Ensemble-based methods for describing protein dynamics (original) (raw)

Molecular Dynamics: Survey of Methods for Simulating the Activity of Proteins

Chemical Reviews, 2006

time-dependent (i.e., kinetic) phenomena. This enables an understanding to be developed of various dynamic aspects of biomolecular structure, recognition, and function. However, when used alone, MD is of limited utility. An MD trajectory (i.e., the progress of simulated structure with respect to time) generally provides data only at the level of atomic positions, velocities, and single-point energies. To obtain the macroscopic properties in which one is usually interested requires the application of statistical mechanics, which connects microscopic simulations and macroscopic observables. Statistical mechanics provides a rigorous framework of mathematical expressions that relate the distributions and motions of atoms and molecules to macroscopic observables such as pressure, heat capacity, and free energies. 17,18 Extraction of these macroscopic observables is therefore possible from the microscopic data, and one can predict, for instance, changes in the binding free energy of a particular drug candidate or the mechanisms and energetic consequences of conformational change in a particular protein. Specific aspects of biomolecular structure, kinetics, and thermodynamics that may be investigated via MD include, for example, macromolecular stability, 19 conformational and allosteric properties, 20 the role of dynamics in enzyme activity, 21,22 molecular recognition and the properties of complexes, 21,23 ion and small molecule transport, 24,25 protein association, 26 protein folding, 27,16 and protein hydration. 28 MD, therefore, provides the opportunity to perform a variety of studies including molecular design (drug design 29 and protein design 30) and structure determination and refinement (Xray,31 NMR, 32 and modeling 33). 3. Molecular Dynamics Methods and Theory Given the structure of a biomolecular system, that is, the relative coordinates of the constituent atoms, there are various computational methods that can be used to investigate and study the dynamics of that system. In the present section, a number of such methods are described and discussed. The majority of important dynamics methodologies are highly dependent upon the availability of a suitable potential-energy function to describe the energy landscape of the system with respect to the aforementioned atomic coordinates. This critical aspect is, therefore, introduced first. 3.1. Potential Functions and the Energy Landscape Choice of an appropriate energy function for describing the intermolecular and intramolecular interactions is critical to a successful (i.e., valid yet tractable) molecular dynamics simulation. In conventional MD simulations, the energy function for nonbonded interactions tends to be a simple pairwise additive function (for computational reasons) of nuclear coordinates only. This use of a single nuclear coordinate to represent atoms is justified in terms of the Born-Oppenheimer approximation. 34 For bonded groups of atoms, that is those that form covalent bonds, bond angles, or dihedral angles, simple two-body, three-body, and four-body terms are used, as described below.

Modeling of Protein Structural Flexibility and Large-Scale Dynamics: Coarse-grained Simulations and Elastic Network Models

2018

Fluctuations of protein three-dimensional structures and large-scale conformational transitions are crucial for the biological function of proteins and their complexes. Experimental studies of such phenomena remain very challenging and therefore molecular modeling can be a good alternative or a valuable supporting tool for the investigation of large molecular systems and long-time events. In this mini-review, we present two alternative approaches to the coarse-grained (CG) modeling of dynamic properties of protein systems. We discuss two CG representations of polypeptide chains used for Monte Carlo dynamics simulations of protein local dynamics and conformational transitions and, on other hand, highly simplified structure-based Elastic Network Models of protein flexibility. In contrast to classical Molecular Dynamics the modeling strategies discussed here allow quite accurate modeling of much larger systems and longer time dynamic phenomena. We briefly describe the main features of ...

Discrete analyses of protein dynamics

Journal of Biomolecular Structure and Dynamics, 2019

Protein structures are highly dynamic macromolecules. This dynamics is often analysed through experimental and/or computational methods only for an isolated or a limited number of proteins. Here, we explore large-scale protein dynamics simulation to observe dynamics of local protein conformations using different perspectives. We analysed molecular dynamics to investigate protein flexibility locally, using classical approaches such as RMSf, solvent accessibility, but also innovative approaches such as local entropy. Firstly, we focussed on classical secondary structures and analysed specifically how βstrand, β−turns, and bends evolve during molecular simulations. We underlined interesting specific bias between β−turns and bends, which are considered as same category, while their dynamics show differences. Secondly, we used a structural alphabet that is able to approximate every part of the protein structures conformations, namely Protein Blocks (PBs) to analyse (i) how each initial local protein conformations evolve during dynamics and (ii) if some exchange can exist among these PBs. Interestingly, the results are largely complex than simple regular/rigid and coil/flexible exchange.

Prediction and validation of protein intermediate states from structurally rich ensembles and coarse-grained simulations

Nature communications, 2016

Protein conformational changes are at the heart of cell functions, from signalling to ion transport. However, the transient nature of the intermediates along transition pathways hampers their experimental detection, making the underlying mechanisms elusive. Here we retrieve dynamic information on the actual transition routes from principal component analysis (PCA) of structurally-rich ensembles and, in combination with coarse-grained simulations, explore the conformational landscapes of five well-studied proteins. Modelling them as elastic networks in a hybrid elastic-network Brownian dynamics simulation (eBDIMS), we generate trajectories connecting stable end-states that spontaneously sample the crystallographic motions, predicting the structures of known intermediates along the paths. We also show that the explored non-linear routes can delimit the lowest energy passages between end-states sampled by atomistic molecular dynamics. The integrative methodology presented here provides...

