Atoms to Phenotypes: Molecular Design Principles of Cellular Energy Metabolism

Modeling Diffusive Motion of Ferredoxin and Plastocyanin on the PSI Domain of Procholorococcus marinus MIT9313

Diffusion of mobile charge carriers, such as ferredoxin and plastocyanin, often constitutes a rate-determining step in photosynthetic energy conversion. The diffusion time scales typically exceed that of other primary bioenergetic processes and remain beyond the reach of direct simulation at the molecular level. We characterize the diffusive kinetics of ferredoxin and plastocyanin upon the photosystem I-rich domain of Prochlorococcus, the most abundant phototroph on Earth by mass. A modeling approach for ferredoxin and plastocyanin diffusion is presented that uses ensembles of coarse-grained molecular dynamics simulations in Martini 2.2P with GROMACS 2021.2. The simulation ensembles are used to construct the diffusion coefficient and drift for ferredoxin and plastocyanin as spatial functions in the photosystem I domain of the MIT9313 ecotype. Four separate models are constructed, corresponding to ferredoxin and plastocyanin in reduced and oxidized states. A single scaling constant of 0.7 is found to be sufficient to adjust the diffusion coefficient obtained from the Martini simulation ensemble to match the in vitro values for both ferredoxin and plastocyanin. A comparison of Martini versions (2.2P, 2.2, 3) is presented with respect to diffusion scaling. The diffusion coefficient and drift together quantify the inhomogeneity of diffusive behavior. Notably, a funnel-like convergence toward the corresponding putative binding positions is observed for both ferredoxin and plastocyanin, even without such a priori foreknowledge supplied in the simulation protocol. The approach presented here is of relevance for studying diffusion kinetics in photosynthetic and other bioenergetic processes.

Trehalose Interferes with the Photosynthetic Electron Transfer Chain of Cereibacter (Rhodobacter) sphaeroides Permeating the Bacterial Chromatophore Membrane

Disaccharide trehalose has been proven in many cases to be particularly effective in preserving the functional and structural integrity of biological macromolecules. In this work, we studied its effect on the electron transfer reactions that occur in the chromatophores of the photosynthetic bacterium Cereibacter sphaeroides. In the presence of a high concentration of trehalose, following the activation of the photochemistry by flashes of light, a slowdown of the electrogenic reactions related to the activity of the photosynthetic reaction center and cytochtome (cyt) bc1 complexes is observable. The kinetics of the third phase of the electrochromic carotenoid shift, due to electrogenic events linked to the reduction in cyt bH heme via the low-potential branch of the cyt bc1 complex and its oxidation by quinone molecule on the Qi site, is about four times slower in the presence of trehalose. In parallel, the reduction in oxidized cyt (c1 + c2) and high-potential cyt bH are strongly slowed down, suggesting that the disaccharide interferes with the electron transfer reactions of the high-potential branch of the bc1 complex. A slowing effect of trehalose on the kinetics of the electrogenic protonation of the secondary quinone acceptor QB in the reaction center complex, measured by direct electrometrical methods, was also found, but was much less pronounced. The direct detection of carbohydrate content indicates that trehalose, at high concentrations, permeates the membrane of chromatophores. The possible mechanisms underlying the observed effect of trehalose on the electron/proton transfer process are discussed in terms of trehalose's propensity to form strong hydrogen bonds with its surroundings.

Dy-SheHeRASADe: A representation of the β sheet dynamics through surface descriptors

Molecular dynamics (MD) simulations are important tools for studying the dynamic motions of macromolecules at the atomic level. With the increasing capabilities of high performance computing, MD simulations are becoming more widely used. This allows molecular modelers to simulate the molecular behavior of large molecular architectures for much longer trajectories. Appropriate visualization of MD trajectories is becoming essential to provide an immediate and intuitive understanding of a molecule's dynamics and function. In this study, we implement a novel 3D graphical representation, Dynamical Sheets Helper for RepresentAtion of SurfAce Descriptors (Dy-SheHeRASADe), to visualize the β sheet secondary structures of proteins in the context of molecular dynamics. Dy-SheHeRASADe is developed in UnityMol, an open source molecular viewer and prototyping platform. We considered β sheet fluctuations and hydrogen bond formation during molecular dynamics simulations to characterize the parts of β sheets with large motions or with labile bonds. We propose two visualization modes based on a surface representation of the β sheets calculated according to the positions of the α carbons and the hydrogen bonds between the β strands. The volumetric mode, in which this surface is enclosed in a semi-transparent volume that represents the fluctuation zone of the sheet during dynamics. The heatmap mode, in which the surface is colored according to the amplitude values of the α carbons. In addition, we quantify the β sheet fluctuations by displaying the values of the largest and smallest movements of the β sheets, the surface area of the sheets, and the number of hydrogen bonds.

BaNDyT: Bayesian Network modeling of molecular Dynamics Trajectories

Bayesian network modeling (BN modeling, or BNM) is an interpretable machine learning method for constructing probabilistic graphical models from the data. In recent years, it has been extensively applied to diverse types of biomedical datasets. Concurrently, our ability to perform long-timescale molecular dynamics (MD) simulations on proteins and other materials has increased exponentially. However, the analysis of MD simulation trajectories has not been data-driven but rather dependent on the user's prior knowledge of the systems, thus limiting the scope and utility of the MD simulations. Recently, we pioneered using BNM for analyzing the MD trajectories of protein complexes. The resulting BN models yield novel fully data-driven insights into the functional importance of the amino acid residues that modulate proteins' function. In this report, we describe the BaNDyT software package that implements the BNM specifically attuned to the MD simulation trajectories data. We believe that BaNDyT is the first software package to include specialized and advanced features for analyzing MD simulation trajectories using a probabilistic graphical network model. We describe here the software's uses, the methods associated with it, and a comprehensive Python interface to the underlying generalist BNM code. This provides a powerful and versatile mechanism for users to control the workflow. As an application example, we have utilized this methodology and associated software to study how membrane proteins, specifically the G protein-coupled receptors, selectively couple to G proteins. The software can be used for analyzing MD trajectories of any protein as well as polymeric materials.

