Computer Model Designs Proteins by Matching Structure to Sequence

IBM's 2025 patent describes a computer system that designs new protein sequences by first understanding a target 3D protein shape and then generating a sequence that would fold into that shape.

What it covers

This patent details a computer method for designing new protein sequences. It uses advanced AI models called transformers. First, it takes a known protein's 3D structure and converts it into a numerical representation (a 'latent fold representation') in a 'second latent space.' Simultaneously, it takes the protein's amino acid sequence and converts it into a numerical representation (a 'latent sequence representation') in a 'first latent space.' The system then trains a decoder to learn how these two representations relate, essentially creating a bridge between protein shape and protein sequence. When given a new target 3D protein structure, the system encodes it and uses the trained decoder to generate a new amino acid sequence that should fold into that specific target structure. For example, it could be used to design a protein that performs a specific function by first defining the necessary 3D shape for that function.

What it doesn't cover

• —Designing biological sequences without using a transformer model encoder
• —Methods that do not involve generating latent sequence representations
• —Methods that do not generate latent fold representations from 3D structures
• —Systems that do not train a decoder to learn a joint latent space between sequence and fold representations
• —Designing sequences for structures not represented in 3D voxels
• —Methods that do not involve inverse folding of three-dimensional structures

The clever bit

The innovation lies in using two separate transformer encoders to create distinct numerical 'fingerprints' for both the protein's sequence and its 3D structure, then training a decoder to map between these fingerprints. This allows the system to 'design backwards' from a desired shape to a workable sequence.

Why it matters

Designing novel proteins with specific functions is crucial for developing new medicines, enzymes for industrial processes, and advanced materials. This patent represents a significant step in using AI to automate and accelerate the discovery of proteins with desired structural and functional properties.

Real-world examples

• 1.Protein design software for drug discovery
• 2.AI-driven enzyme engineering platforms
• 3.Computational biology research tools