Daron M. Standley

This paper benchmarks protein language models against sequence alignment methods for immune receptor retrieval, finding that alignments outperform PLMs at CDR3 level but PLMs match alignments at full variable domain by leveraging germline information.

The paper identifies a planar dimeric form of bovine ATP synthase with minimal membrane bending, contrasting with canonical dimers that induce significant curvature.

The paper identifies MIC11 as an essential gene for Toxoplasma gondii egress, showing it interacts with PLP1 to disrupt host cell membranes.

This paper introduces FACTS, a scalable machine learning framework for multi-disease classification using TCR repertoires, achieving high accuracy and preserving biological signals.

This study finds that BCR repertoire diversity, not TCR diversity, correlates with favorable outcomes in COVID-19 patients.

This study shows that certain SARS-CoV-2 antibodies enhance infection by cross-linking spike proteins, causing NTD-RBD domain decoupling without needing the Fc region.

The paper shows that ZAP-70 mutations in mice lead to a shift in T cell repertoires, with pathogenic Th17 cells resembling Tregs and contributing to autoimmune arthritis.

This paper develops a novel representation of adaptive immune receptors to improve disease classification from blood samples, achieving higher accuracy for infectious disease, autoimmunity, and cancer.

InterClone stores, searches, and clusters adaptive immune receptor repertoires, demonstrating its utility in analyzing SARS-CoV-2 antibodies and TCRs from different donor groups.

This study identifies an allosteric mechanism where TCR-pMHC binding induces CD3 dynamics through conformational changes, with the TCRβ FG loop acting as a gatekeeper and the connecting peptide as a hinge.

This study improves epitope prediction by integrating AlphaFold with the AbAdapt pipeline, showing better prediction of antibody-specific epitope residues.

Canek integrates single-cell transcriptome replicates by removing batch effects with minimal bias, using mutual nearest neighbor and fuzzy logic.