Protein Modeling Methods. Knowledge. Protein Modeling Methods. Fold Recognition. Knowledge-based methods. Introduction to Bioinformatics

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Protein Modeling Methods Introduction to Bioinformatics Iosif Vaisman Ab initio methods Energy-based methods Knowledge-based methods Email: ivaisman@gmu.edu Protein Modeling Methods 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 Pattern searching sequence patterns structure patterns residue composition patterns Threading sequence-structure compatibility structure-sequence compatibility

Threading Segmental Threading Sequence-structure compatibility (fold recognition) Structure-sequence compatibility (inverse folding) Wu and Zhang, 2010 Segmental Threading Threading Only the local environment is taken into account Non-local contacts are assumed with generic peptide No gaps are allowed in the alignment Wu and Zhang, 2010 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 Swiss-Model (http://swissmodel.expasy.org) Modeller (http://salilab.org/modeller) CPHmodels (http://www.cbs.dtu.dk/services/cphmodels) Protein Model Portal (PMP) (http://www.proteinmodelportal.org)

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. http://igc.ethz.ch/gromos/) Model evaluation Swiss-PdbViewer (http://www.expasy.ch/spdbv/mainpage.html) Swiss-Model Request Types First Approach mode. Optimise mode. Combine mode. GPCR mode. 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 Structure verification and validation Bond lengths (Procheck) ----------------------------------------------------------------- Bond labeling Value sigma ----------------------------------------------------------------- C-N C-NH1 (except Pro) 1.329 0.014 C-N (Pro) 1.341 0.016 C-O C-O 1.231 0.020 Calpha-C CH1E-C (except Gly) 1.525 0.021 CH2G*-C (Gly) 1.516 0.018 Calpha-Cbeta CH1E-CH3E (Ala) 1.521 0.033 CH1E-CH1E (Ile,Thr,Val) 1.540 0.027 CH1E-CH2E (the rest) 1.530 0.020 N-Calpha NH1-CH1E (except Gly,Pro) 1.458 0.019 NH1-CH2G* (Gly) 1.451 0.016 N-CH1E (Pro) 1.466 0.015 ----------------------------------------------------------------- Bond angles (Procheck) ------------------------------------------------------------------ Angle labeling Value sigma ------------------------------------------------------------------ C-N-Calpha C-NH1-CH1E (except Gly,Pro) 121.7 1.8 C-NH1-CH2G* (Gly) 120.6 1.7 C-N-CH1E (Pro) 122.6 5.0 Calpha-C-N CH1E-C-NH1 (except Gly,Pro) 116.2 2.0 CH2G*-C-NH1 (Gly) 116.4 2.1 CH1E-C-N (Pro) 116.9 1.5 Calpha-C-O CH1E-C-O (except Gly) 120.8 1.7 CH2G*-C-O (Gly) 120.8 2.1 ------------------------------------------------------------------

Procheck output 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. Ca tetrahedral distortion - standard deviation of the z torsion angle (Ca, N, C, and Cb). 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 Procheck output - backbone G factors Procheck output - all atom G factors

Protein Modeling Methods Potential Energy Functions 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 Boas & Harbury, 2007 Molecular structure representation Potential Energy Function Elementary particles Atoms Groups of atoms Forcefields: AMBER, CHARMM, CVF, ECEPP, GROMOS Non-Bonded Interactions Bond length

Bond length Bond angle Bond angle Bond length and angle (parameters) Torsional angle Torsional angle (parameters)

Non-bonded terms Non-bonded terms (parameters) Potential Energy Function Energy Minimazation Forcefields: AMBER, CHARMM, CVF, ECEPP, GROMOS Energy Minimazation