An unexpected INAD PDZ tandem-mediated PLCβ binding in Drosophila photo receptors
Abstract
INAD assembles key enzymes of Drosophila compound eye photo-transduction pathway into a supramolecular complex, supporting efficient and fast light signaling. However, the molecular mechanism governing the interaction between INAD and NORPA (phospholipase Cβ, PLCβ), a key step for the fast kinetics of the light signaling, is not known. Here, we show that the NORPA C-terminal coiled-coil domain and PDZ-binding motif (CC-PBM) synergistically bind to INAD PDZ45 tandem with an unexpected mode and unprecedentedly high affinity. Guided by the INAD/NORPA complex structure, we discover that INADL is likely a mammalian counterpart of INAD. The INADL PDZ89 tandem specifically binds to PLCβ4 with a strikingly similar mode to that of the INAD/NORPA complex as revealed by the structure of the INADL PDZ89/PLCβ4 CC-PBM complex. Therefore, our study suggests that the highly specific PDZ tandem/PLCβ interactions are an evolutionarily conserved mechanism in PLCβ signaling of the animal kingdom.
Data availability
Diffraction data have been deposited at PDB under the accession numbers 6IRB, 6IRC, 6IRD, and 6IRE.
-
C-terminal domain of Drosophila phospholipase b NORPA, methylatedProtein Data Bank, 6IRC.
-
Complex structure of INADL PDZ89 and PLCb4 C-terminal CC-PBMProtein Data Bank, 6IRD.
-
Complex structure of INAD PDZ45 and NORPA CC-PBMProtein Data Bank, 6IRE.
Article and author information
Author details
Funding
Minister of Science and Technology of China (2014CB910204)
- Mingjie Zhang
Research Grant Council of Hong Kong (C6004-17)
- Mingjie Zhang
Shenzhen Basic Research Grant (JCYJ20170411090807530)
- Wei Liu
Shenzhen Basic Research Grant (JCYJ20160427185712266)
- Wei Liu
National Natural Science Foundation of China (31670765)
- Wei Liu
National Natural Science Foundation of China (31870746)
- Wei Liu
Chinese Academy of Sciences Key Project (XDPB10)
- Tian Xue
Chinese Academy of Sciences Key Project (XDB02010000)
- Tian Xue
Research Grant Council of Hong Kong (AoE-M09-12)
- Mingjie Zhang
National Key R&D Program of China (2016YFA0501903)
- Mingjie Zhang
Natural Science Foundation of Guangdong Province (2016A030312016)
- Mingjie Zhang
Shenzhen Basic Research Grant (JCYJ20160229153100269)
- Wei Liu
National Natural Science Foundation of China (81790644)
- Tian Xue
Chinese Academy of Sciences Key Project (XDA16020603)
- Tian Xue
National Key Basic Research Program of China (2016YFA0400900)
- Tian Xue
GRF grant from RGC of Hong Kong (16104518)
- Fei Ye
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Copyright
© 2018, Ye et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
Metrics
-
- 1,119
- views
-
- 210
- downloads
-
- 10
- citations
Views, downloads and citations are aggregated across all versions of this paper published by eLife.
Download links
Downloads (link to download the article as PDF)
Open citations (links to open the citations from this article in various online reference manager services)
Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)
Further reading
-
- Structural Biology and Molecular Biophysics
The relationship between protein dynamics and function is essential for understanding biological processes and developing effective therapeutics. Functional sites within proteins are critical for activities such as substrate binding, catalysis, and structural changes. Existing computational methods for the predictions of functional residues are trained on sequence, structural, and experimental data, but they do not explicitly model the influence of evolution on protein dynamics. This overlooked contribution is essential as it is known that evolution can fine-tune protein dynamics through compensatory mutations either to improve the proteins’ performance or diversify its function while maintaining the same structural scaffold. To model this critical contribution, we introduce DyNoPy, a computational method that combines residue coevolution analysis with molecular dynamics simulations, revealing hidden correlations between functional sites. DyNoPy constructs a graph model of residue–residue interactions, identifies communities of key residue groups, and annotates critical sites based on their roles. By leveraging the concept of coevolved dynamical couplings—residue pairs with critical dynamical interactions that have been preserved during evolution—DyNoPy offers a powerful method for predicting and analysing protein evolution and dynamics. We demonstrate the effectiveness of DyNoPy on SHV-1 and PDC-3, chromosomally encoded β-lactamases linked to antibiotic resistance, highlighting its potential to inform drug design and address pressing healthcare challenges.
-
- Biochemistry and Chemical Biology
- Structural Biology and Molecular Biophysics
The phase separation of intrinsically disordered proteins is emerging as an important mechanism for cellular organization. However, efforts to connect protein sequences to the physical properties of condensates, that is, the molecular grammar, are hampered by a lack of effective approaches for probing high-resolution structural details. Using a combination of multiscale simulations and fluorescence lifetime imaging microscopy experiments, we systematically explored a series of systems consisting of diblock elastin-like polypeptides (ELPs). The simulations succeeded in reproducing the variation of condensate stability upon amino acid substitution and revealed different microenvironments within a single condensate, which we verified with environmentally sensitive fluorophores. The interspersion of hydrophilic and hydrophobic residues and a lack of secondary structure formation result in an interfacial environment, which explains both the strong correlation between ELP condensate stability and interfacial hydrophobicity scales, as well as the prevalence of protein-water hydrogen bonds. Our study uncovers new mechanisms for condensate stability and organization that may be broadly applicable.