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Related Concept Videos

Conserved Binding Sites01:49

Conserved Binding Sites

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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.
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...
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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.
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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...
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Ligand Binding and Linkage00:49

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Allosteric proteins have more than one ligand binding site; the binding of a ligand to any of these sites influences the binding of ligands to the other sites. When a protein is allosteric, its binding sites are called coupled or linked.  In the case of enzymes, the site that binds to the substrate is known as the active site and the other site is known as the regulatory site. When a ligand binds to the regulatory site, this leads to conformational changes in the protein that can influence...
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Conservation of Protein Domains Over Different Proteins02:26

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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.
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Related Experiment Video

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Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues
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iHyd-LysSite (EPSV): Identifying Hydroxylysine Sites in Protein Using Statistical Formulation by Extracting Enhanced

Muhammad Khalid Mahmood1, Asma Ehsan1, Yaser Daanial Khan1

  • 11Department of Mathematics, University of the Punjab, Lahore, Pakistan; 2Faculty of Information Technology, University of Management and Tecnology, Lahore, Pakistan; 3Gordon Life Science Institute, Boston, MA02478, USA.

Current Genomics
|November 20, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces iHyd-LysSite (EPSV), a computational tool for identifying hydroxylysine sites. The predictor offers a superior alternative to laborious experimental methods for understanding hydroxylation in cellular functions and diseases.

Keywords:
ANNHydroxylysinePTMscross-validationpost-translational modificationspredictive model

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Area of Science:

  • Biochemistry
  • Bioinformatics
  • Molecular Biology

Background:

  • Hydroxylation is a critical post-translational modification (PTM) impacting cellular functions and disease.
  • Hydroxylysine formation involves adding a hydroxyl group to lysine sites, a process vital for protein function.

Purpose of the Study:

  • To develop an accurate and efficient computational method for identifying hydroxylysine sites in protein sequences.
  • To provide an alternative to the difficult, time-consuming, and expensive experimental prediction of hydroxylysine sites.

Main Methods:

  • Utilized a powerful mathematical and statistical methodology incorporating sequence-order effects and composition.
  • Developed the iHyd-LysSite (EPSV) predictor, employing sequence variant techniques for enhanced prediction.
  • Validated the model using self-consistency, independent, 10-fold cross-validation, and jackknife tests with Neural Networks, Random Forest, and Support Vector Machine classifiers.

Main Results:

  • The iHyd-LysSite (EPSV) predictor demonstrated superior predictive performance compared to existing methods.
  • Achieved high sensitivity and specificity rates across multiple validation tests for all employed classifiers.
  • Jackknife test results for sensitivity and specificity reached up to 98.16% and 98.52% respectively, showcasing excellent prediction accuracy.

Conclusions:

  • The proposed iHyd-LysSite (EPSV) tool offers a significant advancement in predicting hydroxylysine sites.
  • This computational approach meets the growing demand for efficient hydroxylysine site identification in biological research.
  • The method provides a reliable and accurate alternative for studying hydroxylation-related cellular functions and diseases.