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Related Concept Videos

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...
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.
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Protein Networks02:26

Protein Networks

An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
Ligand Binding and Linkage00:49

Ligand Binding and Linkage

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 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.
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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.
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A Protocol for Computer-Based Protein Structure and Function Prediction
16:41

A Protocol for Computer-Based Protein Structure and Function Prediction

Published on: November 3, 2011

Efficient design of bio-basis function to predict protein functional sites using kernel-based classifiers.

Pradipta Maji1, Chandra Das

  • 1Machine Intelligence Unit, Indian Statistical Institute, 203 B. T. Road, Kolkata, 700 108, India. pmaji@isical.ac.in

IEEE Transactions on Nanobioscience
|October 5, 2010
PubMed
Summary
This summary is machine-generated.

A novel modified bio-basis function kernel improves protein functional site prediction by mapping amino acid sequences to numerical data. This method efficiently selects relevant bio-basis strings for enhanced pattern recognition.

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Last Updated: Jun 8, 2026

A Protocol for Computer-Based Protein Structure and Function Prediction
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Published on: November 3, 2011

Computational Prediction of Amino Acid Preferences of Potentially Multispecific Peptide-Binding Domains Involved in Protein-Protein Interactions
06:50

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Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules
10:58

Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules

Published on: July 25, 2013

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Machine Learning

Background:

  • Kernel-based algorithms like support vector machines require numerical input for pattern recognition.
  • Amino acid sequences in proteins are non-numerical and need effective encoding for computational analysis.

Purpose of the Study:

  • To propose a new string kernel function for encoding protein sequences.
  • To enhance the prediction of functional sites in proteins using machine learning.

Main Methods:

  • Developed a modified bio-basis function string kernel.
  • Introduced the concept of 'zone of influence' for bio-basis strings.
  • Integrated Fisher ratio and a novel 'degree of resemblance' for bio-basis string selection.

Main Results:

  • The modified bio-basis function maps non-numerical sequence data to a numerical feature space.
  • An efficient method was developed to select a reduced, relevant, and nonredundant set of bio-basis strings.
  • The approach facilitates the application of kernel-based algorithms to protein sequence analysis.

Conclusions:

  • The proposed modified bio-basis function is effective for encoding protein sequences.
  • The novel selection method efficiently identifies key bio-basis strings.
  • This work advances computational methods for predicting protein functional sites.