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Related Concept Videos

End Point Prediction: Gran Plot01:07

End Point Prediction: Gran Plot

A Gran plot is used to predict the equivalence volume or endpoint of a potentiometric or acid-base titration without reaching the endpoint. Typically, titration data is collected as a function of the titrant's volume up to a point less than the equivalence volume and then transformed into a linear format. The straight line is extended to the x-axis, indicating the necessary titrant volume to achieve the equivalence point.
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Orthogonal Trajectories01:26

Orthogonal Trajectories

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Time-Series Graph

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Velocity and Position by Graphical Method

Velocity and position can be calculated from the known function of acceleration as a function of time. The total area under the acceleration-time graph and the velocity-time graph gives the change in velocity and position, respectively. In the case of an airplane, its acceleration is tracked using the inertial navigation system. The pilot provides the input of the airplane's initial position and velocity before takeoff. The inertial navigation system then uses the acceleration data to calculate...
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Relative Motion Analysis using Rotating Axes

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Related Experiment Video

Updated: May 23, 2026

Trajectory Data Analyses for Pedestrian Space-time Activity Study
16:14

Trajectory Data Analyses for Pedestrian Space-time Activity Study

Published on: February 25, 2013

STGAT: A Novel Spatiotemporal Graph Attention Approach for Dynamical Trajectory Prediction.

Haowei Tong1, Ningjie Zhang1, Zhouyu Lu1

  • 1State Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic Developmental Sciences, Department of Bioinformatics and Biostatistics, National Experimental Teaching Center for Life Sciences and Biotechnology, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China.

Chemical Biology & Drug Design
|May 22, 2026
PubMed
Summary

We developed STGAT, a novel spatiotemporal graph attention network model, to accurately predict protein motion trajectories. This AI tool overcomes limitations of traditional molecular dynamics simulations for biological macromolecules.

Keywords:
deep learningmolecular dynamics simulationprotein trajectory predictionspatiotemporal graph attention networks

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Last Updated: May 23, 2026

Trajectory Data Analyses for Pedestrian Space-time Activity Study
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Published on: November 14, 2018

Area of Science:

  • Computational Biology
  • Biophysics
  • Structural Biology

Background:

  • Predicting complex biological macromolecule motion, especially proteins, is a significant challenge.
  • Traditional molecular dynamics (MD) simulations are computationally intensive and limited by short time scales.

Purpose of the Study:

  • To develop an accurate and efficient computational model for predicting protein dynamics trajectories.
  • To overcome the limitations of traditional MD simulations in terms of computational cost and time scale.

Main Methods:

  • Developed the STGAT (spatiotemporal graph attention networks) model.
  • Integrated protein locus timing analysis and graph attention mechanisms.
  • Validated the model on intrinsically disordered proteins (IDP) and structured proteins using RMSD, Laplace Diagram, Rg, and Cα chemical shift.

Main Results:

  • STGAT achieved low RMSD values (below 20 Å, <15 Å for shorter proteins).
  • Predicted protein dynamics showed similar distributions of dihedral angles (φ, ψ) and Rg values compared to MD simulations.
  • Predicted Cα chemical shifts closely matched experimental values.

Conclusions:

  • The STGAT model accurately predicts spatial characteristics and dynamic behavior of proteins, including IDPs.
  • The model demonstrates potential for extending predictions to longer time scales (0-80 ns).
  • STGAT offers a powerful new tool for real-time biomacromolecule dynamics prediction.