Bryan A. Krantz

The paper identifies a 3D enhancer hub at 12q24.31 that regulates HCAR2 and explains shared genetic mechanisms in schizophrenia and multiple sclerosis.

This paper introduces a dynamic translocase and physics-informed machine learning to resolve isobaric amino acids at the single-molecule level with high accuracy.

The paper identifies a conserved metabolic and genetic architecture linked to cognitive plasticity and psychiatric conditions, showing that extreme cognitive abilities and schizophrenia share a common genomic basis governed by metabolic constraints.

This paper proposes dynamical translocases and Physics-Informed Machine Learning to overcome biophysical limitations in single-molecule proteomics, achieving >90% accurate amino acid classification.

The paper identifies structural genomic variances in the HCAR2/HCAR1 locus that disrupt thermodynamic sensing in schizophrenia, linking metabolic dysfunction to clinical symptoms.

This study investigates the role of the phenylalanine clamp loop in peptide translocation through the anthrax toxin nanopore using electrophysiology and mutant analysis, revealing its critical role in creating a hydrophobic trap.

The paper uses single-molecule analysis to show how peptide hydrophobicity, aromaticity, and steric effects govern multi-state translocation through anthrax toxin PA nanopores.

This paper develops a deep learning pipeline for classifying peptides from nanopore translocation events using simulated data, achieving high accuracy for pure samples and reasonable accuracy for mixtures.

This study evaluates engineered nanopore variants for peptide biosensing and classification, finding that different variants excel in distinct tasks, suggesting the use of ensembles for improved performance.

This paper develops a machine learning framework for classifying peptides using nanopore translocation events, showing that engineered features and tree-based models achieve high accuracy.