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STRUCTURAL BIOINFORMATICS Barry Grant University of Michigan www.thegrantlab.org 26-Jan-2016 BIOINF 525 http://bioboot.github.io/bioinf525_w16/

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MODULE OVERVIEW Objective: Provide an introduction to the practice of bioinformatics as well as a practical guide to using common bioinformatics databases and algorithms 1.1. ‣ Introduction to Bioinformatics 1.2. ‣ Sequence Alignment and Database Searching 1.3 ‣ Structural Bioinformatics 1.4 ‣ Genome Informatics: High Throughput Sequencing Applications and Analytical Methods

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Answers to last weeks homework (19/19): Answers week 2 Muddy Point Assessment (11/19): Responses - “More time to finish the assignment” - “I felt there was too much material to cover in one lab” - “The [NCBI] sites were so slow” - “More time with HMMER would be helpful” - “Very nice lab” WEEK TWO REVIEW

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Q18: NW DYNAMIC PROGRAMMING Match: +2 Mismatch: -1 Gap: -2 A G T T C 0 -2 -4 -6 -8 -10 A -2 +2 0 -2 -4 -6 T -4 0 +1 +2 0 -2 T -6 -2 -1 +3 +4 +2 G -8 -4 0 +1 +2 +3 C -10 -6 -2 -1 0 +4 A - T T G C | | | | A G T T - C A T T G C | | | A G T T C

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Check out the “Background Reading” material online: ‣ Achievements & Challenges in Structural Bioinformatics ‣ Protein Structure Prediction ‣ Biomolecular Simulation ‣ Computational Drug Discovery Complete the lecture 1.3 homework questions: http://tinyurl.com/bioinf525-quiz3 THIS WEEK’S HOMEWORK

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“Bioinformatics is the application of computers to the collection, archiving, organization, and analysis of biological data.” … A hybrid of biology and computer science

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“Bioinformatics is the application of computers to the collection, archiving, organization, and analysis of biological data.” Bioinformatics is computer aided biology!

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“Bioinformatics is the application of computers to the collection, archiving, organization, and analysis of biological data.” Bioinformatics is computer aided biology! Goal: Data to Knowledge

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So what is structural bioinformatics?

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So what is structural bioinformatics? Aims to characterize and interpret biomolecules and their assembles at the molecular & atomic level … computer aided structural biology!

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Why should we care?

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Why should we care? Because biomolecules are “nature’s robots” … and because it is only by coiling into specific 3D structures that they are able to perform their functions

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BIOINFORMATICS DATA Genomes DNA & RNA sequence Protein sequence Protein families, motifs and domains Protein structure Protein interactions Chemical entities Pathways Systems Gene expression Literature and ontologies DNA & RNA structure

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STRUCTURAL DATA IS CENTRAL Genomes DNA & RNA sequence DNA & RNA structure Protein sequence Protein families, motifs and domains Protein structure Protein interactions Chemical entities Pathways Systems Gene expression Literature and ontologies

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STRUCTURAL DATA IS CENTRAL Genomes DNA & RNA sequence DNA & RNA structure Protein sequence Protein families, motifs and domains Protein structure Protein interactions Chemical entities Pathways Systems Gene expression Literature and ontologies Sequence > Structure > Function change color to gray and yellow from black and red?

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STRUCTURAL DATA IS CENTRAL Genomes DNA & RNA sequence DNA & RNA structure Protein sequence Protein families, motifs and domains Protein structure Protein interactions Chemical entities Pathways Systems Gene expression Literature and ontologies Sequence > Structure > Function E N E R G E T I C S D Y N A M I C S > >

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• Unfolded chain of amino acid chain • Highly mobile • Inactive • Ordered in a precise 3D arrangment • Stable but dynamic • Active in specific “conformations” • Specific associations & precise reactions Sequence Function Structure

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In daily life, we use machines
 with functional structure and moving parts

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Genomics is a great start …. ▪ But a parts list is not enough to understand how a bicycle works

