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Voltage-dependence gating of ion channels underlies numerous physiological and pathophysiological processes, and disruption of normal voltage gating is the cause of many channelopathies. Here, long timescale atomistic simulations were performed to directly probe voltage-induced gating transitions of the big potassium (BK) channels, where the voltage sensor domain (VSD) movement has been suggested to be distinct from that of canonical Kv channels but remains poorly understood. Using a Core-MT construct without the gating ring, multiple voltage activation transitions were observed at 750 mV, allowing detailed analysis of the activated state of BK VSD and key mechanistic features. Even though the S4 helix remains the principal voltage sensor in BK, its vertical displacement is only ~3 Å and accompanied by significant lateral movements. The nature of the predicted VSD movement is in strong agreement with recent Cryo-EM structural studies of mutant BK channels with constitutively activated VSD. Free energy analysis based on the predicted activation transition yielded a total gating charge of 0.44 e per VSD, consistent with the experimental range of 0.48–0.65 e . We further show that the ability of modest physical movements with a small total gating charge to drive effective voltage gating of BK can be attributed to large gradients in the local electric field as reshaped by the protein. Furthermore, the S4 movement is coupled to the pore opening through a non-canonical pathway that involves the tightly packed S4-S5-S6 interface. These distinct mechanistic features may be relevant to voltage gating of other ion channels where VSDs are not domain-swapped with respect to the pore-gate domain.

Atomistic description of protein fibril formation has been elusive due to the complexity and long time scales of the conformational search. Here, we develop a multiscale approach combining numerous atomistic simulations in explicit solvent to construct Markov State Models (MSMs) of fibril growth. The search for the in-register fully bound fibril state is modeled as a random walk on a rugged two-dimensional energy landscape defined by β-sheet alignment and hydrogen-bonding states, whereas transitions involving states without hydrogen bonds are derived from kinetic clustering. The reversible association/dissociation of an incoming peptide and overall growth kinetics are then computed from MSM simulations. This approach is applied to derive a parameter-free, comprehensive description of fibril elongation of Aβ16–22 and how it is modulated by phenylalanine-to-cyclohexylalanine (CHA) mutations. The trajectories show an aggregation mechanism in which the peptide spends most of its time trapped in misregistered β-sheet states connected by weakly bound states twith short lifetimes. Our results recapitulate the experimental observation that mutants CHA19 and CHA1920 accelerate fibril elongation but have a relatively minor effect on the critical concentration for fibril growth. Importantly, the kinetic consequences of mutations arise from cumulative effects of perturbing the network of productive and nonproductive pathways of fibril growth. This is consistent with the expectation that nonfunctional states will not have evolved efficient folding pathways and, therefore, will require a random search of configuration space. This study highlights the importance of describing the complete energy landscape when studying the elongation mechanism and kinetics of protein fibrils.

Transient receptor potential (TRP) channels are a large and diverse family of transmembrane ion channels that are widely expressed, have important physiological roles, and are associated with many human diseases. These proteins are actively pursued as promising drug targets, benefitting greatly from advances in structural and mechanistic studies of TRP channels. At the same time, the complex, polymodal activation and regulation of TRP channels have presented formidable challenges. In this short review, we summarize recent progresses toward understanding the structural basis of TRP channel function, as well as potential ligand binding sites that could be targeted for therapeutics. A particular focus is on the current understanding of the molecular mechanisms of TRP channel activation and regulation, where many fundamental questions remain unanswered. We believe that a deeper understanding of the functional mechanisms of TRP channels will be critical and likely transformative toward developing successful therapeutic strategies targeting these exciting proteins. This endeavor will require concerted efforts from computation, structural biology, medicinal chemistry, electrophysiology, pharmacology, drug safety and clinical studies.

The calcium-activated chloride channel (CaCC) TMEM16A plays crucial roles in regulating neuronal excitability, smooth muscle contraction, fluid secretion and gut motility. While opening of TMEM16A requires binding of intracellular Ca 2+ , prolonged Ca 2+ -dependent activation results in channel desensitization or rundown, the mechanism of which is unclear. Here we show that phosphatidylinositol (4,5)-bisphosphate (PIP 2 ) regulates TMEM16A channel activation and desensitization via binding to a putative binding site at the cytosolic interface of transmembrane segments (TMs) 3–5. We further demonstrate that the ion-conducting pore of TMEM16A is constituted of two functionally distinct modules: a Ca 2+ -binding module formed by TMs 6–8 and a PIP 2 -binding regulatory module formed by TMs 3–5, which mediate channel activation and desensitization, respectively. PIP 2 dissociation from the regulatory module results in ion-conducting pore collapse and subsequent channel desensitization. Our findings thus provide key insights into the mechanistic understanding of TMEM16 channel gating and lipid-dependent regulation. The calcium-activated chloride channel (CaCC) TMEM16A plays crucial roles in regulating neuronal excitability and muscle contraction. Here authors show that phosphatidylinositol (4,5)-bisphosphate (PIP2) regulates TMEM16A channel activation and desensitization via binding to a putative binding site.

