Protein Secondary Structure Prediction Using Cascaded Convolutional and Recurrent Neural Networks

Protein secondary structure prediction is an important problem in bioinformatics. Inspired by the recent successes of deep neural networks, in this paper, we propose an end-to-end deep network that predicts protein secondary structures from integrated local and global contextual features. Our deep architecture leverages convolutional neural networks with different kernel sizes to extract multiscale local contextual features. In addition, considering long-range dependencies existing in amino acid sequences, we set up a bidirectional neural network consisting of gated recurrent unit to capture global contextual features. Furthermore, multi-task learning is utilized to predict secondary structure labels and amino-acid solvent accessibility simultaneously. Our proposed deep network demonstrates its effectiveness by achieving state-of-the-art performance, i.e., 69.7% Q8 accuracy on the public benchmark CB513, 76.9% Q8 accuracy on CASP10 and 73.1% Q8 accuracy on CASP11. Our model and results are publicly available.Comment: 8 pages, 3 figures, Accepted by International Joint Conferences on Artificial Intelligence (IJCAI

Leveraging Machine Learning Models for Peptide-Protein Interaction Prediction

Peptides play a pivotal role in a wide range of biological activities through participating in up to 40% protein-protein interactions in cellular processes. They also demonstrate remarkable specificity and efficacy, making them promising candidates for drug development. However, predicting peptide-protein complexes by traditional computational approaches, such as Docking and Molecular Dynamics simulations, still remains a challenge due to high computational cost, flexible nature of peptides, and limited structural information of peptide-protein complexes. In recent years, the surge of available biological data has given rise to the development of an increasing number of machine learning models for predicting peptide-protein interactions. These models offer efficient solutions to address the challenges associated with traditional computational approaches. Furthermore, they offer enhanced accuracy, robustness, and interpretability in their predictive outcomes. This review presents a comprehensive overview of machine learning and deep learning models that have emerged in recent years for the prediction of peptide-protein interactions.Comment: 46 pages, 10 figure

Laminar-specific cortico-cortical loops in mouse visual cortex

"Muitas teorias propõem interacções recorrentes através da hierarquia cortical, mas não é claro se os circuitos corticais são selectivamente ligados para implementar cálculos em ciclo. Usando o mapeamento de circuitos subcelulares do método de canal de rodopsina 2 assistido no córtex visual do rato, comparamos a entrada sináptica de alimentação direta (feedforward, FF) ou retroalimentação (feedback, FB) cortico-cortical (CC) às células que se projectam de volta à fonte de entrada (neurónios em ciclo) com células que se projectam para uma área cortical ou subcortical diferente.(...)

Synaptic Plasticity and Hebbian Cell Assemblies

Synaptic dynamics are critical to the function of neuronal circuits on multiple timescales. In the first part of this dissertation, I tested the roles of action potential timing and NMDA receptor composition in long-term modifications to synaptic efficacy. In a computational model I showed that the dynamics of the postsynaptic [Ca2+] time course can be used to map the timing of pre- and postsynaptic action potentials onto experimentally observed changes in synaptic strength. Using dual patch-clamp recordings from cultured hippocampal neurons, I found that NMDAR subtypes can map combinations of pre- and postsynaptic action potentials onto either long-term potentiation (LTP) or depression (LTD). LTP and LTD could even be evoked by the same stimuli, and in such cases the plasticity outcome was determined by the availability of NMDAR subtypes. The expression of LTD was increasingly presynaptic as synaptic connections became more developed. Finally, I found that spike-timing-dependent potentiability is history-dependent, with a non-linear relationship to the number of pre- and postsynaptic action potentials. After LTP induction, subsequent potentiability recovered on a timescale of minutes, and was dependent on the duration of the previous induction. While activity-dependent plasticity is putatively involved in circuit development, I found that it was not required to produce small networks capable of exhibiting rhythmic persistent activity patterns called reverberations. However, positive synaptic scaling produced by network inactivity yielded increased quantal synaptic amplitudes, connectivity, and potentiability, all favoring reverberation. These data suggest that chronic inactivity upregulates synaptic efficacy by both quantal amplification and by the addition of silent synapses, the latter of which are rapidly activated by reverberation. Reverberation in previously inactivated networks also resulted in activity-dependent outbreaks of spontaneous network activity. Applying a model of short-term synaptic dynamics to the network level, I argue that these experimental observations can be explained by the interaction between presynaptic calcium dynamics and short-term synaptic depression on multiple timescales. Together, the experiments and modeling indicate that ongoing activity, synaptic scaling and metaplasticity are required to endow networks with a level of synaptic connectivity and potentiability that supports stimulus-evoked persistent activity patterns but avoids spontaneous activity

Finding the pathology of major depression through effects on gene interaction networks

The disease signature of major depressive disorder is distributed across multiple physical scales and investigative specialties, including genes, cells and brain regions. No single mechanism or pathway currently implicated in depression can reproduce its diverse clinical presentation, which compounds the difficulty in finding consistently disrupted molecular functions. We confront these key roadblocks to depression research - multi-scale and multi-factor pathology - by conducting parallel investigations at the levels of genes, neurons and brain regions, using transcriptome networks to identify collective patterns of dysfunction. Our findings highlight how the collusion of multi-system deficits can form a broad-based, yet variable pathology behind the depressed phenotype. For instance, in a variant of the classic lethality-centrality relationship, we show that in neuropsychiatric disorders including major depression, differentially expressed genes are pushed out to the periphery of gene networks. At the level of cellular function, we develop a molecular signature of depression based on cross-species analysis of human and mouse microarrays from depression-affected areas, and show that these genes form a tight module related to oligodendrocyte function and neuronal growth/structure. At the level of brain-region communication, we find a set of genes and hormones associated with the loss of feedback between the amygdala and anterior cingulate cortex, based on a novel assay of interregional expression synchronization termed "gene coordination". These results indicate that in the absence of a single pathology, depression may be created by dysynergistic effects among genes, cell-types and brain regions, in what we term the "floodgate" model of depression. Beyond our specific biological findings, these studies indicate that gene interaction networks are a coherent framework in which to understand the faint expression changes found in depression and complex neuropsychiatric disorders

