sbdd lbdd fbdd.pptx

sbdd lbdd fbdd.pptx

• 1.
  ASPECT HOMOLOGY MODELINGTHREADING (FOLD RECOGNITION) AB INITIO MODELING PRINCIPLE Based on similarity to known protein structures (templates). Aligns the target sequence to a library of known protein structures without assuming a template. Predicts the protein structure from scratch without using any template. INPUT Target protein sequence and a homologous template structure(s). Target protein sequence only. Target protein sequence only. DATABASE USAGE Requires a database of known protein structures (PDB). Utilizes a structural database but doesn't rely on it exclusively. No specific structural database needed. COMPUTATIONAL COMPLEXITY Generally less computationally intensive compared to ab initio. Intermediate computational complexity. Generally more computationally intensive. SUCCESS RATE Highly successful when there are closely related templates. Moderate success rate, depending on the quality of threading algorithms and available templates. Success rate depends on protein size, complexity, and available computational resources. TEMPLATE DEPENDENCY Strongly dependent on the availability of suitable templates. Moderately dependent on templates but can sometimes find distant homologs. Not dependent on templates; aims to predict from scratch. ACCURACY Range of 90% or higher for the core protein structure. Range of 30% to 70% for global structural accuracy. Range from 10% to 50% or even lower. APPLICABILITY Effective for modeling proteins with close homologs in the PDB Useful for proteins without close homologs but with distant structural relatives. Applicable to any protein, but particularly challenging for large and complex structures. TOOLS SWISS-MODEL, MODELLER I-TASSER, Phyre2 Rosetta, Robetta Pranavi Uppuluri