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      Streptavidin/biotin: Tethering geometry defines unbinding mechanics

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          Abstract

          Single-molecule force spectroscopy reveals how the mechanical stability of a widely used biomolecular complex varies fourfold.

          Abstract

          Macromolecules tend to respond to applied forces in many different ways. Chemistry at high shear forces can be intriguing, with relatively soft bonds becoming very stiff in specific force-loading geometries. Largely used in bionanotechnology, an important case is the streptavidin (SA)/biotin interaction. Although SA’s four subunits have the same affinity, we find that the forces required to break the SA/biotin bond depend strongly on the attachment geometry. With AFM-based single-molecule force spectroscopy (SMFS), we measured unbinding forces of biotin from different SA subunits to range from 100 to more than 400 pN. Using a wide-sampling approach, we carried out hundreds of all-atom steered molecular dynamics (SMD) simulations for the entire system, including molecular linkers. Our strategy revealed the molecular mechanism that causes a fourfold difference in mechanical stability: Certain force-loading geometries induce conformational changes in SA’s binding pocket lowering the energy barrier, which biotin has to overcome to escape the pocket.

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          Dynamic strength of molecular adhesion bonds.

          In biology, molecular linkages at, within, and beneath cell interfaces arise mainly from weak noncovalent interactions. These bonds will fail under any level of pulling force if held for sufficient time. Thus, when tested with ultrasensitive force probes, we expect cohesive material strength and strength of adhesion at interfaces to be time- and loading rate-dependent properties. To examine what can be learned from measurements of bond strength, we have extended Kramers' theory for reaction kinetics in liquids to bond dissociation under force and tested the predictions by smart Monte Carlo (Brownian dynamics) simulations of bond rupture. By definition, bond strength is the force that produces the most frequent failure in repeated tests of breakage, i.e., the peak in the distribution of rupture forces. As verified by the simulations, theory shows that bond strength progresses through three dynamic regimes of loading rate. First, bond strength emerges at a critical rate of loading (> or = 0) at which spontaneous dissociation is just frequent enough to keep the distribution peak at zero force. In the slow-loading regime immediately above the critical rate, strength grows as a weak power of loading rate and reflects initial coupling of force to the bonding potential. At higher rates, there is crossover to a fast regime in which strength continues to increase as the logarithm of the loading rate over many decades independent of the type of attraction. Finally, at ultrafast loading rates approaching the domain of molecular dynamics simulations, the bonding potential is quickly overwhelmed by the rapidly increasing force, so that only naked frictional drag on the structure remains to retard separation. Hence, to expose the energy landscape that governs bond strength, molecular adhesion forces must be examined over an enormous span of time scales. However, a significant gap exists between the time domain of force measurements in the laboratory and the extremely fast scale of molecular motions. Using results from a simulation of biotin-avidin bonds (Izrailev, S., S. Stepaniants, M. Balsera, Y. Oono, and K. Schulten. 1997. Molecular dynamics study of unbinding of the avidin-biotin complex. Biophys. J., this issue), we describe how Brownian dynamics can help bridge the gap between molecular dynamics and probe tests.
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            Optimization of parameters for semiempirical methods VI: more modifications to the NDDO approximations and re-optimization of parameters

            Modern semiempirical methods are of sufficient accuracy when used in the modeling of molecules of the same type as used as reference data in the parameterization. Outside that subset, however, there is an abundance of evidence that these methods are of very limited utility. In an attempt to expand the range of applicability, a new method called PM7 has been developed. PM7 was parameterized using experimental and high-level ab initio reference data, augmented by a new type of reference data intended to better define the structure of parameter space. The resulting method was tested by modeling crystal structures and heats of formation of solids. Two changes were made to the set of approximations: a modification was made to improve the description of noncovalent interactions, and two minor errors in the NDDO formalism were rectified. Average unsigned errors (AUEs) in geometry and ΔH f for PM7 were reduced relative to PM6; for simple gas-phase organic systems, the AUE in bond lengths decreased by about 5 % and the AUE in ΔH f decreased by about 10 %; for organic solids, the AUE in ΔH f dropped by 60 % and the reduction was 33.3 % for geometries. A two-step process (PM7-TS) for calculating the heights of activation barriers has been developed. Using PM7-TS, the AUE in the barrier heights for simple organic reactions was decreased from values of 12.6 kcal/mol-1 in PM6 and 10.8 kcal/mol-1 in PM7 to 3.8 kcal/mol-1. The origins of the errors in NDDO methods have been examined, and were found to be attributable to inadequate and inaccurate reference data. This conclusion provides insight into how these methods can be improved. Electronic supplementary material The online version of this article (doi:10.1007/s00894-012-1667-x) contains supplementary material, which is available to authorized users.
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              Dynamical networks in tRNA:protein complexes.