Architectures of photosynthetic RC-LH1 supercomplexes from Rhodobacter blasticus

The reaction center-light-harvesting complex 1 (RC-LH1) plays an essential role in the primary reactions of bacterial photosynthesis. Here, we present high-resolution structures of native monomeric and dimeric RC-LH1 supercomplexes from Rhodobacter (Rba.) blasticus using cryo-electron microscopy. The RC-LH1 monomer is composed of an RC encircled by an open LH1 ring comprising 15 αβ heterodimers and a PufX transmembrane polypeptide. In the RC-LH1 dimer, two crossing PufX polypeptides mediate dimerization. Unlike Rhodabacter sphaeroides counterpart, Rba. blasticus RC-LH1 dimer has a less bent conformation, lacks the PufY subunit near the LH1 opening, and includes two extra LH1 αβ subunits, forming a more enclosed S-shaped LH1 ring. Spectroscopic assays reveal that these unique structural features are accompanied by changes in the kinetics of quinone/quinol trafficking between RC-LH1 and cytochrome bc1. Our findings reveal the assembly principles and structural variability of photosynthetic RC-LH1 supercomplexes, highlighting diverse strategies used by phototrophic bacteria to optimize light-harvesting and electron transfer in competitive environments.

A Mode Evolution Metric to Extract Reaction Coordinates for Biomolecular Conformational Transitions

The complex, multidimensional energy landscape of biomolecules makes the extraction of suitable, nonintuitive collective variables (CVs) that describe their conformational transitions challenging. At present, dimensionality reduction approaches and machine learning (ML) schemes are employed to obtain CVs from molecular dynamics (MD)/Monte Carlo (MC) trajectories or structural databanks for biomolecules. However, minimum sampling conditions to generate reliable CVs that accurately describe the underlying energy landscape remain unclear. Here, we address this issue by developing a Mode evolution Metric (MeM) to extract CVs that can pinpoint new states and describe local transitions in the vicinity of a reference minimum from nonequilibrated MD/MC trajectories. We present a general mathematical formulation of MeM for both statistical dimensionality reduction and machine learning approaches. Application of MeM to MC trajectories of model potential energy landscapes and MD trajectories of solvated alanine dipeptide reveals that the principal components which locate new states in the vicinity of a reference minimum emerge well before the trajectories locally equilibrate between the associated states. Finally, we demonstrate a possible application of MeM in designing efficient biased sampling schemes to construct accurate energy landscape slices that link transitions between states. MeM can help speed up the search for new minima around a biomolecular conformational state and enable the accurate estimation of thermodynamics for states lying on the energy landscape and the description of associated transitions.

Biomolecular simulations at the exascale: From drug design to organelles and beyond

The rapid advancement in computational power available for research offers to bring not only quantitative improvements, but also qualitative changes in the field of biomolecular simulation. Here, we review the state of biomolecular dynamics simulations at the threshold to exascale resources becoming available. Both developments in parallel and distributed computing will be discussed, providing a perspective on the state of the art of both. A main focus will be on obtaining binding and conformational free energies, with an outlook to macromolecular complexes and (sub)cellular assemblies.

Tuning by Hydrogen Bonding in Photosynthesis

Hydrogen bonding plays a crucial role in stabilizing proteins throughout their folding process. In photosynthetic light-harvesting chromoproteins, enriched with pigment chromophores, hydrogen bonds also fine-tune optical absorption to align with the solar irradiation spectrum. Despite its significance for photosynthesis, the precise mechanism of spectral tuning through hydrogen bonding remains inadequately understood. This study investigates wild-type and genetically engineered LH2 and LH1 light-harvesting complexes from Rhodobacter sphaeroides using a unique set of advanced spectroscopic techniques combined with simple exciton modeling. Our findings reveal an intricate interplay between exciton and site energy shift mechanisms, challenging the prevailing belief that spectral changes observed in these complexes upon the modification of tertiary structure hydrogen bonds almost directly follow shifting site energies. These deeper insights into natural adaptation processes hold great promise for advancing sustainable solar energy conversion technologies.

Advancing biomolecular simulation through exascale HPC, AI and quantum computing

Biomolecular simulation can act as both a digital microscope and a crystal ball; offering the potential for a deeper understanding of experimental observations whilst also presenting a forward-looking avenue for the in silico design and evaluation of hitherto unsynthesized compounds. Indeed, as the intricacy of our scientific inquiries has grown, so too has the computational prowess we seek to deploy in our pursuit of answers. As we enter the Exascale era, this mini-review surveys the computational landscape from both the point of view of the development of new and ever more powerful systems, and the simulations that are run on them. Moreover, as we stand on the cusp of a transformative phase in computational biology, this article offers a contemplative glance into the future, speculating on the profound implications of artificial intelligence and quantum computing for large-scale biomolecular simulations.

Modeling membranes in situ

Molecular dynamics simulations of cellular membranes have come a long way-from simple model lipid bilayers to multicomponent systems capturing the crowded and complex nature of real cell membranes. In this opinionated minireview, we discuss the current challenge to simulate the dynamics of membranes in their native environment, in situ, with the prospect of reaching the level of whole cells and cell organelles using an integrative modeling framework.

Single-Molecule Detection of the Encounter and Productive Electron Transfer Complexes of a Photosynthetic Reaction Center

Small, diffusible redox proteins play an essential role in electron transfer (ET) in respiration and photosynthesis, sustaining life on Earth by shuttling electrons between membrane-bound complexes via finely tuned and reversible interactions. Ensemble kinetic studies show transient ET complexes form in two distinct stages: an "encounter" complex largely mediated by electrostatic interactions, which subsequently, through subtle reorganization of the binding interface, forms a "productive" ET complex stabilized by additional hydrophobic interactions around the redox-active cofactors. Here, using single-molecule force spectroscopy (SMFS) we dissected the transient ET complexes formed between the photosynthetic reaction center-light harvesting complex 1 (RC-LH1) of Rhodobacter sphaeroides and its native electron donor cytochrome c2 (cyt c2). Importantly, SMFS resolves the distribution of interaction forces into low (∼150 pN) and high (∼330 pN) components, with the former more susceptible to salt concentration and to alteration of key charged residues on the RC. Thus, the low force component is suggested to reflect the contribution of electrostatic interactions in forming the initial encounter complex, whereas the high force component reflects the additional stabilization provided by hydrophobic interactions to the productive ET complex. Employing molecular dynamics simulations, we resolve five intermediate states that comprise the encounter, productive ET and leaving complexes, predicting a weak interaction between cyt c2 and the LH1 ring near the RC-L subunit that could lie along the exit path for oxidized cyt c2. The multimodal nature of the interactions of ET complexes captured here may have wider implications for ET in all domains of life.

