Large Biological Systems Research Articles

Dynamic Interplay between the Mode-Randomization and Autodetachment of the Dipole-Bound States of the Anion.

State-specific dynamics of the dipole-bound state (DBS) of the cryogenically cooled deprotonated 4,4'-biphenol anion have been investigated by picosecond time-resolved pump-probe spectroscopy. For DBS vibrational states below the electron-detachment threshold, the relaxation rate is slow to give a lifetime (τ) longer than ∼5 ns, and it is attributed to the nonvalence-to-valence orbital transformation. For the DBS resonances above the detachment threshold, however, the lifetime decreases with the activation of autodetachment, whereas the otherwise zeroth DBS modes seem to be randomized by intramolecular vibrational energy redistribution (IVR), as manifested in the biexponential transients. As the DBS internal energy increases further, the lifetime shows a monotonic decrease to give τ ∼ 50 ps at E'vib ∼ 1700 cm-1. This study demonstrates that IVR may play an important role in the autodetachment dynamics when the density of states rapidly increases with increasing vibrational energy, giving important implications for the electron-transfer dynamics taking place in large biological or astrochemical systems.

A commentary on the ABC consortium and its impact on the development of mesoscopic models of DNA.

In this commentary, it is presented how the Ascona B-DNA (ABC) consortium analyses of the DNA structure and deformability had a great influence in the development of mesoscopic models of DNA. The generation of databases using molecular dynamics ensembles to describe the DNA flexibility, allowed mesoscopic models to improve and become more accurate with a quality similar to that of all-atom MD simulations. All this will be translated in the future in the development of mesoscopic methods that could study and reproduce large biological systems such as chromatin structure, nucleosome arrangements, and protein recognition.

The series of conformational states adopted by rotorless F1-ATPase during its hydrolysis cycle

F1Fo ATP synthase interchanges phosphate transfer energy and proton motive force via a rotary catalytic mechanism and isolated F1-ATPase subcomplexes can also hydrolyze ATP to generate rotation of their central γ rotor subunit. As ATP is hydrolyzed, the F1-ATPase cycles through a series of conformational states that mediates unidirectional rotation of the rotor. However, even in the absence of a rotor, the α and β subunits are still able to pass through a series of conformations, akin to those that generate rotation. Here, we use cryoelectron microscopy to establish the structures of these rotorless states. These structures indicate that cooperativity in this system is likely mediated by contacts between the β subunit lever domains, irrespective of the presence of the γ rotor subunit. These findings provide insight into how long-range information may be transferred in large biological systems.

SPANA: Spatial decomposition analysis for cellular-scale molecular dynamics simulations.

The rapid increase in computational power with the latest supercomputers has enabled atomistic molecular dynamics (MDs) simulations of biomolecules in biological membrane, cytoplasm, and other cellular environments. These environments often contain a million or more atoms to be simulated simultaneously. Therefore, their trajectory analyses involve heavy computations that can become a bottleneck in the computational studies. Spatial decomposition analysis (SPANA) is a set of analysis tools in the Generalized-Ensemble Simulation System (GENESIS) software package that can carry out MD trajectory analyses of large-scale biological simulations using multiple CPU cores in parallel. SPANA applies the spatial decomposition of a large biological system to distribute structural and dynamical analyses into individual CPU cores, which reduces the computational time and the memory size, significantly. SPANA opens new possibilities for detailed atomistic analyses of biomacromolecules as well as solvent water molecules, ions, and metabolites in MD simulation trajectories of very large biological systems containing more than millions of atoms in cellular environments.

Kboolnet: a toolkit for the verification, validation, and visualization of reaction-contingency (rxncon) models

BackgroundComputational models of cell signaling networks are extremely useful tools for the exploration of underlying system behavior and prediction of response to various perturbations. By representing signaling cascades as executable Boolean networks, the previously developed rxncon (“reaction-contingency”) formalism and associated Python package enable accurate and scalable modeling of signal transduction even in large (thousands of components) biological systems. The models are split into reactions, which generate states, and contingencies, that impinge on reactions; this avoids the so-called “combinatorial explosion” of system size. Boolean description of the biological system compensates for the poor availability of kinetic parameters which are necessary for quantitative models. Unfortunately, few tools are available to support rxncon model development, especially for large, intricate systems.ResultsWe present the kboolnet toolkit (https://github.com/Kufalab-UCSD/kboolnet, complete documentation at https://github.com/Kufalab-UCSD/kboolnet/wiki), an R package and a set of scripts that seamlessly integrate with the python-based rxncon software and collectively provide a complete workflow for the verification, validation, and visualization of rxncon models. The verification script VerifyModel.R checks for responsiveness to repeated stimulations as well as consistency of steady state behavior. The validation scripts TruthTable.R, SensitivityAnalysis.R, and ScoreNet.R provide various readouts for the comparison of model predictions to experimental data. In particular, ScoreNet.R compares model predictions to a cloud-stored MIDAS-format experimental database to provide a numerical score for tracking model accuracy. Finally, the visualization scripts allow for graphical representations of model topology and behavior. The entire kboolnet toolkit is cloud-enabled, allowing for easy collaborative development; most scripts also allow for the extraction and analysis of individual user-defined “modules”.ConclusionThe kboolnet toolkit provides a modular, cloud-enabled workflow for the development of rxncon models, as well as their verification, validation, and visualization. This will enable the creation of larger, more comprehensive, and more rigorous models of cell signaling using the rxncon formalism in the future.

