Citation: Olsvik HL, Johansen T (2023) AlphaFold-multimer predicts ATG8 protein binding motifs essential for autophagy analysis. PLoS Biol 21(2): e3002002. https://doi.org/10.1371/journal.pbio.3002002
Posted in: February 8, 2023
Copyright: © 2023 Olsvik, Johansen. That is an open entry article distributed below phrases. Creative Commons Attribution Licenseallowing unrestricted use, distribution, and copy in any medium, supplied the unique creator and supply are cited.
Financing: TJ’s analysis is funded by the Norwegian Analysis Council (grant 249884) and the Norwegian Most cancers Society (grant 190214). Funders had no position in examine design, information assortment and evaluation, choice to publish, or drafting.
Competing pursuits: The authors declared that there are not any competing pursuits.
Proteins are structural and govt macromolecules important for all times in all organic techniques. Perception into protein constructions is required for detailed mechanistic understanding of how they work and remedy completely different duties. Due to this fact, the flexibility to foretell three-dimensional (3D) protein constructions from major sequence info has been an open analysis query for over 50 years. Researching this holy grail of structural bioinformatics has not too long ago led to the event of AlphaFold2, an unbelievable AI-based construction prediction software developed by scientists from Google DeepMind. . How does AlphaFold2 work? Very briefly, it searches sequence databases to seek out sequences just like the enter, generates a number of sequence alignments, makes use of a neural community to extract info, and sends that info to a second neural community that computes a 3D construction. That is completed iteratively. on this situation PLOS BiologyIbrahim and colleagues present how the AlphaFold2 (AF2)-multimer can be utilized as an essential and highly effective new software to efficiently predict pseudo-LC3-interacting area (LIR) motifs (see beneath) in proteins concerned in autophagy processes. .
Macroautophagy (hereinafter known as autophagy) is an evolutionarily conserved lysosomal degradation pathway that maintains homeostasis and carries out high quality management by selectively eradicating broken or extra macromolecules, organelles, and intracellular pathogens. . Autophagy is a multistep course of wherein the double membrane construction is induced, expanded and encapsulated the cargo to degrade in a closed vesicle known as the autophagosome, which then fuses with the lysosomes that digest the cargo. Greater than 40 conserved autophagy-related (ATG) proteins are concerned on this course of. . These embrace the ATG8 household of ubiquitin-like modifiers, represented in people by three LC3- and three GABARAP household proteins. ATG8 proteins are roughly 120 amino acid lengthy proteins anchored to the membranes forming autophagosomes through a C-terminal phosphatidyl ethanolamine lipid moiety. Membrane-conjugated ATG8 proteins have been implicated in cargo sequestration, autophagosome biogenesis and motility, in addition to fusion with lysosomes. Their perform is mediated by particular interactions with proteins containing LIR motifs. generally known as the ATG8 interplay motif (AIM) in crops and fungi. . A canonical LIR motif is an unstructured or β-helix area of 10 to fifteen amino acids, often with a sequence of negatively charged and/or phosphorylatable residues, adopted by a conserved fragrant residue (both W, F, or Y) of the primary adopted by rather more. two much less conserved amino acids and a fourth conserved hydrophobic amino acid (I, L or V). Conserved fragrant and hydrophobic residues are connected in two shut hydrophobic pockets (HP1 and HP2) within the ATG8 proteins (HP1 and HP2).Figure 1).
Figure 1. Workflow of LIR estimates using AlphaFold2-multimer.
(A) The amino acid sequence of the candidate protein (here human selective autophagy receptor p62/SQSTM1) and the ATG8 protein (here human LC3B) are inserted into AlphaFold2-multimer, (B) this then provides the predicted structures of the two proteins in complex. They dock together with the LIR motif inserted into the hydrophobic pockets HP1 and HP2 at the so-called LIR docking site of LC3B, as shown in the zoomed-in appendix at bottom right. This allows both the identification of the already validated LIR motif, the LIR motif of p62/SQSTM1 (see mirror in a), and a structural model of how the tryptophan (W) and leucine (L) residues of the core LIR motif fit. To HP1 and HP2. (C) A sequence logo was generated by WebLogo from validated LIR motifs found in 24 autophagy proteins, with acidic amino acids in red, basic ones in blue, phosphorylatable serines (S) and threonines (T) in green, and hydrophobic amino acids in black.
