BIM Binding Remotely Regulates BAX Activation: Insights from the Free Energy Landscapes

Molecular origin of the differential stabilities of the protofilaments in different polymorphs: molecular dynamics simulation and deep learning

Fragments of α-synuclein, an intrinsically disordered protein, whose misfolding and aggregation are responsible for diseases like Parkinson's disease and others, can co-exist in different polymorphs like 'rod' and 'twister'. Their apparently stable structures have different degrees of tolerance to perturbations like point mutations. The molecular basis of this is investigated using molecular dynamics-based conformational sampling. A charge-swapping mutation, E46K, known to be a reason for the early onset of Parkinson's disease, has differential impact on two polymorphs, and its molecular reason has been probed by investigating the intra-fibril interaction network that is responsible for stabilizing the aggregates. Two different quaternary level arrangement of the peptides in two polymorphs, establishing two different types of interrelations between residues of the peptide monomers, form the basis of their differential stabilities; a Deep Neural Network (DNN)-based analysis has extracted different pairs of residues and their spatial proximities as features to distinguish the states of two polymorphs. It has revealed that difference in these molecular arrangements intrinsically assigns key roles to different sets of residues in two different forms, like a feedback loop from quaternary structure to sequence level; an important insight into the sequence-structure relationship in general. Such atomic level insights were substantiated with the proof of differences in the dynamic correlation between residue pairs, altered mobilities of the sidechains that affects packing and redistribution of the weightage of different principal modes of internal motions in different systems. The identification of key residues with altered significance in different polymorphs is likely to benefit the planned design of fibril breaking molecules.

Mathematical modelling of Alzheimer's disease biomarkers: Targeting Amyloid beta, Tau protein, Apolipoprotein E and Apoptotic pathways

Introduction: The kinetics of brain cell death in Alzheimer's disease (AD) is being studied using mathematical models. These mathematical models utilize techniques like differential equations, stochastic processes, and network theory to explore crucial signalling pathways and interactions between different cell types. One crucial area of research is the intentional cell death known as apoptosis, which is crucial for the nervous system. The main purpose behind the mathematical modelling of this is for identification of which biomarkers and pathways are most influential in the progression of AD. In addition, we can also predict the natural history of the disease, by which we can make early diagnosis. Mathematical modelling of AD: Current mathematical models include the Apolipoprotein E (APOE) Gene Model, the Tau Protein Kinetics Model, and the Amyloid Beta Peptide Kinetic Model. The Bcl-2 and Bax apoptosis theories postulate that the balance of pro- and anti-apoptotic proteins in cells determines whether a cell experiences apoptosis, where the Bcl-2 model, depicts the interaction of pro- and anti-apoptotic proteins, it is also being used in research on cell death in a range of cell types, including neurons and glial cells. How peptides are produced and eliminated in the brain is explained by the Amyloid beta Peptide (Aβ) Kinetics Model. The tau protein kinetics model focuses on production, aggregation, and clearance of tau protein processes, which are hypothesized to be involved in AD. The APOE gene model investigates the connection between the risk of Alzheimer's disease and the APOE gene. These models have been used to predict how Alzheimer's disease would develop and to evaluate how different inhibitors will affect the illness's course. Conclusion: These mathematical models reflect physiological meaningful characteristics and demonstrates robust fits to training data. Incorporating biomarkers like Aβ, Tau, APOE and markers of neuronal loss and cognitive impairment can generate sound predictions of biomarker trajectories over time in Alzheimer's disease.

Adaptive Workflows of Machine Learning Illuminate the Sequential Operation Mechanism of the TAK1's Allosteric Network

