Biology subjects

Fisk, I.

Publications and source records attributed to Fisk, I..

4 recordsLinked to original sources

bioRxiv2025

TMT-based quantitative proteomic assessment of Vicia sativa induced neurotoxicity by β-cyano-L-alanine and γ-glutamyl-β-cyano-L-alanine in SH-SY5Y cells.

{beta}-Cyano-L-alanine (BCA) and {gamma}-glutamyl-{beta}-cyano-L-alanine (GBCA) are the primary antinutritional compounds present within the seeds of the high protein and drought tolerant orphan legume Vicia sativa. Evidence of neurotoxicity is limited to symptom analysis from animal feed trials whilst the molecular mechanisms underlying suspected neurotoxicity in monogastric animals are largely unknown. In this study, we first optimised an in vitro cell-based assay for rapid testing of BCA and GBCA toxicity in the retinoic acid differentiated SH-SY5Y human neuroblastoma cells. Using this system, we then performed proteomics analyses to determine dysregulated expression of proteins in BCA or GBCA treated of differentiated SH-SY5Y cells. Our findings indicate that BCA affects expression of proteins involved in DNA damage and translation whilst GBCA treatment causes dysregulation of those involved in mitosis and cell cycle. Following BCA treatment, we identified changes in many proteins previously suggested to have close association with neurodegenerative diseases including amyotrophic lateral sclerosis and Alzheimers disease as well as cancers. Following GBCA treatment, dysregulation of proteins involved in apoptosis pathways was observed. Finally, the lack of common dysregulated proteins and pathways in BCA or GBCA treated cells indicated that they most likely cause neurotoxicity via distinct mechanisms. SignificanceAntinutritional compounds or toxins limit the use of many potential grain crops including V. sativa (Common Vetch), and BCA and GBCA are the two principal toxic compounds in Common Vetch grain. Vetch grain toxicity is associated with neurotoxic symptoms in animals. Characterizing the proteome of BCA- and GBCA-treated neural cells gives insights into the excitotoxicity mechanism and identification of biomarkers for screening higher quality grain. Quantitative tandem mass tag (TMT) mass spectrometry-based proteome profiling of BCA treated neural cells characterized 6,827 proteins, of which 26 were significantly up-regulated and 73 significantly downregulated. However, TMT proteome analysis of GBCA treated cells identified 76 significantly up regulated and 86 downregulated proteins. These results provide insight into the toxicity of BCA and GBCA and enable future identification of putative direct protein targets. We describe for the first time BCA playing a role in modulating the expression DNA damage proteins and translation, and GBCA playing a role in modulating the expression of mitosis and cell cycle proteins, elucidating the mechanisms of plant-derived toxins in mammalian cell neurotoxicity HighlightsO_LIEstablished in vitro cell-based toxicity assay for {beta}-cyano-L-alanine and {gamma}-glutamyl-{beta}-cyano-L-alanine. C_LIO_LI{beta}-Cyano-L-alanine treatment dysregulated many proteins associated with DNA damage in retinoic acid differentiated SH-SY5Y cells. C_LIO_LI{gamma}-Glutamyl-{beta}-cyano-L-alanine treatment dysregulated proteins associated with cell cycle. C_LI

pharmacology and toxicology↗

bioRxiv2022

OpenFold: Retraining AlphaFold2 yields new insights into its learning mechanisms and capacity for generalization

AlphaFold2 revolutionized structural biology with the ability to predict protein structures with exceptionally high accuracy. Its implementation, however, lacks the code and data required to train new models. These are necessary to (i) tackle new tasks, like protein-ligand complex structure prediction, (ii) investigate the process by which the model learns, which remains poorly understood, and (iii) assess the models generalization capacity to unseen regions of fold space. Here we report OpenFold, a fast, memory-efficient, and trainable implementation of AlphaFold2. We train OpenFold from scratch, fully matching the accuracy of AlphaFold2. Having established parity, we assess OpenFolds capacity to generalize across fold space by retraining it using carefully designed datasets. We find that OpenFold is remarkably robust at generalizing despite extreme reductions in training set size and diversity, including near-complete elisions of classes of secondary structure elements. By analyzing intermediate structures produced by OpenFold during training, we also gain surprising insights into the manner in which the model learns to fold proteins, discovering that spatial dimensions are learned sequentially. Taken together, our studies demonstrate the power and utility of OpenFold, which we believe will prove to be a crucial new resource for the protein modeling community.

bioinformatics↗

bioRxiv2022

Sequence-structure-function relationships in the microbial protein universe

Abstract / SummaryFor the past half-century, structural biologists relied on the notion that similar protein sequences give rise to similar structures and functions. While this assumption has driven research to explore certain parts of the protein universe, it disregards spaces that dont rely on this assumption. Here we explore areas of the protein universe where similar protein functions can be achieved by different sequences and different structures. We predict [~]200,000 structures for diverse protein sequences from 1,003 representative genomes1 across the microbial tree of life, and annotate them functionally on a per-residue basis. Structure prediction is accomplished using the World Community Grid, a large-scale citizen science initiative. The resulting database of structural models is complementary to the AlphaFold database, with regards to domains of life as well as sequence diversity and sequence length. We identify 148 novel folds and describe examples where we map specific functions to structural motifs. We also show that the structural space is continuous and largely saturated, highlighting the need for shifting the focus from obtaining structures to putting them into context, to transform all branches of biology, including a shift from sequence-based to sequence-structure-function based meta-omics analyses.

bioinformatics↗

bioRxiv2020

Common Vetch: a drought tolerant, high protein neglected leguminous crop with potential as a sustainable food source.

Global demand for protein is predicted to increase by 50% by 2050. To meet the increasing demand whilst ensuring sustainability, protein sources that generate low-greenhouse gas emissions are required and protein-rich legume seeds have the potential to make a significant contribution. Legumes like common vetch (Vicia sativa) that grow in marginal cropping zones and are drought tolerant and resilient to changeable annual weather patterns, will be in high demand as the climate changes. In common vetch, the inability to eliminate the {gamma}-glutamyl-{beta}-cyano-alanine (GBCA) toxin present in the seed has hindered its utility as a human and animal food for many decades, leaving this highly resilient species an "orphan" legume. However, the availability of the vetch genome and transcriptome data together with the application of CRISPR-Cas genome editing technologies lay the foundations to eliminate the GBCA toxin constraint. In the near future, we anticipate that a zero-toxin vetch variety will become a significant contributor to global protein demand.

plant biology↗