Revealing Hidden Conformational Space of LOV Protein VIVID Through Rigid Residue Scan Simulations

Visualizing the DNA repair process by a photolyase at atomic resolution

Photolyases, a ubiquitous class of flavoproteins, use blue light to repair DNA photolesions. In this work, we determined the structural mechanism of the photolyase-catalyzed repair of a cyclobutane pyrimidine dimer (CPD) lesion using time-resolved serial femtosecond crystallography (TR-SFX). We obtained 18 snapshots that show time-dependent changes in four reaction loci. We used these results to create a movie that depicts the repair of CPD lesions in the picosecond-to-nanosecond range, followed by the recovery of the enzymatic moieties involved in catalysis, completing the formation of the fully reduced enzyme-product complex at 500 nanoseconds. Finally, back-flip intermediates of the thymine bases to reanneal the DNA were captured at 25 to 200 microseconds. Our data cover the complete molecular mechanism of a photolyase and, importantly, its chemistry and enzymatic catalysis at work across a wide timescale and at atomic resolution.

Dimeric allostery mechanism of the plant circadian clock photoreceptor ZEITLUPE

In Arabidopsis thaliana, the Light-Oxygen-Voltage (LOV) domain containing protein ZEITLUPE (ZTL) integrates light quality, intensity, and duration into regulation of the circadian clock. Recent structural and biochemical studies of ZTL indicate that the protein diverges from other members of the LOV superfamily in its allosteric mechanism, and that the divergent allosteric mechanism hinges upon conservation of two signaling residues G46 and V48 that alter dynamic motions of a Gln residue implicated in signal transduction in all LOV proteins. Here, we delineate the allosteric mechanism of ZTL via an integrated computational approach that employs atomistic simulations of wild type and allosteric variants of ZTL in the functional dark and light states, together with Markov state and supervised machine learning classification models. This approach has unveiled key factors of the ZTL allosteric mechanisms, and identified specific interactions and residues implicated in functional allosteric changes. The final results reveal atomic level insights into allosteric mechanisms of ZTL function that operate via a non-trivial combination of population-shift and dynamics-driven allosteric pathways.

Steric and Electronic Interactions at Gln154 in ZEITLUPE Induce Reorganization of the LOV Domain Dimer Interface

Plants measure light quality, intensity, and duration to coordinate growth and development with daily and seasonal changes in environmental conditions; however, the molecular details linking photochemistry to signal transduction remain incomplete. Two closely related light, oxygen, or voltage (LOV) domain-containing photoreceptor proteins, ZEITLUPE (ZTL) and FLAVIN-BINDING, KELCH REPEAT, F-BOX 1 (FKF1), divergently regulate the protein stability of circadian clock and photoperiodic flowering components to mediate daily and seasonal development. Using structural approaches, we identified that mutations at the Gly46 position led to global rearrangements of the ZTL dimer interface in the isolated ZTL-LOV domain. Specifically, G46S and G46A variants induce a 180° rotation about the ZTL-LOV dimer interface that is coupled to ordering of N- and C-terminal signaling elements. These conformational changes hinge upon rotation of a C-terminal Gln residue (Gln154) analogous to that present in light-state structures of ZTL. In contrast to other LOV proteins, a Q154L variant retains light-state interactions with GIGANTEA (GI), thereby indicating N5 protonation is not required for ZTL signaling. The results presented herein confirm a divergent signaling mechanism within ZTL, whereby steric and electronic effects following adduct formation can be sufficient for signal propagation in LOV proteins containing a Gly residue at position 46. Examination of bacterial LOV structures with Gly residues at the equivalent position suggests that mechanisms of signal transduction in LOV proteins may be fluid across the LOV protein family.

Allosteric Regulation at the Crossroads of New Technologies: Multiscale Modeling, Networks, and Machine Learning

Allosteric regulation is a common mechanism employed by complex biomolecular systems for regulation of activity and adaptability in the cellular environment, serving as an effective molecular tool for cellular communication. As an intrinsic but elusive property, allostery is a ubiquitous phenomenon where binding or disturbing of a distal site in a protein can functionally control its activity and is considered as the "second secret of life." The fundamental biological importance and complexity of these processes require a multi-faceted platform of synergistically integrated approaches for prediction and characterization of allosteric functional states, atomistic reconstruction of allosteric regulatory mechanisms and discovery of allosteric modulators. The unifying theme and overarching goal of allosteric regulation studies in recent years have been integration between emerging experiment and computational approaches and technologies to advance quantitative characterization of allosteric mechanisms in proteins. Despite significant advances, the quantitative characterization and reliable prediction of functional allosteric states, interactions, and mechanisms continue to present highly challenging problems in the field. In this review, we discuss simulation-based multiscale approaches, experiment-informed Markovian models, and network modeling of allostery and information-theoretical approaches that can describe the thermodynamics and hierarchy allosteric states and the molecular basis of allosteric mechanisms. The wealth of structural and functional information along with diversity and complexity of allosteric mechanisms in therapeutically important protein families have provided a well-suited platform for development of data-driven research strategies. Data-centric integration of chemistry, biology and computer science using artificial intelligence technologies has gained a significant momentum and at the forefront of many cross-disciplinary efforts. We discuss new developments in the machine learning field and the emergence of deep learning and deep reinforcement learning applications in modeling of molecular mechanisms and allosteric proteins. The experiment-guided integrated approaches empowered by recent advances in multiscale modeling, network science, and machine learning can lead to more reliable prediction of allosteric regulatory mechanisms and discovery of allosteric modulators for therapeutically important protein targets.

