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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...
Multi-pass Transmembrane Proteins and β-barrels01:09

Multi-pass Transmembrane Proteins and β-barrels

In multi-pass transmembrane proteins, the polypeptide chain crosses the membrane more than once. The transmembrane polypeptide chain either forms an α-helix or β-strand structure. α-Helix containing multi-pass transmembrane proteins are ubiquitous, whereas β-strand containing ones are mainly found in gram-negative bacteria, mitochondria, and chloroplasts.
α-Helix containing multi-pass transmembrane proteins
Multi-pass transmembrane proteins such as G-protein-linked receptors (GPCRs) and...
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...
Multi-species Conserved Sequences02:51

Multi-species Conserved Sequences

Next-generation sequencing technologies have created large genomic databases of a variety of animals and plants. Ever since the human genome project was completed, scientists studied the genome of primates, mammals, and other phylogenetically distant living beings. Such large-scale  studies have provided new insights into the evolutionary relationship between organisms.
Although the genome of each species varies greatly from each other, a few sequences are highly conserved. Such conserved DNA...
Evolutionary Relationships through Genome Comparisons02:54

Evolutionary Relationships through Genome Comparisons

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...
Gene Families01:57

Gene Families

Gene families consist of groups of genes proposed to have originated from a common ancestor. Typically these arise through events in which a gene or genes are mistakenly duplicated during cell division. Unlike their parent genes (which are subject to selection pressure to maintain function), these gene copies do not need to preserve their sequences and may evolve at a relatively faster rate.
Occasionally these regions can be adapted to take on new roles within the organism, becoming novel genes...

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Identification and Classification of Position-specific GABAA Receptor Subunit Missense Variants for Their Role In Hippocampal Pyramidal Neurons
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Recognition of beta-structural motifs using hidden Markov models trained with simulated evolution.

Anoop Kumar1, Lenore Cowen

  • 1Department of Computer Science, Tufts University, Medford, MA, USA. anoop.kumar@tufts.edu

Bioinformatics (Oxford, England)
|June 10, 2010
PubMed
Summary

We improved protein sequence analysis by incorporating pairwise residue dependencies into profile hidden Markov models (HMMs). This enhanced model shows a 5% improvement in recognizing related protein families, advancing evolutionary relationship detection.

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Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues
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Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues

Published on: July 14, 2015

Area of Science:

  • Computational Biology
  • Bioinformatics
  • Structural Bioinformatics

Background:

  • Profile hidden Markov models (HMMs) are effective for recognizing evolutionarily related protein sequences.
  • Standard HMMs do not account for pairwise statistical preferences between residues involved in beta-sheet hydrogen bonds.
  • This limitation hinders accurate remote homology detection, especially for structural motifs.

Purpose of the Study:

  • To develop and evaluate methods for incorporating pairwise dependencies into HMMs.
  • To improve the recognition of remote homologous protein sequences, particularly beta-structural motifs.
  • To assess if models trained on one family can detect other families within the same superfamily.

Main Methods:

  • Developed a novel pairwise hidden Markov model incorporating statistical preferences of hydrogen-bonded residues in beta-sheets.
  • Simulated evolutionary processes to train the enhanced HMMs.
  • Tested the models on the remote homology detection problem for beta-structural motifs using SCOP superfamilies.

Main Results:

  • The enhanced HMMs trained with the pairwise model achieved a median 5% improvement in Area Under the Curve (AUC).
  • This improvement was observed in recognizing beta-structural motifs across different families within the same superfamily.
  • The results demonstrate the effectiveness of capturing pairwise dependencies for homology detection.

Conclusions:

  • Incorporating pairwise residue dependencies into HMMs significantly enhances the detection of remote homologous protein sequences.
  • The developed method improves the recognition of beta-structural motifs, a challenging problem in bioinformatics.
  • The study provides a more accurate statistical model for evolutionary relationship analysis in proteins.