Do All Paths Lead to Rome? How Reliable is Umbrella Sampling Along a Single Path?

Molecular dynamics (MD) simulations are widely applied to estimate absolute binding free energies of protein-ligand and protein-protein complexes. A routinely used method for binding free energy calculations with MD is umbrella sampling (US), which calculates the potential of mean force (PMF) along a single reaction coordinate. Surprisingly, in spite of its widespread use, few validation studies have focused on the convergence of the free energy computed along a single path for specific cases, not addressing the reproducibility of such calculations in general. In this work, we therefore investigate the reproducibility and convergence of US along a standard distance-based reaction coordinate for various protein-protein and protein-ligand complexes, following commonly used guidelines for the setup. We show that repeating the complete US workflow can lead to differences of 2-20 kcal/mol in computed binding free energies. We attribute those discrepancies to small differences in the binding pathways. While these differences are unavoidable in the established US protocol, the popularity of the latter could hint at a lack of awareness of such reproducibility problems. To test if the convergence of PMF profiles can be improved if multiple pathways are sampled simultaneously, we performed additional simulations with an adaptive-biasing method, here the accelerated weight histogram (AWH) approach. Indeed, the PMFs obtained from AHW simulations are consistent and reproducible for the systems tested. To the best of our knowledge, our work is the first to attempt a systematic assessment of the pitfalls in one the most widely used protocols for computing binding affinities. We anticipate therefore that our results will provide an incentive for a critical reassessment of the validity of PMFs computed with US, and make a strong case to further benchmark the performance of adaptive-biasing methods for computing binding affinities.

Sulfoquinovosyl diacylglycerol is required for dimerisation of the Rhodobacter sphaeroides reaction centre-light harvesting 1 core complex

The reaction centre-light harvesting 1 (RC-LH1) core complex is indispensable for anoxygenic photosynthesis. In the purple bacterium Rhodobacter (Rba.) sphaeroides RC-LH1 is produced both as a monomer, in which 14 LH1 subunits form a C-shaped antenna around 1 RC, and as a dimer, where 28 LH1 subunits form an S-shaped antenna surrounding 2 RCs. Alongside the five RC and LH1 subunits, an additional polypeptide known as PufX provides an interface for dimerisation and also prevents LH1 ring closure, introducing a channel for quinone exchange that is essential for photoheterotrophic growth. Structures of Rba. sphaeroides RC-LH1 complexes revealed several new components; protein-Y, which helps to form the quinone channel; protein-Z, of unknown function and seemingly unique to dimers; and a tightly bound sulfoquinovosyl diacylglycerol (SQDG) lipid that interacts with two PufX arginine residues. This lipid lies at the dimer interface alongside weak density for a second molecule, previously proposed to be an ornithine lipid. In this work we have generated strains of Rba. sphaeroides lacking protein-Y, protein-Z, SQDG or ornithine lipids to assess the roles of these previously unknown components in the assembly and activity of RC-LH1. We show that whilst the removal of either protein-Y, protein-Z or ornithine lipids has only subtle effects, SQDG is essential for the formation of RC-LH1 dimers but its absence has no functional effect on the monomeric complex.

De novo design of proteins housing excitonically coupled chlorophyll special pairs

Natural photosystems couple light harvesting to charge separation using a 'special pair' of chlorophyll molecules that accepts excitation energy from the antenna and initiates an electron-transfer cascade. To investigate the photophysics of special pairs independently of the complexities of native photosynthetic proteins, and as a first step toward creating synthetic photosystems for new energy conversion technologies, we designed C2-symmetric proteins that hold two chlorophyll molecules in closely juxtaposed arrangements. X-ray crystallography confirmed that one designed protein binds two chlorophylls in the same orientation as native special pairs, whereas a second designed protein positions them in a previously unseen geometry. Spectroscopy revealed that the chlorophylls are excitonically coupled, and fluorescence lifetime imaging demonstrated energy transfer. The cryo-electron microscopy structure of a designed 24-chlorophyll octahedral nanocage with a special pair on each edge closely matched the design model. The results suggest that the de novo design of artificial photosynthetic systems is within reach of current computational methods.

CHARMM-GUI Multicomponent Assembler for modeling and simulation of complex multicomponent systems

Atomic-scale molecular modeling and simulation are powerful tools for computational biology. However, constructing models with large, densely packed molecules, non-water solvents, or with combinations of multiple biomembranes, polymers, and nanomaterials remains challenging and requires significant time and expertise. Furthermore, existing tools do not support such assemblies under the periodic boundary conditions (PBC) necessary for molecular simulation. Here, we describe Multicomponent Assembler in CHARMM-GUI that automates complex molecular assembly and simulation input preparation under the PBC. In this work, we demonstrate its versatility by preparing 6 challenging systems with varying density of large components: (1) solvated proteins, (2) solvated proteins with a pre-equilibrated membrane, (3) solvated proteins with a sheet-like nanomaterial, (4) solvated proteins with a sheet-like polymer, (5) a mixed membrane-nanomaterial system, and (6) a sheet-like polymer with gaseous solvent. Multicomponent Assembler is expected to be a unique cyberinfrastructure to study complex interactions between small molecules, biomacromolecules, polymers, and nanomaterials.

Protein Effects on the Excitation Energies and Exciton Dynamics of the CP24 Antenna Complex

In this study, the site energy fluctuations, energy transfer dynamics, and some spectroscopic properties of the minor light-harvesting complex CP24 in a membrane environment were determined. For this purpose, a 3 μs-long classical molecular dynamics simulation was performed for the CP24 complex. Furthermore, using the density functional tight binding/molecular mechanics molecular dynamics (DFTB/MM MD) approach, we performed excited state calculations for the chlorophyll a and chlorophyll b molecules in the complex starting from five different positions of the MD trajectory. During the extended simulations, we observed variations in the site energies of the different sets as a result of the fluctuating protein environment. In particular, a water coordination to Chl-b 608 occurred only after about 1 μs in the simulations, demonstrating dynamic changes in the environment of this pigment. From the classical and the DFTB/MM MD simulations, spectral densities and the (time-dependent) Hamiltonian of the complex were determined. Based on these results, three independent strongly coupled chlorophyll clusters were revealed within the complex. In addition, absorption and fluorescence spectra were determined together with the exciton relaxation dynamics, which reasonably well agrees with experimental time scales.

