A Novel Method for Similarity Analysis of Protein Sequences

This paper proposes a novel method for analyzing similarities of protein sequences based on features generated from the hydropathy properties of amino acid sequences. At first using the hydropathy characteristics, the protein sequences are converted into fixed-dimensional feature vectors. Based on these feature vectors, we analyze nine ND5 proteins and construct a phylogenic tree as an application. The phylogeny obtained is generally consistent with the phylogenic tree constructed by MEGA4 software. The experimental results show the utility of our approach.

Percolation of annotation errors through hierarchically structured protein sequence databases

In our previous work, we explored the use of graph-theoretic spectral methods for clustering protein sequences [7]. The nodes of the graph represent a set of proteins to be clustered into families and/or super-families. Edges between nodes are undirected and weighted by the similarities between proteins. We constructed a novel similarity function based on BLAST scores. The similarity values are in turn used to construct a Markov matrix representing transition probabilities between every pair of connected proteins. By analyzing the perturbations to the stationary distribution of the Markov matrix (as in [6,4]), we partition the graph into clusters. In this paper, we compare our method with TribeMCL, which modifies random walks, by reinforcing strong edges and pruning weak ones, such that clusters emerge naturally from the graph [3]. We compare these two methods with respect to their ease of use and the quality of the resulting clusters.

The prediction of protein function as well as the reconstruction of evolutionary genesis employing sequence comparison at large is still the most powerful tool in sequence analysis. Due to the exponential growth of the number of known protein sequences and the subsequent quadratic growth of the similarity matrix, the computation of the Similarity Matrix of Proteins (SIMAP) becomes a computational intensive task. The SIMAP database provides a comprehensive and up-to-date pre-calculation of the protein sequence similarity matrix, sequence-based features and sequence clusters. As of September 2009, SIMAP covers 48 million proteins and more than 23 million non-redundant sequences. Novel features of SIMAP include the expansion of the sequence space by including databases such as ENSEMBL as well as the integration of metagenomes based on their consistent processing and annotation. Furthermore, protein function predictions by Blast2GO are pre-calculated for all sequences in SIMAP and the data access and query functions have been improved. SIMAP assists biologists to query the up-to-date sequence space systematically and facilitates large-scale downstream projects in computational biology. Access to SIMAP is freely provided through the web portal for individuals (http://mips.gsf.de/simap/) and for programmatic access through DAS (http://webclu.bio.wzw.tum.de/das/) and Web-Service (http://mips.gsf.de/webservices/services/SimapService2.0?wsdl).

The basic helix–loop–helix (bHLH) transcription factor gene family is one of the largest gene families and extensively involved in plant growth, development, and stress responses. However, limited studies are available on the gene family in poplar. In this study, we focused on 202 bHLH genes, exploring their DNA and protein sequences and physicochemical properties. According to their protein sequence similarities, we classified the genes into 25 groups with specific motif structures. In order to explore their expressions, we performed gene expression profiling using RNA-Seq and identified 19 genes that display tissue-differential expression patterns without treatment. Furthermore, we also performed gene expression profiling under salt stress. We found 74 differentially expressed genes (DEGs), which are responsive to the treatment. A total of 18 of the 19 genes correspond well to the DEGs. We validated the results using reverse transcription quantitative real-time PCR. This study lays the foundation for future studies on gene cloning, transgenes, and biological mechanisms.

Dicer and Dicer like (DCLs) proteins are essential part of small RNA biogenesis pathway, is a type of RNase III digesting long dsRNA (pre-miRNA) to small RNA segments (miRNA). A total of 20 full length of Dicer like proteins (DCL1, DCL2, DCL3 and DCL4) from different organisms available in NCBI were evaluated by bioinformatics tools to investigate properties, structure of DCLs, domain analysis, multiple sequence alignment and phylogenetics tree construction. All DCLs protein sequences have Ribonuclease III protein family that contains RNaseIII domain including Helicase ATP-binding type-1, Helicase C-terminal, Dicer double-stranded RNA-binding fold, PAZ, Ribonuclease III, Double stranded RNA-binding domain (dsRB). Physicochemical analysis offers data such as pI, EC, Al, GRAVY and instability index about these enzymes. Putative phosphorylation sites were also identified which are found to be conserved in plant species and the results showed that the most abundant phosphorylation site is Serine residues in DCLs proteins. Patterns and profile analysis were performed using Prosite and conserved protein motifs subjected to MEME to obtain the best possible matches. The phylogenetics tree represented three major clusters and similar DCLs protein sequences of different plant species clustered together. The obtained results could be used for further in silico analysis and homology modeling studies. Keywords: Dicer, DCLs, miRNA, RNase, In silico analysis. *Corresponding Author: Ertugrul Filiz (e-mail: ertugrulfiliz@gmail.com). (Received: 25.05.2012 Accepted: 24.01.2013)

Protein remote homology detection is one of the most important problems in bioinformatics. Discriminative methods such as support vector machines (SVM) have shown superior performance. However, the performance of SVM-based methods depends on the vector representations of the protein sequences. Prior works have demonstrated that sequence-order effects are relevant for discrimination, but little work has explored how to incorporate the sequence-order information along with the amino acid physicochemical properties into the prediction. In order to incorporate the sequence-order effects into the protein remote homology detection, the physicochemical distance transformation (PDT) method is proposed. Each protein sequence is converted into a series of numbers by using the physicochemical property scores in the amino acid index (AAIndex), and then the sequence is converted into a fixed length vector by PDT. The sequence-order information can be efficiently included into the feature vector with little computational cost by this approach. Finally, the feature vectors are input into a support vector machine classifier to detect the protein remote homologies. Our experiments on a well-known benchmark show the proposed method SVM-PDT achieves superior or comparable performance with current state-of-the-art methods and its computational cost is considerably superior to those of other methods. When the evolutionary information extracted from the frequency profiles is combined with the PDT method, the profile-based PDT approach can improve the performance by 3.4% and 11.4% in terms of ROC score and ROC50 score respectively. The local sequence-order information of the protein can be efficiently captured by the proposed PDT and the physicochemical properties extracted from the amino acid index are incorporated into the prediction. The physicochemical distance transformation provides a general framework, which would be a valuable tool for protein-level study.

