Biology subjects

Pretorius, D.

Publications and source records attributed to Pretorius, D..

4 recordsLinked to original sources

bioRxiv2025

Designing Novel Solenoid Proteins with In Silico Evolution

Solenoid proteins are elongated tandem repeat proteins with diverse biological functions, making them attractive targets for protein design. Advances in machine learning have transformed our understanding of sequence-structure relationships, enabling new approaches for de novo protein design. Here, we present an in silico evolution platform that couples a solenoid discriminator network with AlphaFold2 as an oracle within a genetic algorithm. Starting from random sequences, we design -, {beta}-, and {beta}-solenoid backbones, generating structures that span natural and novel solenoid space. We experimentally characterise 41 solenoid designs, with -solenoids consistently folding as intended, including one structurally validated design that closely matches the design model. All {beta}-solenoids initially failed, reflecting the difficulty of designing {beta}-strand majority proteins. By introducing terminal capping elements and refining designs based on earlier experimental screens, we generate two {beta}-solenoids that have biophysical properties consistent with their designs. Our approach achieves fold-specific hallucination-based design without depending on explicit structural templates.

synthetic biology↗

bioRxiv2025

Exploring the structural diversity and evolution of the D1 subunit of photosystem II using AlphaFold and Foldtree

While our knowledge of photosystem II has expanded to time-resolved atomic details, the diversity of experimental structures of the enzyme remains limited. Recent advances in protein structure prediction with AlphaFold offer a promising approach to fill this gap in structural diversity in non-model systems. This study used AlphaFold to predict the structures of the D1 protein, the core subunit of photosystem II, across a broad range of photosynthetic organisms. The prediction produced high-confidence structures, and structural alignment analyses highlighted conserved regions across the different D1 groups, which were in line with high pLDDT scoring regions. In contrast, varying pLDDT in the DE loop and terminal regions appear to correlate with different degrees of structural flexibility or disorder. Subsequent structural phylogenetic analysis provided a phylogeny that is in good agreement with previous sequence-based studies. Moreover, the phylogeny supports a parsimonious scenario in which far-red D1 and D1INT evolved from the ancestral form of G4 D1. This study demonstrates the potential of AlphaFold in studies on structural diversity and the evolution of photosynthesis.

bioinformatics↗

bioRxiv2024

RepeatParam: Algorithm for Parameterising Repeat Proteins and Analysis of Repeat Protein Architectures

MotivationTandem repeat proteins consist of repetitive sequence and structure motifs and have diverse roles in Nature for molecular recognition and signalling. The architecture of repeat proteins can be described using simple helical parameters. Understanding these structural features can inform both the function of these proteins and be used to parametrically design new proteins. Despite their importance, no existing program is capable of completely parameterising repeat proteins. ResultsHere we describe a novel repeat protein parameterisation algorithm, RepeatParam, and a comprehensive repeat protein dataset. RepeatParam determines a helix that defines the global protein architecture and a superhelix that describes the relationship between consecutive repeats. We analyse the relationships between helical parameters and families of different repeat proteins. Availabilityhttps://github.com/dpretorius/repeat-protein-parameterisation

biochemistry↗

bioRxiv2024

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

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. GitHubhttps://github.com/gnik2018/SOLeNNoID ZenodoTBC

bioinformatics↗