Rama Ranganathan

This paper introduces a new regularization strategy for generative protein models that improves the inference of multiscale sequence statistics, enabling the generation of functional and diverse protein sequences.

The paper uses a deep sequencing assay to analyze how mutations affect circadian clock phenotypes, revealing factorized oscillator properties and mutation-specific effects on period and amplitude.

BCAR improves barcode-sequence mapping by accurately aligning reads and correcting sequencing errors, outperforming existing methods on simulated and experimental data.

ProT-VAE designs functional proteins by learning sequence-function relationships using a transformer-based variational autoencoder, enabling conditional generation of novel sequences with high functionality.

This paper introduces BioM3, a framework that uses natural language prompts to design functional proteins, achieving state-of-the-art performance in prediction and generating proteins with native-like structures and function.

This paper introduces ProtWave-VAE, a generative model combining VAE and autoregressive methods for protein design, showing it can infer functional patterns and generate synthetic proteins with experimental validation.

This study uses deep generative models to design synthetic SH3 domains functional in yeast stress signaling, showing a VAE can recapitulate natural function and classify orthologs by phylogeny and function.

The paper studies epistasis in a fluorescent protein by analyzing mutations linking two phenotypically distinct variants, revealing high-order interactions and sparse patterns.

This study reveals a hierarchical organization in the kinase domain that enables regulatory plasticity through distinct functional units.

This paper introduces statistical coupling analysis (SCA) to decompose proteins into functional sectors and presents new methods for sector analysis in a Python software package.