6 research outputs found
Distinct structural mechanisms determine substrate affinity and kinase activity of protein kinase CĪ±
Protein kinase CĪ± (PKCĪ±) belongs to the family of AGC kinases that phosphorylate multiple peptide substrates. Although the consensus sequence motif has been identified and used to explain substrate specificity for PKCĪ±, it does not inform the structural basis of substrate-binding and kinase activity for diverse substrates phosphorylated by this kinase. The transient, dynamic, and unstructured nature of this proteināprotein interaction has limited structural mapping of kinaseāsubstrate interfaces. Here, using multiscale MD simulation-based predictions and FRET sensor-based experiments, we investigated the conformational dynamics of the kinaseāsubstrate interface. We found that the binding strength of the kinaseāsubstrate interaction is primarily determined by long-range columbic interactions between basic (Arg/Lys) residues located N-terminally to the phosphorylated Ser/Thr residues in the substrate and by an acidic patch in the kinase catalytic domain. Kinase activity stemmed from conformational flexibility in the region C-terminal to the phosphorylated Ser/Thr residues. Flexibility of the substrateākinase interaction enabled an Arg/Lys two to three amino acids C-terminal to the phosphorylated Ser/Thr to prime a catalytically active conformation, facilitating phosphoryl transfer to the substrate. The structural mechanisms determining substrate binding and catalytic activity formed the basis of diverse binding affinities and kinase activities of PKCĪ± for 14 substrates with varying degrees of sequence conservation. Our findings provide insight into the dynamic properties of the kinaseāsubstrate interaction that govern substrate binding and turnover. Moreover, this study establishes a modeling and experimental method to elucidate the structural dynamics underlying substrate selectivity among eukaryotic kinases
Distinct structural mechanisms determine substrate affinity and kinase activity of protein kinase CĪ±
Protein kinase CĪ± (PKCĪ±) belongs to the family of AGC kinases that phosphorylate multiple peptide substrates. Although the consensus sequence motif has been identified and used to explain substrate specificity for PKCĪ±, it does not inform the structural basis of substrate-binding and kinase activity for diverse substrates phosphorylated by this kinase. The transient, dynamic, and unstructured nature of this proteināprotein interaction has limited structural mapping of kinaseāsubstrate interfaces. Here, using multiscale MD simulation-based predictions and FRET sensor-based experiments, we investigated the conformational dynamics of the kinaseāsubstrate interface. We found that the binding strength of the kinaseāsubstrate interaction is primarily determined by long-range columbic interactions between basic (Arg/Lys) residues located N-terminally to the phosphorylated Ser/Thr residues in the substrate and by an acidic patch in the kinase catalytic domain. Kinase activity stemmed from conformational flexibility in the region C-terminal to the phosphorylated Ser/Thr residues. Flexibility of the substrateākinase interaction enabled an Arg/Lys two to three amino acids C-terminal to the phosphorylated Ser/Thr to prime a catalytically active conformation, facilitating phosphoryl transfer to the substrate. The structural mechanisms determining substrate binding and catalytic activity formed the basis of diverse binding affinities and kinase activities of PKCĪ± for 14 substrates with varying degrees of sequence conservation. Our findings provide insight into the dynamic properties of the kinaseāsubstrate interaction that govern substrate binding and turnover. Moreover, this study establishes a modeling and experimental method to elucidate the structural dynamics underlying substrate selectivity among eukaryotic kinases
Methods for the refinement of protein structure 3D models
The refinement of predicted 3D protein models is crucial in bringing them closer towards experimental accuracy for further computational studies. Refinement approaches can be divided into two main stages: The sampling and scoring stages. Sampling strategies, such as the popular Molecular Dynamics (MD)-based protocols, aim to generate improved 3D models. However, generating 3D models that are closer to the native structure than the initial model remains challenging, as structural deviations from the native basin can be encountered due to force-field inaccuracies. Therefore, different restraint strategies have been applied in order to avoid deviations away from the native structure. For example, the accurate prediction of local errors and/or contacts in the initial models can be used to guide restraints. MD-based protocols, using physics-based force fields and smart restraints, have made significant progress towards a more consistent refinement of 3D models. The scoring stage, including energy functions and Model Quality Assessment Programs (MQAPs) are also used to discriminate near-native conformations from non-native conformations. Nevertheless, there are often very small differences among generated 3D models in refinement pipelines, which makes model discrimination and selection problematic. For this reason, the identification of the most native-like conformations remains a major challenge
Intrinsically Disordered Proteins and Chronic Diseases
This book is an embodiment of a series of articles that were published as part of a Special Issue of Biomolecules. It is dedicated to exploring the role of intrinsically disordered proteins (IDPs) in various chronic diseases. The main goal of the articles is to describe recent progress in elucidating the mechanisms by which IDPs cause various human diseases, such as cancer, cardiovascular disease, amyloidosis, neurodegenerative diseases, diabetes, and genetic diseases, to name a few. Contributed by leading investigators in the field, this compendium serves as a valuable resource for researchers, clinicians as well as postdoctoral fellows and graduate student
Protein Structure Refinement of CASP Target Proteins Using GNEIMO Torsional Dynamics Method
A longstanding
challenge in using computational methods for protein
structure prediction is the refinement of low-resolution structural
models derived from comparative modeling methods into highly accurate
atomistic models useful for detailed structural studies. Previously,
we have developed and demonstrated the utility of the internal coordinate
molecular dynamics (MD) technique, generalized NewtonāEuler
inverse mass operator (GNEIMO), for refinement of small proteins.
Using GNEIMO, the high-frequency degrees of freedom are frozen and
the protein is modeled as a collection of rigid clusters connected
by torsional hinges. This physical model allows larger integration
time steps and focuses the conformational search in the low frequency
torsional degrees of freedom. Here, we have applied GNEIMO with temperature
replica exchange to refine low-resolution protein models of 30 proteins
taken from the continuous assessment of structure prediction (CASP)
competition. We have shown that GNEIMO torsional MD method leads to
refinement of up to 1.3 Ć
in the root-mean-square deviation in
coordinates for 30 CASP target proteins without using any experimental
data as restraints in performing the GNEIMO simulations. This is in
contrast with the unconstrained all-atom Cartesian MD method performed
under the same conditions, where refinement requires the use of restraints
during the simulations