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AI3SD Video: Deep Learning enhanced prediction of protein structure and dynamics

AI3SD Video: Deep Learning enhanced prediction of protein structure and dynamics
AI3SD Video: Deep Learning enhanced prediction of protein structure and dynamics
Proteins exist in several different conformations. These structural changes are often associated with fluctuations at the residue level. Recent findings showed that co-evolutionary analysis coupled with machine-learning techniques improved the prediction precision by providing quantitative distance predictions between pairs of residues. The predicted statistical distance distribution from the Multi Sequence Analysis (MSA) revealed the presence of different local maxima suggesting the flexibility of key residue pairs. Here we investigate the ability of the residue-residue distance prediction to provide insights into the protein conformational ensemble. We combine deep learning approaches with mechanistic modeling to a set of proteins that experimentally showed conformational changes. The predicted protein models were filtered based on their energy scored, RMSD clustered, and the centroids locally refined. The models were compared to the experimental-Molecular Dynamics (MD) relaxed structure by analyzing the backbone residue torsional distribution and the sidechains orientations. Our pipeline not only consents us to retrieve the global experimental folding but also the experimental structural dynamics due to local and global conformational changes. Based on the insight of this study we are proposing a protocol that allows the in-silico investigation of protein dynamics suited for pharmacological research on catalysis and molecular recognition.
AI, AI3SD Event, Artificial Intelligence, Machine Intelligence, Machine Learning, ML, Proteins
Audagnotto, Martina
403fca95-3e5e-40b2-a007-bbafb29d06a3
Frey, Jeremy G.
ba60c559-c4af-44f1-87e6-ce69819bf23f
Kanza, Samantha
b73bcf34-3ff8-4691-bd09-aa657dcff420
Niranjan, Mahesan
5cbaeea8-7288-4b55-a89c-c43d212ddd4f
Audagnotto, Martina
403fca95-3e5e-40b2-a007-bbafb29d06a3
Frey, Jeremy G.
ba60c559-c4af-44f1-87e6-ce69819bf23f
Kanza, Samantha
b73bcf34-3ff8-4691-bd09-aa657dcff420
Niranjan, Mahesan
5cbaeea8-7288-4b55-a89c-c43d212ddd4f

Audagnotto, Martina (2021) AI3SD Video: Deep Learning enhanced prediction of protein structure and dynamics. Frey, Jeremy G., Kanza, Samantha and Niranjan, Mahesan (eds.) AI 4 Proteins Seminar Series 2021. 14 Apr - 17 Jun 2021. (doi:10.5258/SOTON/P0099).

Record type: Conference or Workshop Item (Other)

Abstract

Proteins exist in several different conformations. These structural changes are often associated with fluctuations at the residue level. Recent findings showed that co-evolutionary analysis coupled with machine-learning techniques improved the prediction precision by providing quantitative distance predictions between pairs of residues. The predicted statistical distance distribution from the Multi Sequence Analysis (MSA) revealed the presence of different local maxima suggesting the flexibility of key residue pairs. Here we investigate the ability of the residue-residue distance prediction to provide insights into the protein conformational ensemble. We combine deep learning approaches with mechanistic modeling to a set of proteins that experimentally showed conformational changes. The predicted protein models were filtered based on their energy scored, RMSD clustered, and the centroids locally refined. The models were compared to the experimental-Molecular Dynamics (MD) relaxed structure by analyzing the backbone residue torsional distribution and the sidechains orientations. Our pipeline not only consents us to retrieve the global experimental folding but also the experimental structural dynamics due to local and global conformational changes. Based on the insight of this study we are proposing a protocol that allows the in-silico investigation of protein dynamics suited for pharmacological research on catalysis and molecular recognition.

Video
AI4Proteins-Seminar-Series-MartinaAudagnotto-160621 - Version of Record
Available under License Creative Commons Attribution.
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More information

Published date: 16 June 2021
Additional Information: Martina completed her M.Sc. in Chemistry at the University of Turin, studying the effect of the adsorption of amino acids on titanium dioxide using quantum mechanics approaches. Afterwards, she pursued a PhD in the field of Molecular Dynamics at École Polytechnique de Lausanne (EPFL) under Prof. Dal Peraro’s supervision. Combing X-ray experiments and Molecular Dynamics simulations, Martina investigated the membrane-protein interplay in modeled physiological conditions. Her work highlighted the importance of applying a multilevel approach to achieve a comprehensive picture of biological systems and understanding the dynamic interactions and subsequent events that occur in cells. At the University of California San Diego (UCSD) under the supervision of Prof. Amaro, Prof. Villa and Prof. Taylor, Martina worked as a postdoctoral fellow on an interdisciplinary project to investigate the LRRK2 familial mutations and their association with Parkinson’s Disease. By combining in situ cryo-electron tomography (ET) density map with X-ray structure and homology models, she revealed the atomistic architecture of LRRK2 protein and their organization around the microtubule providing the starting point for future medicinal chemistry studies. Martina is currently working on proteins structure folding prediction methods at AstraZeneca in the team of Christian Tyrchan. By combining deep learning approaches with mechanistic modeling she retrieved not only the global experimental folding but also the experimental structural dynamics due to local and global conformational changes. Based on the insight of this study the proposing protocol will allow the in-silico investigation of protein dynamics suited for pharmacological research on catalysis and molecular recognition.
Venue - Dates: AI 4 Proteins Seminar Series 2021, 2021-04-14 - 2021-06-17
Keywords: AI, AI3SD Event, Artificial Intelligence, Machine Intelligence, Machine Learning, ML, Proteins

Identifiers

Local EPrints ID: 450159
URI: http://eprints.soton.ac.uk/id/eprint/450159
PURE UUID: 18e153f8-d7a8-4b62-bb0b-cdf94739a6dd
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: 14 Jul 2021 16:31
Last modified: 28 Jul 2021 01:54

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