              Community network analysis derived from molecular dynamics simulations is used to identify and compare the signaling pathways in a bacterial glutamyl-tRNA synthetase (GluRS):tRNA(Glu) and an archaeal leucyl-tRNA synthetase (LeuRS):tRNA(Leu) complex. Although the 2 class I synthetases have remarkably different interactions with their cognate tRNAs, the allosteric networks for charging tRNA with the correct amino acid display considerable similarities. A dynamic contact map defines the edges connecting nodes (amino acids and nucleotides) in the physical network whose overall topology is presented as a network of communities, local substructures that are highly intraconnected, but loosely interconnected. Whereas nodes within a single community can communicate through many alternate pathways, the communication between monomers in different communities has to take place through a smaller number of critical edges or interactions. Consistent with this analysis, there are a large number of suboptimal paths that can be used for communication between the identity elements on the tRNAs and the catalytic site in the aaRS:tRNA complexes. Residues and nucleotides in the majority of pathways for intercommunity signal transmission are evolutionarily conserved and are predicted to be important for allosteric signaling. The same monomers are also found in a majority of the suboptimal paths. Modifying these residues or nucleotides has a large effect on the communication pathways in the protein:RNA complex consistent with kinetic data.
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                Author and article information

                Journal
                Sci Adv
                Sci Adv
                SciAdv
                advances
                Science Advances
                American Association for the Advancement of Science
                2375-2548
                March 2020
                25 March 2020
                : 6
                : 13
                : eaay5999
                Affiliations
                [1 ]Lehrstuhl für Angewandte Physik and Center for NanoScience, Ludwig-Maximilians-Universität München, Amalienstr. 54, 80799 Munich, Germany.
                [2 ]NIH Center for Macromolecular Modeling and Bioinformatics, Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA.
                Author notes
                [* ]Corresponding author. Email: gaub@ 123456lmu.de (H.E.G.); rcbernardi@ 123456ks.uiuc.edu (R.C.B.)
                Author information
                http://orcid.org/0000-0002-7037-1793
                http://orcid.org/0000-0002-1986-2693
                http://orcid.org/0000-0002-4220-6088
                http://orcid.org/0000-0003-0758-2026
                Article
                aay5999
                10.1126/sciadv.aay5999
                7096159
                32232150
                8cf0efcd-b604-4fd7-8dc4-879b9ba527cc
                Copyright © 2020 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution License 4.0 (CC BY).

                This is an open-access article distributed under the terms of the Creative Commons Attribution license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 03 July 2019
                : 03 January 2020
                Funding
                Funded by: doi http://dx.doi.org/10.13039/100000001, National Science Foundation;
                Award ID: MCB-1616590
                Funded by: doi http://dx.doi.org/10.13039/100000001, National Science Foundation;
                Award ID: ACI-1713784
                Funded by: doi http://dx.doi.org/10.13039/100000001, National Science Foundation;
                Award ID: ACI-1238993
                Funded by: doi http://dx.doi.org/10.13039/100000001, National Science Foundation;
                Award ID: OCI-0725070
                Funded by: doi http://dx.doi.org/10.13039/100000002, National Institutes of Health;
                Award ID: P41-GM104601
                Funded by: doi http://dx.doi.org/10.13039/501100001659, Deutsche Forschungsgemeinschaft;
                Award ID: Sonderforschungsbereich 1032, Teilprojekt A01
                Categories
                Research Article
                Research Articles
                SciAdv r-articles
                Biochemistry
                Biophysics
                Biophysics
                Custom metadata
                Kyle Solis

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