Model type
Protein sequence transformer; this record is the paper-specific evaluated configuration.
Pipeline
ESM-2 650M embeddings + classifier
This antibody-deamidation pipeline combines ESM-2 sequence embeddings with local sequence information to identify susceptible residues.
Sources
The Accurate Prediction of Antibody Deamidations by Combining High-Throughput Automated Peptide Mapping and Protein Language Model-Based Deep Learning · 3. Results/3.4. Performance Evaluation of Models (paragraph 4); Abstract (paragraph 1)How it worksEvaluated procedure (conceptual)
Conceptual input–method–output guide. Check the procedure text and linked evaluation for fitted components, additional inputs and exact settings.
Sources
The Accurate Prediction of Antibody Deamidations by Combining High-Throughput Automated Peptide Mapping and Protein Language Model-Based Deep Learning · Abstract (paragraph 1); 4. Discussion and Conclusions (paragraph 2)Overview
Biological inputs
Antibody amino-acid sequences and local residue context
Sources
The Accurate Prediction of Antibody Deamidations by Combining High-Throughput Automated Peptide Mapping and Protein Language Model-Based Deep Learning · 1. Introduction (paragraph 5); 3. Results/3.2. The Use of ESM-2 Embedding for Deamidation Site Prediction (paragraph 4)Outputs
Deamidation propensity and extent predictions
Sources
The Accurate Prediction of Antibody Deamidations by Combining High-Throughput Automated Peptide Mapping and Protein Language Model-Based Deep Learning · 3. Results/3.4. Performance Evaluation of Models (paragraph 4); 3. Results/3.2. The Use of ESM-2 Embedding for Deamidation Site Prediction (paragraph 1)Access
Official upstream implementation and usage documentation: https://github.com/facebookresearch/esm/blob/2b369911bb5b4b0dda914521b9475cad1656b2ac/README.md. This pinned documentation revision is not automatically the evaluated weight revision.
Sources
facebookresearch/esm README.md · README.md; installation, model download and usage instructionslimited source coverage · Automated source review, 2026-09-16. All specifications and missing details
Evaluations and results
1 evaluation · 1 result. Different protocols are not a single leaderboard.
Filter evaluations
Applied filters: All linked evaluations
| Tested configuration | Protocol and dataset | Finding | Evidence and details |
|---|---|---|---|
| Pipeline: ESM-2 650M embeddings + classifier | Task: antibody deamidation-site prediction Dataset: antibody peptide-mapping training dataset | 0.944 accuracy fraction · unknown Uncertainty: ± 0.012 Coverage: Not reported scored / Not reported eligible | Author-reported evaluation · Source checkedMethods, coverage and sourceESM-2 650M embeddings + classifier: antibody deamidation-site prediction global contextual embeddings only Aggregation: Not reported The Accurate Prediction of Antibody Deamidations by Combining High-Throughput Automated Peptide Mapping and Protein Language Model-Based Deep Learning · Table 1, Global embeddings only row, Accuracy column |
Source checking is not independent reproduction. Release 2026-09-29-06401fd5b220.
Use this model
How it works, versions and access
Underlying model: ESM-2. Results on this page belong to this pipeline and its evaluated settings.
How it works
How the evaluated method works
A chimeric supervised predictor integrates pretrained protein-language-model embeddings and a local sequence branch. The score belongs to this complete antibody-specific predictor, not to ESM-2 alone.
Sources
The Accurate Prediction of Antibody Deamidations by Combining High-Throughput Automated Peptide Mapping and Protein Language Model-Based Deep Learning · Abstract (paragraph 1); 4. Discussion and Conclusions (paragraph 2)Underlying method and version boundaries
ESM-2 is a transformer protein language-model family. The official repository exposes residue embeddings, sequence-level pooling and models at several sizes; the study configuration determines which of these is evaluated.
Sources
facebookresearch/esm README.md · README.md; introduction, model description, pretrained-model and usage sections at pinned revisionWhat was evaluated
The linked evaluation record identifies ESM-2 650M embeddings + classifier: antibody deamidation-site prediction. Its dataset, split, adaptation and evidence origin remain attached to the reported results.
Sources
The Accurate Prediction of Antibody Deamidations by Combining High-Throughput Automated Peptide Mapping and Protein Language Model-Based Deep Learning · The named evaluation’s methods and comparison table; exact preserved evaluation IDs: evaluation-b2-antibody-deamidation-plm-2024Strengths, limitations and unresolved questions
Strengths and limitations
Strengths and considerations
- Uses sequence inputs without requiring an experimentally solved antibody structure.
