UNIX
        SGI IRIX

HOMOLOGY MODELING
        USES & BACKGROUND
        BASIC GUIDE
        FOLD ASSIGNMENT
        TEMPLATE & ALIGNMENT
        BUILDING THE MODEL
        REFINEMENT & EVALUATION

DOCKING
        USES
        BACKGROUND
        SETTING UP THE SYSTEM
        ACCESSING THE RESULTS

RATIONAL DRUG DESIGN
        USES
        BACKGROUND

MOLECULAR DYNAMICS
        USES
        BACKGROUND
        SETTING UP THE SYSTEM
        ACCESSING THE RESULTS

Homology Modeling

The following document gives some indepth information about homology modeling. A modeling tutorial using DS Modeling (Accelrys) can be found here.

STEP 2: Template selection and alignment

For the template selection, one or more templates can be used. The use of multiple templates is not justified when the sequence spread between parents, relative to the target, is not appropriate for the level of expected model error. If both the average level of sequence identity between target and parents is larger than 40% and the sequence spread is too small between parents, then a single parent is used . The search on the database produces several local alignments according to the best score that correlates both target and template sequences. However, this is not necessarily the best alignment to identify residue correspondences and construct the target protein conformation, because the procedure was tuned to find remote homolgues and not the best alignment. Therefore, although target and templates are likely to be correctly aligned if sharing more than 40% identity, they need to be realigned if they are in the "twilight zone" sharing less than 30% identity.

The optimal alignment between homologous proteins, one of them with known 3D structure (template), is further used for constructing a model of the spatial structure of the target. However, after superposition of protein cores, amino acids from loop regions can be significantly displaced . At least 2/3 of the comparative protein modelling cases are based on less than 40% sequence identity between target and templates. To obtain a reasonable level of accuracy, the models must be based on alignments with few errors. Such alignments can usually be obtained when the sequence identity between the modelled sequence and at least one known structure is larger than 30% . A remarkable improvement is obtained by using multiple alignments of global sequence plus additional structural informations instead of the pair sequence local alignments used on the search of likely relatives. Several alignment programs ( MULTIALIGN ; MULTAL ; CLUSTALW ) have been tested against a database of correctly aligned multiple sequences ( BaliBase) . After all, the recent approaches that include local and pre-processed alignments, like those already found by using PSI-BLAST (i.e. DbClustal ); or those recalculating the local ( i.e. using Lalign ) and pre-processed alignments for segemnt pairs (i.e. using Dialign2 ) as for example the program T-Coffee ; or by iterative refinement of the multiple alignement like the program Prrp have obtained extraordinary good results.

Nevertheless, all these alignements loose the structural information given by those templates for which the conformation is known. On superimposing very similar structures upon one another, one is immediately able to distinguish regions of higher conservation; these are commonly referred to as structurally conserved regions (SCRs), whilst those regions that present the largest differences in conformation are referred as structurally variable regions (SVRs). In order to avoid the lost of structural information we suggest the following re-alignement between the target a sequence and the template:

1) To obtain a multiple structural alignement between the templates with known structure

2) With the sequence alignment obtained previously for these templates proceed to calculate a hidden markov profile to align the target sequence to the HMM profile. or alternatively, use some of the following steps instead:

3') To align the related sequences (target , templates and extra related sequences) with Dbclustal, T-coffee, Prrp, etc.; check for the closest result to the structural alignment and refine manually the alignment of the target sequence.

3'') Use all the different alignments obtained by step 3 and/or 3' to model built several models and evaluate the final model by other means (see "evaluation of the model") to choose the best model.

3''') To align all the related sequences as in 3'; obtain hidden markov profiles with these alignements and align both hidden markov profiles obtained structuraly (from step 2) and sequencially (as in 3'). Several alignements of the target sequence with the templates with known 3D structure are extracted from the final alignments. These alignements will be used to model built several conformations of the target sequence as in 3'' and the resulting models will also be evaluated by other methods in order to choose the best model.
 

Sign Guest Book
View Guest Book