Na+-binding modes involved in thrombin's allosteric response as revealed by molecular dynamics simulations, correlation networks and Markov modeling

Generative adversarial network (GAN) model-based design of potent SARS-CoV-2 Mpro inhibitors using the electron density of ligands and 3D binding pockets: insights from molecular docking, dynamics simulation, and MM-GBSA analysis

Deep learning-based generative adversarial network (GAN) frameworks have recently been developed to expedite the drug discovery process. These models generate novel molecules from scratch and validate them through molecular docking simulation to identify the most promising candidates for a given drug target. In this study, the SARS-CoV-2 main protease (Mpro) was selected as the drug target. Two distinct GAN algorithms were employed to generate novel small molecules. One approach utilized experimental electron density (ED-based) data of ligands for training to generate drug-like molecules, while the second approach leveraged the target binding pocket to capture spatial and bonding relationship between atoms within the binding pockets. The ED-based approach generated approximately 26,000 molecules, whereas the binding pocket-based method produced around 100 molecules. These generated molecules were subsequently ranked based on molecular docking results using the glide XP score (both flexible and rigid docking) and AutoDock Vina. To identify the most potent GAN-derived molecules, molecular docking was also performed on co-crystallized inhibitor molecules of Mpro. The six most promising molecules from these GAN approaches were further evaluated for stability, interactions, and MM-GBSA binding free energy through molecular dynamics simulations. This analysis led to the identification of four potent Mpro inhibitor molecules, all featuring a 2-benzyl-6-bromophenol scaffold. The binding free energies of these compounds were compared with those of other Mpro inhibitors, revealing that our compounds demonstrated better affinity for Mpro than some broad-spectrum protease inhibitors. The dynamic cross-correlation matrix plot indicated strongly correlated and anti-correlated regions, potentially linked to ligand binding.

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.

Thrombin - A Molecular Dynamics Perspective

Thrombin is a crucial enzyme involved in blood coagulation, essential for maintaining circulatory system integrity and preventing excessive bleeding. However, thrombin is also implicated in pathological conditions such as thrombosis and cancer. Despite the application of various experimental techniques, including X-ray crystallography, NMR spectroscopy, and HDXMS, none of these methods can precisely detect thrombin's dynamics and conformational ensembles at high spatial and temporal resolution. Fortunately, molecular dynamics (MD) simulation, a computational technique that allows the investigation of molecular functions and dynamics in atomic detail, can be used to explore thrombin behavior. This review summarizes recent MD simulation studies on thrombin and its interactions with other biomolecules. Specifically, the 17 studies discussed here provide insights into thrombin's switch between 'slow' and 'fast' forms, active and inactive forms, the role of Na+ binding, the effects of light chain mutation, and thrombin's interactions with other biomolecules. The findings of these studies have significant implications for developing new therapies for thrombosis and cancer. By understanding thrombin's complex behavior, researchers can design more effective drugs and treatments that target thrombin.

Molecular mechanism by which spider-driving peptide potentiates coagulation factors

Hemostasis is a crucial process that quickly forms clots at injury sites to prevent bleeding and infections. Dysfunctions in this process can lead to hemorrhagic disorders, such as hemophilia and thrombocytopenia purpura. While hemostatic agents are used in clinical treatments, there is still limited knowledge about potentiators targeting coagulation factors. Recently, LCTx-F2, a procoagulant spider-derived peptide, was discovered. This study employed various methods, including chromogenic substrate analysis and dynamic simulation, to investigate how LCTx-F2 enhances the activity of thrombin and FXIIa. Our findings revealed that LCTx-F2 binds to thrombin and FXIIa in a similar manner, with the N-terminal penetrating the active-site cleft of the enzymes and the intermediate section reinforcing the peptide-enzyme connection. Interestingly, the C-terminal remained at a considerable distance from the enzymes, as evidenced by the retention of affinity for both enzymes using truncated peptide T-F2. Furthermore, results indicated differences in the bonding relationship of critical residues between thrombin and FXIIa, with His13 facilitating binding to thrombin and Arg7 being required for binding to FXIIa. Overall, our study sheds light on the molecular mechanism by which LCTx-F2 potentiates coagulation factors, providing valuable insights that may assist in designing drugs targeting procoagulation factors.

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.

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.