Allostery is a fundamental mechanism driving biomolecular processes that holds significant therapeutic concern. Our study rigorously investigates how two distinct machine-learning algorithms uniquely classify two already close-to-active DFG-in states of TAK1, differing just by the presence or absence of its allosteric activator TAB1, from an ensemble mixture of conformations (obtained from 2.4 μs molecular dynamics (MD) simulations). The novelty, however, lies in understanding the deeper algorithmic potentials to systematically derive a diverse set of differential residue connectivity features that reconstruct the essential mechanistic architecture for TAK1-TAB1 allostery in such a close-to-active biochemical scenario. While the recursive, random forest-based workflow displays the potential of conducting discretized, hierarchical derivation of allosteric features, a multilayer perceptron-based approach gains considerable efficacy in revealing fluid connected patterns of features when hybridized with mutual information scoring. Interestingly, both pipelines benchmark similar directions of functional conformational changes for TAK1's activation. The findings significantly advance the depth of mechanistic understanding by highlighting crucial activation signatures along a directed C-lobe → activation loop → ATP pocket channel of information flow, including (1) the αF-αE biterminal alignments and (2) the "catalytic" drift of the activation loop toward kinase active site. Besides, some novel allosteric hotspots (K253, Y206, N189, etc.) are further recognized as TAB1 sensors, transducers, and responders, including a benchmark E70 mutation site, precisely mapping the important structural segments for sequential allosteric execution. Hence, our work demonstrates how to navigate through greater structural depths and dimensions of dynamic allosteric machineries just by leveraging standard ML methods in suitable streamlined workflows adaptive to the specific system and objectives.

Allosteric Boost by TAB1 on the TAK1 Kinase Favorably Sculpts the Thermodynamic Landscape of Activation

The intricate mechanisms of allosteric regulation in kinases are of general interest to the scientific community for potential therapeutic implications. However, the diversity among kinases and their regulatory routes requires a case-by-case study to widen the repertoire of known mechanisms. The present study achieves this by understanding TAK1 kinase activation by TAB1 as a model phenomenon for the first time. Despite the known capacity of TAK1 to switch between its inactive ("DFG-out") and active-like ("DFG-in") conformations, the questionable role of TAB1 in offering an energetic favor to this has been addressed here using sequential combination of enhanced sampling methods like targeted molecular dynamics (TMD) and Gaussian accelerated molecular dynamics (GaMD). It reveals how a minimal domain of TAB1 sufficiently acts like a "catalytic gear" by favorably sculpting TAK1's thermodynamic landscape (potential of mean force in 2D) that accelerates "in"-"out" conformational switching of the conserved DFG motif. Standard molecular dynamics simulations (∼5 μs) reveal that TAB1 fascinatingly exploits the "lever-like" αF helix of TAK1 kinase domain to remotely propel the DFG motif via subtle helical "unfolding-folding" modifications within the kinase activation loop. The presence of two charged residues on terminal poles of αF helix imparts it, with this unique "lever-like" utility, and this turns out to be one important signature of co-evolution between TAK1 and TAB1. The entire mechanism of TAB1's impact transduction, which is found to be analogous to the moves in the popular "Chinese checker" game, gives a clear proof of the "dynamics-driven allostery" concept in kinases. The findings further benchmark TAK1's known autophosphorylation capacity. A novel insight into kinase allostery is thus provided, which potentiates investigation of similar capacities in other kinases.

The Bak core dimer focuses triacylglycerides in the membrane

Apoptosis, the intrinsic programmed cell death process, is mediated by the Bcl-2 family members Bak and Bax. Activation via formation of symmetric core dimers and oligomerization on the mitochondrial outer membrane (MOM) leads to permeabilization and cell death. Although this process is linked to the MOM, the role of the membrane in facilitating such pores is poorly understood. We recently described Bak core domain dimers, revealing lipid binding sites and an initial role of lipids in oligomerization. Here we describe simulations that identified localized clustering and interaction of triacylglycerides (TAGs) with a minimized Bak dimer construct. Coalescence of TAGs occurred beneath this Bak dimer, mitigating dimer-induced local membrane thinning and curvature in representative coarse-grain MOM and model membrane systems. Furthermore, the effects observed as a result of coarse-grain TAG cluster formation was concentration dependent, scaling from low physiological MOM concentrations to those found in other organelles. We find that increasing the TAG concentration in liposomes mimicking the MOM decreased the ability of activated Bak to permeabilize these liposomes. These results suggest that the presence of TAGs within a Bak-lipid membrane preserves membrane integrity and is associated with reduced membrane stress, suggesting a possible role of TAGs in Bak-mediated apoptosis.

