Biology subjects

Peddinti, G.

Publications and source records attributed to Peddinti, G..

5 recordsLinked to original sources

bioRxiv2026

ActSeekN: A Structural-Motif-Based Pipeline for Interpretable Enzyme Function Annotation

Accurate enzyme function annotation remains a major bottleneck in genome analysis despite the rapid expansion of available protein sequence and structure data. Most existing methods rely on sequence similarity or machine-learning representations, which often perform poorly for proteins with low sequence identity or convergent evolutionary histories. Because enzymatic activity is determined by the three-dimensional arrangement of catalytic and binding-site residues, structure-based approaches offer a mechanistically grounded alternative. However, their broader application has been constrained by the limited size and coverage of curated active-site reference databases. To address this challenge, we developed ActSeekN, a structural-motif-based functional annotation pipeline that combines the ActSeek active-site search algorithm with a newly constructed large-scale reference database derived from AlphaFold-predicted structures, UniProt annotations, and curated catalytic residue information. This framework enables rapid and scalable identification of conserved catalytic motifs across structurally related proteins, allowing function to be transferred on the basis of local three-dimensional catalytic geometry rather than global sequence similarity. In this way, ActSeekN overcomes a central limitation of previous structure-based methods by expanding the searchable space of catalytic motifs while retaining mechanistic interpretability. Benchmarking against state-of-the-art machine-learning approaches demonstrates competitive or superior performance. Applications to yeast, human, and Trichoderma reesei proteomes refine existing annotations, complete partial EC assignments, and identify previously unrecognized enzymatic functions, highlighting ActSeekN as a powerful tool for genome annotation and biotechnology. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=99 SRC="FIGDIR/small/720574v1_ufig1.gif" ALT="Figure 1"> View larger version (37K): org.highwire.dtl.DTLVardef@f5da16org.highwire.dtl.DTLVardef@c0faa4org.highwire.dtl.DTLVardef@18765fforg.highwire.dtl.DTLVardef@3960b9_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2023

Reconstruction of compartmentalized genome-scalemetabolic models using deep learning for over 800fungi

Eukaryotic metabolism is organized into subcellular compartments enclosed by lipid-membranes. Since most metabolites do not move freely across the membranes, the compartmentalization influences metabolic pathway connectivities. Thus, compartmentalizing the pathways also in genome-scale metabolic models (GEMs) is essential for accurate predictions of eukaryotic cells metabolic phenotypes. Compartmentalization has manually been introduced into the model Eukaryote GEMs like for yeast Saccharomyces cerevisiae. Non-model organisms GEMs can be automatically reconstructed from genome data. However, the existing GEM reconstruction methods do not introduce compartmentalization into the models. To that end, we integrated our novel deep learning protein localization prediction and protein functional annotation into a top-down GEM reconstruction for automatically creating species-specific compartmentalized GEMs. We developed also a universal fungal GEM for top-down reconstruction of species-specifically compartmentalized GEMs and reconstructed models for 834 species. The novel protein localization prediction outperformed the state of the art in classifying proteins into multiple compartments and integrating the enzyme localization predictions to functional annotations improved the top-down model reconstruction. Interestingly, the clustering of the fungal GEMs reconstructed with predicted species-specific enzyme localization resembled more closely the phylogenetic relationships of the species than that of the GEMS without the compartmentalization. Compartmentalization of metabolism in fungi differs from other eukaryotes but the enzyme subcellular localizations vary also among fungal species. The Fungal kingdom encompasses species important for human health, environment, and industrial biotechnology. The reconstructed fungal GEM set offers valuable tools for e.g., predicting metabolic phenotypes of e.g., mushrooms or single-cellular fungi, the roles of eukaryotes in microbial communities, designs optimizing eukaryotic hosts for industrial chemical production, and identifying drug targets against pathogenic fungi. Beyond fungi, the compartmentalized GEM reconstruction method allows combining other universal GEMs for developing cell type specific GEMs for higher eukaryotes including plants, insects, and mammals.

systems biology↗

bioRxiv2021

Lager yeast design through meiotic segregation of a fertile Saccharomyces cerevisiae x Saccharomyces eubayanus hybrid

