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ASPECT HOMOLOGY MODELING THREADING (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

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sbdd lbdd fbdd.pptx

  • 1. ASPECT HOMOLOGY MODELING THREADING (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