scholarly journals An Accurate Estimate of the Free Energy and Phase Diagram of All-DNA Bulk Fluids

2018 ◽  
Author(s):  
Emanuele Locatelli ◽  
Lorenzo Rovigatti
Keyword(s):  
Phase Diagram ◽  
Free Energy ◽  
Large Scale ◽  
Nearest Neighbor ◽  
Numerical Study ◽  
Thermodynamic Description ◽  
Numerical Approach ◽  
Quantitative Agreement ◽  
State Theory ◽  
Coarse Grained

We present a numerical study in which large-scale bulk simulations of self-assembled DNA constructs have been carried out with a realistic coarse-grained model. The investigation aims at obtaining a precise, albeit numerically demanding, estimate of the free energy for such systems. We then, in turn, use these accurate results to validate a recently proposed theoretical approach that builds on a liquid-state theory, the Wertheim theory, to compute the phase diagram of all-DNA fluids. This hybrid theoretical/numerical approach, based on the lowest order virial expansion and a nearest-neighbor DNA model, can provide, in an undemanding way, a thermodynamic description of DNA associating fluids that is in semi-quantitative agreement with experiments. We show that the predictions of such scheme are as accurate as the ones obtained with more sophisticated methods. We also demonstrate the flexibility of the approach by incorporating non-trivial additional contributions that go beyond the nearest-neighbor model to compute the DNA hybridization free energy.

scholarly journals Thermodynamics, maximum power, and the dynamics of preferential river flow structures on continents

2012 ◽  
Vol 9 (6) ◽  
pp. 7317-7378 ◽  
Author(s):  
A. Kleidon ◽  
E. Zehe ◽  
U. Ehret ◽  
U. Scherer
Keyword(s):  
Free Energy ◽  
Large Scale ◽  
Scaling Laws ◽  
Preferential Flow ◽  
River Flow ◽  
Thermodynamic Description ◽  
Minimum Energy ◽  
Flow Structures ◽  
Drainage Basins ◽  
Drainage Systems

Abstract. The organization of drainage basins shows some reproducible phenomena, as exemplified by self-similar fractal river network structures and typical scaling laws, and these have been related to energetic optimization principles, such as minimization of stream power, minimum energy expenditure or maximum "access". Here we describe the organization and dynamics of drainage systems using thermodynamics, focusing on the generation, dissipation and transfer of free energy associated with river flow and sediment transport. We argue that the organization of drainage basins reflects the fundamental tendency of natural systems to deplete driving gradients as fast as possible through the maximization of free energy generation, thereby accelerating the dynamics of the system. This effectively results in the maximization of sediment export to deplete topographic gradients as fast as possible and potentially involves large-scale feedbacks to continental uplift. We illustrate this thermodynamic description with a set of three highly simplified models related to water and sediment flow and describe the mechanisms and feedbacks involved in the evolution and dynamics of the associated structures. We close by discussing how this thermodynamic perspective is consistent with previous approaches and the implications that such a thermodynamic description has for the understanding and prediction of sub-grid scale organization of drainage systems and preferential flow structures in general.

lncRNATargets: A platform for lncRNA target prediction based on nucleic acid thermodynamics

2016 ◽  
Vol 14 (04) ◽  
pp. 1650016 ◽  
Author(s):  
Ruifeng Hu ◽  
Xiaobo Sun
Keyword(s):  
Free Energy ◽  
Nucleic Acid ◽  
Large Scale ◽  
Nearest Neighbor ◽  
Binding Free Energy ◽  
Target Prediction ◽  
Easy Access ◽  
Web Based ◽  
Size Limitation ◽  
User Friendly

