Minimalist models for proteins: a comparative analysis

2010 ◽  
Vol 43 (3) ◽  
pp. 333-371 ◽  
Author(s):  
Valentina Tozzini
Keyword(s):  
Phase Diagram ◽  
Comparative Analysis ◽  
Amino Acid ◽  
Degrees Of Freedom ◽  
Predictive Power ◽  
Coarse Graining ◽  
Coarse Grained ◽  
Molecular Systems ◽  
Modern Molecular Biology ◽  
Functional Forms

AbstractThe last decade has witnessed a renewed interest in the coarse-grained (CG) models for biopolymers, also stimulated by the needs of modern molecular biology, dealing with nano- to micro-sized bio-molecular systems and larger than microsecond timescale. This combination of size and timescale is, in fact, hard to access by atomic-based simulations. Coarse graining the system is a route to be followed to overcome these limits, but the ways of practically implementing it are many and different, making the landscape of CG models very vast and complex.In this paper, the CG models are reviewed and their features, applications and performances compared. This analysis, restricted to proteins, focuses on the minimalist models, namely those reducing at minimum the number of degrees of freedom without losing the possibility of explicitly describing the secondary structures. This class includes models using a single or a few interacting centers (beads) for each amino acid.From this analysis several issues emerge. The difficulty in building these models resides in the need for combining transferability/predictive power with the capability of accurately reproducing the structures. It is shown that these aspects could be optimized by accurately choosing the force field (FF) terms and functional forms, and combining different parameterization procedures. In addition, in spite of the variety of the minimalist models, regularities can be found in the parameters values and in FF terms. These are outlined and schematically presented with the aid of a generic phase diagram of the polypeptide in the parameter space and, hopefully, could serve as guidelines for the development of minimalist models incorporating the maximum possible level of predictive power and structural accuracy.

scholarly journals Bottom-Up Coarse-Grained Modeling of DNA

2021 ◽  
Vol 8 ◽  
Author(s):  
Tiedong Sun ◽  
Vishal Minhas ◽  
Nikolay Korolev ◽  
Alexander Mirzoev ◽  
Alexander P. Lyubartsev ◽  
...  
Keyword(s):  
Degrees Of Freedom ◽  
Deoxyribonucleic Acid ◽  
Persistence Length ◽  
Coarse Graining ◽  
Potential Of Mean Force ◽  
Coarse Grained ◽  
Bottom Up ◽  
Slow Dynamic ◽  
Molecular Systems ◽  
Fine Grained

Recent advances in methodology enable effective coarse-grained modeling of deoxyribonucleic acid (DNA) based on underlying atomistic force field simulations. The so-called bottom-up coarse-graining practice separates fast and slow dynamic processes in molecular systems by averaging out fast degrees of freedom represented by the underlying fine-grained model. The resulting effective potential of interaction includes the contribution from fast degrees of freedom effectively in the form of potential of mean force. The pair-wise additive potential is usually adopted to construct the coarse-grained Hamiltonian for its efficiency in a computer simulation. In this review, we present a few well-developed bottom-up coarse-graining methods, discussing their application in modeling DNA properties such as DNA flexibility (persistence length), conformation, “melting,” and DNA condensation.

scholarly journals "Dividing and Conquering" and "Caching" in Molecular Modeling

2021 ◽  
Author(s):  
Xiaoyong Cao ◽  
Pu Tian
Keyword(s):  
Molecular Modeling ◽  
Degrees Of Freedom ◽  
Materials Science ◽  
Force Fields ◽  
Coarse Graining ◽  
Divide And Conquer ◽  
Collective Variables ◽  
Molecular Systems ◽  
State Models ◽  
Molecular Science

