Large Biological Systems Research Articles

The Renormalization Group and Its Applications to Generating Coarse-Grained Models of Large Biological Molecular Systems.

Understanding the dynamics of biomolecules is the key to understanding their biological activities. Computational methods ranging from all-atom molecular dynamics simulations to coarse-grained normal-mode analyses based on simplified elastic networks provide a general framework to studying these dynamics. Despite recent successes in studying very large systems with up to a 100,000,000 atoms, those methods are currently limited to studying small- to medium-sized molecular systems due to computational limitations. One solution to circumvent these limitations is to reduce the size of the system under study. In this paper, we argue that coarse-graining, the standard approach to such size reduction, must define a hierarchy of models of decreasing sizes that are consistent with each other, i.e., that each model contains the information of the dynamics of its predecessor. We propose a new method, Decimate, for generating such a hierarchy within the context of elastic networks for normal-mode analysis. This method is based on the concept of the renormalization group developed in statistical physics. We highlight the details of its implementation, with a special focus on its scalability to large systems of up to millions of atoms. We illustrate its application on two large systems, the capsid of a virus and the ribosome translation complex. We show that highly decimated representations of those systems, containing down to 1% of their original number of atoms, still capture qualitatively and quantitatively their dynamics. Decimate is available as an OpenSource resource.

The significance of the amorphous potential energy landscape for dictating glassy dynamics and driving solid-state crystallisation.

The fundamental origins surrounding the dynamics of disordered solids near their characteristic glass transitions continue to be fiercely debated, even though a vast number of materials can form amorphous solids, including small-molecule organic, inorganic, covalent, metallic, and even large biological systems. The glass-transition temperature, Tg, can be readily detected by a diverse set of techniques, but given that these measurement modalities probe vastly different processes, there has been significant debate regarding the question of why Tg can be detected across all of them. Here we show clear experimental and computational evidence in support of a theory that proposes that the shape and structure of the potential-energy surface (PES) is the fundamental factor underlying the glass-transition processes, regardless of the frequency that experimental methods probe. Whilst this has been proposed previously, we demonstrate, using ab initio molecular-dynamics (AIMD) simulations, that it is of critical importance to carefully consider the complete PES - both the intra-molecular and inter-molecular features - in order to fully understand the entire range of atomic-dynamical processes in disordered solids. Finally, we show that it is possible to utilise this dependence to directly manipulate and harness amorphous dynamics in order to control the behaviour of such solids by using high-powered terahertz pulses to induce crystallisation and preferential crystal-polymorph growth in glasses. Combined, these findings provide compelling evidence that the PES landscape, and the corresponding energy barriers, are the ultimate controlling feature behind the atomic and molecular dynamics of disordered solids, regardless of the frequency at which they occur.

Toward amino acid typing for proteins in FFLUX.

Continuing the development of the FFLUX, a multipolar polarizable force field driven by machine learning, we present a modern approach to atom‐typing and building transferable models for predicting atomic properties in proteins. Amino acid atomic charges in a peptide chain respond to the substitution of a neighboring residue and this response can be categorized in a manner similar to atom‐typing. Using a machine learning method called kriging, we are able to build predictive models for an atom that is defined, not only by its local environment, but also by its neighboring residues, for a minimal additional computational cost. We found that prediction errors were up to 11 times lower when using a model specific to the correct group of neighboring residues, with a mean prediction of ∼0.0015 au. This finding suggests that atoms in a force field should be defined by more than just their immediate atomic neighbors. When comparing an atom in a single alanine to an analogous atom in a deca‐alanine helix, the mean difference in charge is 0.026 au. Meanwhile, the same difference between a trialanine and a deca‐alanine helix is only 0.012 au. When compared to deca‐alanine models, the transferable models are up to 20 times faster to train, and require significantly less ab initio calculation, providing a practical route to modeling large biological systems. © 2016 The Authors. Journal of Computational Chemistry Published by Wiley Periodicals, Inc.

Development of a Simple Electron Transfer and Polarization Model and Its Application to Biological Systems.

