Improving the organization and interactivity of metabolic pathfinding with precomputed pathways.

BACKGROUND: The rapid growth of available knowledge on metabolic processes across thousands of species continues to expand the possibilities of producing chemicals by combining pathways found in different species. Several computational search algorithms have been developed for automating the identification of possible heterologous pathways; however, these searches may return thousands of pathway results. Although the large number of results are in part due to the large number of possible compounds and reactions, a subset of core reaction modules is repeatedly observed in pathway results across multiple searches, suggesting that some subpaths between common compounds were more consistently explored than others.To reduce the resources spent on searching the same metabolic space, a new meta-algorithm for metabolic pathfinding, Hub Pathway search with Atom Tracking (HPAT), was developed to take advantage of a precomputed network of subpath modules. To investigate the efficacy of this method, we created a table describing a network of common hub metabolites and how they are biochemically connected and only offloaded searches to and from this hub network onto an interactive webserver capable of visualizing the resulting pathways. RESULTS: A test set of nineteen known pathways taken from literature and metabolic databases were used to evaluate if HPAT was capable of identifying known pathways. HPAT found the exact pathway for eleven of the nineteen test cases using a diverse set of precomputed subpaths, whereas a comparable pathfinding search algorithm that does not use precomputed subpaths found only seven of the nineteen test cases. The capability of HPAT to find novel pathways was demonstrated by its ability to identify novel 3-hydroxypropanoate (3-HP) synthesis pathways. As for pathway visualization, the new interactive pathway filters enable a reduction of the number of displayed pathways from hundreds down to less than ten pathways in several test cases, illustrating their utility in reducing the amount of presented information while retaining pathways of interest. CONCLUSIONS: This work presents the first step in incorporating a precomputed subpath network into metabolic pathfinding and demonstrates how this leads to a concise, interactive visualization of pathway results. The modular nature of metabolic pathways is exploited to facilitate efficient discovery of alternate pathways.

Machine Learning Guided Atom Mapping of Metabolic Reactions.

Atom mapping of a chemical reaction is a mapping between the atoms in the reactant molecules and the atoms in the product molecules. It encodes the underlying reaction mechanism and, as such, constitutes essential information in computational studies in drug design. Various techniques have been investigated for the automatic computation of the atom mapping of a chemical reaction, approaching the problem as a graph matching problem. The graph abstraction of the chemical problem, though, eliminates crucial chemical information. There have been efforts for enhancing the graph representation by introducing the bond stabilities as edge weights, as they are estimated based on experimental evidence. Here, we present a fully automated optimization-based approach, named AMLGAM (Automated Machine Learning Guided Atom Mapping), that uses machine learning techniques for the estimation of the bond stabilities based on the chemical environment of each bond. The optimization method finds the reaction mechanism which favors the breakage/formation of the less stable bonds. We evaluated our method on a manually curated data set of 382 chemical reactions and ran our method on a much larger and diverse data set of 7400 chemical reactions. We show that the proposed method improves the accuracy over existing techniques based on results published by earlier studies on a common data set and is capable of handling unbalanced reactions.

Evolutionary fates within a microbial population highlight an essential role for protein folding during natural selection.

Systems biology can offer a great deal of insight into evolution by quantitatively linking complex properties such as protein structure, folding, and function to the fitness of an organism. Although the link between diseases such as Alzheimer's and misfolding is well appreciated, directly showing the importance of protein folding to success in evolution has been more difficult. We show here that predicting success during adaptation can depend critically on enzyme kinetic and folding models. We used a 'weak link' method to favor mutations to an essential, but maladapted, adenylate kinase gene within a microbial population that resulted in the identification of five mutants that arose nearly simultaneously and competed for success. Physicochemical characterization of these mutants showed that, although steady-state enzyme activity is important, success within the population is critically dependent on resistance to denaturation and aggregation. A fitness function based on in vitro measurements of enzyme activity, reversible and irreversible unfolding, and the physiological context reproduces in vivo evolutionary fates in the population linking organismal adaptation to its physical basis.

A predictive model of gene expression reveals the role of network motifs in the mating response of yeast.

Cells use signaling pathways to receive and process information about their environment. These nonlinear systems rely on feedback and feedforward regulation to respond appropriately to changing environmental conditions. Mathematical models describing signaling pathways often lack predictive power because they are not trained on data that encompass the diverse time scales on which these regulatory mechanisms operate. We addressed this limitation by measuring transcriptional changes induced by the mating response in Saccharomyces cerevisiae exposed to different dynamic patterns of pheromone. We found that pheromone-induced transcription persisted after pheromone removal and showed long-term adaptation upon sustained pheromone exposure. We developed a model of the regulatory network that captured both characteristics of the mating response. We fit this model to experimental data with an evolutionary algorithm and used the parameterized model to predict scenarios for which it was not trained, including different temporal stimulus profiles and genetic perturbations to pathway components. Our model allowed us to establish the role of four architectural elements of the network in regulating gene expression. These network motifs are incoherent feedforward, positive feedback, negative feedback, and repressor binding. Experimental and computational perturbations to these network motifs established a specific role for each in coordinating the mating response to persistent and dynamic stimulation.

Evolution of a single gene highlights the complexity underlying molecular descriptions of fitness.

