Evidence map›Paper›PMID 38889249›Full record

ArticleAdvanced science (Weinheim, Baden-Wurttemberg, Germany)2024

Investigation of Antibody Tolerance in Methanol for Analytical Purposes: Methanol Effect Patterns and Molecular Mechanisms.

Yingjie Zhang, Jiafei Mi, Weilin Wu, Jie Fei, Bochen Lv, Xuezhi Yu, Kai Wen, Jianzhong Shen, Zhanhui Wang

Abstract read
In one paragraph

Article in Advanced science (Weinheim, Baden-Wurttemberg, Germany), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Yingjie ZhangNational Key Laboratory of Veterinary Public Health and Safety, Beijing Key Laboratory of Detection Technology for Animal-Derived Food, College of Veterinary Medicine, China Agricultural University, Beijing, 100193, P. R. China.
Jiafei MiNational Key Laboratory of Veterinary Public Health and Safety, Beijing Key Laboratory of Detection Technology for Animal-Derived Food, College of Veterinary Medicine, China Agricultural University, Beijing, 100193, P. R. China.
Weilin WuNational Key Laboratory of Veterinary Public Health and Safety, Beijing Key Laboratory of Detection Technology for Animal-Derived Food, College of Veterinary Medicine, China Agricultural University, Beijing, 100193, P. R. China.
Jie FeiNational Key Laboratory of Veterinary Public Health and Safety, Beijing Key Laboratory of Detection Technology for Animal-Derived Food, College of Veterinary Medicine, China Agricultural University, Beijing, 100193, P. R. China.
Bochen LvNational Key Laboratory of Veterinary Public Health and Safety, Beijing Key Laboratory of Detection Technology for Animal-Derived Food, College of Veterinary Medicine, China Agricultural University, Beijing, 100193, P. R. China.
Xuezhi YuNational Key Laboratory of Veterinary Public Health and Safety, Beijing Key Laboratory of Detection Technology for Animal-Derived Food, College of Veterinary Medicine, China Agricultural University, Beijing, 100193, P. R. China.
Kai WenNational Key Laboratory of Veterinary Public Health and Safety, Beijing Key Laboratory of Detection Technology for Animal-Derived Food, College of Veterinary Medicine, China Agricultural University, Beijing, 100193, P. R. China.
Jianzhong ShenNational Key Laboratory of Veterinary Public Health and Safety, Beijing Key Laboratory of Detection Technology for Animal-Derived Food, College of Veterinary Medicine, China Agricultural University, Beijing, 100193, P. R. China.
Zhanhui WangNational Key Laboratory of Veterinary Public Health and Safety, Beijing Key Laboratory of Detection Technology for Animal-Derived Food, College of Veterinary Medicine, China Agricultural University, Beijing, 100193, P. R. China.ORCID 0000-0002-0167-9559

Funding

National Key R&D Program of China 2023YFF1105000National Natural Science Foundation of China 32172905
6 · The paper itself

Abstract

The extraction of targets from biological samples for immunoassays using organic solvents, such as methanol, is often necessary. However, high concentrations of organic solvents in extracts invariably lead to instability of the employed antibody, resulting in poor performance of the immunoassay. Evaluating the tolerance ability and exploring the molecular mechanisms of antibody tolerance in organic solvents are essential for the development of robust immunoassays. In this work, 25 monoclonal antibodies and methanol are utilized as models to address these questions. A novel protocol is initially established to precisely and rapidly determine antibody tolerance in methanol, identifying two distinct methanol effect patterns. Through a detailed investigation of the structural basis, a novel hypothesis regarding methanol effect patterns is proposed, termed "folding-aggregation," which is subsequently validated through molecular dynamics simulations. Furthermore, the investigation of sequence basis reveals significant differences in residue types within the complementarity-determining regions and ligand-binding residues, distinguishing the two antibody methanol effect patterns. Moreover, the methanol effect patterns of the antibodies are defined by germline antibodies. This work represents the first exploration of antibody methanol effect patterns and associated molecular mechanisms, with potential implications for the discovery and engineering of tolerant antibodies for the development of robust immunoassays.

Indexed as

Antibodies, MonoclonalMethanolMolecular Dynamics SimulationSolventsImmunoassayAntibodies, MonoclonalMethanolSolventsantibody methanol effect patternsantibody tolerancefolding and aggregationimmunoassaymolecular mechanism

Identifiers

PMID38889249
PMCPMC11336977

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.