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Related Concept Videos

Conservation of Protein Domains Over Different Proteins02:26

Conservation of Protein Domains Over Different Proteins

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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.
A limited set of protein domains often duplicate and recombine during evolution. These domains can be organized in different combinations to...
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Conserved Binding Sites01:49

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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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Evolutionary Relationships through Genome Comparisons02:54

Evolutionary Relationships through Genome Comparisons

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Genome comparison is one of the excellent ways to interpret the evolutionary relationships between organisms. The basic principle of genome comparison is that if two species share a common feature, it is likely encoded by the DNA sequence conserved between both species. The advent of genome sequencing technologies in the late 20th century enabled scientists to understand the concept of conservation of domains between species and helped them to deduce evolutionary relationships across diverse...
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Protein Families

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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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Protein-protein Interfaces02:04

Protein-protein Interfaces

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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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Gene Evolution - Fast or Slow?02:05

Gene Evolution - Fast or Slow?

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The genomes of eukaryotes are punctuated by long stretches of sequence which do not code for proteins or RNAs. Although some of these regions do contain crucial regulatory sequences, the vast majority of this DNA serves no known function. Typically, these regions of the genome are the ones in which the fastest change, in evolutionary terms, is observed, because there is typically little to no selection pressure acting on these regions to preserve their sequences.
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Related Experiment Video

Updated: May 13, 2025

Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions
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Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions

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EVOLVE: A Web Platform for AI-Based Protein Mutation Prediction and Evolutionary Phase Exploration.

Satyam Sangeet1,2, Anushree Sinha1, Madhav B Nair1

  • 1Department of Chemical Sciences, Indian Institute of Science Education and Research, Kolkata, West Bengal 741246, India.

Journal of Chemical Information and Modeling
|May 1, 2025
PubMed
Summary

EVOLVE is a new web tool that predicts protein mutation sites and their collective effects using machine learning and statistical mechanics. It helps identify potential variants of concern by analyzing mutational entropy, aiding biophysical chemistry research.

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Area of Science:

  • Biophysical Chemistry
  • Computational Biology
  • Protein Engineering

Background:

  • Predicting protein structure-function relationships from sequence data is crucial.
  • Identifying mutation sites is key to understanding protein behavior.
  • Analyzing large sequence spaces presents computational challenges.

Purpose of the Study:

  • To develop a web tool, EVOLVE, for exploring prospective mutation sites and their collective behavior.
  • To address the challenges of large sequence-space analysis in protein research.
  • To provide a method for identifying potential variants of concern.

Main Methods:

  • Integration of statistical mechanics-guided machine learning algorithms.
  • Calculation of mutational entropy using statistical mechanics to identify hotspots.
  • Application of the statistical mechanics of phase transition concept to quantify entropy fluctuations.

Main Results:

  • EVOLVE accurately predicts probable mutational sites and their functional consequences.
  • Validation against viral protein sequences confirms the tool's predictive capabilities.
  • Quantitative identification of Variants of Concern (VOC) and Variants under Monitoring (VUM) is enabled.

Conclusions:

  • EVOLVE offers a user-friendly platform for analyzing protein mutation sites.
  • The tool leverages advanced computational methods for enhanced biophysical insights.
  • EVOLVE aids in understanding protein evolution and identifying significant viral variants.