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This study comprehensively evaluates the performance of five types of probabilistic hierarchical classification methods used for predicting Gene Ontology (GO) terms related to ageing. Of those tested, a new hybrid of a Local Hierarchical Classifier (LHC) and the Predictive Clustering Tree algorithm (LHC-PCT) had the best predictive accuracy results. We also tested the impact of two types of variations in most hierarchical classification algorithms, namely: (a) changing the base algorithm (we tested Naive Bayes and Support Vector Machines), and the impact of (b) using or not the Correlation based Feature Selection (CFS) algorithm in a pre-processing step. In total, we evaluated the predictive performance of 17 variations of hierarchical classifiers across 15 datasets of ageing and longevity-related genes. We conclude that the LHC-PCT algorithm ranks better across several tests (seven out of 12). In addition, we interpreted the models generated by the PCT algorithm to show how hierarchical classification algorithms can be used to extract biological insights out of the ageing-related datasets that we compiled. [ABSTRACT FROM PUBLISHER]

Background: Studying the functions and structures of proteins is important for understanding the molecular mechanisms of life. The number of publicly available protein structures has increasingly become extremely large. Still, the classification of a protein structure remains a difficult, costly, and time consuming task. The difficulties are often due to the essential role of spatial and topological structures in the classification of protein structures. Results: We propose PROTNN, a novel classification approach for protein 3D-structures. Given an unannotated query protein structure and a set of annotated proteins, PROTNN assigns to the query protein the class with the highest number of votes across the k nearest neighbor reference proteins, where k is a user-defined parameter. The search of the nearest neighbor annotated structures is based on a protein-graph representation model and pairwise similarities between vector embedding of the query and the reference protein structures in structural and topological spaces. Conclusions: We demonstrate through an extensive experimental evaluation that PROTNN is able to accurately classify several datasets in an extremely fast runtime compared to state-of-the-art approaches. We further show that PROTNN is able to scale up to a whole PDB dataset in a single-process mode with no parallelization, with a gain of thousands order of magnitude in runtime compared to state-of-the-art approaches. [ABSTRACT FROM AUTHOR]

Proteins and their domains evolve by a set of events commonly including the duplication and divergence of small motifs. The presence of short repetitive regions in domains has generally constituted a difficult case for structural domain classifications and their hierarchies. We developed the Evolutionary Classification Of protein Domains (ECOD) in part to implement a new schema for the classification of these types of proteins. Here we document the ways in which ECOD classifies proteins with small internal repeats, widespread functional motifs, and assemblies of small domain-like fragments in its evolutionary schema. We illustrate the ways in which the structural genomics project impacted the classification and characterization of new structural domains and sequence families over the decade. [ABSTRACT FROM AUTHOR]

Over the last decade, numerous studies have demonstrated the fundamental importance of tandem repeat (TR) proteins in many biological processes. A plethora of new repeat structures have also been solved. The recently published RepeatsDB provides information on TR proteins. However, a detailed structural characterization of repetitive elements is largely missing, as repeat unit annotation is manually curated and currently covers only 3 % of the bona fide TR proteins. Repeat Protein Unit Predictor (ReUPred) is a novel method for the fast automatic prediction of repeat units and repeat classification using an extensive Structure Repeat Unit Library (SRUL) derived from RepeatsDB. ReUPred uses an iterative structural search against the SRUL to find repetitive units. On a test set of solenoid proteins, ReUPred is able to correctly detect 92 % of the proteins. Unlike previous methods, it is also able to correctly classify solenoid repeats in 89 % of cases. It also outperforms two recent state-of-the-art methods for the repeat unit identification problem. The accurate prediction of repeat units increases the number of annotated repeat units by an order of magnitude compared to the sequence-based Pfam classification. ReUPred is implemented in Python for Linux and freely available from the URL: . [ABSTRACT FROM AUTHOR]

The study on principle of protein folding is a cutting-edge topic in life science, and folding type classification is the basis of protein folding research. In this paper, we constructed a template database of BRD-like folding type, and established a comprehensive classification method based on multiple templates. Our method is used for the classification of BRD-like folding. We tested the training set of 12 117 samples, and found that the sensitivity, specificity and MCC were 0.3,0.997 and 0.72 respectively. Then we tested the 2 260 samples of the independent test, and found that the sensitivity, specificity and MCC were 0.1,0.998 and 0.86 respectively. These results indicated that the comprehensive classification method based on multiple templates could be used for the classification of protein folding. [ABSTRACT FROM AUTHOR]

