Repeat proteins: designing new shapes and functions for solenoid folds

Gidley, Frances B; Parmeggiani, Fabio

doi:10.1016/j.sbi.2021.02.002

Cited by 4 publications

(4 citation statements)

References 57 publications

(64 reference statements)

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“…For example, solenoid proteins can bind to nucleic acids (4), catalyse reactions (5), display antifreeze activity (6) and bind to ligands (7). In addition, solenoid proteins are attractive design targets due to their modular architecture and have commercial applications in RNA editing (pentatricopeptide repeat proteins -EditForce) and cancer therapy (DARPins -Molecular Partners), in addition to numerous binding and materials applications developed by academic groups (8). The polypeptide chain in solenoid structures follows a helical path, where individual repeats pack against each other and depend on each other for folding (1).…”

Section: Introductionmentioning

confidence: 99%

SOLeNNoID: A Deep Learning Pipeline For Solenoid Residue Detection in Protein Structures

Nikov,

Pretorius,

Murray

2024

Preprint

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Solenoid proteins are a subset of tandem repeat proteins, which are structurally distinct from globular proteins. Solenoid proteins are defined by their modular, elongated structures, dependent on interactions between adjacent repeats. These proteins are found across all domains of life and have many important functions such as protein binding, enzymatic catalysis, ice binding and nucleic acid binding. Furthermore, engineered variants of solenoid proteins such as DARPins and designed PPR proteins have therapeutic commercial applications. In order to advance the study of natural solenoid proteins and the design of novel solenoid proteins, accurate tools for solenoid detection and annotation are required. As solenoid structures are more conserved than solenoid sequences and owing to recent developments in protein structure prediction, structure-based solenoid detection is preferred. Here we propose SOLeNNoID - a deep learning pipeline for solenoid residue prediction in protein structures. We cover all three solenoid sub-classes: alpha-, alpha/beta- and beta-solenoids. We use a CNN architecture to reason over protein distance matrices and compare our method to existing structure-based methods. Finally, we produce predictions on the entire PDB and demonstrate a 71 percent increase in solenoid-containing entries over the gold-standard RepeatsDB database using our method.

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Section: Introductionmentioning

confidence: 99%

SOLeNNoID: A Deep Learning Pipeline For Solenoid Residue Detection in Protein Structures

Nikov,

Pretorius,

Murray

2024

Preprint

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“…Highly diverse libraries such as Darpins (Binz et al, 2004), ɑReps (Guellouz et al, 2013;Urvoas et al, 2010), and repebodies (Lee et al, 2012) have been demonstrated to generate tight and specific protein binders (Boersma and Pluckthun, 2011). Due to their regularity and modularity, repeat proteins offer new possibilities for engineering protein assemblies and self-assembling nanostructures (Beloqui and Cortajarena, 2020;Bethel et al, 2022;Brunette et al, 2020;Brunette et al, 2015;Gidley and Parmeggiani, 2021;Parmeggiani et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

Engineering of brick and staple components for ordered assembly of synthetic repeat proteins

Miller

Urvoas

Gigant

et al. 2023

Journal of Structural Biology

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“…A significant fraction of TRs, however, remain unstructured. It is expected that new TRs can originate i. a. by replication slippage (Ellegren, 2004) or by duplication of intrinsically disordered regions (Delucchi et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

“…Recent years have seen an increased awareness of the importance of genomic tandem repeats (TRs) as functional features. TRs are adjacent repetitive units in genomic sequences, they are known for their associations with diseases and immune related functions and often play an important role in nucleic acid binding ( Kajava, 2012 ; Delucchi et al, 2020 ; Gidley and Parmeggiani, 2021 ). TRs are found in abundance throughout the three domains of life ( Marcotte et al, 1999 ; Delucchi et al, 2020 ).…”

Section: Introductionmentioning

confidence: 99%

TRAL 2.0: Tandem Repeat Detection With Circular Profile Hidden Markov Models and Evolutionary Aligner

et al. 2021

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The Tandem Repeat Annotation Library (TRAL) focuses on analyzing tandem repeat units in genomic sequences. TRAL can integrate and harmonize tandem repeat annotations from a large number of external tools, and provides a statistical model for evaluating and filtering the detected repeats. TRAL version 2.0 includes new features such as a module for identifying repeats from circular profile hidden Markov models, a new repeat alignment method based on the progressive Poisson Indel Process, an improved installation procedure and a docker container. TRAL is an open-source Python 3 library and is available, together with documentation and tutorials viavital-it.ch/software/tral.

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Repeat proteins: designing new shapes and functions for solenoid folds

Cited by 4 publications

References 57 publications

SOLeNNoID: A Deep Learning Pipeline For Solenoid Residue Detection in Protein Structures

SOLeNNoID: A Deep Learning Pipeline For Solenoid Residue Detection in Protein Structures

Engineering of brick and staple components for ordered assembly of synthetic repeat proteins

TRAL 2.0: Tandem Repeat Detection With Circular Profile Hidden Markov Models and Evolutionary Aligner

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