Accumulation of α-synuclein (αSyn) aggregates in the human brain is the major hallmark of synucleinopathies such as Parkinson’s Disease, Multiple System Atrophy, and Dementia with Lewy bodies. Positron Emission Tomography (PET) plays a vital role in diagnosing these diseases and monitoring their progression by enabling the non-invasive and sensitive detection of αSyn aggregates in the brain. However, developing PET tracers with specific target binding, as well as identifying the binding site and complex structure present significant challenges. Here, we investigated the interaction between lipidic αSyn aggregates and MODAG-005, a new αSyn PET tracer candidate, using nuclear magnetic resonance spectroscopy, cryogenic electron microscopy and molecular dynamics (MD) simulations. Two binding sites of MODAG-005 were found, one on the surface and one in a tubular cavity of the fibril, of which the occupancies were found to depend on the preparation protocols. The cavity binding site is thermodynamically more stable than the external binding site and is the only site occupied when MODAG-005 is applied with liposome as carriers to the aggregates. This is corroborated by MD simulations in which stable interactions between MODAG-005 and glycine containing backbone motifs of the tubular cavity of the aggregates are observed.

Given the growing interest in designing targeted covalent inhibitors, methods for rapidly and accurately probing pKas─and, by extension, the reactivities─of target cysteines are highly desirable. Complementary to cysteine, histidine is similarly relevant due to its frequent presence in protein active sites and its unique ability to exist in two tautomeric states. Here, we demonstrate that nonequilibrium free energy calculations can accurately determine the pKa values of both residues, often outperforming conventional predictors. Importantly, we find that (1) increasing the van der Waals radius of cysteine's sulfur atom, (2) modifying the backbone charges of histidine, and (3) introducing effective polarization by downscaling the side chain partial charges of both residues can all significantly improve pKa prediction accuracy. Using the modified CHARMM36m force field on the full dataset reduces the prediction error from 2.12 ± 0.27 pK to 1.28 ± 0.15 pK and increases the correlation with experiment from 0.25 ± 0.09 to 0.58 ± 0.08. Similarly, using the modified Amber14SB force field decreases the error from 3.21 ± 0.29 pK to 1.69 ± 0.23 pK and improves the correlation from 0.15 ± 0.10 to 0.36 ± 0.10.

BK channels are a class of K+ channels that possess an unusually high conductance and are synergistically gated by intracellular Ca2+ and voltage. Despite the significant array of experimental and computational data, many aspects of their function and dynamics remain unclear-such as how ion permeation is halted in the closed state of the channel. Available CryoEM structures obtained in deactivating conditions capture the channel with a wide, unobstructed pore, in contrast to e.g. a helix bundle crossing observed in some K+ channels. Several hypotheses of BK closure were proposed, including selectivity filter and hydrophobic gating. In this work, we expand on the model of hydrophobic gating and focus on the role of lipids. Based on our atomistic and coarse-grained molecular dynamics simulations, we propose lipid entrance into the pore - either with lipid tails or entire lipid molecules - through the membrane-facing fenestrations to be a critical determinant of BK conductivity. Furthermore, we elucidate the mechanism of BK activation by negatively charged lipids, and suggest that they act by a multi-modal mechanism, which encompasses lipid entry reduction, increase of the K+ occupancy of the pore, and stabilization of the channel’s open-state structure - in broad agreement with experimental data. This presents an example of the crucial role of lipids in regulating BK channel’s activity, and paves the way to further understanding of BK function in such complex environments as cellular membranes. ### Competing Interest Statement The authors have declared no competing interest. Deutsche Forschungsgemeinschaft, https://ror.org/018mejw64

