The University of Southampton
University of Southampton Institutional Repository
Warning ePrints Soton is experiencing an issue with some file downloads not being available. We are working hard to fix this. Please bear with us.

AI3SD Video: Fireflies-Lévy Flights algorithm for peptides conformational optimization

AI3SD Video: Fireflies-Lévy Flights algorithm for peptides conformational optimization
AI3SD Video: Fireflies-Lévy Flights algorithm for peptides conformational optimization
Over the last 50 years, several algorithms and approaches were introduced and improved to tackle the challenges of exploring a large and multidimensional conformational space. Optimisation algorithms are frequently used to guide the search in a conformational space of complex molecules such as proteins. It is a crucial step to access molecular properties corresponding to the most stable conformer. The optimisers are usually buried in docking software with limited tuning possibilities. We implement a Fireflies algorithm with Lévy flights distribution to search for the lowest energy conformations of peptides. The hyperparameters of this bio-inspired metaheuristics algorithm are tuned and its performance is compared with the state-of-the-art method. Our results show that the Fireflies-Lévy flights algorithm is able to improve upon the genetic algorithm method with fewer energy evaluations. To the best of our knowledge, this is the first cheminformatics application that will open the door to additional nature-inspired metaheuristics to support the conformational analysis of large biomolecules.
AI3SD, AI3SD Event, Artificial Intelligence, Machine Intelligence, ML, Machine Learning, Proteins
Hosni, Zied
7d146ffe-e595-4e1c-acb2-761e31ef82f6
Frey, Jeremy G.
ba60c559-c4af-44f1-87e6-ce69819bf23f
Kanza, Samantha
b73bcf34-3ff8-4691-bd09-aa657dcff420
Niranjan, Mahesan
5cbaeea8-7288-4b55-a89c-c43d212ddd4f
Hosni, Zied
7d146ffe-e595-4e1c-acb2-761e31ef82f6
Frey, Jeremy G.
ba60c559-c4af-44f1-87e6-ce69819bf23f
Kanza, Samantha
b73bcf34-3ff8-4691-bd09-aa657dcff420
Niranjan, Mahesan
5cbaeea8-7288-4b55-a89c-c43d212ddd4f

Hosni, Zied (2021) AI3SD Video: Fireflies-Lévy Flights algorithm for peptides conformational optimization. Frey, Jeremy G., Kanza, Samantha and Niranjan, Mahesan (eds.) AI 4 Proteins Seminar Series 2021. 14 Apr - 17 Jun 2021. (doi:10.5258/SOTON/P0109).

Record type: Conference or Workshop Item (Other)

Abstract

Over the last 50 years, several algorithms and approaches were introduced and improved to tackle the challenges of exploring a large and multidimensional conformational space. Optimisation algorithms are frequently used to guide the search in a conformational space of complex molecules such as proteins. It is a crucial step to access molecular properties corresponding to the most stable conformer. The optimisers are usually buried in docking software with limited tuning possibilities. We implement a Fireflies algorithm with Lévy flights distribution to search for the lowest energy conformations of peptides. The hyperparameters of this bio-inspired metaheuristics algorithm are tuned and its performance is compared with the state-of-the-art method. Our results show that the Fireflies-Lévy flights algorithm is able to improve upon the genetic algorithm method with fewer energy evaluations. To the best of our knowledge, this is the first cheminformatics application that will open the door to additional nature-inspired metaheuristics to support the conformational analysis of large biomolecules.

Video
AI4Proteins-Seminar-Series-ZiedHosni-160621 - Version of Record
Available under License Creative Commons Attribution.
Download (216MB)

More information

Published date: 16 June 2021
Additional Information: Zied finished his PhD in the Cronin Group at the University of Glasgow before securing a postdoctoral position in the Bioinformatics Hub in the Centre for Virus Research in Glasgow. During his research experience, he developed the ability to apply his knowledge of computational chemistry and synthesis into practical use in drug discovery, including machine learning tools for polymorphism predictions, artificial intelligence solution development, big data technologies and virtual screening. Before coming to Sheffield, Zied was a research associate in the Centre of Computational Chemistry at Bristol University where he was investigating mechanistic insights of the stereoselectivity in boron-lithium chemistry. He was previously a research associate at Strathclyde Institute of Pharmacy and Biological Sciences at Strathclyde University (UK) where he fully utilised his machine learning for scientific projects, whilst collaborating with several global pharmaceutical companies such as Lilly, AstraZeneca and Novartis, providing the opportunity to liaise with industry professionals and experts.
Venue - Dates: AI 4 Proteins Seminar Series 2021, 2021-04-14 - 2021-06-17
Keywords: AI3SD, AI3SD Event, Artificial Intelligence, Machine Intelligence, ML, Machine Learning, Proteins

Identifiers

Local EPrints ID: 450210
URI: http://eprints.soton.ac.uk/id/eprint/450210
PURE UUID: f13cda58-ba59-4e02-badf-f8fd971cc308
ORCID for Jeremy G. Frey: ORCID iD orcid.org/0000-0003-0842-4302
ORCID for Samantha Kanza: ORCID iD orcid.org/0000-0002-4831-9489

Catalogue record

Date deposited: 15 Jul 2021 16:47
Last modified: 28 Jul 2021 01:54

Export record

Altmetrics

Contributors

Author: Zied Hosni
Editor: Jeremy G. Frey ORCID iD
Editor: Samantha Kanza ORCID iD
Editor: Mahesan Niranjan

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×