Deep Learning Empowers the Discovery of Self‐Assembling Peptides with Over 10 Trillion Sequences

Wang, Jiaqi; Liu, Zihan; Zhao, Shuang; Xu, Tengyan; Wang, Huaimin; Li, Stan Z.; Li, Wenbin

doi:10.1002/advs.202301544

Cited by 3 publications

(1 citation statement)

References 59 publications

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“…In recent years, several works have focused on the theoretical investigation of peptide aggregation behavior. For instance, coarse-grained molecular dynamics (MD) simulation predictions of short-peptide aggregation behavior − and the integration of machine learning to expand the search space − have been reported. Coarse-grained approaches, on the one hand, often lack atomic-scale details, making them less suitable for guiding catalytic design.…”

Section: Introductionmentioning

confidence: 99%

Computation-Driven Rational Design of Self-Assembled Short Peptides for Catalytic Hydrogen Production

Yang,

Wang,

et al. 2024

J. Am. Chem. Soc.

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Self-assembling peptides represent a captivating area of study in nanotechnology and biomaterials. This interest is largely driven by their unique properties and the vast application potential across various fields such as catalytic functions. However, design complexities, including high-dimensional sequence space and structural diversity, pose significant challenges in the study of such systems. In this work, we explored the possibility of self-assembled peptides to catalyze the hydrolysis of hydrosilane for hydrogen production using ab initio calculations and carried out wet-lab experiments to confirm the feasibility of these catalytic reactions under ambient conditions. Further, we delved into the nuanced interplay between sequence, structural conformation, and catalytic activity by combining modeling with experimental techniques such as transmission electron microscopy and nuclear magnetic resonance and proposed a dual mode of the microstructure of the catalytic center. Our results reveal that although research in this area is still at an early stage, the development of self-assembled peptide catalysts for hydrogen production has the potential to provide a more sustainable and efficient alternative to conventional hydrogen production methods. In addition, this work also demonstrates that a computation-driven rational design supplemented by experimental validation is an effective protocol for conducting research on functional self-assembled peptides.

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Section: Introductionmentioning

confidence: 99%

Computation-Driven Rational Design of Self-Assembled Short Peptides for Catalytic Hydrogen Production

Yang,

Wang,

et al. 2024

J. Am. Chem. Soc.

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HydrogelFinder: A Foundation Model for Efficient Self‐Assembling Peptide Discovery Guided by Non‐Peptidal Small Molecules

Ren,

Wei,

Luo

et al. 2024

Advanced Science

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Self‐assembling peptides have numerous applications in medicine, food chemistry, and nanotechnology. However, their discovery has traditionally been serendipitous rather than driven by rational design. Here, HydrogelFinder, a foundation model is developed for the rational design of self‐assembling peptides from scratch. This model explores the self‐assembly properties by molecular structure, leveraging 1,377 self‐assembling non‐peptidal small molecules to navigate chemical space and improve structural diversity. Utilizing HydrogelFinder, 111 peptide candidates are generated and synthesized 17 peptides, subsequently experimentally validating the self‐assembly and biophysical characteristics of nine peptides ranging from 1–10 amino acids—all achieved within a 19‐day workflow. Notably, the two de novo‐designed self‐assembling peptides demonstrated low cytotoxicity and biocompatibility, as confirmed by live/dead assays. This work highlights the capacity of HydrogelFinder to diversify the design of self‐assembling peptides through non‐peptidal small molecules, offering a powerful toolkit and paradigm for future peptide discovery endeavors.

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Transient and elusive intermediate states in self‐assembly processes: An overview

Zhang,

Hu,

Xing

et al. 2024

Responsive Materials

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The transient and elusive intermediate states are the keys in self‐assembly processes, which are common phenomena shaping the structure, properties, and functionalities of assembled materials across many scientific domains. However, the understanding about the intermediate states of self‐assembly process is always challenging and limited. In this review, we focus on these states by combining theoretical and experimental approaches. By examining a wide variety of self‐assembly systems that span from biological to metal–organic nanostructures, this review uncovers the wealth of intermediate states of self‐assembled materials. In addition to combining the current knowledge, it will identify challenges and provide a new insight into the opportunities for future research.

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Deep Learning Empowers the Discovery of Self‐Assembling Peptides with Over 10 Trillion Sequences

Cited by 3 publications

References 59 publications

Computation-Driven Rational Design of Self-Assembled Short Peptides for Catalytic Hydrogen Production

Computation-Driven Rational Design of Self-Assembled Short Peptides for Catalytic Hydrogen Production

HydrogelFinder: A Foundation Model for Efficient Self‐Assembling Peptide Discovery Guided by Non‐Peptidal Small Molecules

Transient and elusive intermediate states in self‐assembly processes: An overview

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