A Vision for the Future of Multiscale Modeling

The rise of modern computer science enabled physical chemistry to make enormous progresses in understanding and harnessing natural and artificial phenomena. Nevertheless, despite the advances achieved over past decades, computational resources are still insufficient to thoroughly simulate extended systems from first principles. Indeed, countless biological, catalytic and photophysical processes require ab initio treatments to be properly described, but the breadth of length and time scales involved makes it practically unfeasible. A way to address these issues is to couple theories and algorithms working at different scales by dividing the system into domains treated at different levels of approximation, ranging from quantum mechanics to classical molecular dynamics, even including continuum electrodynamics. This approach is known as multiscale modeling and its use over the past 60 years has led to remarkable results. Considering the rapid advances in theory, algorithm design, and computing power, we believe multiscale modeling will massively grow into a dominant research methodology in the forthcoming years. Hereby we describe the main approaches developed within its realm, highlighting their achievements and current drawbacks, eventually proposing a plausible direction for future developments considering also the emergence of new computational techniques such as machine learning and quantum computing. We then discuss how advanced multiscale modeling methods could be exploited to address critical scientific challenges, focusing on the simulation of complex light-harvesting processes, such as natural photosynthesis. While doing so, we suggest a cutting-edge computational paradigm consisting in performing simultaneous multiscale calculations on a system allowing the various domains, treated with appropriate accuracy, to move and extend while they properly interact with each other. Although this vision is very ambitious, we believe the quick development of computer science will lead to both massive improvements and widespread use of these techniques, resulting in enormous progresses in physical chemistry and, eventually, in our society.

From complex data to clear insights: visualizing molecular dynamics trajectories

Advances in simulations, combined with technological developments in high-performance computing, have made it possible to produce a physically accurate dynamic representation of complex biological systems involving millions to billions of atoms over increasingly long simulation times. The analysis of these computed simulations is crucial, involving the interpretation of structural and dynamic data to gain insights into the underlying biological processes. However, this analysis becomes increasingly challenging due to the complexity of the generated systems with a large number of individual runs, ranging from hundreds to thousands of trajectories. This massive increase in raw simulation data creates additional processing and visualization challenges. Effective visualization techniques play a vital role in facilitating the analysis and interpretation of molecular dynamics simulations. In this paper, we focus mainly on the techniques and tools that can be used for visualization of molecular dynamics simulations, among which we highlight the few approaches used specifically for this purpose, discussing their advantages and limitations, and addressing the future challenges of molecular dynamics visualization.

Perspective on Integrative Simulations of Bioenergetic Domains

Bioenergetic processes in cells, such as photosynthesis or respiration, integrate many time and length scales, which makes the simulation of energy conversion with a mere single level of theory impossible. Just like the myriad of experimental techniques required to examine each level of organization, an array of overlapping computational techniques is necessary to model energy conversion. Here, a perspective is presented on recent efforts for modeling bioenergetic phenomena with a focus on molecular dynamics simulations and its variants as a primary method. An overview of the various classical, quantum mechanical, enhanced sampling, coarse-grained, Brownian dynamics, and Monte Carlo methods is presented. Example applications discussed include multiscale simulations of membrane-wide electron transport, rate kinetics of ATP turnover from electrochemical gradients, and finally, integrative modeling of the chromatophore, a photosynthetic pseudo-organelle.

Diffusion Analyses along Mean and Gaussian-Curved Membranes with CurD

Curved cellular membranes are both abundant and functionally relevant. While novel tomography approaches reveal the structural details of curved membranes, their dynamics pose an experimental challenge. Curvature especially affects the diffusion of lipids and macromolecules, yet neither experiments nor continuum models distinguish geometric effects from those caused by curvature-induced changes in membrane properties. Molecular simulations could excel here, yet despite community interest toward curved membranes, tools for their analysis are still lacking. Here, we satisfy this demand by introducing CurD, our novel and openly available implementation of the Vertex-oriented Triangle Propagation algorithm to the study of lipid diffusion along membranes with mean and/or Gaussian curvature. This approach, aided by our highly optimized implementation, computes geodetic distances significantly faster than conventional implementations of path-finding algorithms. Our tool, applied to coarse-grained simulations, allows for the first time the analysis of curvature effects on diffusion at size scales relevant to physiological processes such as endocytosis. Our analyses with different membrane geometries reveal that Gaussian curvature plays a surprisingly small role on lipid motion, whereas mean curvature; i.e., the packing of lipid headgroups largely dictates their mobility.

All-Atom Biomolecular Simulation in the Exascale Era

Exascale supercomputers have opened the door to dynamic simulations, facilitated by AI/ML techniques, that model biomolecular motions over unprecedented length and time scales. This new capability holds the potential to revolutionize our understanding of fundamental biological processes. Here we report on some of the major advances that were discussed at a recent CECAM workshop in Pisa, Italy, on the topic with a primary focus on atomic-level simulations. First, we highlight examples of current large-scale biomolecular simulations and the future possibilities enabled by crossing the exascale threshold. Next, we discuss challenges to be overcome in optimizing the usage of these powerful resources. Finally, we close by listing several grand challenge problems that could be investigated with this new computer architecture.

Use of bacterial photosynthetic vesicles to evaluate the effect of ionic liquids on the permeability of biological membranes

Ionic liquids (ILs) are salts composed of a combination of organic or inorganic cations and anions characterized by a low melting point, often below 100 °C. This property, together with an extremely low vapor pressure, low flammability and high thermal stability, makes them suitable for replacing canonical organic solvents, with a reduction of industrial activities impact on the environment. Although in the last decades the eco-compatibility of ILs has been extensively verified through toxicological tests performed on model organisms, a detailed understanding of the interaction of these compounds with biological membranes is far from being exhaustive. In this context, we have chosen to evaluate the effect of some ILs on native membranes by using chromatophores, photosynthetic vesicles that can be isolated from Rhodobacter capsulatus, a member of the purple non‑sulfur bacteria. Here, carotenoids associated with the light-harvesting complex II, act as endogenous spectral probes of the transmembrane electrical potential (ΔΨ). By measuring through time-resolved absorption spectroscopy the evolution of the carotenoid band shift induced by a single excitation of the photosynthetic reaction center, information on the ΔΨ dissipation due to ionic currents across the membrane can be obtained. We found that some ILs cause a rather fast dissipation of the transmembrane ΔΨ even at low concentrations, and that this behavior is dose-dependent. By using two different models to analyze the decay of the carotenoid signals, we attempted to interpret at a mechanistic level the marked increase of ionic permeability caused by specific ILs.

