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NN-align. An artificial neural network-based alignment algorithm for MHC class II peptide binding prediction

BMC BIOINFORMATICS 2009年第1期10卷 1-10页

作者： Nielsen, Morten Lund, Ole Tech Univ Denmark Ctr Biol Sequence Anal Dept Syst Biol DK-2800 Lyngby Denmark

Background: The major histocompatibility complex (MHC) molecule plays a central role in controlling the adaptive immune response to infections. MHC class I molecules present peptides derived from intracellular proteins to cytotoxic T cells, whereas MHC class II molecules stimulate cellular and humoral immunity through presentation of extracellularly derived peptides to helper T cells. Identification of which peptides will bind a given MHC molecule is thus of great importance for the understanding of host-pathogen interactions, and large efforts have been placed in developing algorithms capable of predicting this binding event. Results: Here, we present a novel artificial neural network-based method, NN-align that allows for simultaneous identification of the MHC class II binding core and binding affinity. NN-align is trained using a novel training algorithm that allows for correction of bias in the training data due to redundant binding core representation. Incorporation of information about the residues flanking the peptide-binding core is shown to significantly improve the prediction accuracy. The method is evaluated on a large-scale benchmark consisting of six independent data sets covering 14 human MHC class II alleles, and is demonstrated to outperform other state-of-the-art MHC class II prediction methods. Conclusion: The NN-align method is competitive with the state-of-the-art MHC class II peptide binding prediction algorithms. The method is publicly available at http://***/services/NetMHCII-2.0.

关键词： Major Histocompatibility Complex Major Histocompatibility Complex Class Major Histocompatibility Complex Molecule position specific score matrix Binding Core

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A neural strategy for the inference of SH3 domain-peptide interaction specificity

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BMC BIOINFORMATICS 2005年第Sup4期6卷 1-6页

作者： Ferraro, E Via, A Ausiello, G Helmer-Citterich, M Univ Roma Tor Vergata Dept Biol Ctr Mol Bioinformat Rome Italy

Background: The SH3 domain family is one of the most representative and widely studied cases of so-called Peptide Recognition Modules (PRM). The polyproline II motif PxxP that generally characterizes its ligands does not reflect the complex interaction spectrum of the over 1500 different SH3 domains, and the requirement of a more refined knowledge of their specificity implies the setting up of appropriate experimental and theoretical strategies. Due to the limitations of the current technology for peptide synthesis, several experimental high-throughput approaches have been devised to elucidate protein-protein interaction mechanisms. Such approaches can rely on and take advantage of computational techniques, such as regular expressions or position specific scoring matrices (PSSMs) to pre-process entire proteomes in the search for putative SH3 targets. In this regard, a reliable inference methodology to be used for reducing the sequence space of putative binding peptides represents a valuable support for molecular and cellular biologists. Results: Using as benchmark the peptide sequences obtained from in vitro binding experiments, we set up a neural network model that performs better than PSSM in the detection of SH3 domain interactors. In particular our model is more precise in its predictions, even if its performance can vary among different SH3 domains and is strongly dependent on the number of binding peptides in the benchmark. Conclusion: We show that a neural network can be more effective than standard methods in SH3 domain specificity detection. Neural classifiers identify general SH3 domain binders and domain-specific interactors from a PxxP peptide population, provided that there are a sufficient proportion of true positives in the training sets. This capability can also improve peptide selection for library definition in array experiments. Further advances can be achieved, including properly encoded domain sequences and structural information as input for a

关键词： Binding Peptide Machine Learning Approach position specific score matrix Eps15 Homology position specific score Matrice

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Prediction of conformational B-cell epitopes from 3D structures by random forests with a distance-based feature

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BMC BIOINFORMATICS 2011年第1期12卷 1-10页

作者： Zhang, Wen Xiong, Yi Zhao, Meng Zou, Hua Ye, Xinghuo Liu, Juan Wuhan Univ Sch Comp Wuhan 430072 Peoples R China

