Automatically builds 3D protein models by aligning target sequences with known template structures and satisfying spatial restraints derived from templates.
Provides a comprehensive Python interface that allows users to script custom modeling workflows and integrate MODELLER with other bioinformatics tools.
Specialized functionality for modeling regions where the target and template sequences don't align well, particularly useful for variable loops and insertion regions.
Can combine information from multiple template structures to build hybrid models that incorporate the best features from each template.
Includes built-in model quality assessment using the Discrete Optimized Protein Energy (DOPE) score and other statistical potentials.
Supports various alignment formats and provides tools for refining sequence-structure alignments, which are critical for modeling accuracy.
Pharmaceutical researchers use MODELLER to predict 3D structures of potential drug targets when experimental structures are unavailable. By modeling disease-related proteins, they can identify binding pockets and design small molecules that might modulate protein function. This accelerates early-stage drug discovery, particularly for novel targets without crystal structures.
Bioinformaticians employ MODELLER to infer protein function from structure when sequence-based methods are inconclusive. By comparing modeled structures to known functional sites in template proteins, researchers can predict catalytic residues, binding sites, and molecular interactions. This is particularly valuable for annotating genomes where many proteins have unknown functions.
Researchers studying genetic diseases use MODELLER to model how mutations affect protein structure. By comparing wild-type and mutant models, they can predict structural disruptions that might explain disease mechanisms. This helps prioritize experimental validation of pathogenic variants identified in genomic studies.
Large-scale structural genomics projects integrate MODELLER into automated pipelines to model thousands of proteins efficiently. The Python API allows batch processing and quality control automation, enabling systematic coverage of protein sequence space. This supports efforts to provide structural models for entire proteomes.
Educators use MODELLER in bioinformatics courses to teach homology modeling principles. Students learn complete workflows from sequence alignment to model validation, gaining practical skills in protein structure prediction. The software's academic accessibility makes it ideal for classroom use across institutions.
Protein engineers utilize MODELLER to design modified antibodies or enzymes with improved properties. By modeling how specific mutations affect structure, they can rationally design variants with enhanced stability, specificity, or catalytic activity before experimental testing, reducing laboratory trial-and-error.
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