Ariane Mora

This paper introduces AlphaProtein Novo, a machine-learning pipeline for de novo enzyme design that outperforms natural sequence mining in creating enzymes for new-to-nature chemistry and substrates.

Fluxion generates enzyme function predictions by modeling electron flow trajectories, enabling function generation beyond static classification.

The paper uses genome-informed machine learning to discover a new enzyme, PTC, that catalyzes phenazine thioconjugation from sparse data.

This paper introduces Enzyme-toolkit, a framework for discovering new enzymes with specific activities, demonstrating its effectiveness in identifying enzymes for pollutant degradation.

The paper introduces a framework using MSA-Transformer to generate hybrid proteins between specified source and target proteins, producing sequences with higher consistency and plausibility than random baselines.

Squidly predicts catalytic residues using a biology-informed contrastive learning framework, outperforming existing methods and integrating with BLAST for efficient annotation.

This study uses multispectral satellite imagery and machine learning to classify closely related beech species, achieving high accuracy for assisted gene flow applications.

LevSeq rapidly generates sequence-function data for protein engineering using nanopore sequencing and dual barcoding, enabling directed evolution and machine learning applications.

The paper introduces GRASP, a method for ancestral sequence reconstruction that handles large datasets and indels, demonstrating its ability to generate biologically active enzyme ancestors.

This study evaluates how structural properties of scRNA-seq data affect proximity metric performance and recommends tailored approaches for improved clustering.

The paper investigates the role of PRC2 in mouse CNS development by integrating transcriptomic and epigenetic data to reveal layered regulatory mechanisms.