US 2021/0280268 A1Published application

Protein structure prediction system

AssigneeDnastar, Inc.

InventorsFrederick R. BLATTNER, Steven J. DARNELL, Matthew R. LARSON, Amanda E. MITCHELL, John L. SCHROEDER

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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.

Claims20

Dependent claims

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.

15 more elements

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.

15 more elements

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.

15 more elements

Dependencies are inferred from the claim text.

Facts

Application: 17/326,107Filed 20 May 2021
Priority: 16 Jul 2015
Published: 9 Sep 2021
Term ends: Not estimatedThe source records don’t support an estimate.

Classification9 codes

G16B 15/20
ICT specially adapted for analysing two-dimensional [2D] or three-dimensional [3D] molecular structures, e.g. structural or functional relations…:…
G06N 7/08
Computing arrangements based on specific mathematical models: using chaos models or non-linear system models
G16B 5/00
Ict specially adapted for modelling or simulations in systems biology
G16B 15/00
Ict specially adapted for analysing two-dimensional [2d] or three-dimensional [3d] molecular structures

Family

Application62/193,225Us provisional application · filed 16 Jul 2015
Application15/737,231Continuation · filed 15 Dec 2017
Application17/326,107Continuation
US 11,031,094 B2Related patent
US 2018/0260517 A1Published application
US 2021/0280268 A1This patent

From the US filing history. Foreign family members aren’t listed.

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