Biology subjects

Sen, T. Z.

Publications and source records attributed to Sen, T. Z..

7 recordsLinked to original sources

bioRxiv2025

Genome assembly and population genomic analysis of Aegilops caudata uncover a rich source for disease and insect pest resistance for wheat improvement

Aegilops caudata L. is a rich source of resistance genes to major wheat pathogens and pests, yet its complex genome structure and lack of a reference sequence have hindered genetic analysis, gene discovery, and introgression. Here, we report a high-quality genome sequence assembly of Ae. caudata accession S740-69, integrated with optical mapping to precisely delineate introgressed segments from structurally altered genomic regions in wheat-Ae. caudata translocation lines carrying a novel stem rust resistance gene. Resequencing and phenotyping of 95 diverse accessions, coupled with k-mer-based association mapping, enabled population-level identification of several novel loci for resistance to wheat pests and diseases, such as greenbug and stem rust. Virus-induced gene silencing confirmed Aecau6C01G127270 (SrAect1) as the first functionally characterized stem rust resistance gene from Ae. caudata. This new genomic framework provides a foundation for systematic mining of resistance genes in this species and their efficient introgression into wheat.

genomics↗

bioRxiv2025

Why do some predicted protein structures fold poorly? Benchmarking AlphaFold, ESMFold, and Boltz in maize

Protein structure prediction tools have significantly reduced the time and cost to generate protein structures and accelerated protein discovery and design. However, plant proteins are underrepresented in sequence and structural datasets used to train these programs. To quantify the downstream impact of this deficiency, we benchmarked five structure-prediction programs (AlphaFold 2, AlphaFold 3, ESMFold, Boltz-1, and Boltz-2) across 417 well-characterized Zea mays genes. These "classical" genes represent a set of well-studied genes with known genetic and phenotypic effects. We generated structures for each gene using these programs and compared how sequence, structural, and evolutionary conservation impacted the structures confidence and geometric features. Proteins lacking conserved sequence and/or structural domains had on average 25% to 43% lower confidence scores than proteins having both domains. Proteome-wide phylostratigraphy revealed that species-specific proteins had substantially lower confidence scores than proteins conserved amongst angiosperms and Eukaryotes. Boltz-1 and ESMFold structures had the highest occurrence of structures with severe geometry issues, including overlapping atoms and unlikely bond angles. We also compared computational and experimental alignments of Arabidopsis, maize, rice, wheat, and soybean proteins from the Protein Data Bank and identified structures showing incongruences with experimental data. This study challenges the assertion that protein folding has been completely solved, and urges more investigation into benchmarking and standardized evaluation frameworks to improve model performance and assessment in agricultural crops.

bioinformatics↗

bioRxiv2025

Calcium Dependent Conformational Changes in Human Transglutaminase 2 and its Implications in Celiac Disease

Transglutaminase 2 (TG2) serves as a modifiable transamidating acyltransferase that precipitates calcium-induced protein alterations. The enzyme plays a crucial role in the cell and disease states, such as tissue repair, calcium signal transduction, celiac disease, and cancer. It is implicated in protein crosslinking and has been found in high concentrations in the small intestines of those with celiac disease. The function of TG2 hinges upon calcium ions binding to particular sites on the enzyme. In this study, we delve into the contribution of calcium-responsive transglutaminase 2 (TG2) in celiac disease, utilizing both molecular dynamics simulations and coarse-grained models, and investigate the impact of non-synonymous single nucleotide polymorphisms (nsSNPs) on TG2. Molecular dynamics reveal prominent conformational differences between the open and closed conformations. In the coarse-grained model, key residues are found adjacent to the active site in the open conformation, while in the closed conformation, key residues are distant from the active site. We further explore the functional impact of non-synonymous single nucleotide polymorphisms (nsSNPs) in TG2 using both sequence-based and structure-based computational tools. Through a consensus approach, we identify ten nsSNPs that are predicted to destabilize TG2 or alter its structural flexibility, with mutations such as R48H, E186Q, C277S, and E549G likely to influence active site accessibility and calcium coordination. The findings from this research enhances our understanding of the molecular processes underpinning celiac disease and helps facilitate innovative treatment approaches that target calcium-responsive TG2.

bioinformatics↗

bioRxiv2024

Fusarium Protein Toolkit: AI-powered tools to combat fungal threats to agriculture

