Author response:
We sincerely appreciate the insightful feedback and constructive suggestions provided by the reviewers. We thank reviewers for their valuable support in improving our manuscript.
In response to the public reviews raised by reviewers, we plan to make the following revisions:
(1) Most metadata have been rectified through collaborative review of original literature sources rather than automated processes. We intend to incorporate a detailed discussion on this matter in the revised manuscript.
(2) We will include a corrections table for entries to provide clarity and transparency regarding any amendments made.
(3) Additional references will be included to elucidate the rationale behind the selection of interact residues definition methods and the set threshold. The threshold is not fixed. In fact, we utilized a 5Å cutoff in current version, listing all residues with distances less than 5Å alongside the corresponding distances. The researchers could screen the residues through distance according to their custom cutoff. To offer researchers flexibility, we will also provide interact residues and corresponding distances with higher cutoffs for custom screening. These enhancements will be detailed in the revised manuscript.
(4)We acknowledge the importance of expanding the database to include a wider range of experimental information and complexes with diverse target sizes. Regrettably, immediate updates to address these limitations are not feasible at this time. Thus, we will give an illustration in the later detail response to reviewers.

Armita Nourmohammad

Aleksandra Walczak

eLife Assessment
This useful manuscript provides a newly curated database (termed AACDB) of antibody-antigens structural information, alongside annotations that are either taken and from the PDB, or added de-novo. Sequences, structures, and annotations can be easily downloaded from the AACDB website, speeding up the development of structure-based algorithms and analysis pipelines to characterize antibody-antigen interactions. The methodology presented for this data curation is solid, although some key annotations are missing which would greatly improve the database.