Background: Antigen-antibody interactions are key events in immune system, which provide important clues to the immune processes and responses. In Antigen-antibody interactions, the specific sites on the antigens that are directly bound by the B-cell produced antibodies are well known as B-cell epitopes. The identification of epitopes is a hot topic in bioinformatics because of their potential use in the epitope-based drug design. Although most B-cell epitopes are discontinuous (or conformational), insufficient effort has been put into the conformational epitope prediction, and the performance of existing methods is far from satisfaction. Results: In order to develop the high-accuracy model, we focus on some possible aspects concerning the prediction performance, including the impact of interior residues, different contributions of adjacent residues, and the imbalanced data which contain much more non-epitope residues than epitope residues. In order to address above issues, we take following strategies. Firstly, a concept of 'thick surface patch' instead of 'surface patch' is introduced to describe the local spatial context of each surface residue, which considers the impact of interior residue. The comparison between the thick surface patch and the surface patch shows that interior residues contribute to the recognition of epitopes. Secondly, statistical significance of the distance distribution difference between non-epitope patches and epitope patches is observed, thus an adjacent residue distance feature is presented, which reflects the unequal contributions of adjacent residues to the location of binding sites. Thirdly, a bootstrapping and voting procedure is adopted to deal with the imbalanced dataset. Based on the above ideas, we propose a new method to identify the B-cell conformational epitopes from 3D structures by combining conventional features and the proposed feature, and the random forest (RF) algorithm is used as the classification engine. The experi

关键词： Surface Patch Conformational Epitope Epitope Prediction position specific score matrix Imbalanced Dataset

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PostMod: sequence based prediction of kinase-specific phosphorylation sites with indirect relationship

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BMC BIOINFORMATICS 2010年第Sup1期11卷 1-10页

作者： Jung, Inkyung Matsuyama, Akihisa Yoshida, Minoru Kim, Dongsup Korea Adv Inst Sci & Technol Dept Bio & Brain Engn Taejon 305701 South Korea RIKEN Chem Genet Lab Wako Saitama 3510198 Japan Korea Adv Inst Sci & Technol KAIST Inst BioCentury Taejon 305701 South Korea

Background: Post-translational modifications (PTMs) have a key role in regulating cell functions. Consequently, identification of PTM sites has a significant impact on understanding protein function and revealing cellular signal transductions. Especially, phosphorylation is a ubiquitous process with a large portion of proteins undergoing this modification. Experimental methods to identify phosphorylation sites are labor-intensive and of high-cost. With the exponentially growing protein sequence data, development of computational approaches to predict phosphorylation sites is highly desirable. Results: Here, we present a simple and effective method to recognize phosphorylation sites by combining sequence patterns and evolutionary information and by applying a novel noise-reducing algorithm. We suggested that considering long-range region surrounding a phosphorylation site is important for recognizing phosphorylation peptides. Also, from compared results to AutoMotif in 36 different kinase families, new method outperforms AutoMotif. The mean accuracy, precision, and recall of our method are 0.93, 0.67, and 0.40, respectively, whereas those of AutoMotif with a polynomial kernel are 0.91, 0.47, and 0.17, respectively. Also our method shows better or comparable performance in four main kinase groups, CDK, CK2, PKA, and PKC compared to six existing predictors. Conclusion: Our method is remarkable in that it is powerful and intuitive approach without need of a sophisticated training algorithm. Moreover, our method is generally applicable to other types of PTMs.

关键词： Phosphorylation Site position specific score matrix BLOSUM62 matrix Phosphorylation Peptide Kinase Group

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On the complexity of deriving position specific score matrices from positive and negative sequences

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DISCRETE APPLIED MATHEMATICS 2007年第6-7期155卷 676-685页

作者： Akutsu, Tatsuya Bannai, Hideo Miyano, Satoru Ott, Sascha Kyoto Univ Bioinformat Ctr Inst Chem Res Uji Kyoto 6110011 Japan Univ Tokyo Inst Med Sci Ctr Human Genome Minato Ku Tokyo 1088639 Japan UCL Wolfson Inst Biomed Res London WC1E 6A England

position-specific score matrices (PSSMs) have been applied to various problems in computational molecular biology. In this paper, we study the following problem: given positive examples (sequences) and negative examples (sequences), find a PSSM which correctly discriminates between positive and negative examples. We prove that this problem is solved in polynomial time if the size of a PSSM is bounded by a constant. On the other hand. we prove that this problem is NP-hard if the size is not bounded. We also prove hardness results for deriving multiple PSSMs and related problems. (c) 2006 Elsevier B.V. All rights reserved.

