Mapping phenotypic landscapes using DNA micro-arrays

Inverse metabolic engineering is a useful approach for engineering phenotypes in biological systems. The overarching objective of this approach is to combine the power of evolutionary engineering approaches with the precision of constructive metabolic engineering strategies. Often the difficulty in this approach is elucidating the genetic basis of the phenotypes that emerge as a result of evolutionary mechanisms. As a result of advances in genomics technologies, several techniques now exist that substantially improve researchers ability to identify such genes. Metabolic engineers now have the ability to map phenotypic landscapes of considerable genetic diversity, which should improve understanding of the relationships that exist among phenotype, genotype, and environment. In this mini-review, we will discuss several of such genomics tools that may be useful in developing inverse metabolic engineering strategies and, in particular, mapping phenotypic landscapes.     

Mapping information-rich genotype-phenotype landscapes with genome-scale Perturb-seq

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Mapping the dynamic organization of the nuclear pore complex inside single living cells

Most cellular activities are executed by multi-protein complexes that form the basic functional modules of their molecular machinery. Proteomic approaches can provide an evermore detailed picture of their composition, but do not reveal how these machines are organized dynamically to accomplish their biological function. Here, we present a method to determine the dissociation rates of protein subunits from complexes that have a traceable localization inside single living cells. As a case study, we systematically analysed the dynamic organization of vertebrate nuclear pore complexes (NPCs), large supramolecular complexes of about 30 different polypeptides. NPC components exhibited a wide range of residence times covering five orders of magnitude from seconds to days. We found the central parts of the NPC to be very stable, consistent with a function as a structural scaffold, whereas more peripheral components exhibited more dynamic behaviour, suggesting adaptor as well as regulatory functions. The presented strategy can be applied to many multi-protein complexes and will help to characterize the dynamic behaviour of complex networks of proteins in live cells.     

Mapping folding energy landscapes with theory and experiment

The detailed characterization of the overall free energy landscape associated with the folding process of a protein is the ultimate goal in protein folding studies. Modern experimental techniques and all-atom simulations provide a way to obtain accurate thermodynamic and kinetic measurements, but they are oftentimes restricted to probe limited regions of a protein landscape. Although simplified protein models can access larger regions of the landscape, they are built on assumptions and approximations that can affect the accuracy of the results. We review here recent promising approaches that allow to combine the complementary strengths of theory and experiment for a more complete characterization of a protein folding landscape at multiple resolutions. Recent results and possible applications are discussed.     

Turing patterns inside cells

Concentration gradients inside cells are involved in key processes such as cell division and morphogenesis. Here we show that a model of the enzymatic step catalized by phosphofructokinase (PFK), a step which is responsible for the appearance of homogeneous oscillations in the glycolytic pathway, displays Turing patterns with an intrinsic length-scale that is smaller than a typical cell size. All the parameter values are fully consistent with classic experiments on glycolytic oscillations and equal diffusion coefficients are assumed for ATP and ADP. We identify the enzyme concentration and the glycolytic flux as the possible regulators of the pattern. To the best of our knowledge, this is the first closed example of Turing pattern formation in a model of a vital step of the cell metabolism, with a built-in mechanism for changing the diffusion length of the reactants, and with parameter values that are compatible with experiments. Turing patterns inside cells could provide a check-point that combines mechanical and biochemical information to trigger events during the cell division process.     

Imaging biochemistry inside cells

Proteins provide the building blocks for multicomponent molecular units, or pathways, from which higher cellular functions emerge. These units consist of either assemblies of physically interacting proteins or dispersed biochemical activities connected by rapidly diffusing second messengers, metabolic intermediates, ions or other proteins. It will probably remain within the realm of genetics to identify the ensemble of proteins that constitute these functional units and to establish the first-order connectivity. The dynamics of interactions within these protein machines can be assessed in living cells by the application of fluorescence spectroscopy on a microscopic level, using fluorescent proteins that are introduced within these functional units. Fluorescence is sensitive, specific and non-invasive, and the spectroscopic properties of a fluorescent probe can be analysed to obtain information on its molecular environment. The development and use of sensors based on the genetically encoded variants of green-fluorescent proteins has facilitated the observation of 'live' biochemistry on a microscopic level, with the advantage of preserving the cellular context of biochemical connectivity, compartmentalization and spatial organization. Protein activities and interactions can be imaged and localized within a single cell, allowing correlation with phenomena such as the cell cycle, migration and morphogenesis.     

