Long-lived quantum coherences observed in several photosynthetic pigment-protein complexes at low and at room temperatures have generated a heated debate over the impact that the coupling of electronic excitations to molecular vibrations of the relevant actors (pigments, proteins and solvents) has on the excitation energy transfer process. In this work, we use a combined MD and QM/MMPol strategy to investigate the exciton-phonon interactions in the PE545 light-harvesting complex by computing the spectral densities for each pigment and analyzing their consequences in the exciton dynamics. Insights into the origin of relevant peaks, as well as their differences among individual pigments, are provided by correlating them with normal modes obtained from a quasi-harmonic analysis of the motions sampled by the pigments in the complex. Our results indicate that both the protein and the solvent significantly modulate the intramolecular vibrations of the pigments thus playing an important role in promoting or suppressing certain exciton-phonon interactions. We also find that these low-frequency features are largely smoothed out when the spectral density is averaged over the complex, something difficult to avoid in experiments that underscores the need to combine theory and experiment to understand the origin of quantum coherence in photosynthetic light-harvesting.

Electronic energy transfer is widely used as a molecular ruler to interrogate the structure of biomacromolecules, and performs a key task in photosynthesis by transferring collected energy through specialized pigment–protein complexes. Förster theory, introduced over 70 years ago, allows linking transfer rates to simple structural and spectroscopic properties of the energy‐transferring molecules. In biosystems, however, significant deviations from Förster behavior often arise due to breakdown of the ideal dipole approximation, dielectric screening effects due to the biological environment, or departure from the weak‐coupling regime. In this review, we provide a concise overview of advances in simulations of energy transfer in biomacromolecules that allow overcoming the main limitations of Förster theory. We first discuss advances in quantum chemical methods to compute electronic couplings, their extension to multiscale formulations to include screening effects, and strategies to treat the interplay between coupling fluctuations and energy transfer dynamics. We then examine the spectral overlap term, and how this quantity can be estimated from simulations of the spectral density of exciton–phonon coupling. Finally, we discuss rate theories that can describe energy transfers in situations where strong coupling leads to delocalized excitions, a common situation found in closely packed multichromophoric systems such as photosynthetic complexes and nucleic acids. This article is categorized under: Structure and Mechanism > Computational Biochemistry and Biophysics Theoretical and Physical Chemistry > Spectroscopy

Quantitative models of light harvesting in photosynthetic antenna complexes depend sensitively on the challenging determination of the relative site energies of the pigments. Herein we analyze the basis of the light harvesting properties of four antennae from cryptophyte algae, phycocyanines PC577, PC612, PC630 and PC645, by comparing two alternative theoretical strategies to derive the excitonic Hamiltonian. The first is based on molecular dynamics simulations and subsequent polarizable quantum/molecular mechanics (QM/MMPol) calculations, whereas the second is based on three-layer QM/ MMPol/ddCOSMO calculations performed on optimized geometries of the pigments, where the water solvent is described using the ddCOSMO continuum model. We find the latter approach to be remarkably accurate, suggesting that these four phycobiliproteins share a common energetic ordering PCB 82 < PCB 158 < DBV 51/61 for pigments located in the highly-conserved β chains, whereas bilins in the more divergent α chains cause their spectral differences. In addition, we predict a strong screening of the coupling among central dihydrobiliverdins (DBVs) in "open" form complexes PC577 and PC612 compared to "closed" form ones, which together with the increased interpigment separation explains the attenuation of coherence beatings observed for these complexes.[a] Dr.

Protein tyrosine phosphatases (PTPs) possess a conserved mobile catalytic loop, the WPD-loop, which brings an aspartic acid into the active site where it acts as an acid/base catalyst. Prior experimental...

