For Condensed Formula What You Do To The 3 Bonding Protein Structure Prediction – Primary and Secondary Structure Analysis and Prediction

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Protein Structure Prediction – Primary and Secondary Structure Analysis and Prediction

There are various tools for predicting physical properties using sequence information. Some of the major ones are discussed below:

1. Calculate pI/Mw.

2. protoparam.

3. SAPS.

4. Protein hydrophobicity prediction.

5. PEST and PESTfind.

6. Coil, Paircoil and Multicoil.

1. Calculate pI/Mw:

This tool calculates the isoelectric point and molecular weight of the input sequence.

2. Protoparam:

It is a tool that allows the calculation of various physical and chemical parameters for a given protein.

3. Predicting Protein Hydrophobicity:

Protoscale can be used to predict the hydrophobicity of proteins.

4. PEST and PESTFind:

PEST stands for Proline, Glutamic Acid, Serine and Threonine residues.

PEST identifies potential PEST regions in the introduced probe using the molecular fraction of P, E, S and T components and the hydrophobicity index of the region.

5. Coil, Paracoil and Multicoil:

This program compares the sequences to a database of known parallel two-stranded coils and obtains a similarity score.

Paircoil predicts the location of coil-coil fields in sequence.

The Mulitcoil program predicts the location of coiled-coil regions in amino-acid sequences and classifies the predictions as dimeric or trimeric.

Secondary structure analysis and prediction

Important secondary structure prediction methods are:

1. Chou-Fasman method:

It relies on assigning a set of prediction values ​​to the residue and then applying the algorithm to the conformational parameters and positional frequencies.

2. GOR (Garnier, Osguthorpe and Robson) method:

The accuracy of prediction using this method is about 64%. This method works better for helixes than for sheets.

3. Nearest neighbor method:

This method is based on the hypothesis that short homologous sequences of amino acids have the same secondary structure tendencies.

4. Hidden Markov Models:

It is used to predict the secondary structure of a protein of a given conformational class alpha + beta as used in structural classification databases.

5. Multiple alignment based self-optimization method:

It is a secondary structure prediction program that uses multiple alignments.

2-D structure prediction

This is done in two steps:

1. Predicting inter-residue contacts:

Interresidue contacts can be predicted based on hydrogen bonding patterns between residues. Analyzes of correlated mutations are performed to predict long-range inter-residue contacts.

2. Predicting inter-strand contacts:

Prediction of inter-residue contacts can be simplified by predicting contacts between residues on adjacent strands. This can be done on the basis of mid-force capability.

conclusion

Protein structure prediction which is one of the most useful and important applications in bioinformatics can be done if the amino acid sequence of the protein is known. Many protein identification and characterization tools are available. However, to predict the structure and functions of transmembrane helicases, a specific class of proteins that includes GPCRs, is very essential, as they are important for therapeutic interactions. Although excellent tools and computational methods are available, none of the techniques are fully proven and the field remains very exciting for researchers.

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