iDPF-PseRAAAC: A Web-Server for Identifying the Defensin Peptide Family and Subfamily Using Pseudo Reduced Amino Acid Alphabet Composition

Defensins as one of the most abundant classes of antimicrobial peptides are an essential part of the innate immunity that has evolved in most living organisms from lower organisms to humans. To identify specific defensins as interesting antifungal leads, in this study, we constructed a more rigorous benchmark dataset and the iDPF-PseRAAAC server was developed to predict the defensin family and subfamily. Using reduced dipeptide compositions were used, the overall accuracy of proposed method increased to 95.10% for the defensin family, and 98.39% for the vertebrate subfamily, which is higher than the accuracy from other methods. The jackknife test shows that more than 4% improvement was obtained comparing with the previous method. A free online server was further established for the convenience of most experimental scientists at http://wlxy.imu.edu.cn/college/biostation/fuwu/iDPF-PseRAAAC/index.asp. A friendly guide is provided to describe how to use the web server. We anticipate that iDPF-PseRAAAC may become a useful high-throughput tool for both basic research and drug design.     

IHEC_RAAC: a online platform for identifying human enzyme classes via reduced amino acid cluster strategy

Enzymes have been proven to play considerable roles in disease diagnosis and biological functions. The feature extraction that truly reflects the intrinsic properties of protein is the most critical step for the automatic identification of enzymes. Although lots of feature extraction methods have been proposed, some challenges remain. In this study, we developed a predictor called IHEC_RAAC, which has the capability to identify whether a protein is a human enzyme and distinguish the function of the human enzyme. To improve the feature representation ability, protein sequences were encoded by a new feature-vector called ‘reduced amino acid cluster’. We calculated 673 amino acid reduction alphabets to determine the optimal feature representative scheme. The tenfold cross-validation test showed that the accuracy of IHEC_RAAC to identify human enzymes was 74.66% and further discriminate the human enzyme classes with an accuracy of 54.78%, which was 2.06% and 8.68% higher than the state-of-the-art predictors, respectively. Additionally, the results from the independent dataset indicated that IHEC_RAAC can effectively predict human enzymes and human enzyme classes to further provide guidance for protein research. A user-friendly web server, IHEC_RAAC, is freely accessible at http://bioinfor.imu.edu.cn/ihecraac.

     

Support vector machine approach for protein subcellular localization prediction

MOTIVATION: Subcellular localization is a key functional characteristic of proteins. A fully automatic and reliable prediction system for protein subcellular localization is needed, especially for the analysis of large-scale genome sequences. RESULTS: In this paper, Support Vector Machine has been introduced to predict the subcellular localization of proteins from their amino acid compositions. The total prediction accuracies reach 91.4% for three subcellular locations in prokaryotic organisms and 79.4% for four locations in eukaryotic organisms. Predictions by our approach are robust to errors in the protein N-terminal sequences. This new approach provides superior prediction performance compared with existing algorithms based on amino acid composition and can be a complementary method to other existing methods based on sorting signals. AVAILABILITY: A web server implementing the prediction method is available at http://www.bioinfo.tsinghua.edu.cn/SubLoc/. SUPPLEMENTARY INFORMATION: Supplementary material is available at http://www.bioinfo.tsinghua.edu.cn/SubLoc/.     

SubChlo: Predicting protein subchloroplast locations with pseudo-amino acid composition and the evidence-theoretic K-nearest neighbor (ET-KNN) algorithm

The chloroplast is a type of plant specific subcellular organelle. It is of central importance in several biological processes like photosynthesis and amino acid biosynthesis. Thus, understanding the function of chloroplast proteins is of significant value. Since the function of chloroplast proteins correlates with their subchloroplast locations, the knowledge of their subchloroplast locations can be very helpful in understanding their role in the biological processes. In the current paper, by introducing the evidence-theoretic K-nearest neighbor (ET-KNN) algorithm, we developed a method for predicting the protein subchloroplast locations. This is the first algorithm for predicting the protein subchloroplast locations. We have implemented our algorithm as an online service, SubChlo (http://bioinfo.au.tsinghua.edu.cn/subchlo). This service may be useful to the chloroplast proteome research.     