ABSTRACT Histone post-translational modifications play a critical role in the regulation of gene expression. Methylation of lysines at N-terminal tails of histones has been shown to be involved in such regulation. While this modification was long considered to be irreversible, two different classes of enzymes capable of carrying out the demethylation of histone lysines were recently identified: the oxidases, such as LSD1, and the oxygenases (JmjC-containing). Here, a family-wide analysis of the second of these classes is proposed, with over 300 proteins studied at the sequence level. We show that a correlated evolution analysis yields some position/residue pairs which are critical at comparing JmjC sequences and enables the classification of JmjC domains into five families. A few positions appear more frequently among conditions, such as positions 23 (directly C-terminal to the second iron ligand), 24, 252 and 253 (directly N-terminal to a conserved Asn). Implications of family conditions are studied in detail on PHF2, revealing the meaningfulness of the sequence-derived conditions at the structural level. These results should help obtain insights on the diversity of JmjC-containing proteins solely by considering some of the amino acids present in their JmjC domain. Proteins 2016; 84:397-407. © 2016 Wiley Periodicals, Inc. [ABSTRACT FROM AUTHOR]

As a result of the genome sequencing and structural genomics initiatives, we have a wealth of protein sequence and structural data. However, only about 1% of these proteins have experimental functional annotations. As a result, computational approaches that can predict protein functions are essential in bridging this widening annotation gap. This article reviews the current approaches of protein function prediction using structure and sequence based classification of protein domain family resources with a special focus on functional families in the CATH-Gene3D resource. [ABSTRACT FROM AUTHOR]

Background: Comparison between multiple protein datasets requires the choice of an appropriate reference system and a number of variables to describe their differences. Here we introduce an innovative approach to discriminate multiple protein datasets (multiCM) and to measure enrichments in gene ontology terms (cleverGO) using semantic similarities. Results: We illustrate the powerfulness of our approach by investigating the links between RNA-binding ability and other protein features, such as structural disorder and aggregation, in S. cerevisiae, C. elegans, M. musculus and H. sapiens. Our results are in striking agreement with available experimental evidence and unravel features that are key to understand the mechanisms regulating cellular homeostasis. Conclusions: In an intuitive way, multiCM and cleverGO provide accurate classifications of physico-chemical features and annotations of biological processes, molecular functions and cellular components, which is extremely useful for the discovery and characterization of new trends in protein datasets. The multiCM and cleverGO can be freely accessed on the Web at http://www.tartaglialab.com/cs_multi/submission and http://www.tartaglialab.com/ GO_analyser/universal. Each of the pages contains links to the corresponding documentation and tutorial. [ABSTRACT FROM AUTHOR]

Background: Insects belong to a class that accounts for the majority of animals on earth. With over one million identified species, insects display a huge diversity and occupy extreme environments. At present, there are dozens of fully sequenced insect genomes that cover a range of habitats, social behavior and morphologies. In view of such diverse collection of genomes, revealing evolutionary trends and charting functional relationships of proteins remain challenging. Results: We analyzed the relatedness of 17 complete proteomes representative of proteomes from insects including louse, bee, beetle, ants, flies and mosquitoes, as well as an out-group from the crustaceans. The analyzed proteomes mostly represented the orders of Hymenoptera and Diptera. The 287,405 protein sequences from the 18 proteomes were automatically clustered into 20,933 families, including 799 singletons. A comprehensive analysis based on statistical considerations identified the families that were significantly expanded or reduced in any of the studied organisms. Among all the tested species, ants are characterized by an exceptionally high rate of family gain and loss. By assigning annotations to hundreds of species-specific families, the functional diversity among species and between the major clades (Diptera and Hymenoptera) is revealed. We found that many species-specific families are associated with receptor signaling, stress-related functions and proteases. The highest variability among insects associates with the function of transposition and nucleic acids processes (collectively coined TNAP). Specifically, the wasp and ants have an order of magnitude more TNAP families and proteins relative to species that belong to Diptera (mosquitoes and flies). Conclusions: An unsupervised clustering methodology combined with a comparative functional analysis unveiled proteomic signatures in the major clades of winged insects. We propose that the expansion of TNAP families in Hymenoptera potentially contributes to the accelerated genome dynamics that characterize the wasp and ants. [ABSTRACT FROM AUTHOR]

