In silico structure prediction

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In sillico Stucture Prediction

Primary structure (Amino acid sequence) Secondary structure (-helix,-sheet Tertiary structure (Three-dimensional structure formed by assembly of secondary structures Quaternary structure(Structure formed by more than one polypeptide chains


Experimental data X-ray crystallography

NMR spectroscopy

expensive & time consuming

Computational methodsHomology/comparative modeling

Fold recognition (threading)

Ab initio (de novo, new folds) methods

Homology/comparative modeling

modeling a protein 3D structure using a known experimentally determined structure of a homologous protein as a template

usually provides the most reliable result.

Used when the sequence is similar to a known structure with >30-50% identity).

two proteins belonging to the same family and sharing similar amino acid sequences, will have similar three-dimensional structures


template identification

amino acid sequence alignment (multiple sequence alignment)

alignment correction

backbone generation

generation of loops

side chain generation & optimization

ab initioloop building

overall model optimisation

model verification. Quality criteria, model quality

MSA gives an overview of the general features of the protein family, the degree of conservation, the consensus sequence motifs, etc.

the positions of insertions and deletions should be correct, likewise the conservation of important residues (active site residues)

The modeling software will thread sequence on the template structure. Creates a preliminary model of protein (backbone generation)

Building of missing parts, generation of side chains for replaced residues and optimization of side chain conformations.

At the last step the overall model needs to be optimized followed by verification of model quality.

Softwares used for modeling :

Swiss Model




(1)(2)(1)- sequence of the protein to be predicted(2)- MSA(3)- Homologus Template protein(4)- backbone generation(5)- overall model optimization(3)(4)(5)

Protein threading(fold recognition)

used to model those proteins which have the same fold as proteins of known structures, but do not have homologous proteins with known structure.

Fold recognition alignments are quite different from ordinary sequence alignments since they are evaluated from a structural perspective.

Threading works by using statistical knowledge of the relationship between the structures deposited in the PDB and the sequence of the protein which one wishes to model.

The prediction is made by "threading" (i.e. placing, aligning) each amino acid in the target sequence to a position in the template structure, and evaluating how well the target fits the template.

Steps involved:

The construction of a structure template database:

Select protein structures from the protein structure databases as structural templates.

Databases used are PDB, FSSP, SCOP, or CATH

2. The design of the scoring function:

Design a good scoring function to measure the fitness b/w targetsequences and templates based on the knowledge of the known relationships between the structures and the sequences.

A good scoring function should contain mutation potential, environmentfitness potential, pairwise potential, secondary structure compatibilities, and gap penalties.

The quality of the energy function is closely related to the predictionaccuracy, especially the alignment accuracy.

3. Threading alignment:

Align the target sequence with each of the structure templates by optimizing the designed scoring function.

This step is one of the major tasks of all threading-based structure prediction programs that take into account the pairwise contact potential; otherwise, a dynamic programming algorithm can fulfill it.

4. Threading prediction:

Select the threading alignment that is statistically most probable as the threading prediction.

Then construct a structure model for the target by placing the backbone atoms of the target sequence at their aligned backbone positions of the selected structural template

Protein threading software:







ab initio method

means from the beginning

predicts the native fold from amino acid sequence alone

Methods for ab initio prediction includes;

Molecular Dynamics (MD) simulations

Monte Carlo (MC)

Genetic Algorithms

Softwares used in ab initio methods




(1)(2)(1)- protein sequences(2)- suitable folds

Prediction of protein structure from folds