Dr. Maria Van Kerkhove, PhD visits RPI (April 2023)
Montelione lab member and the Rensselaer Polytechnic Institute NIGMS Fellowship Trainee, Rebecca Greene-Cramer recently welcomed the World Health Organization (WHO) COVID-19 Health Operations and Technical Lead, Maria D. Van Kerkhove, PhD.
Dr. Van Kerkhove was invited as a keynote speaker to RPI to discuss her experience and integral role in monitoring, surveillance, and prevention of emerging and zoonotic disease, as well as future strategies for pandemic prevention and preparedness being developed by the World Health Organization.
Anton-2 supercomputer time by the National Research Council of the National Academy of Science (November 2022)
RPI Researchers awarded Anton-2 supercomputer time by the National Research Council of the National Academy of Science for investigating structural dynamics of membrane proteins that underpin antibiotic resistance.
The goal is to investigate mechanisms of antimicrobial drug transport by Integral Membrane Protein MltA Interacting Protein (MipA) using microsec-long molecular dynamic simulations.
To learn more about this research featured in RPI News click the link below. https://news.rpi.edu/approach/2022/11/21-0
National Institutes of Health Awards Grant for Enhanced NMR Instrumentation (January 2022)
Grant efforts were led by RPI Faculty Gaetano Montelione, Scott McCallum, Marimar Lopez, and Chunyu Wang Grants for 800 MHz NMR System Upgrade and Helium Recovery System. Article.
Hepatitis C drugs multiply effect of COVID-19 antiviral Remdesivir (April 2021)
Proposed mechanism by which FDA-approved hepatitis C virus (HCV) drugs synergize with the SARS-CoV-2 antiviral remdesivir
1921 – 2020
Harold was gifted with a unique combination of intensity and breadth of vision. He realized early in his career that both experimental and theoretical work would be necessary to solve the central problems of protein science, and, most unusually, he excelled in both spheres.
A common binding motif in the ET domain of BRD3 forms polymorphic structural interfaces with host and viral proteins
The extra-terminal (ET) domain of BRD3 is conserved among BET proteins (BRD2, BRD3, BRD4), interacting with multiple host and viral protein-protein networks. Solution NMR studies of complexes formed between BRD3-ET domain with either the 79-residue murine leukemia virus integrase (IN) C-terminal domain (IN329-408), or its 22-residue IN tail peptide (TP) (IN386-407) alone, reveal reveal a 10-residue linker region (IN379-388) tethering the SH3 domain (IN329-378) to the ET-binding motif (IN389-405)-ET complex. This linker has restricted flexibility, impacting the potential range of interdomain orientations in the IN – nucleosome complex.
Image Box Title
Change this description
Schematic Depiction of process for simulating NOESY peak and resonance assignment data for CASP-NMR target N0968S1
Protein structure prediction assisted with sparse NMR data in CASP13
CASP13 has investigated the impact of sparse NMR data on the accuracy of protein structure prediction. NOESY and 15 N-1 H residual dipolar coupling data, typical of that obtained for 15 N,13 C-enriched, perdeuterated proteins up to about 40 kDa, were simulated for 11 CASP13 targets ranging in size from 80 to 326 residues. For several targets, two prediction groups generated models that are more accurate than those produced using baseline methods. Real NMR data collected for a de novo designed protein were also provided to predictors, including one data set in which only backbone resonance assignments were available. Some NMR-assisted prediction groups also did very well with these data. CASP13 also assessed whether incorporation of sparse NMR data improves the accuracy of protein structure prediction relative to nonassisted regular methods. In most cases, incorporation of sparse, noisy NMR data results in models with higher accuracy. The best NMR-assisted models were also compared with the best regular predictions of any CASP13 group for the same target. For six of 13 targets, the most accurate model provided by any NMR-assisted prediction group was more accurate than the most accurate model provided by any regular prediction group; however, for the remaining seven targets, one or more regular prediction method provided a more accurate model than even the best NMR-assisted model. These results suggest a novel approach for protein structure determination, in which advanced prediction methods are first used to generate structural models, and sparse NMR data is then used to validate and/or refine these models.