MutSα's Multi-Domain Allosteric Response to Three DNA Damage Types Revealed by Machine Learning

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.

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.

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.

Chemistry of Fluorinated Pyrimidines in the Era of Personalized Medicine

We review developments in fluorine chemistry contributing to the more precise use of fluorinated pyrimidines (FPs) to treat cancer. 5-Fluorouracil (5-FU) is the most widely used FP and is used to treat > 2 million cancer patients each year. We review methods for 5-FU synthesis, including the incorporation of radioactive and stable isotopes to study 5-FU metabolism and biodistribution. We also review methods for preparing RNA and DNA substituted with FPs for biophysical and mechanistic studies. New insights into how FPs perturb nucleic acid structure and dynamics has resulted from both computational and experimental studies, and we summarize recent results. Beyond the well-established role for inhibiting thymidylate synthase (TS) by the 5-FU metabolite 5-fluoro-2′-deoxyuridine-5′-O-monophosphate (FdUMP), recent studies have implicated new roles for RNA modifying enzymes that are inhibited by 5-FU substitution including tRNA methyltransferase 2 homolog A (TRMT2A) and pseudouridylate synthase in 5-FU cytotoxicity. Furthermore, enzymes not previously implicated in FP activity, including DNA topoisomerase 1 (Top1), were established as mediating FP anti-tumor activity. We review recent literature summarizing the mechanisms by which 5-FU inhibits RNA- and DNA-modifying enzymes and describe the use of polymeric FPs that may enable the more precise use of FPs for cancer treatment in the era of personalized medicine.

Probing light chain mutation effects on thrombin via molecular dynamics simulations and machine learning

Thrombin is a key component for chemotherapeutic and antithrombotic therapy development. As the physiologic and pathologic roles of the light chain still remain vague, here, we continue previous efforts to understand the impacts of the disease-associated single deletion of LYS9 in the light chain. By combining supervised and unsupervised machine learning methodologies and more traditional structural analyses on data from 10 μs molecular dynamics simulations, we show that the conformational ensemble of the ΔK9 mutant is significantly perturbed. Our analyses consistently indicate that LYS9 deletion destabilizes both the catalytic cleft and regulatory functional regions and result in some conformational changes that occur in tens to hundreds of nanosecond scaled motions. We also reveal that the two forms of thrombin each prefer a distinct binding mode of a Na+ ion. We expand our understanding of previous experimental observations and shed light on the mechanisms of the LYS9 deletion associated bleeding disorder by providing consistent but more quantitative and detailed structural analyses than early studies in literature. With a novel application of supervised learning, i.e. the decision tree learning on the hydrogen bonding features in the wild-type and ΔK9 mutant forms of thrombin, we predict that seven pairs of critical hydrogen bonding interactions are significant for establishing distinct behaviors of wild-type thrombin and its ΔK9 mutant form. Our calculations indicate the LYS9 in the light chain has both localized and long-range allosteric effects on thrombin, supporting the opinion that light chain has an important role as an allosteric effector.

Fluctuation correlations as major determinants of structure- and dynamics-driven allosteric effects

Both structure- and dynamics-driven allosteric effects are determined by the correlation of distance fluctuations in proteins.

Visualizing correlated motion with HDBSCAN clustering

Correlated motion analysis provides a method for understanding communication between and dynamic similarities of biopolymer residues and domains. The typical equal-time correlation matrices-frequently visualized with pseudo-colorings or heat maps-quickly convey large regions of highly correlated motion but hide more subtle similarities of motion. Here we propose a complementary method for visualizing correlations within proteins (or general biopolymers) that quickly conveys intuition about which residues have a similar dynamic behavior. For grouping residues, we use the recently developed non-parametric clustering algorithm HDBSCAN. Although the method we propose here can be used to group residues using correlation as a similarity matrix-the most straightforward and intuitive method-it can also be used to more generally determine groups of residues which have similar dynamic properties. We term these latter groups "Dynamic Domains", as they are based not on spatial closeness but rather closeness in the column space of a correlation matrix. We provide examples of this method across three human proteins of varying size and function-the Nf-Kappa-Beta essential modulator, the clotting promoter Thrombin and the mismatch repair protein (dimer) complex MutS-alpha. Although the examples presented here are from all-atom molecular dynamics simulations, this visualization technique can also be used on correlations matrices built from any ensembles of conformations from experiment or computation.

Mechanistic insights into thrombin's switch between "slow" and "fast" forms

Thrombin is a multifunctional enzyme that plays an important role in blood coagulation, cell growth, and metastasis. Depending upon the binding of sodium ions, thrombin presents significantly different enzymatic activities. In the environment with sodium ions, thrombin is highly active in cleaving the coagulated substrates and this is referred to as the "fast" form; in the environment without sodium ions, thrombin turns catalytically less active and is in the "slow" form. Although many experimental studies over the last two decades have attempted to reveal the structural and kinetic differences between these two forms, it remains vague and disputed how the functional switch between the "fast" and "slow" forms is mediated by Na+ cations. In this work, we employ microsecond-scale all-atom molecular dynamics simulations to investigate the differences in the structural ensembles in sodium-bound/unbound and potassium-bound/unbound thrombin. Our calculations indicate that the regulatory regions, including the 60s, γ loops, and exosite I and II, are primarily affected by both the bound and unbound cations. Conformational free energy surfaces, estimated from principal component analysis, further reveal the existence of multiple conformational states. The binding of a cation introduces changes in the distribution of these states. Through comparisons with potassium-binding, the binding of sodium ions appears to shift the population toward conformational states that might be catalytically favorable. Our study of thrombin in the presence of sodium/potassium ions suggests Na+-mediated generalized allostery is the mechanism of thrombin's functional switch between the "fast" and "slow" forms.