The sensation of mechanical stimuli is initiated by elastic gating springs that pull open mechanosensory transduction channels. Searches for gating springs have focused on force-conveying protein tethers such as the amino-terminal ankyrin tether of the Drosophila mechanosensory transduction channel NOMPC. Here, by combining protein domain duplications with mechanical measurements, electrophysiology, molecular dynamics simulations and modeling, we identify the NOMPC gating-spring as the short linker between the ankyrin tether and the channel gate. This linker acts as a Hookean hinge that is ten times more elastic than the tether, with the linker hinge dictating channel gating and the intrinsic stiffness of the gating spring. Our study shows how mechanosensation is initiated molecularly; disentangles gating springs and tethers, and respective paradigms of channel gating; and puts forward gating springs as core ion channel constituents that enable efficient gating by diverse stimuli and in a wide variety of channels. Hehlert et al. report that the gating spring that pulls open mechanosensitive NOMPC channels is not their helical ankyrin tether, but instead an elastic hinge that suspends that tether on the channel gate.

Free energy calculations have become invaluable in protein design, offering a powerful means to rapidly and accurately screen potential variants. Here, we provide a step-by-step protocol that combines molecular dynamics simulations with non-equilibrium alchemical free energy methods to tackle open questions in protein design. For system setup, simulation, and free energy analysis, our workflow relies on two free and open-source packages: pmx and GROMACS. Using the well-characterized protein switch MAD2, we demonstrate how to predict mutation-induced changes in protein behavior. At each step in the protocol, we highlight critical factors for obtaining high-quality free energy estimates and discuss common pitfalls and strategies to avoid them. Finally, we outline how this protocol can be extended to more challenging applications, such as charge-changing mutations, large-scale mutational scans, and allosteric coupling studies.

Drug discovery can be thought as a search for a needle in a haystack. Finding the initial active hit molecules, the optimal decoration of lead molecule analogues, to final clinical candidate selection is an on-going trade-off between applying the best methods versus the cost of assessing the large available chemical space. Computational techniques can impact by narrowing the search-space, but some preferred methods such as binding affinity calculations can still only be performed on a small fraction of the possible molecules. For that purpose, machine learning (ML) strategies are being developed to complement the experimentation and computationally more expensive approaches in navigating and triaging large chemical libraries. In the current study, we explore how an active learning protocol can be combined with first principles based alchemical free energy calculations to identify high affinity phosphodiesterase 2 (PDE2) inhibitors. Firstly, we calibrate the procedure using a large set of experimentally characterised PDE2 binders. The optimized protocol is then used prospectively on a large chemical library to navigate towards potent inhibitors. In the active learning cycle, at every iteration a small fraction of compounds is probed by alchemical calculations and the obtained affinities are used to train ML models. With successive rounds high affinity binders are identified by explicitly evaluating only a small subset of compounds in a large chemical library, thus providing an efficient protocol that robustly identifies a large fraction of true positives.

Alzheimer's disease is a specific neurodegenerative disorder, distinct from normal aging, with a growing unmet medical need. It is characterized by the accumulation of amyloid plaques in the brain, primarily consisting of amyloid beta (Aβ) fibrils. Therapeutic antibodies can slow down the disease, but are associated with potential severe side effects, motivating the development of small molecules to halt disease progression. This study investigates the interaction between the clinical drug candidate small molecule anle138b and lipidic Aβ₄₀ fibrils of type 1 (L1). L1 fibrils were previously shown to closely resemble fibrils from Alzheimer's patients. Using high-resolution structural biology techniques, including cryo-electron microscopy (cryo-EM), nuclear magnetic resonance (NMR) spectroscopy enhanced by dynamic nuclear polarization (DNP), and molecular dynamics (MD) simulations, we find that anle138b selectively binds to a cavity within the fibril. This structural insight provides a deeper understanding of a potential drug-binding mechanism at the atomic level and may inform the development of therapies and diagnostic approaches. In addition, anle138b reduces fibril formation in the presence of lipids by approximately 75%. This may suggest a mechanistic connection to its previously reported activity in animal models of Alzheimer's disease.

