Unveiling interatomic distances influencing the reaction coordinates in alanine dipeptide isomerization: An explainable deep learning approach.

The present work shows that the free energy landscape associated with alanine dipeptide isomerization can be effectively represented by specific interatomic distances without explicit reference to dihedral angles. Conventionally, two stable states of alanine dipeptide in vacuum, i.e., C7eq (ß-sheet structure) and C7ax (left handed α-helix structure), have been primarily characterized using the main chain dihedral angles, φ (C-N-Cα-C) and ψ (N-Cα-C-N). However, our recent deep learning combined with the "Explainable AI" (XAI) framework has shown that the transition state can be adequately captured by a free energy landscape using φ and θ (O-C-N-Cα) [Kikutsuji et al., J. Chem. Phys. 156, 154108 (2022)]. In the perspective of extending these insights to other collective variables, a more detailed characterization of the transition state is required. In this work, we employ interatomic distances and bond angles as input variables for deep learning rather than the conventional and more elaborate dihedral angles. Our approach utilizes deep learning to investigate whether changes in the main chain dihedral angle can be expressed in terms of interatomic distances and bond angles. Furthermore, by incorporating XAI into our predictive analysis, we quantified the importance of each input variable and succeeded in clarifying the specific interatomic distance that affects the transition state. The results indicate that constructing a free energy landscape based on the identified interatomic distance can clearly distinguish between the two stable states and provide a comprehensive explanation for the energy barrier crossing.

Improvement of the Green-Red Förster Resonance Energy Transfer-Based Ca2+ Indicator by Using the Green Fluorescent Protein, Gamillus, with a Trans Chromophore as the Donor.

To monitor the Ca2+ dynamics in cells, various genetically encoded Ca2+ indicators (GECIs) based on Förster resonance energy transfer (FRET) between fluorescent proteins are widely used for live imaging. Conventionally, cyan and yellow fluorescent proteins have been often used as FRET pairs. Meanwhile, bathochromically shifted indicators with green and red fluorescent protein pairs have various advantages, such as low toxicity and autofluorescence in cells. However, it remains difficult to develop them with a similar level of dynamic range as cyan and yellow fluorescent protein pairs. To improve this, we used Gamillus, which has a unique trans-configuration chromophore, as a green fluorescent protein. Based on one of the best high-dynamic-range GECIs, Twitch-NR, we developed a GECI with 1.5-times higher dynamic range (253%), Twitch-GmRR, using RRvT as a red fluorescent protein. Twitch-GmRR had high brightness and photostability and was successfully applied for imaging the Ca2+ dynamics in live cells. Our results suggest that Gamillus with trans-type chromophores contributes to improving the dynamic range of GECIs. Therefore, selection of the cis-trans isomer of the chromophore may be a fundamental approach to improve the dynamic range of green-red FRET indicators, unlimited by GECIs.

Accelerated Molecular Dynamics and AlphaFold Uncover a Missing Conformational State of Transporter Protein OxlT.

Transporter proteins change their conformations to carry their substrate across the cell membrane. The conformational dynamics is vital to understanding the transport function. We have studied the oxalate transporter (OxlT), an oxalate:formate antiporter from Oxalobacter formigenes, significant in avoiding kidney stone formation. The atomic structure of OxlT has been recently solved in the outward-open and occluded states. However, the inward-open conformation is still missing, hindering a complete understanding of the transporter. Here, we performed a Gaussian accelerated molecular dynamics simulation to sample the extensive conformational space of OxlT and successfully predicted the inward-open conformation where cytoplasmic substrate formate binding was preferred over oxalate binding. We also identified critical interactions for the inward-open conformation. The results were complemented by an AlphaFold2 structure prediction. Although AlphaFold2 solely predicted OxlT in the outward-open conformation, mutation of the identified critical residues made it partly predict the inward-open conformation, identifying possible state-shifting mutations.

Molecular Design of FRET Probes Based on Domain Rearrangement of Protein Disulfide Isomerase for Monitoring Intracellular Redox Status.

Multidomain proteins can exhibit sophisticated functions based on cooperative interactions and allosteric regulation through spatial rearrangements of the multiple domains. This study explored the potential of using multidomain proteins as a basis for Förster resonance energy transfer (FRET) biosensors, focusing on protein disulfide isomerase (PDI) as a representative example. PDI, a well-studied multidomain protein, undergoes redox-dependent conformational changes, enabling the exposure of a hydrophobic surface extending across the b' and a' domains that serves as the primary binding site for substrates. Taking advantage of the dynamic domain rearrangements of PDI, we developed FRET-based biosensors by fusing the b' and a' domains of thermophilic fungal PDI with fluorescent proteins as the FRET acceptor and donor, respectively. Both experimental and computational approaches were used to characterize FRET efficiency in different redox states. In vitro and in vivo evaluations demonstrated higher FRET efficiency of this biosensor in the oxidized form, reflecting the domain rearrangement and its responsiveness to intracellular redox environments. This novel approach of exploiting redox-dependent domain dynamics in multidomain proteins offers promising opportunities for designing innovative FRET-based biosensors with potential applications in studying cellular redox regulation and beyond.

