Neil P. King

The paper designs large pseudosymmetric protein nanoparticles using a hierarchical computational method, achieving sizes up to 96 nm with 960 subunits.

This study develops MERS-CoV vaccines using a protein nanoparticle platform, showing they induce strong neutralizing antibodies and protect mice against MERS-CoV challenge.

This study uses deep mutational scanning to analyze H5 hemagglutinin mutations and their impact on pandemic risk factors in influenza viruses.

This study identifies molecular mechanisms of FCoV-23 pathogenicity, including receptor usage and biotype switching, and identifies a cross-reactive antibody for therapeutic development.

This paper develops a modular saponin-based nanoparticle adjuvant platform incorporating TLR agonists, showing improved vaccine potency and breadth against COVID-19 and HIV in mice.

This paper develops a stabilization strategy for sarbecovirus fusion machinery vaccines, showing broad reactivity and protection against SARS-CoV-2 variants.

This paper designs pH-responsive antibody nanoparticles with three distinct protein components that disassemble at tunable pH levels, enabling targeted delivery of biologics.

This study benchmarks ProteinMPNN against Rosetta in designing two-component protein nanomaterials, showing comparable success with significantly reduced computation and no manual refinement.

This study shows that antigen spacing on protein nanoparticles affects antibody responses to vaccination, with decreased spacing improving hemagglutination inhibition and neutralization potency.

This study evaluates trihead nanoparticle immunogens to enhance cross-neutralizing antibody responses against influenza H1 viruses by combining immunofocusing techniques.

This study characterizes the receptor tropism and antigenicity of a clade 3 sarbecovirus, identifying broad ACE2 usage and cross-neutralizing antibodies with implications for vaccine design.

This paper introduces a top-down reinforcement learning approach to design protein nanomaterials, resulting in more compact and robust icosahedral assemblies than previously observed.

This paper computationally designs self-assembling protein rotary machines that sample internal rotational degrees of freedom based on energy landscapes.

This paper expands the Rosetta software suite with tools for glycan modeling, density fitting, and rational sequon design, achieving high accuracy in predicting and building glycan structures.

This paper describes an engineered SARS-CoV-2 RBD variant that shows improved immunogenicity in mice and protective immunity in hamsters.

The paper predicts SARS-CoV-2 B-cell epitopes using computational methods and validates them with ELISA, finding two antigens with high sensitivity and specificity for serological diagnosis of COVID-19.

Designed trimers matched to viral glycoprotein termini yielded tetrahedral, octahedral and icosahedral nanoparticles with cryo-EM-verified structures that display native-like HIV Env, influenza HA and RSV F.