Predicting subcellular localization with AdaBoost Learner

Protein subcellular localization, which tells where a protein resides in a cell, is an important characteristic of a protein, and relates closely to the function of proteins. The prediction of their subcellular localization plays an important role in the prediction of protein function, genome annotation and drug design. Therefore, it is an important and challenging role to predict subcellular localization using bio-informatics approach. In this paper, a robust predictor, AdaBoost Learner is introduced to predict protein subcellular localization based on its amino acid composition. Jackknife cross-validation and independent dataset test were used to demonstrate that Adaboost is a robust and efficient model in predicting protein subcellular localization. As a result, the correct prediction rates were 74.98% and 80.12% for the Jackknife test and independent dataset test respectively, which are higher than using other existing predictors. An online server for predicting subcellular localization of proteins based on AdaBoost classifier was available on http://chemdata.shu. edu.cn/sl12.     

Enhancing protein-vitamin binding residues prediction by multiple heterogeneous subspace SVMs ensemble

Background

Vitamins are typical ligands that play critical roles in various metabolic processes. The accurate identification of the vitamin-binding residues solely based on a protein sequence is of significant importance for the functional annotation of proteins, especially in the post-genomic era, when large volumes of protein sequences are accumulating quickly without being functionally annotated.

Results

In this paper, a new predictor called TargetVita is designed and implemented for predicting protein-vitamin binding residues using protein sequences. In TargetVita, features derived from the position-specific scoring matrix (PSSM), predicted protein secondary structure, and vitamin binding propensity are combined to form the original feature space; then, several feature subspaces are selected by performing different feature selection methods. Finally, based on the selected feature subspaces, heterogeneous SVMs are trained and then ensembled for performing prediction.

Conclusions

The experimental results obtained with four separate vitamin-binding benchmark datasets demonstrate that the proposed TargetVita is superior to the state-of-the-art vitamin-specific predictor, and an average improvement of 10% in terms of the Matthews correlation coefficient (MCC) was achieved over independent validation tests. The TargetVita web server and the datasets used are freely available for academic use at http://csbio.njust.edu.cn/bioinf/TargetVita or http://www.csbio.sjtu.edu.cn/bioinf/TargetVita.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2105-15-297) contains supplementary material, which is available to authorized users.     

Using optimized evidence-theoretic K-nearest neighbor classifier and pseudo-amino acid composition to predict membrane protein types

Knowledge of membrane protein type often provides crucial hints toward determining the function of an uncharacterized membrane protein. With the avalanche of new protein sequences emerging during the post-genomic era, it is highly desirable to develop an automated method that can serve as a high throughput tool in identifying the types of newly found membrane proteins according to their primary sequences, so as to timely make the relevant annotations on them for the reference usage in both basic research and drug discovery. Based on the concept of pseudo-amino acid composition [K.C. Chou, Proteins: Struct. Funct. Genet. 43 (2001) 246-255; Erratum: Proteins: Struct. Funct. Genet. 44 (2001) 60] that has made it possible to incorporate a considerable amount of sequence-order effects by representing a protein sample in terms of a set of discrete numbers, a novel predictor, the so-called "optimized evidence-theoretic K-nearest neighbor" or "OET-KNN" classifier, was proposed. It was demonstrated via the self-consistency test, jackknife test, and independent dataset test that the new predictor, compared with many previous ones, yielded higher success rates in most cases. The new predictor can also be used to improve the prediction quality for, among many other protein attributes, structural class, subcellular localization, enzyme family class, and G-protein coupled receptor type. The OET-KNN classifier will be available as a web-server at http://www.pami.sjtu.edu.cn/kcchou.     

