A neural network model for generating complex birdsong syntax

The singing behavior of songbirds has been investigated as a model of sequence learning and production. The song of the Bengalese finch, Lonchura striata var. domestica, is well described by a finite state automaton including a stochastic transition of the note sequence, which can be regarded as a higher-order Markov process. Focusing on the neural structure of songbirds, we propose a neural network model that generates higher-order Markov processes. The neurons in the robust nucleus of the archistriatum (RA) encode each note; they are activated by RA-projecting neurons in the HVC (used as a proper name). We hypothesize that the same note included in different chunks is encoded by distinct RA-projecting neuron groups. From this assumption, the output sequence of RA is a higher-order Markov process, even though the RA-projecting neurons in the HVC fire on first-order Markov processes. We developed a neural network model of the local circuits in the HVC that explains the mechanism by which RA-projecting neurons transit stochastically on first-order Markov processes. Numerical simulation showed that this model can generate first-order Markov process song sequences.     

A protein interaction network associated with asthma

Identifying candidate genes related to complex diseases or traits and mapping their relationships require a system-level analysis at a cellular scale. The objective of the present study is to systematically analyze the complex effects of interrelated genes and provide a framework for revealing their relationships in association with a specific disease (asthma in this case). We observed that protein-protein interaction (PPI) networks associated with asthma have a power-law connectivity distribution as many other biological networks have. The hub nodes and skeleton substructure of the result network are consistent with the prior knowledge about asthma pathways, and also suggest unknown candidate target genes associated with asthma, including GNB2L1, BRCA1, CBL, and VAV1. In particular, GNB2L1 appears to play a very important role in the asthma network through frequent interactions with key proteins in cellular signaling. This network-based approach represents an alternative method for analyzing the complex effects of candidate genes associated with complex diseases and suggesting a list of gene drug targets. The full list of genes and the analysis details are available in the following online supplementary materials: http://biosoft.kaist.ac.kr:8080/resources/asthma_ppi.     

A mathematical model for prokaryotic protein synthesis

A kinetic model for the synthesis of proteins in prokaryotes is presented and analysed. This model is based on a Markov model for the state of the DNA strand encoding the protein. The states that the DNA strand can occupy are: ready, repressed, or having a mRNA chain of length i in the process of being completed. The case i = 0 corresponds to the RNA polymerase attached, but no nucleotides attached to the chain. The Markov model consists of differential equations for the rates of change of the probabilities. The rate of production of the mRNA molecules is equal to the probability that the chain is assembled to the penultimate nucleotide, times the rate at which that nucleotide is attached. Similarly, the mRNA molecules can also be in different states, including: ready and having an amino acid chain of length j attached. The rate of protein synthesis is the rate at which the chain is completed. A Michaelis-Menten type of analysis is done, assuming that the rate of protein degradation determines the ’slow’ time, and that all the other kinetic rates are ‘fast’. In the self-regulated case, this results in a single ordinary differential equation for the protein concentration.     

Cross-species cluster co-conservation: a new method for generating protein interaction networks

Co-conservation (phylogenetic profiles) is a well-established method for predicting functional relationships between proteins. Several publicly available databases use this method and additional clustering strategies to develop networks of protein interactions (cluster co-conservation (CCC)). CCC has previously been limited to interactions within a single target species. We have extended CCC to develop protein interaction networks based on co-conservation between protein pairs across multiple species, cross-species cluster co-conservation.     

Fundamentals of protein interaction network mapping

Studying protein interaction networks of all proteins in an organism (“interactomes”) remains one of the major challenges in modern biomedicine. Such information is crucial to understanding cellular pathways and developing effective therapies for the treatment of human diseases. Over the past two decades, diverse biochemical, genetic, and cell biological methods have been developed to map interactomes. In this review, we highlight basic principles of interactome mapping. Specifically, we discuss the strengths and weaknesses of individual assays, how to select a method appropriate for the problem being studied, and provide general guidelines for carrying out the necessary follow‐up analyses. In addition, we discuss computational methods to predict, map, and visualize interactomes, and provide a summary of some of the most important interactome resources. We hope that this review serves as both a useful overview of the field and a guide to help more scientists actively employ these powerful approaches in their research.     

