Prediction of protein-protein interactions between viruses and human by an SVM model

<p>Abstract</p> <p>Background</p> <p>Several computational methods have been developed to predict protein-protein interactions from amino acid sequences, but most of those methods are intended for the interactions within a species rather than for interactions across different species. Methods for predicting interactions between homogeneous proteins are not appropriate for finding those between heterogeneous proteins since they do not distinguish the interactions between proteins of the same species from those of different species.</p> <p>Results</p> <p>We developed a new method for representing a protein sequence of variable length in a frequency vector of fixed length, which encodes the relative frequency of three consecutive amino acids of a sequence. We built a support vector machine (SVM) model to predict human proteins that interact with virus proteins. In two types of viruses, human papillomaviruses (HPV) and hepatitis C virus (HCV), our SVM model achieved an average accuracy above 80%, which is higher than that of another SVM model with a different representation scheme. Using the SVM model and Gene Ontology (GO) annotations of proteins, we predicted new interactions between virus proteins and human proteins.</p> <p>Conclusions</p> <p>Encoding the relative frequency of amino acid triplets of a protein sequence is a simple yet powerful representation method for predicting protein-protein interactions across different species. The representation method has several advantages: (1) it enables a prediction model to achieve a better performance than other representations, (2) it generates feature vectors of fixed length regardless of the sequence length, and (3) the same representation is applicable to different types of proteins.</p

FOXO1 Regulates Bacteria-Induced Neutrophil Activity

Neutrophils play an essential role in the innate immune response to microbial infection and are particularly important in clearing bacterial infection. We investigated the role of the transcription factor FOXO1 in the response of neutrophils to bacterial challenge with Porphyromonas gingivalis in vivo and in vitro. In these experiments, the effect of lineage-specific FOXO1 deletion in LyzM.Cre+FOXO1L/L mice was compared with matched littermate controls. FOXO1 deletion negatively affected several critical aspects of neutrophil function in vivo including mobilization of neutrophils from the bone marrow (BM) to the vasculature, recruitment of neutrophils to sites of bacterial inoculation, and clearance of bacteria. In vitro FOXO1 regulated neutrophil chemotaxis and bacterial killing. Moreover, bacteria-induced expression of CXCR2 and CD11b, which are essential for several aspects of neutrophil function, was dependent on FOXO1 in vivo and in vitro. Furthermore, FOXO1 directly interacted with the promoter regions of CXCR2 and CD11b. Bacteria-induced nuclear localization of FOXO1 was dependent upon toll-like receptor (TLR) 2 and/or TLR4 and was significantly reduced by inhibitors of reactive oxygen species (ROS and nitric oxide synthase) and deacetylases (Sirt1 and histone deacetylases). These studies show for the first time that FOXO1 activation by bacterial challenge is needed to mobilize neutrophils to transit from the BM to peripheral tissues in response to infection as well as for bacterial clearance in vivo. Moreover, FOXO1 regulates neutrophil function that facilitates chemotaxis, phagocytosis, and bacterial killing

Biological learning in key-value memory networks

In neuroscience, classical Hopfield networks are the standard biologically plausible model of long-term memory, relying on Hebbian plasticity for storage and attractor dynamics for recall. In contrast, memory-augmented neural networks in machine learning commonly use a key-value mechanism to store and read out memories in a single step. Such augmented networks achieve impressive feats of memory compared to traditional variants, yet it remains unclear whether they can be implemented by biological systems. In our work, we bridge this gap by proposing a set of of biologically plausible three-factor plasticity rules for a basic feedforward key-value memory network. Keys are stored in the input-to-hidden synaptic weights by a "non-Hebbian" rule, controlled only by pre-synaptic activity, and modulated by local third factors which represent dendtritic spikes. Values are stored in the hidden-to-output weights by a Hebbian rule, with the pre-synaptic neuron selected through softmax attention which represents recurrent inhibition. The same rules are recovered when network parameters are meta-learned. Our network performs on par with classical Hopfield networks on autoassociative memory tasks and can be naturally extended to correlated inputs, continual recall, heteroassociative memory, and sequence learning. Importantly, since memories are stored in slots indexed by hidden layer neurons, unlike the fully distributed representation in the classical Hopfield network, they can be individually selected for extended storage or rapid decay. Finally, our memory network can easily be incorporated into a larger neural system, either as a memory bank for an external controller, or as a fast learning system used in conjunction with a slow one. Overall, our results suggest a compelling alternative to the classical Hopfield network as a model of biological long-term memory. Keywords: learning, memory, synaptic plasticity, Hebbian, key-value memory, neural network, three-factor plasticit

Synthesis of a series of novel 3,9-disubstituted phenanthrenes as analogues of known <i>N</i>-methyl-D-aspartate receptor allosteric modulators

9-Substituted phenanthrene-3-carboxylic acids have been reported to have allosteric modulatory activity at the NMDA receptor. This receptor is activated by the excitatory neurotransmitter L-glutamate and has been implicated in a range of neurological disorders such as schizophrenia, epilepsy and chronic pain and neurodegenerative disorders such as Alzheimer’s disease. Herein, the convenient synthesis of a wide range of novel 3,9-disubstituted phenanthrene derivatives starting from a few common intermediates is described. These new phenanthrene derivatives will help to clarify the structural requirements for allosteric modulation of the NMDA receptor

A single-channel mechanism for pharmacological potentiation of GluN1/GluN2A NMDA receptors

AbstractNMDA receptors (NMDARs) contribute to several neuropathological processes. Novel positive allosteric modulators (PAMs) of NMDARs have recently been identified but their effects on NMDAR gating remain largely unknown. To this end, we tested the effect of a newly developed molecule UBP684 on GluN1/GluN2A receptors. We found that UBP684 potentiated the whole-cell currents observed under perforated-patch conditions and slowed receptor deactivation. At the single channel level, UBP684 produced a dramatic reduction in long shut times and a robust increase in mean open time. These changes were similar to those produced by NMDAR mutants in which the ligand-binding domains (LBDs) are locked in the closed clamshell conformation by incorporating a disulfide bridge. Since the locked glutamate-binding clefts primarily contributes to receptor efficacy these results suggests that UBP684 binding may induce switch in conformation similar to glutamate LBD locked state. Consistent with this prediction UBP684 displayed greater potentiation of NMDARs with only the GluN1 LBD locked compared to NMDARs with only the GluN2 LBD locked. Docking studies suggest that UBP684 binds to the GluN1 and GluN2 LBD interface supporting its potential ability in stabilizing the LBD closed conformation. Together these studies identify a novel pharmacological mechanism of facilitating the function of NMDARs.</jats:p