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Multi-species Conserved Sequences02:51

Multi-species Conserved Sequences

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Next-generation sequencing technologies have created large genomic databases of a variety of animals and plants. Ever since the human genome project was completed, scientists studied the genome of primates, mammals, and other phylogenetically distant living beings. Such large-scale  studies have provided new insights into the evolutionary relationship between organisms.
Although the genome of each species varies greatly from each other, a few sequences are highly conserved. Such conserved...
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Peptide Bonds02:43

Peptide Bonds

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A peptide bond covalently attaches amino acids through a dehydration reaction. One amino acid's carboxyl group and another amino acid's amino group combine, releasing a water molecule. The resulting bond is the peptide bond. The products that such linkages form are peptides. As more amino acids join this growing chain, the resulting chain is a polypeptide. Each polypeptide has a free amino group at one end. This end has the N-terminal, or the amino-terminal, and the other end has a free...
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Peptide Identification Using Tandem Mass Spectrometry01:33

Peptide Identification Using Tandem Mass Spectrometry

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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.
This technique helps gather information regarding the protein from which the peptide was obtained and to study the peptides’ amino acid sequence. Identifying peptides from a complex mixture is an important component of the growing field of...
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Protein and Protein Structure02:15

Protein and Protein Structure

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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.
A protein's shape is critical to its function. For example, an enzyme...
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Per-Unit Sequence Models01:26

Per-Unit Sequence Models

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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.
Zero-sequence currents, which are identical in magnitude and phase, generate a neutral current, resulting in voltage drops across the neutral impedance and the low-voltage winding. If the...
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Structural Protein Function01:56

Structural Protein Function

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Structural proteins are a category of proteins responsible for functions ranging from cell shape and movement to providing support to major structures such as bones, cartilage, hair, and muscles. This group includes proteins such as collagen, actin, myosin, and keratin.
Collagen, the most abundant protein in mammals, is found throughout the body. In connective tissue, such as skin, ligaments, and tendons, it provides tensile strength and elasticity.  In bones and teeth, it mineralizes to...
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Video Experimental Relacionado

Updated: Jan 28, 2026

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images
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Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

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XCPP: Un marco de aprendizaje profundo explicable multimodelo para la identificación precisa de péptidos penetrantes

Hafsah Riasat1, Tamim Alkhalifah2, Fahad Alturise3

  • 1Department of Computer Science, School of Systems and Technology, University of Management and Technology, Lahore, Pakistan.

Current drug targets
|January 27, 2026
PubMed
Resumen

Los modelos de aprendizaje profundo predicen con precisión los péptidos penetrantes de células (CPP), cruciales para la administración de fármacos y el diagnóstico. Las redes neuronales convolucionales (CNN) demostraron un rendimiento superior, y el análisis SHAP mejoró la interpretabilidad del modelo.

Palabras clave:
aprendizaje profundobioinformáticaIA explicable (XAI)

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Área de la Ciencia:

  • Bioinformática
  • Biología Computacional
  • Sistemas de Administración de Fármacos

Sus antecedentes:

  • Los péptidos penetrantes de células (CPP) son secuencias cortas de aminoácidos que permiten el transporte de moléculas terapéuticas a través de las membranas celulares.
  • Los CPP ofrecen una plataforma versátil para la administración dirigida de fármacos y el diagnóstico molecular.

Objetivo del estudio:

  • Desarrollar y evaluar modelos de aprendizaje profundo para la predicción in silico precisa de CPP.
  • Identificar las características de secuencia clave que contribuyen a la actividad de los CPP utilizando inteligencia artificial explicable (XAI).

Principales métodos:

  • Análisis de 473 CPP confirmados de la base de datos EnDM-CPP.
  • Cálculo de cuatro descriptores de secuencia (PRIM, RPRIM, AAPIV, AAPIV Inverso).
  • Entrenamiento y prueba de modelos de Redes Neuronales Profundas (DNN), Redes Neuronales Convolucionales (CNN) y Memoria a Corto Plazo (LSTM).
  • Evaluación del modelo utilizando autoconsistencia, pruebas independientes y validación cruzada de 10 veces.
  • Aplicación de valores SHAP para XAI para interpretar las predicciones del modelo.

Principales resultados:

  • El modelo CNN logró la mayor precisión (99,05%) durante la validación cruzada, superando a los modelos DNN y LSTM.
  • Todos los modelos demostraron una precisión de predicción razonable con características de entrada estructuradas.
  • El análisis SHAP identificó con éxito descriptores de secuencia biológicamente relevantes, mejorando la transparencia del modelo.

Conclusiones:

  • El aprendizaje profundo, en particular las CNN, proporciona un marco eficaz para la identificación precisa de CPP.
  • El estudio destaca el potencial de la predicción in silico de CPP para aplicaciones en la administración de fármacos, el diagnóstico y la medicina personalizada.
  • La XAI basada en SHAP aumenta la confianza en las predicciones del modelo al vincular las características de la secuencia con las propiedades biológicas.