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Buehler, M. J.

Publications and source records attributed to Buehler, M. J..

2 recordsLinked to original sources

Nonlinear mechanics of lamin filaments and the meshwork topology build an emergent nuclear lamina

The nuclear lamina - a meshwork of intermediate filaments termed lamins - functions as a mechanotransduction interface between the extracellular matrix and the nucleus via the cytoskeleton. Although lamins are primarily responsible for the mechanical stability of the nucleus in multicellular organisms, in situ characterization of lamin filaments under tension has remained elusive. Here, we apply an integrative approach combining atomic force microscopy, cryo-electron tomography, network analysis, and molecular dynamics simulations to directly measure the mechanical response of single lamin filaments in its three-dimensional meshwork. Endogenous lamin filaments portray non-Hookean behavior - they deform reversibly under a force of a few hundred picoNewtons and stiffen at nanoNewton forces. The filaments are extensible, strong and tough, similar to natural silk and superior to the synthetic polymer Kevlar(R). Graph theory analysis shows that the lamin meshwork is not a random arrangement of filaments but the meshwork topology follows small world properties. Our results suggest that the lamin filaments arrange to form a robust, emergent meshwork that dictates the mechanical properties of individual lamin filaments. The combined approach provides quantitative insights into the structure-function organization of lamins in situ, and implies a role of meshwork topology in laminopathies.

biophysics

Artificial intelligence method to design and fold alpha-helix structural proteins from the primary amino acid sequence

The development of rational techniques to discover new proteins for use in variety of applications ranging from agriculture to biotechnology remains an outstanding materials design problem. The key barrier is to design a sequence to fold into a predictable structure to achieve a certain material function. Focused on alpha-helical proteins, we report a Multi-scale Neighborhood-based Neural Network (MNNN) model to learn how a specific amino acid sequence folds into a protein structure. The algorithm predicts the protein structure without using a template or co-evolutional information at a maximum error of 2.1 [A]. We find that the prediction accuracy is higher than other models and the prediction consumes less than six orders of magnitude time than ab initio folding methods. We demonstrate that MNNN can predict the structure of an unknown protein that agrees with experiments, and our model hence shows a great advantage in the rational design of de novo proteins.\n\n\n\nO_FIG O_LINKSMALLFIG WIDTH=194 HEIGHT=200 SRC=\"FIGDIR/small/660639v2_ufig1.gif\" ALT=\"Figure 1\">\nView larger version (66K):\norg.highwire.dtl.DTLVardef@8771fcorg.highwire.dtl.DTLVardef@4c7255org.highwire.dtl.DTLVardef@e63f1eorg.highwire.dtl.DTLVardef@39e4ad_HPS_FORMAT_FIGEXP M_FIG C_FIG

biochemistry