Hugh K. Haddox

The paper introduces a simulator called antigen-prime to model influenza virus evolution and benchmarks methods for variant assignment and growth rate estimation.

This study uses deep learning to detect entrenchment in antibody germline genes, revealing epistatic constraints from both intra-chain and genetically uncoupled partners.

The paper estimates neutral mutation rates and fitness effects of mutations in influenza proteins using phylogenetic analysis of over 100,000 sequences, revealing context-dependent patterns across viruses.

The paper uses experimental results from de novo protein designs to identify and resolve failure modes in the Rosetta energy function, improving its accuracy in predicting stable protein structures.

This paper introduces a model that separates mutation and selection processes in antibody language models to improve prediction of mutation effects on antibody function.

This paper designs a protein family that binds and senses small molecules using deep learning and physics-based methods, achieving nanomolar sensitivity for cortisol.

This study examines how SARS-CoV-2 evolved to escape 1,603 antibodies by analyzing mutation dynamics and selective pressures over 3.5 years of the pandemic.

This study compares nucleotide context models and protein language models for predicting antibody affinity maturation, finding nucleotide-based models perform better.

This paper designs sequence-specific DNA-binding proteins computationally and validates them experimentally, achieving high specificity and affinity for target sequences.

The paper develops 'thrifty' models of somatic hypermutation using convolutions on 3-mer embeddings, showing they outperform 5-mer models with fewer parameters.

The paper estimates mutation rates across SARS-CoV-2 genome sites and identifies factors influencing these rates, finding significant variability and patterns related to gene boundaries, sequence context, and RNA structure.

The paper maps how HA mutations affect neutralization by serum from different age groups, showing age-dependent antigenic effects and implications for viral evolution.

This paper designs 3D protein crystals with specified lattice architectures using computational methods, achieving atomic accuracy and demonstrating successful experimental validation.

The paper uses deep mutational scanning to analyze CYP2C19 variants, revealing a tradeoff between substrate specificity and protein abundance.

The paper develops a method to identify mutations with shifted effects across DMS experiments on SARS-CoV-2 spike homologs, finding some mutations show >1,000-fold differences in impact across variants.

The paper designs over ten thousand {square}{beta}{beta}{square} proteins, identifies stability determinants, and demonstrates how high-throughput methods can improve protein design success rates.