Evidence map›Paper›PMID 40586846›Full record

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

PRO-LDM: A Conditional Latent Diffusion Model for Protein Sequence Design and Functional Optimization.

Sitao Zhang, Zixuan Jiang, Rundong Huang, Wenting Huang, Siyuan Peng, Shaoxun Mo, Letao Zhu, Peiheng Li, Ziyi Zhang, Emily Pan and 6 more

Abstract read
PubMed Full text at PMC Publisher
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 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Review
  2. 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

16 authors.

Sitao ZhangState Key Laboratory of Microbial Metabolism, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, 200240, China.ORCID https://orcid.org/0000-0002-8170-8080
Zixuan JiangFunction Hub, The Hong Kong University of Science and Technology (Guangzhou), Guangdong, 511453, China.
Rundong HuangDepartment of Mathematics, School of Computation, Information and Technology, Technical University of Munich, 80333, Munich, Germany.
Wenting HuangState Key Laboratory of Microbial Metabolism, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, 200240, China.
Siyuan PengState Key Laboratory of Microbial Metabolism, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, 200240, China.
Shaoxun MoState Key Laboratory of Microbial Metabolism, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, 200240, China.
Letao ZhuState Key Laboratory of Microbial Metabolism, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, 200240, China.
Peiheng LiElectronic and Information Engineering, School of Electronic Information Engineering, Beihang University, Beijing, 100191, China.
Ziyi ZhangFunction Hub, The Hong Kong University of Science and Technology (Guangzhou), Guangdong, 511453, China.
Emily PanThe Lawrenceville School, Lawrenceville, NJ, 08648, USA.
Xi ChenXtalPi, Inc., New Area Free Trade No. 1, Life Science and Technology Industrial Park Phase 1 No. 2-1, Pudong District, Shanghai, 200120, China.
Yunfei LongXtalPi, Inc., New Area Free Trade No. 1, Life Science and Technology Industrial Park Phase 1 No. 2-1, Pudong District, Shanghai, 200120, China.
Qi LiangResearch Center for Intelligent Computing Platforms, Zhejiang Lab, Hangzhou, Zhejiang, 311121, China.
Jin TangResearch Center for Intelligent Computing Platforms, Zhejiang Lab, Hangzhou, Zhejiang, 311121, China.
Renjing XuFunction Hub, The Hong Kong University of Science and Technology (Guangzhou), Guangdong, 511453, China.
Rui QingState Key Laboratory of Microbial Metabolism, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, 200240, China.ORCID https://orcid.org/0000-0002-7952-2295

Funding

National Natural Science Foundation of China 32470105Science and Technology Commission of Shanghai Municipality 24HC2810800
6 · The paper itself

Abstract

The diffusion model has grasped enormous attention in the computer vision field and emerged as a promising algorithm in protein design for precise structure and sequence generation. Here PRO-LDM is introduced: a modular multi-tasking framework combining design fidelity and computational efficiency, by integrating the diffusion model in latent space. The model learns biological representations at local and global levels, to design natural-like species with enhanced diversity, or optimize protein properties and functions. Its modular nature also enables the integration with alternative pre-trained encoders for enhanced generalization capability. Outlier design can be implemented by adjusting the classifier-free guidance that enables PRO-LDM to sample vastly different regions in the latent space. The approach is demonstrated in generating a novel green-fluorescence-protein variant with notably enhanced fluorescence in multiple working scenarios along with increased solubility and stability. The model provides a versatile tool to effectively extract physicochemical and evolutionary information in sequences for designing new proteins with optimized performances.

Indexed as

Protein EngineeringProteinsAlgorithmsDiffusionProteinsconditional generationfunctional optimizationlatent diffusion modelsprotein sequence design

Identifiers

PMID40586846
PMCPMC12442598

What OpenQuestion holds

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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.