Acceleration of generalized replica exchange with solute tempering simulations of large biological systems on massively parallel supercomputer.

Generalized replica exchange with solute tempering (gREST) is one of the enhanced sampling algorithms for proteins or other systems with rugged energy landscapes. Unlike the replica-exchange molecular dynamics (REMD) method, solvent temperatures are the same in all replicas, while solute temperatures are different and are exchanged frequently between replicas for exploring various solute structures. Here, we apply the gREST scheme to large biological systems containing over one million atoms using a large number of processors in a supercomputer. First, communication time on a multi-dimensional torus network is reduced by matching each replica to MPI processors optimally. This is applicable not only to gREST but also to other multi-copy algorithms. Second, energy evaluations, which are necessary for the multistate bennet acceptance ratio (MBAR) method for free energy estimations, are performed on-the-fly during the gREST simulations. Using these two advanced schemes, we observed 57.72 ns/day performance in 128-replica gREST calculations with 1.5 million atoms system using 16,384 nodes in Fugaku. These schemes implemented in the latest version of GENESIS software could open new possibilities to answer unresolved questions on large biomolecular complex systems with slow conformational dynamics.

Benchmarking Molecular Dynamics Force Fields for All-Atom Simulations of Biological Condensates.

Proteins containing intrinsically disordered regions are integral parts of the cellular signaling pathways and common components of biological condensates. Point mutations in the protein sequence, genetic at birth or acquired through aging, can alter the properties of the condensates, marking the onset of neurodegenerative diseases such as ALS and dementia. While the all-atom molecular dynamics method can, in principle, elucidate the conformational changes that arise from point mutations, the applications of this method to protein condensate systems is conditioned upon the availability of molecular force fields that can accurately describe both structured and disordered regions of such proteins. Using the special-purpose Anton 2 supercomputer, we benchmarked the efficacy of nine presently available molecular force fields in describing the structure and dynamics of a Fused in sarcoma (FUS) protein. Five-microsecond simulations of the full-length FUS protein characterized the effect of the force field on the global conformation of the protein, self-interactions among its side chains, solvent accessible surface area, and the diffusion constant. Using the results of dynamic light scattering as a benchmark for the FUS radius of gyration, we identified several force fields that produced FUS conformations within the experimental range. Next, we used these force fields to perform ten-microsecond simulations of two structured RNA binding domains of FUS bound to their respective RNA targets, finding the choice of the force field to affect stability of the RNA-FUS complex. Taken together, our data suggest that a combination of protein and RNA force fields sharing a common four-point water model provides an optimal description of proteins containing both disordered and structured regions and RNA-protein interactions. To make simulations of such systems available beyond the Anton 2 machines, we describe and validate implementation of the best performing force fields in a publicly available molecular dynamics program NAMD. Our NAMD implementation enables simulations of large (tens of millions of atoms) biological condensate systems and makes such simulations accessible to a broader scientific community.

A deep transfer learning-based protocol accelerates full quantum mechanics calculation of protein.

Effective full quantum mechanics (FQM) calculation of protein remains a grand challenge and of great interest in computational biology with substantial applications in drug discovery, protein dynamic simulation and protein folding. However, the huge computational complexity of the existing QM methods impends their applications in large systems. Here, we design a transfer-learning-based deep learning (TDL) protocol for effective FQM calculations (TDL-FQM) on proteins. By incorporating a transfer-learning algorithm into deep neural network (DNN), the TDL-FQM protocol is capable of performing calculations at any given accuracy using models trained from small datasets with high-precision and knowledge learned from large amount of low-level calculations. The high-level double-hybrid DFT functional and high-level quality of basis set is used in this work as a case study to evaluate the performance of TDL-FQM, where the selected 15 proteins are predicted to have a mean absolute error of 0.01kcal/mol/atom for potential energy and an average root mean square error of 1.47kcal/mol/$ {\rm A^{^{ \!\!\!o}}} $ for atomic forces. The proposed TDL-FQM approach accelerates the FQM calculation more than thirty thousand times faster in average and presents more significant benefits in efficiency as the size of protein increases. The ability to learn knowledge from one task to solve related problems demonstrates that the proposed TDL-FQM overcomes the limitation of standard DNN and has a strong power to predict proteins with high precision, which solves the challenge of high precision prediction in large chemical and biological systems.

