Prediction and evolutionary information analysis of protein solvent accessibility using multiple linear regression

Wang, Jung-Ying; Lee, Hahn-Ming; Ahmad, Shandar

doi:10.1002/prot.20620

Cited by 36 publications

(50 citation statements)

References 21 publications

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“…Numerous approaches have been developed for the prediction of solvent accessibility in soluble proteins (for examples, see Rost and Sander 1994;Pascarella et al 1998;Li and Pan 2001;Pollastri et al 2002;Yuan et al 2002;Wang et al 2005). Similarly, lipid accessibility of transmembrane residues is an important prediction task in the fi eld of membrane protein structural bioinformatics.…”

Section: Prediction Of Lipid Accessibilitymentioning

confidence: 99%

Predicting residue and helix contacts in membrane proteins

Fuchs

Kirschner

Frishman

2010

Structural Bioinformatics of Membrane Proteins

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Helix-helix contacts are an important feature of alpha-helical membrane proteins as they defi ne their characteristic helix bundle structure. No bioinformatics approaches for the prediction of pairwise residue contacts in membrane proteins have existed until recently. In this chapter we describe novel contact prediction methods based on residue coevolution and machine learning techniques specifi cally geared towards membrane proteins. While contact prediction accuracies are limited to ~10% using co-evolving residues alone, machine learning methods are able to improve these accuracies signifi cantly to more than 25% by using available membrane protein structures as a training dataset and incorporating membrane protein specifi c sequence features into the prediction process. Importantly, predicted residue contacts allow for identifi cation of interacting transmembrane helices with high accuracy. As diff erent membrane protein structures can be distinguished by their specifi c patt ern of helix interactions, predicted residue contacts may not only serve as structural constraints in modeling experiments, but also constitute valuable information for structural classifi cation of membrane proteins with unknown structure.

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Section: Prediction Of Lipid Accessibilitymentioning

confidence: 99%

Predicting residue and helix contacts in membrane proteins

Fuchs

Kirschner

Frishman

2010

Structural Bioinformatics of Membrane Proteins

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show abstract

“…While the methods for realvalue prediction of solvent accessibility had achieved reasonable accuracy prior to Real -SPINE (a correlation of above 0.60 [199,207] ), prediction of real -value backbone angle appeared more challenging with a correlation coeffi cient between predicted and measured real -value ψ angles at only 0.47 [107] .…”

Section: Real -Spine For Real -Value Prediction Of Backbone Torsion Amentioning

confidence: 99%

Prediction of One‐Dimensional Structural Properties Of Proteins by Integrated Neural Networks

Zhou

Faraggi

2010

Introduction to Protein Structure Prediction

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“…Chan and Dill pointed that the dicted with PSI-PRED, and binary code that burial of core residues is the driving force in protein indicates position of a given residue with folding, which suggests that knowledge of localizarespect to sequence termini. Cross validation of individual residues (surface vs. buried) protion tests on a benchmark dataset show that vides useful information to reconstruct the 3D-our method achieves 14.3 mean absolute structure of proteins [6][7][8]. error and 0.68 correlation.…”

Section: Predicted Relative Solvent Accessibility (Rsa)mentioning

confidence: 99%

Sequence based prediction of relative solvent accessibility using two-stage support vector regression with confidence values

Chen¹,

Kurgan²,

Kurgan³

2008

JBiSE

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ABSTRACTing gap between the number of known protein sequences and the number of known structures. Predicted relative solvent accessibility (RSA)Despite several decades of extensive research in terprovides useful information for prediction of tiary structure prediction, this task is still a big chalbinding sites and reconstruction of the 3D-lenge, especially for sequences that do not have a sigs t r u c t u r e b a s e d o n a p r o t e i n s e q u e n c e .nificant sequence similarity with known structures Recent years observed development of sev- [1]. As a result, the predictions of the solvent accessieral RSA prediction methods including those b i l i t y [ 2 ] a n d t h e s e c o n d a r y s t r u c t u r e [ 3 ] a r e that generate real values and those that preaddressed as an intermediate step towards the predicdict discrete states (buried vs. exposed). We tion of the tertiary structure. The relative solvent propose a novel method for real value predicaccessibility (RSA) reflects the degree to which a restion that aims at minimizing the prediction idue interacts with the solvent molecules. Since proerror when compared with six existing methtein-protein and protein-ligand interactions occur at ods. The proposed method is based on a twothe protein surface, only the residues that have a stage Support Vector Regression (SVR) prelarge surface area exposed to the solvent can possibly dictor. The improved prediction quality is a bind to the ligands and other proteins. As a result, preresult of the developed composite sequence diction of solvent accessibility provides useful inforrepresentation, which includes a custommation for prediction of binding sites [4] and is selected subset of features from the PSIvitally important for understanding the binding mech-BLAST profile, secondary structure preanism of proteins [5]. Chan and Dill pointed that the dicted with PSI-PRED, and binary code that burial of core residues is the driving force in protein indicates position of a given residue with folding, which suggests that knowledge of localizarespect to sequence termini. Cross validation of individual residues (surface vs. buried) protion tests on a benchmark dataset show that vides useful information to reconstruct the 3D-our method achieves 14.3 mean absolute structure of proteins [6][7][8]. error and 0.68 correlation. We also propose aThe existing solvent accessibility prediction methconfidence value that is associated with each ods use the protein sequence, which is converted into predicted RSA values. The confidence is com-a fixed-size feature-based representation, as an input puted based on the difference in predictions to predict the RSA for each of the residues. These from the two-stage SVR and a second two-methods can be divided into two main groups: stage Linear Regression (LR) predictor. TheReal valued predictors predict RSA value (the confidence values can be used to indicate definition is given in the Materials section

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Prediction and evolutionary information analysis of protein solvent accessibility using multiple linear regression

Cited by 36 publications

References 21 publications

Predicting residue and helix contacts in membrane proteins

Predicting residue and helix contacts in membrane proteins

Prediction of One‐Dimensional Structural Properties Of Proteins by Integrated Neural Networks

Sequence based prediction of relative solvent accessibility using two-stage support vector regression with confidence values

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