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AntiBP2: improved version of antibacterial peptide prediction

BMC BIOINFORMATICS 2010年第s1期11卷 1-7页

作者： Lata, Sneh Mishra, Nitish K. Raghava, Gajendra P. S. Inst Microbial Technol Chandigarh India

Background: Antibacterial peptides are one of the effecter molecules of innate immune system. Over the last few decades several antibacterial peptides have successfully approved as drug by FDA, which has prompted an interest in these antibacterial peptides. In our recent study we analyzed 999 antibacterial peptides, which were collected from Antibacterial Peptide Database (APD). We have also developed methods to predict and classify these antibacterial peptides using support vector machine (SVM). Results: During analysis we observed that certain residues are preferred over other in antibacterial peptide, particularly at the N and C terminus. These observation and increased data of antibacterial peptide in APD encouraged us to again develop a new and more robust method for predicting antibacterial peptides in protein from their amino acid sequence or given peptide have antibacterial properties or not. First, the binary patterns of the 15 N terminus residues were used for predicting antibacterial peptide using SVM and achieved accuracy of 85.46% with 0.705 Mathew's Correlation Coefficient (MCC). Then we used the binary pattern of 15 C terminus residues and achieved accuracy of 85.05% with 0.701 MCC, latter on we developed prediction method by combining N & C terminus and achieved an accuracy of 91.64% with 0.831 MCC. Finally we developed SVM based model using amino acid composition of whole peptide and achieved 92.14% accuracy with MCC 0.843. In this study we used five-fold cross validation technique to develop all these models and tested the performance of these models on an independent dataset. We further classify antibacterial peptides according to their sources and achieved an overall accuracy of 98.95%. We further classify antibacterial peptides in their respective family and got a satisfactory result. Conclusion: Among antibacterial peptides, there is preference for certain residues at N and C terminus, which helps to discriminate them from non-antibacterial pep

关键词： support vector machine Antimicrobial Peptide support vector machine model Independent Dataset Antibacterial Peptide

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Prediction of FAD interacting residues in a protein from its primary sequence using evolutionary information

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BMC BIOINFORMATICS 2010年第Sup1期11卷 S48-S48页

作者： Mishra, Nitish K. Raghava, Gajendra P. S. Inst Microbial Technol Sect 39A Chandigarh India

Background: Flavin binding proteins (FBP) plays a critical role in several biological functions such as electron transport system (ETS). These flavoproteins contain very tightly bound, sometimes covalently, flavin adenine dinucleotide (FAD) or flavin mono nucleotide (FMN). The interaction between flavin nucleotide and amino acids of flavoprotein is essential for their functionality. Thus identification of FAD interacting residues in a FBP is an important step for understanding their function and mechanism. Results: In this study, we describe models developed for predicting FAD interacting residues using 15, 17 and 19 window pattern. support vector machine (SVM) based models have been developed using binary pattern of amino acid sequence of protein and achieved maximum accuracy 69.65% with Mathew's Correlation Coefficient (MCC) 0.39 and Area Under Curve (AUC) 0.773. The performance of these models have been improved significantly from 69.65% to 82.86% with MCC 0.66 and AUC 0.904, when evolutionary information is used as input in SVM. The evolutionary information was generated in form of position specific score matrix (PSSM) profile by using PSI-BLAST at e-value 0.001. All models were developed on 198 non-redundant FAD binding protein chains containing 5172 FAD interacting residues and evaluated using fivefold cross-validation technique. Conclusion: This study suggests that evolutionary information of 17 amino acid patterns perform best for FAD interacting residues prediction. We also developed a web server which predicts FAD interacting residues in a protein which is freely available for academics.

关键词： support vector machine Area Under Curve support vector machine model Flavin Adenine Dinucleotide Evolutionary Information

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Identification of NAD interacting residues in proteins

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BMC BIOINFORMATICS 2010年第1期11卷 1-8页

作者： Ansari, Hifzur R. Raghava, Gajendra P. S. Inst Microbial Technol Chandigarh 160036 India