Relative binding free energy (RBFE) calculations have emerged as a powerful tool in drug discovery, capable of achieving experimental-level accuracy. However, the accuracy is compromised by a multitude of factors, including the initial structure modeling. The current study contributes to the quantification of the impact of initial structure modeling on the accuracy across a diverse set of activity cliff pairs. Along with providing a quantitative relation between the resolution of the crystal structure and free energy accuracy, we also demonstrate the incorporation of a secondary solvation tool (SOLVATE) to increase the free energy accuracy, especially when crystal waters are missing. The study also evaluates the reliability of AI-predicted structures in RBFE calculations, showing their effectiveness in predicting RBFE directionality and assigning nominal resolutions to the predicted structures based on free energy accuracy. These findings provide a set of recommendations for the development of more robust RBFE protocols, informing the use of structural data, solvation techniques, and AI-predicted protein models in drug discovery.

Aggregation intermediates play a pivotal role in the assembly of amyloid fibrils, which are central to the pathogenesis of neurodegenerative diseases. The structures of filamentous intermediates and mature fibrils are now efficiently determined by single-particle cryo-electron microscopy. By contrast, smaller pre-fibrillar α-Synuclein (αS) oligomers, crucial for initiating amyloidogenesis, remain largely uncharacterized. We report an atomic-resolution structural characterization of a toxic pre-fibrillar aggregation intermediate (I1) on pathway to the formation of lipidic fibrils, which incorporate lipid molecules on protofilament surfaces during fibril growth on membranes. Super-resolution microscopy reveals a tetrameric state, providing insights into the early oligomeric assembly. Time resolved nuclear magnetic resonance (NMR) measurements uncover a structural reorganization essential for the transition of I1 to mature lipidic L2 fibrils. The reorganization involves the transformation of anti-parallel β-strands during the pre-fibrillar I1 state into a β-arc characteristic of amyloid fibrils. This structural reconfiguration occurs in a conserved structural kernel shared by a vast number of αS-fibril polymorphs including extracted fibrils from Parkinson’s and Lewy Body Dementia patients. Consistent with reports of anti-parallel β-strands being a defining feature of toxic αS pre-fibrillar intermediates, I1 impacts viability of neuroblasts and disrupts cell membranes, resulting in an increased calcium influx. Our results integrate the occurrence of anti-parallel β-strands as salient features of toxic oligomers with their significant role in the amyloid fibril assembly pathway. These structural insights have implications for the development of therapies and biomarkers. An atomistic structure of a toxic pre-fibrillar α-Synuclein intermediate is presented, highlighting structural changes that drive amyloid fibril formation.

Free energy calculations based on molecular dynamics simulations offer a quantitative assessment of biomolecule binding and stability. This chapter discusses accurately estimating the free energy differences employing nonequilibrium alchemy. We cover the theoretical background, technical aspects of free energy simulations, and practical frameworks of efficient simulation set-up. The chapter also details several examples of large-scale quantitative estimations of binding free energy, including protein-ligand, protein-protein, and protein-nucleic acid binding. Overall, we provide a comprehensive guide to state-of-the-art simulation methods for accurate free energy calculations in the field of molecular biophysics and computational chemistry.

Allostery, the phenomenon by which the perturbation of a molecule at one site alters its behavior at a remote functional site, enables control over biomolecular function. Allosteric modulation is a promising avenue for drug discovery and is employed in the design of mechanical metamaterials. However, a general principle of allostery, i.e. a set of quantitative and transferable "ground rules", remains elusive. It is neither a set of structural motifs nor intrinsic motions. Focusing on elastic network models, we here show that an allosteric lever -- a mode-coupling pattern induced by the perturbation -- governs the directional, source-to-target, allosteric communication: a structural perturbation of an allosteric site couples the excitation of localized hard elastic modes with concerted long range soft-mode relaxation. Perturbations of non-allosteric sites instead couple hard and soft modes uniformly. The allosteric response is shown to be generally non-linear and non-reciprocal, and allows for minimal structural distortions to be efficiently transmitted to specific changes at distant sites. Allosteric levers exist in proteins and "pseudoproteins" -- networks designed to display an allosteric response. Interestingly, protein sequences that constitute allosteric transmission channels are shown to be evolutionarily conserved. To illustrate how the results may be applied in drug design, we use them to successfully predict known allosteric sites in proteins.