BackgroundThe fungal genus Fusarium poses significant threats to food security and safety worldwide because it consists of numerous species that cause destructive diseases in crops, as well as mycotoxin contamination. The adverse effects of climate change are exacerbating some existing threats and causing new problems. These challenges highlight the need for innovative solutions, including the development of advanced tools to identify targets to control crop diseases and mycotoxin contamination incited by Fusarium. DescriptionIn response to these challenges, we developed the Fusarium Protein Toolkit (FPT, https://fusarium.maizegdb.org/), a web-based tool that allows users to interrogate the structural and variant landscape within the Fusarium pan-genome. FPT offers a comprehensive approach to understanding and mitigating the detrimental effects of Fusarium on agriculture. The tool displays both AlphaFold and ESMFold-generated protein structure models from six Fusarium species. The structures are accessible through a user-friendly web portal and facilitate comparative analysis, functional annotation inference, and identification of related protein structures. Using a protein language model, FPT predicts the impact of over 270 million coding variants in two of the most agriculturally important species, Fusarium graminearum, which causes Fusarium head blight and trichothecene mycotoxin contamination of cereals, and F. verticillioides, which causes ear rot and fumonisin mycotoxin contamination of maize. To facilitate the assessment of naturally occurring genetic variation, FPT provides variant effect scores for proteins in a Fusarium pan-genome constructed from 22 diverse species. The scores indicate potential functional consequences of amino acid substitutions and are displayed as intuitive heatmaps using the PanEffect framework. ConclusionFPT fills a knowledge gap by providing previously unavailable tools to assess structural and missense variation in proteins produced by Fusarium, the most agriculturally important group of mycotoxin-producing plant pathogens. FPT will deepen our understanding of pathogenic mechanisms in Fusarium, and aid the identification of genetic targets that can be used to develop control strategies that reduce crop diseases and mycotoxin contamination. Such targets are vital to solving the agricultural problems incited by Fusarium, particularly evolving threats affected by climate change. By providing a novel approach to interrogate Fusarium-induced crop diseases, FPT is a crucial step toward safeguarding food security and safety worldwide.

bioinformatics↗

bioRxiv2024

Chromosome-scale genome assembly of bread wheat's wild relative Triticum timopheevii

Wheat (Triticum aestivum) is one of the most important food crops with an urgent need for increase in its production to feed the growing world. Triticum timopheevii (2n = 4x = 28) is an allotetraploid wheat wild relative species containing the At and G genomes that has been exploited in many pre-breeding programmes for wheat improvement. In this study, we report the generation of a chromosome-scale reference genome assembly of T. timopheevii accession PI 94760 based on PacBio HiFi reads and chromosome conformation capture (Hi-C). The assembly comprised a total size of 9.35 Gb, featuring a contig N50 of 42.4 Mb, and 166,325 predicted gene models. DNA methylation analysis showed that the G genome had on average more methylated bases than the At genome. The G genome was also more closely related to the S genome of Aegilops speltoides than to the B genome of hexaploid or tetraploid wheat. In summary, the T. timopheevii genome assembly provides a valuable resource for genome-informed discovery of agronomically important genes for food security.

plant biology↗

bioRxiv2022

FASSO: An AlphaFold based method to assign functional annotations by combining sequence and structure orthology

Methods to predict orthology play an important role in bioinformatics for phylogenetic analysis by identifying orthologs within or across any level of biological classification. Sequence-based reciprocal best hit approaches are commonly used in functional annotation since orthologous genes are expected to share functions. The process is limited as it relies solely on sequence data and does not consider structural information and its role in function. Previously, determining protein structure was highly time-consuming, inaccurate, and limited to the size of the protein, all of which resulted in a structural biology bottleneck. With the release of AlphaFold, there are now over 200 million predicted protein structures, including full proteomes for dozens of key organisms. The reciprocal best structural hit approach uses protein structure alignments to identify structural orthologs. We propose combining both sequence- and structure-based reciprocal best hit approaches to obtain a more accurate and complete set of orthologs across diverse species, called Functional Annotations using Sequence and Structure Orthology (FASSO). Using FASSO, we annotated orthologs between five plant species (maize, sorghum, rice, soybean, Arabidopsis) and three distance outgroups (human, budding yeast, and fission yeast). We inferred over 270,000 functional annotations across the eight proteomes including annotations for over 5,600 uncharacterized proteins. FASSO provides confidence labels on ortholog predictions and flags potential misannotations in existing proteomes. We further demonstrate the utility of the approach by exploring the annotation of the maize proteome.

bioinformatics↗

bioRxiv2021

FINDER: An automated software package to annotate eukaryotic genes from RNA-Seq data and associated protein sequences

BackgroundGene annotation in eukaryotes is a non-trivial task that requires meticulous analysis of accumulated transcript data. Challenges include transcriptionally active regions of the genome that contain overlapping genes, genes that produce numerous transcripts, transposable elements and numerous diverse sequence repeats. Currently available gene annotation software applications depend on pre-constructed full-length gene sequence assemblies which are not guaranteed to be error-free. The origins of these sequences are often uncertain, making it difficult to identify and rectify errors in them. This hinders the creation of an accurate and holistic representation of the transcriptomic landscape across multiple tissue types and experimental conditions. Therefore, to gauge the extent of diversity in gene structures, a comprehensive analysis of genome-wide expression data is imperative. ResultsWe present FINDER, a fully automated computational tool that optimizes the entire process of annotating genes and transcript structures. Unlike current state-of-the-art pipelines, FINDER automates the RNA-Seq pre-processing step by working directly with raw sequence reads and optimizes gene prediction from BRAKER2 by supplementing these reads with associated proteins. The FINDER pipeline (1) reports transcripts and recognizes genes that are expressed under specific conditions, (2) generates all possible alternatively spliced transcripts from expressed RNA-Seq data, (3) analyzes read coverage patterns to modify existing transcript models and create new ones, and (4) scores genes as high- or low-confidence based on the available evidence across multiple datasets. We demonstrate the ability of FINDER to automatically annotate a diverse pool of genomes from eight species. ConclusionsFINDER takes a completely automated approach to annotate genes directly from raw expression data. It is capable of processing eukaryotic genomes of all sizes and requires no manual supervision - ideal for bench researchers with limited experience in handling computational tools.

bioinformatics↗