Many studies have supported that long noncoding RNAs (lncRNAs) perform various functions in various critical biological processes. Advanced experimental and computational technologies allow access to more information on lncRNAs. Determining the functions and action mechanisms of these RNAs on a large scale is urgently needed. We provided lncRNATargets, which is a web-based platform for lncRNA target prediction based on nucleic acid thermodynamics. The nearest-neighbor (NN) model was used to calculate binging-free energy. The main principle of NN model for nucleic acid assumes that identity and orientation of neighbor base pairs determine stability of a given base pair. lncRNATargets features the following options: setting of a specific temperature that allow use not only for human but also for other animals or plants; processing all lncRNAs in high throughput without RNA size limitation that is superior to any other existing tool; and web-based, user-friendly interface, and colored result displays that allow easy access for nonskilled computer operators and provide better understanding of results. This technique could provide accurate calculation on the binding-free energy of lncRNA-target dimers to predict if these structures are well targeted together. lncRNATargets provides high accuracy calculations, and this user-friendly program is available for free at http://www.herbbol.org:8001/lrt/ .

AN APPROACH TO DATA PARALLEL MOLECULAR DYNAMICS FOR LIQUIDS

1993 ◽  
Vol 04 (01) ◽  
pp. 41-48 ◽  
Author(s):  
FREDRIK HEDMAN ◽  
AATTO LAAKSONEN
Keyword(s):  
Molecular Dynamics ◽  
Large Scale ◽  
Nearest Neighbor ◽  
Large Fraction ◽  
Particle Migration ◽  
Coarse Grained ◽  
Time Step ◽  
Data Parallel ◽  
Massively Parallel Computers ◽  
Large Scale Data

An efficient approach to large scale data parallel short-range molecular dynamics for liquids is presented. The method is based on the coarse-grained cell method in which the simulation cell is decomposed into equally sized subcells, with the shortest side being larger than the cut-off radius. To avoid a large fraction of the nonproductive calculations we develop a geometric sorting procedure based on particle distances to subcell boundaries. Due to particle migration, the contents of the subcells need to be updated. This is done with a method based only on nearest-neighbor communications. Special "null-particles" are introduced, which act as buffers during periodic updates and allow for a globally uniform algorithm during the force calculations. The method should be easy to implement on most massively parallel computers of SIMD or MIMD type. We have implemented our code in CM Fortran on an 8K CM200. Communication cost is around 7% of the total cpu time. The overall speed for one million particles is approximately 5.9μs per MD time step and per particle and 5.5μs for five million particles.

scholarly journals Efficient conversion of chemical energy into mechanical work by Hsp70 chaperones

eLife ◽  
2019 ◽  
Vol 8 ◽  
Author(s):  
Salvatore Assenza ◽  
Alberto Stefano Sassi ◽  
Ruth Kellner ◽  
Benjamin Schuler ◽  
Paolo De Los Rios ◽  
...  
Keyword(s):  
Free Energy ◽  
Single Molecule ◽  
Atp Hydrolysis ◽  
Critical Role ◽  
Quantitative Agreement ◽  
Mechanical Work ◽  
Chemical Energy ◽  
Coarse Grained ◽  
Theoretical Approaches ◽  
Non Equilibrium

Hsp70 molecular chaperones are abundant ATP-dependent nanomachines that actively reshape non-native, misfolded proteins and assist a wide variety of essential cellular processes. Here, we combine complementary theoretical approaches to elucidate the structural and thermodynamic details of the chaperone-induced expansion of a substrate protein, with a particular emphasis on the critical role played by ATP hydrolysis. We first determine the conformational free-energy cost of the substrate expansion due to the binding of multiple chaperones using coarse-grained molecular simulations. We then exploit this result to implement a non-equilibrium rate model which estimates the degree of expansion as a function of the free energy provided by ATP hydrolysis. Our results are in quantitative agreement with recent single-molecule FRET experiments and highlight the stark non-equilibrium nature of the process, showing that Hsp70s are optimized to effectively convert chemical energy into mechanical work close to physiological conditions.

scholarly journals Free energy calculations for rings and chains formed by dipolar hard spheres