Sources
The Accurate Prediction of Antibody Deamidations by Combining High-Throughput Automated Peptide Mapping and Protein Language Model-Based Deep Learning · 3. Results/3.5. Independent Dataset Predicting Deamidation Hot Spots (paragraph 1); 3. Results/3.3. Enhanced Prediction by Combining ESM-2 Embedding and Local Sequence Information (paragraph 1)
Limitations and conditions
- Training labels reflect the study’s antibody modalities and peptide-mapping conditions; transfer to other assay conditions requires evidence.
Sources
The Accurate Prediction of Antibody Deamidations by Combining High-Throughput Automated Peptide Mapping and Protein Language Model-Based Deep Learning · 2. Materials and Methods/2.2. Accelerated Thermal Stress (paragraph 1); Abstract (paragraph 1)
Profile review details
Primary full text and the available official implementation/model documentation were inspected. Explanatory claims are source-backed; unresolved exact-configuration metadata is labelled explicitly. This is automated review, not a human review or independent benchmark reproduction.
Stable record: reported-model-4921459942b45fSpecifications
Inputs, training, access and other details
Explanatory profile: limited source coverage · Automated source review, 2026-09-16. Review applies to the cited claims; unresolved fields are listed below. Numerical results retain their own review status.
| Property | Description and evidence |
|---|---|
| Model type | Protein sequence transformer; this record is the paper-specific evaluated configuration.Sourcesfacebookresearch/esm README.md · README.md model description |
| Architecture / procedure | A chimeric supervised predictor integrates pretrained protein-language-model embeddings and a local sequence branch. The score belongs to this complete antibody-specific predictor, not to ESM-2 alone.SourcesThe Accurate Prediction of Antibody Deamidations by Combining High-Throughput Automated Peptide Mapping and Protein Language Model-Based Deep Learning · Abstract (paragraph 1); 4. Discussion and Conclusions (paragraph 2) |
| Biological inputs | Antibody amino-acid sequences and local residue contextSourcesThe Accurate Prediction of Antibody Deamidations by Combining High-Throughput Automated Peptide Mapping and Protein Language Model-Based Deep Learning · 1. Introduction (paragraph 5); 3. Results/3.2. The Use of ESM-2 Embedding for Deamidation Site Prediction (paragraph 4) |
| Outputs | Deamidation propensity and extent predictionsSourcesThe Accurate Prediction of Antibody Deamidations by Combining High-Throughput Automated Peptide Mapping and Protein Language Model-Based Deep Learning · 3. Results/3.4. Performance Evaluation of Models (paragraph 4); 3. Results/3.2. The Use of ESM-2 Embedding for Deamidation Site Prediction (paragraph 1) |
| Parameters | 650-million-parameter ESM-2 backbone; the total trained pipeline parameter count is not established here. · Not reported in inspected sourcesSourcesThe Accurate Prediction of Antibody Deamidations by Combining High-Throughput Automated Peptide Mapping and Protein Language Model-Based Deep Learning · 3. Results/3.2. The Use of ESM-2 Embedding for Deamidation Site Prediction (paragraph 2); 4. Discussion and Conclusions (paragraph 6) |
| Known versions / configuration | esm2_t33_650m_UR50DSourcesThe Accurate Prediction of Antibody Deamidations by Combining High-Throughput Automated Peptide Mapping and Protein Language Model-Based Deep Learning · 3. Results/3.2. The Use of ESM-2 Embedding for Deamidation Site Prediction (paragraph 2); Table antibodies-13-00074-t002 (paragraph 1) |
| Training data / fitting | An antibody deamidation dataset of 2,285 observations assembled with automated peptide mapping.SourcesThe Accurate Prediction of Antibody Deamidations by Combining High-Throughput Automated Peptide Mapping and Protein Language Model-Based Deep Learning · 3. Results/3.5. Independent Dataset Predicting Deamidation Hot Spots (paragraph 1); 3. Results/3.5. Independent Dataset Predicting Deamidation Hot Spots (paragraph 2) |
| Context limits | A maximum input/context length for this exact evaluated configuration is not established by the inspected sources. · Not reported in inspected sourcesSources (2)The Accurate Prediction of Antibody Deamidations by Combining High-Throughput Automated Peptide Mapping and Protein Language Model-Based Deep Learning; facebookresearch/esm README.md · 2. Materials and Methods/2.1. Chemicals and Reagents; 2. Materials and Methods/2.2. Accelerated Thermal Stress ; 2. Materials and Methods/2.3. Automated Peptide Mapping; 2. Materials and Methods/2.4. LC-MS/MS Analysis ; 3. Results/3.7. Model Implementation for High-Throughput Screening Drug Candidates; inspected for explicit maximum input length (dataset lengths and family-wide limits are not substituted); README.md at pinned repository revision |
| Access | Official upstream implementation and usage documentation: https://github.com/facebookresearch/esm/blob/2b369911bb5b4b0dda914521b9475cad1656b2ac/README.md. This pinned documentation revision is not automatically the evaluated weight revision.Sourcesfacebookresearch/esm README.md · README.md; installation, model download and usage instructions |
| Code licence | MIT (upstream repository code at the cited revision; this does not establish every dependency or historical checkpoint licence).Sourcesfacebookresearch/esm LICENSE · LICENSE; complete licence text |
| Weights licence | The inspected model-access documentation does not explicitly identify terms for this exact evaluated checkpoint or fitted head; repository code terms are shown separately. · Not reported in inspected sourcesSourcesfacebookresearch/esm README.md · README.md; checkpoint/access documentation and licence scope |
Evidence
Source checking verifies the cited claim or transcription. It does not establish independent reproduction.
