Alignment protein sequence with

FIGURE 3-30 Aligning protein sequences with the use of gaps. subtilis. Introduction of a gap in the B. subtilis sequence allows a bet-Shown here is the sequence alignment of a short section of the EF-Tu ter alignment of amino acid residues on either side of the gap. Iden-... 

FIGURE 3-30 Aligning protein sequences with the use of gaps. 

Further improvement of the map with these phases may reveal side chains more clearly. Now the trick is to identify some specific side chains so that the known amino-acid sequence of the protein can be aligned with visible features in the map. As mentioned earlier, chain termini are often ill-defined, so we need a foothold for alignment of sequence with map where the map is sharp. Many times the key is a short stretch of sequence containing several bulky hydrophobic residues, like Trp, Phe, and Tyr. Because they are hydro-phobic, they are likely to be in the interior where the map is clearer. Because they are bulky, their side-chain density is more likely to be identifiable. From such a foothold, the detailed model building can begin. 

Alignment of H. pylori protein sequences with homologous sequences from Mcthcmococcus jtmnaschii suggests conservation... 

How can we tell where to align the two sequences The simplest approach is to compare all possible juxtapositions of one protein sequence with another, in each case recording the number of identical residues that are aligned with one... 

A) - Alignment of eubacterial HU proteins sequences with Thermoplasma acidophilum protein HTa... 

DSSP [24] and HSSP [17] are databanks of secondary structure, based on proteins in the PDB. HSSP contains homology-derived structures of proteins, with each protein in PDB providing the basis for including sequence homologies and then aligning those sequences with the sequence of known structure from PDB. This provides implied secondary and tertiary structures for many more proteins than could be found experimentally. 

This branch of bioinformatics is concerned with computational approaches to predict and analyse the spatial structure of proteins and nucleic acids. Whereas in many cases the primary sequence uniquely specifies the 3D structure, the specific rules are not well understood, and the protein folding problem remains largely unsolved. Some aspects of protein structure can already be predicted from amino acid content. Secondary structure can be deduced from the primary sequence with statistics or neural networks. When using a multiple sequence alignment, secondary structure can be predicted with an accuracy above 70%. 

---