A Bayesian network model for protein fold and remote homologue recognition

MOTIVATION: The Bayesian network approach is a framework which combines graphical representation and probability theory, which includes, as a special case, hidden Markov models. Hidden Markov models trained on amino acid sequence or secondary structure data alone have been shown to have potential for addressing the problem of protein fold and superfamily classification. RESULTS: This paper describes a novel implementation of a Bayesian network which simultaneously learns amino acid sequence, secondary structure and residue accessibility for proteins of known three-dimensional structure. An awareness of the errors inherent in predicted secondary structure may be incorporated into the model by means of a confusion matrix. Training and validation data have been derived for a number of protein superfamilies from the Structural Classification of Proteins (SCOP) database. Cross validation results using posterior probability classification demonstrate that the Bayesian network performs better in classifying proteins of known structural superfamily than a hidden Markov model trained on amino acid sequences alone.     

Global geometric affinity for revealing high fidelity protein interaction network

Protein-protein interaction (PPI) network analysis presents an essential role in understanding the functional relationship among proteins in a living biological system. Despite the success of current approaches for understanding the PPI network, the large fraction of missing and spurious PPIs and a low coverage of complete PPI network are the sources of major concern. In this paper, based on the diffusion process, we propose a new concept of global geometric affinity and an accompanying computational scheme to filter the uncertain PPIs, namely, reduce the spurious PPIs and recover the missing PPIs in the network. The main concept defines a diffusion process in which all proteins simultaneously participate to define a similarity metric (global geometric affinity (GGA)) to robustly reflect the internal connectivity among proteins. The robustness of the GGA is attributed to propagating the local connectivity to a global representation of similarity among proteins in a diffusion process. The propagation process is extremely fast as only simple matrix products are required in this computation process and thus our method is geared toward applications in high-throughput PPI networks. Furthermore, we proposed two new approaches that determine the optimal geometric scale of the PPI network and the optimal threshold for assigning the PPI from the GGA matrix. Our approach is tested with three protein-protein interaction networks and performs well with significant random noises of deletions and insertions in true PPIs. Our approach has the potential to benefit biological experiments, to better characterize network data sets, and to drive new discoveries.     

A structural model for the rolA protein and its interaction with DNA

The study of the plant oncogene rolA has been hampered by a lack of structural information. Here we show that, despite a lack of significant sequence similarity to proteins of known structure, the rolA sequence adopts a known fold; that of the papillomavirus E2 DNA-binding domain. This fold is reliably identified by modern threading programs, which consider predicted secondary structure, but not by others. Although the rolA sequence is only around 16% identical to those of the available template structures, a structural model could be built that performed well against protein structure verification programs. The adopted strategy involved alignment corrections, justified by multiple model building and evaluation, with particular attention paid to the hydrophobic core residues. We find that rolA protein is predicted to resemble the template proteins in two key aspects; existence as a dimer and ability to bind DNA. rolA protein has recently been shown experimentally to possess DNA binding ability. This model predicts Lys 24 and Arg 27 to be involved in sequence-specific interactions and eight other residues to hydrogen-bond phosphate groups of the DNA.     

A model explaining the size distribution of gene and protein families

This article deals with the theoretical size distribution of gene and protein families in complete genomes. A simple evolutionary model for the development of such families in which genes in a family are formed or selected against independently and at random, and in which new families are formed by the random splitting of existing families, is used to derive the resulting size distribution. Mathematically this turns out to be the distribution of the state of a homogeneous birth-and-death process after an exponentially distributed time, which it is shown will under certain conditions exhibit the power-law behaviour observed for gene and protein family sizes.     

A model for protein translation: polysome self-organization leads to maximum protein synthesis rates

The genetic information in DNA is transcribed to mRNA and then translated to proteins, which form the building blocks of life. Translation, or protein synthesis, is hence a central cellular process. We have developed a gene-sequence-specific mechanistic model for the translation machinery, which accounts for all the elementary steps of the translation mechanism. We performed a sensitivity analysis to determine the effects of kinetic parameters and concentrations of the translational components on protein synthesis rate. Utilizing our mathematical framework and sensitivity analysis, we investigated the translational kinetic properties of a single mRNA species in Escherichia coli. We propose that translation rate at a given polysome size depends on the complex interplay between ribosomal occupancy of elongation phase intermediate states and ribosome distributions with respect to codon position along the length of the mRNA, and this interplay leads to polysome self-organization that drives translation rate to maximum levels.     

