Wavelet Transform-Based Phylogenetic Analysis of Protein Sequences

With the acceleration of gene sequencing studies, many biological data emerges. By analyzing these data, it contributes greatly to the studies on understanding the metabolic disorders in the organism and increasing the efficiency of the drugs. For this purpose, it is critical to classify the data in a way that is accurate, fast and low-cost according to its characteristics and relationships. Besides experimental methods, machine learning and bioinformatics methods are used. Artificial neural networks, support vector machines, flexible calculation methods are frequently used methods. However, the effectiveness of these methods on biosecence data depends on the method of using the method with the most appropriate parameters and converting protein sequences into numerical sequences. When the sequences are transformed with amino acid frequencies, the properties of amino acids are ignored. For this purpose, handling the physicochemical (hydrophobicity, hydrophilicity ...) properties of amino acids increases the performance of classification techniques. The phylogenetic tree is the best method to visualize the classification among species. In the project, the wavelet transform used in the analysis of digital signals has been adapted to protein sequences defined by hydrophobicity values. Each protein sequence was defined to correspond to a signal, the wavelet transform was divided into approach and detail components, and the similarities between them were calculated, and the phylogenetic tree of the species was created. As an application, phylogenetic trees of ND5 protein sequences of 22 species were created in the MatlabR2017 program of NeighborJoining (NJ) and Unweighed Pair Group Method of Aritmetic Averages (UPGMA) methods

Incorporating Multiple Biology based Knowledge to Amplify the Prophecy of Enzyme Sub-Functional Classes

Based on current in silico methods, enzyme sub-functional classes is distinguished from sequence level information, local order or sequence length and order knowledge. To date, no work has been done to predict the enzyme subclasses efficiently corresponding to the ENZYME database. In order to precisely predict the sub-functional classes of enzyme, we propose a derivative feature vector labelled as APH which unifies amino acid composition, dipeptide composition, hydrophobicity and hydrophilicity. Support Vector Machine is used for prediction and the performance is evaluated using accuracy obtained over 99% and Matthew’s Correlation Coefficient (MCC) over 0.99 with the aid of biological validation from in vivo studies

Exploiting Complex Protein Domain Networks for Protein Function Annotation

International audienceHuge numbers of protein sequences are now available in public databases. In order to exploit more fully this valuable biological data, these sequences need to be annotated with functional properties such as Enzyme Commission (EC) numbers and Gene Ontology terms. The UniProt Knowledgebase (UniProtKB) is currently the largest and most comprehensive resource for protein sequence and annotation data. In the March 2018 release of UniProtKB, some 556,000 sequences have been manually curated but over 111 million sequences still lack functional annotations. The ability to annotate automatically these unannotated sequences would represent a major advance for the field of bioinformatics. Here, we present a novel network-based approach called GrAPFI for the automatic functional annotation of protein sequences. The underlying assumption of GrAPFI is that proteins may be related to each other by the protein domains, families, and super-families that they share. Several protein domain databases exist such as In-terPro, Pfam, SMART, CDD, Gene3D, and Prosite, for example. Our approach uses Interpro domains, because the InterPro database contains information from several other major protein family and domain databases. Our results show that GrAPFI achieves better EC number annotation performance than several other previously described approaches

GrAPFI: predicting enzymatic function of proteins from domain similarity graphs

This work is dedicated to the memory of David W. Ritchie, who recently passed away.International audienceBackground: Thanks to recent developments in genomic sequencing technologies, the number of protein sequences in public databases is growing enormously. To enrich and exploit this immensely valuable data, it is essential to annotate these sequences with functional properties such as Enzyme Commission (EC) numbers, for example. The January 2019 release of the Uniprot Knowledge base (UniprotKB) contains around 140 million protein sequences. However, only about half of a million of these (UniprotKB/SwissProt) have been reviewed and functionally annotated by expert curators using data extracted from the literature and computational analyses. To reduce the gap between the annotated and unannotated protein sequences, it is essential to develop accurate automatic protein function annotation techniques. Results: In this work, we present GrAPFI (Graph-based Automatic Protein Function Inference) for automatically annotating proteins with EC number functional descriptors from a protein domain similarity graph. We validated the performance of GrAPFI using six reference proteomes in UniprotKB/SwissProt, namely Human, Mouse, Rat, Yeast, E. Coli and Arabidopsis thaliana. We also compared GrAPFI with existing EC prediction approaches such as ECPred, DEEPre, and SVMProt. This shows that GrAPFI achieves better accuracy and comparable or better coverage with respect to these earlier approaches. Conclusions: GrAPFI is a novel protein function annotation tool that performs automatic inference on a network of proteins that are related according to their domain composition. Our evaluation of GrAPFI shows that it gives better performance than other state of the art methods. GrAPFI is available at https://gitlab.inria.fr/bsarker/bmc_grapfi.git as a stand alone tool written in Python