Development of scoring functions for antibody sequence assessment and optimization
Open accessAbstract
Antibody development is still associated with substantial risks and difficulties as single mutations can radically change molecule properties like thermodynamic stability, solubility or viscosity. Since antibody generation methodologies cannot select and optimize for molecule properties which are important for biotechnological applications, careful sequence analysis and optimization is necessary to develop antibodies that fulfil the ambitious requirements of future drugs. While efforts to grab the physical principles of undesired molecule properties from the very bottom are becoming increasingly powerful, the wealth of publically available antibody sequences provides an alternative way to develop early assessment strategies for antibodies using a statistical approach which is the objective of this paper. Here, publically available sequences were used to develop heuristic potentials for the framework regions of heavy and light chains of antibodies of human and murine origin. The potentials take into account position dependent probabilities of individual amino acids but also conditional probabilities which are inevitable for sequence assessment and optimization. It is shown that the potentials derived from human sequences clearly distinguish between human sequences and sequences from mice and, hence, can be used as a measure of humaness which compares a given sequence with the phenotypic pool of human sequences instead of comparing sequence identities to germline genes. Following this line, it is demonstrated that, using the developed potentials, humanization of an antibody can be described as a simple mathematical optimization problem and that the in-silico generated framework variants closely resemble native sequences in terms of predicted immunogenicity.
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More sophisticated methods that take positional correlations between residues into account could be superior to Humanness score and HSC.
“Humanness score and HSC are primarily sequence distance-based approaches and it has been demonstrated that more sophisticated methods that take the positional correlations between residues into account could be superior [187, 188].”
From Computational approaches to therapeutic antibody design: established methods and emerging trends (2020) · cites this paper as [188]
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- Authors
- Seeliger, D.
- Affiliations
- Boehringer Ingelheim (Germany) (DE)
- Corresponding author
- Seeliger, Daniel
- ORCID
- Seeliger, Daniel
- Venue
- PLoS ONE
- Publisher
- Public Library of Science (PLoS)
- Published
- 2013-10-21
- Volume
- 8
- Issue
- 10
- Pages
- e76909
- ISSN
- 1932-6203 (ISSN-L)
- PMC
- PMC3804498
- OpenAlex
- W1974285609
- Cited by
- 24
- References
- 42
- Citations by year
- 2025: 1 · 2024: 5 · 2023: 1 · 2022: 2 · 2021: 3 · 2020: 3 · 2019: 2 · 2018: 5 · 2017: 1 · 2015: 1
- FWCI
- 0.26
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- PLoS ONE (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, other-oa)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Monoclonal and Polyclonal Antibodies Research
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › vaccines and immunoinformatics approaches
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Keywords
- antibody optimization, scoring functions, heuristic potential, humanization, immunogenicity prediction, conditional probability, therapeutic antibodies
- MeSH terms
- Rituximab, Algorithms, Amino Acid Sequence, Animals, Antibodies, Antibodies, Monoclonal, Antibody Specificity, Humans, Immunoglobulin Variable Region, Immunoglobulin Heavy Chains, Immunoglobulin Light Chains, Molecular Sequence Data, Sequence Analysis, Models, Immunological, Mice, Antibodies, Monoclonal, Murine-Derived
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