Bioinformatics Protein Structure Secondary Structure Prediction

Protein Secondary Structure Prediction

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Predictions of one-dimensional protein structures such as secondary structures is useful for predicting three-dimensional structure and important for understanding the sequence-structure relationship.

As an alternative to experimental techniques, structure analysis and prediction tools help predict protein structure according to their amino-acid sequences. Solving the structure of a given protein is highly important in medicine (for example, in drug design) and biotechnology (for example, in the design of novel enzymes). The field of computational protein prediction is thus evolving constantly, following the increase in computational power of machines and the development of intelligent algorithms.

The early methods suffered from a lack of data. Predictions were performed on single sequences rather than families of homologous sequences, and there were relatively few known 3D structures from which to derive parameters. Probably the most famous early methods are those of;

  • Chou & Fasman
  • Garnier, Osguthorbe
  • Robson (GOR) and Lim

There are now many web servers for structure prediction, some of the famous are enlisted here;

  • JPRED Consensus prediction 
  • DSC King & Sternberg 
  • PREDATOR Frischman & Argos (EMBL)
  • PHD
  • ZPRED server 
  •  nnPredict 
  •  BMERC PSA Server 
  •  SSP (Nearest-neighbor) 

The  aim of secondary structure prediction is to provide the location of alpha helices, and beta strands within a protein or protein family.

Protein secondary structures are traditionally characterized as 3 general states;

  • helix (H)
  • strand (E)
  • coil (C)

From these general three states, the DSSP program proposed a finer characterization of the secondary structures by extending the three states into eight states;

  • helix (G)
  • α-helix (H)
  • π-helix
  • β-stand (E)
  • bridge (B)
  • turn (T)
  • bend (S)

A great number of structure prediction software are developed for dedicated protein features and particularity, such as disorder prediction, dynamics prediction, structure conservation prediction, etc. Approaches include homology modeling, protein threading, ab initio methods, secondary structure prediction, and transmembrane helix and signal peptide prediction. Choosing the right method always begins by using the primary sequence of the unknown protein and searching the protein database for homologues.

Secondary structure prediction tools predict local secondary structures based only on the amino acid sequence of the protein. Predicted structures are then compared to the DSSP score, which is calculated based on the crystallographic structure of the protein .

Prediction methods for secondary structure mainly rely on databases of known protein structures and modern machine learning methods such as neural nets and support vector machines.

Advantages of predicting the secondary structures of Proteins 

  • tertiary structure prediction 
  • Protein function prediction
  • Protein classification
  • Predicting structural change
  • detection and alignment of remote homology between proteins
  • on detecting transmembrane regions, solvent-accessible residues, and other important features of molecules
  • Detection of hydrophobic region and hydrophilic region



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