Small molecule kinase inhibitors are critical in the modern treatment of cancers, evidenced by the existence of over 80 FDA-approved small-molecule kinase inhibitors. Unfortunately, intrinsic or acquired resistance, often causing therapy discontinuation, is frequently caused by mutations in the kinase therapeutic target. The advent of clinical tumor sequencing has opened additional opportunities for precision oncology to improve patient outcomes by pairing optimal therapies with tumor mutation profiles. However, modern precision oncology efforts are hindered by lack of sufficient biochemical or clinical evidence to classify each mutation as resistant or sensitive to existing inhibitors. Structure-based methods show promising accuracy in retrospective benchmarks at predicting whether a kinase mutation will perturb inhibitor binding, but comparisons are made by pooling disparate experimental measurements across different conditions. We present the first prospective benchmark of structure-based approaches on a blinded dataset of in-cell kinase inhibitor affinities to Abl kinase mutants using a NanoBRET reporter assay. We compare NanoBRET results to structure-based methods and their ability to estimate the impact of mutations on inhibitor binding (measured as ΔΔG). Comparing physics-based simulations, Rosetta, and previous machine learning models, we find that structure-based methods accurately classify kinase mutations as inhibitor-resistant or inhibitor-sensitizing, and each approach has a similar degree of accuracy. We show that physics-based simulations are best suited to estimate ΔΔG of mutations that are distal to the kinase active site. To probe modes of failure, we retrospectively investigate two clinically significant mutations poorly predicted by our methods, T315A and L298F, and find that starting configurations and protonation states significantly alter the accuracy of our predictions. Our experimental and computational measurements provide a benchmark for estimating the impact of mutations on inhibitor binding affinity for future methods and structure-based models. These structure-based methods have potential utility in identifying optimal therapies for tumor-specific mutations, predicting resistance mutations in the absence of clinical data, and identifying potential sensitizing mutations to established inhibitors.

The modulator pocket is a cryptic site discovered in the TREK1 (K2P2.1) K2P channel. This pocket, located close to the selectivity filter, accommodates agonists that enhance the channel's activity. Since its discovery, equivalent sites in other K2P channels have been shown to bind various ligands, both endogenous and exogenous. In this review, we attempt to elucidate how the modulator pocket contributes to K2P channel activation. To this end, we first describe the gating mechanisms reported in the literature and rationalize their modes of action. We then highlight previous experimental and computational evidence for agonists that bind to the modulator pocket, together with mutations at this site that affect gating. Finally, we elaborate how the activation signal arising from the modulator pocket is transduced to the gates in K2P channels. In doing so, we outline a potential common modulator pocket architecture across K2P channels: a largely amphipathic structure - consistent with the expected properties of a pocket exposed at the interface between a hydrophobic membrane and the aqueous solvent - but still with some important channel-sequence-variations. This architecture and its key differences can be leveraged for the design of new selective and potent modulators.

ABSTRACT We benchmarked the performance of the GROMACS 2024 molecular dynamics (MD) code on a modern high‐performance computing (HPC) cluster with AMD CPUs on up to 65,536 CPU cores. We used five different MD systems, ranging in size from about 82,000 to 204 million atoms, and evaluated their performance using two different Message Passing Interface (MPI) libraries, Intel‐MPI and Open‐MPI. The largest system showed near‐perfect strong scaling up to 512 nodes or 65,536 cores, maintaining a parallel efficiency above 0.9 even at the highest level of parallelization. Energy efficiency for a given number of nodes was generally equal to or slightly better than parallel efficiency. We achieved peak performances of 687 ns/d for the 82k atom system, 116 ns/d for the 53M atom system, and about 35 ns/d for the largest 204M atom system. These results demonstrate that highly optimized software running on a state‐of‐the‐art HPC cluster provides sufficient computing power to simulate biomolecular systems at the mesoscale of viruses and organelles, and potentially small cells in the near future.