The Role of Electrostatic Interactions in IFIT5-RNA Complexes Predicted by the UBDB+EPMM Method.

Electrostatic energy has a significant contribution to intermolecular interaction energy, especially in biological systems. Unfortunately, precise quantum mechanics calculations are not feasible for large biological systems; hence, simpler calculation methods are required. We propose a method called UBDB+EPMM (University at Buffalo Pseudoatom DataBank + Exact Potential Multipole Moments), which shortens computational time without losing accuracy. Here, we characterize electrostatic interactions in selected complexes of IFIT proteins with RNA. IFIT proteins are effectors of the innate immune system, and by binding foreign RNA, they prevent the synthesis of viral proteins in human host cells; hence, they block the propagation of viruses. We show that by using the UBDB+EPMM method it is possible to describe protein-RNA interactions not only qualitatively but also quantitatively. Looking at the charge penetration contribution to electrostatic interactions, we find all amino acid residues with strong local interactions. Moreover, we confirm that electrostatic interaction of IFIT5 with pppRNA does not depend on the sequence of the RNA.

Galvani Offset Potential and Constant-pH Simulations of Membrane Proteins.

A central problem in computational biophysics is the treatment of titratable residues in molecular dynamics simulations of large biological macromolecular systems. Conventional simulation methods ascribe a fixed ionization state to titratable residues in accordance with their pKa and the pH of the system, assuming that an effective average model will be able to capture the predominant behavior of the system. While this assumption may be justifiable in many cases, it is certainly limited, and it is important to design alternative methodologies allowing a more realistic treatment. Constant-pH simulation methods provide powerful approaches to handle titratable residues more realistically by allowing the ionization state to vary statistically during the simulation. Extending the molecular mechanical (MM) potential energy function to a family of potential functions accounting for different ionization states, constant-pH simulations are designed to sample all accessible configurations and ionization states, properly weighted according to their Boltzmann factor. Because protonation and deprotonation events correspond to a change in the total charge, difficulties arise when the long-range Coulomb interaction is treated on the basis of an idealized infinite simulation model and periodic boundary conditions with particle-mesh Ewald lattice sums. Charging free-energy calculations performed under these conditions in aqueous solution depend on the Galvani potential of the bulk water phase. This has important implications for the equilibrium and nonequilibrium constant-pH simulation methods grounded in the relative free-energy difference corresponding to the protonated and unprotonated residues. Here, the effect of the Galvani potential is clarified, and a simple practical solution is introduced to address this issue in constant-pH simulations of the acid-sensing ion channel (ASIC).

Fragment Localized Molecular Orbitals

We introduce the concept of fragment localized molecular orbitals (FLMOs), which are Hartree–Fock molecular orbitals localized in specific fragments constituting a molecular system. In physical terms, we minimize the local electronic energies of the different fragments, at the cost of maximizing the repulsion between them. To showcase the approach, we rationalize the main interactions occurring in large biological systems in terms of interactions between the fragments of the system. In particular, we study an anticancer drug intercalated within DNA and retinal in anabaena sensory rhodopsin as prototypes of molecular systems embedded in biological matrixes. Finally, the FLMOs are exploited to rationalize the formation of two oligomers, prototypes of amyloid diseases, such as Parkinson and Alzheimer.

Protein‐protein interactions play significant roles in molecular motors

Dynein is a molecular motor for cargo transportation and force generation in cells. Dysfunction of dynein is associated with many diseases, such as ciliopathies, lissencephaly and other neurodegeneration disorders. Understanding the functions of dynein is crucial for developing new treatments of such diseases. Electrostatic interactions play important roles in proteins including dyneins. A lot of efforts have been made to study the electrostatic interactions in biological systems. However, it is extremely challenging to accurately calculate the electrostatic interactions in large biological systems such as dynein. I will introduce a novel multi‐scale simulation approach which is used to study dynein’s motion along microtubules. The electrostatic binding funnel around microtubule is observed, which drags the dynein to the binding pocket. The electrostatic forces on dynein residues form a torsion which reorients the dynein when it is in an un‐native orientation. Furthermore, the electrostatic component of the binding energy of dynein and microtubule strongly affects the velocity and run length of the dynein. These results reveal the mechanisms of dynein’s motility and functions along microtubule, which shed light on treatments of molecular motor related diseases.