Structure and mechanism of oxalate transporter OxlT in an oxalate-degrading bacterium in the gut microbiota.

An oxalate-degrading bacterium in the gut microbiota absorbs food-derived oxalate to use this as a carbon and energy source, thereby reducing the risk of kidney stone formation in host animals. The bacterial oxalate transporter OxlT selectively uptakes oxalate from the gut to bacterial cells with a strict discrimination from other nutrient carboxylates. Here, we present crystal structures of oxalate-bound and ligand-free OxlT in two distinct conformations, occluded and outward-facing states. The ligand-binding pocket contains basic residues that form salt bridges with oxalate while preventing the conformational switch to the occluded state without an acidic substrate. The occluded pocket can accommodate oxalate but not larger dicarboxylates, such as metabolic intermediates. The permeation pathways from the pocket are completely blocked by extensive interdomain interactions, which can be opened solely by a flip of a single side chain neighbouring the substrate. This study shows the structural basis underlying metabolic interactions enabling favourable symbiosis.

Molecular mechanism on forcible ejection of ATPase inhibitory factor 1 from mitochondrial ATP synthase.

IF1 is a natural inhibitor protein for mitochondrial FoF1 ATP synthase that blocks catalysis and rotation of the F1 by deeply inserting its N-terminal helices into F1. A unique feature of IF1 is condition-dependent inhibition; although IF1 inhibits ATP hydrolysis by F1, IF1 inhibition is relieved under ATP synthesis conditions. To elucidate this condition-dependent inhibition mechanism, we have performed single-molecule manipulation experiments on IF1-inhibited bovine mitochondrial F1 (bMF1). The results show that IF1-inhibited F1 is efficiently activated only when F1 is rotated in the clockwise (ATP synthesis) direction, but not in the counterclockwise direction. The observed rotational-direction-dependent activation explains the condition-dependent mechanism of IF1 inhibition. Investigation of mutant IF1 with N-terminal truncations shows that the interaction with the γ subunit at the N-terminal regions is crucial for rotational-direction-dependent ejection, and the middle long helix is responsible for the inhibition of F1.

Explaining reaction coordinates of alanine dipeptide isomerization obtained from deep neural networks using Explainable Artificial Intelligence (XAI).

A method for obtaining appropriate reaction coordinates is required to identify transition states distinguishing the product and reactant in complex molecular systems. Recently, abundant research has been devoted to obtaining reaction coordinates using artificial neural networks from deep learning literature, where many collective variables are typically utilized in the input layer. However, it is difficult to explain the details of which collective variables contribute to the predicted reaction coordinates owing to the complexity of the nonlinear functions in deep neural networks. To overcome this limitation, we used Explainable Artificial Intelligence (XAI) methods of the Local Interpretable Model-agnostic Explanation (LIME) and the game theory-based framework known as Shapley Additive exPlanations (SHAP). We demonstrated that XAI enables us to obtain the degree of contribution of each collective variable to reaction coordinates that is determined by nonlinear regressions with deep learning for the committor of the alanine dipeptide isomerization in vacuum. In particular, both LIME and SHAP provide important features to the predicted reaction coordinates, which are characterized by appropriate dihedral angles consistent with those previously reported from the committor test analysis. The present study offers an AI-aided framework to explain the appropriate reaction coordinates, which acquires considerable significance when the number of degrees of freedom increases.

Optimizing Go-MARTINI Coarse-Grained Model for F-BAR Protein on Lipid Membrane.

Coarse-grained (CG) molecular dynamics (MD) simulations allow us to access much larger length and time scales than atomistic MD simulations, providing an attractive alternative to the conventional simulations. Based on the well-known MARTINI CG force field, the recently developed Go-MARTINI model for proteins describes large-amplitude structural dynamics, which has not been possible with the commonly used elastic network model. Using the Go-MARTINI model, we conduct MD simulations of the F-BAR Pacsin1 protein on lipid membrane. We observe that structural changes of the non-globular protein are largely dependent on the definition of the native contacts in the Go model. To address this issue, we introduced a simple cutoff scheme and tuned the cutoff distance of the native contacts and the interaction strength of the Lennard-Jones potentials in the Go-MARTINI model. With the optimized Go-MARTINI model, we show that it reproduces structural fluctuations of the Pacsin1 dimer from atomistic simulations. We also show that two Pacsin1 dimers properly assemble through lateral interaction on the lipid membrane. Our work presents a first step towards describing membrane remodeling processes in the Go-MARTINI CG framework by simulating a crucial step of protein assembly on the membrane.

