Direct correlation analysis improves fold recognition

Sadowski, Michael I.; Maksimiak, Katarzyna; Taylor, William R.

doi:10.1016/j.compbiolchem.2011.08.002

Cited by 22 publications

(24 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Recently, the application of sparse inverse covariance matrices to contact prediction has led to a significant improvement in the accuracy of such predictions,29–34 raising the possibility of using such predictions as a source of information on possible domain boundaries, an earlier study of which was performed by Rigden 35. In this article, we explore a number of methods to exploit this new information and show that a simple kernel‐smoothing predictor can provide accurate information of use in domain prediction with an improvement of between 8 and 20% over other ab initio predictors.…”

Section: Introductionmentioning

confidence: 94%

Prediction of protein domain boundaries from inverse covariances

Sadowski¹

2012

Proteins

Self Cite

View full text Add to dashboard Cite

It has been known even since relatively few structures had been solved that longer protein chains often contain multiple domains, which may fold separately and play the role of reusable functional modules found in many contexts. In many structural biology tasks, in particular structure prediction, it is of great use to be able to identify domains within the structure and analyze these regions separately. However, when using sequence data alone this task has proven exceptionally difficult, with relatively little improvement over the naive method of choosing boundaries based on size distributions of observed domains. The recent significant improvement in contact prediction provides a new source of information for domain prediction. We test several methods for using this information including a kernel smoothing-based approach and methods based on building alpha-carbon models and compare performance with a length-based predictor, a homology search method and four published sequence-based predictors: DOMCUT, DomPRO, DLP-SVM, and SCOOBY-DOmain. We show that the kernel-smoothing method is significantly better than the other ab initio predictors when both single-domain and multidomain targets are considered and is not significantly different to the homology-based method. Considering only multidomain targets the kernel-smoothing method outperforms all of the published methods except DLP-SVM. The kernel smoothing method therefore represents a potentially useful improvement to ab initio domain prediction. Proteins 2013. © 2012 Wiley Periodicals, Inc.

show abstract

Section: Introductionmentioning

confidence: 94%

Prediction of protein domain boundaries from inverse covariances

Sadowski¹

2012

Proteins

Self Cite

View full text Add to dashboard Cite

show abstract

“…Previously, such attempts were hindered, principally, by a lack of sequence data; however, with the recent acceleration in the accumulation of known sequences, this limitation is becoming less of a restriction. Recent attempts to use residue covariation have been based on families of many sequences11 and have met with greatly improved success 12…”

Section: Introductionmentioning

confidence: 99%

Correlation for k_La Prediction of Airlift Loop Reactors Including the Gas Phase Residence Time Effect

et al. 2015

View full text Add to dashboard Cite

Experimental mass transfer data and literature data of airlift loop reactors are analyzed concerning the effect of the gas phase residence time on the volumetric mass transfer coefficient kLa. It becomes evident that a neglect of the oxygen depletion can be regarded as a diminishment of the mass transfer driving force. The latter is found to be an important cause of the mass transfer dependency on the actual apparatus height of airlift loop reactors. Therefore, a new correlation is presented that describes the volumetric mass transfer coefficients of airlift loop reactors by including the effect of gas phase transient component concentration depletion.

show abstract

“…We have shown in this work that the application of predicted contacts derived from mutual information, can be applied with improved effect to the combinatorial fold generation level of the PLATO model construction method compared with previous results where it was applied only as a post-filter [6]. This entailed using the predicted contacts at the level of secondary structure elements (SSEs) rather than at the residue level.…”

Section: Discussionmentioning

confidence: 99%

“…Using these large sequence families, it now appears that the information in pairwise residue correlations is approaching a threshold at which useful structural constraints can be obtained [6]. In that work, we showed that predicted contacts derived from processing the mutual information (MI) between positions in a multiple sequence alignment could be used to select the correct fold from a large collection of well constructed decoys.…”

Section: Introductionmentioning

confidence: 99%

Structural Constraints on the Covariance Matrix Derived from Multiple Aligned Protein Sequences

Taylor¹,

Sadowski²

2011

PLoS ONE

Self Cite

View full text Add to dashboard Cite

Residue contact predictions were calculated based on the mutual information observed between pairs of positions in large multiple protein sequence alignments. Where previously only the statistical properties of these data have been considered important, we introduce new measures to impose constraints that make the contact map more consistent with a three dimensional structure. These included global (bulk) properties and local secondary structure properties. The latter allowed the contact constraints to be employed at the level of filtering pairs of secondary structure contacts which led to a more efficient (lower-level) implementation in the PLATO structure prediction server. Where previously the measure of success with this method had been whether the correct fold was predicted in the top 10 ranked models, with the current implementation, our summary statistic is the number of correct folds included in the top 10 models — which is on average over 50 percent.

show abstract

Direct correlation analysis improves fold recognition

Cited by 22 publications

References 30 publications

Prediction of protein domain boundaries from inverse covariances

Prediction of protein domain boundaries from inverse covariances

Correlation for k_La Prediction of Airlift Loop Reactors Including the Gas Phase Residence Time Effect

Structural Constraints on the Covariance Matrix Derived from Multiple Aligned Protein Sequences

Contact Info

Product

Resources

About

Direct correlation analysis improves fold recognition

Cited by 22 publications

References 30 publications

Prediction of protein domain boundaries from inverse covariances

Prediction of protein domain boundaries from inverse covariances

Correlation for kLa Prediction of Airlift Loop Reactors Including the Gas Phase Residence Time Effect

Structural Constraints on the Covariance Matrix Derived from Multiple Aligned Protein Sequences

Contact Info

Product

Resources

About

Correlation for k_La Prediction of Airlift Loop Reactors Including the Gas Phase Residence Time Effect