Targeting plant UBX proteins: AI-enhanced lessons from distant cousins

Zhang, Junrui; Vancea, Alexandra; Arold, Stefan T.

doi:10.1016/j.tplants.2022.05.012

Cited by 6 publications

(8 citation statements)

References 42 publications

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“…The resulting PUX-based NAnobody Degraders (P-NADs, pronounced /ˈpiː.nʌts/) P-NAD3, P-NAD4, and P-NAD5 (named according to their base being PUX3, 4, or 5) were 384 ± 4 residues long (Figure 1C,D). AlphaFold models and their predicted aligned errors plots (PAEs) were consistent with a "beads-on-string" model where all domains are flexibly linked, except for helix1 which was predicted to bind the SEP domain in all P-NADs (Supplementary Fig 1A , B) (Zhang, 2022). To probe the influence of the helix1-SHP fragment, we created an additional fragment for PUX5 where the helix and SEP domain were deleted (P-NAD5SHP-UBX, Figure 1C).…”

Section: Design Of Nanobody-ubx Degraderssupporting

confidence: 58%

“…The integration of in vitro and cellular assays enables the utilisation of P-NADs to explore the not fully understood functionalities of the various domains and motifs of UBX domain adaptor proteins (Zhang, 2022). For instance, our findings indicate that P-NADs can facilitate the unfolding of a non-ubiquitinated GFP by CDC48.…”

Section: Discussionmentioning

confidence: 99%

“…The only exception here is P-NAD5 SHP-UBX , which binds CDC48A-ND1 in a 1:1 complex, suggesting that the binding stoichiometry is influenced by the helix1-SEP fragment. PUX3, 4 and 5 contain an additional putative SHP box N-terminal to the SEP domain (F 168 XGXGRXL in P-NAD4, see Figure 1D ) which might contribute to binding (Zhang, 2022). However, more data is required to confirm the stoichiometry and its molecular basis.…”

Section: Discussionmentioning

confidence: 99%

“…Given that nanobodies can be obtained against many proteins, these nanobody degraders enable the creation of PROTACs for proteins that could not be targeted by small molecules. However, nanobody-based degraders still depend on the ubiquitination machinery of the target cell, introducing some limitations: (i) The target protein must have a well-positioned lysine residue on a surface site accessible to the E2 ubiquitin-conjugating enzyme (W. Zhang, 2022). (ii) Most organisms host a plethora of E3 ligases in their genome (> 600 in humans), each with highly different structures and features (Li, 2008).…”

Section: Introductionmentioning

confidence: 99%

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P-NADs:PUX-basedNAnobody Degraders for Ubiquitin-Independent Degradation of Target Proteins

Wang,

Chistov,

Zhang

et al. 2023

Preprint

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Targeted protein degradation (TPD) allows cells to maintain a functional proteome and to rapidly adapt to changing conditions. Methods that repurpose TPD for the deactivation of specific proteins have demonstrated significant potential in therapeutic and research applications. Most of these methods are based on proteolysis targeting chimaera (PROTAC) which link the protein target to an E3 ubiquitin ligase binding moiety, resulting in the ubiquitin-based degradation of the target protein. In this study, we introduce a method for ubiquitin-independent TPD based on nanobody-conjugated plant ubiquitin regulatory X domain-containing (PUX) adaptor proteins. We show that thePUX-basedNAnobodyDegraders (P-NADs) can unfold a target protein through the Arabidopsis and human orthologues of the CDC48 unfoldase without the need for ubiquitination or initiating motifs. Despite originating from plants, P-NAD plasmids can be transfected into a human cell line, where produced proteins use the endogenous CDC48 machinery for ubiquitin-independent TPD. Thus, P-NADs pave the road for ubiquitin-independent therapeutic TPD approaches. The P-NAD design combined with establishedin vitroand cellular assays make this system also a versatile platform for elucidating functional aspects of CDC48-based TPD in plants and animals.