关键词： sequence motif position specific score matrix weight matrix profile NP-hard

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Prediction of beta-turns at over 80% accuracy based on an ensemble of predicted secondary structures and multiple alignments

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BMC BIOINFORMATICS 2008年第1期9卷 1-14页

作者： Zheng, Ce Kurgan, Lukasz Univ Alberta Dept Elect & Comp Engn Edmonton AB Canada

Background: beta-turn is a secondary protein structure type that plays significant role in protein folding, stability, and molecular recognition. To date, several methods for prediction of beta-turns from protein sequences were developed, but they are characterized by relatively poor prediction quality. The novelty of the proposed sequence-based beta-turn predictor stems from the usage of a window based information extracted from four predicted three-state secondary structures, which together with a selected set of position specific scoring matrix (PSSM) values serve as an input to the support vector machine (SVM) predictor. Results: We show that (1) all four predicted secondary structures are useful;(2) the most useful information extracted from the predicted secondary structure includes the structure of the predicted residue, secondary structure content in a window around the predicted residue, and features that indicate whether the predicted residue is inside a secondary structure segment;(3) the PSSM values of Asn, Asp, Gly, Ile, Leu, Met, Pro, and Val were among the top ranked features, which corroborates with recent studies. The Asn, Asp, Gly, and Pro indicate potential beta-turns, while the remaining four amino acids are useful to predict non-beta-turns. Empirical evaluation using three nonredundant datasets shows favorable Q(total), Q(predicted) and MCC values when compared with over a dozen of modern competing methods. Our method is the first to break the 80% Q(total) barrier and achieves Q(total) = 80.9%, MCC = 0.47, and Q(predicted) higher by over 6% when compared with the second best method. We use feature selection to reduce the dimensionality of the feature vector used as the input for the proposed prediction method. The applied feature set is smaller by 86, 62 and 37% when compared with the second and two third-best (with respect to MCC) competing methods, respectively. Conclusion: Experiments show that the proposed method constitutes an improvement o

关键词： Support Vector Machine Secondary Structure Support Vector Machine Classifier Matthews Correlation Coefficient position specific score matrix

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Concomitant prediction of function and fold at the domain level with GO-based profiles

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BMC BIOINFORMATICS 2013年第3-sup期14卷 1-9页

作者： Lopez, Daniel Pazos, Florencio Natl Ctr Biotechnol CNB CSIC Computat Syst Biol Grp Madrid 28049 Spain

Predicting the function of newly sequenced proteins is crucial due to the pace at which these raw sequences are being obtained. Almost all resources for predicting protein function assign functional terms to whole chains, and do not distinguish which particular domain is responsible for the allocated function. This is not a limitation of the methodologies themselves but it is due to the fact that in the databases of functional annotations these methods use for transferring functional terms to new proteins, these annotations are done on a whole-chain basis. Nevertheless, domains are the basic evolutionary and often functional units of proteins. In many cases, the domains of a protein chain have distinct molecular functions, independent from each other. For that reason resources with functional annotations at the domain level, as well as methodologies for predicting function for individual domains adapted to these resources are required. We present a methodology for predicting the molecular function of individual domains, based on a previously developed database of functional annotations at the domain level. The approach, which we show outperforms a standard method based on sequence searches in assigning function, concomitantly predicts the structural fold of the domains and can give hints on the functionally important residues associated to the predicted function.

关键词： Gene Ontology Structural Alignment Domain Level position specific score matrix Predict Protein Function

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Improving protein fold recognition using the amalgamation of evolutionary-based and structural based information

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BMC BIOINFORMATICS 2014年第16-Sup期15卷 1-9页

作者： Paliwal, Kuldip K. Sharma, Alok Lyons, James Dehzangi, Abdollah Griffith Univ Sch Engn Brisbane Qld 4111 Australia Univ S Pacific Sch Engn & Phys Suva Fiji IIIS Brisbane Qld Australia NICTA Brisbane Qld Australia

Deciphering three dimensional structure of a protein sequence is a challenging task in biological science. Protein fold recognition and protein secondary structure prediction are transitional steps in identifying the three dimensional structure of a protein. For protein fold recognition, evolutionary-based information of amino acid sequences from the position specific scoring matrix (PSSM) has been recently applied with improved results. On the other hand, the SPINE-X predictor has been developed and applied for protein secondary structure prediction. Several reported methods for protein fold recognition have only limited accuracy. In this paper, we have developed a strategy of combining evolutionary-based information (from PSSM) and predicted secondary structure using SPINE-X to improve protein fold recognition. The strategy is based on finding the probabilities of amino acid pairs (AAP). The proposed method has been tested on several protein benchmark datasets and an improvement of 8.9% recognition accuracy has been achieved. We have achieved, for the first time over 90% and 75% prediction accuracies for sequence similarity values below 40% and 25%, respectively. We also obtain 90.6% and 77.0% prediction accuracies, respectively, for the Extended Ding and Dubchak and Taguchi and Gromiha benchmark protein fold recognition datasets widely used for in the literature.