Fluorescence fluctuation microscopy: a diversified arsenal of methods to investigate molecular dynamics inside cells

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Mapping viability loci using molecular markers

In genetic mapping experiments, some molecular markers often show distorted segregation ratios. We hypothesize that these markers are linked to some viability loci that cause the observed segregation ratios to deviate from Mendelian expectations. Although statistical methods for mapping viability loci have been developed for line-crossing experiments, methods for viability mapping in outbred populations have not been developed yet. In this study, we develop a method for mapping viability loci in outbred populations using a full-sib family as an example. We develop a maximum likelihood (ML) method that uses the observed marker genotypes as data and the proportions of the genotypes of the viability locus as parameters. The ML solutions are obtained via the expectation-maximization algorithm. Application and efficiencies of the method are demonstrated and tested using a set of simulated data. We conclude that mapping viability loci can be accomplished using similar statistical techniques used in quantitative trait locus mapping for quantitative traits.     

Experimental fitness landscapes to understand the molecular evolution of RNA-based life

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Peptoid conformational free energy landscapes from implicit-solvent molecular simulations in AMBER

To test the accuracy of existing AMBER force field models in predicting peptoid conformation and dynamics, we simulated a set of model peptoid molecules recently examined by Butterfoss et al. (JACS 2009, 131, 16798-16807) using QM methods as well as three peptoid sequences with experimentally determined structures. We found that AMBER force fields, when used with a Generalized Born/Surface Area (GBSA) implicit solvation model, could accurately reproduce the peptoid torsional landscape as well as the major conformers of known peptoid structures. Enhanced sampling by replica exchange molecular dynamics (REMD) using temperatures from 300 to 800 K was used to sample over cis-trans isomerization barriers. Compared to (Nrch)5 and cyclo-octasarcosyl, the free energy of N-(2-nitro-3-hydroxyl phenyl)glycine-N-(phenyl)glycine has the most "foldable" free energy landscape, due to deep trans-amide minima dictated by N-aryl sidechains. For peptoids with (S)-N (1-phenylethyl) (Nspe) side chains, we observe a discrepancy in backbone dihedral propensities between molecular simulations and QM calculations, which may be due to force field effects or the inability to capture n --> n* interactions. For these residues, an empirical phi-angle biasing potential can "rescue" the backbone propensities seen in QM. This approach can serve as a general strategy for addressing force fields without resorting to a complete reparameterization. Overall, this study demonstrates the utility of implicit-solvent REMD simulations for efficient sampling to predict peptoid conformational landscapes, providing a potential tool for first-principles design of sequences with specific folding properties.     

An inside look at hippocampal silent cells

In this issue of Neuron, Greenberg and colleagues revise our understanding of how activity-dependent MeCP2 phosphorylation regulates distinct aspects of brain development and circuit function. The study also suggests a prominent role for MeCP2 in the regulation of global chromatin state in?vivo.     

Conformational landscapes of membrane proteins delineated by enhanced sampling molecular dynamics simulations

The expansion of computational power, better parameterization of force fields, and the development of novel algorithms to enhance the sampling of the free energy landscapes of proteins have allowed molecular dynamics (MD) simulations to become an indispensable tool to understand the function of biomolecules. The temporal and spatial resolution of MD simulations allows for the study of a vast number of processes of interest. Here, we review the computational efforts to uncover the conformational free energy landscapes of a subset of membrane proteins: ion channels, transporters and G-protein coupled receptors. We focus on the various enhanced sampling techniques used to study these questions, how the conclusions come together to build a coherent picture, and the relationship between simulation outcomes and experimental observables.     

Imaging proteins inside cells with fluorescent tags

Watching biological molecules provides clues to their function and regulation. Some of the most powerful methods of labeling proteins for imaging use genetically encoded fluorescent fusion tags. There are four standard genetic methods of covalently tagging a protein with a fluorescent probe for cellular imaging. These use (i) autofluorescent proteins, (ii) self-labeling enzymes, (iii) enzymes that catalyze the attachment of a probe to a target sequence, and (iv) biarsenical dyes that target tetracysteine motifs. Each of these techniques has advantages and disadvantages. In this review, we cover new developments in these methods and discuss practical considerations for their use in imaging proteins inside living cells.