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Conservation of Protein Domains Over Different Proteins02:26

Conservation of Protein Domains Over Different Proteins

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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...
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Two structural features of the DNA molecule provide a basis for the mechanisms of heredity: the four nucleotide bases and its double-stranded nature. The Watson-Crick model of double-helical DNA structure, proposed in 1952, drew heavily upon the X-ray crystallography work of researchers Rosalind Franklin and Maurice Wilkins. Watson, Crick, and Wilkins jointly received the Nobel Prize in Physiology or Medicine for their work in 1962. Franklin was, controversially, excluded from the prize for...
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Genome Size and the Evolution of New Genes03:21

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While every living organism has a genome of some kind (be it RNA, or DNA), there is considerable variation in the sizes of these blueprints. One major factor that impacts genome size is whether the organism is prokaryotic or eukaryotic. In prokaryotes, the genome contains little to no non-coding sequence, such that genes are tightly clustered in groups or operons sequentially along the chromosome. Conversely, the genes in eukaryotes are punctuated by long stretches of non-coding sequence.
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Per-Unit Sequence Models01:26

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An ideal Y-Y transformer, grounded through neutral impedances, displays per-unit sequence networks akin to those of a single-phase ideal transformer when subjected to balanced positive- or negative-sequence currents. These currents do not produce neutral currents, and their associated voltage drops.
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Proteins: From Genes to Degradation02:11

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Within a biological system, the DNA encodes the RNA, and the nucleotide sequence in the RNA further defines the amino acid sequence in the protein. This is referred to as “The Central Dogma of Molecular Biology” - a term coined by Francis Crick.  Central dogma is a firm principle in biology that defines the flow of genetic information within any life form. The two fundamental steps in central dogma are - transcription and translation.
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Related Experiment Video

Updated: Oct 14, 2025

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

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The generative capacity of probabilistic protein sequence models.

Francisco McGee1,2,3, Sandro Hauri4,5, Quentin Novinger2,5

  • 1Center for Biophysics and Computational Biology, Temple University, Philadelphia, 19122, USA.

Nature Communications
|November 3, 2021
PubMed
Summary

Generative protein sequence models (GPSMs) like Potts models and VAEs are evaluated for their ability to capture complex mutation patterns. The Potts model best reproduces natural sequence statistics, highlighting the importance of higher-order epistasis in GPSM accuracy.

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Area of Science:

  • Computational biology
  • Bioinformatics
  • Machine learning in biology

Background:

  • Generative protein sequence models (GPSMs) like Potts models and variational autoencoders (VAEs) are increasingly used for protein engineering and understanding fitness landscapes.
  • Current evaluation metrics for GPSMs do not fully capture their ability to reproduce complex, multi-residue mutational patterns arising from epistasis in natural protein sequences.

Purpose of the Study:

  • To develop and apply novel sequence statistics for assessing the "generative capacity" of different GPSMs.
  • To compare the performance of the Potts model, VAE, and a site-independent model in capturing higher-order mutational patterns.

Main Methods:

  • Development of a suite of sequence statistics to quantify generative capacity.
  • Evaluation of three GPSMs: pairwise Potts Hamiltonian, VAE, and site-independent model.
  • Comparison of model-generated statistics against those from natural protein sequences.

Main Results:

  • The Potts model demonstrated the highest generative capacity, accurately reproducing higher-order mutational statistics observed in natural sequences.
  • The VAE's generative capacity was intermediate, outperforming the site-independent model but falling short of the Potts model.
  • The study highlights the significance of higher-order covariation and epistasis in evaluating GPSMs.

Conclusions:

  • A new framework for evaluating GPSM accuracy based on higher-order statistics and epistasis has been established.
  • The findings provide insights into the strengths and limitations of current GPSMs for modeling protein sequence evolution.
  • This work has broader implications for the development and interpretation of probabilistic sequence models across biological sciences.