Abstract: Systems and methods for building a comprehensive genome scale metabolic model. The method includes determining that at least one of a hypothetical profile annotation and an uncharacterized profile annotation is available in a profile annotation associated with a protein. Further, the method includes performing a machine learning procedure on at least one of the hypothetical profile annotation and the uncharacterized hypothetical profile annotation after a fuzzy string matching and ranking procedure. Further, the method includes obtaining all the possible protein annotations based on a fuzzy string matching procedure. Further, the method includes performing a rank procedure on at least one of the hypothetical profile annotation and the uncharacterized profile alternative annotation. Further, the method includes identifying all possible metabolic reactions for the protein annotation obtained by the fuzzy string matching procedure.

Abstract: Embodiments herein disclose a method for identifying co-evolving sites and and at least one substituent amino acid residue. The method includes obtaining a current state of a protein and an ancestral state of the protein. Further, the method includes determining at least one amino acid substitution along with at least one co-evolving site associated with the protein based on the current state of the protein and the ancestral state of the protein. Further, the method includes assessing the at least one amino acid substitution as a function of a nucleotide substitution in the protein. Further, the method includes assessing at least one co-evolving site substitution based on the at least one assessed amino acid substitution. Further, the method includes identifying the co-evolving sites and at least one substituent amino acid residue.

Abstract: Systems and methods for building a comprehensive genome scale metabolic model. The method includes determining that at least one of a hypothetical profile annotation and an uncharacterized profile annotation is available in a profile annotation associated with a protein. Further, the method includes performing a machine learning procedure on at least one of the hypothetical profile annotation and the uncharacterized hypothetical profile annotation after a fuzzy string matching and ranking procedure. Further, the method includes obtaining all the possible protein annotations based on a fuzzy string matching procedure. Further, the method includes performing a rank procedure on at least one of the hypothetical profile annotation and the uncharacterized profile alternative annotation. Further, the method includes identifying all possible metabolic reactions for the protein annotation obtained by the fuzzy string matching procedure.

Abstract: Accordingly, embodiments herein disclose a method for handling an enzyme function through at least one change at multiple sites in an enzyme. The method includes detecting, by an electronic device, one or more functionally linked site pairs from the multiple sites in the enzyme. Further, the method includes determining, by the electronic device, at least one criticality assessment function for the one or more functionally linked site pairs. Further, the method includes prioritizing, by the electronic device, the functionally linked site pairs based on at least one of the criticality assessment function, a functional linkage strength, and a spatial linkage metric. Further, the method includes identifying, by the electronic device, the multiple sites from the prioritized functionally linked site pairs as optimum sites for introducing a change to handle the enzyme function.

Abstract: An apparatus and methods for assessing an ability of an organism(s) to metabolize toxic compounds, in a computing environment. Embodiments herein relates to the field of metabolic engineering in biochemical pathways and more particularly to an apparatus and methods for assessing an ability of an organism, a strain of the organism or strain of a different organism(s), to metabolize toxic compounds. The method includes receiving an input corresponding to at least one of a metabolite/compound data, at least one reaction data and at least one pathway data. The received input may be assessed to identify toxic compound(s). Further, reactions and pathway(s) may be determined in the identified toxic compound(s). Furthermore, the identified reactions and pathway(s) may be classified as potential toxic generating route(s) or toxic degrading route(s). The assessment can be used for performing comparative assessment of different strains of an organism/different organisms to achieve reaction diversity.

Abstract: Methods and systems for annotating regulatory regions of a microbial genome. A method disclosed herein includes extracting data related to at least one promoter of the regulatory regions of the microbial genome, wherein the data includes at least one promoter sequence and data available for at least one promoter subtype. Based on extracted at least one feature of the at least one promoter sequence, the method further includes configuring at least one predictive model using the deep learning based neural network to predict the at least one promoter subtype associated with the at least one promoter sequence. The method further includes annotating at least one unknown promoter sequence into the at least one promoter subtype using the at least one configured predictive model.

Abstract: A method for predicting a change in a functional property of a biomolecule includes obtaining a plurality of biomolecules and mutation data associated with at least one functional property; determining a net change in the at least one functional property based on at least one parameter associated with the mutation data for each biomolecule at a site; encoding a plurality of sequenced features for the mutation data associated with each biomolecule; configuring a prediction model to detect a relationship of the sequence features and the net change in the at least one functional property of the mutation data associated with each biomolecule; and predicting a change in the at least one functional property of a target biomolecule based on the prediction model for the mutation data at a user specified site.

Abstract: A method of predicting an amino acid substitution includes: receiving input of information regarding a structure of an enzyme along with the site of the enzyme in proximity to a bound ligand; identifying a functional atom of a wild type (WT) amino acid at the site of interest and a functional atom of the ligand; confirming properties of the functional atom of the WT amino acid and the functional atom of the ligand; detecting whether an interaction exists between the functional atom of the WT amino acid and the functional atom of the ligand; selecting alternative amino acids according to a result of the detecting of the interaction; determining a score for each of the selected alternative amino acids, respectively; ranking the selected alternative amino acids, based on the scores; and predicting substitutions of alternative amino acids having high rankings from among the selected alternative amino acids.