Background: Small molecular cofactors or ligands play a crucial role in the proper functioning of cells. Accurate annotation of their target proteins and binding sites is required for the complete understanding of reaction mechanisms. Nicotinamide adenine dinucleotide (NAD(+) or NAD) is one of the most commonly used organic cofactors in living cells, which plays a critical role in cellular metabolism, storage and regulatory processes. In the past, several NAD binding proteins (NADBP) have been reported in the literature, which are responsible for a wide-range of activities in the cell. Attempts have been made to derive a rule for the binding of NAD(+) to its target proteins. However, so far an efficient model could not be derived due to the time consuming process of structure determination, and limitations of similarity based approaches. Thus a sequence and non-similarity based method is needed to characterize the NAD binding sites to help in the annotation. In this study attempts have been made to predict NAD binding proteins and their interacting residues (NIRs) from amino acid sequence using bioinformatics tools. Results: We extracted 1556 proteins chains from 555 NAD binding proteins whose structure is available in Protein Data Bank. Then we removed all redundant protein chains and finally obtained 195 non-redundant NAD binding protein chains, where no two chains have more than 40% sequence identity. In this study all models were developed and evaluated using five-fold cross validation technique on the above dataset of 195 NAD binding proteins. While certain type of residues are preferred (e.g. Gly, Tyr, Thr, His) in NAD interaction, residues like Ala, Glu, Leu, Lys are not preferred. A support vector machine (SVM) based method has been developed using various window lengths of amino acid sequence for predicting NAD interacting residues and obtained maximum Matthew's correlation coefficient (MCC) 0.47 with accuracy 74.13% at window length 17. We also developed a

关键词： support vector machine Protein Data Bank Aldose Reductase support vector machine model Nicotinamide Adenine Dinucleotide

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support vector machine Predictive Control for Superheated Steam Temperature Based on Particle Swarm Optimization

Support Vector Machine Predictive Control for Superheated St...

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Asia-Pacific Power and Energy Engineering Conference (APPEEC)

作者： Zhao, Dandan Liang, Ping South China Univ Technol Sch Elect Power Guangzhou Guangdong Peoples R China

ISBN: (纸本)9781424448135

The processes with characters of nonlinear and time-varying is common in power plant. In order to achieve high control performance and effectiveness, a predictive control strategy combining support vector machine model and particle swarm optimization algorithm is proposed. Simulation of the superheated steam temperature system was done in a 600MW supercritical once-through boiler. Experimental results show good control performance in terms of reference command tracking ability and steady state errors. This method is better than PID control in robustness and dynamic performance.

关键词： support vector machine model particle swarm optimization predictive control superheated steam temperature

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APIS: accurate prediction of hot spots in protein interfaces by combining protrusion index with solvent accessibility

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BMC BIOINFORMATICS 2010年第1期11卷 1-14页

作者： Xia, Jun-Feng Zhao, Xing-Ming Song, Jiangning Huang, De-Shuang Chinese Acad Sci Hefei Inst Intelligent Machines Intelligent Comp Lab Hefei 230031 Anhui Peoples R China Univ Sci & Technol China Sch Life Sci Hefei 230027 Anhui Peoples R China Shanghai Univ Inst Syst Biol Shanghai 200444 Peoples R China Kyoto Univ Inst Chem Res Bioinformat Ctr Kyoto 6110011 Japan Monash Univ Dept Biochem & Mol Biol Clayton Vic 3800 Australia

Background: It is well known that most of the binding free energy of protein interaction is contributed by a few key hot spot residues. These residues are crucial for understanding the function of proteins and studying their interactions. Experimental hot spots detection methods such as alanine scanning mutagenesis are not applicable on a large scale since they are time consuming and expensive. Therefore, reliable and efficient computational methods for identifying hot spots are greatly desired and urgently required. Results: In this work, we introduce an efficient approach that uses support vector machine (SVM) to predict hot spot residues in protein interfaces. We systematically investigate a wide variety of 62 features from a combination of protein sequence and structure information. Then, to remove redundant and irrelevant features and improve the prediction performance, feature selection is employed using the F-score method. Based on the selected features, nine individual-feature based predictors are developed to identify hot spots using SVMs. Furthermore, a new ensemble classifier, namely APIS (A combined model based on Protrusion Index and Solvent accessibility), is developed to further improve the prediction accuracy. The results on two benchmark datasets, ASEdb and BID, show that this proposed method yields significantly better prediction accuracy than those previously published in the literature. In addition, we also demonstrate the predictive power of our proposed method by modelling two protein complexes: the calmodulin/myosin light chain kinase complex and the heat shock locus gene products U and V complex, which indicate that our method can identify more hot spots in these two complexes compared with other state-of-the-art methods. Conclusion: We have developed an accurate prediction model for hot spot residues, given the structure of a protein complex. A major contribution of this study is to propose several new features based on the protrusion index

关键词： support vector machine support vector machine model Solvent Accessibility Accessible Surface Area Interface Residue

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Prediction of GTP interacting residues, dipeptides and tripeptides in a protein from its evolutionary information

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BMC BIOINFORMATICS 2010年第1期11卷 1-9页

作者： Chauhan, Jagat S. Mishra, Nitish K. Raghava, Gajendra P. S. Inst Microbial Technol IMTECH Bioinformat Ctr Chandigarh 160036 India