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… but not the end ▪ We want the full spatiotemporal picture, and an ability to control it ▪ Broad applications, including drug design, medical diagnostics, chemical manufacturing, and energy

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Extracted from The Inner Life of a Cell by Cellular Visions and Harvard [YouTube link: https://www.youtube.com/watch?v=y-uuk4Pr2i8 ]

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• Unfolded chain of amino acid chain • Highly mobile • Inactive • Ordered in a precise 3D arrangment • Stable but dynamic • Active in specific “conformations” • Specific associations & precise reactions Sequence Function Structure

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KEY CONCEPT: ENERGY LANDSCAPE Native Compact, Ordered Unfolded Expanded, Disordered

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KEY CONCEPT: ENERGY LANDSCAPE Native Compact, Ordered Molten Globule Unfolded Compact, Disordered Expanded, Disordered 0.1 microseconds 1 millisecond Barrier Height Barrier crossing time ~exp(Barrier Height)

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KEY CONCEPT: ENERGY LANDSCAPE Native State(s) Compact, Ordered Molten Globule State Unfolded State Compact, Disordered Expanded, Disordered 0.1 microseconds 1 millisecond Barrier Height Barrier crossing time ~exp(Barrier Height) Multiple Native Conformations (e.g. ligand bound and unbound)

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OUTLINE: ‣ Overview of structural bioinformatics • Major motivations, goals and challenges ‣ Fundamentals of protein structure • Composition, form, forces and dynamics ‣ Representing and interpreting protein structure • Modeling energy as a function of structure ‣ Example application areas • Predicting functional dynamics & drug discovery

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OUTLINE: ‣ Overview of structural bioinformatics • Major motivations, goals and challenges ‣ Fundamentals of protein structure • Composition, form, forces and dynamics ‣ Representing and interpreting protein structure • Modeling energy as a function of structure ‣ Example application areas • Predicting functional dynamics & drug discovery

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TRADITIONAL FOCUS PROTEIN, DNA  AND SMALL MOLECULE DATA SETS  WITH MOLECULAR STRUCTURE Protein (PDB) DNA (NDB) Small Molecules (CCDB)

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Motivation 1: Detailed understanding of molecular interactions Provides an invaluable structural context for conservation and mechanistic analysis leading to functional insight.

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Motivation 1: Detailed understanding of molecular interactions Computational modeling can provide detailed insight into functional interactions, their regulation and potential consequences of perturbation. Grant et al. PLoS. Comp. Biol. (2010)

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Motivation 2: Lots of structural data is becoming available 115,306 (1/20/2016) Data from: http://www.rcsb.org/pdb/statistics/ Structural Genomics has contributed to driving down the cost and time required for structural determination

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Motivation 2: Lots of structural data is becoming available Structural Genomics has contributed to driving down the cost and time required for structural determination purification expression cloning struc. refinement struc. validation annotation publication phasing data collection xtal screening tracing bl xtal mounting crystallization imaging harvesting target selection PDB Image Credit: “Structure determination assembly line” Adam Godzik

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Motivation 3: Theoretical and computational predictions have been, and continue to be, enormously valuable and influential!

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SUMMARY OF KEY MOTIVATIONS Sequence > Structure > Function • Structure determines function, so understanding structure helps our understanding of function Structure is more conserved than sequence • Structure allows identification of more distant evolutionary relationships Structure is encoded in sequence • Understanding the determinants of structure allows design and manipulation of proteins for industrial and medical advantage

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Residue No. Goals: • Analysis • Visualization • Comparison • Prediction • Design Grant et al. JMB. (2007)

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Goals: • Analysis • Visualization • Comparison • Prediction • Design Scarabelli and Grant. PLoS. Comp. Biol. (2013)

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Goals: • Analysis • Visualization • Comparison • Prediction • Design Scarabelli and Grant. PLoS. Comp. Biol. (2013)