The gating mechanism of transmembrane ion channels is crucial for understanding how these proteins control ion flow across membranes in various physiological processes. Big potassium (BK) channels are particularly interesting with large single-channel conductance and dual regulation by membrane voltage and intracellular Ca 2+ . Recent atomistic structures of BK channels failed to identify structural features that could physically block the ion flow in the closed state. Here, we show that gating of BK channels does not seem to require a physical gate. Instead, changes in the pore shape and surface hydrophobicity in the Ca 2+ -free state allow the channel to readily undergo hydrophobic dewetting transitions, giving rise to a large free energy barrier for K + permeation. Importantly, the dry pore remains physically open and is readily accessible to quaternary ammonium channel blockers. The hydrophobic gating mechanism is also consistent with scanning mutagenesis studies showing that modulation of pore hydrophobicity is correlated with activation properties. BK channels are regulated by membrane voltage and intracellular Ca2+ but the structural features that block the ion flow in the closed state remain unknown. Here authors use molecular dynamics simulation and show that a physical gate is not required; instead ion flow is regulated by hydrophobic dewetting due to changes in pore shape and surface hydrophobicity.

Memristive devices are promising candidates to emulate biological computing. However, the typical switching voltages (0.2-2 V) in previously described devices are much higher than the amplitude in biological counterparts. Here we demonstrate a type of diffusive memristor, fabricated from the protein nanowires harvested from the bacterium Geobacter sulfurreducens , that functions at the biological voltages of 40-100 mV. Memristive function at biological voltages is possible because the protein nanowires catalyze metallization. Artificial neurons built from these memristors not only function at biological action potentials (e.g., 100 mV, 1 ms) but also exhibit temporal integration close to that in biological neurons. The potential of using the memristor to directly process biosensing signals is also demonstrated. Designing energy efficient systems capable to directly process signals at biological voltages remains a challenge. Here, the authors propose a bio-compatible memristor device based on protein-nanowire dielectric, harvested from the bacterium Geobactor sulfurreducens, working at biological voltages.

Machine learning has achieved remarkable success across a broad range of scientific and engineering disciplines, particularly its use for predicting native protein structures from sequence information alone. However, biomolecules are inherently dynamic, and there is a pressing need for accurate predictions of dynamic structural ensembles across multiple functional levels. These problems range from the relatively well-defined task of predicting conformational dynamics around the native state of a protein, which traditional molecular dynamics (MD) simulations are particularly adept at handling, to generating large-scale conformational transitions connecting distinct functional states of structured proteins or numerous marginally stable states within the dynamic ensembles of intrinsically disordered proteins. Machine learning has been increasingly applied to learn low-dimensional representations of protein conformational spaces, which can then be used to drive additional MD sampling or directly generate novel conformations. These methods promise to greatly reduce the computational cost of generating dynamic protein ensembles, compared to traditional MD simulations. In this review, we examine recent progress in machine learning approaches towards generative modeling of dynamic protein ensembles and emphasize the crucial importance of integrating advances in machine learning, structural data, and physical principles to achieve these ambitious goals.

Epigallocatechin gallate (EGCG) from green tea can induce apoptosis in cancerous cells, but the underlying molecular mechanisms remain poorly understood. Using SPR and NMR, here we report a direct, μM interaction between EGCG and the tumor suppressor p53 ( K D  = 1.6 ± 1.4 μM), with the disordered N-terminal domain (NTD) identified as the major binding site ( K D  = 4 ± 2 μM). Large scale atomistic simulations (>100 μs), SAXS and AUC demonstrate that EGCG-NTD interaction is dynamic and EGCG causes the emergence of a subpopulation of compact bound conformations. The EGCG-p53 interaction disrupts p53 interaction with its regulatory E3 ligase MDM2 and inhibits ubiquitination of p53 by MDM2 in an in vitro ubiquitination assay, likely stabilizing p53 for anti-tumor activity. Our work provides insights into the mechanisms for EGCG’s anticancer activity and identifies p53 NTD as a target for cancer drug discovery through dynamic interactions with small molecules. Epigallocatechin gallate (EGCG) is a catechin flavonoid which induces apoptosis in cancerous cells, but the underlying molecular mechanisms remain poorly understood. Here authors use an interdisciplinary approach to show a direct interaction between EGCG and the tumor suppressor p53 and demonstrate that EGCG inhibits ubiquitination of p53 by MDM2.

TMEM16A is a widely expressed Ca2+-activated Cl− channel that regulates crucial physiological functions including fluid secretion, neuronal excitability, and smooth muscle contraction. There is a critical need to understand the molecular mechanisms of TMEM16A gating and regulation. However, high-resolution TMEM16A structures have failed to reveal an activated state with an unobstructed permeation pathway even with saturating Ca2+. This has been attributed to the requirement of PIP2 for preventing TMEM16A desensitization. Here, atomistic simulations show that specific binding of PIP2 to TMEM16A can lead to spontaneous opening of the permeation pathway in the Ca2+-bound state. The predicted activated state is highly consistent with a wide range of mutagenesis and functional data. It yields a maximal Cl− conductance of ~1 pS, similar to experimental estimates, and recapitulates the selectivity of larger SCN− over Cl−. The resulting molecular mechanism of activation provides a basis for understanding the interplay of multiple signals in controlling TMEM16A channel function.Chen and Jia investigate the synergistic regulating role of Ca2+ binding and the signaling lipid PIP2 in TMEM16A channel gating. Their study is significant as it provides new insights into the activated state of TMEM16A and highlights an example of functional importance of lipids in regulating membrane-associated proteins.