Automating the search of molecular motor templates by evolutionary methods

Fernández, José‐Jesús; Vico, Francisco J.

doi:10.1016/j.biosystems.2011.07.002

Cited by 3 publications

(3 citation statements)

References 71 publications

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“…Furthermore, designed elastic network structures that reproduce “protein-like” features can be considered and compared with ENMs of real proteins. For example, machine-like motions induced by ligand binding were demonstrated [60], and models of linear motors were constructed [110,111]. ENMs mimicking allosteric effects could be constructed through evolutionary optimization of the structure [112].…”

Section: Discussionmentioning

confidence: 99%

Coarse-Grained Protein Dynamics Studies Using Elastic Network Models

Togashi

Flechsig

2018

IJMS

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Elastic networks have been used as simple models of proteins to study their slow structural dynamics. They consist of point-like particles connected by linear Hookean springs and hence are convenient for linear normal mode analysis around a given reference structure. Furthermore, dynamic simulations using these models can provide new insights. As the computational cost associated with these models is considerably lower compared to that of all-atom models, they are also convenient for comparative studies between multiple protein structures. In this review, we introduce examples of coarse-grained molecular dynamics studies using elastic network models and their derivatives, focusing on the nonlinear phenomena, and discuss their applicability to large-scale macromolecular assemblies.

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Section: Discussionmentioning

confidence: 99%

Coarse-Grained Protein Dynamics Studies Using Elastic Network Models

Togashi

Flechsig

2018

IJMS

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“…However, little is known about the underlying mechanisms contributing to such motion. Our mechanical screening method can be combined with artificial structures and evolutionary simulations in order to extract valuable information on the general relationship among molecular structures, motion, and functions [17,38].…”

Section: Automated Screening and Its Applicationsmentioning

confidence: 99%

Screening for mechanical responses of proteins using coarse-grained elastic network models

Togashi

2016

NOLTA

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Abstract:Owing to recent progress in structural biology, the structures of a number of proteins have been resolved and deposited into databases such as the Protein Data Bank (PDB). Many proteins function as molecular machines or show allosteric effects, which require integrated communication between different parts of the protein structure; however, the general principles underlying such communication are not yet clear. All-atom molecular dynamics simulation is a powerful tool for tracking molecular motion, but is still far too computationally costly to be applied widely. In this paper, we present a simple method for assessing the properties of mechanical communications using coarse-grained steered molecular dynamics simulations with elastic network models. The method was tested by screening for a certain mechanical property among protein structures in the PDB.

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“…In this section, we present a third and last model of morphogenetic design based on behavior-finding (published in [18]). As in the previous studies, agents are represented by mass-spring networks, and their structure is the result of an evolutionary search for agents able to perform a specified behavior.…”

Section: Behavior-finding In a Non-developmental Modelmentioning

confidence: 99%

Behavior-Finding: Morphogenetic Designs Shaped by Function

Lobo

Fernández

Vico

2012

Morphogenetic Engineering

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Evolution has shaped an incredible diversity of multicellular living organisms, whose complex forms are self-made through a robust developmental process. This fundamental combination of biological evolution and development has served as an inspiration for novel engineering design methodologies, with the goal to overcome the scalability problems suffered by classical top-down approaches. Top-down methodologies are based on the manual decomposition of the design into modular, independent subunits. In contrast, recent computational morphogenetic techniques have shown that they were able to automatically generate truly complex innovative designs. Algorithms based on evolutionary computation and artificial development have been proposed to automatically design both the structures, within certain constraints, and the controllers that optimize their function. However, the driving force of biological evolution does not resemble an enumeration of design requirements, but much rather relies on the interaction of organisms within the environment. Similarly, controllers do not evolve nor develop separately, but are woven into the organism's morphology. In this chapter, we discuss evolutionary morphogenetic algorithms inspired by these important aspects of biological evolution. The proposed methodologies could contribute to the automation of processes that design "organic" structures, whose morphologies and controllers are intended to solve a functional problem. The performance of the algorithms is tested on a class of optimization problems that we call behavior-finding. These challenges are not explicitly based on morphology or controller constraints, but only on the solving abilities and efficacy of the design. Our results show that morphogenetic algorithms are well suited to behavior-finding.

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Automating the search of molecular motor templates by evolutionary methods

Cited by 3 publications

References 71 publications

Coarse-Grained Protein Dynamics Studies Using Elastic Network Models

Coarse-Grained Protein Dynamics Studies Using Elastic Network Models

Screening for mechanical responses of proteins using coarse-grained elastic network models

Behavior-Finding: Morphogenetic Designs Shaped by Function

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