In this article, a novel approach for extracting features from protein sequences is proposed. This approach extracts only six features corresponding to each protein sequence. These features are computed by globally considering the probabilities of occurrences of the amino acids in different positions within the superfamily which locally belongs to the six exchange groups. Then, these features are used as an input to the Neural Network formed by Boolean-Like Training Algorithm (BLTA). The BLTA is used to classify the protein sequences obtained from the Protein Information Resource (PIR). To investigate the efficacy of proposed feature extraction approach, the experimentation is performed on two superfamilies, namely Ras and Globin using tenfold cross validation. The highest Classification Accuracy achieved is 100.00 ± 00.00 with Computational Time 170.49 ± 70.87 (s) are remarkably better in comparison to the Classification Accuracies and Computational Time achieved by Mansouri, Bandyopadhyay and Wang. The experimental results demonstrate that the proposed approach extracts the most significant and lesser number of features for each protein sequence due to which it results in considerably potential improvement in Classification Accuracy and takes less Computational Time in comparison with other well-known feature extraction approaches. [ABSTRACT FROM AUTHOR]

STRIKE is an algorithm which predicts protein-protein interactions (PPIs) and determines that proteins interact if they contain similar substrings of amino acids. Unlike other methods for PPI prediction, STRIKE is able to achieve reasonable improvement over the existing PPI prediction methods. Although its high accuracy as a PPI prediction method, STRIKE consumes a large execution time and hence it is considered to be a compute-intensive application. In this paper, we develop and implement a parallel STRIKE algorithm for high-performance computing (HPC) systems. Using a large-scale cluster, the execution time of the parallel implementation of this bioinformatics algorithm was reduced from about a week on a serial uniprocessor machine to about 16.5 h on 16 computing nodes, down to about 2 h on 128 parallel nodes. Communication overheads between nodes are thoroughly studied. [ABSTRACT FROM AUTHOR]

Contact sites between amino acids characterize important structural features of a protein. We investigated characteristics of contact sites in a representative set of proteins and their relations between protein class or topology. For this purpose, we used a non-redundant set of 5872 protein domains, identically categorized by CATH and SCOP databases. The proteins represented alpha, beta, and alpha+beta classes. Contact maps of protein structures were obtained for a selected set of physical distances in the main backbone and separations in protein sequences. For each set a dependency between contact degree and distance parameters was quantified. We indicated residues forming contact sites most frequently and unique amino acid pairs which created contact sites most often within each structural class. Contact characteristics of specific topologies were compared to the characteristics of their protein classes showing protein groups with a distinguished contact characteristic. We showed that our results could be used to improve the performance of recent top contact predictor - direct coupling analysis. Our work provides values of contact site propensities that can be involved in bioinformatic databases. [ABSTRACT FROM AUTHOR]

ABSTRACT In this article, we develop a quantitative comparison method for two arbitrary protein structures. This method uses a root-mean-square deviation characterization and employs a series expansion of the protein's shape function in terms of the Wigner-D functions to define a new criterion, which is called a 'similarity value.' We further demonstrate that the expansion coefficients for the shape function obtained with the help of the Wigner-D functions correspond to structure factors. Our method addresses the common problem of comparing two proteins with different numbers of atoms. We illustrate it with a worked example. Proteins 2014; 82:2756-2769. © 2014 Wiley Periodicals, Inc. [ABSTRACT FROM AUTHOR]

Protein classification is the first step to functional annotation; SCOP and Pfam databases are currently the most relevant protein classification schemes. However, the disproportion in the number of three dimensional (3D) protein structures generated versus their classification into relevant superfamilies/families emphasizes the need for automated classification schemes. Predicting function of novel proteins based on sequence information alone has proven to be a major challenge. The present study focuses on the use of physicochemical parameters in conjunction with machine learning algorithms (Naive Bayes, Decision Trees, Random Forest and Support Vector Machines) to classify proteins into their respective SCOP superfamily/Pfam family, using sequence derived information. Spectrophores™, a 1D descriptor of the 3D molecular field surrounding a structure was used as a benchmark to compare the performance of the physicochemical parameters. The machine learning algorithms were modified to select features based on information gain for each SCOP superfamily/Pfam family. The effect of combining physicochemical parameters and spectrophores on classification accuracy (CA) was studied. Machine learning algorithms trained with the physicochemical parameters consistently classified SCOP superfamilies and Pfam families with a classification accuracy above 90%, while spectrophores performed with a CA of around 85%. Feature selection improved classification accuracy for both physicochemical parameters and spectrophores based machine learning algorithms. Combining both attributes resulted in a marginal loss of performance. Physicochemical parameters were able to classify proteins from both schemes with classification accuracy ranging from 90-96%. These results suggest the usefulness of this method in classifying proteins from amino acid sequences. [ABSTRACT FROM AUTHOR]