Molecular modeling is widely utilized in subjects including but not limited to physics, chemistry, biology, materials science and engineering. Impressive progress has been made in development of theories, algorithms and software packages. To divide and conquer, and to cache intermediate results have been long standing principles in development of algorithms. Not surprisingly, Most of important methodological advancements in more than half century of molecule modeling are various implementations of these two fundamental principles. In the mainstream classical computational molecular science based on force fields parameterization by coarse graining, tremendous efforts have been invested on two lines of algorithm development. The first is coarse graining, which is to represent multiple basic particles in higher resolution modeling as a single larger and softer particle in lower resolution counterpart, with resulting force fields of partial transferability at the expense of some information loss. The second is enhanced sampling, which realizes "dividing and conquering" and/or "caching" in configurational space with focus either on reaction coordinates and collective variables as in metadynamics and related algorithms, or on the transition matrix and state discretization as in Markov state models. For this line of algorithms, spatial resolution is maintained but no transferability is available. Deep learning has been utilized to realize more efficient and accurate ways of "dividing and conquering" and "caching" along these two lines of algorithmic research. We proposed and demonstrated the local free energy landscape approach, a new framework for classical computational molecular science and a third class of algorithm that facilitates molecular modeling through partially transferable in resolution "caching" of distributions for local clusters of molecular degrees of freedom. Differences, connections and potential interactions among these three algorithmic directions are discussed, with the hope to stimulate development of more elegant, efficient and reliable formulations and algorithms for "dividing and conquering" and "caching" in complex molecular systems.

scholarly journals Exploring the landscape of model representations

2020 ◽  
Vol 117 (39) ◽  
pp. 24061-24068 ◽  
Author(s):  
Thomas T. Foley ◽  
Katherine M. Kidder ◽  
M. Scott Shell ◽  
W. G. Noid
Keyword(s):  
Statistical Physics ◽  
Degrees Of Freedom ◽  
Large Scale ◽  
Coarse Graining ◽  
Order Parameters ◽  
Microscopic Model ◽  
Coarse Grained ◽  
Proper Order ◽  
Complex Phenomena ◽  
Protein Fluctuations

The success of any physical model critically depends upon adopting an appropriate representation for the phenomenon of interest. Unfortunately, it remains generally challenging to identify the essential degrees of freedom or, equivalently, the proper order parameters for describing complex phenomena. Here we develop a statistical physics framework for exploring and quantitatively characterizing the space of order parameters for representing physical systems. Specifically, we examine the space of low-resolution representations that correspond to particle-based coarse-grained (CG) models for a simple microscopic model of protein fluctuations. We employ Monte Carlo (MC) methods to sample this space and determine the density of states for CG representations as a function of their ability to preserve the configurational information, I, and large-scale fluctuations, Q, of the microscopic model. These two metrics are uncorrelated in high-resolution representations but become anticorrelated at lower resolutions. Moreover, our MC simulations suggest an emergent length scale for coarse-graining proteins, as well as a qualitative distinction between good and bad representations of proteins. Finally, we relate our work to recent approaches for clustering graphs and detecting communities in networks.

scholarly journals “Dividing and Conquering” and “Caching” in Molecular Modeling

2021 ◽  
Vol 22 (9) ◽  
pp. 5053
Author(s):  
Xiaoyong Cao ◽  
Pu Tian
Keyword(s):  
Molecular Modeling ◽  
Degrees Of Freedom ◽  
Materials Science ◽  
Coarse Graining ◽  
Divide And Conquer ◽  
Collective Variables ◽  
Molecular Systems ◽  
Materials Science And Engineering ◽  
State Models ◽  
Molecular Science

Molecular modeling is widely utilized in subjects including but not limited to physics, chemistry, biology, materials science and engineering. Impressive progress has been made in development of theories, algorithms and software packages. To divide and conquer, and to cache intermediate results have been long standing principles in development of algorithms. Not surprisingly, most important methodological advancements in more than half century of molecular modeling are various implementations of these two fundamental principles. In the mainstream classical computational molecular science, tremendous efforts have been invested on two lines of algorithm development. The first is coarse graining, which is to represent multiple basic particles in higher resolution modeling as a single larger and softer particle in lower resolution counterpart, with resulting force fields of partial transferability at the expense of some information loss. The second is enhanced sampling, which realizes “dividing and conquering” and/or “caching” in configurational space with focus either on reaction coordinates and collective variables as in metadynamics and related algorithms, or on the transition matrix and state discretization as in Markov state models. For this line of algorithms, spatial resolution is maintained but results are not transferable. Deep learning has been utilized to realize more efficient and accurate ways of “dividing and conquering” and “caching” along these two lines of algorithmic research. We proposed and demonstrated the local free energy landscape approach, a new framework for classical computational molecular science. This framework is based on a third class of algorithm that facilitates molecular modeling through partially transferable in resolution “caching” of distributions for local clusters of molecular degrees of freedom. Differences, connections and potential interactions among these three algorithmic directions are discussed, with the hope to stimulate development of more elegant, efficient and reliable formulations and algorithms for “dividing and conquering” and “caching” in complex molecular systems.

