Assessment of domain boundary predictions and the prediction of intramolecular contacts in CASP8

Ezkurdia, Iakes; Graña, Osvaldo; Izarzugaza, José M. G.; Tress, Michael L.

doi:10.1002/prot.22554

Cited by 65 publications

(73 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The most commonly used are based on C α -C α , C β -C β or the closest heavy atom distances in sidechains. The relative improvement of PconsC2 is independent on the definition and therefore we chose to present only the results using the same contact definition as used in CASP (Critical Assessment of protein Structure Prediction), that is C β -C β , C α for Glycine, distance between the amino-acids ≤ 8Å [42].…”

Section: Methodsmentioning

confidence: 99%

Improved Contact Predictions Using the Recognition of Protein Like Contact Patterns

et al. 2014

View full text Add to dashboard Cite

Given sufficient large protein families, and using a global statistical inference approach, it is possible to obtain sufficient accuracy in protein residue contact predictions to predict the structure of many proteins. However, these approaches do not consider the fact that the contacts in a protein are neither randomly, nor independently distributed, but actually follow precise rules governed by the structure of the protein and thus are interdependent. Here, we present PconsC2, a novel method that uses a deep learning approach to identify protein-like contact patterns to improve contact predictions. A substantial enhancement can be seen for all contacts independently on the number of aligned sequences, residue separation or secondary structure type, but is largest for β-sheet containing proteins. In addition to being superior to earlier methods based on statistical inferences, in comparison to state of the art methods using machine learning, PconsC2 is superior for families with more than 100 effective sequence homologs. The improved contact prediction enables improved structure prediction.

show abstract

Section: Methodsmentioning

confidence: 99%

Improved Contact Predictions Using the Recognition of Protein Like Contact Patterns

et al. 2014

View full text Add to dashboard Cite

show abstract

“…Such approaches have worked favorably in cases for smaller proteins that have fewer than ∼90 amino acids 7 and need to be combined with experimental data for larger proteins 8,9 . Other approaches attempt to predict residue contacts using three-dimensional information with machine-learning techniques, such as support vector machines, random forests and neural networks, but contact prediction accuracy remained “still quite low” 10 with substantial improvements to models achieved only for some small proteins 11,12 . Clearly, and unfortunately, the de novo structure prediction problem does not scale 13 , the conformational search space increases exponentially as the size of the protein increases, presenting a fundamental computational challenge, even for fragment-based methods 14 .…”

mentioning

confidence: 99%

Protein structure prediction from sequence variation

2012

View full text Add to dashboard Cite

Genomic sequences contain rich evolutionary information about functional constraints on macromolecules such as proteins. This information can be efficiently mined to detect evolutionary couplings between residues in proteins and address the long-standing challenge to compute protein three-dimensional structures from amino acid sequences. Substantial progress has recently been made on this problem owing to the explosive growth in available sequences and the application of global statistical methods. In addition to three-dimensional structure, the improved understanding of covariation may help identify functional residues involved in ligand binding, protein-complex formation and conformational changes. We expect computation of covariation patterns to complement experimental structural biology in elucidating the full spectrum of protein structures, their functional interactions and evolutionary dynamics.

show abstract

“…Xd is a weighted harmonic average difference between the distance distribution of the predicted contacts and the all-pairs distance distribution; it also measures how the distribution of distances for predicted positives differs from the distribution of all pairs of residues in the target domains (Ezkurdia et al, 2009;Grana et al, 2005).…”

Section: Performance Assessmentmentioning

confidence: 99%

Predicting residue–residue contacts using random forest models

Fang

2011

Bioinformatics

View full text Add to dashboard Cite

Motivation: Protein residue-residue contact prediction can be useful in predicting protein 3D structures. Current algorithms for such a purpose leave room for improvement. Results: We develop ProC_S3, a set of Random Forest algorithmbased models, for predicting residue-residue contact maps. The models are constructed based on a collection of 1490 nonredundant, high-resolution protein structures using >1280 sequencebased features. A new amino acid residue contact propensity matrix and a new set of seven amino acid groups based on contact preference are developed and used in ProC_S3. ProC_S3 delivers a 3-fold cross-validated accuracy of 26.9% with coverage of 4.7% for top L/5 predictions (L is the number of residues in a protein) of long-range contacts (sequence separation ≥ 24). Further benchmark tests deliver an accuracy of 29.7% and coverage of 5.6% for an independent set of 329 proteins. In the recently completed Ninth Community Wide Experiment on the Critical Assessment of Techniques for Protein Structure Prediction (CASP9), ProC_S3 is ranked as No. 1, No. 3, and No. 2 accuracies in the top L/5, L/10 and best 5 predictions of long-range contacts, respectively, among 18 automatic prediction servers.

show abstract

Assessment of domain boundary predictions and the prediction of intramolecular contacts in CASP8

Cited by 65 publications

References 24 publications

Improved Contact Predictions Using the Recognition of Protein Like Contact Patterns

Improved Contact Predictions Using the Recognition of Protein Like Contact Patterns

Protein structure prediction from sequence variation

Predicting residue–residue contacts using random forest models

Contact Info

Product

Resources

About