Optimal connectivity results for spheres in the curve graph of low and medium complexity surfaces

Answering a question of Wright, we show that spheres of any radius are always connected in the curve graph of surfaces $\Sigma_{2,0}, \Sigma_{1,3},$ and $\Sigma_{0,6}$, and the union of two consecutive spheres is always connected for $\Sigma_{0, 5}$ and $\Sigma_{1,2}$. We also classify the connected components of spheres of radius 2 in the curve graph of $\Sigma_{0, 5}$ and $\Sigma_{1,2}$

Preferential binding of allosteric modulators to active and inactive conformational states of metabotropic glutamate receptors

Metabotropic glutamate receptors (mGluRs) are G protein coupled receptors that play important roles in synaptic plasticity and other neuro-physiological and pathological processes. Allosteric mGluR ligands are particularly promising drug targets because of their modulatory effects – enhancing or suppressing the response of mGluRs to glutamate. The mechanism by which this modulation occurs is not known. Here, we propose the hypothesis that positive and negative modulators will differentially stabilize the active and inactive conformations of the receptors, respectively. To test this hypothesis, we have generated computational models of the transmembrane regions of different mGluR subtypes in two different conformations. The inactive conformation was modeled using the crystal structure of the inactive, dark state of rhodopsin as template and the active conformation was created based on a recent model of the light-activated state of rhodopsin. Ligands for which the nature of their allosteric effects on mGluRs is experimentally known were docked to the modeled mGluR structures using ArgusLab and Autodock softwares. We find that the allosteric ligand binding pockets of mGluRs are overlapping with the retinal binding pocket of rhodopsin, and that ligands have strong preferences for the active and inactive states depending on their modulatory nature. In 8 out of 14 cases (57%), the negative modulators bound the inactive conformations with significant preference using both docking programs, and 6 out of 9 cases (67%), the positive modulators bound the active conformations. Considering results by the individual programs only, even higher correlations were observed: 12/14 (86%) and 8/9 (89%) for ArgusLab and 10/14 (71%) and 7/9 (78%) for AutoDock. These findings strongly support the hypothesis that mGluR allosteric modulation occurs via stabilization of different conformations analogous to those identified in rhodopsin where they are induced by photochemical isomerization of the retinal ligand – despite the extensive differences in sequences between mGluRs and rhodopsin

A mixture of feature experts approach for protein-protein interaction prediction

High-throughput methods can directly detect the set of interacting proteins in yeast but the results are often incomplete and exhibit high false positive and false negative rates. A number of researchers have recently presented methods for integrating direct and indirect data for predicting interactions. However, due to missing data and the high redundancy among the features used, different samples may benefit from different features based on the set of attributes available. In addition, in many cases it is hard to directly determine which of the datasets led to the prediction, which is an important issue for the biologists using these predications to design new experiments. To address these challenges we use a Mixture-of-Experts method. We split the data into four (roughly) homogeneous sets. The individual experts use logistic regression and their scores are combined using another logistic regression. However, when combining the scores the weighting of each expert depends on the set of input attributes. Thus different experts will have different influence on the prediction depending on the available features. We applied our method to predict the set of interacting proteins in yeast. Our method improved upon the best previous methods for this task. In addition, using the weighting of the experts the prediction can be easily evaluated by biologists based on the features that they feel are the most reliable.

Bioinformatics of Corals: Investigating Heterogeneous Omics Data from Coral Holobionts for Insight into Reef Health and Resillience

Coral reefs are home to over 2 million species and provide habitat for roughly 25% of all marine animals, but they are being severely threatened by pollution and climate change. A large amount of genomic, transcriptomic and other -omics data from different species of reef building corals, the uni-cellular dinoagellates, plus the coral microbiome (where corals have possibly the most complex microbiome yet discovered, consisting of over 20,000 different species), is becoming increasingly available for corals. This new data present an opportunity for bioinformatics researchers and computational biologists to contribute to a timely, compelling, and urgent investigation of critical factors that influence reef health and resilience. This paper summarizes the content of the Bioinformatics of Corals workshop, that is being held as part of PSB 2021. It is particularly relevant for this workshop to occur at PSB, given the abundance of and reliance on coral reefs in Hawaii and the conference’s traditional association with the region

Transmembrane helix prediction using amino acid property features and latent semantic analysis

