Special Issue "Computational Discovery of Antibodies"

A special issue of Antibodies (ISSN 2073-4468).

Deadline for manuscript submissions: closed (31 October 2022) | Viewed by 4782

Special Issue Editors

John & Johnson R&D, Janssen, Spring House, PA 19477, USA
Interests: antibody discovery; engineering and expression; developability; technology development; computational design
Biologics Research, Sanofi, Framingham, MA 01701, USA
Interests: antibody discovery; immunoinformatics; protein/antibody engineering; computational biology
Special Issues, Collections and Topics in MDPI journals
Dr. Abhinandan Raghavan
E-Mail Website
Guest Editor
Otsuka America Pharmaceutical Inc., Princeton, NJ 08540, USA
Interests: bioinformatics; systems biology; antibody engineering

Special Issue Information

Dear Colleagues,

Computational or in silico antibody discovery has emerged as a powerful method capable of generating antibodies with pre-defined specificities and sequence properties in a time- and cost-effective manner. The avalanche of human antibody repertoire data through next-generation sequencing (NGS), increasing numbers of experimental structures of antibodies, along with improved structure prediction, docking, and molecular dynamic simulation methods, have paved the way for computational antibody discovery. Though the computational design of antibodies has been around for some time, the newly available knowledge of antibody immunogenetics and paratope diversification at repertoire level from the NGS data, as well as advanced computational approaches in antibody modeling and docking that are also driven by artificial intelligence now make the computational discovery of antibodies a powerful third-generation method following in vivo (immunization) and in vitro (e.g., phage display) methods. This Special Issue will gather original research articles and topical reviews covering a wide range of aspects related to the computational discovery of antibodies.

This Special Issue of Antibodies focuses on: (1) NGS repertoire (BCR-seq, Ig-Seq) mining for antibody discovery (biases in gene assembly and chain pairing); (2) Computational approaches for antibody modeling, in silico antibody engineering, and developability prediction applicable to antibody discovery; (3) Computational methods for antigen–antibody interaction predictions, analysis of large NGS datasets, and antibody libraries and screening including NGS-driven and bioinformatics-aided strategies for antibody discovery; as well as (4) AI-assisted (machine- and deep-learning methods) de novo antibody design and development.

Dr. Partha S. Chowdhury
Dr. Ponraj Prabakaran
Dr. Abhinandan Raghavan
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Antibodies is an international peer-reviewed open access quarterly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.


  • antibody discovery
  • antibody engineering
  • antibody design
  • antigen–antibody interaction
  • computational biology
  • artificial intelligence
  • machine learning
  • deep learning
  • next-generation sequencing
  • antibiome
  • antibodyome
  • immunogenetics
  • BCR
  • NGS

Published Papers (1 paper)

Order results
Result details
Select all
Export citation of selected articles as:


19 pages, 2152 KiB  
On the Rapid Calculation of Binding Affinities for Antigen and Antibody Design and Affinity Maturation Simulations
Antibodies 2022, 11(3), 51; https://doi.org/10.3390/antib11030051 - 03 Aug 2022
Cited by 3 | Viewed by 3592
The accurate and efficient calculation of protein-protein binding affinities is an essential component in antibody and antigen design and optimization, and in computer modeling of antibody affinity maturation. Such calculations remain challenging despite advances in computer hardware and algorithms, primarily because proteins are [...] Read more.
The accurate and efficient calculation of protein-protein binding affinities is an essential component in antibody and antigen design and optimization, and in computer modeling of antibody affinity maturation. Such calculations remain challenging despite advances in computer hardware and algorithms, primarily because proteins are flexible molecules, and thus, require explicit or implicit incorporation of multiple conformational states into the computational procedure. The astronomical size of the amino acid sequence space further compounds the challenge by requiring predictions to be computed within a short time so that many sequence variants can be tested. In this study, we compare three classes of methods for antibody/antigen (Ab/Ag) binding affinity calculations: (i) a method that relies on the physical separation of the Ab/Ag complex in equilibrium molecular dynamics (MD) simulations, (ii) a collection of 18 scoring functions that act on an ensemble of structures created using homology modeling software, and (iii) methods based on the molecular mechanics-generalized Born surface area (MM-GBSA) energy decomposition, in which the individual contributions of the energy terms are scaled to optimize agreement with the experiment. When applied to a set of 49 antibody mutations in two Ab/HIV gp120 complexes, all of the methods are found to have modest accuracy, with the highest Pearson correlations reaching about 0.6. In particular, the most computationally intensive method, i.e., MD simulation, did not outperform several scoring functions. The optimized energy decomposition methods provided marginally higher accuracy, but at the expense of requiring experimental data for parametrization. Within each method class, we examined the effect of the number of independent computational replicates, i.e., modeled structures or reinitialized MD simulations, on the prediction accuracy. We suggest using about ten modeled structures for scoring methods, and about five simulation replicates for MD simulations as a rule of thumb for obtaining reasonable convergence. We anticipate that our study will be a useful resource for practitioners working to incorporate binding affinity calculations within their protein design and optimization process. Full article
(This article belongs to the Special Issue Computational Discovery of Antibodies)
Show Figures

Figure 1

Back to TopTop