts statistical models of protein covariation.

Review 9. Hybrid methods for combined experimental and computational determination of protein structure.

10. Origins of coevolution between residues distant in protein 3D structures.