Amelia Villegas-Morcillo

The Quagmire of Immunogenicity-Guided Antibody Design: Labels Limit Progress

The paper evaluates antibody design methods using immunogenicity labels and finds that format-based approaches outperform sequence-based predictors, but label inaccuracies limit progress.

All-Atom Protein Sequence Design using Discrete Diffusion Models

This paper introduces a discrete diffusion model for protein sequence design using all-atom representations, showing improved novelty and diversity compared to amino acid-based models.

Guiding diffusion models for antibody sequence and structure co-design with developability properties

This paper designs antibody sequences and structures using a diffusion model guided by developability properties like solubility and folding stability.

So ManyFolds, So Little Time: Efficient Protein Structure Prediction With pLMs and MSAs

The paper presents a streamlined AlphaFold variant using pLMs and MSAs, showing that combining or using either modality achieves comparable performance to models trained exclusively on one.

An Analysis of Protein Language Model Embeddings for Fold Prediction

This paper evaluates protein language model embeddings for fold prediction using fine-tuning neural networks and finds that transformer-based embeddings with specific models improve performance.

Unsupervised protein embeddings outperform hand-crafted sequence and structure features at predicting molecular function

The paper shows that unsupervised protein sequence embeddings outperform traditional features in predicting molecular function, with a simple model achieving state-of-the-art results.