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Homology Modeling The following document gives some indepth information about homology modeling. A modeling tutorial using DS Modeling (Accelrys) can be found here. STEP 1: Fold assignment To start the modeling process, we have to identify the template and define an alignment (residue-by-residue equivalences between the target and the template sequences. In homology modelling the stretches to be built are chosen according to their sequence alignment, consequently this is the most crucial step in a modeling process. Any errors at this stage are usually impossible to correct later . The sequences of the fold having the larger similarity with the target sequence will be taken as parents or templates. Currently, around 40% of all protein sequences can have at least one domain modelled on a related known protein structure . In particular, some proteins can have very low sequence identity and yet all share the same fold and a closely related function . The current theory of evolution would hold that such structures, having diverged from a common ancestor, often retain some functional and sequence similarity . In addition, divergent evolution has been recently reported on the basis of a biochemical pathway evolution for some proteins with a common (ba)8 barrel fold for which sequence similarity was not detected . Originally, searches of homologous sequences to the target were done with local alignement programs as for example: FASTA ; SSEARCH or BLAST that are able to find identities shared between pairs of related sequences. With the high rate at which new sequences become available from genomic initiatives the importance of the sensitive methods of recognizing distant homologies has increased. Such methods are the main source of annotation, hence in the last decade very sensitive approaches have been developed to recognise fold. They have succeeded in different degrees of identification of relationships between remote homologues. These methods include:
2) Advanced sequence comparison procedures that take into account multiple sequence alignments with a position specific scoring system , either provided by a coherent theory for profile methods using machine learning probabilistic models (Hidden Markov Models) ; by a position specific iterative BLAST (PSI-BLAST) ; or by searching in sequence space using intermediate sequences (ISS) . These methods were shown to get better results than simple threading . 3) Finally, new approaches incorporating sequence profiles and knowledge-based threading potential have been used, improving the recognition of remote homologues
2) It exploits the transitivity of homology like the intermediate sequence search , by which a query sequence is aligned to a database (i.e. SWISS-PROT) . Then, all aligned sequences with high significance similarity (E-values<0.001) are used as new seeds and this is iterated until no new sequences are found. This procedure implies a larger search than the obtained by a single sequence search.
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