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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 3: Model building Methods of model building
Several algorithms have been developed in order to obtain a rigid body superimposition between sequences no directly related (JIG-SAW , COMPOSER , among others). SCR construction follows the original approach of Greer using sequentially similar SCR from homologous proteins to define the new core from a multiple alignment: 1) superimposing the known structures of homologous proteins (parents) using the SCRs to construct a framework; 2) superimposing the closest template sequence to the target sequence in the averaged main chain of framework; 3) building the SVRs main chain conformations by fitting compatible structures in the anchored stumps of the framework (see section on SVRs modelling for identification of the stretches to use); and 4) completing the target structure by modelling the side-chains of the target sequence. The methods based on the satisfaction of spatial restraints (like MODELLER ) are based on generating as many constraints (or restraints) as possible from the structural alignments of the parents and building the target structure like in the NMR methods (using additional energetic restraints according to the correct stereochemistry of the protein polymer). It is clear that regions where the structure of the homologous templates can not be structurally aligned, or where an alignment between the target and the multiple alignment of the templates is not given, will have to be built with an additional function. Most of the structural changes are produced in the loop regions, but occasional secondary structures may also be involved in variable regions . In the case of multiple superimposed parents the coordinates are separated into conserved secondary structure elements and conserved loops. SVRs modelling can be seen as a mini protein folding problem, consequently the number of methods for predicting loop conformation are twofold: ab initio methods and adopting database searching techniques or knowledge-based approaches 1. The ab initio prediction is based on a conformational search guided by a scoring or energy function: (f,y) space sampling ; minimum perturbation random tweak method ; systematic conformational search ; global energy minimization , local energy minimization ; molecular dynamics simulations ; genetic algorithms ; Monte Carlo and molecular dynamics ; Monte Carlo sampling ; multiple copy sampling ; searching discrete conformations by dynamic programming ; self-consistent field optimization ; among others (for a review see ) 2. The database approach to loop prediction consists of finding a segment of main chain that fits the two stem regions of a loop. The procedure has improved since the early works on modeling and in the last few years instead of a single conformation a number of loop conformations are selected for each gap that is as uniformely spread as possible . Hence, the remaining loops from the multiple parent modelling and all loops in the single parent modelling are modelled from database searches in three different databases: 1) homologous structures ; 2) cluster database of loops ; and 3) nonredundant database of proteins with less than 25% homology and accuracy higher than 2.5 A. The requirements of the chosen loop cluster of conformations are twofold: 1) the fitting between the two bracing secondary structures, and 2) a sequence pattern presented in the target loop to model. This procedure is based on the successful work on canonical loop structures of immunoglobulin complementary determining regions (CDR) by Chothia et al.. Nevertheless, the database search is valid only for short and medium sized loops or for special cases where homologous proteins share some structural commonalities on the loops although still being considered variable regions (as is the case for immunoglobulins ). Up to date classifications of long loops have failed, and it has been demonstrated that a correlation between the geometric variables describing the loop stems is needed in order to obtain such classification. This was only asserted for short and medium sized loops .
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