Building Representation Learning Models for Antibody Comprehension

Barton, Justin; Gaspariunas, Aretas; Galson, Jacob D.; Leem, Jinwoo

doi:10.1101/cshperspect.a041462

Cited by 2 publications

(1 citation statement)

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“…Another modification that we envisage is model-driven, especially making use of the language models [ 43 ]. The annotations that we offer are devoid of the larger sequence context which language models capture.…”

Section: Discussionmentioning

confidence: 99%

LAP: Liability Antibody Profiler by sequence & structural mapping of natural and therapeutic antibodies

Satława,

Tarkowski,

Wróbel

et al. 2024

PLoS Comput Biol

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Antibody-based therapeutics must not undergo chemical modifications that would impair their efficacy or hinder their developability. A commonly used technique to de-risk lead biotherapeutic candidates annotates chemical liability motifs on their sequence. By analyzing sequences from all major sources of data (therapeutics, patents, GenBank, literature, and next-generation sequencing outputs), we find that almost all antibodies contain an average of 3–4 such liability motifs in their paratopes, irrespective of the source dataset. This is in line with the common wisdom that liability motif annotation is over-predictive. Therefore, we have compiled three computational flags to prioritize liability motifs for removal from lead drug candidates: 1. germline, to reflect naturally occurring motifs, 2. therapeutic, reflecting chemical liability motifs found in therapeutic antibodies, and 3. surface, indicative of structural accessibility for chemical modification. We show that these flags annotate approximately 60% of liability motifs as benign, that is, the flagged liabilities have a smaller probability of undergoing degradation as benchmarked on two experimental datasets covering deamidation, isomerization, and oxidation. We combined the liability detection and flags into a tool called Liability Antibody Profiler (LAP), publicly available at lap.naturalantibody.com. We anticipate that LAP will save time and effort in de-risking therapeutic molecules.

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Section: Discussionmentioning

confidence: 99%