scholarly journals The Automated Optimisation of a Coarse-Grained Force Field Using Free Energy Data

2020 ◽  
Author(s):  
Javier Caceres-Delpiano ◽  
Lee-Ping Wang ◽  
Jonathan W. Essex
Keyword(s):  
Free Energy ◽  
Force Field ◽  
Degrees Of Freedom ◽  
Coarse Grained ◽  
Free Energies ◽  
Energy Data ◽  
Molecular Systems ◽  
Atomistic Models ◽  
The Cost ◽  
Long Timescales

AbstractAtomistic models provide a detailed representation of molecular systems, but are sometimes inadequate for simulations of large systems over long timescales. Coarse-grained models enable accelerated simulations by reducing the number of degrees of freedom, at the cost of reduced accuracy. New optimisation processes to parameterise these models could improve their quality and range of applicability. We present an automated approach for the optimisation of coarse-grained force fields, by reproducing free energy data derived from atomistic molecular simulations. To illustrate the approach, we implemented hydration free energy gradients as a new target for force field optimisation in ForceBalance and applied it successfully to optimise the un-charged side-chains and the protein backbone in the SIRAH protein coarse-grain force field. The optimised parameters closely reproduced hydration free energies of atomistic models and gave improved agreement with experiment.

scholarly journals Less is more: Coarse-grained integrative modeling of large biomolecular assemblies with HADDOCK

2019 ◽  
Author(s):  
Jorge Roel-Touris ◽  
Charleen G. Don ◽  
Rodrigo V. Honorato ◽  
João P.G.L.M Rodrigues ◽  
Alexandre M.J.J. Bonvin
Keyword(s):  
Protein Interactions ◽  
Degrees Of Freedom ◽  
Energy Landscape ◽  
3D Structure ◽  
Coarse Graining ◽  
Protein Docking ◽  
Coarse Grained ◽  
Speed Increase ◽  
Less Is More ◽  
Similar Accuracy

ABSTRACTPredicting the 3D structure of protein interactions remains a challenge in the field of computational structural biology. This is in part due to difficulties in sampling the complex energy landscape of multiple interacting flexible polypeptide chains. Coarse-graining approaches, which reduce the number of degrees of freedom of the system, help address this limitation by smoothing the energy landscape, allowing an easier identification of the global energy minimum. They also accelerate the calculations, allowing to model larger assemblies. Here, we present the implementation of the MARTINI coarse-grained force field for proteins into HADDOCK, our integrative modelling platform. Docking and refinement are performed at the coarse-grained level and the resulting models are then converted back to atomistic resolution through a distance restraints-guided morphing procedure. Our protocol, tested on the largest complexes of the protein docking benchmark 5, shows an overall ~7-fold speed increase compared to standard all-atom calculations, while maintaining a similar accuracy and yielding substantially more near-native solutions. To showcase the potential of our method, we performed simultaneous 7 body docking to model the 1:6 KaiC-KaiB complex, integrating mutagenesis and hydrogen/deuterium exchange data from mass spectrometry with symmetry restraints, and validated the resulting models against a recently published cryo-EM structure.

Coarse-Grained Molecular Dynamics Simulation of Lysozyme Protein Crystals

2011 ◽  
Vol 6 (1) ◽  
Author(s):  
Nafiseh Farhadian ◽  
Mojtaba Shariaty-Niassar ◽  
Kourosh Malek ◽  
Ali Maghari
Keyword(s):  
Molecular Dynamics ◽  
Degrees Of Freedom ◽  
Coarse Graining ◽  
Dynamics Simulation ◽  
Coarse Grained ◽  
Protein Crystal ◽  
Protein Crystals ◽  
Biological Phenomena ◽  
The Stability ◽  
Coarse Grained Molecular Dynamics