Soft Matter ◽  
2017 ◽  
Vol 13 (43) ◽  
pp. 7870-7878 ◽  
Author(s):  
Michela Ronti ◽  
Lorenzo Rovigatti ◽  
José M. Tavares ◽  
Alexey O. Ivanov ◽  
Sofia S. Kantorovich ◽  
...  
Keyword(s):  
Phase Diagram ◽  
Free Energy ◽  
Hard Spheres ◽  
Numerical Approach ◽  
Free Energy Calculations ◽  
Free Energies ◽  
Energy Calculations

A numerical approach to calculate the free energies of DHS particles in chains and rings, exploring the unknown low-T region of the phase diagram of DHS.

scholarly journals Compression stiffening of fibrous networks with stiff inclusions

2020 ◽  
Vol 117 (35) ◽  
pp. 21037-21044
Author(s):  
Jordan L. Shivers ◽  
Jingchen Feng ◽  
Anne S. G. van Oosten ◽  
Herbert Levine ◽  
Paul A. Janmey ◽  
...  
Keyword(s):  
Phase Diagram ◽  
Qualitative Agreement ◽  
Three Dimensional ◽  
Quantitative Agreement ◽  
Coarse Grained ◽  
Fiber Network ◽  
Coarse Grained Model ◽  
Fibrous Network ◽  
Stress Propagation ◽  
Stiffening Effect

Tissues commonly consist of cells embedded within a fibrous biopolymer network. Whereas cell-free reconstituted biopolymer networks typically soften under applied uniaxial compression, various tissues, including liver, brain, and fat, have been observed to instead stiffen when compressed. The mechanism for this compression-stiffening effect is not yet clear. Here, we demonstrate that when a material composed of stiff inclusions embedded in a fibrous network is compressed, heterogeneous rearrangement of the inclusions can induce tension within the interstitial network, leading to a macroscopic crossover from an initial bending-dominated softening regime to a stretching-dominated stiffening regime, which occurs before and independently of jamming of the inclusions. Using a coarse-grained particle-network model, we first establish a phase diagram for compression-driven, stretching-dominated stress propagation and jamming in uniaxially compressed two- and three-dimensional systems. Then, we demonstrate that a more detailed computational model of stiff inclusions in a subisostatic semiflexible fiber network exhibits quantitative agreement with the predictions of our coarse-grained model as well as qualitative agreement with experiments.

scholarly journals Free-energy landscape of ion-channel voltage-sensor–domain activation

2014 ◽  
Vol 112 (1) ◽  
pp. 124-129 ◽  
Author(s):  
Lucie Delemotte ◽  
Marina A. Kasimova ◽  
Michael L. Klein ◽  
Mounir Tarek ◽  
Vincenzo Carnevale
Keyword(s):  
Free Energy ◽  
Energy Landscape ◽  
State Theory ◽  
Voltage Sensor ◽  
Reaction Coordinate ◽  
Coarse Grained ◽  
Free Energy Landscape ◽  
Activation Process ◽  
Discrete State ◽  
Gating Charge

Voltage sensor domains (VSDs) are membrane-bound protein modules that confer voltage sensitivity to membrane proteins. VSDs sense changes in the transmembrane voltage and convert the electrical signal into a conformational change called activation. Activation involves a reorganization of the membrane protein charges that is detected experimentally as transient currents. These so-called gating currents have been investigated extensively within the theoretical framework of so-called discrete-state Markov models (DMMs), whereby activation is conceptualized as a series of transitions across a discrete set of states. Historically, the interpretation of DMM transition rates in terms of transition state theory has been instrumental in shaping our view of the activation process, whose free-energy profile is currently envisioned as composed of a few local minima separated by steep barriers. Here we use atomistic level modeling and well-tempered metadynamics to calculate the configurational free energy along a single transition from first principles. We show that this transition is intrinsically multidimensional and described by a rough free-energy landscape. Remarkably, a coarse-grained description of the system, based on the use of the gating charge as reaction coordinate, reveals a smooth profile with a single barrier, consistent with phenomenological models. Our results bridge the gap between microscopic and macroscopic descriptions of activation dynamics and show that choosing the gating charge as reaction coordinate masks the topological complexity of the network of microstates participating in the transition. Importantly, full characterization of the latter is a prerequisite to rationalize modulation of this process by lipids, toxins, drugs, and genetic mutations.

