[HTML][HTML] Advances in computational structure-based antibody design
Antibodies are currently the most important class of biotherapeutics and are used to treat
numerous diseases. Recent advances in computational methods are ushering in a new era …
numerous diseases. Recent advances in computational methods are ushering in a new era …
Approaches to expand the conventional toolbox for discovery and selection of antibodies with drug-like physicochemical properties
Antibody drugs should exhibit not only high-binding affinity for their target antigens but also
favorable physicochemical drug-like properties. Such drug-like biophysical properties are …
favorable physicochemical drug-like properties. Such drug-like biophysical properties are …
Germline-encoded amino acid–binding motifs drive immunodominant public antibody responses
EL Shrock, RT Timms, T Kula, EL Mena, AP West Jr… - Science, 2023 - science.org
Despite the vast diversity of the antibody repertoire, infected individuals often mount
antibody responses to precisely the same epitopes within antigens. The immunological …
antibody responses to precisely the same epitopes within antigens. The immunological …
ImmuneBuilder: Deep-Learning models for predicting the structures of immune proteins
Immune receptor proteins play a key role in the immune system and have shown great
promise as biotherapeutics. The structure of these proteins is critical for understanding their …
promise as biotherapeutics. The structure of these proteins is critical for understanding their …
The 2022 Nucleic Acids Research database issue and the online molecular biology database collection
DJ Rigden, XM Fernández - Nucleic acids research, 2022 - academic.oup.com
Abstract The 2022 Nucleic Acids Research Database Issue contains 185 papers, including
87 papers reporting on new databases and 85 updates from resources previously published …
87 papers reporting on new databases and 85 updates from resources previously published …
Toward real-world automated antibody design with combinatorial Bayesian optimization
Antibodies are multimeric proteins capable of highly specific molecular recognition. The
complementarity determining region 3 of the antibody variable heavy chain (CDRH3) often …
complementarity determining region 3 of the antibody variable heavy chain (CDRH3) often …
Unsupervised protein-ligand binding energy prediction via neural euler's rotation equation
Protein-ligand binding prediction is a fundamental problem in AI-driven drug discovery.
Previous work focused on supervised learning methods for small molecules where binding …
Previous work focused on supervised learning methods for small molecules where binding …
The Patent and Literature Antibody Database (PLAbDab): an evolving reference set of functionally diverse, literature-annotated antibody sequences and structures
Antibodies are key proteins of the adaptive immune system, and there exists a large body of
academic literature and patents dedicated to their study and concomitant conversion into …
academic literature and patents dedicated to their study and concomitant conversion into …
A machine learning strategy for the identification of key in silico descriptors and prediction models for IgG monoclonal antibody developability properties
Identification of favorable biophysical properties for protein therapeutics as part of
developability assessment is a crucial part of the preclinical development process …
developability assessment is a crucial part of the preclinical development process …
Towards the accurate modelling of antibody− antigen complexes from sequence using machine learning and information-driven docking
Motivation Antibody− antigen complex modelling is an important step in computational
workflows for therapeutic antibody design. While experimentally determined structures of …
workflows for therapeutic antibody design. While experimentally determined structures of …