Analysis of the Binding Sites on BAX and the Mechanism of BAX Activators through Extensive Molecular Dynamics Simulations

The BAX protein is a pro-apoptotic member of the Bcl-2 family, which triggers apoptosis by causing permeabilization of the mitochondrial outer membrane. However, the activation mechanism of BAX is far from being understood. Although a few small-molecule BAX activators have been reported in the literature, their crystal structures in complex with BAX have not been resolved. So far, their binding modes were modeled at most by simple molecular docking efforts. Lack of an in-depth understanding of the activation mechanism of BAX hinders the development of more effective BAX activators. In this work, we employed cosolvent molecular dynamics simulation to detect the potential binding sites on the surface of BAX and performed a long-time molecular dynamics simulation (50 μs in total) to derive the possible binding modes of three BAX activators (i.e., BAM7, BTC-8, and BTSA1) reported in the literature. Our results indicate that the trigger, S184, and vMIA sites are the three major binding sites on the full-length BAX structure. Moreover, the canonical hydrophobic groove is clearly detected on the α9-truncated BAX structure, which is consistent with the outcomes of relevant experimental studies. Interestingly, it is observed that solvent probes bind to the trigger bottom pocket more stably than the PPI trigger site. Each activator was subjected to unbiased molecular dynamics simulations started at the three major binding sites in five parallel jobs. Our MD results indicate that all three activators tend to stay at the trigger site with favorable MM-GB/SA binding energies. BAM7 and BTSA1 can enter the trigger bottom pocket and thereby enhance the movement of the α1-α2 loop, which may be a key factor at the early stage of BAX activation. Our molecular modeling results may provide useful guidance for designing smart biological experiments to further explore BAX activation and directing structure-based efforts toward discovering more effective BAX activators.

FLT3 tyrosine kinase inhibitors synergize with BCL-2 inhibition to eliminate FLT3/ITD acute leukemia cells through BIM activation

Tyrosine kinase inhibitors (TKIs) targeting FLT3 have shown activity but when used alone have achieved limited success in clinical trials, suggesting the need for combination with other drugs. We investigated the combination of FLT3 TKIs (Gilteritinib or Sorafenib), with Venetoclax, a BCL-2 selective inhibitor (BCL-2i), on FLT3/ITD leukemia cells. The combination of a FLT3 TKI and a BCL-2i synergistically reduced cell proliferation and enhanced apoptosis/cell death in FLT3/ITD cell lines and primary AML samples. Venetoclax also re-sensitized FLT3 TKI-resistant cells to Gilteritinib or Sorafenib treatment, mediated through MAPK pathway inhibition. Gilteritinib treatment alone dissociated BIM from MCL-1 but increased the binding of BIM to BCL-2. Venetoclax treatment enhanced the binding of BIM to MCL-1 but dissociated BIM from BCL-2. Treatment with the drugs together resulted in dissociation of BIM from both BCL-2 and MCL-1, with an increased binding of BIM to the cell death mediator BAX, leading to increased apoptosis. These findings suggest that Venetoclax mitigates the unintended pro-survival effects of FLT3 TKI mainly through the dissociation of BIM and BCL-2 and also decreased BIM expression. This study provides evidence that the addition of BCL-2i enhances the effect of FLT3 TKI therapy in FLT3/ITD AML treatment.

Dissecting the thermodynamic contributions of the charged residues in the membrane anchoring of Bcl-xl C-terminal domain

The C-terminal helix of the Bcl-xl is known to initiate the membrane insertion of the protein by anchoring into the mitochondrial outer membrane. The C-terminal charged residues of that helix, R232 and K233, are reported to have an important structural role in the process of that insertion. The present work provides a quantitative understanding of the thermodynamic contribution of these residues on the membrane insertion energy-profile, calculated from the Adaptive Biasing Force based MD simulations of 2.67 μs altogether. Interestingly, the effect of the single neutralizing mutations at the C-terminus, i.e. K233A or R232A, is easily tolerated by the peptide without impacting the nature of insertion energy-profile, indicating the efficiency of one positively charged residue to drive the insertion. Whereas a double mutant, i.e. R232A and K233A, makes a significant impact on the energy-profile by destabilizing the membrane-associated states, as well as the membrane-embedded states. The finding provides molecular-level mechanistic insight. The water-mediated interaction formed by the peptide polar side chains within the bilayer core is found to modulate the membrane response during peptide insertion and that subsequently regulates the insertion mechanism. Mutation of the C-terminal residues eventually alters such a cascade of interactions that results in an insertion through energetically more expensive pathway. Since any one of the positively charged residues at the terminal is critical to ensure the membrane insertion, it appears that the natural selection of 'two' instead of 'one' charged residue is redundant in the context of membrane anchoring but may be important for other biochemical events.