Procheck output. Bond angles (Procheck) Structure verification and validation Bond lengths (Procheck) Introduction to Bioinformatics.

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1 Structure verification and validation Bond lengths (Procheck) Introduction to Bioinformatics Iosif Vaisman Bond labeling Value sigma C-N C-NH1 (except Pro) C-N (Pro) C-O C-O Calpha-C CH1E-C (except Gly) CH2G*-C (Gly) Calpha-Cbeta CH1E-CH3E (Ala) CH1E-CH1E (Ile,Thr,Val) CH1E-CH2E (the rest) N-Calpha NH1-CH1E (except Gly,Pro) NH1-CH2G* (Gly) N-CH1E (Pro) Bond angles (Procheck) Angle labeling Value sigma C-N-Calpha C-NH1-CH1E (except Gly,Pro) C-NH1-CH2G* (Gly) C-N-CH1E (Pro) Calpha-C-N CH1E-C-NH1 (except Gly,Pro) CH2G*-C-NH1 (Gly) CH1E-C-N (Pro) Calpha-C-O CH1E-C-O (except Gly) CH2G*-C-O (Gly) Procheck output a. Ramachandran plot quality - percentage of the protein's residues that are in the core regions of the Ramachandran plot. b. Peptide bond planarity - standard deviation of the protein structure's omega torsion angles. c. Bad non-bonded interactions - number of bad contacts per 100 residues. d. Cα tetrahedral distortion - standard deviation of the ζ torsion angle (Cα, N, C, and Cβ). e. Main-chain hydrogen bond energy - standard deviation of the hydrogen bond energies for main-chain hydrogen bonds. f. Overall G-factor - average of different G- factors for each residue in the structure. Procheck output Procheck output

2 Procheck output Procheck output - backbone G factors Procheck output - all atom G factors Dynamics of Database Growth EMBL PDB PDB Current Holdings (11/07/07) PDB Structures by Resolution Proteins Nucleic Acids Molecule Type Protein/ NA Compl Other Total X-ray Exp. Meth. NMR EM Other Total

3 PDB Redundancy PDB Growth Method Description 95% identity (one chain) 90% identity (one chain) 70% identity (one chain) 50% identity (one chain) 40% identity (one chain) 30% identity (one chain) # of Clusters Growth of New Folds in PDB Growth of New Folds in PDB "new folds" (blue) and "old folds" (orange) Secondary Structure: Computational Problems Structural classes of proteins Secondary structure characterization Secondary structure assignment Secondary structure prediction Protein structure classification all α all β α/β

4 Protein Structure Classification SCOP - Structural Classification of Proteins FSSP - Fold classification based on Structure-Structure alignment of Proteins CATH - Class, architecture, topology and homologous superfamily Current release: PDB Entries (June 2009) Domains. The SCOP database aims to provide a detailed and comprehensive description of the structural and evolutionary relationships between all proteins whose structure is known. Proteins are classified to reflect both structural and evolutionary relatedness. Many levels exist in the hierarchy; the principal levels are family, superfamily and fold Family: Clear evolutionarily relationship Superfamily: Probable common evolutionary origin Fold: Major structural similarity Family: Clear evolutionarily relationship Proteins clustered together into families are clearly evolutionarily related. Generally, this means that pairwise residue identities between the proteins are 30% and greater. However, in some cases similar functions and structures provide definitive evidence of common descent in the absense of high sequence identity; for example, many globins form a family though some members have sequence identities of only 15%. Superfamily: Probable common evolutionary origin Proteins that have low sequence identities, but whose structural and functional features suggest that a common evolutionary origin is probable are placed together in superfamilies. For example, actin, the ATPase domain of the heat shock protein, and hexakinase together form a superfamily. Fold: Major structural similarity Proteins are defined as having a common fold if they have the same major secondary structures in the same arrangement and with the same topological connections. Different proteins with the same fold often have peripheral elements of secondary structure and turn regions that differ in size and conformation. In some cases, these differing peripheral regions may comprise half the structure. Proteins placed together in the same fold category may not have a common evolutionary origin: the structural similarities could arise just from the physics and chemistry of proteins favoring certain packing arrangements and chain topologies. SCOP Statistics Class Folds Super Families families All alpha proteins All beta proteins Alpha and beta proteins (a/b) Alpha and beta proteins (a+b) Multi-domain proteins Membrane and cell surface proteins Small proteins Total

5 SCOP Statistics Protein Modeling Methods Class All alpha proteins All beta proteins Alpha/beta proteins (a/b) Alpha+beta proteins (a+b) Multi-domain proteins Membrane and cell surface proteins Small proteins Total Number of folds Number of superfamilies Number of families Ab initio methods: solution of a protein folding problem search in conformational space Energy-based methods: energy minimization molecular simulation Knowledge-based methods: homology modeling fold recogniion Knowledge Knowledge is... a pattern that exceeds certain threshold of interestingness. Factors that contribute to interestingness: coverage confidence statistical significance simplicity unexpectedness actionability Knowledge-based methods Finding patterns in known structures Deriving rules (usually in the form of PMF) Applying the rules Fold Recognition Threading Pattern searching sequence patterns structure patterns residue composition patterns Threading sequence-structure compatibility structure-sequence compatibility Sequence-structure compatibility (fold recognition) Structure-sequence compatibility (inverse folding)

6 Threading Only the local environment is taken into account Non-local contacts are assumed with generic peptide No gaps are allowed in the alignment Homology Modeling Identification of structurally conserved regions (using multiple alignment) Backbone construction (based on SCR) Loop construction (KB or conformational search) Side-chain restoration (KB, rotamer, or MM) Structure verification and evaluation Structure refinement (energy minimization) Homology Modeling Programs Modeller ( Swiss-Model ( Whatif ( Swiss-Model Method: Knowledge-based approach. Requirements: At least one known 3D-structure of a related protein. Good quality sequence alignements. Procedures: Superposition of related 3D-structures. Generation of a multiple a alignement. Generation of a framework for the new sequence. Rebuild lacking loops. Complete and correct backbone. Correct and rebuild side chains. Verify model structure quality and check packing. Refine structure by energy minimisation and molecular dynamics. Methods and Programs used by Swiss-Model Sequence Alignment BLAST (Altschul S.F., J. Mol. Biol. 215:403, 1990) SIM (Huang, X., Miller, M. Adv. Appl. Math.12:337, 1991) ProModII (Peitsch, M.C. Unpublished, Server-specific tool) Knowledge Based Protein Modelling ProMod (Peitsch M.C. Biochem Soc Trans 24:274, 1996) Energy Minimisation Gromos96 (van Gunsteren W.F. Model evaluation Swiss-PdbViewer ( Swiss-Model Request Types First Approach mode. Optimise mode. Combine mode. GPCR mode.

7 Model Confidence Factors The Model B-factors are determined as follows: The number of template structures used for model building. The deviation of the model from the template structures. The Distance trap value used for framework building. The Model B-factor is computed as: 85.0 * (1/ # selected template str.) * (Distance trap / 2.5) and 99.9 for all atoms added during loop and side-chain building