Background: Guanosine triphosphate (GTP)-binding proteins play an important role in regulation of G-protein. Thus prediction of GTP interacting residues in a protein is one of the major challenges in the field of the computational biology. In this study, an attempt has been made to develop a computational method for predicting GTP interacting residues in a protein with high accuracy (Acc), precision (Prec) and recall (Rc). Result: All the models developed in this study have been trained and tested on a non-redundant (40% similarity) dataset using five-fold cross-validation. Firstly, we have developed neural network based models using single sequence and PSSM profile and achieved maximum Matthews Correlation Coefficient (MCC) 0.24 (Acc 61.30%) and 0.39 (Acc 68.88%) respectively. Secondly, we have developed a support vector machine (SVM) based models using single sequence and PSSM profile and achieved maximum MCC 0.37 (Prec 0.73, Rc 0.57, Acc 67.98%) and 0.55 (Prec 0.80, Rc 0.73, Acc 77.17%) respectively. In this work, we have introduced a new concept of predicting GTP interacting dipeptide (two consecutive GTP interacting residues) and tripeptide (three consecutive GTP interacting residues) for the first time. We have developed SVM based model for predicting GTP interacting dipeptides using PSSM profile and achieved MCC 0.64 with precision 0.87, recall 0.74 and accuracy 81.37%. Similarly, SVM based model have been developed for predicting GTP interacting tripeptides using PSSM profile and achieved MCC 0.70 with precision 0.93, recall 0.73 and accuracy 83.98%. Conclusion: These results show that PSSM based method performs better than single sequence based method. The prediction models based on dipeptides or tripeptides are more accurate than the traditional model based on single residue. A web server "GTPBinder" http://***/raghava/gtpbinder/based on above models has been developed for predicting GTP interacting residues in a protein.

关键词： support vector machine support vector machine model Matthews Correlation Coefficient Evolutionary Information Negative Pattern

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In-silico prediction of blood-secretory human proteins using a ranking algorithm

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BMC BIOINFORMATICS 2010年第1期11卷 250-250页

作者： Liu, Qi Cui, Juan Yang, Qiang Xu, Ying Hong Kong Univ Sci & Technol Dept Comp Sci & Engn Hong Kong Hong Kong Peoples R China Tongji Univ Coll Life Sci & Biotechnol Shanghai Peoples R China Univ Georgia Dept Biochem & Mol Biol Computat Syst Biol Lab Athens GA 30602 USA Univ Georgia Inst Bioinformat Athens GA 30602 USA Jilin Univ Coll Comp Sci & Technol Changchun 130023 Jilin Peoples R China

Background: Computational identification of blood-secretory proteins, especially proteins with differentially expressed genes in diseased tissues, can provide highly useful information in linking transcriptomic data to proteomic studies for targeted disease biomarker discovery in serum. Results: A new algorithm for prediction of blood-secretory proteins is presented using an information-retrieval technique, called manifold ranking. On a dataset containing 305 known blood-secretory human proteins and a large number of other proteins that are either not blood-secretory or unknown, the new method performs better than the previous published method, measured in terms of the area under the recall-precision curve (AUC). A key advantage of the presented method is that it does not explicitly require a negative training set, which could often be noisy or difficult to derive for most biological problems, hence making our method more applicable than classification-based data mining methods in general biological studies. Conclusion: We believe that our program will prove to be very useful to biomedical researchers who are interested in finding serum markers, especially when they have candidate proteins derived through transcriptomic or proteomic analyses of diseased tissues. A computer program is developed for prediction of blood-secretory proteins based on manifold ranking, which is accessible at our website http://***/publications/materials/qiliu/blood_secretory_***.

关键词： support vector machine Negative Sample support vector machine model Ranking Algorithm True Sample

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KiDoQ: using docking based energy scores to develop ligand based model for predicting antibacterials

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BMC BIOINFORMATICS 2010年第1期11卷 125-125页

作者： Garg, Aarti Tewari, Rupinder Raghava, Gajendra P. S. Inst Microbial Technol Bioinformat Ctr Chandigarh India Panjab Univ Dept Biotechnol Chandigarh 160014 India

