Framework for protein design from partial sequence with memory-efficient global attention method

A machine-learning based method for protein sequence design, comprising:

• generating a portion of a sequence and removing noise in input residue context;
• encoding and processing the portion of the sequence and backbone structure to obtain graph features;
• performing memory-efficient global graph attention layers to propagate the graph features and learn global residue interactions;
• and
• generating an entire sequence non-iteratively.

A computer program product, comprising:

• a non-transitory computer-executable storage device having computer readable program instructions embodied thereon that when executed by a computer cause the computer to perform machine-learning based method for protein sequence design, the computer-executable program instruction comprising:
• generating a portion of a sequence and removing noise in input residue context;
• encoding and processing the portion of the sequence and backbone structure to obtain graph features;
• performing memory-efficient global graph attention layers to propagate the graph features and learn global residue interactions;
• and
• generating an entire sequence non-iteratively.