Crystalline chitin hydrolase is a burnt-bridge Brownian motor.

Motor proteins are essential units of life and are well-designed nanomachines working under thermal fluctuations. These proteins control moving direction by consuming chemical energy or by dissipating electrochemical potentials. Chitinase A from bacterium Serratia marcescens (SmChiA) processively moves along crystalline chitin by hydrolysis of a single polymer chain to soluble chitobiose. Recently, we directly observed the stepping motions of SmChiA labeled with a gold nanoparticle by dark-field scattering imaging to investigate the moving mechanism. Time constants analysis revealed that SmChiA moves back and forth along the chain freely, because forward and backward states have a similar free energy level. The similar probabilities of forward-step events (83.5%=69.3%+14.2%) from distributions of step sizes and chain-hydrolysis (86.3%=(1/2.9)/(1/2.9+1/18.3)×100) calculated from the ratios of time constants of hydrolysis and the backward step indicated that SmChiA moves forward as a result of shortening of the chain by a chitobiose unit, which stabilizes the backward state. Furthermore, X-ray crystal structures of sliding intermediate and molecular dynamics simulations showed that SmChiA slides forward and backward under thermal fluctuation without large conformational changes of the protein. Our results demonstrate that SmChiA is a burnt-bridge Brownian ratchet motor.

Learning reaction coordinates via cross-entropy minimization: Application to alanine dipeptide.

We propose a cross-entropy minimization method for finding the reaction coordinate from a large number of collective variables in complex molecular systems. This method is an extension of the likelihood maximization approach describing the committor function with a sigmoid. By design, the reaction coordinate as a function of various collective variables is optimized such that the distribution of the committor pB * values generated from molecular dynamics simulations can be described in a sigmoidal manner. We also introduce the L2-norm regularization used in the machine learning field to prevent overfitting when the number of considered collective variables is large. The current method is applied to study the isomerization of alanine dipeptide in vacuum, where 45 dihedral angles are used as candidate variables. The regularization parameter is determined by cross-validation using training and test datasets. It is demonstrated that the optimal reaction coordinate involves important dihedral angles, which are consistent with the previously reported results. Furthermore, the points with pB *∼0.5 clearly indicate a separatrix distinguishing reactant and product states on the potential of mean force using the extracted dihedral angles.

Chemical-State-Dependent Free Energy Profile from Single-Molecule Trajectories of Biomolecular Motors: Application to Processive Chitinase.

The mechanism of biomolecular motors has been elucidated using single-molecule experiments for visualizing motor motion. However, it remains elusive that how changes in the chemical state during the catalytic cycle of motors lead to unidirectional motions. In this study, we use single-molecule trajectories to estimate an underlying diffusion model with chemical-state-dependent free energy profile. To consider nonequilibrium trajectories driven by the chemical energy consumed by biomolecular motors, we develop a novel framework based on a hidden Markov model, wherein switching among multiple energy profiles occurs reflecting the chemical state changes in motors. The method is tested using simulation trajectories and applied to single-molecule trajectories of processive chitinase, a linear motor that is driven by the hydrolysis energy of a single chitin chain. The chemical-state-dependent free energy profile underlying the burnt-bridge Brownian ratchet mechanism of processive chitinase is determined. The novel framework allows us to connect the chemical state changes to the unidirectional motion of biomolecular motors.

Ion Binding and Selectivity of the Na+/H+ Antiporter MjNhaP1 from Experiment and Simulation.

Cells employ membrane-embedded antiporter proteins to control their pH, salt concentration, and volume. The large family of cation/proton antiporters is dominated by Na+/H+ antiporters that exchange sodium ions against protons, but homologous K+/H+ exchangers have recently been characterized. We show experimentally that the electroneutral antiporter NhaP1 of Methanocaldococcus jannaschii (MjNhaP1) is highly selective for Na+ ions. We then characterize the ion selectivity in both the inward-open and outward-open states of MjNhaP1 using classical molecular dynamics simulations, free energy calculations, and hybrid quantum/classical (QM/MM) simulations. We show that MjNhaP1 is highly selective for binding of Na+ over K+ in the inward-open state, yet it is only weakly selective in the outward-open state. These findings are consistent with the function of MjNhaP1 as a sodium-driven deacidifier of the cytosol that maintains a high cytosolic K+ concentration in environments of high salinity. By combining experiment and computation, we gain mechanistic insight into the Na+/H+ transport mechanism and help elucidate the molecular basis for ion selectivity in cation/proton exchangers.

