Light Chain Mutation Effects on the Dynamics of Thrombin

The Light Chain Allosterically Enhances the Protease Activity of Murine Urokinase-Type Plasminogen Activator

The active form of the murine urokinase-type plasminogen activator (muPA) is formed by a 27-residue disordered light chain connecting the amino-terminal fragment (ATF) with the serine protease domain. The two chains are tethered by a disulfide bond between C1CT in the disordered light chain and C122CT in the protease domain. Previous work showed that the presence of the disordered light chain affected the inhibition of the protease domain by antibodies. Here we show that the disordered light chain induced a 3.7-fold increase in kcat of the protease domain of muPA. In addition, hydrogen-deuterium exchange mass spectrometry (HDX-MS) and accelerated molecular dynamics (AMD) were performed to identify the interactions between the disordered light chain and the protease domain. HDX-MS revealed that the light chain is contacting the 110s, the turn between the β10- and β11-strand, and the β7-strand. A reduction in deuterium uptake was also observed in the activation loop, the 140s loop and the 220s loop, which forms the S1-specificty pocket where the substrate binds. These loops are further away from where the light chain seems to be interacting with the protease domain. Our results suggest that the light chain most likely increases the activity of muPA by allosterically favoring conformations in which the specificity pocket is formed. We propose a model by which the allostery would be transmitted through the β-strands of the β-barrels to the loops on the other side of the protease domain.

Allosteric Modulation of Thrombin by Thrombomodulin: Insights from Logistic Regression and Statistical Analysis of Molecular Dynamics Simulations

Thrombomodulin (TM), a transmembrane receptor integral to the anticoagulant pathway, governs thrombin's substrate specificity via interaction with thrombin's anion-binding exosite I. Despite its established role, the precise mechanisms underlying this regulatory function are yet to be fully unraveled. In this study, we deepen the understanding of these mechanisms through eight independent 1 μs all-atom simulations, analyzing thrombin both in its free form and when bound to TM fragments TM456 and TM56. Our investigations revealed distinct and significant conformational changes in thrombin mediated by the binding of TM56 and TM456. While TM56 predominantly influences motions within exosite I, TM456 orchestrates coordinated alterations across various loop regions, thereby unveiling a multifaceted modulatory role that extends beyond that of TM56. A highlight of our study is the identification of critical hydrogen bonds that undergo transformations during TM56 and TM456 binding, shedding light on the pivotal allosteric influence exerted by TM4 on thrombin's structural dynamics. This work offers a nuanced appreciation of TM's regulatory role in blood coagulation, paving the way for innovative approaches in the development of anticoagulant therapies and expanding the horizons in oncology therapeutics through a deeper understanding of molecular interactions in the coagulation pathway.

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

Hydrogen bonds play a critical role in the folding and stability of proteins, such as proteins and nucleic acids, by providing strong and directional interactions. They help to maintain the secondary and 3D structure of proteins, and structural changes in these molecules often result from the formation or breaking of hydrogen bonds. To gain insights into these hydrogen bonding networks, we applied two machine learning models - a logistic regression model and a decision tree model - to study four variants of thrombin: wild-type, ΔK9, E8K, and R4A. Our results showed that both models have their unique advantages. The logistic regression model highlighted potential key residues (GLU295) in thrombin's allosteric pathways, while the decision tree model identified important hydrogen bonding motifs. This information can aid in understanding the mechanisms of folding in proteins and has potential applications in drug design and other therapies. The use of these two models highlights their usefulness in studying hydrogen bonding networks in proteins.

Molecular dynamics simulation study on the structures of fascin mutants

Fascin is a filamentous actin (F-actin) bundling protein, which cross-links F-actin into bundles and becomes an important component of filopodia on the cell surface. Fascin is overexpressed in many types of cancers. The mutation of fascin affects its ability to bind to F-actin and the progress of cancer. In this paper, we have studied the effects of residues of K22, K41, K43, K241, K358, K399, and K471 using molecular dynamics (MD) simulation. For the strong-effect residues, that is, K22, K41, K43, K358, and K471, our results show that the mutation of K to A leads to large values of root mean square fluctuation (RMSF) around the mutated residues, indicating those residues are important for the flexibility and thermal stability. On the other hand, based on residue cross-correlation analysis, alanine mutations of these residues reinforce the correlation between residues. Together with the RMSF data, the local flexibility is extended to the entire protein by the strong correlations to influence the dynamics and function of fascin. By contrast, for the mutants of K241A and K399A those do not affect the function of fascin, the RMSF data do not show significant differences compared with wild-type fascin. These findings are in a good agreement with experimental studies.

Simulations suggest double sodium binding induces unexpected conformational changes in thrombin

Thrombin is a Na[Formula: see text]-activated serine protease existing in two forms targeted to procoagulant and anticoagulant activities, respectively. There is one Na[Formula: see text]-binding site that has been the focus of the study of the thrombin. However, molecular dynamics (MD) simulations suggest that there might be actually two Na[Formula: see text]-binding sites in thrombin and that Na[Formula: see text] ions can even bind to two sites simultaneously. In this study, we performed 12 independent 2-µs all-atom MD simulations for the wild-type (WT) thrombin and we studied the effects of the different Na[Formula: see text] binding modes on thrombin. From the root-mean-square fluctuations (RMSF) for the [Formula: see text]-carbons, we see that the atomic fluctuations mainly change in the 60s, 170s, and 220s loops, and the connection (residue 167 to 170). The correlation matrices for different binding modes suggest regions that may play an important role in thrombin's allosteric response and provide us a possible allosteric pathway for the sodium binding. Amorim-Hennig (AH) clustering tells us how the structure of the regions of interest changes on sodium binding. Principal component analysis (PCA) shows us how the different regions of thrombin change conformation together with sodium binding. Solvent-accessible surface area (SASA) exposes the conformational change in exosite I and catalytic triad. Finally, we argue that the double binding mode might be an inactive mode and that the kinetic scheme for the Na[Formula: see text] binding to thrombin might be a multiple-step mechanism rather than a 2-step mechanism.

Exosite Binding in Thrombin: A Global Structural/Dynamic Overview of Complexes with Aptamers and Other Ligands

Thrombin is the key enzyme of the entire hemostatic process since it is able to exert both procoagulant and anticoagulant functions; therefore, it represents an attractive target for the developments of biomolecules with therapeutic potential. Thrombin can perform its many functional activities because of its ability to recognize a wide variety of substrates, inhibitors, and cofactors. These molecules frequently are bound to positively charged regions on the surface of protein called exosites. In this review, we carried out extensive analyses of the structural determinants of thrombin partnerships by surveying literature data as well as the structural content of the Protein Data Bank (PDB). In particular, we used the information collected on functional, natural, and synthetic molecular ligands to define the anatomy of the exosites and to quantify the interface area between thrombin and exosite ligands. In this framework, we reviewed in detail the specificity of thrombin binding to aptamers, a class of compounds with intriguing pharmaceutical properties. Although these compounds anchor to protein using conservative patterns on its surface, the present analysis highlights some interesting peculiarities. Moreover, the impact of thrombin binding aptamers in the elucidation of the cross-talk between the two distant exosites is illustrated. Collectively, the data and the work here reviewed may provide insights into the design of novel thrombin inhibitors.