scholarly journals A Generalised Mechano-Kinetic Model For Use in Multiscale Simulation Protocols

2020 ◽  
Author(s):  
Benjamin S. Hanson ◽  
Daniel J. Read
Keyword(s):  
High Resolution ◽  
Kinetic Model ◽  
Conformational Changes ◽  
Large Scale ◽  
Computational Cost ◽  
Multiscale Simulation ◽  
State Theory ◽  
Coarse Grained ◽  
Simulation Framework ◽  
State Models

AbstractMany biophysical systems and proteins undergo mesoscopic conformational changes in order to perform their biological function. However, these conformational changes often result from a cascade of atomistic interactions within a much larger overall object. For simulations of such systems, the computational cost of retaining high-resolution structural and dynamical information whilst at the same time observing large scale motions over long times is high. Furthermore, this cost is only going to increase as ever larger biological objects are observed experimentally at high resolution.With insight from the theory of Markov state models and transition state theory, we derive a generalised mechano-kinetic simulation framework which aims to compensate for these disparate time scales. The framework models continuous dynamical motion at coarse-grained length scales whilst simultaneously including fine-detail, chemical reactions or conformational changes implicitly using a kinetic model. The kinetic model is coupled to the dynamic model in a highly generalised manner, such that it can be applied to any defined continuous energy landscape, and indeed, any existing dynamic simulation framework. We present a series of analytical examples to validate the framework, and showcase its capabilities for studying more complex systems by designing a simulation of minimalist molecular motor.

Large-Scale Assessment of Binding Free Energy Calculations in Active Drug Discovery Projects

2020 ◽  
Author(s):  
Christina Schindler ◽  
Hannah Baumann ◽  
Andreas Blum ◽  
Dietrich Böse ◽  
Hans-Peter Buchstaller ◽  
...  
Keyword(s):  
Free Energy ◽  
Drug Discovery ◽  
Large Scale ◽  
Active Drug ◽  
Binding Free Energy ◽  
Free Energy Calculations ◽  
Energy Calculation ◽  
Large Scale Assessment ◽  
New Public ◽  
Binding Free Energy Calculations

Here we present an evaluation of the binding affinity prediction accuracy of the free energy calculation method FEP+ on internal active drug discovery projects and on a large new public benchmark set.<br>

Reaction-based Enumeration, Active Learning, and Free Energy Calculations to Rapidly Explore Synthetically Tractable Chemical Space and Optimize Potency of Cyclin Dependent Kinase 2 Inhibitors

2019 ◽  
Author(s):  
Kyle Konze ◽  
Pieter Bos ◽  
Markus Dahlgren ◽  
Karl Leswing ◽  
Ivan Tubert-Brohman ◽  
...  
Keyword(s):  
Free Energy ◽  
Drug Discovery ◽  
Active Learning ◽  
Large Scale ◽  
Chemical Space ◽  
Population Based ◽  
Free Energy Calculations ◽  
Computational Technique ◽  
Cyclin Dependent Kinase ◽  
Energy Calculations

We report a new computational technique, PathFinder, that uses retrosynthetic analysis followed by combinatorial synthesis to generate novel compounds in synthetically accessible chemical space. Coupling PathFinder with active learning and cloud-based free energy calculations allows for large-scale potency predictions of compounds on a timescale that impacts drug discovery. The process is further accelerated by using a combination of population-based statistics and active learning techniques. Using this approach, we rapidly optimized R-groups and core hops for inhibitors of cyclin-dependent kinase 2. We explored greater than 300 thousand ideas and identified 35 ligands with diverse commercially available R-groups and a predicted IC<sub>50</sub> < 100 nM, and four unique cores with a predicted IC<sub>50</sub> < 100 nM. The rapid turnaround time, and scale of chemical exploration, suggests that this is a useful approach to accelerate the discovery of novel chemical matter in drug discovery campaigns.

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