Automated descriptors for high-throughput screening of peptide self-assembly

Raj Kumar Rajaram Baskaran; Alexander van Teijlingen; Tell Tuttle

doi:10.1039/D4FD00201F

Automated descriptors for high-throughput screening of peptide self-assembly

Raj Kumar Rajaram Baskaran, Alexander van Teijlingen, Tell Tuttle^*

^*Corresponding author for this work

Pure And Applied Chemistry

Research output: Contribution to journal › Article › peer-review

Abstract

We present five automated descriptors: Aggregate Detection Index (ADI); Sheet Formation Index (SFI); Vesicle Formation Index (VFI); Tube Formation Index (TFI); and Fiber Formation Index (FFI), that have been designed for analysing peptide self-assembly in molecular dynamics simulations. These descriptors, implemented as Python modules within a Conda environment, enhance analytical precision and enable the development of screening methods tailored to specific structural targets rather than general aggregation. Initially tested on the FF dipeptide, the descriptors were validated using a comprehensive dipeptide dataset. This approach facilitates the identification of promising self-assembling moieties with nanoscale properties directly linked to macroscale functions, such as hydrogel formation.

Original language	English
Journal	Faraday Discussions
Early online date	28 Jan 2025
DOIs	https://doi.org/10.1039/D4FD00201F
Publication status	E-pub ahead of print - 28 Jan 2025

Funding

This research was supported by the Horizon Europe Marie Skłodowska-Curie Action (MSCA) MultiSMART (grant no. 101072585), with local funding of this international network being provided by EPSRC (grant no. EP/X029980/1). Results were obtained using the EPSRC-funded ARCHIE-WeSt High-Performance Computer (https://www.archie-west.ac.uk; EPSRC grant no. EP/K000586/1)

Keywords

descriptors
molecular dynamics simulations

Access to Document

10.1039/D4FD00201FLicence: CC BY 3.0

Baskaran-etal-2025-Automated-descriptors-for-high-throughput-screening-of-peptide-self-assemblyProof, 20.3 MBLicence: CC BY 3.0

Cite this

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title = "Automated descriptors for high-throughput screening of peptide self-assembly",

abstract = "We present five automated descriptors: Aggregate Detection Index (ADI); Sheet Formation Index (SFI); Vesicle Formation Index (VFI); Tube Formation Index (TFI); and Fiber Formation Index (FFI), that have been designed for analysing peptide self-assembly in molecular dynamics simulations. These descriptors, implemented as Python modules within a Conda environment, enhance analytical precision and enable the development of screening methods tailored to specific structural targets rather than general aggregation. Initially tested on the FF dipeptide, the descriptors were validated using a comprehensive dipeptide dataset. This approach facilitates the identification of promising self-assembling moieties with nanoscale properties directly linked to macroscale functions, such as hydrogel formation.",

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author = "{Rajaram Baskaran}, {Raj Kumar} and {van Teijlingen}, Alexander and Tell Tuttle",

year = "2025",

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doi = "10.1039/D4FD00201F",

language = "English",

journal = "Faraday Discussions",

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N2 - We present five automated descriptors: Aggregate Detection Index (ADI); Sheet Formation Index (SFI); Vesicle Formation Index (VFI); Tube Formation Index (TFI); and Fiber Formation Index (FFI), that have been designed for analysing peptide self-assembly in molecular dynamics simulations. These descriptors, implemented as Python modules within a Conda environment, enhance analytical precision and enable the development of screening methods tailored to specific structural targets rather than general aggregation. Initially tested on the FF dipeptide, the descriptors were validated using a comprehensive dipeptide dataset. This approach facilitates the identification of promising self-assembling moieties with nanoscale properties directly linked to macroscale functions, such as hydrogel formation.

AB - We present five automated descriptors: Aggregate Detection Index (ADI); Sheet Formation Index (SFI); Vesicle Formation Index (VFI); Tube Formation Index (TFI); and Fiber Formation Index (FFI), that have been designed for analysing peptide self-assembly in molecular dynamics simulations. These descriptors, implemented as Python modules within a Conda environment, enhance analytical precision and enable the development of screening methods tailored to specific structural targets rather than general aggregation. Initially tested on the FF dipeptide, the descriptors were validated using a comprehensive dipeptide dataset. This approach facilitates the identification of promising self-assembling moieties with nanoscale properties directly linked to macroscale functions, such as hydrogel formation.

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KW - molecular dynamics simulations

UR - https://github.com/Tuttlelab/Descriptors

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SN - 1359-6640

JO - Faraday Discussions

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ER -

Automated descriptors for high-throughput screening of peptide self-assembly

Abstract

Funding

Keywords

Access to Document

Other files and links

Fingerprint

Multi-component Soft Materials Advanced Research Training Network : MultiSMART 2021 - Horizon MSCA DN 01

Data for: "Automated descriptors to relate peptide self-assembly with macroscopic properties, enabling machine learning for targeted supramolecular gel discovery"

ARCHIE-WeSt (UOSHPC)

Cite this

Automated descriptors for high-throughput screening of peptide self-assembly

Abstract

Funding

Keywords

Access to Document

Other files and links

Fingerprint

Projects

Multi-component Soft Materials Advanced Research Training Network : MultiSMART 2021 - Horizon MSCA DN 01

Datasets

Data for: "Automated descriptors to relate peptide self-assembly with macroscopic properties, enabling machine learning for targeted supramolecular gel discovery"

Equipment

ARCHIE-WeSt (UOSHPC)

Cite this