We discuss how methods based on hidden Markov models performed in the fold-recognition section of the CASP2 experiment. Hidden Markov models were built for a representative set of just over 1,000 structures from the Protein Data Bank (PDB). Each CASP2 target sequence was scored against this library
Predicting protein structure using only sequence information
β Scribed by Kevin Karplus; Christian Barrett; Melissa Cline; Mark Diekhans; Leslie Grate; Richard Hughey
- Publisher
- John Wiley and Sons
- Year
- 1999
- Tongue
- English
- Weight
- 111 KB
- Volume
- 37
- Category
- Article
- ISSN
- 0887-3585
No coin nor oath required. For personal study only.
β¦ Synopsis
This paper presents results of blind predictions submitted to the CASP3 protein structure prediction experiment. We made predictions using the SAM-T98 method, an iterative hidden Markov model-based method for constructing protein family profiles. The method is purely sequencebased, using no structural information, and yet was able to predict structures as well as all but five of the structure-based methods in CASP3. Proteins
π SIMILAR VOLUMES
We present an analysis of the blind predictions submitted to the fold recognition category for the second meeting on the Critical Assessment of techniques for protein Structure Prediction. Our method achieves fold recognition from predicted secondary structure sequences using hidden Markov models (H
Homology or comparative modeling is aimed at modeling the three-dimensional structure of a target sequence of unknown structure using the framework of an already known fold. Traditionally, homology modeling has been applied to targets with clear sequence similarity to proteins of known structure. Be
Analysis of our fold recognition results in the 3rd Critical Assessment in Structure Prediction (CASP3) experiment, using the programs THREADER 2 and GenTHREADER, shows an encouraging level of overall success. Of the 23 submitted predictions, 20 targets showed no clear sequence similarity to protein
A new dataset of 396 protein domains is developed and used to evaluate the performance of the protein secondary structure prediction algorithms DSC, PHD, NNSSP, and PREDATOR. The maximum theoretical Q 3 accuracy for combination of these methods is shown to be 78%. A simple consensus prediction on th