Design of a Novel Globular Protein Fold with Atomic-Level Accuracy

Similar documents
Supporting Online Material for

Protein Structure Prediction, Engineering & Design CHEM 430

Presenter: She Zhang

Introduction to Comparative Protein Modeling. Chapter 4 Part I

Protein structure prediction. CS/CME/BioE/Biophys/BMI 279 Oct. 10 and 12, 2017 Ron Dror

CMPS 3110: Bioinformatics. Tertiary Structure Prediction

CMPS 6630: Introduction to Computational Biology and Bioinformatics. Tertiary Structure Prediction

Protein Dynamics. The space-filling structures of myoglobin and hemoglobin show that there are no pathways for O 2 to reach the heme iron.

Programme Last week s quiz results + Summary Fold recognition Break Exercise: Modelling remote homologues

HOMOLOGY MODELING. The sequence alignment and template structure are then used to produce a structural model of the target.

Bioinformatics. Macromolecular structure

Protein structure prediction. CS/CME/BioE/Biophys/BMI 279 Oct. 10 and 12, 2017 Ron Dror

Orientational degeneracy in the presence of one alignment tensor.

Alpha-helical Topology and Tertiary Structure Prediction of Globular Proteins Scott R. McAllister Christodoulos A. Floudas Princeton University

Template Free Protein Structure Modeling Jianlin Cheng, PhD

Basics of protein structure

Protein Structure Determination

Molecular Modeling. Prediction of Protein 3D Structure from Sequence. Vimalkumar Velayudhan. May 21, 2007

Giri Narasimhan. CAP 5510: Introduction to Bioinformatics. ECS 254; Phone: x3748

Homology Modeling (Comparative Structure Modeling) GBCB 5874: Problem Solving in GBCB

CAP 5510 Lecture 3 Protein Structures

Prediction and refinement of NMR structures from sparse experimental data

Section II Understanding the Protein Data Bank

Protein Structure Prediction

Introduction to" Protein Structure

SUPPLEMENTARY INFORMATION

Examples of Protein Modeling. Protein Modeling. Primary Structure. Protein Structure Description. Protein Sequence Sources. Importing Sequences to MOE

07/30/2012 Rose=a Conference. Principle for designing! ideal protein structures! Nobuyasu & Rie Koga University of Washington

Homology Modeling. Roberto Lins EPFL - summer semester 2005

Protein Structure Prediction II Lecturer: Serafim Batzoglou Scribe: Samy Hamdouche

Protein Structure. W. M. Grogan, Ph.D. OBJECTIVES

Template Free Protein Structure Modeling Jianlin Cheng, PhD

Biomolecules: lecture 10

Protein Structure Prediction

Template-Based Modeling of Protein Structure

proteins Ab initio protein structure assembly using continuous structure fragments and optimized knowledge-based force field

Building 3D models of proteins

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

Computer simulations of protein folding with a small number of distance restraints

TetraBASE: A Side Chain-Independent Statistical Energy for Designing Realistically Packed Protein Backbones

Protein Structure Prediction

Physiochemical Properties of Residues

Protein folding. Today s Outline

FlexPepDock In a nutshell

arxiv: v1 [cond-mat.soft] 22 Oct 2007

Lecture 26: Polymers: DNA Packing and Protein folding 26.1 Problem Set 4 due today. Reading for Lectures 22 24: PKT Chapter 8 [ ].

CS612 - Algorithms in Bioinformatics

Importance of chirality and reduced flexibility of protein side chains: A study with square and tetrahedral lattice models

Polypeptide Folding Using Monte Carlo Sampling, Concerted Rotation, and Continuum Solvation

Introduction to Computational Structural Biology

Supplementary Figure 1. Aligned sequences of yeast IDH1 (top) and IDH2 (bottom) with isocitrate

Quiz 2 Morphology of Complex Materials

Copyright Mark Brandt, Ph.D A third method, cryogenic electron microscopy has seen increasing use over the past few years.

