Phosphorylation-Induced Conformational Dynamics and Inhibition of Janus Kinase 1 by Suppressors of Cytokine Signaling 1

Abstract

The dysfunction of the JAK/STAT (Janus kinase/ signal transducers and activators of transcription) pathway results in several pathophysiological conditions, including autoimmune disorders. The negative feedback regulators of the JAK/STAT signaling pathway, suppressors of cytokine signaling (SOCS), act as a natural inhibitor of JAK and inhibit aberrant activity. SOCS1 is the most potent member of the SOCS family, whose kinase inhibitory region targets the substrate-binding groove of JAK with high affinity and blocks the phosphorylation of JAK kinases. Overall, we performed an aggregate of 13 μs molecular dynamics simulations on the activation loop's three different phosphorylation (double and single) states. Results from our simulations show that the single Tyr 1034 phosphorylation could stabilize the JAK1/ SOCS1 complex as well as the flexible activation segment. The phosphate-binding loop (P-loop) shows conformational variability at dual and single phosphorylated states. Principal component analysis and protein structure network (PSN) analysis reveal that the different phosphorylation states and SOCS1 binding induce intermediate inactive conformations of JAK1, which could be a better target for future JAK1 selective drug design. PSN analysis suggests that the com-pY1034 system is stabilized due to higher values of network hubs than the other two complex systems. Moreover, the binding free energy calculations suggest that pTyr 1034 states show a higher affinity toward SOCS1 than the dual and pTyr 1035 states. We believe that the mechanistic understanding of JAK1/SOCS1 complexation will aid future studies related to peptide inhibitors based on SOCS1.

Microsecond dynamics of H10N7 influenza neuraminidase reveals the plasticity of loop regions and drug resistance due to the R292K mutation

“…The first two principal components (PC1 and PC2), which were the same as the covariance matrix's first two eigenvectors, were determined using internal scripts. The 2‐dimensional free energy landscape plots were then constructed from PC1 and PC2, utilizing Matplotlib 46 …”

“…The utilization of free energy landscapes constructed using different collective variables has been widely employed in diverse studies to illustrate the impact of protein modifications that induce notable structural alterations. 46,47 The Cpptraj module of AMBER18 was used to remove solvent and ions from the 2 Â 1.0 μs MD trajectories of apo neuraminidase and ligand-bound neuraminidase (wildtype and R292K) complexes before processing the MD trajectories for principal component analysis (PCA). 48 For each MD simulation trajectory, the covariance matrix C α atoms ð Þbetween the i th and j th residues was constructed.…”

“…The 2-dimensional free energy landscape plots were then constructed from PC1 and PC2, utilizing Matplotlib. 46 Moreover, we performed the dihedral angle's PCA to investigate the conformational dynamics of the loop areas and plotted their free energy landscape 49 using the principal vectors (V1 and V2). Here, we mainly considered all backbone dihedral angles of the 150-loop and 340-loop.…”

“…The inhibitors' binding free energies or affinity to neuraminidase were calculated by the widely used molecular mechanics Poisson-Boltzmann surface area (MM-PBSA) method. 46,[51][52][53][54] In this study, 2000 structural frames obtained at regular intervals from the stable trajectories towards the end of each production simulation were used to perform the MM-PBSA calculations. The following equations determine the binding affinity (ΔG bind ),…”