Principles and Overview of Sampling Methods for Modeling Macromolecular Structure and Dynamics

Maximova, Tatiana; Moffatt, Ryan; Ma, Buyong; Nussinov, Ruth; Shehu, Amarda

doi:10.1371/journal.pcbi.1004619

Cited by 203 publications

(167 citation statements)

References 663 publications

(717 reference statements)

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“…Nevertheless, adequate sampling of our acyl-enzyme systems on the catalytic time scale of second to minutes remains out of reach to current free MD capabilities. To model such long time scale structural transitions, a variety of MD algorithms has been developed to enhance sampling capabilities and to accelerate the discovery of viable pathway excursions between two states (49). Here, we employed several enhanced sampling methods that took advantage of experimental data from our mesotrypsin-APLP2-KD* crystal structure, which revealed a large post-cleavage conformational change in the substrate.…”

Section: Discussionmentioning

confidence: 99%

An Acrobatic Substrate Metamorphosis Reveals a Requirement for Substrate Conformational Dynamics in Trypsin Proteolysis

Kayode

Wang

Pendlebury

et al. 2016

Journal of Biological Chemistry

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Edited by George DeMartinoThe molecular basis of enzyme catalytic power and specificity derives from dynamic interactions between enzyme and substrate during catalysis. Although considerable effort has been devoted to understanding how conformational dynamics within enzymes affect catalysis, the role of conformational dynamics within protein substrates has not been addressed. Here, we examine the importance of substrate dynamics in the cleavage of Kunitz-bovine pancreatic trypsin inhibitor protease inhibitors by mesotrypsin, finding that the varied conformational dynamics of structurally similar substrates can profoundly impact the rate of catalysis. A 1.4-Å crystal structure of a mesotrypsinproduct complex formed with a rapidly cleaved substrate reveals a dramatic conformational change in the substrate upon proteolysis. By using long all-atom molecular dynamics simulations of acyl-enzyme intermediates with proteolysis rates spanning 3 orders of magnitude, we identify global and local dynamic features of substrates on the nanosecond-microsecond time scale that correlate with enzymatic rates and explain differential susceptibility to proteolysis. By integrating multiple enhanced sampling methods for molecular dynamics, we model a viable conformational pathway between substrate-like and productlike states, linking substrate dynamics on the nanosecond-microsecond time scale with large collective substrate motions on the much slower time scale of catalysis. Our findings implicate substrate flexibility as a critical determinant of catalysis.Protein function is determined by macromolecular geometry conferred by the folded state, as noted by Anfinsen nearly 40 years ago (1); however, recent years have brought fresh meaning to this paradigm with an increasing appreciation of the temporal dependence of protein structure. Proteins constantly sample varied conformational fluctuations about the time-averaged structures that we observe crystallographically or spectroscopically, and these conformational dynamics are in many cases closely coupled to protein function (2-4). Nowhere is this clearer than for enzymes, proteins evolved to accelerate biological chemical reactions. Varied examples have revealed that enzyme conformational dynamics can facilitate substrate binding, progression along the catalytic reaction coordinate, and product release (5-10). Most studies of protein dynamics in enzyme catalysis have naturally focused on conformational changes within the enzyme. However, for the many enzymes that catalyze reactions of protein substrates, an overlooked source of potentially relevant dynamics lies within the substrate.Trypsins are serine proteases, a class of proteolytic enzymes that have been well characterized and used to dissect and understand catalysis, most often using short oligopeptide model substrates as proxies for natural protein substrates. Following formation of a noncovalent Michaelis complex, the catalytic mechanism proceeds through two sequential steps (Scheme 1) (11). In the first step, the enzyme serine...

