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Analysis of institutional authors

Papadopoulos, AmCorresponding AuthorAlvarez, FAuthor

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January 1, 2026
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ParaSurf: a surface-based deep learning approach for paratope-antigen interaction prediction

Publicated to: BIOINFORMATICS. 41 (2): btaf062- - 2025-02-01 41(2), DOI: 10.1093/bioinformatics/btaf062

Authors:

Papadopoulos, AM; Axenopoulos, A; Iatrou, A; Stamatopoulos, K; Alvarez, F; Daras, P
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Affiliations

Aristotle Univ Thessaloniki - Author
Informat Technol Inst, Ctr Res & Technol Hellas - Author
Inst Appl Biosci, Ctr Res & Technol Hellas - Author
Univ Politecn Madrid - Author
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Abstract

Motivation Identifying antibody binding sites, is crucial for developing vaccines and therapeutic antibodies, processes that are time-consuming and costly. Accurate prediction of the paratope's binding site can speed up the development by improving our understanding of antibody-antigen interactions.Results We present ParaSurf, a deep learning model that significantly enhances paratope prediction by incorporating both surface geometric and non-geometric factors. Trained and tested on three prominent antibody-antigen benchmarks, ParaSurf achieves state-of-the-art results across nearly all metrics. Unlike models restricted to the variable region, ParaSurf demonstrates the ability to accurately predict binding scores across the entire Fab region of the antibody. Additionally, we conducted an extensive analysis using the largest of the three datasets employed, focusing on three key components: (i) a detailed evaluation of paratope prediction for each complementarity-determining region loop, (ii) the performance of models trained exclusively on the heavy chain, and (iii) the results of training models solely on the light chain without incorporating data from the heavy chain.Availability and implementation Source code for ParaSurf, along with the datasets used, preprocessing pipeline, and trained model weights, are freely available at https://github.com/aggelos-michael-papadopoulos/ParaSurf.
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Keywords

Antibody combining siteAntibody-antigen interactions (ppis)AntigenAntigensBinding sites, antibodyBinding-site predictionBioinformaticsComputational biologyDeep learningImmunologyModelParatope predictionProceduresProgramProteinSoftwareSurface-based deep learning

Quality index

Bibliometric impact. Analysis of the contribution and dissemination channel

The work has been published in the journal BIOINFORMATICS due to its progression and the good impact it has achieved in recent years, according to the agency WoS (JCR), it has become a reference in its field. In the year of publication of the work, 2025, it was in position 8/86, thus managing to position itself as a Q1 (Primer Cuartil), in the category Biochemical Research Methods.

Independientemente del impacto esperado determinado por el canal de difusión, es importante destacar el impacto real observado de la propia aportación.

Según las diferentes agencias de indexación, el número de citas acumuladas por esta publicación hasta la fecha 2026-04-13:

  • WoS: 3
  • Scopus: 2
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Impact and social visibility

From the perspective of influence or social adoption, and based on metrics associated with mentions and interactions provided by agencies specializing in calculating the so-called "Alternative or Social Metrics," we can highlight as of 2026-04-13:

  • The use, from an academic perspective evidenced by the Altmetric agency indicator referring to aggregations made by the personal bibliographic manager Mendeley, gives us a total of: 10.
  • The use of this contribution in bookmarks, code forks, additions to favorite lists for recurrent reading, as well as general views, indicates that someone is using the publication as a basis for their current work. This may be a notable indicator of future more formal and academic citations. This claim is supported by the result of the "Capture" indicator, which yields a total of: 10 (PlumX).

With a more dissemination-oriented intent and targeting more general audiences, we can observe other more global scores such as:

  • The Total Score from Altmetric: 2.
  • The number of mentions on the social network X (formerly Twitter): 2 (Altmetric).

It is essential to present evidence supporting full alignment with institutional principles and guidelines on Open Science and the Conservation and Dissemination of Intellectual Heritage. A clear example of this is:

  • The work has been submitted to a journal whose editorial policy allows open Open Access publication.
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Leadership analysis of institutional authors

This work has been carried out with international collaboration, specifically with researchers from: Greece; Macedonia.

There is a significant leadership presence as some of the institution’s authors appear as the first or last signer, detailed as follows: First Author (PAPADOPOULOS, ANGELOS-MICHAIL) .

the author responsible for correspondence tasks has been PAPADOPOULOS, ANGELOS-MICHAIL.

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