Gaetano Thomas Montelione

The paper analyzes NMR chemical shift perturbations in protein complexes to show that structural plasticity is widespread, with 74% of significant CSPs occurring non-locally to the binding site.

AlphaFold-NMR reveals hidden conformational states of proteins using AI-driven sampling and Bayesian validation, uncovering structural heterogeneity in Gaussia luciferase and Cyclin-Dependent Kinase 2-Associated Protein 1.

The paper introduces NEF, a standard for NMR data exchange, and demonstrates its application in structure determination, metabolomics, and ligand screening.

The paper investigates how sequence variations in BET ET domains influence paralog-specific peptide recognition through loop dynamics and conformational changes.

This paper evaluates methods for modeling multi-state protein and RNA systems in CASP16, finding improved accuracy for some targets but persistent challenges in capturing structural details for others.

The paper evaluates computational methods for predicting interdomain pose distributions in D-L-D proteins using NMR and SAXS data, finding none accurately match experimental results.

The paper designs proteins that bind intrinsically disordered regions with high affinity and specificity across diverse targets.

This paper provides protocols for producing and characterizing native SARS-CoV-2 PLpro in E. coli with high yields and supports inhibitor studies using NMR and fluorescence assays.

CSP_Rank improves protein-peptide complex structure modeling by integrating AlphaFold2 with NMR chemical shift perturbation data through Bayesian conformational selection.

The paper assesses how memorization affects AlphaFold's ability to model multiple conformational states of symmetric SLC proteins and proposes a combined ESM-template-based method to improve this.

The paper develops a standardized system for validating biomolecular structures against NMR restraints using NEF and NMR-STAR formats, implemented in the wwPDB OneDep system.

This paper develops an ITC assay to measure kinetic and inhibition parameters of SARS-CoV-2 3CL-protease, revealing two inhibition modes and deriving kinetic constants with high precision.

The paper assesses AlphaFold2 models against experimental NMR data for 9 monomeric proteins not used in training, finding they fit the data as well as or better than existing NMR structures.

The paper introduces SpecDB, a relational database for archiving NMR FID data and metadata, to support machine learning in biomolecular NMR analysis.

The paper introduces REDCRAFT, a software package that determines protein structures from RDC data alone, successfully applying it to proteins of varying sizes and demonstrating its accuracy compared to NOE-based methods.