Computational method for predicting functional sites of biological molecules

A non-transitory computer readable medium comprising instructions for generating prediction models for prediction of biomolecule-to-biomolecule interaction sites, the instructions, when executed by at least one processor, comprising functionality to:

• receive training data including data representative of a plurality of biomolecules having known biomolecule-to-biomolecule interaction sites;
• for each biomolecule of the plurality of biomolecules
• generate a plurality of three dimensional probability density maps, each three dimensional probability density map representing a probability of a non-covalent interacting atom on a surface of the biomolecule interacting with a corresponding atom type of a plurality of atom types;
• for each surface atom of a plurality of surface atoms of the biomolecule, calculate a plurality of attributes, each attribute associated with a different one of the plurality of atom types;
• train a prediction model for each of the atom types of the plurality of atom types based on the attributes calculated for each biomolecule of the plurality of biomolecules.

A non-transitory computer readable medium comprising instructions for determining clusters of amino acid conformations, the instructions, when executed by at least one processor, comprising functionality to:

• for each protein of a plurality of proteins in a protein database:
• for each amino acid type of a plurality of amino acid types:
• determine data characterizing a conformation of each instance of the amino acid type in the protein including determining a vector of torsion angle elements for each instance of the amino acid type in the protein;
• and
• process the data characterizing the conformation determined for each instance of each type of amino acid for each protein in the protein database to identify clusters of amino acid instances having similar conformation characteristics.

A non-transitory computer readable medium comprising instructions for building a protein atomistic non-covalent interacting database, the instructions, when executed by at least one processor, comprising functionality to:

• for each protein of a plurality of proteins in a first protein database:
• identify non-covalent interacting atom pairs at the protein interior;
• for each atom of each identified pair of non-covalent interacting atoms:
• determine data representative of an amino acid type associated with the atom, a conformational type associated with the atom, an atom type associated with the atom, an interacting atom type associated with the atom, and a spatial relationship between the atom and each of the interacting atom types;
• store the data in the protein atomistic non-covalent interacting database;
• for each protein of a plurality of proteins in a second protein database:
• determine data representative of water oxygen distributions around surface amino acids of the protein;
• and
• store the data in the protein atomistic non-covalent interacting database.
• for each protein of a plurality of proteins in a protein-interacting partner database:
• identify non-covalent interacting atom pairs where the pairs
• include an atom at the protein surface and an atom in an interacting partner;
• for each atom of each identified pair of non-covalent interacting atoms:
• determine data representative of the interacting partner atom distribution around surface amino acids of the protein;
• store the data in the protein atomistic non-covalent interacting database;

A non-transitory computer readable medium comprising instructions for generating probability density maps of non-covalent interacting atoms for a query protein, the instructions, when executed by at least one processor, comprising functionality to:

• for each amino acid type of the query protein:
• determine data characterizing a conformation of each instance of the amino acid type in the query protein including determining a vector of torsion angle elements for each instance of the amino acid type in the query protein;
• and
• process the data characterizing the conformation determined for each instance of each type of amino acid for the query protein to identify clusters of amino acid instances having similar conformation characteristics:
• for each atom of the query protein:
• determine an atom type of the atom, a parent amino acid of the atom, and a cluster to which the parent amino acid is a member;
• query a protein atomistic non-covalent interacting database to retrieve data based on the atom type of the atom, the parent amino acid of the atom, and the cluster to which the parent amino acid is a member characterizing a non-covalent interaction of the atom with each atom type of a plurality of interacting atom types;
• process the retrieved data for each atom of the query protein to determine a probability of each interacting atom type interacting with an atom on the surface of the query protein;
• and
• generate a probability density map for each interacting atom type based on the determined probability.

A system for generating prediction models for prediction of biomolecule-to-biomolecule interaction sites, the system comprising:

• a processor operatively interconnected with a first database and a second database, the first data base including training data including data representative of a number of biomolecules having known biomolecule-to-biomolecule interaction sites;
• the processor configured to, for each biomolecule of the plurality of biomolecules, generate a plurality of three dimensional probability density maps, each three dimensional probability density map representing a probability of a non-covalent interacting atom on a surface of the biomolecule interacting with a corresponding atom type of a plurality of atom types;
• the processor further configured to, for each surface atom of a plurality of surface atoms of the biomolecule:
• calculate a plurality of attributes, each attribute associated with a different one of the plurality of atom types;
• train a prediction model for each of the atom types of the plurality of atom types based on the attributes calculated for each biomolecule of the plurality of biomolecules;
• and
• store the trained prediction model in the second database.

A system for determining clusters of amino acid conformations, the system comprising:

• a processor operatively interconnected with a protein database;
• the processor configured to, for each protein of a plurality of proteins in the protein database:
• determine, for each amino acid type of a plurality of amino acid types, data characterizing a conformation of each instance of the amino acid type in the protein including determining a vector of torsion angle elements for each instance of the amino acid type in the protein;
• and
• process the data characterizing the conformation determined for each instance of each type of amino acid for each protein in the protein database to identify clusters of amino acid instances having similar conformation characteristics.

A system for building a protein atomistic non-covalent interacting database, the system comprising:

• a processor operatively interconnected with a first protein database, a second protein database and a protein-interacting partner database;
• the processor configured to, for each protein of a plurality of proteins in the first protein database:
• identify non-covalent interacting atom pairs at the protein interior;
• wherein the processor is further configured to, for each atom of each identified pair of non-covalent interacting atoms:
• determine data representative of an amino acid type associated with the atom, a conformational type associated with the atom, an atom type associated with the atom, an interacting atom type associated with the atom, and a spatial relationship between the atom and each of the interacting atom types;
• store the data in the protein atomistic non-covalent interacting database;
• the processor further configured to, for each protein of a plurality of proteins in a second protein database:
• determine data representative of water oxygen distributions around surface amino acids of the protein;
• and
• store the data in the protein atomistic non-covalent interacting database.
• the processor further configured to, for each protein of a plurality of proteins in a protein-interacting partner database:
• identify non-covalent interacting atom pairs where the pairs include an atom at the protein surface and an atom in an interacting partner;
• the processor further configured to, for each atom of each identified pair of non-covalent interacting atoms:
• determine data representative of the interacting partner atom distribution around surface amino acids of the protein;
• and
• store the data in the protein atomistic non-covalent interacting database.