Biological responses to mechanical stress require strain-sensing molecules, whose mechanically induced conformational changes are relayed to signaling cascades mediating changes in cell and tissue properties. In vertebrate muscle, the giant elastic protein titin is involved in strain sensing via its C-terminal kinase domain (TK) at the sarcomeric M-band and contributes to the adaptation of muscle in response to changes in mechanical strain. TK is regulated in a unique dual autoinhibition mechanism by a C-terminal regulatory tail, blocking the ATP binding site, and tyrosine autoinhibition of the catalytic base. For access to the ATP binding site and phosphorylation of the autoinhibitory tyrosine, the C-terminal autoinhibitory tail needs to be removed. Here, we use AFM-based single-molecule force spectroscopy, molecular dynamics simulations, and enzymatics to study the conformational changes during strain-induced activation of human TK. We show that mechanical strain activates ATP binding before unfolding of the structural titin domains, and that TK can thus act as a biological force sensor. Furthermore, we identify the steps in which the autoinhibition of TK is mechanically relieved at low forces, leading to binding of the cosubstrate ATP and priming the enzyme for subsequent autophosphorylation and substrate turnover.

Use the force: Force–volume atomic force microscopy (AFM) can image native membrane proteins and quantify and map their chemical and physical properties at molecular resolution (see images). For the light‐driven proton pump bacteriorhodopsin (BR), the data shows that lipids form a flexible framework embedding a mechanically anisotropic proton pump, and that the BR adopts different structurally stable conformations that are important for proton pumping.

We develop a general minimally coupled subspace approach (MCSA) to compute absolute entropies of macromolecules, such as proteins, from computer generated canonical ensembles. Our approach overcomes limitations of current estimates such as the quasi-harmonic approximation which neglects non-linear and higher-order correlations as well as multi-minima characteristics of protein energy landscapes. Here, Full Correlation Analysis, adaptive kernel density estimation, and mutual information expansions are combined and high accuracy is demonstrated for a number of test systems ranging from alkanes to a 14 residue peptide. We further computed the configurational entropy for the full 67-residue cofactor of the TATA box binding protein illustrating that MCSA yields improved results also for large macromolecular systems.

Proteins are usually described and classified according to amino acid sequence, structure or function. Here, we develop a minimally biased scheme to compare and classify proteins according to their internal mobility patterns. This approach is based on the notion that proteins not only fold into recurring structural motifs but might also be carrying out only a limited set of recurring mobility motifs. The complete set of these patterns, which we tentatively call the dynasome, spans a multi-dimensional space with axes, the dynasome descriptors, characterizing different aspects of protein dynamics. The unique dynamic fingerprint of each protein is represented as a vector in the dynasome space. The difference between any two vectors, consequently, gives a reliable measure of the difference between the corresponding protein dynamics. We characterize the properties of the dynasome by comparing the dynamics fingerprints obtained from molecular dynamics simulations of 112 proteins but our approach is, in principle, not restricted to any specific source of data of protein dynamics. We conclude that: 1. the dynasome consists of a continuum of proteins, rather than well separated classes. 2. For the majority of proteins we observe strong correlations between structure and dynamics. 3. Proteins with similar function carry out similar dynamics, which suggests a new method to improve protein function annotation based on protein dynamics.