Here we present a new method for point charge calculation which we call QET (charges by electron transfer). The intent of this work is to develop a method that can be useful for studying charge transfer in large biological systems. It is based on the intuitive framework of the QEQ method with the key difference being that the QET method tracks all pairwise electron transfers by augmenting the QEQ pseudoenergy function with a distance dependent cost function for each electron transfer. This approach solves the key limitation of the QEQ method which is its handling of formally charged groups. First, we parametrize the QET method by fitting to electrostatic potentials calculated using ab initio quantum mechanics on over 11,000 small molecules. On an external test set of over 2500 small molecules the QET method achieves a mean absolute error of 1.37 kcal/mol/electron when compared to the ab initio electrostatic potentials. Second, we examine the conformational dependence of the charges on over 2700 tripeptides. With the tripeptide data set, we show that the conformational effects account for approximately 0.4 kcal/mol/electron on the electrostatic potentials. Third, we test the QET method for its ability to reproduce the effects of polarization and electron transfer on 1000 water clusters. For the water clusters, we show that the QET method captures about 50% of the polarization and electron transfer effects. Finally, we examine the effects of electron transfer and polarizability on the electrostatic interaction between p38 and 94 small molecule ligands. When used in conjunction with the Generalized-Born continuum solvent model, polarization and electron transfer with the QET model lead to an average change of 17 kcal/mol on the calculated electrostatic component of ΔG.

A computational method for the investigation of multistable systems and its application to genetic switches.

BackgroundGenetic switches exhibit multistability, form the basis of epigenetic memory, and are found in natural decision making systems, such as cell fate determination in developmental pathways. Synthetic genetic switches can be used for recording the presence of different environmental signals, for changing phenotype using synthetic inputs and as building blocks for higher-level sequential logic circuits. Understanding how multistable switches can be constructed and how they function within larger biological systems is therefore key to synthetic biology.ResultsHere we present a new computational tool, called StabilityFinder, that takes advantage of sequential Monte Carlo methods to identify regions of parameter space capable of producing multistable behaviour, while handling uncertainty in biochemical rate constants and initial conditions. The algorithm works by clustering trajectories in phase space, and iteratively minimizing a distance metric. Here we examine a collection of models of genetic switches, ranging from the deterministic Gardner toggle switch to stochastic models containing different positive feedback connections. We uncover the design principles behind making bistable, tristable and quadristable switches, and find that rate of gene expression is a key parameter. We demonstrate the ability of the framework to examine more complex systems and examine the design principles of a three gene switch. Our framework allows us to relax the assumptions that are often used in genetic switch models and we show that more complex abstractions are still capable of multistable behaviour.ConclusionsOur results suggest many ways in which genetic switches can be enhanced and offer designs for the construction of novel switches. Our analysis also highlights subtle changes in correlation of experimentally tunable parameters that can lead to bifurcations in deterministic and stochastic systems. Overall we demonstrate that StabilityFinder will be a valuable tool in the future design and construction of novel gene networks.Electronic supplementary materialThe online version of this article (doi:10.1186/s12918-016-0375-z) contains supplementary material, which is available to authorized users.

High-performance computing in systems biology: accelerating the simulation and analysis of large and complex biological systems

High-performance computing in systems biology: accelerating the simulation and analysis of large and complex biological systems

TMFF-A Two-Bead Multipole Force Field for Coarse-Grained Molecular Dynamics Simulation of Protein.

Coarse-grained (CG) models are desirable for studying large and complex biological systems. In this paper, we propose a new two-bead multipole force field (TMFF) in which electric multipoles up to the quadrupole are included in the CG force field. The inclusion of electric multipoles in the proposed CG force field enables a more realistic description of the anisotropic electrostatic interactions in the protein system and, thus, provides an improvement over the standard isotropic two-bead CG models. In order to test the accuracy of the new CG force field model, extensive molecular dynamics simulations were carried out for a series of benchmark protein systems. These simulation studies showed that the TMFF model can realistically reproduce the structural and dynamical properties of proteins, as demonstrated by the close agreement of the CG results with those from the corresponding all-atom simulations in terms of root-mean-square deviations (RMSDs) and root-mean-square fluctuations (RMSFs) of the protein backbones. The current two-bead model is highly coarse-grained and is 50-fold more efficient than all-atom method in MD simulation of proteins in explicit water.