Excitation energy transfer in proteoliposomes reconstituted with LH2 and RC-LH1 complexes from Rhodobacter sphaeroides

Light-harvesting 2 (LH2) and reaction-centre light-harvesting 1 (RC-LH1) complexes purified from the photosynthetic bacterium Rhodobacter (Rba.) sphaeroides were reconstituted into proteoliposomes either separately, or together at three different LH2:RC-LH1 ratios, for excitation energy transfer studies. Atomic force microscopy (AFM) was used to investigate the distribution and association of the complexes within the proteoliposome membranes. Absorption and fluorescence emission spectra were similar for LH2 complexes in detergent and liposomes, indicating that reconstitution retains the structural and optical properties of the LH2 complexes. Analysis of fluorescence emission shows that when LH2 forms an extensive series of contacts with other such complexes, fluorescence is quenched by 52.6 ± 1.4%. In mixed proteoliposomes, specific excitation of carotenoids in LH2 donor complexes resulted in emission of fluorescence from acceptor RC-LH1 complexes engineered to assemble with no carotenoids. Extents of energy transfer were measured by fluorescence lifetime microscopy; the 0.72 ± 0.08 ns lifetime in LH2-only membranes decreases to 0.43 ± 0.04 ns with a ratio of 2:1 LH2 to RC-LH1, and to 0.35 ± 0.05 ns for a 1:1 ratio, corresponding to energy transfer efficiencies of 40 ± 14% and 51 ± 18%, respectively. No further improvement is seen with a 0.5:1 LH2 to RC-LH1 ratio. Thus, LH2 and RC-LH1 complexes perform their light harvesting and energy transfer roles when reconstituted into proteoliposomes, providing a way to integrate native, non-native, engineered and de novo designed light-harvesting complexes into functional photosynthetic systems.

AMBERff at Scale: Multimillion-Atom Simulations with AMBER Force Fields in NAMD

All-atom molecular dynamics (MD) simulations are an essential structural biology technique with increasing application to multimillion-atom systems, including viruses and cellular machinery. Classical MD simulations rely on parameter sets, such as the AMBER family of force fields (AMBERff), to accurately describe molecular motion. Here, we present an implementation of AMBERff for use in NAMD that overcomes previous limitations to enable high-performance, massively parallel simulations encompassing up to two billion atoms. Single-point potential energy comparisons and case studies on model systems demonstrate that the implementation produces results that are as accurate as running AMBERff in its native engine.

Development of nano-delivery systems for loaded bioactive compounds: using molecular dynamics simulations

Over the past decade, a remarkable surge in the development of functional nano-delivery systems loaded with bioactive compounds for healthcare has been witnessed. Notably, the demanding requirements of high solubility, prolonged circulation, high tissue penetration capability, and strong targeting ability of nanocarriers have posed interdisciplinary research challenges to the community. While extensive experimental studies have been conducted to understand the construction of nano-delivery systems and their metabolic behavior in vivo, less is known about these molecular mechanisms and kinetic pathways during their metabolic process in vivo, and lacking effective means for high-throughput screening. Molecular dynamics (MD) simulation techniques provide a reliable tool for investigating the design of nano-delivery carriers encapsulating these functional ingredients, elucidating the synthesis, translocation, and delivery of nanocarriers. This review introduces the basic MD principles, discusses how to apply MD simulation to design nanocarriers, evaluates the ability of nanocarriers to adhere to or cross gastrointestinal mucosa, and regulates plasma proteins in vivo. Moreover, we presented the critical role of MD simulation in developing delivery systems for precise nutrition and prospects for the future. This review aims to provide insights into the implications of MD simulation techniques for designing and optimizing nano-delivery systems in the healthcare food industry.

Passive permeability controls synthesis for the allelochemical sorgoleone in sorghum root exudate

Competition for soil nutrients and water with other plants foster competition within the biosphere for access to these limited resources. The roots for the common grain sorghum produce multiple small molecules that are released via root exudates into the soil to compete with other plants. Sorgoleone is one such compound, which suppresses weed growth near sorghum by acting as a quinone analog and interferes with photosynthesis. Since sorghum also grows photosynthetically, and may be susceptible to sorgoleone action if present in tissues above ground, it is essential to exude sorgoleone efficiently. However, since the P450 enzymes that synthesize sorgoleone are intracellular, the release mechanism for sorgoleone remain unclear. In this study, we conducted an in silico assessment for sorgoleone and its precursors to passively permeate biological membranes. To facilitate accurate simulation, CHARMM parameters were newly optimized for sorgoleone and its precursors. These parameters were used to conduct 1 μs of unbiased molecular dynamics simulations to compare the permeability of sorgoleone with its precursors molecules. We find that interleaflet transfer is maximized for sorgoleone, suggesting that the precursor molecules may remain in the same leaflet for access by biosynthetic P450 enzymes. Since no sorgoleone was extracted during unbiased simulations, we compute a permeability coefficient using the inhomogeneous solubility diffusion model. The requisite free energy and diffusivity profiles for sorgoleone through a sorghum membrane model were determined through Replica Exchange Umbrella Sampling (REUS) simulations. The REUS calculations highlight that any soluble sorgoleone would quickly insert into a lipid bilayer, and would readily transit. When sorgoleone forms aggregates in root exudate as indicated by our equilibrium simulations, aggregate formation would lower the effective concentration in aqueous solution, creating a concentration gradient that would facilitate passive transport. This suggests that sorgoleone synthesis occurs within sorghum root cells and that sorgoleone is exuded by permeating through the cell membrane without the need for a transport protein once the extracellular sorgoleone aggregate is formed.

Structural diversity and modularity of photosynthetic RC-LH1 complexes

Bacterial photosynthesis is essential for sustaining life on Earth as it aids in carbon assimilation, atmospheric composition, and ecosystem maintenance. Many bacteria utilize anoxygenic photosynthesis to convert sunlight into chemical energy while producing organic matter. The core machinery of anoxygenic photosynthesis performed by purple photosynthetic bacteria and Chloroflexales is the reaction center-light-harvesting 1 (RC-LH1) pigment-protein supercomplex. In this review, we discuss recent structural studies of RC-LH1 core complexes based on the advancement in structural biology techniques. These studies have provided fundamental insights into the assembly mechanisms, structural variations, and modularity of RC-LH1 complexes across different bacterial species, highlighting their functional adaptability. Understanding the natural architectures of RC-LH1 complexes will facilitate the design and engineering of artificial photosynthetic systems, which can enhance photosynthetic efficiency and potentially find applications in sustainable energy production and carbon capture.

