Eigenvector centrality for characterization of protein allosteric pathways

Detecting multiple communities using quantum annealing on the D-Wave system

A very important problem in combinatorial optimization is the partitioning of a network into communities of densely connected nodes; where the connectivity between nodes inside a particular community is large compared to the connectivity between nodes belonging to different ones. This problem is known as community detection, and has become very important in various fields of science including chemistry, biology and social sciences. The problem of community detection is a twofold problem that consists of determining the number of communities and, at the same time, finding those communities. This drastically increases the solution space for heuristics to work on, compared to traditional graph partitioning problems. In many of the scientific domains in which graphs are used, there is the need to have the ability to partition a graph into communities with the "highest quality" possible since the presence of even small isolated communities can become crucial to explain a particular phenomenon. We have explored community detection using the power of quantum annealers, and in particular the D-Wave 2X and 2000Q machines. It turns out that the problem of detecting at most two communities naturally fits into the architecture of a quantum annealer with almost no need of reformulation. This paper addresses a systematic study of detecting two or more communities in a network using a quantum annealer.

A Tripartite Microbial-Environment Network Indicates How Crucial Microbes Influence the Microbial Community Ecology

Current technologies could identify the abundance and functions of specific microbes, and evaluate their individual effects on microbial ecology. However, these microbes interact with each other, as well as environmental factors, in the form of complex network. Determination of their combined ecological influences remains a challenge. In this study, we developed a tripartite microbial-environment network (TMEN) analysis method that integrates microbial abundance, metabolic function, and environmental data as a tripartite network to investigate the combined ecological effects of microbes. Applying TMEN to analyzing the microbial-environment community structure in the sediments of Hangzhou Bay, one of the most seriously polluted coastal areas in China, we found that microbes were well-organized into 4 bacterial communities and 9 archaeal communities. The total organic carbon, sulfate, chemical oxygen demand, salinity, and nitrogen-related indexes were detected as crucial environmental factors in the microbial-environmental network. With close interactions with these environmental factors, Nitrospirales and Methanimicrococcu were identified as hub microbes with connection advantage. Our TMEN method could close the gap between lack of efficient statistical and computational approaches and the booming of large-scale microbial genomic and environmental data. Based on TMEN, we discovered a potential microbial ecological mechanism that crucial species with significant influence on the microbial community ecology would possess one or two of the community advantages for enhancing their ecological status and essentiality, including abundance advantage and connection advantage.

Allosteric Motions of the CRISPR-Cas9 HNH Nuclease Probed by NMR and Molecular Dynamics

CRISPR-Cas9 is a widely employed genome-editing tool with functionality reliant on the ability of the Cas9 endonuclease to introduce site-specific breaks in double-stranded DNA. In this system, an intriguing allosteric communication has been suggested to control its DNA cleavage activity through flexibility of the catalytic HNH domain. Here, solution NMR experiments and a novel Gaussian-accelerated molecular dynamics (GaMD) simulation method are used to capture the structural and dynamic determinants of allosteric signaling within the HNH domain. We reveal the existence of a millisecond time scale dynamic pathway that spans HNH from the region interfacing the adjacent RuvC nuclease and propagates up to the DNA recognition lobe in full-length CRISPR-Cas9. These findings reveal a potential route of signal transduction within the CRISPR-Cas9 HNH nuclease, advancing our understanding of the allosteric pathway of activation. Further, considering the role of allosteric signaling in the specificity of CRISPR-Cas9, this work poses the mechanistic basis for novel engineering efforts aimed at improving its genome-editing capability.

Structural Energy Landscapes and Plasticity of the Microstates of Apo Escherichia coli cAMP Receptor Protein

The theory for allostery has evolved to a modern energy landscape ensemble theory, the major feature of which is the existence of multiple microstates in equilibrium. The properties of microstates are not well defined due to their transient nature. Characterization of apo protein microstates is important because the specific complex of the ligand-bound microstate defines the biological function. The information needed to link biological function and structure is a quantitative correlation of the energy landscapes between the apo and holo protein states. We employed the Escherichia coli cAMP receptor protein (CRP) system to test the features embedded in the ensemble theory because multiple crystalline apo and holo structures are available. Small angle X-ray scattering data eliminated one of the three apo states but not the other two. We defined the underlying energy landscape differences among the apo microstates by employing the computation algorithm COREX/BEST. The same connectivity patterns among residues in apo CRP are retained upon binding of cAMP. The microstates of apo CRP differ from one another by minor structural perturbations, resulting in changes in the energy landscapes of the various domains of CRP. Using the differences in energy landscapes among these apo states, we computed the cAMP binding energetics that were compared with solution biophysical results. Only one of the three apo microstates yielded data consistent with the solution data. The relative magnitude of changes in energy landscapes embedded in various apo microstates apparently defines the ultimate outcome of the cooperativity of binding.

NMR and computational methods for molecular resolution of allosteric pathways in enzyme complexes

Allostery is a ubiquitous biological mechanism in which a distant binding site is coupled to and drastically alters the function of a catalytic site in a protein. Allostery provides a high level of spatial and temporal control of the integrity and activity of biomolecular assembles composed of proteins, nucleic acids, or small molecules. Understanding the physical forces that drive allosteric coupling is critical to harnessing this process for use in bioengineering, de novo protein design, and drug discovery. Current microscopic models of allostery highlight the importance of energetics, structural rearrangements, and conformational fluctuations, and in this review, we discuss the synergistic use of solution NMR spectroscopy and computational methods to probe these phenomena in allosteric systems, particularly protein-nucleic acid complexes. This combination of experimental and theoretical techniques facilitates an unparalleled detection of subtle changes to structural and dynamic equilibria in biomolecules with atomic resolution, and we provide a detailed discussion of specialized NMR experiments as well as the complementary methods that provide valuable insight into allosteric pathways in silico. Lastly, we highlight two case studies to demonstrate the adaptability of this approach to enzymes of varying size and mechanistic complexity.