Local microcircuitry of PaS shows distinct and common features of excitatory and inhibitory connectivity

The parasubiculum (PaS) is located within the parahippocampal region, where it is thought to be involved in the processing of spatial navigational information. It contains a number of functionally specialized neuron types including grid cells, head direction cells, and border cells; and provides input into layer 2 of the medial entorhinal cortex where grid cells are abundantly located. The local circuitry within the PaS remains so far undefined but may provide clues as to the emergence of spatially tuned firing properties of neurons in this region. We used simultaneous patch-clamp recordings to determine the connectivity rates between the 3 major groups of neurons found in the PaS. We find high rates of interconnectivity between the pyramidal class and interneurons, as well as features of pyramid-to-pyramid interactions indicative of a nonrandom network. The microcircuit that we uncover shares both similarities and divergences to those from other parahippocampal regions also involved in spatial navigation

A series of PDB related databases for everyday needs

The Protein Data Bank (PDB) is the world-wide repository of macromolecular structure information. We present a series of databases that run parallel to the PDB. Each database holds one entry, if possible, for each PDB entry. DSSP holds the secondary structure of the proteins. PDBREPORT holds reports on the structure quality and lists errors. HSSP holds a multiple sequence alignment for all proteins. The PDBFINDER holds easy to parse summaries of the PDB file content, augmented with essentials from the other systems. PDB_REDO holds re-refined, and often improved, copies of all structures solved by X-ray. WHY_NOT summarizes why certain files could not be produced. All these systems are updated weekly. The data sets can be used for the analysis of properties of protein structures in areas ranging from structural genomics, to cancer biology and protein design

Predicting Function and Structure using Bioinformatics Protocols:Study of the Intracellular Regions of the Jagged and Delta Protein Families

The type I membrane-spanning proteins Jagged (Jagged-i and -2) and Delta (Delta-l, - 3 and -4) are the human ligands of Notch receptors, which mediate key signaling events in cell differentiation and morphogenesis. The Jagged and Delta proteins are composed of a relatively large extracellular region and of a 100-150 residue, yet uncharacterized cytoplasmic tail, which has been recently found to be important in Notch bi-directional signaling. We applied bioinformatics methods to analyze the intracellular region of human Notch ligands, and to predict their structural and functional properties. We searched databases for orthologues, and found that while the intracellular region is evolutionaiy well conserved within the same ligand type, a wide variability is observed in different ligands. No significant similarity was found between the intracellular region of Jagged and Delta and proteins of known 3D structure. Globularity and disorder predictions indeed suggest that these regions are largely unstructured. However, secondary structure predictions show that these regions have some propensity to form local secondary structure elements. Functional predictions based on pattern recognition imply that the specificity in the Notch machinery response might be related to specific post-translational modifications and binding motifs in the ligand cytoplasmic tail, rather than to specific interactions between the receptors and the extracellular region of the ligands. We also speculate that, given the unusual amino acid composition, the cytoplasmic tail of Jagged and Delta might be involved in zinc binding

Dopaminergic Modulation Shapes Sensorimotor Processing in the Drosophila Mushroom Body

To survive in a complex and dynamic environment, animals must adapt their behavior based on their current needs and prior experiences. This flexibility is often mediated by neuromodulation within neural circuits that link sensory representations to alternative behavioral responses depending on contextual cues and learned associations. In Drosophila, the mushroom body is a prominent neural structure essential for olfactory learning. Dopaminergic neurons convey salient information about reward and punishment to the mushroom body in order to adjust synaptic connectivity between Kenyon cells, the neurons representing olfactory stimuli, and the mushroom body output neurons that ultimately influence behavior. However, we still lack a mechanistic understanding of how the dopaminergic neurons represent the moment-tomoment experience of a fly and drive changes in this sensory-to-motor transformation. Furthermore, very little is known about how the output neuron pathways lead to the execution of appropriate odor-related behaviors. We took advantage of the mushroom body’s modular circuit organization to investigate how the dopaminergic neuron population encodes different contextual cues. In vivo functional imaging of the dopaminergic neurons reveals that they represent both external reinforcement stimuli, like sugar rewards or punitive electric shock, as well as the fly’s motor state, through coordinated and partially antagonistic activity patterns across the population. This multiplexing of motor and reward signals by the dopaminergic neurons parallels the dual roles of dopaminergic inputs to the vertebrate basal ganglia, thus demonstrating a conserved link between these distantly related neural circuits. We proceed to demonstrate that this dopaminergic signal in the mushroom body modifies neurotransmission with synaptic specificity and temporal precision to coordinately regulate the propagation of sensory signals through the output neurons. To explore how these output pathways ultimately influence olfactory navigation we have developed a closed loop olfactory paradigm in which we can monitor and manipulate the mushroom body output neurons as a fly navigates in a virtual olfactory environment. We have begun to probe the mushroom body circuitry in the context of olfactory navigation. These preliminary investigations have led to the identification of putative pathways for linking mushroom body output with the circuits that implement odor-tracking behavior and the characterization of the complex sensorimotor representations in the dopaminergic network. Our work reveals that the Drosophila dopaminergic system modulates mushroom body output at both acute and enduring timescales to guide immediate behaviors and learned responses