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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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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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Application of I TASSER, trRosetta, UCSF Chimera, HADDOCK server, and HEX loria for De Novo and In Silico Design of Proteins
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AutoCoEv-A High-Throughput In Silico Pipeline for Predicting Inter-Protein Coevolution.

Petar B Petrov1,2, Luqman O Awoniyi1,2, Vid Šuštar1

  • 1MediCity Research Laboratories, Institute of Biomedicine, University of Turku, 20014 Turku, Finland.

International Journal of Molecular Sciences
|March 25, 2022
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Summary

Researchers developed AutoCoEv, a computational pipeline to efficiently identify protein-protein interactions by analyzing coevolution in large datasets. This tool predicts novel functional partners and molecular clusters, advancing our understanding of cellular networks.

Keywords:
automationcoevolutioncorrelationinteractionpipelineprotein

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Area of Science:

  • Computational Biology
  • Bioinformatics
  • Systems Biology

Background:

  • Protein-protein interactions are crucial for cellular functions and form complex regulatory networks.
  • Understanding these interactions is key to deciphering protein functions and cellular mechanisms.
  • Coevolution analysis is a powerful method for identifying functional protein partners, but is computationally intensive.

Purpose of the Study:

  • To develop a user-friendly, open-source computational pipeline, AutoCoEv, for efficient coevolution analysis of large protein datasets.
  • To automate and parallelize the workflow for searching coevolutionary relationships among numerous proteins.
  • To enhance the statistical rigor of coevolution detection and analysis.

Main Methods:

  • Developed AutoCoEv, a pipeline integrating 15 programs, with CAPS2 as the core coevolution detection software.
  • Implemented automation and parallelization for large-scale in silico evolutionary analysis.
  • Patched CAPS2 to improve statistical output, including multiple comparison corrections.

Main Results:

  • Successfully applied AutoCoEv to analyze coevolution among 324 proteins near B lymphocyte lipid rafts.
  • Detected significant coevolutionary relationships, predicting numerous novel protein partners.
  • Identified previously unknown clusters of functionally related molecules.

Conclusions:

  • AutoCoEv enables efficient and cost-effective prediction of functional interactions from large protein datasets.
  • The pipeline facilitates the discovery of novel protein partners and functional modules.
  • AutoCoEv significantly advances the application of coevolution analysis in biological research.