From Byte to Bench to Bedside: Molecular Dynamics Simulations and Drug Discovery

Molecular dynamics (MD) simulations and computer-aided drug design (CADD) have advanced substantially over the past two decades, thanks to continuous computer hardware and software improvements. Given these advancements, MD simulations are poised to become even more powerful tools for investigating the dynamic interactions between potential small-molecule drugs and their target proteins, with significant implications for pharmacological research.

Multifidelity Machine Learning for Molecular Excitation Energies

The accurate but fast calculation of molecular excited states is still a very challenging topic. For many applications, detailed knowledge of the energy funnel in larger molecular aggregates is of key importance, requiring highly accurate excitation energies. To this end, machine learning techniques can be a very useful tool, though the cost of generating highly accurate training data sets still remains a severe challenge. To overcome this hurdle, this work proposes the use of multifidelity machine learning where very little training data from high accuracies is combined with cheaper and less accurate data to achieve the accuracy of the costlier level. In the present study, the approach is employed to predict vertical excitation energies to the first excited state for three molecules of increasing size, namely, benzene, naphthalene, and anthracene. The energies are trained and tested for conformations stemming from classical molecular dynamics and density functional based tight-binding simulations. It can be shown that the multifidelity machine learning model can achieve the same accuracy as a machine learning model built only on high-cost training data while expending a much lower computational effort to generate the data. The numerical gain observed in these benchmark test calculations was over a factor of 30 but certainly can be much higher for high-accuracy data.

Role of hydrogen-bond networks on the donor side of photosynthetic reaction centers from purple bacteria

For the last decades, significant progress has been made in studying the biological functions of H-bond networks in membrane proteins, proton transporters, receptors, and photosynthetic reaction centers. Increasing availability of the X-ray crystal and cryo-electron microscopy structures of photosynthetic complexes resolved with high atomic resolution provides a platform for their comparative analysis. It allows identifying structural factors that are ensuring the high quantum yield of the photochemical reactions and are responsible for the stability of the membrane complexes. The H-bond networks are known to be responsible for proton transport associated with electron transfer from the primary to the secondary quinone as well as in the processes of water oxidation in photosystem II. Participation of such networks in reactions proceeding on the periplasmic side of bacterial photosynthetic reaction centers is less studied. This review summarizes the current understanding of the role of H-bond networks on the donor side of photosynthetic reaction centers from purple bacteria. It is discussed that the networks may be involved in providing close association with mobile electron carriers, in light-induced proton transport, in regulation of the redox properties of bacteriochlorophyll cofactors, and in stabilization of the membrane protein structure at the interface of membrane and soluble phases.

Generation of Electric Potential Difference by Chromatophores from Photosynthetic Bacteria in the Presence of Trehalose under Continuous Illumination

Measurement of electrical potential difference (Δψ) in membrane vesicles (chromatophores) from the purple bacterium Rhodobacter sphaeroides associated with the surface of a nitrocellulose membrane filter (MF) impregnated with a phospholipid solution in decane or immersed into it in the presence of exogenous mediators and disaccharide trehalose demonstrated an increase in the amplitude and stabilization of the signal under continuous illumination. The mediators were the ascorbate/N,N,N'N'-tetramethyl-p-phenylenediamine pair and ubiquinone-0 (electron donor and acceptor, respectively). Although stabilization of photoelectric responses upon long-term continuous illumination was observed for both variants of chromatophore immobilization, only the samples immersed into the MF retained the functional activity of reaction centers (RCs) for a month when stored in the dark at room temperature, which might be due to the preservation of integrity of chromatophore proteins inside the MF pores. The stabilizing effect of the bioprotector trehalose could be related to its effect on both the RC proteins and the phospholipid bilayer membrane. The results obtained will expand current ideas on the use of semi-synthetic structures based on various intact photosynthetic systems capable of converting solar energy into its electrochemical form.

CHARMM-GUI Multicomponent Assembler for Modeling and Simulation of Complex Multicomponent Systems

Atomic-scale molecular modeling and simulation are powerful tools for computational biology. However, constructing models with large, densely packed molecules, non-water solvents, or with combinations of multiple biomembranes, polymers, and nanomaterials remains challenging and requires significant time and expertise. Furthermore, existing tools do not support such assemblies under the periodic boundary conditions (PBC) necessary for molecular simulation. Here, we describe Multicomponent Assembler in CHARMM-GUI that automates complex molecular assembly and simulation input preparation under the PBC. We demonstrate its versatility by preparing 6 challenging systems with varying density of large components: (1) solvated proteins, (2) solvated proteins with a pre-equilibrated membrane, (3) solvated proteins with a sheet-like nanomaterial, (4) solvated proteins with a sheet-like polymer, (5) a mixed membrane-nanomaterial system, and (6) a sheet-like polymer with gaseous solvent. Multicomponent Assembler is expected to be a unique cyberinfrastructure to facilitate innovative studies of complex interactions between small (organic and inorganic) molecules, biomacromolecules, polymers, and nanomaterials.

VMD as a Platform for Interactive Small Molecule Preparation and Visualization in Quantum and Classical Simulations

Modeling and simulation of small molecules such as drugs and biological cofactors have been both a major focus of computational chemistry for decades and a growing need among computational biophysicists who seek to investigate the interaction of different types of ligands with biomolecules. Of particular interest in this regard are quantum mechanical (QM) calculations that are used to more accurately describe such small molecules, which can be of heterogeneous structures and chemistry, either in purely QM calculations or in hybrid QM/molecular mechanics (MM) simulations. QM programs are also used to develop MM force field parameters for small molecules to be used along with established force fields for biomolecules in classical simulations. With this growing need in mind, here we report a set of software tools developed and closely integrated within the broadly used molecular visualization/analysis program, VMD, that allow the user to construct, modify, and parametrize small molecules and prepare them for QM, hybrid QM/MM, or classical simulations. The tools also provide interactive analysis and visualization capabilities in an easy-to-use and integrated environment. In this paper, we briefly report on these tools and their major features and capabilities, along with examples of how they can facilitate molecular research in computational biophysics that might be otherwise prohibitively complex.