Background: Identification of novel drug targets and their inhibitors is a major challenge in the field of drug designing and development. Diaminopimelic acid (DAP) pathway is a unique lysine biosynthetic pathway present in bacteria, however absent in mammals. This pathway is vital for bacteria due to its critical role in cell wall biosynthesis. One of the essential enzymes of this pathway is dihydrodipicolinate synthase (DHDPS), considered to be crucial for the bacterial survival. In view of its importance, the development and prediction of potent inhibitors against DHDPS may be valuable to design effective drugs against bacteria, in general. Results: This paper describes a methodology for predicting novel/potent inhibitors against DHDPS. Here, quantitative structure activity relationship (QSAR) models were trained and tested on experimentally verified 23 enzyme's inhibitors having inhibitory value (K-i) in the range of 0.005-22(mM). These inhibitors were docked at the active site of DHDPS (1YXD) using AutoDock software, which resulted in 11 energy-based descriptors. For QSAR modeling, Multiple Linear Regression (MLR) model was engendered using best four energy-based descriptors yielding correlation values R/q(2) of 0.82/0.67 and MAE of 2.43. Additionally, support vector machine (SVM) based model was developed with three crucial descriptors selected using F-stepping remove-one approach, which enhanced the performance by attaining R/q(2) values of 0.93/0.80 and MAE of 1.89. To validate the performance of QSAR models, external cross-validation procedure was adopted which accomplished high training/testing correlation values (q(2)/r(2)) in the range of 0.78-0.83/0.93-0.95. Conclusions: Our results suggests that ligand-receptor binding interactions for DHDPS employing QSAR modeling seems to be a promising approach for prediction of antibacterial agents. To serve the experimentalist to develop novel/potent inhibitors, a webserver "KiDoQ" has been developed http://crdd.o

关键词： support vector machine Quantitative Structure Activity Relationship support vector machine model Quantitative Structure Activity Relationship model Free Binding Energy

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Prediction and classification of aminoacyl tRNA synthetases using PROSITE domains

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BMC GENOMICS 2010年第1期11卷 507-507页

作者： Panwar, Bharat Raghava, Gajendra P. S. Bioinformat Ctr Inst Microbial Technol Chandigarh India

Background: Aminoacyl tRNA synthetases (aaRSs) catalyse the first step of protein synthesis in all organisms. They are responsible for the precise attachment of amino acids to their cognate transfer RNAs. There are twenty different types of aaRSs, unique for each amino acid. These aaRSs have been divided into two classes, each comprising ten enzymes. It is important to predict and classify aaRSs in order to understand protein synthesis. Results: In this study, all models were developed on a non-redundant dataset containing 117 aaRSs and an equal number of non-aaRSs, in which no two sequences have more than 30% similarity. First, we applied the similarity search technique, BLAST, and achieved a maximum accuracy of 67.52%. We observed that 62% of tRNA synthetases contain one or more domains from amongst the following four PROSITE domains: PS50862, PS00178, PS50860 and PS50861. An SVM-based model was developed to discriminate between aaRSs, and non-aaRSs, and achieved a maximum MCC of 0.68 with accuracy of 83.73%, using selective dipeptide composition. We developed a hybrid approach and achieved a maximum MCC of 0.72 with accuracy of 85.49%, where SVM model developed using selected dipeptide composition and information of four PROSITE domains. We further developed an SVM-based model for classifying the aaRSs into class-1 and class-2, using selective dipeptide composition and achieved an MCC of 0.79. We also observed that two domains (PS00178, PS50889) in class-1 and three domains (PS50862, PS50860, PS50861) in class-2 were preferred. A hybrid method was developed using these domains as descriptor, along with selected dipeptide composition, and achieved an MCC of 0.87 with a sensitivity of 94.55% and an accuracy of 93.19%. All models were evaluated using a five-fold cross-validation technique. Conclusions: We have analyzed protein sequences of aaRSs (class-1 and class-2) and non-aaRSs and identified interesting patterns. The high accuracy achieved by our SVM models usin

关键词： support vector machine Amino Acid Composition Dipeptide support vector machine model machine Learning Technique

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Analysis of syntactic and semantic features for fine-grained event-spatial understanding in outbreak news reports

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JOURNAL OF BIOMEDICAL SEMANTICS 2010年第2010期1卷 3-3页

作者： Chanlekha, Hutchatai Collier, Nigel Res Org Informat & Syst Natl Inst Informat Chiyoda Ku Tokyo 1018430 Japan

Background: Previous studies have suggested that epidemiological reasoning needs a fine-grained modelling of events, especially their spatial and temporal attributes. While the temporal analysis of events has been intensively studied, far less attention has been paid to their spatial analysis. This article aims at filling the gap concerning automatic event-spatial attribute analysis in order to support health surveillance and epidemiological reasoning. Results: In this work, we propose a methodology that provides a detailed analysis on each event reported in news articles to recover the most specific locations where it occurs. Various features for recognizing spatial attributes of the events were studied and incorporated into the models which were trained by several machine learning techniques. The best performance for spatial attribute recognition is very promising;85.9% F-score (86.75% precision/85.1% recall). Conclusions: We extended our work on event-spatial attribute recognition by focusing on machine learning techniques, which are CRF, SVM, and Decision tree. Our approach avoided the costly development of an external knowledge base by employing the feature sources that can be acquired locally from the analyzed document. The results showed that the CRF model performed the best. Our study indicated that the nearest location and previous event location are the most important features for the CRF and SVM model, while the location extracted from the verb's subject is the most important to the Decision tree model.

关键词： support vector machine Spatial Attribute Noun Phrase Relative Clause support vector machine model

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