Systems and methods to predict structures and properties of biomolecule-ligand complexes and uses thereof
View Patent ↗Embodiments herein describe systems and methods to predict biomolecule-ligand complexes and uses thereof. Many embodiments generate candidate poses and docking scores for ligands known to interact with a target protein as well as a candidate ligand. Based on similarities present in the candidate poses and docking scores, a pose for the candidate ligand is identified. Many embodiments determine whether a candidate ligand can bind to a target and, if so, determine an affinity. Many embodiments can be used for virtual screening or lead optimization.
1 . A method for predicting ligand-protein interaction comprising:
obtaining a set of ligands known to bind to a target protein;
generating a set of candidate poses for each ligand in the set of ligands and a candidate ligand; and
determining a pose for the candidate ligand based on a similarity of the candidate poses for the candidate ligand and the candidate poses for each ligand in the set of ligands;
wherein the similarity is based on pairwise comparisons between each ligand in the set of ligands and every other ligand in the set of ligands and pairwise comparisons between the candidate ligand and every ligand in the set of ligands.
2 . The method of claim 1 , wherein generating a set of candidate poses is accomplished by docking at least one ligand from the set of ligands using docking software.
3 . The method of claim 1 , wherein at least one ligand in the set of ligands has an experimentally determined pose; and wherein the pose for the at least one ligand is set to the experimentally determined pose.
4 . The method of claim 1 , wherein the similarity is further based on pairwise comparisons of substructures between each ligand in the set of ligands and every other ligand in the set of ligands and pairwise comparisons between the candidate ligand and every ligand in the set of ligands.
5 . The method of claim 1 , wherein the similarity is further based on pairwise comparisons of a protein-ligand interaction between each ligand in the set of ligands and every other ligand in the set of ligands and pairwise comparisons between the candidate ligand and every ligand in the set of ligands.
6 . The method of claim 1 , wherein determining a pose for the candidate ligand comprises determining a pose for each ligand in the set of ligands that minimizes the potential of all ligands.
7 . The method of claim 6 , wherein the potential is determined by:
E
(
L
)
=
(
n
-
1
)
E
dock
(
L
)
+
E
pairwise
(
L
)
,
where L=( 1 , . . . , n ) indexes the candidate poses for each ligand, n is the number of ligands, E dock represents the standard docking scores for the set of ligand poses, and E pairwise represents a pairwise contribution to the total score.
8 . The method of claim 7 , wherein E pairwise ((L)=Σ (i,j),i≠j e pairwise ( i , j ).
9 . A method for virtual screening, comprising:
obtaining a set of ligands known to bind to a target protein;
determining a pose for each ligand in the set of ligands by generating a set of candidate poses for ligands in the set of ligands, wherein the pose is based on a similarity of the candidate poses for each ligand in the set of ligands; and
determining a binding affinity of at least one candidate ligand by determining whether the candidate ligands binds to the target protein based on similarities to each ligand in the set of ligands and determining an energy if the candidate ligand binds to the target ligand;
wherein the similarity is based on pairwise comparisons between each ligand in the set of ligands and every other ligand in the set of ligands.
10 . The method of claim 9 , wherein determining a binding affinity of a candidate ligand determines a binding affinity of at least two candidate ligands; the method further comprising obtaining a library of candidate ligands, wherein determining a binding affinity of at least one candidate ligand determines a binding affinity of at least one candidate ligand in the library of candidate ligands.
11 . The method of claim 9 , further comprising fixing a pose for each ligand in the set of ligands prior to determining a binding affinity of at least one candidate ligand.
12 . The method of claim 9 , wherein the similarities are further based on pairwise comparisons of substructures between each ligand in the set of ligands and every other ligand in the set of ligands and pairwise comparisons between the candidate ligand and every ligand in the set of ligands.
13 . The method of claim 9 , wherein the similarities are further based on pairwise comparisons of a protein-ligand interaction between each ligand in the set of ligands and every other ligand in the set of ligands and pairwise comparisons between the candidate ligand and every ligand in the set of ligands.
14 . The method of claim 9 , wherein determining a pose for the candidate ligand comprises determining a pose for each ligand in the set of ligands that minimizes the potential of all ligands.
15 . The method of claim 14 wherein the potential is determined by:
E
(
L
)
=
(
n
-
1
)
E
dock
(
L
)
+
E
pairwise
(
L
)
,
where L=( 1 , . . . , n ) indexes the candidate poses for each ligand, n is the number of ligands, E dock represents the standard docking scores for the set of ligand poses, and E pairwise represents a pairwise contribution to the total score.
16 . The method of claim 15 , wherein E pairwise (L)=Σ (i,j),i≠j e pairwise ( i , j ).
17 . The method of claim 7 , wherein e pairwise is determined using a machine learning algorithm, wherein the machine learning algorithm is fit to a benchmark set of pairs of correct poses and pairs of incorrect poses for a diverse set of proteins.
18 . The method of claim 15 , wherein e pairwise is determined using a machine learning algorithm, wherein the machine learning algorithm is fit to a benchmark set of pairs of correct poses and pairs of incorrect poses for a diverse set of proteins.