Time-Lagged Independent Component Analysis of Random Walks and Protein Dynamics

Time-lagged independent component analysis (tICA) is a widely used dimension reduction method for the analysis of molecular dynamics (MD) trajectories and has proven particularly useful for the construction of protein dynamics Markov models. It identifies those “slow” collective degrees of freedom onto which the projections of a given trajectory show maximal autocorrelation for a given lag time. Here we ask how much information on the actual protein dynamics and, in particular, the free energy landscape that governs these dynamics the tICA-projections of MD-trajectories contain, as opposed to noise due to the inherently stochastic nature of each trajectory. To answer this question, we have analyzed the tICA-projections of high dimensional random walks using a combination of analytical and numerical methods. We find that the projections resemble cosine functions and strongly depend on the lag time, exhibiting strikingly complex behavior. In particular, and contrary to previous studies of principal component projections, the projections change noncontinuously with increasing lag time. The tICA-projections of selected 1 μs protein trajectories and those of random walks are strikingly similar, particularly for larger proteins, suggesting that these trajectories contain only little information on the energy landscape that governs the actual protein dynamics. Further the tICA-projections of random walks show clusters very similar to those observed for the protein trajectories, suggesting that clusters in the tICA-projections of protein trajectories do not necessarily reflect local minima in the free energy landscape. We also conclude that, in addition to the previous finding that certain ensemble properties of nonconverged protein trajectories resemble those of random walks; this is also true for their time correlations.

Light Chain Mutation Effects on the Dynamics of Thrombin

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 disease-associated 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.

An investigation into the allosteric mechanism of GPCR A2A adenosine receptor with trajectory-based information theory and complex network model

G protein-coupled receptors (GPCRs), a large superfamily of transmembrane (TM) proteins, allosterically transduce the signal of ligand binding in the extracellular (EC) domain to couple to effector proteins in the intracellular (IC) domain, therefore forming the largest class of drug targets. The A_2A adenosine receptor (A_2AAR), a class-A GPCR, has been extensively studied as it offers numerous possibilities for therapeutic applications. However, the mechanism of allosteric communication between EC and IC domains is not completely clear. In this work, we utilize torsional mutual information to quantify the correlated motions of residue pairs from its molecular dynamics (MD) simulation trajectories, and further use the complex network model to obtain allosteric pipelines and hubs. The identified allosteric communication pipelines mainly transmit the signal from EC domain to the cytoplasmic ends of TM helix 5 (TM5), TM6 and TM7. The allosteric hubs, mostly located at TM5, TM6 and TM7, play an important role in mediating allosteric signal transmission to keep the receptor rigid and prevent G protein from binding to IC domain, which can explain the reason why their mutations distant from ligand-binding site do not affect the ligand binding affinity but affect the ligand efficacy. Additionally, we identify the key residues located in antagonist ZM241385 binding pocket which mediate multiple allosteric pathways and have been experimentally proven to play a critical role in affecting the ligand potency. This study is helpful for understanding the allosteric communication mechanism of A_2AAR, and can provide valuable information for the structure-based drug design of GPCRs.Communicated by Ramaswamy H. Sarma.

Molecular dynamics simulations of human α-thrombin in different structural contexts: evidence for an aptamer-guided cooperation between the two exosites

Human α-thrombin (thrombin) is a multifunctional enzyme that plays a pivotal role in the coagulation pathway. Thrombin activity can be effectively modulated by G-quadruplex-based oligonucleotide aptamers that specifically interact with the two positively charged regions (exosites I and II) on the protein surface. Although insightful atomic-level snapshots of the recognition between thrombin and aptamers have been recently achieved through crystallographic analyses, some dynamic aspects of this interaction have not been fully characterized. We here report molecular dynamics simulations of thrombin in different association states: ligand-free and binary/ternary complexes with the aptamers TBA (at exosite I) and HD22_27mer (at exosite II). The simulations carried out on the binary and ternary complexes formed by thrombin with these aptamers provide a dynamic view of the interactions that stabilize them in a crystal-free environment. Interestingly, the analysis of the dynamics of the exosites in different thrombin binding states clearly indicates that the HD22_27mer binding at the exosite II favours conformations of exosite I that are prone to the TBA binding. Similar effects are observed upon the binding of TBA to the exosite I. These observations provide an atomic-level picture of the exosite inter-communication in thrombin and explain the experimentally detected cooperativity of the TBA/HD22_27mer binding.

Sodium-induced population shift drives activation of thrombin

The equilibrium between active E and inactive E* forms of thrombin is assumed to be governed by the allosteric binding of a Na+ ion. Here we use molecular dynamics simulations and Markov state models to sample transitions between active and inactive states. With these calculations we are able to compare thermodynamic and kinetic properties depending on the presence of Na+. For the first time, we directly observe sodium-induced conformational changes in long-timescale computer simulations. Thereby, we are able to explain the resulting change in activity. We observe a stabilization of the active form in presence of Na+ and a shift towards the inactive form in Na+-free simulations. We identify key structural features to quantify and monitor this conformational shift. These include the accessibility of the S1 pocket and the reorientation of W215, of R221a and of the Na+ loop. The structural characteristics exhibit dynamics at various timescales: Conformational changes in the Na+ binding loop constitute the slowest observed movement. Depending on its orientation, it induces conformational shifts in the nearby substrate binding site. Only after this shift, residue W215 is able to move freely, allowing thrombin to adopt a binding-competent conformation.