Multiple program/multiple data molecular dynamics method with multiple time step integrator for large biological systems.

Parallelization of molecular dynamics (MD) simulation is essential for investigating conformational dynamics of large biological systems, such as ribosomes, viruses, and multiple proteins in cellular environments. To improve efficiency in the parallel computation, we have to reduce the amount of data transfer between processors by introducing domain decomposition schemes. Also, it is important to optimize the computational balance between real-space non-bonded interactions and reciprocal-space interactions for long-range electrostatic interactions. Here, we introduce a novel parallelization scheme for large-scale MD simulations on massively parallel supercomputers consisting of only CPUs. We make use of a multiple program/multiple data (MPMD) approach for separating the real-space and reciprocal-space computations on different processors. We also utilize the r-RESPA multiple time step integrator on the framework of the MPMD approach in an efficient way: when the reciprocal-space computations are skipped in r-RESPA, processors assigned for them are utilized for half of the real-space computations. The new scheme allows us to use twice as many as processors that are available in the conventional single program approach. The best performances of all-atom MD simulations for 1 million (STMV), 8.5 million (8_STMV), and 28.8 million (27_STMV) atom systems on K computer are 65, 36, and 24 ns/day, respectively. The MPMD scheme can accelerate 23.4, 10.2, and 9.2 ns/day from the maximum performance of single-program approach for STMV, 8_STMV, and 27_STMV systems, respectively, which correspond to 57%, 39%, and 60% speed up. This suggests significant speedups by increasing the number of processors without losing parallel computational efficiency. © 2016 Wiley Periodicals, Inc.

Graphics Processing Unit Acceleration and Parallelization of GENESIS for Large-Scale Molecular Dynamics Simulations.

The graphics processing unit (GPU) has become a popular computational platform for molecular dynamics (MD) simulations of biomolecules. A significant speedup in the simulations of small- or medium-size systems using only a few computer nodes with a single or multiple GPUs has been reported. Because of GPU memory limitation and slow communication between GPUs on different computer nodes, it is not straightforward to accelerate MD simulations of large biological systems that contain a few million or more atoms on massively parallel supercomputers with GPUs. In this study, we develop a new scheme in our MD software, GENESIS, to reduce the total computational time on such computers. Computationally intensive real-space nonbonded interactions are computed mainly on GPUs in the scheme, while less intensive bonded interactions and communication-intensive reciprocal-space interactions are performed on CPUs. On the basis of the midpoint cell method as a domain decomposition scheme, we invent the single particle interaction list for reducing the GPU memory usage. Since total computational time is limited by the reciprocal-space computation, we utilize the RESPA multiple time-step integration and reduce the CPU resting time by assigning a subset of nonbonded interactions on CPUs as well as on GPUs when the reciprocal-space computation is skipped. We validated our GPU implementations in GENESIS on BPTI and a membrane protein, porin, by MD simulations and an alanine-tripeptide by REMD simulations. Benchmark calculations on TSUBAME supercomputer showed that an MD simulation of a million atoms system was scalable up to 256 computer nodes with GPUs.

The Use of Multiscale Molecular Simulations in Understanding a Relationship between the Structure and Function of Biological Systems of the Brain: The Application to Monoamine Oxidase Enzymes.