A model for protein-DNA interaction dynamics

We map a simplified version of the protein-DNA interaction problem into an Ising-model in a random magnetic field. The model includes a "head" which moves along the chain while interacting with the underlying spins. The head moves by using the statistical fluctuations of base openings. A Monte Carlo (MC) simulation of this model reveals the possibility of biased diffusion in one direction, followed by sequence identification and binding. The model provides some insight into the mechanisms used by some repressor proteins to diffuse and bind to specific DNA-binding sites.     

Molecular basis for evolving modularity in the yeast protein interaction network

Scale-free networks are generically defined by a power-law distribution of node connectivities. Vastly different graph topologies fit this law, ranging from the assortative, with frequent similar-degree node connections, to a modular structure. Using a metric to determine the extent of modularity, we examined the yeast protein network and found it to be significantly self-dissimilar. By orthologous node categorization, we established the evolutionary trend in the network, from an “emerging” assortative network to a present-day modular topology. The evolving topology fits a generic connectivity distribution but with a progressive enrichment in intramodule hubs that avoid each other. Primeval tolerance to random node failure is shown to evolve toward resilience to hub failure, thus removing the fragility often ascribed to scale-free networks. This trend is algorithmically reproduced by adopting a connectivity accretion law that disfavors like-degree connections for large-degree nodes. The selective advantage of this trend relates to the need to prevent a failed hub from inducing failure in an adjacent hub. The molecular basis for the evolutionary trend is likely rooted in the high-entropy penalty entailed in the association of two intramodular hubs.     

Functional evolution of the yeast protein interaction network

Protein interactions are central to most biological processes. We investigated the dynamics of emergence of the protein interaction network of Saccharomyces cerevisiae by mapping origins of proteins on an evolutionary tree. We demonstrate that evolutionary periods are characterized by distinct connectivity levels of the emerging proteins. We found that the most-connected group of proteins dates to the eukaryotic radiation, and the more ancient group of pre-eukaryotic proteins is less connected. We show that functional classes have different average connectivity levels and that the time of emergence of these functional classes parallels the observed connectivity variation in evolution. We take these findings as evidence that the evolution of function might be the reason for the differences in connectivity throughout evolutionary time. We propose that the understanding of the mechanisms that generate the scale-free protein interaction network, and possibly other biological networks, requires consideration of protein function.     

A transient homotypic interaction model for the influenza A virus NS1 protein effector domain

Influenza A virus NS1 protein is a multifunctional virulence factor consisting of an RNA binding domain (RBD), a short linker, an effector domain (ED), and a C-terminal 'tail'. Although poorly understood, NS1 multimerization may autoregulate its actions. While RBD dimerization seems functionally conserved, two possible apo ED dimers have been proposed (helix-helix and strand-strand). Here, we analyze all available RBD, ED, and full-length NS1 structures, including four novel crystal structures obtained using EDs from divergent human and avian viruses, as well as two forms of a monomeric ED mutant. The data reveal the helix-helix interface as the only strictly conserved ED homodimeric contact. Furthermore, a mutant NS1 unable to form the helix-helix dimer is compromised in its ability to bind dsRNA efficiently, implying that ED multimerization influences RBD activity. Our bioinformatical work also suggests that the helix-helix interface is variable and transient, thereby allowing two ED monomers to twist relative to one another and possibly separate. In this regard, we found a mAb that recognizes NS1 via a residue completely buried within the ED helix-helix interface, and which may help highlight potential different conformational populations of NS1 (putatively termed 'helix-closed' and 'helix-open') in virus-infected cells. 'Helix-closed' conformations appear to enhance dsRNA binding, and 'helix-open' conformations allow otherwise inaccessible interactions with host factors. Our data support a new model of NS1 regulation in which the RBD remains dimeric throughout infection, while the ED switches between several quaternary states in order to expand its functional space. Such a concept may be applicable to other small multifunctional proteins.