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Goals: • Analysis • Visualization • Comparison • Prediction • Design kinesin G-protein myosin Grant et al. unpublished

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Goals: • Analysis • Visualization • Comparison • Prediction • Design Grant et al. PLoS One (2011, 2012)

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Goals: • Analysis • Visualization • Comparison • Prediction • Design Grant et al. PLoS Biology (2011)

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MAJOR RESEARCH AREAS  AND CHALLENGES Include but are not limited to: • Protein classification • Structure prediction from sequence • Binding site detection • Binding prediction and drug design • Modeling molecular motions • Predicting physical properties (stability, binding affinities) • Design of structure and function • etc... With applications to Biology, Medicine, Agriculture and Industry

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‣ Overview of structural bioinformatics • Major motivations, goals and challenges ‣ Fundamentals of protein structure • Composition, form, forces and dynamics ‣ Representing and interpreting protein structure • Modeling energy as a function of structure ‣ Example application areas • Predicting functional dynamics & drug discovery NEXT UP:

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HIERARCHICAL STRUCTURE OF PROTEINS Primary Secondary Tertiary Quaternary amino acid residues Alpha helix Polypeptide chain Assembled subunits > > > Image from: http://www.ncbi.nlm.nih.gov/books/NBK21581/

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RECAP: AMINO ACID NOMENCLATURE main chain (backbone) side chain (R group) Image from: http://www.ncbi.nlm.nih.gov/books/NBK21581/

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AMINO ACIDS CAN BE GROUPED BY THE PHYSIOCHEMICAL PROPERTIES Image from: http://www.ncbi.nlm.nih.gov/books/NBK21581/

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AMINO ACIDS POLYMERIZE THROUGH PEPTIDE BOND FORMATION side%chains% backbone% Image from: http://www.ncbi.nlm.nih.gov/books/NBK21581/

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PEPTIDES CAN ADOPT DIFFERENT CONFORMATIONS BY VARYING THEIR  PHI & PSI BACKBONE TORSIONS Peptide%bond%is%planer% (Cα,%C,%O,%N,%H,%Cα%%all% lie%in%the%same%plane) φ" ψ Bond%angles%and%lengths% are%largely%invariant C?terminal N?terminal Image from: http://www.ncbi.nlm.nih.gov/books/NBK21581/

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PHI VS PSI PLOTS ARE KNOWN AS RAMACHANDRAN DIAGRAMS • Steric%hindrance%dictates%torsion%angle%preference%% • Ramachandran%plot%show%preferred%regions%of%%φ%and%ψ%dihedral% angles%which%correspond%to%major%forms%of%secondary"structure Alpha Helix Beta Sheet Image from: http://www.ncbi.nlm.nih.gov/books/NBK21581/

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MAJOR SECONDARY STRUCTURE TYPES ALPHA HELIX & BETA SHEET Hydrogen%bond:"i→i+4 α4helix" • Most%common%from%has%3.6%residues%per%turn% (number%of%residues%in%one%full%rotation)%%% • Hydrogen%bonds%(dashed%lines)%between% residue%i"and%i+4"stabilize%the%structure% • The%side%chains%(in%green)%protrude%outward% •310 ?helix%and%π?helix%forms%are%less%common Image from: http://www.ncbi.nlm.nih.gov/books/NBK21581/

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MAJOR SECONDARY STRUCTURE TYPES ALPHA HELIX & BETA SHEET In%antiparallel"β4sheets" •Adjacent%β?strands%run%in%opposite%directions%% •Hydrogen%bonds%(dashed%lines)%between%NH%and%CO% stabilize%the%structure% •The%side%chains%(in%green)%are%above%and%below%the%sheet Image from: http://www.ncbi.nlm.nih.gov/books/NBK21581/

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MAJOR SECONDARY STRUCTURE TYPES ALPHA HELIX & BETA SHEET In%parallel"β4sheets" •Adjacent%β?strands%run%in%same%direction% •Hydrogen%bonds%(dashed%lines)%between%NH%and%CO% stabilize%the%structure% •The%side%chains%(in%green)%are%above%and%below%the%sheet Image from: http://www.ncbi.nlm.nih.gov/books/NBK21581/

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What Does a Protein Look like?

