# US 2018/0025108 A1 · Computational method for predicting functional sites of biological molecules

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- Number: US 2018/0025108 A1
- Document ID: US20180025108A1
- Status: Published application
- Kind: A1
- Application number: 15/670,478
- Filed: 2017-08-07
- Published: 2018-01-25
- Earliest claimed: 2013-03-15
- Assignees: Academia Sinica
- Applicants: Academia Sinica
- Inventors: An-Suei YANG; Hung-Pin PENG; Jhih-Wei JIAN
- Web page: https://patentgrep.com/patent/US20180025108A1

## CPC

- G16B 5/20: Probabilistic models
- G16B 5/00: ICT specially adapted for modelling or simulations in systems biology, e.g. gene-regulatory networks, protein interaction networks or metabolic networks
- G16B 40/00: ICT specially adapted for biostatistics; ICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
- G16B 50/10: Ontologies; Annotations
- G16B 50/00: ICT programming tools or database systems specially adapted for bioinformatics
- G16B: BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY

## Abstract

In a general aspect, a method for inferring one or more biomolecule-to-biomolecule interaction sites includes receiving data representative of a plurality of prediction models. Each prediction model is associated with a different atom type of a plurality of atom types and characterizes biomolecule-to-biomolecule interaction site specific patterns common to a plurality of three dimensional probability density maps. Each three dimensional probability density map is associated with a corresponding biomolecule of a plurality of biomolecules included in a training data set and represents a probability of a non-covalent interacting atom on a surface of the corresponding biomolecule interacting with the atom type associated with the prediction model. Data representative of a query biomolecule is received, the data including one or more unknown biomolecule-to-biomolecule interaction sites. The one or more unknown biomolecule-to-biomolecule interaction sites of the query biomolecule are inferred based on the data representative of the plurality of prediction models.

## Claims

### Claim 1

A non-transitory computer readable medium comprising instructions for inferring one or more biomolecule-to-biomolecule interaction sites, the instructions, when executed by at least one processor, comprising functionality to:
receive data representative of a plurality of prediction models, each prediction model associated with a different atom type of a plurality of atom types and characterizing biomolecule-to-biomolecule interaction site specific patterns common to a plurality of three dimensional probability density maps, each three dimensional probability density map associated with a corresponding biomolecule of a plurality of biomolecules included in a training data set and representative of a probability of a non-covalent interacting atom on a surface of the corresponding biomolecule interacting with the atom type associated with the prediction model;
receive data representative of a query biomolecule including one or more unknown biomolecule-to-biomolecule interaction sites; and
infer the one or more unknown biomolecule-to-biomolecule interaction sites of the query biomolecule based on the data representative of the plurality of prediction models.

### Claim 2 (depends on 1)

The non-transitory computer readable medium of claim 1 wherein each of the plurality of biomolecules included in the training data set is a member of a known protein-protein complex and the query biomolecule is a protein.

### Claim 3 (depends on 1)

The non-transitory computer readable medium of claim 1 wherein each of the plurality of biomolecules included in the training data set is a member of a known protein-carbohydrate complex and the query biomolecule is a protein.

### Claim 4

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.

### Claim 5 (depends on 4)

The non-transitory computer readable medium of claim 4 wherein each of the plurality of biomolecules is a protein.

### Claim 6

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.

### Claim 7 (depends on 6)

The non-transitory computer readable medium of claim 6 wherein the instructions to 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 includes instructions to determine an optimal number of clusters.

### Claim 8 (depends on 6)

The non-transitory computer readable medium of claim 6 further comprising instructions to identify a centroid of each of the identified clusters.