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The flow of genetic information in cells from DNA to mRNA to protein is described by the central dogma, which states that genes specify the sequence of mRNAs, which in turn specify the sequence of amino acids making up all proteins. The decoding of one molecule to another is performed by specific proteins and RNAs. Because the information stored in DNA is so central to cellular function, it makes intuitive sense that the cell would make mRNA copies of this information for protein synthesis...
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Single-Strand DNA Binding Proteins01:03

Single-Strand DNA Binding Proteins

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For successful DNA replication, the unwinding of double-stranded DNA must be accompanied by stabilization and protection of the separated single strands of the DNA. This crucial task is performed by single-strand DNA-binding (SSB) proteins. They bind to the DNA in a sequence-independent manner, which means that the nitrogenous bases of the DNA need not be present in a specific order for binding of SSB proteins to it. The binding of SSB proteins straightens single-stranded DNA (ssDNA) and makes...
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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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Drug binding to proteins is a key aspect of pharmacokinetics and can influence a drug's distribution, absorption, and elimination in the body. Several factors, including the drug's physiochemical properties, protein concentration, disease states, and the number of binding sites on the protein, influence this process.
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Covalently Linked Protein Regulators02:04

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Proteins can undergo many types of post-translational modifications, often in response to changes in their environment. These modifications play an important role in the function and stability of these proteins. Covalently linked molecules include functional groups, such as methyl, acetyl, and phosphate groups, and also small proteins, such as ubiquitin. There are around 200 different types of covalent regulators that have been identified.
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iDLDDG: predicción de cambios en la estabilidad de proteínas a partir de mutaciones sin sentido en proteínas de unión

Xuan Yu1, Fang Ge2,3, Dong-Jun Yu3

  • 1Department of Computer Science, City University of Hong Kong, 83 Tat Chee Ave, Kowloon Tong, Hong Kong SAR(HKG), 999077, China.

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Resumen

La predicción de mutaciones sin sentido en proteínas de unión a ADN es crucial para comprender las enfermedades. Nuestro nuevo marco de aprendizaje profundo, iDLDDG, diferencia con precisión los efectos en proteínas de unión a ADN de doble y simple hebra, mejorando la predicción de mutaciones.

Palabras clave:
proteína de unión a ADNbioinformáticaaprendizaje profundomutación sin sentidointeracción proteína-ADN

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Área de la Ciencia:

  • Genomics and Bioinformatics; Molecular Biology; Computational Biology

Sus antecedentes:

  • Accurate prediction of missense mutations' impact on protein-DNA binding affinity is vital for disease mechanism research and therapeutic development.
  • Existing models often fail to account for the distinct characteristics of mutations in double-stranded DNA-binding proteins (DSBs) and single-stranded DNA-binding proteins (SSBs).

Objetivo del estudio:

  • To develop a computational framework for accurately predicting the effects of missense mutations on protein-DNA binding affinity in both DSBs and SSBs.
  • To establish a method that rigorously differentiates mutation mechanisms between DSBs and SSBs.

Principales métodos:

  • Constructed a comprehensive dataset from diverse sources.
  • Developed iDLDDG, a deep learning framework integrating sequence-based embeddings (ESM2, ProtTrans, ESM1v) with multi-scale structural and evolutionary information.
  • Employed an entropy-based algorithm to identify 181 optimal residues for modeling biophysical constraints, enhancing predictive accuracy and efficiency.

Principales resultados:

  • iDLDDG achieved state-of-the-art performance, with a 10-fold cross-validation Pearson Correlation Coefficient (PCC) of 0.755 on the MPD276 dataset.
  • Achieved a PCC of 0.632 on independent test sets covering both DSBs and SSBs, significantly outperforming existing methods.
  • Demonstrated the framework's ability to differentiate mutation mechanisms between DSBs and SSBs.

Conclusiones:

  • iDLDDG provides a foundation for high-accuracy prediction of pathological mutations in DNA-binding proteins.
  • This work establishes the first computational framework capable of rigorously distinguishing DSB and SSB mutation mechanisms.
  • The enhanced predictive accuracy and computational efficiency support large-scale assessments of mutation effects in DNA-binding proteins.