Computational Identification of Protein Pupylation Sites by Using Profile-Based Composition of k-Spaced Amino Acid Pairs

Prokaryotic proteins are regulated by pupylation, a type of post-translational modification that contributes to cellular function in bacterial organisms. In pupylation process, the prokaryotic ubiquitin-like protein (Pup) tagging is functionally analogous to ubiquitination in order to tag target proteins for proteasomal degradation. To date, several experimental methods have been developed to identify pupylated proteins and their pupylation sites, but these experimental methods are generally laborious and costly. Therefore, computational methods that can accurately predict potential pupylation sites based on protein sequence information are highly desirable. In this paper, a novel predictor termed as pbPUP has been developed for accurate prediction of pupylation sites. In particular, a sophisticated sequence encoding scheme [i.e. the profile-based composition of k-spaced amino acid pairs (pbCKSAAP)] is used to represent the sequence patterns and evolutionary information of the sequence fragments surrounding pupylation sites. Then, a Support Vector Machine (SVM) classifier is trained using the pbCKSAAP encoding scheme. The final pbPUP predictor achieves an AUC value of 0.849 in10-fold cross-validation tests and outperforms other existing predictors on a comprehensive independent test dataset. The proposed method is anticipated to be a helpful computational resource for the prediction of pupylation sites. The web server and curated datasets in this study are freely available at http://protein.cau.edu.cn/pbPUP/.     

Multi Label Learning for Prediction of Human Protein Subcellular Localizations

Predicting protein subcellular locations has attracted much attention in the past decade. However, one of the most challenging problems is that many proteins were found simultaneously existing in, or moving between, two or more different cell components in a eukaryotic cell. Seldom previous predictors were able to deal with such multiplex proteins although they have extremely important implications in future drug discovery in terms of their specific subcellular targeting. Approximately 20% of the human proteome consists of such multiplex proteins with multiple sample labels. In order to efficiently handle such multiplex human proteins, we have developed a novel multi-label (ML) learning and prediction framework called ML-PLoc, which decomposes the multi-label prediction problem into multiple independent binary classification problems. ML-PLoc is constructed based on support vector machine (SVM) and sequential evolution information. Experimental results show that ML-PLoc can achieve an overall accuracy 64.6% and recall ratio 67.2% on a benchmark dataset consisting of 14 human subcellular locations, and is very powerful for dealing with multiplex proteins. The current approach represents a new strategy to deal with the multi-label biological problems. ML-PLoc software is freely available for academic use at: http://www.csbio.sjtu.edu.cn/bioinf/ML-PLoc.     

Gene function prediction based on Gene Ontology Hierarchy Preserving Hashing

Gene Ontology (GO) uses structured vocabularies (or terms) to describe the molecular functions, biological roles, and cellular locations of gene products in a hierarchical ontology. GO annotations associate genes with GO terms and indicate the given gene products carrying out the biological functions described by the relevant terms. However, predicting correct GO annotations for genes from a massive set of GO terms as defined by GO is a difficult challenge. To combat with this challenge, we introduce a Gene Ontology Hierarchy Preserving Hashing (HPHash) based semantic method for gene function prediction. HPHash firstly measures the taxonomic similarity between GO terms. It then uses a hierarchy preserving hashing technique to keep the hierarchical order between GO terms, and to optimize a series of hashing functions to encode massive GO terms via compact binary codes. After that, HPHash utilizes these hashing functions to project the gene-term association matrix into a low-dimensional one and performs semantic similarity based gene function prediction in the low-dimensional space. Experimental results on three model species (Homo sapiens, Mus musculus and Rattus norvegicus) for interspecies gene function prediction show that HPHash performs better than other related approaches and it is robust to the number of hash functions. In addition, we also take HPHash as a plugin for BLAST based gene function prediction. From the experimental results, HPHash again significantly improves the prediction performance. The codes of HPHash are available at: http://mlda.swu.edu.cn/codes.php?name=HPHash.     