关键词： Support Vector Machine Support Vector Machine Classifier Feature Extraction Method Fold Recognition position specific score matrix

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Prediction of B-cell epitopes using evolutionary information and propensity scales

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BMC BIOINFORMATICS 2013年第2-sup期14卷 1-9页

作者： Lin, Scott Yi-Heng Cheng, Cheng-Wei Su, Emily Chia-Yu Taipei Med Univ Coll Med Sch Med Taipei Taiwan Acad Sinica Inst Informat Sci Taipei Taiwan Taipei Med Univ Grad Inst Biomed Informat Taipei Taiwan

Background: Development of computational tools that can accurately predict presence and location of B-cell epitopes on pathogenic proteins has a valuable application to the field of vaccinology. Because of the highly variable yet enigmatic nature of B-cell epitopes, their prediction presents a great challenge to computational immunologists. Methods: We propose a method, BEEPro (B-cell epitope prediction by evolutionary information and propensity scales), which adapts a linear averaging scheme on 16 properties using a support vector machine model to predict both linear and conformational B-cell epitopes. These 16 properties include position specific scoring matrix (PSSM), an amino acid ratio scale, and a set of 14 physicochemical scales obtained via a feature selection process. Finally, a three-way data split procedure is used during the validation process to prevent over-estimation of prediction performance and avoid bias in our experiment results. Results: In our experiment, first we use a non-redundant linear B-cell epitope dataset curated by Sollner et al. for feature selection and parameter optimization. Evaluated by a three-way data split procedure, BEEPro achieves significant improvement with the area under the receiver operating curve (AUC) = 0.9987, accuracy = 99.29%, mathew's correlation coefficient (MCC) = 0.9281, sensitivity = 0.9604, specificity = 0.9946, positive predictive value (PPV) = 0.9042 for the Sollner dataset. In addition, the same parameters are used to evaluate performance on other independent linear B-cell epitope test datasets, BEEPro attains an AUC which ranges from 0.9874 to 0.9950 and an accuracy which ranges from 93.73% to 97.31%. Moreover, five-fold cross-validation on one benchmark conformational B-cell epitope dataset yields an accuracy of 92.14% and AUC of 0.9066. Conclusions: Compared with other current models, our method achieves a significant improvement with respect to AUC, accuracy, MCC, sensitivity, specificity, and PPV. Thus,

关键词： Positive Predictive Value Evolutionary Information Conformational Epitope position specific score matrix Epitope Residue

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Systematic analysis of human kinase genes: a large number of genes and alternative splicing events result in functional and structural diversity

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BMC BIOINFORMATICS 2005年第Sup4期6卷 S20-S20页

作者： Milanesi, L Petrillo, M Sepe, L Boccia, A D'Agostino, N Passamano, M Di Nardo, S Tasco, G Casadio, R Paolella, G CNR ITB Milan Italy CILEA Segrate Italy CEINGE Biotecnol Avanzate Naples Italy Univ Naples Federico II Dipartimento Biochim & Biotecnol Med Naples Italy Univ Bologna Dept Biol I-40126 Bologna Italy Univ Molise Dipartimento SAVA Molise Italy

Background: Protein kinases are a well defined family of proteins, characterized by the presence of a common kinase catalytic domain and playing a significant role in many important cellular processes, such as proliferation, maintenance of cell shape, apoptosys. In many members of the family, additional non-kinase domains contribute further specialization, resulting in subcellular localization, protein binding and regulation of activity, among others. About 500 genes encode members of the kinase family in the human genome, and although many of them represent well known genes, a larger number of genes code for proteins of more recent identification, or for unknown proteins identified as kinase only after computational studies. Results: A systematic in silico study performed on the human genome, led to the identification of 5 genes, on chromosome 1, 11, 13, 15 and 16 respectively, and 1 pseudogene on chromosome X;some of these genes are reported as kinases from NCBI but are absent in other databases, such as KinBase. Comparative analysis of 483 gene regions and subsequent computational analysis, aimed at identifying unannotated exons, indicates that a large number of kinase may code for alternately spliced forms or be incorrectly annotated. An InterProScan automated analysis was perfomed to study domain distribution and combination in the various families. At the same time, other structural features were also added to the annotation process, including the putative presence of transmembrane alpha helices, and the cystein propensity to participate into a disulfide bridge. Conclusion: The predicted human kinome was extended by identifiying both additional genes and potential splice variants, resulting in a varied panorama where functionality may be searched at the gene and protein level. Structural analysis of kinase proteins domains as defined in multiple sources together with transmembrane alpha helices and signal peptide prediction provides hints to function assignmen

关键词： Kinase Gene Alternative Splice Event position specific score matrix Kinase Catalytic Domain Domain Combination

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