Many biological phenomena of interest occur on a time scale that is too great to be studied by atomistic simulations. The use of coarse-graining methods to represent a system can alleviate this restriction by reducing the number of degrees of freedom thus extending the time and length scale in molecular modeling. Coarse-grained molecular dynamics (CGMD) technique was employed to simulate diffusion of water in the nanopores of lysozyme protein crystals. Good agreement was obtained between the atomistic and CG simulations in view of the stability of the protein crystal structure and water transport properties. Our simulations demonstrate that the CG method is a suitable technique for simulation the solvent diffusion process in the lysozyme protein crystal and also can be a good technique to predict the behavior of solvent and solutes in the biological systems at longer length and time scales.

scholarly journals Systematic Coarse-Grained Models for Molecular Systems Using Entropy

Proceedings ◽  
2020 ◽  
Vol 46 (1) ◽  
pp. 27
Author(s):  
Evangelia Kalligiannaki ◽  
Vagelis Harmandaris ◽  
Markos Katsoulakis
Keyword(s):  
Bootstrap Method ◽  
Coarse Graining ◽  
Research Area ◽  
Potential Of Mean Force ◽  
Coarse Grained ◽  
Molecular Systems ◽  
Matching Methods ◽  
Force Matching ◽  
Mesoscopic Models ◽  
Body Potential

The development of systematic coarse-grained mesoscopic models for complex molecular systems is an intense research area. Here we first give an overview of different methods for obtaining optimal parametrized coarse-grained models, starting from detailed atomistic representation for high dimensional molecular systems. We focus on methods based on information theory, such as relative entropy, showing that they provide parameterizations of coarse-grained models at equilibrium by minimizing a fitting functional over a parameter space. We also connect them with structural-based (inverse Boltzmann) and force matching methods. All the methods mentioned in principle are employed to approximate a many-body potential, the (n-body) potential of mean force, describing the equilibrium distribution of coarse-grained sites observed in simulations of atomically detailed models. We also present in a mathematically consistent way the entropy and force matching methods and their equivalence, which we derive for general nonlinear coarse-graining maps. We apply, and compare, the above-described methodologies in several molecular systems: A simple fluid (methane), water and a polymer (polyethylene) bulk system. Finally, for the latter we also provide reliable confidence intervals using a statistical analysis resampling technique, the bootstrap method.

The multiscale coarse-graining method. X. Improved algorithms for constructing coarse-grained potentials for molecular systems

2012 ◽  
Vol 136 (19) ◽  
pp. 194115 ◽  
Author(s):  
Avisek Das ◽  
Lanyuan Lu ◽  
Hans C. Andersen ◽  
Gregory A. Voth
Keyword(s):  
Coarse Graining ◽  
Coarse Grained ◽  
Molecular Systems

Diffusion Wavelet Decomposition for Coarse-Graining of Polymer Chains

2015 ◽  
Vol 1753 ◽  
Author(s):  
B. Christopher Rinderspacher ◽  
Jaydeep P. Bardhan ◽  
Ahmed E. Ismail
Keyword(s):  
Degrees Of Freedom ◽  
Large Scale ◽  
Coarse Graining ◽  
Polymer Chains ◽  
Coarse Grained ◽  
Small Scale ◽  
Bonding Structure ◽  
Ring Structures ◽  
Spatial Coordinates ◽  
Molecular Bonding

ABSTRACTHere we present an alternative approach to coarse graining, based on the multiresolution diffusion-wavelet approach to operator compression, which does not require explicit atomistic-to-coarse-grained mappings. Our diffusion-wavelet method takes as input the topology and sparsity of the molecular bonding structure of a system, and returns as output a hierarchical set of degrees of freedom (DoFs) of system-specific coarse-grained variables. Importantly, the hierarchical compression provides a clear framework for modeling at many model scales (levels), beyond the common two-level CG representation. Our results show that the resulting hierarchy separates localized modes, such as a single C-C vibrational mode, from larger-scale motions, e.g., long-range concerted backbone vibrational modes. Our approach correctly captures small-scale chemical features, such as cellulose ring structures, and alkane side chains or CH2 units, as well as large-scale features of the backbone. In particular, the new method’s finest-scale modes describe DoFs similar to united atom models and other chemically-defined CG models. Modes at coarser levels describe increasingly large connected portions of the target polymers. For polyethylene and polystyrene, spatial coordinates and their associated forces were compressed by up to two orders of magnitude. The compression in forces is of particular interest as this allows larger timesteps as well as reducing the number of DoFs.

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