anonymous

Reviewer #1 (Public review):
This manuscript introduces a useful curation pipeline of antibody-antigen structures downloaded from the PDB database. The antibody-antigen structures are presented in a new database called AACDB, alongside annotations that were either corrected from those present in the PDB database or added de-novo with a solid methodology. Sequences, structures, and annotations can be very easily downloaded from the AACDB website, speeding up the development of structure-based algorithms and analysis pipelines to characterize antibody-antigen interactions. However, AACDB is missing some key annotations that would greatly enhance its usefulness.
Here are detailed comments regarding the three strengths above:
(1) I think potentially the most significant contribution of this database is the manual data curation to fix errors present in the PDB entries, by cross-referencing with the literature. However, as a reviewer, validating the extent and the impact of these corrections is hard, since the authors only provided a few anecdotal examples in their manuscript.
I have personally verified some of the examples presented by the authors and found that SAbDab appears to fix the mistakes related to the misidentification of antibody chains, but not other annotations.
(a) "the species of the antibody in 7WRL was incorrectly labeled as "SARS coronavirus B012" in both PDB and SabDab" → I have verified the mistake and fix, and that SAbDab does not fix is, just uses the pdb annotation. 
(b) "1NSN, the resolution should be 2.9 , but it was incorrectly labeled as 2.8" → I have verified the mistake and fix, and that saabdab does not fix it, just uses the PDB annotation. 
(c) "mislabeling of antibody chains as other proteins (e.g. in 3KS0, the light chain of B2B4 antibody was misnamed as heme domain of flavocytochrome b2)" → SAbDab fixes this as well in this case. 
(d) "misidentification of heavy chains as light chains (e.g. both two chains of antibody were labeled as light chain in 5EBW)" → SAbDab fixes this as well in this case.
I personally believe the authors should make public the corrections made, and describe the procedures - if systematic - to identify and correct the mistakes. For example, what was the exact procedure (e.g. where were sequences found, how were the sequences aligned, etc.) to find mutations? Was the procedure run on every entry?
(2) I believe the splitting of the pdb files is a valuable contribution as it standardizes the distribution of antibody-antigen complexes. Indeed, there is great heterogeneity in how many copies of the same structure are present in the structure uploaded to the PDB, generating potential artifacts for machine learning applications to pick up on. That being said, I have two thoughts both for the authors and the broader community. First, in the case of multiple antibodies binding to different epitopes on the same antigen, one should not ignore the potentially stabilizing effect that the binding of one antibody has on the complex, thereby enabling the binding of the second antibody. In general, I urge the community to think about what is the most appropriate spatial context to consider when modeling the stability of interactions from crystal structure data. Second, and in a similar vein, some antigens occur naturally as homomultimers - e.g. influenza hemagglutinin is a homotrimer. Therefore, to analyze the stability of a full-antigen-antibody structure, I believe it would be necessary to consider the full homo-trimer, whereas, in the current curation of AACDB with the proposed data splitting, only the monomers are present.
(3) I think the annotation of interface residues is a useful addition to structural datasets, but their current presentation is lacking on several fronts.
I think the manuscript is lacking in justification about the numbers used as cutoffs (1A^2 for change in SASA and 5A for maximum distance for contact) The authors just cite other papers applying these two types of cutoffs, but the underlying physico-chemical reasons are not explicit even in these papers. I think that, if the authors want AACDB to be used globally for benchmarks, they should provide direct sources of explanations of the cutoffs used, or provide multiple cutoffs. Indeed, different cutoffs are often used (e.g. ATOM3D uses 6A instead of 5A to determine contact between a protein and a small molecule <a href="https://datasets-benchmarks-proceedings.neurips.cc/paper/2021/hash/c45147dee729311ef5b5c3003946c48f-Abstract-round1.html">https://datasets-benchmarks-proceedings.neurips.cc/paper/2021/hash/c45147dee729311ef5b5c3003946c48f-Abstract-round1.html</a>)
I think the authors should provide a figure with statistics pertaining to the interface atoms. I think showing any distribution differences between interface atoms determined according to either strategy (number of atoms, correlation between change in SASA and distance...) would be fundamental to understanding the two strategies. I think other statistics would constitute an enhancement as well (e.g. proportion of heavy vs. light chain residues).
Some obvious limitations of AACDB in its current form include:
AACDB only contains entries with protein-based antigens of at most 50 amino acids in length. This excludes non-protein-based antigens, such as carbohydrate- and nucleotide-based, as well as short peptide antigens.
AACDB does not include annotations of binding affinity, which are present in SAbDab and have been proven useful both for characterizing drivers of antibody-antigen interactions (cite <a href="https://www.sciencedirect.com/science/article/pii/S0969212624004362?via%3Dihub">https://www.sciencedirect.com/science/article/pii/S0969212624004362?via%3Dihub</a>) and for benchmarking antigen-specific antibody-design algorithms (cite <a href="https://www.biorxiv.org/content/10.1101/2023.12.10.570461v1)">https://www.biorxiv.org/content/10.1101/2023.12.10.570461v1)</a>).
In conclusion, I believe AACDB has the potential to be a more standardized and error-light database for antibody-antigen complex structures. It is, however, hard to evaluate the extent to which errors have been corrected since the authors do not provide a list of the errors or a step-by-step procedure for fixing the errors. Unfortunately, AACDB is currently missing binding affinity annotations, which hinders its usefulness.

Reviewer #2 (Public review):
Summary:
Antibodies, thanks to their high binding affinity and specificity to cognate protein targets, are increasingly used as research and therapeutic tools. In this work, Zhou et al. have created, curated, and made publicly available a new database of antibody-antigen complexes to support research in the field of antibody modelling, development, and engineering.
Strengths:
The authors have performed a manual curation of antibody-antigen complexes from the Protein Data Bank, rectifying annotation errors; they have added two methods to estimate paratope-epitope interfaces; they have produced a web interface that is capable of both effective visualisation and of summarising the key useful information in one page. The database is also cross-linked to other databases that contain information relevant to antibody developability and therapeutic applications.
Weaknesses:
The database does not import all the experimental information from PDB and contains only complexes with large protein targets.

AACDB: Antigen-Antibody Complex Database — a Comprehensive Database Unlocking Insights into Interaction Interface

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