Multimodal generative neural networks and molecular dynamics based identification of PDK1 PIF-pocket modulators-Reference-Cited by-同舟云学术

Multimodal generative neural networks and molecular dynamics based identification of PDK1 PIF-pocket modulators

Published:2022 Issue:9 Volume:7 Page:1085-1092
ISSN:2058-9689
Container-title:Molecular Systems Design & Engineering
language:en
Short-container-title:Mol. Syst. Des. Eng.

Author:

Vennila Kailasam N.¹^ORCID,Elango Kuppanagounder P.¹^ORCID

Affiliation:

1. The Gandhigram Rural Institute-Deemed to be University, Tamilnadu, 624302 India

Abstract

Dynamics associated generative neural networks can predict novel PPI modulators for PDK1.

Funder

Council of Scientific and Industrial Research, India

Publisher

Royal Society of Chemistry (RSC)

Subject

Materials Chemistry,Industrial and Manufacturing Engineering,Process Chemistry and Technology,Energy Engineering and Power Technology,Biomedical Engineering,Chemical Engineering (miscellaneous),Chemistry (miscellaneous)

Link

http://pubs.rsc.org/en/content/articlepdf/2022/ME/D2ME00051B

Reference32 articles.

1. High resolution crystal structure of the human PDK1 catalytic domain defines the regulatory phosphopeptide docking site

2. The chemical diversity and structure-based discovery of allosteric modulators for the PIF-pocket of protein kinase PDK1

3. Bidirectional Allosteric Communication between the ATP-Binding Site and the Regulatory PIF Pocket in PDK1 Protein Kinase

4. Discovery of PDK1 Kinase Inhibitors with a Novel Mechanism of Action by Ultrahigh Throughput Screening

5. Identification of Allosteric PIF-Pocket Ligands for PDK1 using NMR-Based Fragment Screening and1H-15N TROSY Experiments

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