Prediction of Protein Function from Tertiary Structure of the Active Site in Heme Proteins by Convolutional Neural Network.

Structure-function relationships in proteins have been one of the crucial scientific topics in recent research. Heme proteins have diverse and pivotal biological functions. Therefore, clarifying their structure-function correlation is significant to understand their functional mechanism and is informative for various fields of science. In this study, we constructed convolutional neural network models for predicting protein functions from the tertiary structures of heme-binding sites (active sites) of heme proteins to examine the structure-function correlation. As a result, we succeeded in the classification of oxygen-binding protein (OB), oxidoreductase (OR), proteins with both functions (OB-OR), and electron transport protein (ET) with high accuracy. Although the misclassification rate for OR and ET was high, the rates between OB and ET and between OB and OR were almost zero, indicating that the prediction model works well between protein groups with quite different functions. However, predicting the function of proteins modified with amino acid mutation(s) remains a challenge. Our findings indicate a structure-function correlation in the active site of heme proteins. This study is expected to be applied to the prediction of more detailed protein functions such as catalytic reactions.

Elucidation of the Correlation between Heme Distortion and Tertiary Structure of the Heme-Binding Pocket Using a Convolutional Neural Network.

Heme proteins serve diverse and pivotal biological functions. Therefore, clarifying the mechanisms of these diverse functions of heme is a crucial scientific topic. Distortion of heme porphyrin is one of the key factors regulating the chemical properties of heme. Here, we constructed convolutional neural network models for predicting heme distortion from the tertiary structure of the heme-binding pocket to examine their correlation. For saddling, ruffling, doming, and waving distortions, the experimental structure and predicted values were closely correlated. Furthermore, we assessed the correlation between the cavity shape and molecular structure of heme and demonstrated that hemes in protein pockets with similar structures exhibit near-identical structures, indicating the regulation of heme distortion through the protein environment. These findings indicate that the tertiary structure of the heme-binding pocket is one of the factors regulating the distortion of heme porphyrin, thereby controlling the chemical properties of heme relevant to the protein function; this implies a structure-function correlation in heme proteins.

PyDISH: database and analysis tools for heme porphyrin distortion in heme proteins.

Heme participates in a wide range of biological functions such as oxygen transport, electron transport, oxygen reduction, transcriptional regulation and so on. While the mechanism of each function has been investigated for many heme proteins, the origin of the diversity of the heme functions is still unclear and a crucial scientific issue. We have constructed a database of heme proteins, named Python-based database and analyzer for DIStortion of Heme porphyrin (PyDISH), which also contains some analysis tools. The aim of PyDISH is to integrate the information on the structures of hemes and heme proteins and the functions of heme proteins. This database will provide the structure-function relationships focusing on heme porphyrin distortion and lead to the elucidation of the origin of the functional diversity of heme proteins. In addition, the insights obtained from the database can be used for the design of protein function. PyDISH contains the structural data of more than 13 000 hemes extracted from the Protein Data Bank, including heme porphyrin distortion, axial ligands coordinating to the heme and the orientation of the propionate sidechains of heme. PyDISH also has information about the protein domains, including Uniprot ID, protein fold by CATH ID, organism, coordination distance and so on. The analytical tools implemented in PyDISH allow users to not only browse and download the data but also analyze the structures of heme porphyrin by using the analytical tools implemented in PyDISH. PyDISH users will be able to utilize the obtained results for the design of protein function. Database URL: http://pydish.bio.info.hiroshima-cu.ac.jp/.

Spiking network simulation code for petascale computers.

Brain-scale networks exhibit a breathtaking heterogeneity in the dynamical properties and parameters of their constituents. At cellular resolution, the entities of theory are neurons and synapses and over the past decade researchers have learned to manage the heterogeneity of neurons and synapses with efficient data structures. Already early parallel simulation codes stored synapses in a distributed fashion such that a synapse solely consumes memory on the compute node harboring the target neuron. As petaflop computers with some 100,000 nodes become increasingly available for neuroscience, new challenges arise for neuronal network simulation software: Each neuron contacts on the order of 10,000 other neurons and thus has targets only on a fraction of all compute nodes; furthermore, for any given source neuron, at most a single synapse is typically created on any compute node. From the viewpoint of an individual compute node, the heterogeneity in the synaptic target lists thus collapses along two dimensions: the dimension of the types of synapses and the dimension of the number of synapses of a given type. Here we present a data structure taking advantage of this double collapse using metaprogramming techniques. After introducing the relevant scaling scenario for brain-scale simulations, we quantitatively discuss the performance on two supercomputers. We show that the novel architecture scales to the largest petascale supercomputers available today.

Supercomputers ready for use as discovery machines for neuroscience.

NEST is a widely used tool to simulate biological spiking neural networks. Here we explain the improvements, guided by a mathematical model of memory consumption, that enable us to exploit for the first time the computational power of the K supercomputer for neuroscience. Multi-threaded components for wiring and simulation combine 8 cores per MPI process to achieve excellent scaling. K is capable of simulating networks corresponding to a brain area with 10(8) neurons and 10(12) synapses in the worst case scenario of random connectivity; for larger networks of the brain its hierarchical organization can be exploited to constrain the number of communicating computer nodes. We discuss the limits of the software technology, comparing maximum filling scaling plots for K and the JUGENE BG/P system. The usability of these machines for network simulations has become comparable to running simulations on a single PC. Turn-around times in the range of minutes even for the largest systems enable a quasi interactive working style and render simulations on this scale a practical tool for computational neuroscience.

A new formalization of a meta-game using the lambda calculus.

This paper presents a new game system formalism. The system describes both strategies and a game master (who computes scores in a given game system) in terms of lambda-calculus. This formalism revisits the prisoner's dilemma game, to discuss how meta-strategies emerge in this classical game, even without repetition. We have also examined the evolution of meta-strategies in lambda formalism.