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Section: Discussionmentioning

confidence: 99%

An Acrobatic Substrate Metamorphosis Reveals a Requirement for Substrate Conformational Dynamics in Trypsin Proteolysis

Kayode

Wang

Pendlebury

et al. 2016

Journal of Biological Chemistry

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“…Typically, in protein structure prediction, it is assumed that the native conformational state of a protein is homogeneous and contains similar conformations (corresponding to one basin) [72]. However, there is a growing realization in protein structure modeling and CASP, stemming from many biological studies [73], that one needs to consider the multiplicity of native conformations; that is, a protein may utilize different, biologically-active conformational states that correspond to different basins in the landscape [16]. Despite this realization, the assessment in CASP of template-free methods is conducted with respect to one native structure withheld from the modelers.…”

Section: The Energy Landscapementioning

confidence: 99%

“…Specifically, for the evaluation presented in this paper, the method utilizes Principal Component Analysis (PCA) [75] to extract collective, variance-preserving coordinates from decoy structures. PCA and other linear dimensionality reduction techniques are shown effective for analysis of protein structures in various applications of interest in computational biology [16,76,77].…”

Section: From Landscape Reconstruction To Basinsmentioning

confidence: 99%

An Energy Landscape Treatment of Decoy Selection in Template-Free Protein Structure Prediction

2018

Self Cite

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Abstract:The energy landscape, which organizes microstates by energies, has shed light on many cellular processes governed by dynamic biological macromolecules leveraging their structural dynamics to regulate interactions with molecular partners. In particular, the protein energy landscape has been central to understanding the relationship between protein structure, dynamics, and function. The landscape view, however, remains underutilized in an important problem in protein modeling, decoy selection in template-free protein structure prediction. Given the amino-acid sequence of a protein, template-free methods compute thousands of structures, known as decoys, as part of an optimization process that seeks minima of an energy function. Selecting biologically-active/native structures from the computed decoys remains challenging. Research has shown that energy is an unreliable indicator of nativeness. In this paper, we advocate that, while comparison of energies is not informative for structures that already populate minima of an energy function, the landscape view exposes the overall organization of generated decoys. As we demonstrate, such organization highlights macrostates that contain native decoys. We present two different computational approaches to extracting such organization and demonstrate through the presented findings that a landscape-driven treatment is promising in furthering research on decoy selection.

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“…Also, AMPs isolated from sources other than venom are not discussed and the reader is referred to some of the many available reviews on AMPs . Finally, challenges and limitations that are common to all biomolecular simulations such as the choice of force field, the use of enhanced sampling methods and the problem of sampling errors and convergence are only discussed in the context of how they affect the accuracy and reliability of venom peptide–membrane simulations.…”

Section: Introductionmentioning

confidence: 99%

Molecular simulations of venom peptide‐membrane interactions: Progress and challenges

Deplazes

2018

Peptide Science

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Because of their wide range of biological activities venom peptides are a valuable source of lead molecules for the development of pharmaceuticals, pharmacological tools and insecticides. Many venom peptides work by modulating the activity of ion channels and receptors or by irreversibly damaging cell membranes. In many cases, the mechanism of action is intrinsically linked to the ability of the peptide to bind to or partition into membranes. Thus, understanding the biological activity of these venom peptides requires characterizing their membrane binding properties. This review presents an overview of the recent developments and challenges in using biomolecular simulations to study venom peptide‐membrane interactions. The review is focused on (i) gating modifier peptides that target voltage‐gated ion channels, (ii) venom peptides that inhibit mechanosensitive ion channels, and (iii) pore‐forming venom peptides. The methods and approaches used to study venom peptide‐membrane interactions are discussed with a particular focus on the challenges specific to these systems and the type of questions that can (and cannot) be addressed using state‐of‐the‐art simulation techniques. The review concludes with an outlook on future aims and directions in the field.

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Principles and Overview of Sampling Methods for Modeling Macromolecular Structure and Dynamics

Cited by 203 publications

References 663 publications

An Acrobatic Substrate Metamorphosis Reveals a Requirement for Substrate Conformational Dynamics in Trypsin Proteolysis

An Acrobatic Substrate Metamorphosis Reveals a Requirement for Substrate Conformational Dynamics in Trypsin Proteolysis

An Energy Landscape Treatment of Decoy Selection in Template-Free Protein Structure Prediction

Molecular simulations of venom peptide‐membrane interactions: Progress and challenges

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