Background: The function of a protein can be deciphered with higher accuracy from its structure than from its amino acid sequence. Due to the huge gap in the available protein sequence and structural space, tools that can generate functionally homogeneous clusters using only the sequence information, hold great importance. For this, traditional alignment-based tools work well in most cases and clustering is performed on the basis of sequence similarity. But, in the case of multi-domain proteins, the alignment quality might be poor due to varied lengths of the proteins, domain shuffling or circular permutations. Multi-domain proteins are ubiquitous in nature, hence alignment-free tools, which overcome the shortcomings of alignment-based protein comparison methods, are required. Further, existing tools classify proteins using only domain-level information and hence miss out on the information encoded in the tethered regions or accessory domains. Our method, on the other hand, takes into account the full-length sequence of a protein, consolidating the complete sequence information to understand a given protein better. Results: Our web-server, CLAP (Classification of Proteins), is one such alignment-free software for automatic classification of protein sequences. It utilizes a pattern-matching algorithm that assigns local matching scores (LMS) to residues that are a part of the matched patterns between two sequences being compared. CLAP works on full-length sequences and does not require prior domain definitions. Pilot studies undertaken previously on protein kinases and immunoglobulins have shown that CLAP yields clusters, which have high functional and domain architectural similarity. Moreover, parsing at a statistically determined cut-off resulted in clusters that corroborated with the sub-family level classification of that particular domain family. Conclusions: CLAP is a useful protein-clustering tool, independent of domain assignment, domain order, sequence length and domain diversity. Our method can be used for any set of protein sequences, yielding functionally relevant clusters with high domain architectural homogeneity. The CLAP web server is freely available for academic use at http://nslab.mbu.iisc.ernet.in/clap/. [ABSTRACT FROM AUTHOR]

Abstract: The cell death (CD) is a dynamic biological function involved in physiological and pathological processes. Due to the complexity of CD, there is a demand for fast theoretical methods that can help to find new CD molecular targets. The current work presents the first classification model to predict CD-related proteins based on Markov Mean Properties. These protein descriptors have been calculated with the MInD-Prot tool using the topological information of the amino acid contact networks of the 2423 protein chains, five atom physicochemical properties and the protein 3D regions. The Machine Learning algorithms from Weka were used to find the best classification model for CD-related protein chains using all 20 attributes. The most accurate algorithm to solve this problem was K*. After several feature subset methods, the best model found is based on only 11 variables and is characterized by the Area Under the Receiver Operating Characteristic Curve (AUROC) of 0.992 and the true positive rate (TP Rate) of 88.2% (validation set). 7409 protein chains labeled with “unknown function” in the PDB Databank were analyzed with the best model in order to predict the CD-related biological activity. Thus, several proteins have been predicted to have CD-related function in Homo sapiens: 3DRX–involved in virus-host interaction biological process, protein homooligomerization; 4DWF–involved in cell differentiation, chromatin modification, DNA damage response, protein stabilization; 1IUR–involved in ATP binding, chaperone binding; 1J7D–involved in DNA double-strand break processing, histone ubiquitination, nucleotide-binding oligomerization; 1UTU–linked with DNA repair, regulation of transcription; 3EEC–participating to the cellular membrane organization, egress of virus within host cell, class mediator resulting in cell cycle arrest, negative regulation of ubiquitin-protein ligase activity involved in mitotic cell cycle and apoptotic process. Other proteins from bacteria predicted as CD-related are 2G3V - a CAG pathogenicity island protein 13 from Helicobacter pylori, 4G5A - a hypothetical protein in Bacteroides thetaiotaomicron, 1YLK–involved in the nitrogen metabolism of Mycobacterium tuberculosis, and 1XSV - with possible DNA/RNA binding domains. The results demonstrated the possibility to predict CD-related proteins using molecular information encoded into the protein 3D structure. Thus, the current work demonstrated the possibility to predict new molecular targets involved in cell-death processes. [Copyright &y& Elsevier]