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Section: Design Of Nanobody-ubx Degraderssupporting

confidence: 58%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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P-NADs:PUX-basedNAnobody Degraders for Ubiquitin-Independent Degradation of Target Proteins

Wang,

Chistov,

Zhang

et al. 2023

Preprint

Self Cite

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“…Hence, the PAE can reveal extents of domains and contacts between distant parts of a protein. More information on the PAE plot can be found in the "Predicted aligned error tutorial" copied below each entry of the AlphaFold database, and in Zhang et al (2022). The PAE plot is interactive and can be used to visualize individual domains in the 3D model by selecting (by a mouse-click and drag) a region of interest.…”

mentioning

confidence: 99%

Leveraging AI Advances and Online Tools for Structure‐Based Variant Analysis

et al. 2023

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Understanding how a gene variant affects protein function is important in life science, as it helps explain traits or dysfunctions in organisms. In a clinical setting, this understanding makes it possible to improve and personalize patient care. Bioinformatic tools often only assign a pathogenicity score, rather than providing information about the molecular basis for phenotypes. Experimental testing can furnish this information, but this is slow and costly and requires expertise and equipment not available in a clinical setting. Conversely, mapping a gene variant onto the three‐dimensional (3D) protein structure provides a fast molecular assessment free of charge. Before 2021, this type of analysis was severely limited by the availability of experimentally determined 3D protein structures. Advances in artificial intelligence algorithms now allow confident prediction of protein structural features from sequence alone. The aim of the protocols presented here is to enable non‐experts to use databases and online tools to investigate the molecular effect of a genetic variant. The Basic Protocol relies only on the online resources AlphaFold, Protein Structure Database, and UniProt. Alternate Protocols document the usage of the Protein Data Bank, SWISS‐MODEL, ColabFold, and PyMOL for structure‐based variant analysis. © 2023 The Authors. Current Protocols published by Wiley Periodicals LLC.Basic Protocol: 3D Mapping based on UniProt and AlphaFoldAlternate Protocol 1: Using experimental models from the PDBAlternate Protocol 2: Using information from homology modeling with SWISS‐MODELAlternate Protocol 3: Predicting 3D structures with ColabFoldAlternate Protocol 4: Structure visualization and analysis with PyMOL

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Deep learning methods for protein structure prediction

Qin,

Chen,

Peng

et al. 2024

MedComm – Future Medicine

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Protein structure prediction (PSP) has been a prominent topic in bioinformatics and computational biology, aiming to predict protein function and structure from sequence data. The three‐dimensional conformation of proteins is pivotal for their intricate biological roles. With the advancement of computational capabilities and the adoption of deep learning (DL) technologies (especially Transformer network architectures), the PSP field has ushered in a brand‐new era of “neuralization.” Here, we focus on reviewing the evolution of PSP from traditional to modern deep learning‐based approaches and the characteristics of various structural prediction methods. This emphasizes the advantages of deep learning‐based hybrid prediction methods over traditional approaches. This study also provides a summary analysis of widely used bioinformatics databases and the latest structure prediction models. It discusses deep learning networks and algorithmic optimization for model training, validation, and evaluation. In addition, a summary discussion of the major advances in deep learning‐based protein structure prediction is presented. The update of AlphaFold 3 further extends the boundaries of prediction models, especially in protein‐small molecule structure prediction. This marks a key shift toward a holistic approach in biomolecular structure elucidation, aiming at solving almost all sequence‐to‐structure puzzles in various biological phenomena.

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Targeting plant UBX proteins: AI-enhanced lessons from distant cousins

Cited by 6 publications

References 42 publications

P-NADs:PUX-basedNAnobody Degraders for Ubiquitin-Independent Degradation of Target Proteins

P-NADs:PUX-basedNAnobody Degraders for Ubiquitin-Independent Degradation of Target Proteins

Leveraging AI Advances and Online Tools for Structure‐Based Variant Analysis

Deep learning methods for protein structure prediction

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