From peg to plasmid: system building for MD simulations of flexible macromolecules

Molecular dynamics simulations are increasingly used to characterize static and dynamic processes in large biological systems such as organelles or virions, with the ultimate goal of simulating a whole cell. Programs for constructing the initial coordinates of the envelope as well as packing the interior with proteins and small molecules exist. However, generating input parameters and realistic coordinates for more flexible macromolecules such as DNA, RNA, or carbohydrates, especially close to all-atom resolution, is less routinely done.

Unraveling membrane properties at the organelle-level with LipidDyn

Cellular membranes are formed from different lipids in various amounts and proportions depending on the subcellular localization. The lipid composition of membranes is sensitive to changes in the cellular environment, and its alterations are linked to several diseases. Lipids not only form lipid-lipid interactions but also interact with other biomolecules, including proteins.Molecular dynamics (MD) simulations are a powerful tool to study the properties of cellular membranes and membrane-protein interactions on different timescales and resolutions. Over the last few years, software and hardware for biomolecular simulations have been optimized to routinely run long simulations of large and complex biological systems. On the other hand, high-throughput techniques based on lipidomics provide accurate estimates of the composition of cellular membranes at the level of subcellular compartments. Lipidomic data can be analyzed to design biologically relevant models of membranes for MD simulations. Similar applications easily result in a massive amount of simulation data where the bottleneck becomes the analysis of the data. In this context, we developed LipidDyn, a Python-based pipeline to streamline the analyses of MD simulations of membranes of different compositions. Once the simulations are collected, LipidDyn provides average properties and time series for several membrane properties such as area per lipid, thickness, order parameters, diffusion motions, lipid density, and lipid enrichment/depletion. The calculations exploit parallelization, and the pipeline includes graphical outputs in a publication-ready form. We applied LipidDyn to different case studies to illustrate its potential, including membranes from cellular compartments and transmembrane protein domains. LipidDyn is available free of charge under the GNU General Public License from https://github.com/ELELAB/LipidDyn.

Structural Studies of Inclusion Complexes of HP-β-CD and Cetirizine by Combination of ROESY and Molecular Modelling Methods

Cyclodextrin inclusion complexes (CD ICs) mimics the biological systems in molecular and chiral recognition mechanisms and therefore used as models in understanding molecular and chiral recognition because of their small size as compared to large biological systems. Cross peaks observed in the ROESY spectra of the mixture of cetirizine (CIT) and HP-β-CD demonstrate the portions of the guest encapsulated in the cavity that helped in establishing the probable orientations of complexes. Protons of both chlorobenzene and phenyl rings showed interactions with the cavity protons suggesting the formation of two complexes. Molecular mechanics (MM) and molecular dynamics (MD) studies were performed only from the wider end of the CD cavity because of steric hinderance of hydroxypropyl groups on the narrow side of CD cavity. Using the fusion of 2D ROESY and molecular modelling studies, the final structures of ensembles of the inclusion complexes were established.

Extending Libraries of Extremely Localized Molecular Orbitals to Metal Organic Frameworks: A Preliminary Investigation

Libraries of extremely localized molecular orbitals (ELMOs) have been recently assembled to reconstruct approximate wavefunctions of very large biological systems, such as polypeptides and proteins. In this paper, we investigate for the first time the possibility of using ELMO transferability to also quickly obtain wavefunctions, electron densities, and electrostatic potentials of three-dimensional coordination polymers such as metal organic frameworks (MOFs). To accomplish this task, we propose a protocol that, in addition to exploiting the usual exportability of extremely localized molecular orbitals, also takes advantage of the novel QM/ELMO (quantum mechanics/extremely localized molecular orbital) approach to properly describe the secondary building units of MOFs. As a benchmark test, our technique has been applied to the well-known metal organic framework HKUST-1 ({Cu3(BTC)2}n, with BTC=1,3,5-benzenetricarboxylate) to quickly calculate electrostatic potential maps in the small and large cavities inside the network. On the basis of the obtained results, we envisage further improvements and applications of this strategy, which can be also seen as a starting point to perform less computationally expensive quantum mechanical calculations on metal organic frameworks with the goal of investigating transformation phenomena such as chemisorption.

A Step toward the Quantification of Noncovalent Interactions in Large Biological Systems: The Independent Gradient Model-Extremely Localized Molecular Orbital Approach.

