Function Prediction of Human Proteins

Due to the rapidness in research, accumulation of biological data is happening at an overwhelming rate. Advanced computation techniques are required to gather the useful information from this enormous amount of protein data such that the knowledge is practically useful and easily interpretable. For instance, drug discoverers need biological or computational methods to predict the functions of proteins, responsible for different sort of diseases in human body. Since traditional biological methods were time consuming and comparatively expensive, various computational methods have been introduced in the respective research areas. In this project, we have tried to generate machine learning models that predict the protein function of unknown proteins and analyze their performance to get a model with highest accuracy. Protein function's sequence annotations such as Amino Acid modifications, Molecule Processing and other structural features like Active Site, Beta strand, Chain, etc. along with it even protein mass and length are considered for prediction of protein functions. To further improve the accuracy feature selection has been performed. According to the enzyme nomenclature scheme the protein are classified into 6 groups. This enzyme classes is nothing but the crystalize reactions of proteins and shows the functions of it.

A strategy for large-scale comparison of evolutionary- and reaction-based classifications of enzyme function

Determining the molecular function of enzymes discovered by genome sequencing represents a primary foundation for understanding many aspects of biology. Historically, classification of enzyme reactions has used the enzyme nomenclature system developed to describe the overall reactions performed by biochemically characterized enzymes, irrespective of their associated sequences. In contrast, functional classification and assignment for the millions of protein sequences of unknown function now available is largely done in two computational steps, first by similarity-based assignment of newly obtained sequences to homologous groups, followed by transferring to them the known functions of similar biochemically characterized homologs. Due to the fundamental differences in their etiologies and practice, `how’ these chemistry- and evolution-centric functional classification systems relate to each other has been difficult to explore on a large scale. To investigate this issue in a new way, we integrated two published ontologies that had previously described each of these classification systems independently. The resulting infrastructure was then used to compare the functional assignments obtained from each classification system for the well-studied and functionally diverse enolase superfamily. Mapping these function assignments to protein structure and reaction similarity networks shows a profound and complex disconnect between the homology- and chemistry-based classification systems. This conclusion mirrors previous observations suggesting that except for closely related sequences, facile annotation transfer from small numbers of characterized enzymes to the huge number uncharacterized homologs to which they are related is problematic. Our extension of these comparisons to large enzyme superfamilies in a computationally intelligent manner provides a foundation for new directions in protein function prediction for the huge proportion of sequences of unknown function represented in major databases. Interactive sequence, reaction, substrate and product similarity networks computed for this work for the enolase and two other superfamilies are freely available for download from the Structure Function Linkage Database Archive (http://sfld.rbvi.ucsf.edu).

A model system of gene-enzyme nomenclature in the genome era applied to aromatic biosynthesis

Background: The accurate annotation of functional roles for newly sequenced genes of genomes is not a simple matter. Function is, of course, related to amino-acid sequence and to domain structure but not always in straightforward ways. Even where given functional roles have been identified experimentally, the application of an uneven and erratic nomenclature has generated confusion on the part of annotators and has produced errors that tend to become progressively compounded in database repositories. Results: The pathway that is deployed in nature for aromatic biosynthesis exemplifies an accumulation of chaotic nomenclature and a variety of annotation dilemmas. We view this pathway as one that is sufficiently complex to pose most of the common problems, and yet is one that at the same time is of a manageable size. A set of guidelines has been developed for naming genes of aromatic-pathway biosynthesis and the corresponding gene products, and we suggest that these can be generalized for application to other metabolic pathways. Conclusion: A system of nomenclature for aromatic biosynthesis is presented that is logical, consistent, and evolutionarily informative.

A model system of gene-enzyme nomenclature in the genome era applied to aromatic biosynthesis

Background: The accurate annotation of functional roles for newly sequenced genes of genomes is not a simple matter. Function is, of course, related to amino-acid sequence and to domain structure but not always in straightforward ways. Even where given functional roles have been identified experimentally, the application of an uneven and erratic nomenclature has generated confusion on the part of annotators and has produced errors that tend to become progressively compounded in database repositories. Results: The pathway that is deployed in nature for aromatic biosynthesis exemplifies an accumulation of chaotic nomenclature and a variety of annotation dilemmas. We view this pathway as one that is sufficiently complex to pose most of the common problems, and yet is one that at the same time is of a manageable size. A set of guidelines has been developed for naming genes of aromatic-pathway biosynthesis and the corresponding gene products, and we suggest that these can be generalized for application to other metabolic pathways. Conclusion: A system of nomenclature for aromatic biosynthesis is presented that is logical, consistent, and evolutionarily informative.

N-ACETYL-β-D-GLUCOSAMINIDASE OF VIBRIO CHOLERAE

Aim. Study N-acetyl-β-D-glucosaminidase (chitobiase) (EC 3.2.1.30) in strains of Vibrio cholerae of O1/non-O1 serogroups of various origin, that is a component of chitinolytic complex taking into account object of isolation and epidemiologic significance of strains. Materials and methods. Cultures of V.cholerae O1/non-O1 serogroup strains were obtained from the museum of live culture of Rostov RIPC. Enzymatic activity analysis was carried out in Hitachi F-2500 fluorescent spectrophotometer using FL Solutions licensed software. NCBI databases were used during enzyme characteristics. Results. N-acetyl-β-D-glucosaminidase in V.cholerae O1/non-O1 serogroup strains was detected, purified by column chromatography, studied and characterized by a number of physical-chemical and biological properties. Comparative computer analysis of amino acid sequence of N-acetyl-β-D-glucosaminidases of V.cholerae (VC2217 gene), Serratia marcescens etc. has allowed to attribute the enzyme from V.cholerae to glycosyl-hydrolases (chi-tobiases) of family 20 and classify it according to enzyme nomenclature as EC 3.2.1.30. Conclusion. N-acetyl-β-D-glucosaminidase in V.cholerae of O1/non-O1 serogroups of various origin and epidemiologic significance, participating in chitin utilization was studied and characterized for the first time, and its possible role in biology of cholera causative agent was shown.