Background: Amino acid sequences and features extracted from such sequences have been used to predict many protein properties, such as subcellular localization or solubility, using classifier algorithms. Although software tools are available for both feature extraction and classifier construction, their application is not straightforward, requiring users to install various packages and to convert data into different formats. This lack of easily accessible software hampers quick, explorative use of sequence-based classification techniques by biologists. Results: We have developed the web-based software tool SPiCE for exploring sequence-based features of proteins in predefined classes. It offers data upload/download, sequence-based feature calculation, data visualization and protein classifier construction and testing in a single integrated, interactive environment. To illustrate its use, two example datasets are included showing the identification of differences in amino acid composition between proteins yielding low and high production levels in fungi and low and high expression levels in yeast, respectively. Conclusions: SPiCE is an easy-to-use online tool for extracting and exploring sequence-based features of sets of proteins, allowing non-experts to apply advanced classification techniques. The tool is available at http://helix.ewi. tudelft.nl/spice. [ABSTRACT FROM AUTHOR]

Abstract: DNA-binding proteins play a vitally important role in many biological processes. Prediction of DNA-binding proteins from amino acid sequence is a significant but not fairly resolved scientific problem. Chaos game representation (CGR) investigates the patterns hidden in protein sequences, and visually reveals previously unknown structure. Fractal dimensions (FD) are good tools to measure sizes of complex, highly irregular geometric objects. In order to extract the intrinsic correlation with DNA-binding property from protein sequences, CGR algorithm, fractal dimension and amino acid composition are applied to formulate the numerical features of protein samples in this paper. Seven groups of features are extracted, which can be computed directly from the primary sequence, and each group is evaluated by the 10-fold cross-validation test and Jackknife test. Comparing the results of numerical experiments, the group of amino acid composition and fractal dimension (21-dimension vector) gets the best result, the average accuracy is 81.82% and average Matthew's correlation coefficient (MCC) is 0.6017. This resulting predictor is also compared with existing method DNA-Prot and shows better performances. [Copyright &y& Elsevier]

BACKGROUND: Protein composition, amino acid profile and nutritional value of the lotus seed and its Osborne fractions were investigated. The seed was rich in protein with 19.85%, and showed well balanced amino acid composition compared with FAO/WHO pattern, Its nutritive properties were similar to those observed in the reference soybean protein. Phenylalanine, tyrosine, leucine and lysine were the limiting amino acids in the seed proteins. The albumin and globulin were the main protein fraction, the amino acid profile and nutritional value were close to the seed protein. RESULTS: Changes in transition temperature and thermal stability were observed through different solvent extractions. Albumin possessed the predominant thermal stability (81.4 °C) followed by globulin (74.49 °C), prolamin (69 °C) and glutelin (65.6 °C). So, solvent compositions influence the profile of AAs and their nutritive value, and aqueous solvent with 0.1 mol L−1 NaCl was an efficient protein solubiliser. CONCLUSION: The results indicated that the extraction processes influenced the lotus seed protein quality and thermal stability. Overall, the study revealed that the lotus seed protein was nutritionally well-balanced protein and might be of significant importance in the formulation of diets for humans. © 2012 Society of Chemical Industry [ABSTRACT FROM AUTHOR]

Phage has high specificity for its host recognition. As a natural enemy of bacteria, it has been used to treat super bacteria many times. Identifying phage proteins from the original sequence is very important for understanding the relationship between phage and host bacteria and developing new antimicrobial agents. However, traditional experimental methods are both expensive and time-consuming. In this study, an ensemble learning-based feature selection method is proposed to find important features for phage protein identification. The method uses four types of protein sequence-derived features, quantifies the importance of each feature by adding perturbations to the features to influence the results, and finally splices the important features among the four types of features. In addition, we analyzed the selected features and their biological significance., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Liu, Cui, Chen and Liu.)

