Light Chain Mutation Effects on the Dynamics of Thrombin

Abstract

Thrombin plays an important role in the process of hemostasis and blood coagulation. Studies in thrombin can help us find ways to treat cancer because thrombin is able to reduce the characteristic hypercoagulability of cancer. Thrombin is composed of two chains, the light chain and the heavy chain. The function of the heavy chain has been largely explored, while the function of the light chain was obscured until several disease-associated mutations in the light chain come to light. In this study, we want to explore the dynamic and conformation effects of mutations on the light chain further to determine possible associations between mutation, conformational changes, and disease. The study, which is a follow-up for our studies on apo thrombin and the mutant, ΔK9, mainly focuses on the mutants E8K and R4A. E8K is a diseaseassociated mutation, and R4A is used to study the role of Arg4, which is suggested experimentally to play a critical role for thrombin's catalytic activities. We performed five all-atom one microsecond-scale molecular dynamics (MD) simulations for both E8K and R4A, and quantified the changes in the conformational ensemble of the mutants. From the root-mean-square fluctuations (RMSF) for the α-carbons, we find that the atomic fluctuations change in the mutants in the 60s loop and γ loop. The correlation coefficients for the α-carbons indicate that the correlation relation for atom-pairs in the protein is also impacted. The clustering analysis and the principal component analysis (PCA) consistently tell us that the catalytic pocket and the regulatory loops are destabilized by the mutations. We also find that there are two binding modes for Na + by clustering the vector difference between the Na + ions and the 220s loop. After further analysis, we find that there is a relation between the Na + binding and the rigidification of the γ loop, which may shed light on the mysterious role of the γ loop in thrombin.

Unraveling the Role of Hydrogen Bonds in Thrombin via Two Machine Learning Methods

“…The top-ranked hydrogen bond, LEU62-GLY189, is located between the 30s loop and the gamma loop. The absence of this bond could explain why these two loops become more flexible in ΔK9 as shown in our previous research . In the (WT, E8K) pair (Figure (b),(c)), all of the top 10 important hydrogen bonds have positive beta values, indicating that E8K has an increased number of hydrogen bonds.…”

“…We used a 9 Å cutoff distance and a 7.5 Å switching distance for the van der Waals and electrostatic forces. These parameters are the default values in ACEMD3, and the same choice can be seen in a variety of MD simulation studies on several different systems by a variety of investigators − as well as in our previous studies of thrombin. − …”

Computational Dissection of the Role of Trp305 in the Regulation of the Death-Associated Protein Kinase–Calmodulin Interaction

“…The two-dimensional energy landscapes of the PC1 and PC2 revealed that the WT, W305Y, and W305D systems had one (A), three (B1, B2, and B3), and three (C1, C2, and C3) conformational sub-basins ( Figure 4 ), respectively. We next extracted the structures from these sub-basins and conducted clustering analysis on these structures for each system [ 46 , 47 ]. Then, the representative structure extracted from each sub-basin from the W305Y and W305D mutant systems was aligned with that from the WT system to unravel the detailed conformational changes in the protein domains triggered by the Trp305 mutations.…”

Molecular dynamics simulation study on the structures of fascin mutants

“…We used hierarchical clustering (HDBSCAN) to analyze the trajectory and obtained the clustering results of wild-type fascin and mutant fascin. 52,53 The results show that when the residues were mutated, the results of protein clustering became different (Figure S2).…”