Protein Structure Prediction using String Kernels

Rangwala, Huzefa; DeRonne, Kevin W.; Karypis, George

doi:10.1002/9780470124642.ch8

Cited by 6 publications

(7 citation statements)

References 55 publications

(98 reference statements)

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“…String kernels (Haussler, 1999;Lodhi et al, 2002;Leslie et al, 2002Leslie et al, , 2004Saigo et al, 2004) are a class of kernel methods that have been successfully used in many sequence classification tasks (Leslie et al, 2002(Leslie et al, , 2004Saigo et al, 2004;Zaki et al, 2005;Rangwala et al, 2006;Wu et al, 2006). In these applications, a protein sequence is viewed as a string defined on a finite alphabet of 20 amino acids.…”

Section: String Kernelsmentioning

confidence: 99%

“…Therefore, one objective of this study was to explore a class of kernel methods, namely string kernels, in addition to the widely used radial bias function (RBF) kernel. Our choice of string kernels was motivated by their successful application in a number of bioinformatics classification tasks, including protein remote homology detection (Leslie et al, 2002(Leslie et al, , 2004Zaki et al, 2005), protein structure prediction (Rangwala et al, 2006), protein binding site prediction (Wu et al, 2006), and major histocompatibility complex (MHC) binding peptide prediction (Salomon and Flower, 2006). In addition, we introduce the subsequence kernel (SSK), which has been successfully used in text classification (Lodhi et al, 2002), but has been under-explored in macromolecular sequence classification applications.…”

Section: Introductionmentioning

confidence: 99%

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Predicting linear B‐cell epitopes using string kernels

EL-Manzalawy

Dobbs

Honavar

2008

J of Molecular Recognition

648

464

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The identification and characterization of B-cell epitopes play an important role in vaccine design, immunodiagnostic tests, and antibody production. Therefore, computational tools for reliably predicting linear B-cell epitopes are highly desirable. We evaluated Support Vector Machine (SVM) classifiers trained utilizing five different kernel methods using fivefold cross-validation on a homology-reduced data set of 701 linear B-cell epitopes, extracted from Bcipep database, and 701 non-epitopes, randomly extracted from SwissProt sequences. Based on the results of our computational experiments, we propose BCPred, a novel method for predicting linear B-cell epitopes using the subsequence kernel. We show that the predictive performance of BCPred (AUC = 0.758) outperforms 11 SVM-based classifiers developed and evaluated in our experiments as well as our implementation of AAP (AUC = 0.7), a recently proposed method for predicting linear B-cell epitopes using amino acid pair antigenicity. Furthermore, we compared BCPred with AAP and ABCPred, a method that uses recurrent neural networks, using two data sets of unique B-cell epitopes that had been previously used to evaluate ABCPred. Analysis of the data sets used and the results of this comparison show that conclusions about the relative performance of different B-cell epitope prediction methods drawn on the basis of experiments using data sets of unique B-cell epitopes are likely to yield overly optimistic estimates of performance of evaluated methods. This argues for the use of carefully homology-reduced data sets in comparing B-cell epitope prediction methods to avoid misleading conclusions about how different methods compare to each other. Our homologyreduced data set and implementations of BCPred as well as the APP method are publicly available through our web-based server, BCPREDS, at: http://ailab.cs.iastate.edu/bcpreds/.

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Section: String Kernelsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Predicting linear B‐cell epitopes using string kernels

EL-Manzalawy

Dobbs

Honavar

2008

J of Molecular Recognition

648

464

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“…A diverse range of techniques have been tried, including Markov Models [27,26] and the prediction of likely geometry using empirical knowledge based on recognised peptides [2]. Very recently, machine learning has been applied to a scoring-based system of string kernels [38]: that report also includes a excellent introduction to the essential aspects of folding and structure prediction.…”

Section: Figure 3 Solvent-accessible Surfacementioning

confidence: 99%

Protein Structure Computation

Skone

Cameron

2007

2007 Frontiers in the Convergence of Bioscience and Information Technologies

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The field of protein structure representation and manipulation has received a great amount of interest during the past fifteen years. Research has come from the biochemical, medical, and physical sciences, in particular for the development of rational drug discovery methods. One of the most significant problems is that of docking: identifying automatically whether two molecules might bind to each other is of central importance to the development of novel medicinal compounds. We present a summary of protein computation principles in the context of an implementation of the well-established FFT docking algorithm.

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“…String kernels (Leslie et al, 2002(Leslie et al, , 2004Lodhi et al, 2002;Saigo et al, 2004;Haussler, 1999) are a class of kernel methods that have been successfully used in many sequence classification tasks (Leslie et al, 2002(Leslie et al, , 2004Saigo et al, 2004;Zaki et al, 2005;Rangwala et al, 2006;Wu et al, 2006). In these applications, a protein sequence is viewed as a string defined on a finite alphabet of 20 amino acids.…”

Section: String Kernelsmentioning

confidence: 99%