Evidence table
Inspect claims, sources and review details
Trace each statement to its source and review. A context-only reference supports the record generally; it does not verify an individual field. Source checking does not reproduce an experiment.
One row per statement and cited source. Multiple citations are not independent evaluations. Shared locators are labelled explicitly.
22 evidence rows matching the loaded filters
| Property and statement | Original source and location | Review and provenance |
|---|---|---|
| Diagram caption Conceptual input–method–output guide. Check the procedure text and linked evaluation for fitted components, additional inputs and exact settings. Individual claims | The Accurate Prediction of Antibody Deamidations by Combining High-Throughput Automated Peptide Mapping and Protein Language Model-Based Deep Learning Abstract (paragraph 1); 4. Discussion and Conclusions (paragraph 2) Version: journal full text in PMC | source checked automated source review · 2026-09-16 Audit detailsPrimary full text and the available official implementation/model documentation were inspected. Explanatory claims are source-backed; unresolved exact-configuration metadata is labelled explicitly. This is automated review, not a human review or independent benchmark reproduction. Field: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
Diagram steps
| The Accurate Prediction of Antibody Deamidations by Combining High-Throughput Automated Peptide Mapping and Protein Language Model-Based Deep Learning Abstract (paragraph 1); 4. Discussion and Conclusions (paragraph 2) Version: journal full text in PMC | source checked automated source review · 2026-09-16 Audit detailsPrimary full text and the available official implementation/model documentation were inspected. Explanatory claims are source-backed; unresolved exact-configuration metadata is labelled explicitly. This is automated review, not a human review or independent benchmark reproduction. Field: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Diagram title Evaluated procedure (conceptual) Individual claims | The Accurate Prediction of Antibody Deamidations by Combining High-Throughput Automated Peptide Mapping and Protein Language Model-Based Deep Learning Abstract (paragraph 1); 4. Discussion and Conclusions (paragraph 2) Version: journal full text in PMC | source checked automated source review · 2026-09-16 Audit detailsPrimary full text and the available official implementation/model documentation were inspected. Explanatory claims are source-backed; unresolved exact-configuration metadata is labelled explicitly. This is automated review, not a human review or independent benchmark reproduction. Field: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Model type Protein sequence transformer; this record is the paper-specific evaluated configuration. Individual claims | facebookresearch/esm README.md README.md model description Version: 2b369911bb5b4b0dda914521b9475cad1656b2ac | source checked automated source review · 2026-09-16 Audit detailsPrimary full text and the available official implementation/model documentation were inspected. Explanatory claims are source-backed; unresolved exact-configuration metadata is labelled explicitly. This is automated review, not a human review or independent benchmark reproduction. Field: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Architecture / procedure A chimeric supervised predictor integrates pretrained protein-language-model embeddings and a local sequence branch. The score belongs to this complete antibody-specific predictor, not to ESM-2 alone. Individual claims | The Accurate Prediction of Antibody Deamidations by Combining High-Throughput Automated Peptide Mapping and Protein Language Model-Based Deep Learning Abstract (paragraph 1); 4. Discussion and Conclusions (paragraph 2) Version: journal full text in PMC | source checked automated source review · 2026-09-16 Audit detailsPrimary full text and the available official implementation/model documentation were inspected. Explanatory claims are source-backed; unresolved exact-configuration metadata is labelled explicitly. This is automated review, not a human review or independent benchmark reproduction. Field: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Weights licence The inspected model-access documentation does not explicitly identify terms for this exact evaluated checkpoint or fitted head; repository code terms are shown separately. Individual claims | facebookresearch/esm README.md README.md; checkpoint/access documentation and licence scope Version: 2b369911bb5b4b0dda914521b9475cad1656b2ac | unreported automated source review · 2026-09-16 Audit detailsPrimary full text and the available official implementation/model documentation were inspected. Explanatory claims are source-backed; unresolved exact-configuration metadata is labelled explicitly. This is automated review, not a human review or independent benchmark reproduction. Field: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Biological inputs Antibody amino-acid sequences and local residue context