Enhanced protein structure prediction using protein homolog discovery and constrained distograms

A system for predicting protein structures based on protein sequences, the system comprising:

• at least one hardware processor;
• at least one non-transitory computer-readable storage medium storing processor-executable instructions that, when executed by the at least one hardware processor, cause the at least one hardware processor to perform:
• generating a contact map using a direct coupling analysis algorithm based on homologs of an input protein sequence;
• providing the generated contact map to a first machine learning model as input, to generate a resulting distogram;
• determining, based at least on the resulting distogram, at least one pair of solvent-exposed amino acids in the protein for which the distance between the two amino acids is to be measured using an in vitro experiment;
• importing in vitro measured distances;
• generating an updated distogram, that is constrained by the in vitro measured distances;
• and
• based on the updated distogram, generating a predicted protein structure.

A method of training a machine learning model for distogram prediction, the method comprising:

• using at least one computer hardware processor to perform:
• accessing a plurality of target distograms, wherein each target distogram of the plurality of target distograms represents a target output of the machine learning model;
• accessing a plurality of input contact maps, wherein each input contact map of the plurality of input contact maps:
• corresponds to a target distogram of the plurality of target distograms and represents an input to the machine learning model for the corresponding target distogram;
• and
• comprises an output of a direct coupling analysis algorithm, based on homologs of an input protein sequence;
• and
• training the machine learning model using the plurality of target distograms and the plurality of input contact maps corresponding to the plurality of target distograms, to obtain a trained machine learning model.

A computer readable medium on which is stored software which when implemented by a processor causes that processor to perform the steps of:

• (i) generating a multiple sequence alignment (MSA) of sequences homologous to a protein of interest;
• (ii) performing in silico a three-dimensional structure prediction of the MSA using a structure prediction algorithm to produce a distogram;
• (iii) identifying in silico at least one pair of solvent-exposed amino acids in the MSA;
• (iv) determining in vitro the distance between the two amino acids of the at least one pair;
• and
• (v) constraining the structure prediction algorithm using the collected distance measurements.

A computer system configured to perform:

• accessing a plurality of target distograms, wherein each target distogram of the plurality of target distograms represents a target output of the machine learning model;
• accessing a plurality of input contact maps, wherein each input contact map of the plurality of input contact maps:
• corresponds to a target distogram of the plurality of target distograms and represents an input to the machine learning model for the corresponding target distogram;
• and
• comprises an output of a direct coupling analysis algorithm, based on homologs of an input protein sequence;
• and
• training the machine learning model using the plurality of target distograms and the plurality of input contact maps corresponding to the plurality of target distograms, to obtain a trained machine learning model.