Although proteins are considered as nonconductors that transfer electrons only up to 1 to 2 nanometers via tunneling, Geobacter sulfurreducens transports respiratory electrons over micrometers, to insoluble acceptors or syntrophic partner cells, via nanowires composed of polymerized cytochrome OmcS. However, the mechanism enabling this long-range conduction is unclear. Here, we demonstrate that individual nanowires exhibit theoretically predicted hopping conductance, at rate (>10 10 s −1 ) comparable to synthetic molecular wires, with negligible carrier loss over micrometers. Unexpectedly, nanowires show a 300-fold increase in their intrinsic conductance upon cooling, which vanishes upon deuteration. Computations show that cooling causes a massive rearrangement of hydrogen bonding networks in nanowires. Cooling makes hemes more planar, as revealed by Raman spectroscopy and simulations, and lowers their reduction potential. We find that the protein surrounding the hemes acts as a temperature-sensitive switch that controls charge transport by sensing environmental perturbations. Rational engineering of heme environments could enable systematic tuning of extracellular respiration.

Determining the principal energy pathways for allosteric communication in biomolecules, that occur as a result of thermal motion, remains challenging due to the intrinsic complexity of the systems involved. Graph theory provides an approach for making sense of such complexity, where allosteric proteins can be represented as networks of amino acids. In this work, we establish the eigenvector centrality metric in terms of the mutual information, as a mean of elucidating the allosteric mechanism that regulates the enzymatic activity of proteins. Moreover, we propose a strategy to characterize the range of the physical interactions that underlie the allosteric process. In particular, the well known enzyme, imidazol glycerol phosphate synthase (IGPS), is utilized to test the proposed methodology. The eigenvector centrality measurement successfully describes the allosteric pathways of IGPS, and allows to pinpoint key amino acids in terms of their relevance in the momentum transfer process. The resulting insight can be utilized for refining the control of IGPS activity, widening the scope for its engineering. Furthermore, we propose a new centrality metric quantifying the relevance of the surroundings of each residue. In addition, the proposed technique is validated against experimental solution NMR measurements yielding fully consistent results. Overall, the methodologies proposed in the present work constitute a powerful and cost effective strategy to gain insight on the allosteric mechanism of proteins.Allostery is a ubiquitous process of physico-chemical regulation in biological macromolecules such as enzymes. The fundamental step in the allosteric regulation is the binding of a ligand at a particular enzymatic site affecting the activity at a different and often very distant position of the protein. While allosteric processes have long been of interest, especially due to their relevance in developing potent and selective therapeutics, the mechanism for energy transfer between allosteric sites remains poorly understood. Thus, establishing a molecular level understanding of communication pathways between the physically distant enzymatic sites is crucial for the design of innovative drug therapies[1, 2] and protein engineering [3][4][5].Recently, there have been significant efforts toward the development of computational tools to support, interpret and/or predict experimental evidences for elucidation of allosteric pathways in proteins [2,[6][7][8][9][10][11][12]. Network analysis has been extensively used in this context, by incorporating concepts and methodologies from graph theory into the realm of molecular dynamics simulations [13][14][15][16][17][18][19] For instance, community network analysis (CNA) has emerged as a powerful and increasingly popular approach to analyze the dynamics of enzymes and protein/DNA (and/or RNA) complexes and to detect possible allosteric pathways [20][21][22][23][24][25][26].In these network theory-based approaches, a protein is represented as a network consisting of a set of nodes, n...

The applications of multiscale quantum-classical (QM-MM) approaches have shown an extraordinary expansion and diversification in the last couple of decades. A great proportion of these efforts have been devoted to interpreting and reproducing spectroscopic experiments in a variety of complex environments such as solutions, interfaces, and biological systems. Today, QM-MM-based computational spectroscopy methods constitute accomplished tools with refined predictive power. The present review summarizes the advances that have been made in QM-MM approaches to UV-visible, Raman, IR, NMR, electron paramagnetic resonance, and Mössbauer spectroscopies, providing in every case an introductory discussion of the corresponding methodological background. A representative number of applications are presented to illustrate the historical evolution and the state of the art of this field, highlighting the advantages and limitations of the available methodologies. Finally, we present our view of the perspectives and open challenges in the field.