# US 2021/0280268 A1 · Protein structure prediction system

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- Number: US 2021/0280268 A1
- Document ID: US20210280268A1
- Status: Published application
- Kind: A1
- Application number: 17/326,107
- Filed: 2021-05-20
- Published: 2021-09-09
- Earliest claimed: 2015-07-16
- Assignees: Dnastar, Inc.
- Applicants: Dnastar, Inc.
- Inventors: Frederick R. BLATTNER; Steven J. DARNELL; Matthew R. LARSON; Amanda E. MITCHELL; John L. SCHROEDER
- Web page: https://patentgrep.com/patent/US20210280268A1

## CPC

- G16B 15/20: Protein or domain folding
- G06N 7/08: using chaos models or non-linear system 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 15/00: ICT specially adapted for analysing two-dimensional [2D] or three-dimensional [3D] molecular structures, e.g. structural or functional relations or structure alignment
- G16B 30/00: ICT specially adapted for sequence analysis involving nucleotides or amino acids
- G16B 30/10: Sequence alignment; Homology search
- G16B 99/00: Subject matter not provided for in other groups of this subclass
- G16B: BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
- G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS

## Abstract

The present invention is an accelerated conformational sampling method for predicting target peptide and protein structures comprising a process of determining energy minimized synthetic templates using a simple system for modeling individual molecular bonds within the subject peptide or protein. Use of these synthetic templates greatly reduces the computational resources necessary for optimally determining structural features of the target peptide or protein. The present invention also provides methods for rapid and efficient analysis of the effect of mutations on target peptides and proteins.

## Claims

### Claim 1

A conformational sampling method for predicting the structure of an amino acid sequence, the amino acid sequence comprising a plurality of residues, comprising:
a) creating a sequence profile matrix of the amino acid sequence;
b) determining an alignment of each respective residue of the amino acid sequence against the sequence profile matrix;
c) identifying internal contacts for one or more residues in the plurality of residues using the alignment;
d) collecting the features of the sequence profile matrix and internal contacts for each residue in the plurality of residues into a collected feature matrix;
e) aligning the collected feature matrix using one or more threading models with a structural feature database of original templates;
f) selecting a plurality of optimally aligned original templates from the aligning e);
g) calculating normal modes of motion for each original template in the plurality of optimally aligned original templates;
h) perturbing each respective original template in the plurality of optimally aligned original templates, for each pair of calculated normal modes, thereby collectively creating a plurality of synthetic templates;
i) scoring the energy difference between each original template and the corresponding synthetic template;
j) selecting a subset of synthetic templates from the plurality of synthetic templates based on satisfaction of a predetermined cut-off criterion;
k) replacing or supplementing the original templates in the plurality of optimally aligned original templates with the corresponding selected subset of synthetic templates to generate a plurality of modeling templates;
l) calculating distance and contact restraints within modeling templates of the plurality of modeling templates;
m) performing, with modeling templates in the plurality of modeling templates, Markov Chain Monte Carlo simulations, thereby obtaining simulation results;
n) clustering the simulation results, wherein the clustering comprises a plurality of clusters, each cluster in the plurality of clusters representing models from the performing m);
o) selecting representative models of each cluster in the plurality of clusters;
p) refining the representative models of the selecting o) by energy minimization; and
q) selecting the lowest energy refined representative model as the predicted structure of the amino acid sequence.

### Claim 2 (depends on 1)

The method of claim 1, wherein creating the sequence profile matrix comprises predicting the secondary structure of the amino acid sequence.

### Claim 3 (depends on 1)

The method of claim 1, wherein creating the sequence profile matrix comprises predicting solvent accessibility of the amino acid sequence.

### Claim 4 (depends on 1)

The method of claim 1, wherein the predetermined cut-off criterion is a predetermined percentile score and the predetermined percentile score is the 65th percentile.

### Claim 5 (depends on 1)

The method of claim 1, wherein the original templates of k) are supplemented with the corresponding subset of selected synthetic templates.

### Claim 6 (depends on 1)

The method of claim 1, wherein the original templates of k) are replaced with the corresponding subset of selected synthetic templates.