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ATP phosphoribosyltransferase catalyses the first step of histidine biosynthesis and is controlled via a complex allosteric mechanism where the regulatory protein HisZ enhances catalysis by the catalytic protein HisGS while mediating allosteric inhibition by histidine. Activation by HisZ was proposed to position HisGS Arg56 to stabilise departure of the pyrophosphate leaving group. Here we report active-site mutants of HisGS with impaired reaction chemistry which can be allosterically restored by HisZ despite the HisZ:HisGS interface lying ~20 Å away from the active site. MD simulations indicate HisZ binding constrains the dynamics of HisGS to favour a preorganised active site where both Arg56 and Arg32 are poised to stabilise leaving-group departure in WT-HisGS. In the Arg56Ala-HisGS mutant, HisZ modulates Arg32 dynamics so that it can partially compensate for the absence of Arg56. These results illustrate how remote protein-protein interactions translate into catalytic resilience by restoring damaged electrostatic preorganisation at the active site.

Recent years have seen an explosion of interest in understanding the physicochemical parameters that shape enzyme evolution, as well as substantial advances in computational enzyme design. This review discusses three areas where evolutionary information can be used as part of the design process: (i) using ancestral sequence reconstruction (ASR) to generate new starting points for enzyme design efforts; (ii) learning from how nature uses conformational dynamics in enzyme evolution to mimic this process in silico; and (iii) modular design of enzymes from smaller fragments, again mimicking the process by which nature appears to create new protein folds. Using showcase examples, we highlight the importance of incorporating evolutionary information to continue to push forward the boundaries of enzyme design studies. Computational enzyme design based on protein evolution: an overviewRoughly three decades have passed since the first attempts to design new enzymes using computational approaches [1,2], and the field has matured considerably since then. While the earliest attempts at computational enzyme design focused primarily on side-chain positioning [1][2][3][4] or on focusing the search space for in vitro directed evolution (see Glossary) studies [5], subsequent work broadly expanded the scope of the field, including the fully de novo design of new enzymes [6] (typically followed by optimization using directed evolution) and the repurposing of existing enzymes to catalyze ever more complex chemical reactions [7,8]. In addition, computational design approaches are becoming ever-more streamlined, such that there now exists a range of powerful web servers that can assist in the design process [9].In principle, computational design approaches can take two very loosely defined directions: structure-based design approaches that require some level of knowledge of the system of interest, including information about the chemical mechanisms, transition states, and key catalytic residues involved; and sequence-based design approaches that can, for example, draw on evolutionary information to predict potential hotspots for protein engineering as well as new variants with desired physicochemical properties, something that is in particular increasingly being achieved using machine-learning approaches [10].Computational approaches that require minimal knowledge of the molecular details of the chemical processes involved are attractive for their speed and efficiency, as exploring the underlying mechanisms and transition states typically requires significant experimental and/or computational effort. However, much like their experimental counterparts, such approaches are likely to hit optimization plateaus [11,12] where further improvement in activity becomes extremely challenging, and without knowledge of the underlying chemistry it can be difficult-to-impossible to overcome such plateaus. Therefore, rather than competing with each other, sequence-and structure-based approaches are highly complementary as each provides different type...

DNA-binding proteins play an important role in gene regulation and cellular function. The transcription factors MarA and Rob are two homologous members of the AraC/XylS family that regulate multidrug resistance. They share a common DNA-binding domain, and Rob possesses an additional C-terminal domain that permits binding of low-molecular weight effectors. Both proteins possess two helix-turn-helix (HTH) motifs capable of binding DNA; however, while MarA interacts with its promoter through both HTH-motifs, prior studies indicate that Rob binding to DNA via a single HTH-motif is sufficient for tight binding. In the present work, we perform microsecond time scale all-atom simulations of the binding of both transcription factors to different DNA sequences to understand the determinants of DNA recognition and binding. Our simulations characterize sequence-dependent changes in dynamical behavior upon DNA binding, showcasing the role of Arg40 of the N-terminal HTH-motif in allowing for specific tight binding. Finally, our simulations demonstrate that an acidic C-terminal loop of Rob can control the DNA binding mode, facilitating interconversion between the distinct DNA binding modes observed in MarA and Rob. In doing so, we provide detailed molecular insight into DNA binding and recognition by these proteins, which in turn is an important step toward the efficient design of antivirulence agents that target these proteins.