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Related Concept Videos

Conserved Binding Sites01:49

Conserved Binding Sites

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 analyses the...
Protein-protein Interfaces02:04

Protein-protein Interfaces

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 polypeptide...
Intrinsically Disordered Proteins02:18

Intrinsically Disordered Proteins

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...
Ligand Binding Sites02:40

Ligand Binding Sites

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.
Protein-ligand interactions are quite specific; even though numerous potential ligands surround a cellular protein at any given time, only a particular ligand can bind to that protein. Moreover, a ligand binds only to a dedicated area on the surface of the protein, known as the...
Conservation of Protein Domains Over Different Proteins02:26

Conservation of Protein Domains Over Different Proteins

Protein domains are small structurally independent units that are part of a single amino acid chain.  Although these domains are often structurally independent, they may rely on synergistic effects to perform their functions as part of a larger protein. Protein domains may be conserved within the same organism, as well as across different organisms.
A limited set of protein domains often duplicate and recombine during evolution. These domains can be organized in different combinations to form...
Protein Folding01:25

Protein Folding

Proteins are chains of amino acids linked together by peptide bonds. Upon synthesis, a protein folds into a three-dimensional conformation, critical to its biological function. Interactions between its constituent amino acids guide protein folding, and hence the protein structure is primarily dependent on its amino acid sequence.
Protein Structure Is Critical to Its Biological Function
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Computational Prediction of Amino Acid Preferences of Potentially Multispecific Peptide-Binding Domains Involved in Protein-Protein Interactions
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Computational Prediction of Amino Acid Preferences of Potentially Multispecific Peptide-Binding Domains Involved in Protein-Protein Interactions

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Predicting binding within disordered protein regions to structurally characterised peptide-binding domains.

Waqasuddin Khan1, Fergal Duffy, Gianluca Pollastri

  • 1Complex and Adaptive Systems Laboratory, University College Dublin, Dublin, Ireland ; Hussain Ebrahim Jamal Research Institute of Chemistry, International Center for Chemical and Biological Sciences, University of Karachi, Karachi, Pakistan.

Plos One
|September 11, 2013
PubMed
Summary

Predicting protein binding sites is challenging. Combining protein docking scores with machine learning, like a bidirectional recurrent neural network (BRNN), significantly improves the identification of short linear motifs (SLiMs) within disordered protein regions.

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Area of Science:

  • Computational Biology
  • Protein Structure and Dynamics
  • Bioinformatics

Background:

  • Disordered protein regions frequently mediate crucial protein-protein interactions.
  • Identifying short linear motifs (SLiMs) within these disordered regions that are responsible for binding is a significant challenge.
  • Accurate prediction of these binding regions is vital for understanding cellular mechanisms and drug discovery.

Purpose of the Study:

  • To evaluate the efficacy of protein docking simulations in predicting SLiM binding sites within disordered protein regions.
  • To develop and assess a machine learning approach that integrates docking scores with other predictive features for improved SLiM identification.
  • To enhance the prediction accuracy of peptide binding regions in proteins.

Main Methods:

  • Assembled a dataset of SLiM-containing proteins with known PDB structures, focusing on SLiMs within disordered regions.
  • Utilized Vina docking scores to assess the binding affinity of tripeptide segments within disordered regions to their respective protein receptors.
  • Trained a bidirectional recurrent neural network (BRNN) incorporating protein sequence, predicted secondary structure, Vina docking scores, and predicted disorder scores.

Main Results:

  • Vina docking scores alone showed weak discrimination between binding and non-binding peptides (AUC 0.58).
  • The bidirectional recurrent neural network (BRNN) model achieved significantly improved performance (AUC 0.72) by integrating multiple data sources.
  • Combining docking information with machine learning approaches markedly enhanced the identification of peptide binding regions compared to single-source predictions.

Conclusions:

  • Protein docking scores alone have limited power in pinpointing SLiMs within larger disordered protein segments.
  • Machine learning models that integrate docking scores with other predictive features offer a superior strategy for identifying peptide binding regions.
  • This combined approach serves as a robust predictor of binding to peptide-binding sites, though not for specific receptor interactions.