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Protein Families02:47

Protein Families

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Protein families are groups of homologous proteins; that is, they have similarities in amino acid sequences and three-dimensional structures. Protein families usually occur because of gene duplication, where an additional copy of a gene is inserted into the genome of an organism.   Mutations that change the amino acids but still allow the protein to be properly synthesized, will lead to new protein family members.   If these new proteins contain similar amino acids in key...
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Peptide Identification Using Tandem Mass Spectrometry01:33

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Tandem mass spectrometry, also known as MS/MS or MS2, is an analytical technique that employs two mass analyzers. Essentially it is a series of mass spectrometers that helps isolate a particular biomolecule and then helps study its chemical properties.
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Signal sequences are short amino acid sequences that guide newly synthesized proteins to their proper location within the cell. Classical signal sequences are fifteen to sixty amino acids long and present at the N-terminus of a polypeptide chain. Each signal sequence has a conserved segment of basic residues towards their N terminus, a hydrophobic core, and a C-terminus rich in polar residues. The C-terminus also contains a signal cleavage site and features a -3 -1 sequence motif. The -3-1...
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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.
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Protein Networks02:26

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An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
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Proteins are polymers of amino acids linked together by peptide bonds. Proteins and polypeptides are interchangeably used to refer to long chains of amino acids. However, polypeptides have a molecular weight of fewer than 10,000 daltons, while proteins have greater molecular weight.  Polypeptides with less than 20 amino acids are called oligopeptides or simply peptides. Interactions among the constituent amino acid side chains of proteins help them fold into a stable 3-dimensional...
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TriplEP-CPP: Algorithm for Predicting the Properties of Peptide Sequences.

Maria Serebrennikova1,2, Ekaterina Grafskaia1, Dmitriy Maltsev3,4,5

  • 1Laboratory of Genetic Engineering, Lopukhin Federal Research and Clinical Center of Physical-Chemical Medicine of Federal Medical Biological Agency, Moscow 119435, Russia.

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Summary

Machine learning accelerates the discovery of cell-penetrating peptides (CPPs) for drug delivery. This study identified a novel, low-toxicity CPP from jellyfish venom, enhancing targeted therapeutic applications.

Keywords:
cell penetrating peptides (CPP)functional activity predictionintracellular deliverymachine learningprotein-lipid interactionstructural-dynamic properties

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Area of Science:

  • Biochemistry
  • Pharmacology
  • Bioinformatics

Background:

  • Drug delivery systems aim to increase intracellular drug concentrations for improved efficacy and reduced toxicity.
  • Cell-penetrating peptides (CPPs) are a promising tool for facilitating the transport of molecules into cells.
  • Traditional methods for CPP development are time-consuming and costly.

Purpose of the Study:

  • To develop a machine learning-based predictive algorithm for identifying novel CPP sequences.
  • To screen potential CPPs for cytotoxicity and cellular penetration capabilities.
  • To discover new CPPs from natural sources, specifically the Rhilema esculentum venom proteome.

Main Methods:

  • Utilized machine learning models, including k-nearest neighbors, gradient boosting, and random forest algorithms.
  • Employed molecular descriptors to train the predictive algorithm for CPP sequence identification.
  • Conducted experimental validation of predicted CPPs, assessing cytotoxicity and cell penetration.

Main Results:

  • A predictive algorithm for novel CPP identification was successfully created.
  • Several potential CPP candidates were identified and evaluated.
  • A novel, low-toxicity CPP was discovered from the Rhilema esculentum venom proteome.

Conclusions:

  • Machine learning significantly enhances the efficiency and reduces the cost of discovering new CPPs.
  • The identified CPP from jellyfish venom demonstrates potential for safe and effective intracellular drug delivery.
  • This approach paves the way for accelerated development of targeted therapeutic agents.