Biology subjects

Kadyan, S.

Publications and source records attributed to Kadyan, S..

4 recordsLinked to original sources

bioRxiv2026

Sex Effect of E-liquid Constituents on Atherosclerosis and Gut Dysbiosis in ApoE-/- Mice

RationaleThe role of sex in the effects of vaping and individual aerosolized e-liquid constituents on atherosclerosis, vascular aging, and gut microbiome remodeling remains poorly characterized. ObjectiveTo determine the contribution of e-cigarette aerosol components to vascular senescence, atherosclerosis, and gut microbiome dysbiosis in ApoE-/- mice and vascular smooth muscle cell (VSMC) viability and senescence. MethodsMale and female ApoE-/- mice were exposed to e-liquid constituents (vehicle, vehicle plus nicotine, and vehicle plus nicotine plus menthol) for 48 minutes per day, 5 days per week for 16 weeks, with vascular pathology assessed in vivo. VSMCs isolated from aortas of wild-type and ApoE-/- male and female mice were exposed to aerosolized e-liquids and evaluated for cellular senescence. ResultsExposure to all tested e-liquid formulations, including vehicle, nicotine-containing, and menthol-containing aerosols, increased atherosclerosis in both male and female mice, with the most robust effects observed in the nicotine-containing formulation and in the descending aorta. Females exhibited greater sensitivity to e-liquid exposure, with increased plaque accumulation in both the aortic arch and descending aorta, while the addition of menthol was associated with reduced plaque burden compared with nicotine alone in both sexes. Novel findings show that e-liquid exposure also altered gut microbial composition in a sex- and exposure-dependent manner, with nicotine causing the greatest dysbiosis and menthol exerting modulatory, but not restorative, effects. Notably, Alloprevotella emerged as a key discriminating genus associated with reduced plaque burden, supporting a potential link between gut microbial remodeling, inflammatory regulation, and atherosclerosis. ConclusionsThese findings demonstrate that individual e-liquid aerosol components increase atherosclerosis and alter the gut microbiome in a sex-specific manner, with nicotine producing the most pronounced pro-atherogenic effects and the addition of menthol reducing these effects, without eliminating overall atherosclerotic risk.

physiology↗

bioRxiv2025

On inputs to deep learning for RNA 3D structure prediction

Today, there are several effective deep learning models for predicting the 3D structure of proteins. Building on their success, models have been developed for predicting the 3D structure of non-coding RNAs. Unfortunately, these models are much less accurate than their protein counterparts. In this paper, we highlight differences between protein and RNA structure, and demonstrate methods for deep learning targeted at addressing those differences, with the aim of prompting discussion on these topics. We present an RNA-specific pipeline for generating structural Multiple Sequence Alignments (MSAs). Derived from the structural alignments, we introduce engineered evolutionary features that strongly inform RNA structure. Further, from the crystal structure, we derive structural features describing RNA base pairing. These evolutionary and structural features can be used in loss functions at different stages of training. Finally, we discuss different cropping strategies informed by RNA structure.

bioinformatics↗

bioRxiv2024

RNA3DB: a dataset for training and benchmarking deep learning models for RNA structure prediction

With advances in protein structure prediction thanks to deep learning models like AlphaFold, RNA structure prediction has recently received increased attention from deep learning researchers. RNAs introduce substantial challenges due to the sparser availability and lower structural diversity of the experimentally resolved RNA structures in comparison to protein structures. These challenges are often poorly addressed by the existing literature, many of which report inflated performance due to using training and testing sets with significant structural overlap. Further, the most recent Critical Assessment of Structure Prediction (CASP15) has shown that deep learning models for RNA structure are currently outperformed by traditional methods. In this paper we present RNA3DB, a dataset of structured RNAs, derived from the Protein Data Bank (PDB), that is designed for training and benchmarking deep learning models. The RNA3DB method arranges the RNA 3D chains into distinct groups (Components) that are non-redundant both with regard to sequence as well as structure, providing a robust way of dividing training, validation, and testing sets. Any split of these structurally-dissimilar Components are guaranteed to produce test and validations sets that are distinct by sequence and structure from those in the training set. We provide the RNA3DB dataset, a particular train/test split of the RNA3DB Components (in an approximate 70/30 ratio) that will be updated periodically. We also provide the RNA3DB methodology along with the source-code, with the goal of creating a reproducible and customizable tool for producing structurally-dissimilar dataset splits for structural RNAs. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=140 SRC="FIGDIR/small/578025v3_ufig1.gif" ALT="Figure 1"> View larger version (39K): org.highwire.dtl.DTLVardef@cb3aa2org.highwire.dtl.DTLVardef@7685fdorg.highwire.dtl.DTLVardef@156ca35org.highwire.dtl.DTLVardef@f58d72_HPS_FORMAT_FIGEXP M_FIG C_FIG HighlightsO_LIWhile there is a recent surge in applying deep learning to RNA structure prediction, domain experts have raised concerns about generalization and current trends in benchmarking. C_LIO_LIMany of the concerns primarily relate to how novel RNA families-i.e. families unseen in the training set-are benchmarked, and whether the models are effective at handling such cases. Performance on bench-marks reflective of real-world applications, such as CASP15 and RNA-Puzzles, is poor for RNA deep learning models. C_LIO_LIWe present a dataset-RNA3DB-that is designed for training and bench-marking deep learning models for RNA structure prediction. RNA3DB provides coverage of all RNA chains found in the Protein Data Bank (PDB). C_LIO_LIRNA3DB is clustered into groups that are both sequentially and structurally non-redundant, providing a robust way of creating training, validation, and testing sets for deep learning models. Along with the dataset, we also provide a transparent methodology as well as the source-code, making our tool both reproducible and customizable. C_LI

bioinformatics↗

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↗