Prediction of transmembrane (TM) helices by statistical methods suffers from lack of sufficient training data. Current best methods use hundreds or even thousands of free parameters in their models which are tuned to fit the little data available for training. Further, they are often restricted to the generally accepted topology "cytoplasmic-transmembrane-extracellular" and cannot adapt to membrane proteins that do not conform to this topology. Recent crystal structures of channel proteins have revealed novel architectures showing that the above topology may not be as universal as previously believed. Thus, there is a need for methods that can better predict TM helices even in novel topologies and families

Vibrational resonance, allostery, and activation in rhodopsin-like G protein-coupled receptors

G protein-coupled receptors are a large family of membrane proteins activated by a variety of structurally diverse ligands making them highly adaptable signaling molecules. Despite recent advances in the structural biology of this protein family, the mechanism by which ligands induce allosteric changes in protein structure and dynamics for its signaling function remains a mystery. Here, we propose the use of terahertz spectroscopy combined with molecular dynamics simulation and protein evolutionary network modeling to address the mechanism of activation by directly probing the concerted fluctuations of retinal ligand and transmembrane helices in rhodopsin. This approach allows us to examine the role of conformational heterogeneity in the selection and stabilization of specific signaling pathways in the photo-activation of the receptor. We demonstrate that ligand-induced shifts in the conformational equilibrium prompt vibrational resonances in the protein structure that link the dynamics of conserved interactions with fluctuations of the active-state ligand. The connection of vibrational modes creates an allosteric association of coupled fluctuations that forms a coherent signaling pathway from the receptor ligand-binding pocket to the G-protein activation region. Our evolutionary analysis of rhodopsin-like GPCRs suggest that specific allosteric sites play a pivotal role in activating structural fluctuations that allosterically modulate functional signals

Characterisation of denatured states of sensory rhodopsin II by solution-state NMR.

Sensory rhodopsin II (pSRII), a retinal-binding photophobic receptor from Natronomonas pharaonis, is a novel model system for membrane protein folding studies. Recently, the SDS-denatured states and the kinetics for reversible unfolding of pSRII have been investigated, opening the door to the first detailed characterisation of denatured states of a membrane protein by solution-state nuclear magnetic resonance (NMR) using uniformly 15N-labelled pSRII. SDS denaturation and acid denaturation of pSRII both lead to fraying of helix ends but otherwise small structural changes in the transmembrane domain, consistent with little changes in secondary structure and disruption of the retinal-binding pocket and tertiary structure. Widespread changes in the backbone amide dynamics are detected in the form of line broadening, indicative of μs-to-ms timescale conformational exchange in the transmembrane region. Detailed analysis of chemical shift and intensity changes lead to high-resolution molecular insights on structural and dynamics changes in SDS- and acid-denatured pSRII, thus highlighting differences in the unfolding pathways under the two different denaturing conditions. These results will form the foundation for furthering our understanding on the folding and unfolding pathways of retinal-binding proteins and membrane proteins in general, and also for investigating the importance of ligand-binding in the folding pathways of other ligand-binding membrane proteins, such as GPCRs

Characterization of Denatured States and Reversible Unfolding of Sensory Rhodopsin II.

Our understanding on the folding of membrane proteins lags behind that of soluble proteins due to challenges posed by the exposure of hydrophobic regions during in vitro chemical denaturation and refolding experiments. While different folding models are accepted for soluble proteins, only the two-stage model and the long-range interactions model have been proposed so far for helical membrane proteins. To address our knowledge gap on how different membrane proteins traverse their folding pathways, we have systematically investigated the structural features of SDS-denatured states and the kinetics for reversible unfolding of sensory rhodopsin II (pSRII), a retinal-binding photophobic receptor from Natronomonas pharaonis. pSRII is difficult to denature, and only SDS can dislodge the retinal chromophore without rapid aggregation. Even in 30% SDS (0.998 ΧSDS), pSRII retains the equivalent of six out of seven transmembrane helices, while the retinal-binding pocket is disrupted, with transmembrane residues becoming more solvent exposed. Folding of pSRII from an SDS-denatured state harboring a covalently bound retinal chromophore shows deviations from an apparent two-state behavior. SDS denaturation to form the sensory opsin apo-protein is reversible. We report pSRII as a new model protein which is suitable for membrane protein folding studies and has a unique folding mechanism that differs from those of bacteriorhodopsin and bovine rhodopsin.BBSR