114 Grundlagen der Bioinformatik, SS 09, D. Huson, July 6, 2009

Protein Structures. 11/19/2002 Lecture 24 1

Improving De novo Protein Structure Prediction using Contact Maps Information

Analysis and Prediction of Protein Structure (I)

Protein Folding Prof. Eugene Shakhnovich

Contact map guided ab initio structure prediction

Modeling for 3D structure prediction

From Amino Acids to Proteins - in 4 Easy Steps

Lecture 2 and 3: Review of forces (ctd.) and elementary statistical mechanics. Contributions to protein stability

COMP 598 Advanced Computational Biology Methods & Research. Introduction. Jérôme Waldispühl School of Computer Science McGill University

Motif Prediction in Amino Acid Interaction Networks

D Dobbs ISU - BCB 444/544X 1

Syllabus BINF Computational Biology Core Course

Ab-initio protein structure prediction

Acta Crystallographica Section D

THE TANGO ALGORITHM: SECONDARY STRUCTURE PROPENSITIES, STATISTICAL MECHANICS APPROXIMATION

SCOP. all-β class. all-α class, 3 different folds. T4 endonuclease V. 4-helical cytokines. Globin-like

Neural Networks for Protein Structure Prediction Brown, JMB CS 466 Saurabh Sinha

CAP 5510: Introduction to Bioinformatics CGS 5166: Bioinformatics Tools. Giri Narasimhan

Protein structure analysis. Risto Laakso 10th January 2005

Protein Structure Determination from Pseudocontact Shifts Using ROSETTA

INDEXING METHODS FOR PROTEIN TERTIARY AND PREDICTED STRUCTURES

Protein Structure Prediction 11/11/05

Molecular Mechanics. I. Quantum mechanical treatment of molecular systems

Type II Kinase Inhibitors Show an Unexpected Inhibition Mode against Parkinson s Disease-Linked LRRK2 Mutant G2019S.

Timothy Chan. Bojana Jankovic. Viet Le. Igor Naverniouk. March 15, 2004

ion mobility spectrometry IR spectroscopy

Figure 1. Molecules geometries of 5021 and Each neutral group in CHARMM topology was grouped in dash circle.

Homology Modeling I. Growth of the Protein Data Bank PDB. Basel, September 30, EMBnet course: Introduction to Protein Structure Bioinformatics

Gary Kleiger, Anjanabha Saha, Steven Lewis, Brian Kuhlman, and Raymond J. Deshaies

Improving the Physical Realism and Structural Accuracy of Protein Models by a Two-Step Atomic-Level Energy Minimization

CHAPTER 29 HW: AMINO ACIDS + PROTEINS

NMR, X-ray Diffraction, Protein Structure, and RasMol

Molecular Modeling Lecture 7. Homology modeling insertions/deletions manual realignment

Structure to Function. Molecular Bioinformatics, X3, 2006

Protein Modeling. Generating, Evaluating and Refining Protein Homology Models

A General Model for Amino Acid Interaction Networks

A Method for the Improvement of Threading-Based Protein Models

Biochemistry,530:,, Introduc5on,to,Structural,Biology, Autumn,Quarter,2015,

Course Notes: Topics in Computational. Structural Biology.

Details of Protein Structure

Lecture 2-3: Review of forces (ctd.) and elementary statistical mechanics. Contributions to protein stability

Chemical Shift Restraints Tools and Methods. Andrea Cavalli

Why Do Protein Structures Recur?

