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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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Intrinsically disordered proteins are a group of proteins that do not fold into specific three-dimensional structures. Their structural flexibility allows them to complement ordered proteins to perform functions that are inaccessible to rigid structures. They are more common in eukaryotes than prokaryotes and may either be exclusively intrinsically disordered or hybrid proteins, consisting of a mix of ordered and disordered regions. The absence of a rigid structure in these proteins can be...
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AIUPred - Binding: Energy Embedding to Identify Disordered Binding Regions.

Gábor Erdős1, Norbert Deutsch1, Zsuzsanna Dosztányi1

  • 1Department of Biochemistry, Eötvös Loránd University, Pázmány Péter stny 1/c, Budapest H-1117, Hungary.

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Predicting functional sites in intrinsically disordered regions (IDRs) is challenging. AIUPred-binding uses energy embeddings and pathogenicity scores to accurately identify these critical disordered binding regions.

Keywords:
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Area of Science:

  • Biochemistry
  • Computational Biology
  • Bioinformatics

Background:

  • Intrinsically disordered regions (IDRs) are crucial for cellular functions, mediating interactions via disordered binding regions.
  • Experimental characterization of IDRs is difficult, necessitating efficient computational prediction tools.
  • Predicting functional sites in IDRs is hampered by dataset limitations and existing methodologies.

Purpose of the Study:

  • To develop and present AIUPred-binding, a novel computational tool for predicting functional sites within intrinsically disordered regions.
  • To improve the accuracy of identifying disordered binding regions by leveraging advanced computational approaches.

Main Methods:

  • Utilized a high-dimensional mathematical representation of structural energies termed 'energy embedding'.
  • Integrated pathogenicity scores derived from AlphaMissense.
  • Employed a transfer learning approach for enhanced prediction accuracy.
  • Developed AIUPred-binding as a novel prediction tool.

Main Results:

  • AIUPred-binding demonstrated improved accuracy in identifying functional sites within IDRs.
  • The tool effectively discerns subtle features within disordered regions.
  • Addressed biases and challenges inherent in manually curated datasets.
  • AIUPred-binding is integrated into the AIUPred web framework.

Conclusions:

  • AIUPred-binding offers a versatile and efficient resource for studying the functional roles of IDRs.
  • The tool overcomes limitations of previous methods for predicting disordered binding regions.
  • AIUPred-binding is freely accessible, promoting wider research in the field.