PreprintCited by 4Protein Structure and DynamicsLipid Membrane Structure and BehaviorMachine Learning in Bioinformatics

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Assembly of Protein Complexes In and On the Membrane with Predicted Spatial Arrangement Constraints

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Christoffer, Charles, Harini, Kannan, Archit, Gupta, Kihara, Daisuke·2023·Journal of Molecular Biology

Preprint — published as 10.1016/j.jmb.2024.168486 · Paper page

Abstract

Membrane proteins play crucial roles in various cellular processes, and their interactions with other proteins in and on the membrane are essential for their proper functioning. While an increasing number of structures of more membrane proteins are being determined, the available structure data is still sparse. To gain insights into the mechanisms of membrane protein complexes, computational docking methods are necessary due to the challenge of experimental determination. Here, we introduce Mem-LZerD, a rigid-body membrane docking algorithm designed to take advantage of modern membrane modeling and protein docking techniques to facilitate the docking of membrane protein complexes. Mem-LZerD is based on the LZerD protein docking algorithm, which has been constantly among the top servers in many rounds of CAPRI protein docking assessment. By employing a combination of geometric hashing, newly constrained by the predicted membrane height and tilt angle, and model scoring accounting for the energy of membrane insertion, we demonstrate the capability of Mem-LZerD to model diverse membrane protein-protein complexes. Mem-LZerD successfully performed unbound docking on 13 of 21 (61.9%) transmembrane complexes in an established benchmark, more than shown by previous approaches. It was additionally tested on new datasets of 44 transmembrane complexes and 92 peripheral membrane protein complexes, of which it successfully modeled 35 (79.5%) and 15 (16.3%) complexes respectively. When non-blind orientations of peripheral targets were included, the number of successes increased to 54 (58.7%). We further demonstrate that Mem-LZerD produces complex models which are suitable for molecular dynamics simulation. Mem-LZerD is made available at https://lzerd.kiharalab.org .

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Metadata

Authors
Christoffer, C., Harini, K., Archit, G., & Kihara, D.
Affiliations
  • Purdue University West Lafayette (US)
  • SRM Institute of Science and Technology (IN)
Corresponding author
Kihara, Daisuke
Venue
Journal of Molecular Biology
Publisher
openRxiv
Published
2023-10-23
Volume
436
Issue
6
Pages
168486-168486
ISSN
0022-2836 (ISSN-L)
OpenAlex
W4391649588
Cited by
4
References
1
Citations by year
2026: 1 · 2025: 2 · 2024: 1
FWCI
0.36
Type
journal article
Language
en
Open access
Locations
  • Journal of Molecular Biology (acceptedVersion)
  • bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
  • PubMed (publishedVersion)
  • PubMed Central (acceptedVersion)
  • Europe PMC (PubMed Central) (acceptedVersion)
  • PubMed Central (submittedVersion, cc-by-nc-nd)
Funders
  • National Science Foundation (1925643, 2137603, 2003635, 2138259, 2138296, 2138307, 2151678, 2138286, 2146026)
  • National Sleep Foundation
  • Purdue University (2138296, 21-38307, 2138286, 21-37603, CIS230021, 21-38259)
  • National Institutes of Health (2138307, 2138286, 2138296, 2137603, 2138259)
  • Science and Engineering Research Board
  • National Institute of General Medical Sciences (GM132024, R01GM123055, R01GM133840, T32GM132024)
  • Office of Advanced Cyberinfrastructure (#2138286, #2138296, 2138259, #2138307, 2137603)
  • Division of Civil, Mechanical and Manufacturing Innovation (CMMI1825941)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Lipid Membrane Structure and Behavior
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
Keywords
membrane protein complexes, membrane protein-protein interactions, rigid-body docking, peripheral membrane proteins, geometric hashing, molecular dynamics simulation

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