In a protein, nearby titratable sites can be coupled: the (de)protonation of one may affect the other. The degree of this interaction depends on several factors and can influence the measured pKa. Here, we derive a formalism based on double free energy differences (ΔΔG) for quantifying the individual site pKa values of coupled residues. As ΔΔG values can be obtained by means of alchemical free energy calculations, the presented approach allows for a convenient estimation of coupled residue pKas in practice. We demonstrate that our approach and a previously proposed microscopic pKa formalism, can be combined with non-equilibrium (NEQ) alchemical free energy calculations to resolve pH-dependent protein pKa values. Toy models and both, regular and constant-pH molecular dynamics simulations, alongside experimental data, are used to validate this approach. Our results highlight the insights gleaned when coupling and microstate probabilities are analyzed and suggest extensions to more complex enzymatic contexts. Furthermore, we find that naïvely computed pKa values that ignore coupling, can be significantly improved when coupling is accounted for, in some cases reducing the error by half. In short, our results suggest that free energy methods can resolve the pKa values of both uncoupled and coupled residues.

TWIK-related potassium channel 1 (TREK1), a two-pore-domain mammalian potassium (K+) channel, regulates the resting potential across cell membranes, presenting a promising therapeutic target for neuropathy treatment. The gating of this channel converges in the conformation of the narrowest part of the pore: the selectivity filter (SF). Various hypotheses explain TREK1 gating modulation, including the dynamics of loops connecting the SF with transmembrane helices and the stability of hydrogen bond (HB) networks adjacent to the SF. Recently, two small molecules (Q6F and Q5F) were reported as activators that affect TREK1 by increasing its open probability in single-channel current measurements. Here, using molecular dynamics simulations, we investigate the effect of these ligands on the previously proposed modulation mechanisms of TREK1 gating compared to the apo channel. Our findings reveal that loop dynamics at the upper region of the SF exhibit only a weak correlation with permeation events/nonpermeation periods, whereas the HB network behind the SF appears more correlated. These nonpermeation periods arise from both distinct mechanisms: a C-type inactivation (resulting from dilation at the top of the SF), which has been described previously, and a carbonyl flipping in an SF binding site. We find that, besides the prevention of C-type inactivation in the channel, the ligands increase the probability of permeation by modulating the dynamics of the carbonyl flipping, influenced by a threonine residue at the bottom of the SF. These results offer insights for rational ligand design to optimize the gating modulation of TREK1 and related K+ channels.

The Coulomb interactions in molecular simulations are inherently approximated due to the finite size of the molecular box sizes amenable to current-day compute power. Several methods exist for treating long-range electrostatic interactions, yet these approaches are subject to various finite-size-related artifacts. Lattice-sum methods are frequently used to approximate long-range interactions; however, these approaches also suffer from artifacts which become particularly pronounced for free-energy calculations that involve charge changes. The artifacts, however, also affect the sampling when plain simulations are performed, leading to a biased ensemble. Here, we investigate two previously described model systems to determine if artifacts continue to play a role when overall neutral boxes are considered, in the context of both free-energy calculations and sampling. We find that ensuring that no net-charge changes take place, while maintaining a neutral simulation box, may be sufficient provided that the simulation boxes are large enough. Addition of salt to the solution (when appropriate) can further alleviate the remaining artifacts in the sampling or the calculated free-energy differences. We provide practical guidelines to avoid finite-size artifacts.

Protein fold-switches are important in many biological processes and exhibit great structural diversity. This makes them perfect for protein design studies with the aim to make programmable proteins with new functions. Even though protein design has made remarkable progress in the last years, designing fold switching proteins de novo is still a challenging task. We apply alchemical free energy simulations with PMX to calculate the mutation free energy differences between different protein conformations.