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Related Concept Videos

Structuralism01:26

Structuralism

Structuralism, an early psychological theory developed by Wilhelm Wundt and his student Edward Bradford Titchener, sought to dissect the human mind into its most fundamental components. Wundt's groundbreaking work in his laboratory set the stage for Titchener to define structuralism's goal as cataloging the "atoms" of the mind—sensations, images, and feelings—akin to how chemists identify elements of matter.
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Application of I TASSER, trRosetta, UCSF Chimera, HADDOCK server, and HEX loria for De Novo and In Silico Design of Proteins
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Toward a "structural BLAST": using structural relationships to infer function.

Fabian Dey1, Qiangfeng Cliff Zhang, Donald Petrey

  • 1Department of Biochemistry and Molecular Biophysics, Howard Hughes Medical Institute, Center for Computational Biology and Bioinformatics and Initiative in Systems Biology, Columbia University, New York, New York 10032, USA.

Protein Science : a Publication of the Protein Society
|January 26, 2013
PubMed
Summary
This summary is machine-generated.

We developed a "structural BLAST" method using homology modeling and machine learning to predict protein function from structure. This approach accurately infers protein-protein interactions and expands structural biology

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A Protocol for Computer-Based Protein Structure and Function Prediction
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A Protocol for Computer-Based Protein Structure and Function Prediction

Published on: November 3, 2011

Area of Science:

  • Structural biology
  • Bioinformatics
  • Computational biology

Background:

  • Inferring protein function from structure is crucial for understanding biological systems.
  • Existing methods often lack comprehensive coverage or accuracy in predicting protein function.
  • Integrating structural information with other biological data can enhance functional annotation.

Purpose of the Study:

  • To present a novel computational framework for inferring protein function directly from protein structure.
  • To develop and validate a structure-based algorithm for predicting protein-protein interactions.
  • To expand the utility of protein structure data in large-scale functional genomics and systems biology.

Main Methods:

  • Utilizing homology modeling to generate protein structure models.
  • Employing machine learning techniques to analyze global and local geometric relationships in protein structures.
  • Developing a "structural BLAST" approach for broad searches of protein structure space.
  • Implementing the PrePPI algorithm, which combines structure-derived data with non-structural evidence using Bayesian methods for interaction prediction.

Main Results:

  • The "structural BLAST" approach achieves high genomic coverage for function inference.
  • The PrePPI algorithm demonstrates accuracy comparable to high-throughput experiments for predicting protein-protein interactions.
  • Bayesian integration of structural and non-structural data improves the reliability of predicted interactions.

Conclusions:

  • Protein structure analysis, combined with machine learning and homology modeling, provides a powerful strategy for inferring protein function.
  • The developed methods significantly enhance the annotation of protein function, particularly in systems-level biological applications.
  • This structure-based approach offers a scalable and accurate means to expand our understanding of the proteome.