HIGHLIGHTS Computational techniques provide accurate descriptions of the structure and dynamics of biological systems, contributing to their understanding at an atomic level.Classical MD simulations are a precious computational tool for the processes where no chemical reactions take place.QM calculations provide valuable information about the enzyme activity, being able to distinguish among several mechanistic pathways, provided a carefully selected cluster model of the enzyme is considered.Multiscale QM/MM simulation is the method of choice for the computational treatment of enzyme reactions offering quantitative agreement with experimentally determined reaction parameters.Molecular simulation provide insight into the mechanism of both the catalytic activity and inhibition of monoamine oxidases, thus aiding in the rational design of their inhibitors that are all employed and antidepressants and antiparkinsonian drugs. Aging society and therewith associated neurodegenerative and neuropsychiatric diseases, including depression, Alzheimer's disease, obsessive disorders, and Parkinson's disease, urgently require novel drug candidates. Targets include monoamine oxidases A and B (MAOs), acetylcholinesterase (AChE), butyrylcholinesterase (BChE), and various receptors and transporters. For rational drug design it is particularly important to combine experimental synthetic, kinetic, toxicological, and pharmacological information with structural and computational work. This paper describes the application of various modern computational biochemistry methods in order to improve the understanding of a relationship between the structure and function of large biological systems including ion channels, transporters, receptors, and metabolic enzymes. The methods covered stem from classical molecular dynamics simulations to understand the physical basis and the time evolution of the structures, to combined QM, and QM/MM approaches to probe the chemical mechanisms of enzymatic activities and their inhibition. As an illustrative example, the later will focus on the monoamine oxidase family of enzymes, which catalyze the degradation of amine neurotransmitters in various parts of the brain, the imbalance of which is associated with the development and progression of a range of neurodegenerative disorders. Inhibitors that act mainly on MAO A are used in the treatment of depression, due to their ability to raise serotonin concentrations, while MAO B inhibitors decrease dopamine degradation and improve motor control in patients with Parkinson disease. Our results give strong support that both MAO isoforms, A and B, operate through the hydride transfer mechanism. Relevance of MAO catalyzed reactions and MAO inhibition in the context of neurodegeneration will be discussed.

A Novel Method to Verify Multilevel Computational Models of Biological Systems Using Multiscale Spatio-Temporal Meta Model Checking.

Insights gained from multilevel computational models of biological systems can be translated into real-life applications only if the model correctness has been verified first. One of the most frequently employed in silico techniques for computational model verification is model checking. Traditional model checking approaches only consider the evolution of numeric values, such as concentrations, over time and are appropriate for computational models of small scale systems (e.g. intracellular networks). However for gaining a systems level understanding of how biological organisms function it is essential to consider more complex large scale biological systems (e.g. organs). Verifying computational models of such systems requires capturing both how numeric values and properties of (emergent) spatial structures (e.g. area of multicellular population) change over time and across multiple levels of organization, which are not considered by existing model checking approaches. To address this limitation we have developed a novel approximate probabilistic multiscale spatio-temporal meta model checking methodology for verifying multilevel computational models relative to specifications describing the desired/expected system behaviour. The methodology is generic and supports computational models encoded using various high-level modelling formalisms because it is defined relative to time series data and not the models used to generate it. In addition, the methodology can be automatically adapted to case study specific types of spatial structures and properties using the spatio-temporal meta model checking concept. To automate the computational model verification process we have implemented the model checking approach in the software tool Mule (http://mule.modelchecking.org). Its applicability is illustrated against four systems biology computational models previously published in the literature encoding the rat cardiovascular system dynamics, the uterine contractions of labour, the Xenopus laevis cell cycle and the acute inflammation of the gut and lung. Our methodology and software will enable computational biologists to efficiently develop reliable multilevel computational models of biological systems.

Process Simulation of Complex Biological Pathways in Physical Reactive Space and Reformulated for Massively Parallel Computing Platforms.

Biological systems encompass complexity that far surpasses many artificial systems. Modeling and simulation of large and complex biochemical pathways is a computationally intensive challenge. Traditional tools, such as ordinary differential equations, partial differential equations, stochastic master equations, and Gillespie type methods, are all limited either by their modeling fidelity or computational efficiency or both. In this work, we present a scalable computational framework based on modeling biochemical reactions in explicit 3D space, that is suitable for studying the behavior of large and complex biological pathways. The framework is designed to exploit parallelism and scalability offered by commodity massively parallel processors such as the graphics processing units (GPUs) and other parallel computing platforms. The reaction modeling in 3D space is aimed at enhancing the realism of the model compared to traditional modeling tools and framework. We introduce the Parallel Select algorithm that is key to breaking the sequential bottleneck limiting the performance of most other tools designed to study biochemical interactions. The algorithm is designed to be computationally tractable, handle hundreds of interacting chemical species and millions of independent agents by considering all-particle interactions within the system. We also present an implementation of the framework on the popular graphics processing units and apply it to the simulation study of JAK-STAT Signal Transduction Pathway. The computational framework will offer a deeper insight into various biological processes within the cell and help us observe key events as they unfold in space and time. This will advance the current state-of-the-art in simulation study of large scale biological systems and also enable the realistic simulation study of macro-biological cultures, where inter-cellular interactions are prevalent.