Proteins are the executors of cellular physiological activities, and accurate structural and function elucidation are crucial for the refined mapping of proteins. As a feature engineering method, the reduction of amino acid composition is not only an important method for protein structure and function analysis, but also opens a broad horizon for the complex field of machine learning. Representing sequences with fewer amino acid types greatly reduces the complexity and noise of traditional feature engineering in dimension, and provides more interpretable predictive models for machine learning to capture key features. In this paper, we systematically reviewed the strategy and method studies of the reduced amino acid (RAA) alphabets, and summarized its main research in protein sequence alignment, functional classification, and prediction of structural properties, respectively. In the end, we gave a comprehensive analysis of 672 RAA alphabets from 74 reduction methods., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2022 The Authors.)

Alternative RNA splicing in multicellular organisms is regulated by a large group of proteins of mainly unknown origin. To predict the functions of these proteins, classification of their domains at the sequence and structural level is necessary. We have focused on four groups of splicing regulators, the heterogeneous nuclear ribonucleoprotein (hnRNP), serine-arginine (SR), embryonic lethal, abnormal vision (ELAV)-like, and CUG-BP and ETR-like factor (CELF) proteins, that show increasing diversity among metazoa. Sequence and phylogenetic analyses were used to obtain a broader understanding of their evolutionary relationships. Surprisingly, when we characterised sequence similarities across full-length sequences and conserved domains of ten metazoan species, we found some hnRNPs were more closely related to SR, ELAV-like and CELF proteins than to other hnRNPs. Phylogenetic analyses and the distribution of the RRM domains suggest that these proteins diversified before the last common ancestor of the metazoans studied here through domain acquisition and duplication to create genes of mixed evolutionary origin. We propose that these proteins were derived independently rather than through the expansion of a single protein family. Our results highlight inconsistencies in the current classification system for these regulators, which does not adequately reflect their evolutionary relationships, and suggests that a domain-based classification scheme may have more utility. [ABSTRACT FROM AUTHOR]

Abstract: Protein phosphorylation plays a fundamental role in the biology of apicomplexan parasites. Many apicomplexan protein kinases are substantially different from their mammalian orthologues, and thus constitute a landscape of potential drug targets. Here, we integrate genomic, biochemical, genetic and evolutionary information to provide an integrated and up-to-date analysis of twelve apicomplexan kinomes. All kinome sequences are available through the Kinomer database. [Copyright &y& Elsevier]

An extensive comparative analysis of orbivirus genomes revealed four cases of unclear numeration and protein designation, due to confused reference to protein size or segment size by which they are encoded. A concise nomenclature based on type species, sequence homology and functional characteristics independent of segment or protein size is suggested. [ABSTRACT FROM AUTHOR]

Abstract: Background: Throughout evolution, mutations in particular regions of some protein structures have resulted in extra covalent bonds that increase the overall robustness of the fold: disulfide bonds. The two strategically placed cysteines can also have a more direct role in protein function, either by assisting thiol or disulfide exchange, or through allosteric effects. In this work, we verified how the structural similarities between disulfides can reflect functional and evolutionary relationships between different proteins. We analyzed the conformational patterns of the disulfide bonds in a set of disulfide-rich proteins that included twelve SCOP superfamilies: thioredoxin-like and eleven superfamilies containing small disulfide-rich proteins (SDP). Results: The twenty conformations considered in the present study were characterized by both structural and energetic parameters. The corresponding frequencies present diverse patterns for the different superfamilies. The least-strained conformations are more abundant for the SDP superfamilies, while the “catalytic” +/−RHook is dominant for the thioredoxin-like superfamily. The “allosteric” –RHSaple is moderately abundant for BBI, Crisp and Thioredoxin-like superfamilies and less frequent for the remaining superfamilies. Using a hierarchical clustering analysis we found that the twelve superfamilies were grouped in biologically significant clusters. Conclusions: In this work, we carried out an extensive statistical analysis of the conformational motifs for the disulfide bonds present in a set of disulfide-rich proteins. We show that the conformational patterns observed in disulfide bonds are sufficient to group proteins that share both functional and structural patterns and can therefore be used as a criterion for protein classification. [ABSTRACT FROM AUTHOR]