Evolution by natural selection is the driving force behind the endless variation we see in nature, yet our understanding of how changes at the molecular level give rise to different phenotypes and altered fitness at the population level remains inadequate. The reproductive fitness of an organism is the most basic metric that describes the chance that an organism will succeed or fail in its environment and it depends upon a complex network of inter- and intramolecular interactions. A deeper understanding of the quantitative relationships relating molecular evolution to adaptation, and consequently fitness, can guide our understanding of important issues in biomedicine such as drug resistance and the engineering of new organisms with applications to biotechnology. We have developed the "weak link" approach to determine how changes in molecular structure and function can relate to fitness and evolutionary outcomes. By replacing adenylate kinase (AK), an essential gene, in a thermophile with a homologous AK from a mesophile we have created a maladapted weak link that produces a temperature-sensitive phenotype. The recombinant strain adapts to nonpermissive temperatures through point mutations to the weak link that increase both stability and activity of the enzyme AK at higher temperatures. Here, we propose a fitness function relating enzyme activity to growth rate and use it to create a dynamic model of a population of bacterial cells. Using metabolic control analysis we show that the growth rate exhibits thresholdlike behavior, saturating at high enzyme activity as other reactions in the energy metabolism pathway become rate limiting. The dynamic model accurately recapitulates observed evolutionary outcomes. These findings suggest that in vitro enzyme kinetic data, in combination with metabolic network analysis, can be used to create fitness functions and dynamic models of evolution within simple metabolic systems.

An adaptive mutation in adenylate kinase that increases organismal fitness is linked to stability-activity trade-offs.

Protein function is a balance between activity and stability. However, the relevance of stability-activity trade-offs for protein evolution and their impact on organismal fitness have been difficult to determine. Previously, we have linked organismal survival at increasing temperatures to adaptive changes to a single protein sequence through allelic replacement of an essential gene, adenylate kinase (adk), in a thermophile. In vivo continuous evolution of the temperature-sensitive thermophile has shown that the first step toward increased organismal fitness is mutation of glutamine-199 to arginine in the mesophilic enzyme (AKsub Q199R). Here, we show that although substitution of Arg-199 did confer a modest increase in stability (0.6 kcal mol(-1)at 20 degrees C; DeltaT(m) = 3.0 degrees C), it is a large change in the activity profile of the enzyme that is responsible for its exceptional robustness during the earlier experimental evolution study. Kinetic studies of AKsub Q199R show that it has a strong loss of enzymatic activity (>50%) at lower temperatures (20-45 degrees C) and a subsequent increase at elevated temperatures. The stability-activity trade-off observed for AKsub Q199R was linked to the rigidification of the overall structure through stabilization of a polypeptide loop containing Arg-199 that is part of the ATP-binding site of the enzyme. Structural analysis revealed the formation of new ionic interactions facilitated by Arg-199. Our results suggest that stability-activity trade-offs are employed readily as an evolutionary strategy during natural selection to increase organismal fitness.

A review of parameters and heuristics for guiding metabolic pathfinding.

Recent developments in metabolic engineering have led to the successful biosynthesis of valuable products, such as the precursor of the antimalarial compound, artemisinin, and opioid precursor, thebaine. Synthesizing these traditionally plant-derived compounds in genetically modified yeast cells introduces the possibility of significantly reducing the total time and resources required for their production, and in turn, allows these valuable compounds to become cheaper and more readily available. Most biosynthesis pathways used in metabolic engineering applications have been discovered manually, requiring a tedious search of existing literature and metabolic databases. However, the recent rapid development of available metabolic information has enabled the development of automated approaches for identifying novel pathways. Computer-assisted pathfinding has the potential to save biochemists time in the initial discovery steps of metabolic engineering. In this paper, we review the parameters and heuristics used to guide the search in recent pathfinding algorithms. These parameters and heuristics capture information on the metabolic network structure, compound structures, reaction features, and organism-specificity of pathways. No one metabolic pathfinding algorithm or search parameter stands out as the best to use broadly for solving the pathfinding problem, as each method and parameter has its own strengths and shortcomings. As assisted pathfinding approaches continue to become more sophisticated, the development of better methods for visualizing pathway results and integrating these results into existing metabolic engineering practices is also important for encouraging wider use of these pathfinding methods.

Pheromone-induced morphogenesis and gradient tracking are dependent on the MAPK Fus3 binding to Gα.

Mitogen-activated protein kinase (MAPK) pathways control many cellular processes, including differentiation and proliferation. These pathways commonly activate MAPK isoforms that have redundant or overlapping function. However, recent studies have revealed circumstances in which MAPK isoforms have specialized, nonoverlapping roles in differentiation. The mechanisms that underlie this specialization are not well understood. To address this question, we sought to establish regulatory mechanisms that are unique to the MAPK Fus3 in pheromone-induced mating and chemotropic fate transitions of the budding yeast Saccharomyces cerevisiae. Our investigations reveal a previously unappreciated role for inactive Fus3 as a potent negative regulator of pheromone-induced chemotropism. We show that this inhibitory role is dependent on inactive Fus3 binding to the α-subunit of the heterotrimeric G-protein. Further analysis revealed that the binding of catalytically active Fus3 to the G-protein is required for gradient tracking and serves to suppress cell-to-cell variability between mating and chemotropic fates in a population of pheromone-responding cells.