### Claim 7

A computer system for predicting the structure of an amino acid sequence, the amino acid sequence comprising a plurality of residues, the computer system comprising at least one processor and memory storing at least one program for execution by the at least one processor, the at least one program comprising instructions for:
a) creating a sequence profile matrix of the amino acid sequence;
b) determining an alignment of each respective residue of the amino acid sequence against the sequence profile matrix;
c) identifying internal contacts for one or more residues in the plurality of residues using the alignment;
d) collecting the features of the sequence profile matrix and internal contacts for each residue in the plurality of residues into a collected feature matrix;
e) aligning the collected feature matrix using one or more threading models with a structural feature database of original templates;
f) selecting a plurality of optimally aligned original templates from the aligning e);
g) calculating normal modes of motion for each original template in the plurality of optimally aligned original templates;
h) perturbing each respective original template in the plurality of optimally aligned original templates, for each pair of calculated normal modes, thereby collectively creating a plurality of synthetic templates;
i) scoring the energy difference between each original template and the corresponding synthetic template;
j) selecting a subset of synthetic templates from the plurality of synthetic templates based on satisfaction of a predetermined cut-off criterion;
k) replacing or supplementing the original templates in the plurality of optimally aligned original templates with the corresponding selected subset of synthetic templates to generate a plurality of modeling templates;
l) calculating distance and contact restraints within modeling templates of the plurality of modeling templates;
m) performing, with modeling templates in the plurality of modeling templates, Markov Chain Monte Carlo simulations, thereby obtaining simulation results;
n) clustering the simulation results, wherein the clustering comprises a plurality of clusters, each cluster in the plurality of clusters representing models from the performing m);
o) selecting representative models of each cluster in the plurality of clusters;
p) refining the representative models of the selecting o) by energy minimization; and
q) selecting the lowest energy refined representative model as the predicted structure of the amino acid sequence.

### Claim 8 (depends on 7)

The computer system of claim 7, wherein creating the sequence profile matrix comprises predicting the secondary structure of the amino acid sequence.

### Claim 9 (depends on 7)

The computer system of claim 7, wherein creating the sequence profile matrix comprises predicting solvent accessibility of the amino acid sequence.

### Claim 10 (depends on 7)

The computer system of claim 7, wherein the predetermined cut-off criterion is a predetermined percentile score and the predetermined percentile score is the 65th percentile.

### Claim 11 (depends on 7)

The computer system of claim 7, wherein the original templates of k) are supplemented with the corresponding subset of selected synthetic templates.

### Claim 12 (depends on 7)

The computer system of claim 7, wherein the original templates of k) are replaced with the corresponding subset of selected synthetic templates.

### Claim 13

A non-transitory computer readable storage medium storing a computational module for predicting the structure of an amino acid sequence, the amino acid sequence comprising a plurality of residues, the computational module comprising instructions for:
a) creating a sequence profile matrix of the amino acid sequence;
b) determining an alignment of each respective residue of the amino acid sequence against the sequence profile matrix;
c) identifying internal contacts for one or more residues in the plurality of residues using the alignment;
d) collecting the features of the sequence profile matrix and internal contacts for each residue in the plurality of residues into a collected feature matrix;
e) aligning the collected feature matrix using one or more threading models with a structural feature database of original templates;
f) selecting a plurality of optimally aligned original templates from the aligning e);
g) calculating normal modes of motion for each original template in the plurality of optimally aligned original templates;
h) perturbing each respective original template in the plurality of optimally aligned original templates, for each pair of calculated normal modes, thereby collectively creating a plurality of synthetic templates;
i) scoring the energy difference between each original template and the corresponding synthetic template;
j) selecting a subset of synthetic templates from the plurality of synthetic templates based on satisfaction of a predetermined cut-off criterion;
k) replacing or supplementing the original templates in the plurality of optimally aligned original templates with the corresponding selected subset of synthetic templates to generate a plurality of modeling templates;
l) calculating distance and contact restraints within modeling templates of the plurality of modeling templates;
m) performing, with modeling templates in the plurality of modeling templates, Markov Chain Monte Carlo simulations, thereby obtaining simulation results;
n) clustering the simulation results, wherein the clustering comprises a plurality of clusters, each cluster in the plurality of clusters representing models from the performing m);
o) selecting representative models of each cluster in the plurality of clusters;
p) refining the representative models of the selecting o) by energy minimization; and
q) selecting the lowest energy refined representative model as the predicted structure of the amino acid sequence.

### Claim 14 (depends on 13)

The non-transitory computer readable storage medium of claim 13, wherein creating the sequence profile matrix comprises predicting the secondary structure of the amino acid sequence.

### Claim 15 (depends on 13)

The non-transitory computer readable storage medium of claim 13, wherein creating the sequence profile matrix comprises predicting solvent accessibility of the amino acid sequence.

### Claim 16 (depends on 13)

The non-transitory computer readable storage medium of claim 13, wherein the predetermined cut-off criterion is a predetermined percentile score and the predetermined percentile score is the 65th percentile.

### Claim 17 (depends on 13)

The non-transitory computer readable storage medium of claim 13, wherein the original templates of k) are supplemented with the corresponding subset of selected synthetic templates.

### Claim 18 (depends on 13)

The non-transitory computer readable storage medium of claim 13, wherein the original templates of k) are replaced with the corresponding subset of selected synthetic templates.

### Claim 19 (depends on 1)

A method for modeling protein-protein interactions, comprising the steps of claim 1.

### Claim 20 (depends on 1)

A method for modeling protein-ligand interactions, comprising the steps of claim 1.