Abstract: Existing protein structure classifications group proteins by overall structural similarity at the highest level and by evolutionary relationships at the lowest level, deriving higher-level groups by pairwise structure comparison. For this to be successful requires that large changes in structure are relatively rare in evolution and that proteins with no detectable evolutionary relationship do not converge on similar global chain conformations since this creates conflicts between structural and evolutionary consistency. Analysis of global structural changes using core topological descriptions for 4261 domains from classes C and D of the SCOP database and new measures of topological distance and consistency of classification showed that the topological consistency of SCOP folds is highly variable with some folds having no consistent description and significant overlaps between groups including some members of separate folds with identical topological descriptions. Topological clustering shows that including sufficient indels to allow family members to be joined would also require joining several distinct folds. We conclude that evolutionary changes in the global topology of protein domains are the root cause of many difficulties for present approaches to structure classification using pairwise comparison. As a resolution we propose that a purely structural classification should be created using an approach similar to that adopted by the Gene Ontology in which proteins are assigned labels describing structure. [ABSTRACT FROM AUTHOR]

One of the major research directions in bioinformatics is that of predicting the protein superfamily in large databases and classifying a given set of protein domains into superfamilies. The classification reflects the structural, evolutionary and functional relatedness. These relationships are embodied in hierarchical classification such as Structural Classification of Protein (SCOP), which is manually curated. Such classification is essential for the structural and functional analysis of proteins. Yet, a large number of proteins remain unclassified. We have proposed an unsupervised machine-learning FuzzyART neural network algorithm to classify a given set of proteins into SCOP superfamilies. The proposed method is fast learning and uses an atypical non-linear pattern recognition technique. In this approach, we have constructed a similarity matrix from p-values of BLAST all-against-all, trained the network with FuzzyART unsupervised learning algorithm using the similarity matrix as input vectors and finally the trained network offers SCOP superfamily level classification. In this experiment, we have evaluated the performance of our method with existing techniques on six different datasets. We have shown that the trained network is able to classify a given similarity matrix of a set of sequences into SCOP superfamilies at high classification accuracy. [ABSTRACT FROM AUTHOR]

Abstract: Classification of protein sequences has important applications in areas such as disease diagnosis, treatment development and drug design. In this paper we present a highly accurate classifier called the g-MARS (gapped Markov Chain with support vector machine) protein classifier. It models the structure of a protein sequence by measuring the transition probabilities between pairs of amino acids. This results in a Markov chain style model for each protein sequence. Then, to capture the similarity among non-exactly matching protein sequences, we show that this model can be generalized to incorporate gaps in the Markov chain. Theoretical justification for the power of our gapped feature space model is provided through its connections to analysis methods for nonlinear dynamical systems. We perform an experimental study and compare g-MARS to several other state-of-the-art protein classifiers. Overall, we demonstrate that g-MARS has high accuracy and operates efficiently on a diverse range of protein families. [ABSTRACT FROM AUTHOR]

Abstract: In this work, we propose a method for protein classification that combines different texture descriptors extracted from the 2-D distance matrix obtained from the 3-D tertiary structure of a given protein. Instead of considering all atoms in the protein, the distance matrix is calculated by considering only those atoms that belong to the protein backbone. The positive results reported in this paper offer further experimental confirmation that the distance matrix contains sufficient information for describing a protein. Moreover, we show that combining features extracted from the primary structure with features extracted from the distance matrix increases the performance of our classification system. We demonstrate this finding by comparing the performance of an ensemble of classifiers that uses the combined features. The classifiers used in our experiments are support vector machines and random subspace of support vector machines. The experimental results, validated using three different datasets (protein fold recognition, DNA-binding proteins recognition, biological processes, and molecular functions recognition) along with different texture feature extraction methods (variants of local binary patterns, Radon feature transform based approaches, and Haralick descriptors) demonstrate the effectiveness of the proposed approach. Particularly interesting are the results in the classification of 27 types of structural properties: our proposed approach achieves significant improvement compared with other reported methods. [Copyright &y& Elsevier]

Classification of proteins as precursors of bioactive peptides is presented in this work. To achieve this aim, the worldwide available computer databases such as BIOPEP, CATH, PDB, and SCOP were applied. The main qualitative criterion to classify the proteins was the integrated coefficient of biological activity of protein (C) defined as a square root of the sum of squares of (A) for different activities divided by the number of activities, where (A) denotes the frequency of occurrence of fragments with a given activity in a protein sequence and is described as the number of fragments with a given activity divided by the number of amino acid residues of a protein chain taken for an analysis. Taking into consideration the coefficient (C) calculated for 126 animal and plant proteins, three families were distinguished. In the family containing proteins -- the poorest source of bioactive fragments, were e.g. leguminlike chains of pumpkin, ginkgo biloba isolated from primary endosperm, vicia faba, and faba bean. Proteins being the best source of bioactive fragments (e.g. proteins derived from milk, bovine and chicken meat and wheat) were classified into the 1st family. It was found out that such a family classification is not identical with protein classification according to the criteria proposed and applied in the other computer databases. However, some proteins contained similar bioactive fragments within the sequence chains as well as possessed similar functions or structural motifs (e.g. TIM barrel motif). It can be presumed about the evolutionary similarity of proteins as a source of bioactive peptides. [ABSTRACT FROM AUTHOR]