Identification of Metabolic Pathway Systems.

The estimation of parameters in even moderately large biological systems is a significant challenge. This challenge is greatly exacerbated if the mathematical formats of appropriate process descriptions are unknown. To address this challenge, the method of dynamic flux estimation (DFE) was proposed for the analysis of metabolic time series data. Under ideal conditions, the first phase of DFE yields numerical representations of all fluxes within a metabolic pathway system, either as values at each time point or as plots against their substrates and modulators. However, this numerical result does not reveal the mathematical format of each flux. Thus, the second phase of DFE selects functional formats that are consistent with the numerical trends obtained from the first phase. While greatly facilitating metabolic data analysis, DFE is only directly applicable if the pathway system contains as many dependent variables as fluxes. Because most actual systems contain more fluxes than metabolite pools, this requirement is seldom satisfied. Auxiliary methods have been proposed to alleviate this issue, but they are not general. Here we propose strategies that extend DFE toward general, slightly underdetermined pathway systems.

CHARMM-GUI Martini Maker for Coarse-Grained Simulations with the Martini Force Field.

Coarse-grained simulations are widely used to study large biological systems. Nonetheless, building such simulation systems becomes nontrivial, especially when membranes with various lipid types are involved. Taking advantage of the frameworks in all-atom CHARMM-GUI modules, we have developed CHARMM-GUI Martini Maker for building solution, micelle, bilayer, and vesicle systems as well as systems with randomly distributed lipids using the Martini force field. Martini Maker supports 82 lipid types and different flavors of the Martini force field, including polar and nonpolar Martini, Dry Martini, and ElNeDyn (an elastic network model for proteins). The qualities of the systems generated by Martini Maker are validated by simulations of various examples involving proteins and lipids. We expect Martini Maker to be a useful tool for modeling large, complicated biomolecular systems in a user-friendly way.

GENESIS: a hybrid-parallel and multi-scale molecular dynamics simulator with enhanced sampling algorithms for biomolecular and cellular simulations.

GENESIS (Generalized-Ensemble Simulation System) is a new software package for molecular dynamics (MD) simulations of macromolecules. It has two MD simulators, called ATDYN and SPDYN. ATDYN is parallelized based on an atomic decomposition algorithm for the simulations of all-atom force-field models as well as coarse-grained Go-like models. SPDYN is highly parallelized based on a domain decomposition scheme, allowing large-scale MD simulations on supercomputers. Hybrid schemes combining OpenMP and MPI are used in both simulators to target modern multicore computer architectures. Key advantages of GENESIS are (1) the highly parallel performance of SPDYN for very large biological systems consisting of more than one million atoms and (2) the availability of various REMD algorithms (T-REMD, REUS, multi-dimensional REMD for both all-atom and Go-like models under the NVT, NPT, NPAT, and NPγT ensembles). The former is achieved by a combination of the midpoint cell method and the efficient three-dimensional Fast Fourier Transform algorithm, where the domain decomposition space is shared in real-space and reciprocal-space calculations. Other features in SPDYN, such as avoiding concurrent memory access, reducing communication times, and usage of parallel input/output files, also contribute to the performance. We show the REMD simulation results of a mixed (POPC/DMPC) lipid bilayer as a real application using GENESIS. GENESIS is released as free software under the GPLv2 licence and can be easily modified for the development of new algorithms and molecular models. WIREs Comput Mol Sci 2015, 5:310–323. doi: 10.1002/wcms.1220

Assembling the pieces of macromolecular complexes: Hybrid structural biology approaches.

