DDMF: A Method for Mining Relatively Important Nodes Based on Distance Distribution and Multi-Index Fusion

In research on complex networks, mining relatively important nodes is a challenging and practical work. However, little research has been done on mining relatively important nodes in complex networks, and the existing relatively important node mining algorithms cannot take into account the indicators of both precision and applicability. Aiming at the scarcity of relatively important node mining algorithms and the limitations of existing algorithms, this paper proposes a relatively important node mining method based on distance distribution and multi-index fusion (DDMF). First, the distance distribution of each node is generated according to the shortest path between nodes in the network; then, the cosine similarity, Euclidean distance and relative entropy are fused, and the entropy weight method is used to calculate the weights of different indexes; Finally, by calculating the relative importance score of nodes in the network, the relatively important nodes are mined. Through verification and analysis on real network datasets in different fields, the results show that the DDMF method outperforms other relatively important node mining algorithms in precision, recall, and AUC value.

DEER Spectroscopy of Channelrhodopsin-2 Helix B Movements in Trapped Photocycle Intermediates

The light-gated dimeric cation channel channelrhodopsin-2 (ChR2) has been established as one of the most important optogenetic tools. During its functional cycle, ChR2 undergoes conformational changes, the most prominent ones include a movement of transmembrane helix B. In the present work, we assign this movement to a trapped photocycle intermediate using DEER spectroscopy combined with sample illumination inside the microwave resonator, allowing trapping and relaxation of defined ChR2 intermediates at different temperatures between 180 and 278 K. Intradimer distances measured between spin-labeled positions 79 located in helix B of ChR2 in the dark state and upon light activation and relaxation at 180 K were similar. In contrast, light activation at 180 K and 30 min relaxation at between 230 and 255 K results in significant changes of the distance distribution. We show that the light-induced movement of helix B is correlated with the presence of the P480 state of ChR2. We hypothesize that conformational changes occurring in this area are key elements responsible for desensitizing the channel for cation conduction.

On Time-Dependent Trip Distance Distribution with For-Hire Vehicle Trips in Chicago

For transportation system analysis in a new space dimension with respect to individual trips’ remaining distances, vehicle trips demand has two main components: the departure time and the trip distance. In particular, the trip distance distribution (TDD) is a direct input to the bathtub model in the new space dimension, and is a very important variable to consider in many applications, such as the development of distance-based congestion pricing strategies or mileage tax. For a good understanding of the demand pattern, both the distribution of trip initiation and trip distance should be calibrated from real data. In this paper, it is assumed that the demand pattern can be described by the joint distribution of trip distance and departure time. In other words, TDD is assumed to be time-dependent, and a calibration and validation methodology of the joint probability is proposed, based on log-likelihood maximization and the Kolmogorov–Smirnov test. The calibration method is applied to empirical for-hire vehicle trips in Chicago, and it is concluded that TDD varies more within a day than across weekdays. The hypothesis that TDD follows a negative exponential, log-normal, or Gamma distribution is rejected. However, the best fit is systematically observed for the time-dependent log-normal probability density function. In the future, other trip distributions should be considered and also non-parametric probability density estimation should be explored for a better understanding of the demand pattern.

Geodesic-based distance reveals non-linear topological features in neural activity from mouse visual cortex

An increasingly popular approach to the analysis of neural data is to treat activity patterns as being constrained to and sampled from a manifold, which can be characterized by its topology. The persistent homology method identifies the type and number of holes in the manifold thereby yielding functional information about the coding and dynamic properties of the underlying neural network. In this work we give examples of highly non-linear manifolds in which the persistent homology algorithm fails when it uses the Euclidean distance which does not always yield a good approximation of the true distance distribution of a point cloud sampled from a manifold. To deal with this issue we propose a simple strategy for the estimation of the geodesic distance which is a better approximation of the true distance distribution and can be used to successfully identify highly non-linear features with persistent homology. To document the utility of our method we model a circular manifold, based on orthogonal sinusoidal basis functions and compare how the chosen metric determines the performance of the persistent homology algorithm. Furthermore we discuss the robustness of our method across different manifold properties and point out strategies for interpreting its results as well as some possible pitfalls of its application. Finally we apply this analysis to neural data coming from the Visual Coding - Neuropixels dataset recorded in mouse visual cortex after stimulation with drifting gratings at the Allen Institute. We find that different manifolds with a non-trivial topology can be seen across regions and stimulus properties. Finally, we discuss what these manifolds say about visual computation and how they depend on stimulus parameters.

Correlation of N-glycan dynamics and interaction network with allosteric antigen binding and Fc receptor recognition

Aim: Fragment crystallizable (Fc) glycans modulate Fc conformations and functions, and glycan may also regulate antigen recognition. In the antibody drug development, glycosylation patterns affect antibody drug characteristics and quality control. In order to provide a global feature of N-glycan interactions in response to antigen and Fc receptor bindings, the interactions among Fc N-glycans and N-glycans’ interaction with Fc CH2 and CH3 domains have been studied. Methods: Molecular dynamics simulations were used to generate conformation ensembles of free antibody, antibody-antigen complex, antibody-human Fc-gamma-receptor-I (hFcγRI) and antibody-antigen-hFcγRI, the hydrogen bonds and radial distance distribution involving N-glycans carbohydrate chains have been analyzed. Results: Two important interaction patterns have been observed. The first is the strong but non-specific interactions between two carbohydrate chains in free antibody. Secondly, it has been found that N-glycans carbohydrate chains can directly interact with CH3 domain in free antibody, and that the distance distribution between carbohydrate chains and CH3 domain clearly differentiate the free antibody, antibody-antigen complex, antibody-hFcγRI complex, and final antibody-antigen-hFcγRI complex. Conclusions: N-glycans partially acts as allosteric sensor and respond to antigen and hFcγRI binding.

Characterization of Weak Protein Domain Structure by Spin-Label Distance Distributions

Function of intrinsically disordered proteins may depend on deviation of their conformational ensemble from that of a random coil. Such deviation may be hard to characterize and quantify, if it is weak. We explored the potential of distance distributions between spin labels, as they can be measured by electron paramagnetic resonance techniques, for aiding such characterization. On the example of the intrinsically disordered N-terminal domain 1–267 of fused in sarcoma (FUS) we examined what such distance distributions can and cannot reveal on the random-coil reference state. On the example of the glycine-rich domain 188–320 of heterogeneous nuclear ribonucleoprotein A1 (hnRNP A1) we studied whether deviation from a random-coil ensemble can be robustly detected with 19 distance distribution restraints. We discuss limitations imposed by ill-posedness of the conversion of primary data to distance distributions and propose overlap of distance distributions as a fit criterion that can tackle this problem. For testing consistency and size sufficiency of the restraint set, we propose jack-knife resampling. At current desktop computers, our approach is expected to be viable for domains up to 150 residues and for between 10 and 50 distance distribution restraints.