Abstract: Objective: In this paper we propose a new feature extractor for peptide/protein classification based on the calculation of texture descriptors. Representing a peptide/protein using a matrix descriptor, instead of a vector, allows to deal with the peptide/protein as an image and to use texture descriptors for representation purposes. Methods and materials: A matrix descriptor, which is a squared matrix of the dimension of the peptide/protein, is obtained considering a partial ordering of the amino acids of the peptide/protein according to their value of a given physicochemical property. Each matrix descriptor is considered as a texture image and several texture descriptors are considered to obtain a compact representation which is scale invariant (i.e. independent on the length of the peptide\protein). The texture descriptors tested in this work are: local binary patterns (LBP), discrete cosine transform (DCT) and Daubechies wavelets. Results and conclusion: The experimental section reports several tests, aimed at supporting our ideas, performed on the following datasets: vaccine dataset for the predictions of peptides that bind human leukocyte antigens; human immunodeficiency virus (HIV-1) protease cleavage site prediction dataset and membrane proteins type dataset. The experimental results confirm the usefulness of the novel descriptors: the performance obtained by our system on the three difficult datasets is quite high, indicating that the proposed method is a feasible system for extracting information from peptides and proteins. The performance obtained by each of the three texture descriptors calculated from the matrix-based representation, and coupled to a support vector machine classifier, is lower than the performance obtained by other vector-based descriptors based on physicochemical properties proposed in the literature. Anyway the new descriptors bring different information and our tests show that the texture descriptors and the vector-based descriptors can be combined to improve the overall performance of the system. In particular the proposed approach improves the state-of-the-art results in two out of three tested problems (HIV-1 protease cleavage site prediction dataset and membrane proteins type dataset). [Copyright &y& Elsevier]

Abstract: Fold recognition is a challenging field strongly associated with protein function determination, which is crucial for biologists and the pharmaceutical industry. Hidden Markov models (HMMs) have been widely used for this purpose. In this paper we demonstrate how the fold recognition performance of a recently introduced HMM with a reduced state-space topology can be improved. Our method employs an efficient architecture and a low complexity training algorithm based on likelihood maximization. The fold recognition performance of the model is further improved in two steps. In the first step we use a smaller model architecture based on the {E,H,L} alphabet instead of the DSSP secondary structure alphabet. In the second step secondary structure information (predicted or true) is additionally used in scoring the test set sequences. The Protein Data Bank and the annotation of the SCOP database are used for the training and evaluation of the proposed methodology. The results show that the fold recognition accuracy is substantially improved in both steps. Specifically, it is increased by 2.9% in the first step to 22%. In the second step it further increases and reaches up to 30% when predicted secondary structure information is additionally used and it increases even more and reaches up to 34.7% when we use the true secondary structure. The major advantage of the proposed improvements is that the fold recognition performance is substantially increased while the size of the model and the computational complexity of scoring are decreased. [Copyright &y& Elsevier]

Abstract: Variable predictive model based class discrimination (VPMCD) algorithm is proposed as an effective protein secondary structure classification tool. The algorithm mathematically represents the characteristics amino acid interactions specific to each protein structure and exploits them further to distinguish different structures. The new concept and the VPMCD classifier are established using well-studied datasets containing four protein classes as benchmark. The protein samples selected from SCOP and PDB databases with varying homology (25–100%) and non-uniform distribution of class samples provide challenging classification problem. The performance of the new method is compared with advanced classification algorithms like component coupled, SVM and neural networks. VPMCD provides superior performance for high homology datasets. 100% classification is achieved for self-consistency test and an improvement of 5% prediction accuracy is obtained during Jackknife test. The sensitivity of the new algorithm is investigated by varying model structures/types and sequence homology. Simpler to implement VPMCD algorithm is observed to be a robust classification technique and shows potential for effective extensions to other clinical diagnosis and data mining applications in biological systems. [Copyright &y& Elsevier]