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Free Content Editorial [Hot topic: Special Issue on Intelligent Computing in Protein Science (Guest Editor: De-Shuang Huang)]

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We are very pleased to offer this special issue to the readers of Protein and Peptide Letters by selecting the candidate papers from the 2009 International Conference on Intelligent Computing (ICIC), held on September 16-19, 2009 in Ulsan, Korea. Eleven papers representing less than five percent of all eligible papers accepted at the ICIC2009 are selected for inclusion in this special issue.

In recent years, we have witnessed intelligent computing techniques, such as artificial intelligence, machine learning, feature selection, ensembles and others being dedicated to various research aspects of bioinformatics, chemoinformatics, computational biology, system biology, etc. Meanwhile, intelligent computing research has been enriched by the development of more solid mathematical frameworks, elaborating more efficient and powerful algorithms. More importantly, it has been driven by its application to many amazing research fields, such as bioinformatics.

Currently, intelligent computing techniques for protein bioinformatics are being used to encode protein and peptide biological information, extract biological features, recognize biological patterns, mine and comprehend biological data, build models of biological systems and processes, and automatically form theories from the unprecedentedly vast experimental biological data on protein sequence, genetics, structure, interactions and functions, etc. Its main objective is to find the rule and useful biological information and extract biological knowledge from limited observation examples that cannot be obtained using classical biological methods and theories. It extends the rule to predict and infer protein's structure, function, interaction networks, and other significant aspects of protein and peptide science. Hence, the intelligent computing technique, an in silico method, is supplementary to conventional experimental methods. This special issue includes eleven papers on how to use intelligent computing techniques to solve problems in protein bioinformatics.

Four papers in this issue focus on computational algorithms application in protein-protein interaction (PPI) prediction and PPI network analysis. Xia, et al. discuss the existing computational methods for PPI prediction from protein genetics, sequence and structure information, etc. Yang et al. predict protein-protein interactions using local descriptors that account for the interactions between residues in both continuous and discontinuous regions of a protein sequence and using a k-nearest neighbor classification system. Wang et al., infer PPI with a hybrid Genetic Algorithm /Support Vector Machine method by representing the protein with its domains, where they consider the effects of duplication, combination transformation of the domain composition using genetic algorithm (GA) and discover the optimal transformation with a support vector machine (SVM) classifier. Lee et al. provide a fast and adaptive approach to revel the highly evolutionarily conserved motif mode of a yeast protein interaction network through intelligent agent-based distributed computing method.

The next two papers present intelligent computing methods on protein interaction site predictions. Han, et al. compute the interaction propensity of three consecutive amino acids (called amino acid triplet or triple amino acids) from the structure data of protein-RNA complexes and predict RNA-binding sites in proteins using SVM with the interaction propensity, which are proved to be more effective than other amino acid biochemical properties. Wang, et al. use a radial basis function neural network (RBFNN) ensemble model to predict protein interaction sites in heterocomplexes by classifing protein surface residues into interaction sites or non-interaction sites and judging the prediction output by majority voting.

Another two papers focus on protein supersecondary structure prediction. Specifically, Xia et al. propose a two-stage SVM classifier with new physicochemical and structural properties-based coding schemes to predict π-turns that are irregular secondary structure elements consisting of short backbone fragments (six-amino-acid residues) where the backbone reverses its overall direction and play an important role in proteins from both the structural and functional points of view. Xia et al. further propose a SVM ensemble with majority voting strategy and new feature representation scheme based on auto covariance combining with protein secondary structure and residue conformation propensity to predict β-hairpins in proteins more accurately.

The last three papers focus on applying feature selection method, iterative partitioning technique and simple linear regression predictor to TAP binding peptides, protein repeats, and peptide drift time in ion mobility-mass spectrometry, respectively. More specifically, Li, et al. present a new feature selection approach to predict and analyze TAP-peptide binding specificity using Forward Attribute Reduction based on Neighborhood Model (FARNeM). Zhang, et al. propose weight protein sequence with respective probabilities of occurrence at each position and present the iterative partitioning technique to define and locate the loose repeats and the strict repeats with the weighted sequence. Wang, et al. introduce a method for predicting peptide drift time in ion mobility-mass spectrometry (IMMS) with a numeric descriptor, i.e. molecular weight for peptide representation and a simple linear regression predictor for peptides drift time prediction....
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Document Type: Research Article

Publication date: 2010-09-01

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  • Protein & Peptide Letters publishes short papers in all important aspects of protein and peptide research, including structural studies, recombinant expression, function, synthesis, enzymology, immunology, molecular modeling, drug design etc. Manuscripts must have a significant element of novelty, timeliness and urgency that merit rapid publication. Reports of crystallisation, and preliminary structure determinations of biologically important proteins are acceptable. Purely theoretical papers are also acceptable provided they provide new insight into the principles of protein/peptide structure and function.
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