AI-based AlphaFold2 significantly expands the structural space of the autophagy pathway

ABSTRACT

A structural understanding of entire cellular processes has been an uncharted realm, until now. The artificial intelligence-based tool AlphaFold2 (AF2) has substantially changed the prediction accuracy, and predicted models of entire proteomes are now available. Here, we have examined AF2’s prediction of 38 core macroautophagic/autophagic proteins and 378 interacting partners representing the human autophagic interactome. Prior to AF2, ~50% of the proteins lacked atomistic level resolution and we found significant improvement in structural coverage by AF2, with an addition of ~ 47% of the residues modeled with reasonable confidence. We also augmented this structural information with μs timescale molecular dynamics simulations, in particular, ATG2, ATG10, and ATG14. ATG2A, a bipartite membrane protein with rodlike architecture was predicted with high accuracy and our simulations revealed dynamic transitions of cavity-lining residues that might play a critical role in regulating lipid transfer. In addition, a promising approach of multimeric prediction by AF2 revealed the architecture of ATG7-ATG10, a tetrameric complex that participates in conjugation machinery in autophagy. By combining computational and experimental approaches, we demonstrated that three salt bridges were crucial to ATG7-ATG10 complex formation and mutating these residues abrogated the binding. We have also generated a web resource with curated AF2 structural models, simulated conformational ensemble, and structural analysis that will be highly pertinent to the autophagy community. Altogether, our work presents a robust pipeline to utilize AF2 as a tool for a starting point to provide the dynamic behavior of molecules in a given biological pathway.

Abbreviations

AF2: AlphaFold2; AF2-Mult: AlphaFold2 multimer; ATG: autophagy-related; CTD: C-terminal domain; ECTD: extreme C-terminal domain; FR: flexible region; MD: molecular dynamics; NTD: N-terminal domain; pLDDT: predicted local distance difference test; UBL: ubiquitin-like

KEYWORDS:

• AlphaFold2
• ATG7-ATG10 complex
• autophagic interactome
• autophagy
• molecular dynamics simulations

Acknowledgements

This work was supported by funding from CSIR, and the Department of Science and Technology (DST). NM is thankful to DST-SERB NPDF grant, SK to CSIR for fellowship and LT acknowledges the support from intramural CSIR-IGIB grant OLP 1163 and India Alliance DBT/Wellcome trust grant (IA/21/2/505925). NJ would like to thank DBT Ramalingaswami fellowship grant (35/2019). We are grateful to IBDC, Regional Centre for Biotechnology, for providing supercomputing facility. SV acknowledges the RCB-GSK Ph.D program in Bioinformatics and Biostatistics at RCB, Faridabad for fellowship. We also acknowledge the compute support from CSIR-IGIB and appreciate Mr. Vikas Pandey for IT support at CSIR IGIB. We would like to thank Dr. Souvik Maiti for providing experimental infrastructural support, and Dr. Debasisa Mohanty for helping in benchmarking of simulations. We would like to thank all the CSB lab members for support during analysis.

Disclosure statement

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Data availability statement

The data files related to AF2 analysis, and AF2 predicted structural models, and MD simulations have been made available at https://rapsap.igib.res.in.

Supplementary data

Supplemental data for this article can be accessed online at https://doi.org/10.1080/15548627.2023.2238578

Additional information

Funding

The work was supported by the Department of Biotechnology, Ministry of Science and Technology, India [35/2019]; CSIR [OLP 1163, IA/21/2/505925]; DST-SERB [PDF/2020/002046].