This article presents a new approach to detect coiled coil and leucine zipper (L-Zip) motifs in protein sequences. The approach is based on protein scale calculation and sequence analysis. For this purpose, the wavelet-based local extrema extraction is employed, and window-based variations of local extrema afterward. This, in turn, provided a way to distinguish coiled coil subsequences and potential L-Zip motifs. The approach is validated on carefully chosen protein sequences that return inconclusive results within known frameworks for L-Zip detection, for example, 2ZIP. The results show that this new approach represents an improvement over previously presented approaches.

Protein sequence analysis is an important tool for researchers to study on bio-informatics and molecular biology, such as proteins structure and function prediction, phylogenetic classification and different conservation pattern recognition. It is a significant open issue to quickly efficiently find the similar proteins from a large scale of protein repository. This paper proposes a new method based on Discrete Wavelet Transform (DWT) to measure the similarity of protein sequences, i.e. the ACDWT model, as well as two amino acid encoding methods (HPC and ADCC) according to hydropathy properties and dissociation constants respectively. The model employs only the approximation coefficients of DWT so that the feature vector is short. That brings the proposed model a great running time promotion. According to the phylogenic trees about nine ND5 proteins made from our model and others, the experimental results show that our model is efficient and a little better than the others.

Novel 20-D descriptors of protein sequences and it’s applications in similarity analysis

Comparative protein analysis of the chitin metabolic pathway in extant organisms: A complex network approach

The physicochemical properties of eight extracellular proteinases secreted by psychrotrophic bacteria of dairy origin have been studied. Seven of these proteinases were able to withstand ultra heat treatment (UHT) with D values at 140 degrees C ranging from 2 to 300 s. The six Pseudomonas fluorescens proteinases were glycoproteins of mol. wt 47000-49500. The two Serratia marcescens proteinases, of mol. wt of 51000, did not contain carbohydrate but in other respects were similar to the Pseudomonas proteinases. The proteinases were inhibited by various metal chelators and all contained Ca and Zn in similar proportions. Their amino acid compositions were similar, with alanine as the N-terminal group, cysteine completely absent and very low levels of methionine. Isoelectric points ranged from 5.10 to 8.25. Their physical and chemical properties enabled them to be classified as alkaline metalloendopeptidases. A similarity index (S delta n) was used to predict sequence homology between ten proteinases of known amino acid composition. Comparisons of S delta n of these proteinases showed only minor sequence differences except for those of Ps. fluorescens MC60. Heat resistance could not be related wholly to similarities in protein sequence, but could be related both to the strength of stabilizing Ca2+-protein interactions and to the randomness inherent within the folding of the peptide chain.

BackgroundThe rapid burgeoning of available protein data makes the use of clustering within families of proteins increasingly important. The challenge is to identify subfamilies of evolutionarily related sequences. This identification reveals phylogenetic relationships, which provide prior knowledge to help researchers understand biological phenomena. A good evolutionary model is essential to achieve a clustering that reflects the biological reality, and an accurate estimate of protein sequence similarity is crucial to the building of such a model. Most existing algorithms estimate this similarity using techniques that are not necessarily biologically plausible, especially for hard-to-align sequences such as proteins with different domain structures, which cause many difficulties for the alignment-dependent algorithms. In this paper, we propose a novel similarity measure based on matching amino acid subsequences. This measure, named SMS for Substitution Matching Similarity, is especially designed for application to non-aligned protein sequences. It allows us to develop a new alignment-free algorithm, named CLUSS, for clustering protein families. To the best of our knowledge, this is the first alignment-free algorithm for clustering protein sequences. Unlike other clustering algorithms, CLUSS is effective on both alignable and non-alignable protein families. In the rest of the paper, we use the term "phylogenetic" in the sense of "relatedness of biological functions".ResultsTo show the effectiveness of CLUSS, we performed an extensive clustering on COG database. To demonstrate its ability to deal with hard-to-align sequences, we tested it on the GH2 family. In addition, we carried out experimental comparisons of CLUSS with a variety of mainstream algorithms. These comparisons were made on hard-to-align and easy-to-align protein sequences. The results of these experiments show the superiority of CLUSS in yielding clusters of proteins with similar functional activity.ConclusionWe have developed an effective method and tool for clustering protein sequences to meet the needs of biologists in terms of phylogenetic analysis and prediction of biological functions. Compared to existing clustering methods, CLUSS more accurately highlights the functional characteristics of the clustered families. It provides biologists with a new and plausible instrument for the analysis of protein sequences, especially those that cause problems for the alignment-dependent algorithms.

Adsorptive Biphasic Column Technology for Protein Sequence Analysis and Protein Chemical Modification

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