Machine Learning Integrating Protein Structure, Sequence, and Dynamics to Predict the Enzyme Activity of Bovine Enterokinase Variants-Reference-Cited by-同舟云学术

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Machine Learning Integrating Protein Structure, Sequence, and Dynamics to Predict the Enzyme Activity of Bovine Enterokinase Variants

Published:2024-02-22 Issue:7 Volume:64 Page:2681-2694
ISSN:1549-9596
Container-title:Journal of Chemical Information and Modeling
language:en
Short-container-title:J. Chem. Inf. Model.

Author:

Venanzi Niccolo Alberto Elia¹,Basciu Andrea²,Vargiu Attilio Vittorio²^ORCID,Kiparissides Alexandros¹³,Dalby Paul A.¹^ORCID,Dikicioglu Duygu¹^ORCID

Affiliation:

1. Department of Biochemical Engineering, University College London, Gower Street, WC1E 6BT London, U.K.

2. Department of Physics, University of Cagliari, Cittadella Universitaria, I-09042 Monserrato, Cagliari, Italy

3. Department of Chemical Engineering, Aristotle University of Thessaloniki, 54 124 Thessaloniki, Greece

Funder

Engineering and Physical Sciences Research Council

Publisher

American Chemical Society (ACS)

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.jcim.3c00999

Reference87 articles.

1. Estimated Research and Development Investment Needed to Bring a New Medicine to Market, 2009-2018

2. Protein Engineering: Past, Present, and Future

3. Natural Selection and the Concept of a Protein Space

4. The distribution of fitness effects among beneficial mutations in Fisher's geometric model of adaptation

5. Strategy and success for the directed evolution of enzymes

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1. Utilizing Molecular Dynamics Simulations, Machine Learning, Cryo-EM, and NMR Spectroscopy to Predict and Validate Protein Dynamics;International Journal of Molecular Sciences;2024-09-08

2. Alpha- glucosidase inhibition analysis and in silico studies of new tetrahydroquinazolin-4(1H)-one derivatives;Results in Chemistry;2024-07

3. Editorial: Machine Learning in Bio-cheminformatics;Journal of Chemical Information and Modeling;2024-04-08

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