The independent gradient model (IGM) is a recent electron density-based computational method that enables to detect and quantify covalent and noncovalent interactions. When applied to large systems, the original version of the technique still relies on promolecular electron densities given by the sum of spherically averaged atomic electron distributions, which leads to approximate evaluations of the inter- and intramolecular interactions occurring in systems of biological interest. To overcome this drawback and perform IGM analyses based on quantum mechanically rigorous electron densities also for macromolecular systems, we coupled the IGM approach with the recently constructed libraries of extremely localized molecular orbitals (ELMOs) that allow fast and reliable reconstructions of polypeptide and protein electron densities. The validation tests performed on small polypeptides and peptide dimers have shown that the novel IGM-ELMO strategy provides results that are systematically closer to the fully quantum mechanical ones and outperforms the IGM method based on the crude promolecular approximation, but still keeping a quite low computational cost. The results of the test calculations carried out on proteins have also confirmed the trends observed for the IGM analyses conducted on small systems. This makes us envisage the future application of the novel IGM-ELMO approach to unravel complicated noncovalent interaction networks (e.g., in protein-protein contacts) or to rationally design new drugs through molecular docking calculations and virtual high-throughput screenings.

Ligand Affinities within the Open-Boundary Molecular Mechanics/Coarse-Grained Framework (I): Alchemical Transformations within the Hamiltonian Adaptive Resolution Scheme.

Our recently developed Open-Boundary Molecular Mechanics/Coarse Grained (OB-MM/CG) framework predicts ligand poses in important pharmaceutical targets, such as G-protein Coupled Receptors, even when experimental structural information is lacking. The approach, which is based on GROMOS and AMBER force fields, allows for grand-canonical simulations of protein-ligand complexes by using the Hamiltonian Adaptive Resolution Scheme (H-AdResS) for the solvent. Here, we present a key step toward the estimation of ligand binding affinities for their targets within this approach. This is the implementation of the H-AdResS in the GROMACS code. The accuracy of our implementation is established by calculating hydration free energies of several molecules in water by means of alchemical transformations. The deviations of the GROMOS- and AMBER-based H-AdResS results from the reference fully atomistic simulations are smaller than the accuracy of the force field and/or they are in the range of the published results. Importantly, our predictions are in good agreement with experimental data. The current implementation paves the way to the use of the OB-MM/CG framework for the study of large biological systems.

StructureMan: A Structure Manipulation Tool to Study Large Scale Biomolecular Interactions.

Studying biomolecular interactions is a crucial but challenging task. Due to their large scales, many biomolecular interactions are difficult to be simulated via all atom models. An effective approach to investigate the biomolecular interactions is highly demanded in many areas. Here we introduce a Structure Manipulation (StructureMan) program to operate the structures when studying the large-scale biomolecular interactions. This novel StructureMan tool provides comprehensive operations which can be utilized to study the interactions in various large biological systems. Combining with electrostatic calculation programs such as DelPhi and DelPhiForce, StructureMan was implemented to reveal the detailed electrostatic features in two large biological examples, the viral capsid and molecular motor-microtubule complexes. Applications on these two examples revealed interesting binding mechanisms in the viral capsid and molecular motor. Such applications demonstrated that the StructureMan can be widely used when studying the biomolecular interactions in large scale biological problems. This novel tool provides an alternative approach to efficiently study the biomolecular interactions, especially for large scale biology systems. The StructureMan tool is available at our website: http://compbio.utep.edu/static/downloads/script-for-munipulation2.zip.

Single-Molecule Fluorescence Methods to Study Protein Exchange Kinetics in Supramolecular Complexes.

Recent single-molecule studies have demonstrated that the composition of multi-protein complexes can strike a balance between stability and dynamics. Proteins can dynamically exchange in and out of the complex depending on their concentration in solution. These exchange dynamics are a key determinant of the molecular pathways available to multi-protein complexes. It is therefore important that we develop robust and reproducible assays to study protein exchange. Using DNA replication as an example, we describe three single-molecule fluorescence assays used to study protein exchange dynamics. In the chase exchange assay, fluorescently labeled proteins are challenged by unlabeled proteins, where exchangeresults in the disappearance of the fluorescence signal. In the FRAP exchange assay, fluorescently labeled proteins are photobleached before exchange is measured by an increase in fluorescence as non-bleached proteins exchange into the complex. Finally, in the two-color exchange assay, proteins are labeled with two different fluorophores and exchange is visualized by detecting changes in color. All three assays compliment in their ability toelucidate the dynamic behavior of proteins in large biological systems.