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Related Experiment Video

Updated: Feb 15, 2026

Probing RNA Structure with Dimethyl Sulfate Mutational Profiling with Sequencing In Vitro and in Cells

Probing RNA Structure with Dimethyl Sulfate Mutational Profiling with Sequencing In Vitro and in Cells

Published on: December 9, 2022

Scoring profile-to-profile sequence alignments.

Guoli Wang¹, Roland L Dunbrack

¹Institute for Cancer Research, Fox Chase Cancer Center, 333 Cottman Avenue, Philadelphia, PA 19111, USA.

Protein Science : a Publication of the Protein Society

|May 21, 2004

Summary

This summary is machine-generated.

Profile-profile alignment enhances sequence alignment accuracy. Optimizing parameters like gap penalties and incorporating structural information significantly improves search sensitivity and specificity for homologous proteins.

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Area of Science:

Bioinformatics
Computational Biology
Structural Biology

Background:

Sequence alignment profiles are powerful tools for accurate sequence alignments.
Profile-profile comparison yields more accurate and longer alignments than traditional methods.
Evaluating scoring schemes requires large-scale benchmarks of homologous proteins with known structural alignments.

Purpose of the Study:

Investigate properties of profile-profile alignments using a benchmark dataset.
Evaluate the impact of various parameters and algorithms on alignment accuracy, sensitivity, and specificity.
Determine optimal strategies for creating accurate profile-profile alignments.

Main Methods:

Utilized a benchmark of homologous proteins with structurally determined sequence alignments.
Investigated multiple sequence alignment construction, sequence weighting, amino acid frequency determination, column scoring, gap scoring, and inclusion of structural information.
Optimized gap penalties and evaluated different column-column scoring functions.

Main Results:

Optimized gap penalties led to similar alignment accuracy across most column-column scoring functions.
Certain scoring functions demonstrated superior search sensitivity and specificity.
Position-specific weighting schemes outperformed sequence-specific schemes for amino acid counts.
Removing gapped positions in profiles and incorporating secondary structure information improved alignment quality.

Conclusions:

Profile-profile alignment methods can be significantly optimized through careful parameter selection and the inclusion of structural data.
Optimized weighting schemes and gap penalties are crucial for high-performance sequence alignment.
Secondary structure information enhances the accuracy of profile-profile alignments, aiding in the study of homologous proteins.