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Related Concept Videos

Protein Organization01:24

Protein Organization

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Proteins are polymers of amino acid residues. They are versatile and responsible for different cellular functions, including DNA replication, molecular transport, catalysis, and structural support. Proteins have a hierarchical structure comprising at least three levels of organization: primary, secondary, and tertiary structure. Some large proteins have a quaternary structure where individual protein subunits are linked together.
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Protein Organization01:13

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Protein and Protein Structure02:15

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Proteins are one of the most abundant organic molecules in living systems and have the most diverse range of functions of all macromolecules. Proteins may be structural, regulatory, contractile, or protective. They may serve in transport, storage, or membranes; or they may be toxins or enzymes. Their structures, like their functions, vary greatly. They are all, however, amino acid polymers arranged in a linear sequence.
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Protein Folding01:25

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Proteins are chains of amino acids linked together by peptide bonds. Upon synthesis, a protein folds into a three-dimensional conformation, critical to its biological function. Interactions between its constituent amino acids guide protein folding, and hence the protein structure is primarily dependent on its amino acid sequence.
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Updated: Dec 24, 2025

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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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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Experimentally-driven protein structure modeling.

Nikolay V Dokholyan1

  • 1Department of Pharmacology, Penn State University College of Medicine, Hershey, PA 17033, USA; Department of Biochemistry & Molecular Biology, Penn State College of Medicine, Hershey, PA 17033, USA.; Department of Chemistry, Pennsylvania State University, University Park, PA 16802, USA.; Department of Biomedical Engineering, Pennsylvania State University, University Park, PA 16802, USA.

Journal of Proteomics
|April 9, 2020
PubMed
Summary
This summary is machine-generated.

Experimentally-driven computational modeling reveals protein structures and dynamics. This hybrid approach integrates diverse experimental data for a comprehensive molecular view, overcoming limitations of traditional methods.

Keywords:
Discrete molecular dynamicsMass spectrometryMolecular dynamics simulationsMolecular modelingStatistical mechanicsStructural proteomics

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Area of Science:

  • Structural Biology
  • Computational Chemistry
  • Biophysics

Background:

  • Modern science utilizes theoretical, experimental, and computational methods to study molecular structures and dynamics.
  • Experimental techniques provide data at specific time and length scales, requiring tailored computational approaches.
  • Integrating multi-scale experimental data offers a comprehensive understanding of molecular structure and motion.

Purpose of the Study:

  • To describe computational approaches for utilizing experimental data to determine protein structure and dynamics.
  • To review the limitations and resolution of constraints-based modeling.
  • To highlight the significance of experimentally-driven computational modeling as an alternative to traditional methods.

Main Methods:

  • Utilizing experimental data within computational frameworks.
  • Tailoring computational methods to match experimental time and length scales.
  • Integrating data from various experiments for a holistic molecular picture.

Main Results:

  • Development of computational approaches to analyze experimental data for protein structure and dynamics.
  • Identification of limitations and resolution constraints in modeling.
  • Demonstration of the power of hybrid experimental-computational methods.

Conclusions:

  • Experimentally-driven computational modeling is a rapidly advancing field.
  • Hybrid approaches provide powerful insights into disordered proteins and molecular dynamics.
  • This review outlines various methods in experimentally-driven computational structure modeling.