Computational Identification of Protein-Protein Interactions in Rice Based on the Predicted Rice Interactome Network

Plant protein-protein interaction networks have not been identified by large-scale experiments. In order to better understand the protein interactions in rice, the Predicted Rice Interactome Network (PRIN; http://bis.zju.edu.cn/ prin/) presented 76,585 predicted interactions involving 5,049 rice proteins. After mapping genomic features of rice (GO annotation, subcellular localization prediction, and gene expression), we found that a well-annotated and biologically significant network is rich enough to capture many significant functional linkages within higher-order biological systems, such as pathways and biological processes. Furthermore, we took MADS-box do- main-containing proteins and circadian rhythm signaling pathways as examples to demonstrate that functional protein complexes and biological pathways could be effectively expanded in our predicted network. The expanded molecular network in PRIN has considerably improved the capability of these analyses to integrate existing knowledge and provide novel insights into the function and coordination of genes and gene networks.     

Hierarchical Classification of Protein Folds Using a Novel Ensemble Classifier

The analysis of biological information from protein sequences is important for the study of cellular functions and interactions, and protein fold recognition plays a key role in the prediction of protein structures. Unfortunately, the prediction of protein fold patterns is challenging due to the existence of compound protein structures. Here, we processed the latest release of the Structural Classification of Proteins (SCOP, version 1.75) database and exploited novel techniques to impressively increase the accuracy of protein fold classification. The techniques proposed in this paper include ensemble classifying and a hierarchical framework, in the first layer of which similar or redundant sequences were deleted in two manners; a set of base classifiers, fused by various selection strategies, divides the input into seven classes; in the second layer of which, an analogous ensemble method is adopted to predict all protein folds. To our knowledge, it is the first time all protein folds can be intelligently detected hierarchically. Compared with prior studies, our experimental results demonstrated the efficiency and effectiveness of our proposed method, which achieved a success rate of 74.21%, which is much higher than results obtained with previous methods (ranging from 45.6% to 70.5%). When applied to the second layer of classification, the prediction accuracy was in the range between 23.13% and 46.05%. This value, which may not be remarkably high, is scientifically admirable and encouraging as compared to the relatively low counts of proteins from most fold recognition programs. The web server Hierarchical Protein Fold Prediction (HPFP) is available at http://datamining.xmu.edu.cn/software/hpfp.     

Predicting homo-oligomers and hetero-oligomers by pseudo-amino acid composition: an approach from discrete wavelet transformation

Many proteins exist in vivo as oligomers with different quaternary structural attributes rather than as individual chains. These proteins are the structural components of various biological functions, including cooperative effects, allosteric mechanisms and ion-channel gating. With the dramatic increase in the number of protein sequences submitted to the public databank, it is important for both basic research and drug discovery research to acquire the knowledge about possible quaternary structural attributes of their interested proteins in a timely manner. A high-throughput method (DWT_SVM), fusing discrete wavelet transform (DWT) and support vector machine (SVM) classifier algorithm with various physicochemical features, has been developed to predict protein quaternary structure. The accuracy in distinguishing candidate proteins as homo-oligomer or hetero-oligomer using the dataset R₂₇₂₀ was 85.95% and 85.49% respectively by jackknife, showing that DWT_SVM is guide promising in predicting protein quaternary structures. The online service is available at http://bioinfo.ncu.edu.cn/Services.aspx. Protein sequences in FASTA format can be directly fed to the system OligoPred. The processed results will be presented in a diagram that includes the information of feature extraction and the classification error rate.     

iDNA-Prot: identification of DNA binding proteins using random forest with grey model

DNA-binding proteins play crucial roles in various cellular processes. Developing high throughput tools for rapidly and effectively identifying DNA-binding proteins is one of the major challenges in the field of genome annotation. Although many efforts have been made in this regard, further effort is needed to enhance the prediction power. By incorporating the features into the general form of pseudo amino acid composition that were extracted from protein sequences via the "grey model" and by adopting the random forest operation engine, we proposed a new predictor, called iDNA-Prot, for identifying uncharacterized proteins as DNA-binding proteins or non-DNA binding proteins based on their amino acid sequences information alone. The overall success rate by iDNA-Prot was 83.96% that was obtained via jackknife tests on a newly constructed stringent benchmark dataset in which none of the proteins included has ≥25% pairwise sequence identity to any other in a same subset. In addition to achieving high success rate, the computational time for iDNA-Prot is remarkably shorter in comparison with the relevant existing predictors. Hence it is anticipated that iDNA-Prot may become a useful high throughput tool for large-scale analysis of DNA-binding proteins. As a user-friendly web-server, iDNA-Prot is freely accessible to the public at the web-site on http://icpr.jci.edu.cn/bioinfo/iDNA-Prot or http://www.jci-bioinfo.cn/iDNA-Prot. Moreover, for the convenience of the vast majority of experimental scientists, a step-by-step guide is provided on how to use the web-server to get the desired results.     