Hybrid approaches in structural biology have gained considerable interest for uncovering the molecular architectures of large and transient biological systems. In particular, MS-based methods and structural electron microscopy can complement conventional tools, such as X-ray crystallography and NMR spectroscopy. However, bringing together the data derived from diverse sources requires sophisticated methods that can efficiently deal with intrinsic ambiguities and heterogeneities of the vast amount of data available. Here, we highlight hybrid approaches for studying dynamic assemblies, such as transient soluble and integral membrane protein complexes. In this review, we emphasize the integration of the wide range of emerging MS-based methods, such as ion mobility, native MS, hydrogen-deuterium exchange MS and chemical cross-linking MS, with data acquired from cryo electron microscopy and X-ray crystallography and further provide a future outlook of hybrid structural biology approaches.

Estimating dosimetric quantities of radon progeny using human CT scan data and small tissue volume analysis with Geant4 code system

Estimating the health effects of radon exposure is of great interest because radon is considered the second leading cause of lung cancer after smoking. The dose-response curve is not well understood at low-dose levels where radon exposure is estimated. Therefore, the health mechanisms of radiation due to radon progeny at the cellular and molecular levels are of interest for providing an indication of a possible threshold value above which the exposure may indicate cancer formation. In this paper we present a macroscopic and cellular level numerical analysis of the radon-induced dose estimates based on the Geant4 code system. Macroscopic estimates are assessed based on patient-specific computer tomography scans that provide geometries easily applicable to modeling radiation effects of the radon progeny sources. A small tissue volumes analysis based on the Geant4 code system is developed so as to provide information about the interactions and particle track structures at the microscopic (cellular) levels producing the dosimetric effects of radon short-lived progenies. The results presented in this paper also call attention to the capabilities of Geant4 to provide radon-related dosimetric parameters of large and small-scale biological systems.

CHARMM-GUI Martini Maker for Coarse-Grained Simulations

Coarse-grained simulations are widely used to study large biological systems. We have developed CHARMM-GUI Martini Maker for building solution, micelle, bilayer, and vesicle systems using Martini, a coarse-grained force field for biological molecules such as lipids, proteins, and carbohydrates. The supported force field includes martini, martini with polarizable water, dry martini, and ElNeDyn (an elastic network model for proteins). The qualities of the output systems are validated by simulation of various examples and comparison of the coarse-grained simulations to all-atom simulations. We expect this module to be a useful tool for modeling large, complicated systems in a user-friendly way.

BROMOC suite: Monte Carlo/Brownian dynamics suite for studies of ion permeation and DNA transport in biological and artificial pores with effective potentials.

The transport of ions and solutes by biological pores is central for cellular processes and has a variety of applications in modern biotechnology. The time scale involved in the polymer transport across a nanopore is beyond the accessibility of conventional MD simulations. Moreover, experimental studies lack sufficient resolution to provide details on the molecular underpinning of the transport mechanisms. BROMOC, the code presented herein, performs Brownian dynamics simulations, both serial and parallel, up to several milliseconds long. BROMOC can be used to model large biological systems. IMC-MACRO software allows for the development of effective potentials for solute-ion interactions based on radial distribution function from all-atom MD. BROMOC Suite also provides a versatile set of tools to do a wide variety of preprocessing and postsimulation analysis. We illustrate a potential application with ion and ssDNA transport in MspA nanopore.

Biocellion: accelerating computer simulation of multicellular biological system models.

Biological system behaviors are often the outcome of complex interactions among a large number of cells and their biotic and abiotic environment. Computational biologists attempt to understand, predict and manipulate biological system behavior through mathematical modeling and computer simulation. Discrete agent-based modeling (in combination with high-resolution grids to model the extracellular environment) is a popular approach for building biological system models. However, the computational complexity of this approach forces computational biologists to resort to coarser resolution approaches to simulate large biological systems. High-performance parallel computers have the potential to address the computing challenge, but writing efficient software for parallel computers is difficult and time-consuming. We have developed Biocellion, a high-performance software framework, to solve this computing challenge using parallel computers. To support a wide range of multicellular biological system models, Biocellion asks users to provide their model specifics by filling the function body of pre-defined model routines. Using Biocellion, modelers without parallel computing expertise can efficiently exploit parallel computers with less effort than writing sequential programs from scratch. We simulate cell sorting, microbial patterning and a bacterial system in soil aggregate as case studies. Biocellion runs on x86 compatible systems with the 64 bit Linux operating system and is freely available for academic use. Visit http://biocellion.com for additional information.