Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Videos

fRMSDPred: predicting local RMSD between structural fragments using sequence information.

Huzefa Rangwala1, George Karypis

  • 1Computer Science & Engineering, University of Minnesota, Minneapolis, MN 55455, USA. rangwala@cs.umn.edu

Computational Systems Bioinformatics. Computational Systems Bioinformatics Conference
|October 24, 2007
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

PropMolFlow: property-guided molecule generation with geometry-complete flow matching.

Nature computational science·2026
Same author

EGGNet, a Generalizable Geometric Deep Learning Framework for Protein Complex Pose Scoring.

ACS omega·2024
Same author

TraverseNet: Unifying Space and Time in Message Passing for Traffic Forecasting.

IEEE transactions on neural networks and learning systems·2022
Same author

A knowledge graph of clinical trials ([Formula: see text]).

Scientific reports·2022
Same author

Benchmarking Accuracy and Generalizability of Four Graph Neural Networks Using Large In Vitro ADME Datasets from Different Chemical Spaces.

Molecular informatics·2022
Same author

DGL-LifeSci: An Open-Source Toolkit for Deep Learning on Graphs in Life Science.

ACS omega·2021
Same journal

Novel Gene Discovery in the Human Malaria Parasite using Nucleosome Positioning Data.

Computational systems bioinformatics. Computational Systems Bioinformatics Conference·2014
Same journal

Proceedings of Computational Systems Bioinformatics 2008. August 26-29, 2008. Palo Alto, California, USA.

Computational systems bioinformatics. Computational Systems Bioinformatics Conference·2009
Same journal

Graph wavelet alignment kernels for drug virtual screening.

Computational systems bioinformatics. Computational Systems Bioinformatics Conference·2009
Same journal

Fast multisegment alignments for temporal expression profiles.

Computational systems bioinformatics. Computational Systems Bioinformatics Conference·2009
Same journal

Knowledge representation and data mining for biological imaging.

Computational systems bioinformatics. Computational Systems Bioinformatics Conference·2009
Same journal

Efficient haplotype inference from pedigrees with missing data using linear systems with disjoint-set data structures.

Computational systems bioinformatics. Computational Systems Bioinformatics Conference·2009
See all related articles

This study introduces machine learning methods to predict protein fragment similarities, improving protein structure alignment accuracy. These novel approaches enhance comparative modeling for more reliable protein structure prediction.

Area of Science:

  • Computational Biology
  • Bioinformatics
  • Machine Learning

Background:

  • Comparative modeling is crucial for protein structure prediction.
  • Accurate sequence-structure alignment is key to improving modeling effectiveness.
  • Current alignment methods can be enhanced by incorporating predicted structural information.

Purpose of the Study:

  • To develop machine learning approaches for estimating the Root Mean Square Deviation (RMSD) between protein fragments.
  • To utilize fragment-level RMSD predictions for constructing and assessing sequence-structure alignments.
  • To identify high-quality alignment segments for improved protein structure prediction.

Main Methods:

  • Supervised learning framework utilizing support vector regression and classification.

Related Experiment Videos

  • Incorporation of protein profiles and predicted secondary structure.
  • Development of effective information encoding schemes and novel second-order pairwise exponential kernel functions.

    • Main Results:

    • Algorithms effectively predict fragment-level RMSD values.
    • Predicted RMSD aids in alignment construction and quality assessment.
    • Demonstrated superior performance compared to traditional profile-to-profile scoring schemes.

    Conclusions:

    • Machine learning-based fragment-level RMSD prediction significantly enhances protein structure alignment.
    • The proposed methods offer a robust approach for improving comparative modeling.
    • This work provides a valuable tool for bioinformatics and computational biology research.