Fostering discoveries in the era of exascale computing: How the next generation of supercomputers empowers computational and experimental biophysics alike

Over a century ago, physicists started broadly relying on theoretical models to guide new experiments. Soon thereafter, chemists began doing the same. Now, biological research enters a new era when experiment and theory walk hand in hand. Novel software and specialized hardware became essential to understand experimental data and propose new models. In fact, current petascale computing resources already allow researchers to reach unprecedented levels of simulation throughput to connect in silico and in vitro experiments. The reduction in cost and improved access allowed a large number of research groups to adopt supercomputing resources and techniques. Here, we outline how large-scale computing has evolved to expand decades-old research, spark new research efforts, and continuously connect simulation and observation. For instance, multiple publicly and privately funded groups have dedicated extensive resources to develop artificial intelligence tools for computational biophysics, from accelerating quantum chemistry calculations to proposing protein structure models. Moreover, advances in computer hardware have accelerated data processing from single-molecule experimental observations and simulations of chemical reactions occurring throughout entire cells. The combination of software and hardware has opened the way for exascale computing and the production of the first public exascale supercomputer, Frontier, inaugurated by the Oak Ridge National Laboratory in 2022. Ultimately, the popularization and development of computational techniques and the training of researchers to use them will only accelerate the diversification of tools and learning resources for future generations.

Mesoscale simulations: An indispensable approach to understand biomembranes

Computer simulation techniques form a versatile tool, a computational microscope, for exploring biological processes. This tool has been particularly effective in exploring different features of biological membranes. In recent years, thanks to elegant multiscale simulation schemes, some fundamental limitations of investigations by distinct simulation techniques have been resolved. As a result, we are now capable of exploring processes spanning multiple scales beyond the capacity of any single technique. In this perspective, we argue that mesoscale simulations require more attention and must be further developed to fill evident gaps in a quest toward simulating and modeling living cell membranes.

De novo design of energy transfer proteins housing excitonically coupled chlorophyll special pairs

Natural photosystems couple light harvesting to charge separation using a "special pair" of chlorophyll molecules that accepts excitation energy from the antenna and initiates an electron-transfer cascade. To investigate the photophysics of special pairs independent of complexities of native photosynthetic proteins, and as a first step towards synthetic photosystems for new energy conversion technologies, we designed C2-symmetric proteins that precisely position chlorophyll dimers. X-ray crystallography shows that one designed protein binds two chlorophylls in a binding orientation matching native special pairs, while a second positions them in a previously unseen geometry. Spectroscopy reveals excitonic coupling, and fluorescence lifetime imaging demonstrates energy transfer. We designed special pair proteins to assemble into 24-chlorophyll octahedral nanocages; the design model and cryo-EM structure are nearly identical. The design accuracy and energy transfer function of these special pair proteins suggest that de novo design of artificial photosynthetic systems is within reach of current computational methods.

Recent progress in atomistic modeling of light-harvesting complexes: a mini review

In this mini review, we focus on recent advances in the atomistic modeling of biological light-harvesting (LH) complexes. Because of their size and sophisticated electronic structures, multiscale methods are required to investigate the dynamical and spectroscopic properties of such complexes. The excitation energies, in this context also known as site energies, excitonic couplings, and spectral densities are key quantities which usually need to be extracted to be able to determine the exciton dynamics and spectroscopic properties. The recently developed multiscale approach based on the numerically efficient density functional tight-binding framework followed by excited state calculations has been shown to be superior to the scheme based on pure classical molecular dynamics simulations. The enhanced approach, which improves the description of the internal vibrational dynamics of the pigment molecules, yields spectral densities in good agreement with the experimental counterparts for various bacterial and plant LH systems. Here, we provide a brief overview of those results and described the theoretical foundation of the multiscale protocol.

Cryo-EM structure of a monomeric RC-LH1-PufX supercomplex with high-carotenoid content from Rhodobacter capsulatus

In purple photosynthetic bacteria, the photochemical reaction center (RC) and light-harvesting complex 1 (LH1) assemble to form monomeric or dimeric RC-LH1 membrane complexes, essential for bacterial photosynthesis. Here, we report a 2.59-Å resolution cryoelectron microscopy (cryo-EM) structure of the RC-LH1 supercomplex from Rhodobacter capsulatus. We show that Rba. capsulatus RC-LH1 complexes are exclusively monomers in which the RC is surrounded by a 15-subunit LH1 ring. Incorporation of a transmembrane polypeptide PufX leads to a large opening within the LH1 ring. Each LH1 subunit associates two carotenoids and two bacteriochlorophylls, which is similar to Rba. sphaeroides RC-LH1 but more than one carotenoid per LH1 in Rba. veldkampii RC-LH1 monomer. Collectively, the unique Rba. capsulatus RC-LH1-PufX represents an intermediate structure between Rba. sphaeroides and Rba. veldkampii RC-LH1-PufX. Comparison of PufX from the three Rhodobacter species indicates the important residues involved in dimerization of RC-LH1.

Rotary biomolecular motor-powered supramolecular colloidal motor

Cells orchestrate the motion and force of hundreds of protein motors to perform various mechanical tasks over multiple length scales. However, engineering active biomimetic materials from protein motors that consume energy to propel continuous motion of micrometer-sized assembling systems remains challenging. Here, we report rotary biomolecular motor-powered supramolecular (RBMS) colloidal motors that are hierarchically assembled from a purified chromatophore membrane containing F_OF₁-ATP synthase molecular motors, and an assembled polyelectrolyte microcapsule. The micro-sized RBMS motor with asymmetric distribution of F_OF₁-ATPases can autonomously move under light illumination and is collectively powered by hundreds of rotary biomolecular motors. The propulsive mechanism is that a transmembrane proton gradient generated by a photochemical reaction drives F_OF₁-ATPases to rotate for ATP biosynthesis, which creates a local chemical field for self-diffusiophoretic force. Such an active supramolecular architecture endowed with motility and biosynthesis offers a promising platform for intelligent colloidal motors resembling the propulsive units in swimming bacteria.

