MODELLER is used for homology or comparative modeling of protein three-dimensional structures. The user provides an alignment of a sequence to be modeled with known related structures and MODELLER automatically calculates a model containing all non-hydrogen atoms. MODELLER implements comparative protein structure modeling by satisfaction of spatial restraints, and can perform many additional tasks, including de novo modeling of loops in protein structures, optimization of various models of protein structure with respect to a flexibly defined objective function, multiple alignment of protein sequences and/or structures, clustering, searching of sequence databases, comparison of protein structures, etc. These tutorials set on basic & advanced modeling with help to ease the usage of Modeller and give a better insight of this software.
This tutorial focuses on using Modeller version 9.9 on the Windows platform.
Click here to learn Modeller Download & Installation.
Model a sequence with high identity to a template.
Aligning TvLDH with the template.
Model a sequence based on multiple templates and bound to a ligand.
Loop refinement-Modeling the loop using ab-initio methods.
Modeling using a known ligand bound to the binding site.
Increase the accuracy of the modeling exercise by iterating the 4 step process based on Moulding.
The alignment-modeling-evaluation cycle. The case of Haloferax volcanii dihydrofolate reductase.
The alignment of known sequence 4dfr with unknown sequence HVDFR.
To output information to the alignment file in the PAP format.
Calculation of initial model using PIR alignment file.
Evaluation of model using DOPE potential.
Model a sequence based on a low identity to a template.
This exercise uses resources external to MODELLER in order to select a template for a difficult case of protein structure prediction.
Sequence search for putative gene of interest.
Template search using BLAST and PSI-BLAST to find suitable homologous sequence.
Target sequence submission to mGenThreader server for fold assignment.
Model a sequence using both template and cryo-EM data.
The exercise assesses the quality of generated models and loops by rigid fitting into cryo-EM maps, and improves them with flexible EM fitting.
Assess the quality of the models.
Fit models into cryo-EM maps.
Refine models with loop modeling.
Flexible Cryo-EM fitting with Flex-EM.
This page was last modified on 11 December 2012, at 18:00.