LIR motifs had been first recognized at selective autophagy receptors that bind to each cargo and LC3 or GABARAP proteins, facilitating the uptake of cargo for selective autophagy. Nonetheless, different ATG proteins, adapters, transport proteins, some protein kinases, and autophagy substrates can even bind on to ATG8 proteins through a LIR motif. . The LIR/AIM motif has grow to be a spotlight in autophagy analysis, as ATG8 household proteins are central in selective autophagy and in each step of autophagy from autophagosome biogenesis, enlargement, transport and fusion to lysosomes. To determine and validate candidate LIR motifs in autophagy substrates, potential autophagy receptors or proteins concerned in autophagy processes are essential. How would you outline a brand new LIR motif? Till now, a normal process has been to make use of an everyday expression algorithm based mostly on a consensus sequence compiled from predefined motifs to determine candidate motifs within the linear amino acid sequence of a protein of curiosity. It’s the most used web-based iLIR utility. . iLIR provides a rating for the presence of the motif in a doubtlessly unstructured area, however doesn’t account for 3D construction as such. The expected LIR motif could also be dysfunctional as a result of it’s embedded with the incorrect spacing and geometry of the facet chains that inhibit docking to HP1 and HP2, or as a result of it’s a part of a helix. Furthermore, the present model of iLIR doesn’t have any penalty in opposition to choosing candidate motifs that often have binding-inhibiting residues within the core LIR equivalent to proline (P), glycine (G), lysine (Ok), and arginine (R). .
As talked about above, AlphaFold2 revolutionized the prediction of protein construction. . The flexibility to determine amino acid sequence motifs that bind to particular proteins is one other central drawback in molecular biology. The latest AlphaFold-Multimer (AF2-multimer) now predicts interplay surfaces between two proteins . on this situation PLOS BiologyIbrahim and colleagues reveal how the AF2-multimer can be utilized to efficiently predict LIR motifs of each identified and unknown LIR-containing proteins, in addition to to determine non-canonical LIR motifs (Figure 1) . This can be a large step ahead that the autophagy analysis neighborhood will profit from. Ibrahim and colleagues mixed protein modeling information from the AF2-multimer with phylogenetic evaluation of protein sequences and binding validation in protein-protein interplay experiments. In circumstances the place a protein has multiple LIR motif, it was smart to introduce level mutations into the established LIR motif to search for further LIR motifs. Searches for LIR motifs weren’t restricted to brief amino acid sequences. Sequences of 1,478 residues lengthy had been used to foretell interactions utilizing the AF2-multimer. The process works for protein sequences between kingdoms. The authors used this to determine ATG8-binding constructions in proteins which have already been proven to bind to ATG8s. The excessive accuracy of 90% for figuring out LIR motifs in 33 proteins is excellent. Potential of different related instruments like AF2-multimer and ColabFold , autophagy and LIR motifs are usually not lacking. It’s also of basic curiosity as a method and gear for locating sequence motifs and/or 3D constructions with a point of conservation that bind to particular proteins.
Are these 3D predictions within the poor man’s silico crystal constructions? The process can not substitute X-ray crystal constructions, NMR, or cryo-electron microscopy. Nonetheless, it’s a highly effective software for rapidly figuring out residues that could be essential for complicated formation after which experimentally testing them with site-directed mutagenesis and interplay research. This can clearly speed up research of construction exercise affiliation in autophagy analysis and past. The computation time to carry out AF2-multimer predictions is a limitation and is very depending on sequence size. Scanning full-length sequences of many proteins for LIRs would take quite a lot of time. At present, post-translational modifications, significantly phosphorylations, which can be typically essential within the regulation of protein-protein interactions are usually not processed by the AF2-multimer. Future growth envisions the inclusion of the flexibility to foretell the results of phosphorylations and calculate their binding affinity.
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