Protein Structure Prediction

1. Protein Structure Prediction

4. Protein Secondary Structure Prediction • Refers to the prediction of the conformational state of each amino acid residue of a protein sequence as one of the 3 possible states – helices, strands or coils. • Methods • Ab-Initio Based Method - predict secondary structures based on statistical calculations of the residues of a single query sequence. • Homology Based Method –based on secondary structural patterns conserved among multiple homologous sequences.

5. Ab-Initio Based Method • Chou-Fasman Method • GOR Method

6. Chou-Fasman Method • Determines the propensity of each residue to be in the helix, strand or turn conformation using observed frequencies found in protein crystal structures. • From PDB database, calculate the propensity for a given amino acid to adopt a certain secondary structure-type

7. Example: #Total residues= 20,000, #Ala residues = 2,000, #helix residues = 4,000, #Ala in helix = 500 P(a, aai) = 500/20,000, p(a) = 4,000/20,000, p(aai) = 2,000/20,000 P = 500 / (4,000/10) = 1.25

8. Amino acid propensities

9. Helix • Scan for window of 6 residues where average score > 1 (4 residues for helix and 2 residues for strand) • Propagate in both directions until 4 (or 3) residue window with mean propensity < 1 • Move forward and repeat • Strand • Scan for window of 5 residues where average score > 1 (3 residues for helix and 2 residues for strand) • Propagate in both directions until 4 (or 3) residue window with mean propensity < 1 • Move forward and repeat • Conflict solution Any region containing overlapping alpha-helical and beta-strand assignments are taken to be helical if the average P(helix) > P(strand). It is a beta strand if the average P(strand) > P(helix).

10. GOR Method • It is also based on propensity of each residue to be in one of the 4 conformational states, helix, strand, turn and coil. • It takes short range interactions of neighboring residues into account. • It examine a window of every 17 residues and sums up propensity scores for all residues for each of the 4 states. • The highest scored state defines the conformational state for the center residue in the window (9 position).

11. Homology – Based Method • It combines the ab initio secondary structure prediction of individual sequences and alignment information from multiple homologous sequences (>35% identity). • The idea behind this approach is that close protein homologs should adopt the same secondary and tertiary structure.

12. Prediction with Neural-Networks • It is a machine learning process that requires a structure of multiple layers of interconnected variables or nodes. • The input is an amino acid sequence and the output is the probability of a residue to adopt a particular structure. • Between input and output are many connected hidden layers where the machine learning takes place. • It has to be first trained by sequences with known structures. • The weight functions in hidden layers are optimized so they can relate input to output correctly.

14. Several third generation prediction algorithm • PHD • PSIPRED • HMMSTR • SOPMA • IPRED • PredictProtein

15. Protein Tertiary Structure Prediction • Homology Modeling • Threading and fold recognition

16. Homology Modeling Homology modeling, also known as comparative modeling of protein, refers to constructing an atomic-resolution model of the "target" protein from its amino acid sequence and an experimental three-dimensional structure of a related homologous protein (the "template").

17. The steps required in homology modeling • Template recognition and initial alignment • Backbone generation • Loop modeling • Side-chain modeling • Model optimization • Model validation

18. Flowchart showing steps involved in homology modeling

19. Homology Modeling Servers and Software. • SWISS-MODEL, An Automated Comparative Protein Modelling Server, TorstenSchwede, Manuel C. Peitsch & Nicolas Guex, ExPASy, Geneva, Switzerland. • You just submit your sequence! It finds the best template (if one exists), aligns the sequences, and returns the PDB file to you automatically. You can choose whether to get back a 3D alignment of the model with the template(s), or just the model. • DeepView: Integrated with SWISS-MODEL. See above under Tutorials. • WHAT IF Web Interface (click on Build/check/repair model). Roland Krause, GertVriend, Univ. Nijmegen (in USA, say "Nigh-maygen"), Netherlands.

20. Modeller (Sali and Blundell 1993)Modeller is a program for comparative protein structure modelling by satisfaction of spatial restraints. It can be described as “Modeling by satisfaction of restraints” uses a set of restraints derived from an alignment and the model is obtained by minimization of these restraints. These restraints can be from related protein structures or NMR experiments. User gives an alignment of sequences to be modelled with known structures. Modeller calculates a model with all non hydrogen atoms. It also performs comparison of protein structures or sequences, clustering of proteins, searching of sequence databases.

21. Threading and Fold Recognition • Proteins often adopt similar folds despite no significant sequence or functional similarity. • For many proteins there will be suitable template structures in PDB. • Unfortunately, lack of sequence similarity will mean that many of these are undetected by sequence-only comparison done in homology modelling.

22. Fold recognition methods attempt to detect the fold that is compatible with a particular query sequence. • Unlike sequence-only comparison, these methods take advantage of the extra information made available by 3D structure. • In effect, fold prediction methods turn the protein folding problem on its head: rather than predicting how a sequence will fold, they predict how well a fold will fit a sequence.

23. Threading and fold recognition programs • 3D-PSSM • GenThreader • Fugue • MUSTER