Lood van Niekerk

A high-throughput platform for biophysical antibody developability assessment to enable AI/ML model training

The paper introduces a high-throughput platform to generate large datasets for training machine learning models to predict antibody developability properties.

Proteome-wide model for human disease genetics

This paper introduces popEVE, a deep generative model for proteome-wide disease variant prediction, identifying 442 genes in a developmental disorder cohort, including 123 novel candidates.

ProteinGym: Large-Scale Benchmarks for Protein Design and Fitness Prediction

ProteinGym unifies over 250 deep mutational scans and ~65k clinical variants to benchmark over 70 models in zero-shot and supervised settings; TranceptEVE ranks best overall.

TranceptEVE: Combining Family-specific and Family-agnostic Models of Protein Sequences for Improved Fitness Prediction

Blending an alignment-based family model with an autoregressive protein language model, weighted by how deep the alignment is, reportedly improves mutation-effect prediction against experimental assays and ClinVar.