Presentation Outline. Prediction of Protein Secondary Structure using Neural Networks at Better than 70% Accuracy

Protein structure (and biomolecular structure more generally) CS/CME/BioE/Biophys/BMI 279 Sept. 28 and Oct. 3, 2017 Ron Dror

Small Molecules as Rotamers: Generation and Docking in

Transcription:

Design of a Novel Globular Protein Fold with Atomic-Level Accuracy Brian Kuhlman, Gautam Dantas, Gregory C. Ireton, Gabriele Varani, Barry L. Stoddard, David Baker Presented by Kate Stafford 4 May 05

Protein Structure Prediction Methods Ab initio structure prediction: uses quantum mechanics to model atom by atom Relatively inaccurate, very slow The only option for totally novel structures Homology modeling: relies on sequence alignment with a template of known structure More accurate, uses less computer time Very dependent on quality of alignment, which is dependent on sequence identity Impossible if no similar sequences have solved structures Tertiary structure is very difficult, but good algorithms for secondary structure prediction exist

Protein Design: Why? Designing a protein de novo is a good test of current modeling methods, especially energy functions and solvent models New protein designs could lead to the development of novel catalytic functionality

Top7: A Totally Artificial Protein Top7 is a 93-residue α/β protein whose topology and sequence are not found in the PDB or in SCOP This means Top7 s fold is in a region of conformational space not explored by (currently known) biological structures Top7 s crystal structure has a backbone RMSD of 1.7Å compared to the designed model For comparison: <3 is excellent for a homology-based model of a small- to medium-sized protein with high sequence identity to the template Figure removed due to copyright reasons. Please see: Kuhlman B., G. Dantas, G. C.Ireton, G. Varani, B. L.Stoddard, and D. Baker. "Design of a novel globular protein fold with atomic-level accuracy." Science 302, no. 5649 (Nov 21, 2003): 1364-8. Blue = model Red = crystal structure

Top7 Design Protocol I: Starting Structures The target topology was selected first, specifically because it was not known in nature For a given topology, geometric constraints must be identified (e.g., hydrogen bonding in sheets and helices) A set of 3D models satisfying the constraints was generated by combining 3- and 9-residue PDB fragments of the appropriate predicted secondary structure This gave 172 backbone structures differing from each other by 2-3Å (not very much) Figure removed due to copyright reasons. Please see: Kuhlman B., G. Dantas, G. C.Ireton, G. Varani, B. L.Stoddard, and D. Baker. "Design of a novel globular protein fold with atomic-level accuracy." Science 302, no. 5649 (Nov 21, 2003): 1364-8. Target topology with final Top7 sequence. Arrows represent hydrogen bonds. Pink hexagons are sheets, blue squares are helices, green circles are loops.

Top7 Design Protocol II: Starting Sequences A starting sequence was designed for each structure by searching an amino acid rotamer library All amino acids except cysteine were allowed at 71 positions The remaining 22 positions are on the sheet surfaces and were restricted to polar amino acids Unsurprisingly, these starting models had a high free energy

Top7 Design Protocol III: Optimization Backbone optimization: use Monte Carlo minimization to alter structure so that it better accommodates existing sequence 1. Perturb 1-5 randomly selected torsion angles 2. Optimize sterics by cycling through rotamers 3. Optimize torsion angles within 10 residues Sequence optimization: rotamers of new amino acids explored for low-energy side-chain packing 15 alternating cycles of each gives a final energy-minimized structure

Top7 Characteristics Final backbone model is only 1.1Å different from starting model Only 31% of residues are retained from initial to final design The synthesized protein is thermally stable to 98C Many side chains are superimposable (for comparison: 50% of native residue-residue contacts are reproduced in a decent model) Figure removed due to copyright reasons. Please see: Kuhlman B., G. Dantas, G. C.Ireton, G. Varani, B. L.Stoddard, and D. Baker. "Design of a novel globular protein fold with atomic-level accuracy." Science 302, no. 5649 (Nov 21, 2003): 1364-8. Blue = model Red = crystal structure

Top7 Structure Figure removed due to copyright reasons. Please see: Kuhlman B., G. Dantas, G. C.Ireton, G. Varani, B. L.Stoddard, and D. Baker. "Design of a novel globular protein fold with atomic-level accuracy." Science 302, no. 5649 (Nov 21, 2003): 1364-8.