16 research outputs found
Patent: Dual Function Proteins for Treating Metabolic Disorders
The present invention relates to new proteins comprising fibroblast growth factor 2 1 (FGF21 ) and other metabolic regulators known to improve metabolic profiles in subjects to whom they are administered
Efforts toward the direct experimental characterization of enzyme microenvironments: tyrosine100 in dihydrofolate reductase.
State secrets: Site-specific deuteration and FTIR studies reveal that Tyr100 in dihydrofolate reductase plays an important role in catalysis, with a strong electrostatic coupling occurring between Tyr100 and the charge that develops in the hydride-transfer transition state (see picture, NADP(+) purple, Tyr100 green). However, relaying correlated motions that facilitate catalysis from distal sites of the protein to the hydride donor may also be involved
Estimation of Hydrogen-Exchange Protection Factors from MD Simulation Based on Amide Hydrogen Bonding Analysis
Site-specific labeling of proteins with NMR-active unnatural amino acids
A large number of amino acids other than the canonical amino acids can now be easily incorporated in vivo into proteins at genetically encoded positions. The technology requires an orthogonal tRNA/aminoacyl-tRNA synthetase pair specific for the unnatural amino acid that is added to the media while a TAG amber or frame shift codon specifies the incorporation site in the protein to be studied. These unnatural amino acids can be isotopically labeled and provide unique opportunities for site-specific labeling of proteins for NMR studies. In this perspective, we discuss these opportunities including new photocaged unnatural amino acids, outline usage of metal chelating and spin-labeled unnatural amino acids and expand the approach to in-cell NMR experiments
Estimation of Hydrogen-Exchange Protection Factors from MD Simulation Based on Amide Hydrogen Bonding Analysis
Hydrogen
exchange (HX) studies have provided critical insight into our understanding
of protein folding, structure, and dynamics. More recently, hydrogen
exchange mass spectrometry (HX-MS) has become a widely applicable
tool for HX studies. The interpretation of the wealth of data generated
by HX-MS experiments as well as other HX methods would greatly benefit
from the availability of exchange predictions derived from structures
or models for comparison with experiment. Most reported computational
HX modeling studies have employed solvent-accessible-surface-area
based metrics in attempts to interpret HX data on the basis of structures
or models. In this study, a computational HX-MS prediction method
based on classification of the amide hydrogen bonding modes mimicking
the local unfolding model is demonstrated. Analysis of the NH bonding
configurations from molecular dynamics (MD) simulation snapshots is
used to determine partitioning over bonded and nonbonded NH states
and is directly mapped into a protection factor (PF) using a logistics
growth function. Predicted PFs are then used for calculating deuteration
values of peptides and compared with experimental data. Hydrogen exchange
MS data for fatty acid synthase thioesterase (FAS-TE) collected for
a range of pHs and temperatures was used for detailed evaluation of
the approach. High correlation between prediction and experiment for
observable fragment peptides is observed in the FAS-TE and additional
benchmarking systems that included various apo/holo proteins for which
literature data were available. In addition, it is shown that HX modeling
can improve experimental resolution through decomposition of in-exchange
curves into rate classes, which correlate with prediction from MD.
Successful rate class decompositions provide further evidence that
the presented approach captures the underlying physical processes
correctly at the single residue level. This assessment is further
strengthened in a comparison of residue resolved protection factor
predictions for staphylococcal nuclease with NMR data, which was also
used to compare prediction performance with other algorithms described
in the literature. The demonstrated transferable and scalable MD based
HX prediction approach adds significantly to the available tools for
HX-MS data interpretation based on available structures and models
Estimation of Hydrogen-Exchange Protection Factors from MD Simulation Based on Amide Hydrogen Bonding Analysis
Hydrogen
exchange (HX) studies have provided critical insight into our understanding
of protein folding, structure, and dynamics. More recently, hydrogen
exchange mass spectrometry (HX-MS) has become a widely applicable
tool for HX studies. The interpretation of the wealth of data generated
by HX-MS experiments as well as other HX methods would greatly benefit
from the availability of exchange predictions derived from structures
or models for comparison with experiment. Most reported computational
HX modeling studies have employed solvent-accessible-surface-area
based metrics in attempts to interpret HX data on the basis of structures
or models. In this study, a computational HX-MS prediction method
based on classification of the amide hydrogen bonding modes mimicking
the local unfolding model is demonstrated. Analysis of the NH bonding
configurations from molecular dynamics (MD) simulation snapshots is
used to determine partitioning over bonded and nonbonded NH states
and is directly mapped into a protection factor (PF) using a logistics
growth function. Predicted PFs are then used for calculating deuteration
values of peptides and compared with experimental data. Hydrogen exchange
MS data for fatty acid synthase thioesterase (FAS-TE) collected for
a range of pHs and temperatures was used for detailed evaluation of
the approach. High correlation between prediction and experiment for
observable fragment peptides is observed in the FAS-TE and additional
benchmarking systems that included various apo/holo proteins for which
literature data were available. In addition, it is shown that HX modeling
can improve experimental resolution through decomposition of in-exchange
curves into rate classes, which correlate with prediction from MD.
Successful rate class decompositions provide further evidence that
the presented approach captures the underlying physical processes
correctly at the single residue level. This assessment is further
strengthened in a comparison of residue resolved protection factor
predictions for staphylococcal nuclease with NMR data, which was also
used to compare prediction performance with other algorithms described
in the literature. The demonstrated transferable and scalable MD based
HX prediction approach adds significantly to the available tools for
HX-MS data interpretation based on available structures and models
Preclinical Characterization and Phase 1 Study of the Anti-HER2-TLR7 Immune Stimulator Antibody Conjugate NJH395 in Patients with HER2-positive Malignancies
Immune stimulator antibody conjugates (ISACs) combining tumor-targeting monoclonal antibodies with potent immunostimulatory agents allow targeted delivery of immune activators in tumors. NJH395 is a novel, first-in-class ISAC comprising a Toll‐like receptor 7 (TLR7) agonist conjugated to an anti-HER2 antibody via a non-cleavable linker-payload. Preclinical characterization showed ISAC-mediated activation of myeloid cells in the presence of antigen-expressing cancer cells, with antigen targeting and TLR7 agonism contributing to antitumor activity. Safety, efficacy, immunogenicity, pharmacokinetics, and pharmacodynamics were investigated in a phase 1, multicenter, open-label study in patients with HER2-positive malignancies (NCT03696771). Data from 18 patients enrolled in single ascending dose escalation demonstrated delivery of the TLR7 agonist payload in HER2-positive tumor cells and induction of type I IFN response, which interrelated with preferential immune modulation in the tumor microenvironment. Cytokine release syndrome was a common but manageable drug-related adverse event. Antidrug antibody formation and neuroinflammation at high doses represented significant clinical challenges