Biology subjects

Chithrananda, S.

Publications and source records attributed to Chithrananda, S..

3 recordsLinked to original sources

bioRxiv2024

Mapping the combinatorial coding between olfactory receptors and perception with deep learning

The sense of smell remains poorly understood, especially in contrast to visual and auditory coding. At the core of our sense of smell is the olfactory information flow, in which odorant molecules activate a subset of our olfactory receptors and combinations of unique receptor activations code for unique odors. Understanding this relationship is crucial for unraveling the mysteries of human olfaction and its potential therapeutic applications. Despite this, predicting molecule-OR interactions remains incredibly difficult. Here, we develop a novel, biologically-inspired approach denoted MolOR that first maps odorant molecules to their respective olfactory receptor (OR) activation profiles and subsequently predicts their odor percepts. Despite a lack of overlap between molecules with OR activation data and percept annotations, our joint model improves percept prediction by leveraging the OR activation profile of each odorant as auxiliary features in predicting its percepts. We extend this cross receptor-percept approach, showing that sets of molecules with very different structures but similar percepts, a common challenge for chemosensory prediction, have similar predicted OR activation profiles. Lastly, we further probe the odorant-OR models predictive ability, showing it can distinguish binding patterns across unique OR families, as well as between protein-coding genes or frequently occuring pseudogenes in the human olfactory subgenome. This work may aid in the potential discovery of novel odorant ligands targeting functions of orphan ORs, and in further characterizing the relationship between chemical structures and percepts. In doing so, we hope to advance our understanding of olfactory perception and the design of new odorants with desired perceptual qualities.

molecular biology↗

bioRxiv2024

Functional protein mining with conformal guarantees

1Molecular structure prediction and homology detection provide a promising path to discovering new protein function and evolutionary relationships. However, current approaches lack statistical reliability assurances, limiting their practical utility for selecting proteins for further experimental and in-silico characterization. To address this challenge, we introduce a novel approach to protein search leveraging principles from conformal prediction, offering a framework that ensures statistical guarantees with user-specified risk and provides calibrated probabilities (rather than raw ML scores) for any protein search model. Our method (1) lets users select many biologically-relevant loss metrics (i.e. false discovery rate) and assigns reliable functional probabilities for annotating genes of unknown function; (2) achieves state-of-the-art performance in enzyme classification without training new models; and (3) robustly and rapidly pre-filters proteins for computationally intensive structural alignment algorithms. Our framework enhances the reliability of protein homology detection and enables the discovery of new proteins with likely desirable functional properties.

bioinformatics↗

bioRxiv2024

RNA language models predict mutations that improve RNA function

Structured RNA lies at the heart of many central biological processes, from gene expression to catalysis. While advances in deep learning enable the prediction of accurate protein structural models, RNA structure prediction is not possible at present due to a lack of abundant high-quality reference data1. Furthermore, available sequence data are generally not associated with organismal phenotypes that could inform RNA function2-4. We created GARNET (Gtdb Acquired RNa with Environmental Temperatures), a new database for RNA structural and functional analysis anchored to the Genome Taxonomy Database (GTDB)5. GARNET links RNA sequences derived from GTDB genomes to experimental and predicted optimal growth temperatures of GTDB reference organisms. This enables construction of deep and diverse RNA sequence alignments to be used for machine learning. Using GARNET, we define the minimal requirements for a sequence- and structure-aware RNA generative model. We also develop a GPT-like language model for RNA in which overlapping triplet tokenization provides optimal encoding. Leveraging hyperthermophilic RNAs in GARNET and these RNA generative models, we identified mutations in ribosomal RNA that confer increased thermostability to the Escherichia coli ribosome. The GTDB- derived data and deep learning models presented here provide a foundation for understanding the connections between RNA sequence, structure, and function.

synthetic biology↗