Directed kinetic transition network model

Molecular dynamics simulations contain detailed kinetic information related to the functional states of proteins and macromolecules, but this information is obscured by the high dimensionality of configurational space. Markov state models and transition network models are widely applied to extract kinetic descriptors from equilibrium molecular dynamics simulations. In this study, we developed the Directed Kinetic Transition Network (DKTN)-a graph representation of a master equation which is appropriate for describing nonequilibrium kinetics. DKTN models the transition rate matrix among different states under detailed balance. Adopting the mixing time from the Markov chain, we use the half mixing time as the criterion to identify critical state transition regarding the protein conformational change. The similarity between the master equation and the Kolmogorov equation suggests that the DKTN model can be reformulated into the continuous-time Markov chain model, which is a general case of the Markov chain without a specific lag time. We selected a photo-sensitive protein, vivid, as a model system to illustrate the usage of the DKTN model. Overall, the DKTN model provides a graph representation of the master equation based on chemical kinetics to model the protein conformational change without the underlying assumption of the Markovian property.

Allosteric mechanism of the circadian protein Vivid resolved through Markov state model and machine learning analysis

The fungal circadian clock photoreceptor Vivid (VVD) contains a photosensitive allosteric light, oxygen, voltage (LOV) domain that undergoes a large N-terminal conformational change. The mechanism by which a blue-light driven covalent bond formation leads to a global conformational change remains unclear, which hinders the further development of VVD as an optogenetic tool. We answered this question through a novel computational platform integrating Markov state models, machine learning methods, and newly developed community analysis algorithms. Applying this new integrative approach, we provided a quantitative evaluation of the contribution from the covalent bond to the protein global conformational change, and proposed an atomistic allosteric mechanism leading to the discovery of the unexpected importance of A'α/Aβ and previously overlooked Eα/Fα loops in the conformational change. This approach could be applicable to other allosteric proteins in general to provide interpretable atomistic representations of their otherwise elusive allosteric mechanisms.

t-Distributed Stochastic Neighbor Embedding Method with the Least Information Loss for Macromolecular Simulations

Dimensionality reduction methods are usually applied on molecular dynamics simulations of macromolecules for analysis and visualization purposes. It is normally desired that suitable dimensionality reduction methods could clearly distinguish functionally important states with different conformations for the systems of interest. However, common dimensionality reduction methods for macromolecules simulations, including predefined order parameters and collective variables (CVs), principal component analysis (PCA), and time-structure based independent component analysis (t-ICA), only have limited success due to significant key structural information loss. Here, we introduced the t-distributed stochastic neighbor embedding (t-SNE) method as a dimensionality reduction method with minimum structural information loss widely used in bioinformatics for analyses of macromolecules, especially biomacromolecules simulations. It is demonstrated that both one-dimensional (1D) and two-dimensional (2D) models of the t-SNE method are superior to distinguish important functional states of a model allosteric protein system for free energy and mechanistic analysis. Projections of the model protein simulations onto 1D and 2D t-SNE surfaces provide both clear visual cues and quantitative information, which is not readily available using other methods, regarding the transition mechanism between two important functional states of this protein.

Blue-Light Receptors for Optogenetics

Sensory photoreceptors underpin light-dependent adaptations of organismal physiology, development, and behavior in nature. Adapted for optogenetics, sensory photoreceptors become genetically encoded actuators and reporters to enable the noninvasive, spatiotemporally accurate and reversible control by light of cellular processes. Rooted in a mechanistic understanding of natural photoreceptors, artificial photoreceptors with customized light-gated function have been engineered that greatly expand the scope of optogenetics beyond the original application of light-controlled ion flow. As we survey presently, UV/blue-light-sensitive photoreceptors have particularly allowed optogenetics to transcend its initial neuroscience applications by unlocking numerous additional cellular processes and parameters for optogenetic intervention, including gene expression, DNA recombination, subcellular localization, cytoskeleton dynamics, intracellular protein stability, signal transduction cascades, apoptosis, and enzyme activity. The engineering of novel photoreceptors benefits from powerful and reusable design strategies, most importantly light-dependent protein association and (un)folding reactions. Additionally, modified versions of these same sensory photoreceptors serve as fluorescent proteins and generators of singlet oxygen, thereby further enriching the optogenetic toolkit. The available and upcoming UV/blue-light-sensitive actuators and reporters enable the detailed and quantitative interrogation of cellular signal networks and processes in increasingly more precise and illuminating manners.

Recognition of protein allosteric states and residues: Machine learning approaches

Allostery is a process by which proteins transmit the effect of perturbation at one site to a distal functional site upon certain perturbation. As an intrinsically global effect of protein dynamics, it is difficult to associate protein allostery with individual residues, hindering effective selection of key residues for mutagenesis studies. The machine learning models including decision tree (DT) and artificial neural network (ANN) models were applied to develop classification model for a cell signaling allosteric protein with two states showing extremely similar tertiary structures in both crystallographic structures and molecular dynamics simulations. Both DT and ANN models were developed with 75% and 80% of predicting accuracy, respectively. Good agreement between machine learning models and previous experimental as well as computational studies of the same protein validates this approach as an alternative way to analyze protein dynamics simulations and allostery. In addition, the difference of distributions of key features in two allosteric states also underlies the population shift hypothesis of dynamics-driven allostery model. © 2018 Wiley Periodicals, Inc.