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
Interests: antibody discovery; engineering and expression; developability; technology development; computational design
Interests: antibody discovery; immunoinformatics; protein/antibody engineering; computational biology
Special Issues, Collections and Topics in MDPI journals
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
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