Curvature induction and sensing of the F-BAR protein Pacsin1 on lipid membranes via molecular dynamics simulations.

F-Bin/Amphiphysin/Rvs (F-BAR) domain proteins play essential roles in biological processes that involve membrane remodelling, such as endocytosis and exocytosis. It has been shown that such proteins transform the lipid membrane into tubes. Notably, Pacsin1 from the Pacsin/Syndapin subfamily has the ability to transform the membrane into various morphologies: striated tubes, featureless wide and thin tubes, and pearling vesicles. The molecular mechanism of this interesting ability remains elusive. In this study, we performed all-atom (AA) and coarse-grained (CG) molecular dynamics simulations to investigate the curvature induction and sensing mechanisms of Pacsin1 on a membrane. From AA simulations, we show that Pacsin1 has internal structural flexibility. In CG simulations with parameters tuned from the AA simulations, spontaneous assembly of two Pacsin1 dimers through lateral interaction is observed. Based on the complex structure, we show that the regularly assembled Pacsin1 dimers bend a tensionless membrane. We also show that a single Pacsin1 dimer senses the membrane curvature, binding to a buckled membrane with a preferred curvature. These results provide molecular insights into polymorphic membrane remodelling.

Mechanism of the electroneutral sodium/proton antiporter PaNhaP from transition-path shooting.

Na+/H+ antiporters exchange sodium ions and protons on opposite sides of lipid membranes. The electroneutral Na+/H+ antiporter NhaP from archaea Pyrococcus abyssi (PaNhaP) is a functional homolog of the human Na+/H+ exchanger NHE1, which is an important drug target. Here we resolve the Na+ and H+ transport cycle of PaNhaP by transition-path sampling. The resulting molecular dynamics trajectories of repeated ion transport events proceed without bias force, and overcome the enormous time-scale gap between seconds-scale ion exchange and microseconds simulations. The simulations reveal a hydrophobic gate to the extracellular side that opens and closes in response to the transporter domain motion. Weakening the gate by mutagenesis makes the transporter faster, suggesting that the gate balances competing demands of fidelity and efficiency. Transition-path sampling and a committor-based reaction coordinate optimization identify the essential motions and interactions that realize conformational alternation between the two access states in transporter function.

Processive chitinase is Brownian monorail operated by fast catalysis after peeling rail from crystalline chitin.

Processive chitinase is a linear molecular motor which moves on the surface of crystalline chitin driven by processive hydrolysis of single chitin chain. Here, we analyse the mechanism underlying unidirectional movement of Serratia marcescens chitinase A (SmChiA) using high-precision single-molecule imaging, X-ray crystallography, and all-atom molecular dynamics simulation. SmChiA shows fast unidirectional movement of ~50 nm s-1 with 1 nm forward and backward steps, consistent with the length of reaction product chitobiose. Analysis of the kinetic isotope effect reveals fast substrate-assisted catalysis with time constant of ~3 ms. Decrystallization of the single chitin chain from crystal surface is the rate-limiting step of movement with time constant of ~17 ms, achieved by binding free energy at the product-binding site of SmChiA. Our results demonstrate that SmChiA operates as a burnt-bridge Brownian ratchet wherein the Brownian motion along the single chitin chain is rectified forward by substrate-assisted catalysis.

Transition path sampling of rare events by shooting from the top.

Transition path sampling is a powerful tool in the study of rare events. Shooting trial trajectories from configurations along existing transition paths proved particularly efficient in the sampling of reactive trajectories. However, most shooting attempts tend not to result in transition paths, in particular in cases where the transition dynamics has diffusive character. To overcome the resulting efficiency problem, we developed an algorithm for "shooting from the top." We first define a shooting range through which all paths have to pass and then shoot off trial trajectories only from within this range. For a well chosen shooting range, nearly every shot is successful, resulting in an accepted transition path. To deal with multiple mechanisms, weighted shooting ranges can be used. To cope with the problem of unsuitably placed shooting ranges, we developed an algorithm that iteratively improves the location of the shooting range. The transition path sampling procedure is illustrated for models of diffusive and Langevin dynamics. The method should be particularly useful in cases where the transition paths are long so that only relatively few shots are possible, yet reasonable order parameters are known.