Conditional Sequence-Structure Integration: A Novel Approach for Precision Antibody Engineering and Affinity Optimization
Bibliographic record
Abstract
Abstract Antibodies, or immunoglobulins, are integral to the immune response, playing a crucial role in recognizing and neutralizing external threats such as pathogens. However, the design of these molecules is complex due to the limited availability of paired structural antibody-antigen data and the intricacies of structurally non-deterministic regions. In this paper, we introduce a novel approach to designing antibodies by integrating structural and sequence information of antigens. Our approach employs a protein structural encoder to capture both sequence and conformational details of antigen. The encoded antigen information is then fed into an antibody language model (aLM) to generate antibody sequences. By adding cross-attention layers, aLM effectively incorporates the antigen information from the encoder. For optimal model training, we utilized the Causal Masked Language Modeling (CMLM) objective. Unlike other methods that require additional contextual information, such as epitope residues or a docked antibody framework, our model excels at predicting the antibody sequence without the need for any supplementary data. Our enhanced methodology demonstrates superior performance when compared to existing models in the RAbD benchmark for antibody design and SKEPMI for antibody optimization.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".