Yeasts in the lager brewing group are closely related and consequently do not exhibit significant genetic variability. Here, an artificial Saccharomyces cerevisiae x Saccharomyces eubayanus tetraploid interspecies hybrid was created by rare mating, and its ability to sporulate and produce viable gametes was exploited to generate phenotypic diversity. Four spore clones obtained from a single ascus were isolated, and their brewing-relevant phenotypes were assessed. These F1 spore clones were found to differ with respect to fermentation performance under lager brewing conditions (15 {degrees}C, 15 {degrees}Plato), production of volatile aroma compounds, flocculation potential and temperature tolerance. One spore clone, selected for its rapid fermentation and acetate ester production was sporulated to produce an F2 generation, again comprised of four spore clones from a single ascus. Again, phenotypic diversity was introduced. In two of these F2 clones, the fermentation performance was maintained and acetate ester production was improved relative to the F1 parent and the original hybrid strain. Strains also performed well in comparison to a commercial lager yeast strain. Spore clones varied in ploidy and chromosome copy numbers, and faster wort fermentation was observed in strains with a higher ploidy. An F2 spore clone was also subjected to 10 consecutive wort fermentations, and single cells were isolated from the resulting yeast slurry. These isolates also exhibited variable fermentation performance and chromosome copy numbers, highlighting the instability of polyploid interspecific hybrids. These results demonstrate the value of this natural approach to increase the phenotypic diversity of lager brewing yeast strains. Contribution to the fieldLager beer fermentations have traditionally been carried out with natural S. cerevisiae x S. eubayanus hybrids. These strains possess both the ability to tolerate low temperatures and the ability to utilize efficiently wort sugars. However, being closely related, strains within the group exhibit limited phenotypic variability. Since the recent discovery of wild strains of S. eubayanus, it has been possible to generate lager yeast hybrids artificially, thereby increasing the genetic and phenotypic diversity of lager brewing strains. Here, to demonstrate the potential for further increased diversity, a constructed tetraploid hybrid was sporulated and spore clones derived from a single ascus were evaluated with respect to fermentation performance (sugar utilization, stress tolerance and volatile aroma synthesis). Meiosis introduced variability in a number of key parameters. One fertile spore clone from this F1 generation was sporulated to introduce further diversity and to demonstrate the potential of clone selection in steering phenotypes in a desirable direction. Genome instability of hybrids was observed, but this can be exploited to further increase diversity. This was demonstrated by assessing performance of variants isolated after ten consecutive rounds of fermentation. The approach allows for the introduction of phenotypic diversity without the need for targeted genetic modification.

microbiology↗

bioRxiv2020

Production of biopolymer precursors beta-alanine and L-lactic acid from CO2 with metabolically versatile Rhodococcus opacus DSM 43205

Hydrogen oxidizing autotrophic bacteria are promising hosts for CO2 conversion into chemicals. In this work, we engineered the metabolically versatile lithoautotrophic bacterium Rhodococcus opacus strain DSM 43205 for synthesis of polymer precursors. Aspartate decarboxylase (panD) or lactate dehydrogenase (ldh) were expressed for beta-alanine or L-lactic acid production, respectively. The heterotrophic cultivations on glucose produced 25 mg L-1 beta-alanine and 742 mg L-1 L-lactic acid, while autotrophic cultivations with CO2, H2 and O2 resulted in the production of 1.8 mg L-1 beta-alanine and 146 mg L-1 L-lactic acid. Beta-alanine was also produced at 345 {micro}g L-1 from CO2 in electrobioreactors, where H2 and O2 were provided by water electrolysis. This work demonstrates that R. opacus DSM 43205 can be readily engineered to produce chemicals from CO2 and provides base for its further metabolic engineering.

bioengineering↗

bioRxiv2018

Validation and automation of a high-throughput multi-targeted method for semi-quantification of endogenous metabolites from different biological matrices using tandem mass spectrometry

The use of metabolomics profiling to understand metabolism under different physiological states has increased in recent years, which created the need for robust analytical platforms. Here, we present a validated method for targeted and semi-quantitative analysis of 102 polar metabolites that covers major metabolic pathways from 24 classes in a single 17.5-min assay. The method has been optimized for a wide range of biological matrices from various organisms, and involves automated sample preparation, and data processing using in-house developed R package. To ensure reliability, the method was validated for accuracy, precision, selectivity, specificity, linearity, recovery, and stability according to European Medicines Agency guidelines. We demonstrated excellent repeatability of the retention times (CV<4%), calibration curves (R2[≥]0.980) in their respective wide dynamic concentration ranges (CV<3%), and concentrations (CV<25%) of quality control samples interspersed within 25 batches analyzed over a period of one-year. The robustness was demonstrated through high correlation between metabolite concentrations measured using our method and NIST reference values (R2=0.967), including cross-platform comparability against the BIOCRATES AbsoluteIDQp180 kit (R2=0.975) and NMR analyses (R2=0.884). We have shown that our method can be successfully applied in many biomedical research fields and clinical trials, including epidemiological studies for biomarker discovery. In summary, a thorough validation demonstrated that our method is reproducible, robust, reliable, and suitable for metabolomics studies.

biochemistry↗