Modeling Protein Structure, Dynamics and Thermodynamics with Reduced Representation of Conformational Space

John von Neumann …, 2006

In this contribution we describe a successful approach to protein modeling which is based on reduced representation of protein conformational space, all-atom-refinement, evaluation and selection of the best molecular models. During the sixth CASP (Critical Assessment of protein Structure Prediction) community-wide experiment our methodology (referred further as CABS) proven to be one of the best performing methods for protein structure prediction, applied both for comparative modeling and to de novo folding. The newest applications of the CABS modeling technology include: study of protein folding thermodynamic, dynamics in the denatured state and folding pathways, structure prediction based on sparse and inaccurate experimental data and prediction of protein-protein interactions or flexible ligand docking. The CABS reduced model could be easily integrated with the all-atom approaches providing solid starting point for reliable multiscale simulations of large biomolecular systems.

Bridging the Atomic and Coarse-Grained Descriptions of Collective Motions in Proteins

Multiscale Approaches to Protein Modeling, 2010

In proteins and enzymes the necessity that the native state is thermodynamically stable must be appropriately balanced by the capability of the structure to sustain conformational changes and efficiently interconvert among different functionally relevant conformers. This subtle equilibrium reverberates in the complexity of the free-energy landscape which is endowed by a variety of local minima of varying depth and breadth corresponding to the salient structural states of the molecules. In this chapter we will present some concepts and computational algorithms that can be used to characterize the internal dynamics of proteins and relate it to their "functional mechanics." We will apply these concepts to the analysis of a molecular dynamics simulation of adenylate kinase, a protein for which the structural rearrangement is known to be crucial for the accomplishment of its biological function. We will show that, despite the structural heterogeneity of the explored conformational ensemble, the generalized directions accounting for conformational fluctuations within and across the visited conformational substates are robust and can be described by a limited set of collective coordinates. Finally, as a term of comparison, we will show that in the case of HIV-1 Trans-Activator of Transcription (TAT), a naturally unstructured protein, the lack of any hierarchical organization of the free-energy minima results in a poor consistency of the essential dynamical spaces sampled during the dynamical evolution of the system.

Molecular Dynamics Simulations of Peptides and Proteins with Amplified Collective Motions

Biophysical Journal, 2003

We present a novel method that uses the collective modes obtained with a coarse-grained model/anisotropic network model to guide the atomic-level simulations. Based on this model, local collective modes can be calculated according to a single configuration in the conformational space of the protein. In the molecular dynamics simulations, the motions along the slowest few modes are coupled to a higher temperature by the weak coupling method to amplify the collective motions. This amplified-collective-motion (ACM) method is applied to two test systems. One is an S-peptide analog. We realized the refolding of the denatured peptide in eight simulations out of 10 using the method. The other system is bacteriophage T4 lysozyme. Much more extensive domain motions between the N-terminal and C-terminal domain of T4 lysozyme are observed in the ACM simulation compared to a conventional simulation. The ACM method allows for extensive sampling in conformational space while still restricting the sampled configurations within low energy areas. The method can be applied in both explicit and implicit solvent simulations, and may be further applied to important biological problems, such as long timescale functional motions, protein folding/unfolding, and structure prediction.

Describing protein folding kinetics by molecular dynamics simulations

2004

In this work we demonstrate the use of a rigorous formalism for the extraction of state-to-state transition functions as a way to study the kinetics of protein folding in the context of a Markov chain. The approach is illustrated by its application to two different systems: a blocked alanine dipeptide in a vacuum and the C-terminal â-hairpin motif from protein G in water. The first system displays some of the desired features of the approach, whereas the second illustrates some of the challenges that must be overcome to apply the method to more complex biomolecular systems. For both example systems, Boltzmann weighted conformations produced by a replica exchange Monte Carlo procedure were used as starting states for kinetic trajectories. The alanine dipeptide displays Markovian behavior in a state space defined with respect to -ª torsion angles. In contrast, Markovian behavior was not observed for the â-hairpin in a state space where all possible native hydrogen bonding patterns wer...

Coarse-Grained Modeling of Protein Dynamics

Springer Series in Bio-/Neuroinformatics, 2014

ABSTRACT Simulations of protein dynamics may work on different levels of molecular detail. The levels of simplification (coarse-graining) can range from very low to atomic resolution and may concern different simulation aspects (including protein representation, interaction schemes or models of molecular motion). So-called coarse-grained (CG) models offer many advantages, unreachable by classical simulation tools, as demonstrated in numerous studies of protein dynamics. Followed by a brief introduction, we present example applications of CG models for efficient predictions of biophysical mechanisms. We discuss the following topics: mechanisms of chaperonin action, mechanical properties of proteins, membrane proteins, protein-protein interactions and intrinsically unfolded proteins. Presently, these areas represent emerging application fields of CG simulation models.

Computer simulation of proteins: thermodynamics and structure prediction

The European Physical Journal D, 2009

Over the last decade, computer simulations have become an increasingly important tool to study proteins. They now regularly complement experimental investigations and often are the only instrument to probe processes in the cell. Here, we summarize some of the algorithmic advances and review recent results that exemplify the progress over the last years. Our focus is on the thermodynamics and structure prediction of proteins, with information on the kinetics and dynamics inferred only indirectly.