Million-atom molecular dynamics simulations reveal the interfacial interactions and assembly of plant PSII-LHCII supercomplex

Protein-protein interface interactions dictate efficient excitation energy transfer from light-harvesting antennas to the photosystem II (PSII) core. In this work, we construct a 1.2 million atom-scale model of plant C₂S₂-type PSII-LHCII supercomplex and perform microsecond-scale molecular dynamics (MD) simulations to explore the interactions and assembly mechanisms of the sizeable PSII-LHCII supercomplex. We optimize the nonbonding interactions of the PSII-LHCII cryo-EM structure using microsecond-scale MD simulations. Binding free energy calculations with component decompositions reveal that hydrophobic interactions predominantly drive antenna-core association and the antenna-antenna interactions are relatively weak. Despite the positive electrostatic interaction energies, hydrogen bonds and salt bridges mainly provide directional or anchoring forces for interface binding. Analysis of the roles of small intrinsic subunits of PSII suggests that LHCII and CP26 first interact with small intrinsic subunits and then bind to the core proteins, whereas CP29 adopts a one-step binding process to the PSII core without the assistance of other factors. Our study provides insights into the molecular underpinnings of the self-organization and regulation of plant PSII-LHCII. It lays the framework for deciphering the general assembly principles of photosynthetic supercomplexes and possibly other macromolecular structures. The finding also has implications for repurposing photosynthetic systems to enhance photosynthesis.

Molecular dynamics simulation of an entire cell

The ultimate microscope, directed at a cell, would reveal the dynamics of all the cell's components with atomic resolution. In contrast to their real-world counterparts, computational microscopes are currently on the brink of meeting this challenge. In this perspective, we show how an integrative approach can be employed to model an entire cell, the minimal cell, JCVI-syn3A, at full complexity. This step opens the way to interrogate the cell's spatio-temporal evolution with molecular dynamics simulations, an approach that can be extended to other cell types in the near future.

Across atoms to crossing continents: Application of similarity measures to biological location data

Biological processes involve movements across all measurable scales. Similarity measures can be applied to compare and analyze these movements but differ in how differences in movement are aggregated across space and time. The present study reviews frequently-used similarity measures, such as the Hausdorff distance, Fréchet distance, Dynamic Time Warping, and Longest Common Subsequence, jointly with several measures less used in biological applications (Wasserstein distance, weak Fréchet distance, and Kullback-Leibler divergence), and provides computational tools for each of them that may be used in computational biology. We illustrate the use of the selected similarity measures in diagnosing differences within two extremely contrasting sets of biological data, which, remarkably, may both be relevant for magnetic field perception by migratory birds. Specifically, we assess and discuss cryptochrome protein conformational dynamics and extreme migratory trajectories of songbirds between Alaska and Africa. We highlight how similarity measures contrast regarding computational complexity and discuss those which can be useful in noise elimination or, conversely, are sensitive to spatiotemporal scales.

A brief history of visualizing membrane systems in molecular dynamics simulations

Understanding lipid dynamics and function, from the level of single, isolated molecules to large assemblies, is more than ever an intensive area of research. The interactions of lipids with other molecules, particularly membrane proteins, are now extensively studied. With advances in the development of force fields for molecular dynamics simulations (MD) and increases in computational resources, the creation of realistic and complex membrane systems is now common. In this perspective, we will review four decades of the history of molecular dynamics simulations applied to membranes and lipids through the prism of molecular graphics.

Identification of amino acid residues in a proton release pathway near the bacteriochlorophyll dimer in reaction centers from Rhodobacter sphaeroides

Insight into control of proton transfer, a crucial attribute of cellular functions, can be gained from investigations of bacterial reaction centers. While the uptake of protons associated with the reduction of the quinone is well characterized, the release of protons associated with the oxidized bacteriochlorophyll dimer has been poorly understood. Optical spectroscopy and proton release/uptake measurements were used to examine the proton release characteristics of twelve mutant reaction centers, each containing a change in an amino acid residue near the bacteriochlorophyll dimer. The mutant reaction centers had optical spectra similar to wild-type and were capable of transferring electrons to the quinones after light excitation of the bacteriochlorophyll dimer. They exhibited a large range in the extent of proton release and in the slow recovery of the optical signal for the oxidized dimer upon continuous illumination. Key roles were indicated for six amino acid residues, Thr L130, Asp L155, Ser L244, Arg M164, Ser M190, and His M193. Analysis of the results points to a hydrogen-bond network that contains these residues, with several additional residues and bound water molecules, forming a proton transfer pathway. In addition to proton transfer, the properties of the pathway are proposed to be responsible for the very slow charge recombination kinetics observed after continuous illumination. The characteristics of this pathway are compared to proton transfer pathways near the secondary quinone as well as those found in photosystem II and cytochrome c oxidase.

Mechanisms of E. coli chemotaxis signaling pathways visualized using cryoET and computational approaches

Chemotaxis signaling pathways enable bacteria to sense and respond to their chemical environment and, in some species, are critical for lifestyle processes such as biofilm formation and pathogenesis. The signal transduction underlying chemotaxis behavior is mediated by large, highly ordered protein complexes known as chemosensory arrays. For nearly two decades, cryo-electron tomography (cryoET) has been used to image chemosensory arrays, providing an increasingly detailed understanding of their structure and function. In this mini-review, we provide an overview of the use of cryoET to study chemosensory arrays, including imaging strategies, key results, and outstanding questions. We further discuss the application of molecular modeling and simulation techniques to complement structure determination efforts and provide insight into signaling mechanisms. We close the review with a brief outlook, highlighting promising future directions for the field.

Mesoscience in cell biology and cancer research

Mesoscale characteristics and their interdimensional correlation are the focus of contemporary interdisciplinary research. Mesoscience is a discipline that has the potential to radically update the existing knowledge structure, which differs from the conventional unit-scale and system-scale research models, revealing a previously untouchable area for scientific research. Integrative biology research aims to dissect the complex problems of life systems by conducting comprehensive research and integrating various disciplines from all biological levels of the living organism. However, the mesoscientific issues between different research units are neglected and challenging. Mesoscale research in biology requires the integration of research theories and methods from other disciplines (mathematics, physics, engineering, and even visual imaging) to investigate theoretical and frontier questions of biological processes through experiments, computations, and modeling. We reviewed  integrative paradigms and methods for the biological mesoscale problems (focusing on oncology research) and prospected the potential of their multiple dimensions and upcoming challenges. We expect to establish an interactive and collaborative theoretical platform for further expanding the depth and width of our understanding on the nature of biology.