Different packing of external residues can explain differences in the thermostability of proteins from thermophilic and mesophilic organisms

MOTIVATION: Understanding the basis of protein stability in thermophilic organisms raises a general question: what structural properties of proteins are responsible for the higher thermostability of proteins from thermophilic organisms compared to proteins from mesophilic organisms? RESULTS: A unique database of 373 structurally well-aligned protein pairs from thermophilic and mesophilic organisms is constructed. Comparison of proteins from thermophilic and mesophilic organisms has shown that the external, water-accessible residues of the first group are more closely packed than those of the second. Packing of interior parts of proteins (residues inaccessible to water molecules) is the same in both cases. The analysis of amino acid composition of external residues of proteins from thermophilic organisms revealed an increased fraction of such amino acids as Lys, Arg and Glu, and a decreased fraction of Ala, Asp, Asn, Gln, Thr, Ser and His. Our theoretical investigation of folding/unfolding behavior confirms the experimental observations that the interactions that differ in thermophilic and mesophilic proteins form only after the passing of the transition state during folding. Thus, different packing of external residues can explain differences in thermostability of proteins from thermophilic and mesophilic organisms. AVAILABILITY: The database of 373 structurally well-aligned protein pairs is available at http://phys.protres.ru/resources/termo_meso_base.html. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.     

A seqlet-based maximum entropy Markov approach for protein secondary structure prediction

1 Introduction The prediction of protein structure and function from amino acid sequences is one of the most impor-tant problems in molecular biology. This problem is becoming more pressing as the number of known pro-tein sequences is explored as a result of genome and other sequencing projects, and the protein sequence- structure gap is widening rapidly[1]. Therefore, com-putational tools to predict protein structures are needed to narrow the widening gap. Although the prediction of three dim…     

Hot spot prediction in protein-protein interactions by an ensemble system

Background

Hot spot residues are functional sites in protein interaction interfaces. The identification of hot spot residues is time-consuming and laborious using experimental methods. In order to address the issue, many computational methods have been developed to predict hot spot residues. Moreover, most prediction methods are based on structural features, sequence characteristics, and/or other protein features.

Results

This paper proposed an ensemble learning method to predict hot spot residues that only uses sequence features and the relative accessible surface area of amino acid sequences. In this work, a novel feature selection technique was developed, an auto-correlation function combined with a sliding window technique was applied to obtain the characteristics of amino acid residues in protein sequence, and an ensemble classifier with SVM and KNN base classifiers was built to achieve the best classification performance.

Conclusion

The experimental results showed that our model yields the highest F1 score of 0.92 and an MCC value of 0.87 on ASEdb dataset. Compared with other machine learning methods, our model achieves a big improvement in hot spot prediction.

Availability

http://deeplearner.ahu.edu.cn/web/HotspotEL.htm.

     

A complexity-based method for predicting protein subcellular location

A complexity-based approach is proposed to predict subcellular location of proteins. Instead of extracting features from protein sequences as done previously, our approach is based on a complexity decomposition of symbol sequences. In the first step, distance between each pair of protein sequences is evaluated by the conditional complexity of one sequence given the other. Subcellular location of a protein is then determined using the k-nearest neighbor algorithm. Using three widely used data sets created by Reinhardt and Hubbard, Park and Kanehisa, and Gardy et al., our approach shows an improvement in prediction accuracy over those based on the amino acid composition and Markov model of protein sequences.