Explicit consideration of topological and parameter uncertainty gives new insights into a well-established model of glycolysis

Previous models of glycolysis in the sleeping sickness parasite Trypanosoma brucei assumed that the core part of glycolysis in this unicellular parasite is tightly compartimentalized within an organelle, the glycosome, which had previously been shown to contain most of the glycolytic enzymes. The glycosomes were assumed to be largely impermeable, and exchange of metabolites between the cytosol and the glycosome was assumed to be regulated by specific transporters in the glycosomal membrane. This tight compartmentalization was considered to be essential for parasite viability. Recently, size-specific metabolite pores were discovered in the membrane of glycosomes. These channels are proposed to allow smaller metabolites to diffuse across the membrane but not larger ones. In light of this new finding, we re-analyzed the model taking into account uncertainty about the topology of the metabolic system in T. brucei, as well as uncertainty about the values of all parameters of individual enzymatic reactions. Our analysis shows that these newly-discovered nonspecific pores are not necessarily incompatible with our current knowledge of the glycosomal metabolic system, provided that the known cytosolic activities of the glycosomal enzymes play an important role in the regulation of glycolytic fluxes and the concentration of metabolic intermediates of the pathway

Transforming Graph Representations for Statistical Relational Learning

Relational data representations have become an increasingly important topic due to the recent proliferation of network datasets (e.g., social, biological, information networks) and a corresponding increase in the application of statistical relational learning (SRL) algorithms to these domains. In this article, we examine a range of representation issues for graph-based relational data. Since the choice of relational data representation for the nodes, links, and features can dramatically affect the capabilities of SRL algorithms, we survey approaches and opportunities for relational representation transformation designed to improve the performance of these algorithms. This leads us to introduce an intuitive taxonomy for data representation transformations in relational domains that incorporates link transformation and node transformation as symmetric representation tasks. In particular, the transformation tasks for both nodes and links include (i) predicting their existence, (ii) predicting their label or type, (iii) estimating their weight or importance, and (iv) systematically constructing their relevant features. We motivate our taxonomy through detailed examples and use it to survey and compare competing approaches for each of these tasks. We also discuss general conditions for transforming links, nodes, and features. Finally, we highlight challenges that remain to be addressed

A simple physical model for scaling in protein-protein interaction networks

It has recently been demonstrated that many biological networks exhibit a scale-free topology where the probability of observing a node with a certain number of edges (k) follows a power law: i.e. p(k) ~ k^-g. This observation has been reproduced by evolutionary models. Here we consider the network of protein-protein interactions and demonstrate that two published independent measurements of these interactions produce graphs that are only weakly correlated with one another despite their strikingly similar topology. We then propose a physical model based on the fundamental principle that (de)solvation is a major physical factor in protein-protein interactions. This model reproduces not only the scale-free nature of such graphs but also a number of higher-order correlations in these networks. A key support of the model is provided by the discovery of a significant correlation between number of interactions made by a protein and the fraction of hydrophobic residues on its surface. The model presented in this paper represents the first physical model for experimentally determined protein-protein interactions that comprehensively reproduces the topological features of interaction networks. These results have profound implications for understanding not only protein-protein interactions but also other types of scale-free networks.Comment: 50 pages, 17 figure

Comment on "Deficiencies in molecular dynamics simulation-based prediction of protein-DNA binding free energy landscapes"

Sequence-specific DNA binding transcription factors play an essential role in the transcriptional regulation of all organisms. The development of reliable in silico methods to predict the binding affinity landscapes of transcription factors thus promises to provide rapid screening of transcription factor specificities and, at the same time, yield valuable insight into the atomistic details of the interactions driving those specificities. Recent literature has reported highly discrepant results on the current ability of state-of-the-art atomistic molecular dynamics simulations to reproduce experimental binding free energy landscapes for transcription factors. Here, we resolve one important discrepancy by noting that in the case of alchemical free energy calculations involving base pair mutations, a common convention used in improving end point convergence of mixed potentials in fact can lead to erroneous results. The underlying cause for inaccurate double free energy difference estimates is specific to the particular implementation of the alchemical transformation protocol. Using the Gromacs simulation package, which is not affected by this issue, we obtain free energy landscapes in agreement with the experimental measurements; equivalent results are obtained for a small set of test cases with a modified version of the AMBER package. Our findings provide a consistent and optimistic outlook on the current state of prediction of protein-DNA binding free energy interactions using molecular dynamics simulations and an important precaution for appropriate end point handling in a broad range of free energy calculations

TopologyNet: Topology based deep convolutional neural networks for biomolecular property predictions

Although deep learning approaches have had tremendous success in image, video and audio processing, computer vision, and speech recognition, their applications to three-dimensional (3D) biomolecular structural data sets have been hindered by the entangled geometric complexity and biological complexity. We introduce topology, i.e., element specific persistent homology (ESPH), to untangle geometric complexity and biological complexity. ESPH represents 3D complex geometry by one-dimensional (1D) topological invariants and retains crucial biological information via a multichannel image representation. It is able to reveal hidden structure-function relationships in biomolecules. We further integrate ESPH and convolutional neural networks to construct a multichannel topological neural network (TopologyNet) for the predictions of protein-ligand binding affinities and protein stability changes upon mutation. To overcome the limitations to deep learning arising from small and noisy training sets, we present a multitask topological convolutional neural network (MT-TCNN). We demonstrate that the present TopologyNet architectures outperform other state-of-the-art methods in the predictions of protein-ligand binding affinities, globular protein mutation impacts, and membrane protein mutation impacts.Comment: 20 pages, 8 figures, 5 table