Antibody Structure Prediction Without MSA Using Language Models
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Solution Overview
Problem
Existing protein structure prediction methods, particularly for antibodies, are time-consuming and reliant on Multiple Sequence Alignments (MSAs, which are ineffective for orphan proteins or fast-evolving antibodies, leading to reduced prediction precision.
Innovation Solution
A non-MSA-based approach using an antibody language model (ALM) with self-attention layers and a structure prediction model, trained on an antibody database, to generate residue and attention weight encodings, which are transformed into single and pair representations, and optimized with domain-specific loss functions for accurate antibody structure prediction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If MSA-based deep learning methods (AlphaFold2, RoseTTAFold) are used for protein structure prediction, then prediction accuracy is improved, but computation time increases significantly
Solution Approach 1:
The patent extracts and removes the MSA dependency from the structure prediction pipeline. By using a protein language model (PLM) that processes only the target sequence without requiring MSA input, the method eliminates the time-consuming MSA generation step while maintaining accurate structure prediction through learned sequence representations
Solution Approach 2:
The patent performs preliminary pre-training of the protein language model on large-scale protein sequence data before actual structure prediction. This preliminary action enables the model to learn evolutionary and structural patterns in advance, allowing it to achieve high prediction accuracy without requiring MSA computation during the actual prediction phase
2Measurement precision
If MSA-based methods are used for antibody structure prediction, then prediction precision is improved, but the method becomes ineffective for orphan proteins and fast-evolving antibodies
Solution Approach 1:
The patent creates a universal protein language model that can handle all protein types including orphan proteins and fast-evolving antibodies. The PLM is trained on diverse protein sequences and learns generalizable patterns that work across different protein families, making the method applicable to proteins regardless of whether they have homologous sequences for MSA construction
Solution Approach 2:
By removing the MSA requirement, the patent enables structure prediction for proteins that previously could not be predicted accurately. Orphan proteins and fast-evolving antibodies without sufficient homologous information can now be predicted using only their target sequence input to the PLM
3Loss of time
If large protein language models (DeepAb, ABlooper, IgFold) are used for antibody structure prediction, then computation time is reduced, but prediction precision is lost
Solution Approach 1:
The patent optimizes the PLM architecture parameters specifically for antibody structure prediction. By adjusting model depth, width, and attention mechanisms to match antibody structural characteristics, the method achieves both fast computation and high precision predictions for antibody structures
Solution Approach 2:
The patent performs domain-specific pre-training and fine-tuning on antibody sequences and structures before actual prediction. This preliminary action on antibody-specific data enables the model to achieve high prediction precision while maintaining fast computation speeds, outperforming general-purpose PLMs
Data Source
AI summary
Disclosed herein are methods, systems, and apparatus, including computer programs encoded on computer storage media, for antibody structure prediction. In an example method, a target antibody sequence of a target antibody that includes a sequence of amino acids is received. The target antibody sequence is processed by an antibody language model (ALM) to obtain a residue encoding and an attention weight encoding without performing multiple sequence alignment (MSA), wherein the ALM is a protein language model trained from antibody sequences, and the ALM comprises a plurality of self-attention layers. The residue encoding and the attention weight encoding are transformed into a single representation and a pair representation that are input into a structure prediction model. A predicted structure of the target antibody is determined using the structure prediction model.