Large-Scale Conformational Changes and Protein Function: Breaking the in silico Barrier

Large-scale conformational changes are essential to link protein structures with their function at the cell and organism scale, but have been elusive both experimentally and computationally. Over the past few years developments in cryo-electron microscopy and crystallography techniques have started to reveal multiple snapshots of increasingly large and flexible systems, deemed impossible only short time ago. As structural information accumulates, theoretical methods become central to understand how different conformers interconvert to mediate biological function. Here we briefly survey current in silico methods to tackle large conformational changes, reviewing recent examples of cross-validation of experiments and computational predictions, which show how the integration of different scale simulations with biological information is already starting to break the barriers between the in silico, in vitro, and in vivo worlds, shedding new light onto complex biological problems inaccessible so far.

Light Regulation of Enzyme Allostery through Photo-responsive Unnatural Amino Acids

Imidazole glycerol phosphate synthase (ImGPS) is an allosteric bienzyme complex in which substrate binding to the synthase subunit HisF stimulates the glutaminase subunit HisH. To control this stimulation with light, we have incorporated the photo-responsive unnatural amino acids phenylalanine-4'-azobenzene (AzoF), o-nitropiperonyl-O-tyrosine (NPY), and methyl-o-nitropiperonyllysine (mNPK) at strategic positions of HisF. The light-mediated isomerization of AzoF at position 55 (fS55AzoF^E ↔ fS55AzoF^Z) resulted in a reversible 10-fold regulation of HisH activity. The light-mediated decaging of NPY at position 39 (fY39NPY → fY39) and of mNPK at position 99 (fK99mNPK → fK99) led to a 4- to 6-fold increase of HisH activity. Molecular dynamics simulations explained how the unnatural amino acids interfere with the allosteric machinery of ImGPS and revealed additional aspects of HisH stimulation in wild-type ImGPS. Our findings show that unnatural amino acids can be used as a powerful tool for the spatiotemporal control of a central metabolic enzyme complex by light.

Exploring Allosteric Pathways of a V-Type Enzyme with Dynamical Perturbation Networks

Elucidation of the allosteric pathways in proteins is a computational challenge that strongly benefits from combination of atomistic molecular dynamics (MD) simulations and coarse-grained analysis of the complex dynamical network of chemical interactions based on graph theory. Here, we introduce and assess the performances of the dynamical perturbation network analysis of allosteric pathways in a prototypical V-type allosteric enzyme. Dynamical atomic contacts obtained from MD simulations are used to weight the allosteric protein graph, which involves an extended network of contacts perturbed by the effector binding in the allosteric site. The outcome showed good agreement with previously reported theoretical and experimental extended studies and it provided recognition of new potential allosteric spots that can be exploited in future mutagenesis experiments. Overall, the dynamical perturbation network analysis proved to be a powerful computational tool, complementary to other network-based approaches that can assist the full exploitation of allosteric phenomena for advances in protein engineering and rational drug design.

Water Network Dynamics Next to the Oxygen-Evolving Complex of Photosystem II

The influence of the environment on the functionality of the oxygen-evolving complex (OEC) of photosystem II has long been a subject of great interest. In particular, various water channels, which could serve as pathways for substrate water diffusion, or proton translocation, are thought to be critical to catalytic performance of the OEC. Here, we address the dynamical nature of hydrogen bonding along the water channels by performing molecular dynamics (MD) simulations of the OEC and its surrounding protein environment in the S1 and S2 states. Through the eigenvector centrality (EC) analysis, we are able to determine the characteristics of the water network and assign potential functions to the major channels, namely that the narrow and broad channels are likely candidates for proton/water transport, while the large channel may serve as a path for larger ions such as chloride and manganese thought to be essential during PSII assembly.

Advances in the Computational Identification of Allosteric Sites and Pathways in Proteins

With the increasing difficulty to develop new drugs and the emergence of resistance to traditional orthosteric-site inhibitors, the search for alternatives is finally approaching the focus on allosteric sites. Allosteric sites offer opportunities to regulate many pharmacologically targeted pathways by inhibition or activation. In addition, allosteric sites tend to be less conserved than the functional site, which may facilitate the design of specific effectors in the protein families for which specific orthosteric inhibitors have proved difficult to design. Furthermore, recent evidence suggests that all proteins might be susceptible of allosteric regulation, increasing the space of druggable targets. Computational identification of allosteric sites has therefore become an active field of research. The problem can be approached from two sides: (1) the identification of allosteric-communication pathways between the functional site and potential allosteric sites and (2) the functional-site-independent identification of allosteric sites. While the first approach tends to be more laborious and thus restricted to a single protein, the second tends to be more amenable to larger-scale analysis, thus providing tools for the two drug discovery scenarios: the analysis of known targets and the screening for new potential targets. Here, I show some basic concepts and methods useful to the identification of allosteric sites and pathways, in line with these two approaches. I describe them in some detail to build a clear framework, at the risk of losing the interest of experts. Examples of recent studies involving these methods are also illustrated, focusing on the techniques rather than on their findings on allosterism.