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Related Concept Videos

Conservation of Protein Domains Over Different Proteins02:26

Conservation of Protein Domains Over Different Proteins

Protein domains are small structurally independent units that are part of a single amino acid chain.  Although these domains are often structurally independent, they may rely on synergistic effects to perform their functions as part of a larger protein. Protein domains may be conserved within the same organism, as well as across different organisms.
A limited set of protein domains often duplicate and recombine during evolution. These domains can be organized in different combinations to form...
Intrinsically Disordered Proteins02:18

Intrinsically Disordered Proteins

Intrinsically disordered proteins are a group of proteins that do not fold into specific three-dimensional structures. Their structural flexibility allows them to complement ordered proteins to perform functions that are inaccessible to rigid structures. They are more common in eukaryotes than prokaryotes and may either be exclusively intrinsically disordered or hybrid proteins, consisting of a mix of ordered and disordered regions. The absence of a rigid structure in these proteins can be...
Protein Organization01:24

Protein Organization

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.
The primary structure of a protein is its amino acid sequence.
Conservation of Protein Domains02:26

Conservation of Protein Domains

Protein domains are small structurally independent units that are part of a single amino acid chain.  Although these domains are often structurally independent, they may rely on synergistic effects to perform their functions as part of a larger protein. Protein domains may be conserved within the same organism, as well as across different organisms.
A limited set of protein domains often duplicate and recombine during evolution. These domains can be organized in different combinations to form...
Protein Complexes with Interchangeable Parts01:57

Protein Complexes with Interchangeable Parts

Groups of proteins may form a complex where each protein in this complex has a different role in the overall execution of the complex’s function. Often some of the proteins in the complex can be replaced by a closely related variant to give a complex that contains many of the same components yet is functionally distinct.
The SCF ubiquitin ligase is a protein complex of five individual proteins. This complex attaches ubiquitin to other target proteins to mark them for degradation. In order to...
Protein Complexes with Interchangeable Parts01:57

Protein Complexes with Interchangeable Parts

Groups of proteins may form a complex where each protein in this complex has a different role in the overall execution of the complex’s function. Often some of the proteins in the complex can be replaced by a closely related variant to give a complex that contains many of the same components yet is functionally distinct.
The SCF ubiquitin ligase is a protein complex of five individual proteins. This complex attaches ubiquitin to other target proteins to mark them for degradation. In order to...

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Related Experiment Video

Updated: Jun 13, 2026

Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules
10:58

Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules

Published on: July 25, 2013

Entropy quantum computing for fixed-backbone protein design.

Babak Emami1, Wesley Dyk2, David Haycraft2

  • 1Quantum Computing Inc. (QCi), 5 Marine View Plaza, Suite 214, Hoboken, NJ, 07030, USA. bemami@quantumcomputinginc.com.

Scientific Reports
|June 11, 2026
PubMed
Summary
This summary is machine-generated.

Computational protein design (CPD) is optimized using a novel quantum computing approach. This method finds low-energy protein configurations, approaching optimal solutions for biotechnology applications.

Keywords:
Classical benchmarkingEntropy quantum optimizationOptimizationProtein design

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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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Application of I TASSER, trRosetta, UCSF Chimera, HADDOCK server, and HEX loria for De Novo and In Silico Design of Proteins
05:08

Application of I TASSER, trRosetta, UCSF Chimera, HADDOCK server, and HEX loria for De Novo and In Silico Design of Proteins

Published on: July 8, 2025

Area of Science:

  • Biotechnology
  • Computational Biology
  • Quantum Computing

Background:

  • Computational protein design (CPD) is crucial for enzyme engineering and therapeutics.
  • The combinatorial complexity of CPD challenges traditional optimization methods.

Purpose of the Study:

  • To formulate fixed-backbone CPD as a quadratic Hamiltonian for quantum computing.
  • To evaluate the performance of Quantum Computing Inc.'s Dirac-3 photonic entropy computing platform for CPD.

Main Methods:

  • Formulating fixed-backbone CPD as a quadratic Hamiltonian over rotamer variables.
  • Utilizing Quantum Computing Inc.'s Dirac-3 photonic entropy computing platform.
  • Benchmarking against an exact classical cost function network (CFN) solver for optimal baselines.

Main Results:

  • Dirac-3 achieved best-observed solutions within 0.16-2.47% of optimal energies on benchmark proteins (493-943 variables).
  • The quantum approach identified low-energy configurations for directly solvable CPD instances.
  • Runtime analysis showed CFN faster in absolute terms, while Dirac-3 exhibited moderate runtime growth with problem size.

Conclusions:

  • The proposed quantum formulation is effective for optimization in directly solvable CPD instances.
  • Results establish a benchmark for entropy-based optimization in CPD.
  • Further exploration of decomposition-based approaches for larger instances is noted.