Individual claims | The Accurate Prediction of Antibody Deamidations by Combining High-Throughput Automated Peptide Mapping and Protein Language Model-Based Deep Learning 1. Introduction (paragraph 5); 3. Results/3.2. The Use of ESM-2 Embedding for Deamidation Site Prediction (paragraph 4) Version: journal full text in PMC | source checked automated source review · 2026-09-16 Audit detailsPrimary full text and the available official implementation/model documentation were inspected. Explanatory claims are source-backed; unresolved exact-configuration metadata is labelled explicitly. This is automated review, not a human review or independent benchmark reproduction. Field: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Outputs Deamidation propensity and extent predictions Individual claims | The Accurate Prediction of Antibody Deamidations by Combining High-Throughput Automated Peptide Mapping and Protein Language Model-Based Deep Learning 3. Results/3.4. Performance Evaluation of Models (paragraph 4); 3. Results/3.2. The Use of ESM-2 Embedding for Deamidation Site Prediction (paragraph 1) Version: journal full text in PMC | source checked automated source review · 2026-09-16 Audit detailsPrimary full text and the available official implementation/model documentation were inspected. Explanatory claims are source-backed; unresolved exact-configuration metadata is labelled explicitly. This is automated review, not a human review or independent benchmark reproduction. Field: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Parameters 650-million-parameter ESM-2 backbone; the total trained pipeline parameter count is not established here. Individual claims | The Accurate Prediction of Antibody Deamidations by Combining High-Throughput Automated Peptide Mapping and Protein Language Model-Based Deep Learning 3. Results/3.2. The Use of ESM-2 Embedding for Deamidation Site Prediction (paragraph 2); 4. Discussion and Conclusions (paragraph 6) Version: journal full text in PMC | unreported automated source review · 2026-09-16 Audit detailsPrimary full text and the available official implementation/model documentation were inspected. Explanatory claims are source-backed; unresolved exact-configuration metadata is labelled explicitly. This is automated review, not a human review or independent benchmark reproduction. Field: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Known versions / configuration esm2_t33_650m_UR50D Individual claims | The Accurate Prediction of Antibody Deamidations by Combining High-Throughput Automated Peptide Mapping and Protein Language Model-Based Deep Learning 3. Results/3.2. The Use of ESM-2 Embedding for Deamidation Site Prediction (paragraph 2); Table antibodies-13-00074-t002 (paragraph 1) Version: journal full text in PMC | source checked automated source review · 2026-09-16 Audit detailsPrimary full text and the available official implementation/model documentation were inspected. Explanatory claims are source-backed; unresolved exact-configuration metadata is labelled explicitly. This is automated review, not a human review or independent benchmark reproduction. Field: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
Sources and history
View linked audit checks and correction history
Release 2026-09-29-06401fd5b220 · Record review: needs review
3 source records and release history
- The Accurate Prediction of Antibody Deamidations by Combining High-Throughput Automated Peptide Mapping and Protein Language Model-Based Deep Learning · Original source · journal full text in PMC
- facebookresearch/esm README.md · Original source · 2b369911bb5b4b0dda914521b9475cad1656b2ac
- facebookresearch/esm LICENSE · Original source · 2b369911bb5b4b0dda914521b9475cad1656b2ac
Technical metadata and extraction receipts
Stable ID: reported-model-4921459942b45f
- areas
- proteins-complexes
- entity level
- method
- version
- esm2_t33_650m_UR50D
- reported name
- ESM-2 650M embeddings + classifier
- historical missing metadata
- checkpoint revision: not_reported_in_legacy_extract; training data: not_reported_in_legacy_extract; licence: not_reported_in_legacy_extract
- metadata review scope
- historical_missing_metadata preserves the original discovery state. Current descriptive evidence and missingness are recorded in profile.facts; numerical-result review is separate.
- legacy kinds
- model
- entity classification
- review date: 2026-09-17; rationale: This record identifies a composed analysis workflow with separately identifiable upstream models, representations or tools and a downstream prediction/scoring procedure. Results belong to that complete composition rather than to an upstream model alone.; source ids: antibody-deamidation-plm-2024; evidence-reported-base-esm-readme-md; source locator: Abstract (paragraph 1); 4. Discussion and Conclusions (paragraph 2) | README.md model description | 3. Results/3.4. Performance Evaluation of Models (paragraph 4); Abstract (paragraph 1); ambiguities: This is the paper-specific pipeline identity; unspecified component checkpoints or implementation versions are not inferred from its name.