Limits of multifunctionality in tunable networks

Rocks, Jason W.; Ronellenfitsch, Henrik; Liu, Andrea J.; Nagel, Sidney R.; Katifori, Eleni

doi:10.1073/pnas.1806790116

Cited by 61 publications

(68 citation statements)

References 49 publications

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“…As in Ref. [5], we find power-law scaling: N c T ∼ N γc and w ∼ N γw with γ c ≈ γ w ≈ 0.7 for β = 0. As we increase β, γ c and γ w increase, saturating to unity (insets to Fig.…”

Section: Tuning Of Flow Networksupporting

confidence: 86%

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Tuning and jamming reduced to their minima

2019

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Inspired by protein folding, we smooth out the complex cost function landscapes of two processes, the tuning of networks, and the jamming of ideal spheres. In both processes, geometrical frustration plays a role -tuning pressure differences between pairs of target nodes far from the source in a flow network impedes tuning of nearby pairs more than the reverse process, while unjamming the system in one region can make it more difficult to unjam elsewhere. By modifying the cost functions to control the order in which functions are tuned or regions unjam, we smooth out local minima while leaving global minima unaffected, increasing the success rate for reaching global minima. arXiv:1901.00674v3 [cond-mat.soft]

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Section: Tuning Of Flow Networksupporting

confidence: 86%

“…Here we study two systems with rough landscapes (Fig. 1), namely tuned flow networks [5] and sphere packings [1,2,20]. In both systems, we show that we can smooth out local minima, increasing the ability to reach global minima.…”

Section: Introductionmentioning

confidence: 82%

Tuning and jamming reduced to their minima

2019

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“…The effect of bond removal on the elastic properties of spring networks has also been investigated in the context of material science [ 42 ]. It has been further demonstrated that tuning based on adding or removing links can be employed to generate networks which display multifunctional responses [ 43 ]. Yan et al considered 2D on-lattice models which evolve under the swapping of springs to accomplish an allosteric task [ 19 ], and deduced design and physical aspects in emerging functional networks.…”

Section: Models Of Allosteric Protein Machinesmentioning

confidence: 99%

Designed Elastic Networks: Models of Complex Protein Machinery

Flechsig

Togashi

2018

IJMS

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Recently, the design of mechanical networks with protein-inspired responses has become increasingly popular. Here, we review contributions which were motivated by studies of protein dynamics employing coarse-grained elastic network models. First, the concept of evolutionary optimization that we developed to design network structures which execute prescribed tasks is explained. We then review what presumably marks the origin of the idea to design complex functional networks which encode protein-inspired behavior, namely the design of an elastic network structure which emulates the cycles of ATP-powered conformational motion in protein machines. Two recent applications are reviewed. First, the construction of a model molecular motor, whose operation incorporates both the tight coupling power stroke as well as the loose coupling Brownian ratchet mechanism, is discussed. Second, the evolutionary design of network structures which encode optimal long-range communication between remote sites and represent mechanical models of allosteric proteins is presented. We discuss the prospects of designed protein-mimicking elastic networks as model systems to elucidate the design principles and functional signatures underlying the operation of complex protein machinery.

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“…directly to allostery in a protein. 86,87 After applying a 12 Å distance cut-o↵ to the derivative edge metric, contacts at the HisF-HisH interface are highlighted. A structural representation of the edges with large magnitude derivative values from Equation 5 is provided in Figure 4A.…”

Section: Derivative Centrality Metric Of Hessianmentioning

confidence: 99%

Residue-Level Allostery Propagates Through the Effective Coarse-Grained Hessian

Lake

Davidson

Klem

et al. 2019

Preprint

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AbstractThe long-ranged coupling between residues that gives rise to allostery in a protein is built up from short-ranged physical interactions. Computational tools used to predict this coupling and its functional relevance have relied on the application of graph theoretical metrics to residue-level correlations measured from all-atom molecular dynamics (aaMD) simulations. The short-ranged interactions that yield these long-ranged residue-level correlations are quantified by the effective coarse-grained Hessian. Here we compute an effective harmonic coarse-grained Hessian from aaMD simulations of a benchmark allosteric protein, IGPS, and demonstrate the improved locality of this graph Laplacian over two other connectivity matrices. Additionally, two centrality metrics are developed that indicate the direct and indirect importance of each residue at producing the covariance between the effector binding pocket and the active site. The residue importance indicated by these two metrics is corroborated by previous mutagenesis experiments and leads to unique functional insights; in contrast to previous computational analyses, our results suggest that fP76-hK181 is the most important contact for conveying direct allosteric paths across the HisF-HisH interface. The connectivity around fD98 is found to be important at affecting allostery through indirect means.

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Limits of multifunctionality in tunable networks

Cited by 61 publications

References 49 publications

Tuning and jamming reduced to their minima

Tuning and jamming reduced to their minima

Designed Elastic Networks: Models of Complex Protein Machinery

Residue-Level Allostery Propagates Through the Effective Coarse-Grained Hessian

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