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Related Concept Videos

Ribosome Profiling02:24

Ribosome Profiling

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Ribosome profiling or ribo-sequencing is a deep sequencing technique that produces a snapshot of active translation in a cell. It selectively sequences the mRNAs protected by ribosomes to get an insight into a cell’s translation landscape at any given point in time.
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Many proteins form complexes to carry out their functions, making protein-protein interactions (PPIs) essential for an organism's survival. Most PPIs are stabilized by numerous weak noncovalent chemical forces. The physical shape of the interfaces determines the way two proteins interact. Many globular proteins have closely-matching shapes on their surfaces, which form a large number of weak bonds. Additionally, many PPIs occur between two helices or between a surface cleft and a...
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Protein Dynamics in Living Cells01:19

Protein Dynamics in Living Cells

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Different fluorescence-based techniques are used to study the protein dynamics in living cells. These techniques include FRAP, FRET, and PET.
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Conserved Binding Sites01:49

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Many proteins’ biological role depends on their interactions with their ligands, small molecules that bind to specific locations on the protein known as ligand-binding sites. Ligand-binding sites are often conserved among homologous proteins as these sites are critical for protein function.
Binding sites are often located in large pockets, and if their location on a protein’s surface is unknown, it can be predicted using various approaches. The energetic method computationally...
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Leaky Scanning02:28

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During most eukaryotic translation processes, the small 40S ribosome subunit scans an mRNA from its 5' end until it encounters the first start AUG codon. The large 60S ribosomal subunit then joins the smaller one to initiate protein synthesis. The location of the translation initiation is largely determined by the nucleotides near the start codon as there may be multiple translation initiation sites present on the mRNA.  Marilyn Kozak discovered that the sequence RCCAUGG (where R...
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Ligand Binding Sites02:40

Ligand Binding Sites

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Proteins are dynamic macromolecules that carry out a wide variety of essential processes; however, the activities of most proteins depend on their interactions with other molecules or ions, known as ligands.
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Related Experiment Video

Updated: Jan 9, 2026

De novo Identification of Actively Translated Open Reading Frames with Ribosome Profiling Data
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De novo Identification of Actively Translated Open Reading Frames with Ribosome Profiling Data

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Decoding protein binding plasticity via integrated deep ribosome display and deep learning.

Mengtong Tang1,2,3, Jiawei Li1,2,3, Zhixi Li1,2

  • 1Frontiers Science Center for Synthetic Biology (Ministry of Education), School of Synthetic Biology and Biomanufacturing, Tianjin University, Tianjin, China.

Communications Biology
|December 2, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces an AI-driven platform for exploring protein interaction plasticity, decoding binding activity from millions of peptides. The framework enables data-driven design of novel synthetic peptides with tailored binding properties.

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Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues
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Area of Science:

  • Biochemistry and Molecular Biology
  • Artificial Intelligence in Biology
  • Protein Engineering

Background:

  • Protein interaction plasticity is crucial for biological networks and protein design.
  • Exploring sequence space for protein interactions is challenging due to its vast dimensionality.

Purpose of the Study:

  • To develop a platform integrating deep experimental screening and deep learning to decode protein interaction plasticity.
  • To enable systematic exploration of protein interactions and data-driven design of synthetic peptides.

Main Methods:

  • Developed a novel ribosome display system to generate a dataset of 47.8 million unique peptides.
  • Employed a deep learning architecture trained on sequence context, enrichment dynamics, and subsequence abundance.
  • Utilized sequence dimensionality reduction and motif elicitation techniques.

Main Results:

  • Achieved high accuracy (Pearson's r = 0.902) in predicting Streptactin-binding activity using deep learning.
  • Identified 799 strong-binding sequences with a canonical motif.
  • Discovered 219 sequences with novel motifs and divergent docking conformations.

Conclusions:

  • The integrated experimental-AI framework effectively decodes protein interaction plasticity.
  • Findings reveal significant depth and breadth in protein-binding plasticity.
  • The platform facilitates exploration of protein interactions and synthetic peptide design.