Evidence map›Paper›PMID 40271725›Full record

ArticleAdvanced science (Weinheim, Baden-Wurttemberg, Germany)2025

Enhancing the Protein Stability of an Anticancer VHH-Fc Heavy Chain Antibody through Computational Modeling and Variant Design.

Yuan Fang, Menghua Song, Tianning Pu, Xiaoqing Song, Kailu Xu, Pengcheng Shen, Ting Cao, Yiman Zhao, Simon Hsu, Dongmei Han and 1 more

Abstract read
In one paragraph

Article in Advanced science (Weinheim, Baden-Wurttemberg, Germany), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
  4. Review
  5. 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

11 authors.

Yuan FangState Key Laboratory of Genetics and Development of Complex Phenotypes, Shanghai Engineering Research Center of Industrial Microorganisms, MOE Engineering Research Center of Gene Technology, School of Life Sciences, Fudan University, Shanghai, 200438, China.
Menghua SongDepartment of Technical Operations, Shanghai Henlius Biotech, Inc., Shanghai, 200233, China.
Tianning PuDepartment of Technical Operations, Shanghai Henlius Biotech, Inc., Shanghai, 200233, China.
Xiaoqing SongDepartment of Technical Operations, Shanghai Henlius Biotech, Inc., Shanghai, 200233, China.
Kailu XuDepartment of Technical Operations, Shanghai Henlius Biotech, Inc., Shanghai, 200233, China.
Pengcheng ShenDepartment of Technical Operations, Shanghai Henlius Biotech, Inc., Shanghai, 200233, China.
Ting CaoDepartment of Technical Operations, Shanghai Henlius Biotech, Inc., Shanghai, 200233, China.
Yiman ZhaoDepartment of Technical Operations, Shanghai Henlius Biotech, Inc., Shanghai, 200233, China.
Simon HsuDepartment of Technical Operations, Shanghai Henlius Biotech, Inc., Shanghai, 200233, China.
Dongmei HanDepartment of Technical Operations, Shanghai Henlius Biotech, Inc., Shanghai, 200233, China.
Qiang HuangState Key Laboratory of Genetics and Development of Complex Phenotypes, Shanghai Engineering Research Center of Industrial Microorganisms, MOE Engineering Research Center of Gene Technology, School of Life Sciences, Fudan University, Shanghai, 200438, China.ORCID https://orcid.org/0000-0001-5238-1704

Funding

National Key Research and Development Program of China 2021YFA0910604National Natural Science Foundation of China 31671386National Natural Science Foundation of China 31971377
6 · The paper itself

Abstract

VHHs (also known as nanobodies) are important therapeutic antibodies. To prolong their half-life in bloodstream, VHHs are usually fused to the Fc fragment of full-length antibodies. However, stability is often the main challenge for their commercialization, and methods to improve stability are still lacking. Here, an in silico pipeline is developed for analyzing the stability of an anticancer VHH-Fc fusion antibody (VFA01) and designing its stable variants. Computational modeling is used to analyze the VFA01 structure and evaluate its conformational stability, disulfide bond reduction state, and aggregation and degradation tendency. By building mechanistic models of aggregation and degradation, the hotspot residues affecting stability: C130, F57, Y106, L120, and W111 are identified. Based on them, a series of VFA01 variants are designed and obtained a variant M11 (C130S/W111F/F57K) whose stability is significantly enhanced compared to VFA01: there are no visible particles in solution, and the change rate of DLS average hydrodynamic size, SEC HMW%, and CE-SDS purity are improved by 6.2-, 3.4-, and 1.5-fold, respectively. Both antigen-binding activity and production yield are also improved by about 1.5-fold. The results show that our computational pipeline is a very promising approach for improving the protein stability of therapeutic VHH-Fc fusion antibodies.

Indexed as

Immunoglobulin Fc FragmentsImmunoglobulin Heavy ChainsSingle-Domain AntibodiesComputer SimulationHumansProtein StabilityImmunoglobulin Fc FragmentsImmunoglobulin Heavy ChainsSingle-Domain Antibodiesheavy chain antibodyprotein designsingle‐domain antibodystructure‐based modelingtherapeutic protein

Identifiers

PMID40271725
PMCPMC12199382

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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.