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• Proteins%are%stable%(and%hidden)%in%water

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• Proteins%closely%interact%with%water

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• Proteins%are%close%packed%solid%but%flexible%objects%(globular)

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• Due%to%their%large%size%and%%complexity%it%is%often% hard%to%see%whats%important%in%the%structure%

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• Backbone%or%main?chain%representation%can%help% trace%chain%topology%

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• Backbone%or%main?chain%representation%can%help% trace%chain%topology%&%reveal%secondary%structure

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• Simplified%secondary%structure%representations%are% commonly%used%to%communicate%structural%details%% • Now%we%can%clearly%see%2o,%3o%and%4o%structure% • Coiled%chain%of%connected%secondary%structures

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DISPLACEMENTS REFLECT INTRINSIC FLEXIBILITY Superposition%of%all%482%structures%in%RCSB%PDB% (23/09/2015)%

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DISPLACEMENTS REFLECT INTRINSIC FLEXIBILITY Principal%component%analysis%(PCA)%of%experimental%structures%

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KEY CONCEPT: ENERGY LANDSCAPE Native State(s) Compact, Ordered Molten Globule State Unfolded State Compact, Disordered Expanded, Disordered 0.1 microseconds 1 millisecond Barrier Height Barrier crossing time ~exp(Barrier Height) Multiple Native Conformations (e.g. ligand bound and unbound)

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Key%forces%affec`ng%structure: • H?bonding% • Van%der%Waals% • Electrosta`cs% • Hydrophobicity% • Disulfide%Bridges 150°%<%θ%<%180° d 2.6%Å%<%d%<%3.1Å

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Repulsion% Airac`on% d 3%Å%<%d%<%4Å Key%forces%affec`ng%structure: • H?bonding% • Van%der%Waals% • Electrosta`cs% • Hydrophobicity% • Disulfide%Bridges

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• H?bonding% • Van%der%Waals% • Electrosta`cs% • Hydrophobicity% • Disulfide%Bridges (some%`me%called%IONIC%BONDs%or%SALT%BRIDGEs) E = Energy k = constant D = Dielectric constant (vacuum = 1; H2 O = 80) q1 & q2 = electronic charges (Coulombs) r = distance (Å) Coulomb’s"law d%%%%%%%%%%d%=%2.8%Å Key%forces%affec`ng%structure:

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• H?bonding% • Van%der%Waals% • Electrosta`cs% • Hydrophobicity% • Disulfide%Bridges The%force%that%causes%hydrophobic%molecules%or%nonpolar%por`ons%of%molecules%to% aggregate%together%rather%than%to%dissolve%in%water%is%called%Hydrophobicity%(Greek," “water"fearing”).%This%is%not%a%separate%bonding%force;%rather,%it%is%the%result%of%the% energy%required%to%insert%a%nonpolar%molecule%into%water. Key%forces%affec`ng%structure:

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Forces%affec`ng%structure: • H?bonding% • Van%der%Waals% • Electrosta`cs% • Hydrophobicity% • Disulfide%Bridges 10 Other%names:% cys`ne%bridge% disulfide%bridge Hair%contains%lots%of%disulfide%bonds% which%are%broken%and%reformed%by%heat

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‣ Overview of structural bioinformatics • Major motivations, goals and challenges ‣ Fundamentals of protein structure • Composition, form, forces and dynamics ‣ Representing and interpreting protein structure • Modeling energy as a function of structure ‣ Example application areas • Predicting functional dynamics & drug discovery NEXT UP:

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PDB Growing but not as rapidly as Sequence repositories It is highly biased towards crystallography of enzymes

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Search: HIV

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Search: 1HSG (PDB ID)

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Slide Credit: RCSB PDB

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PDB FILE FORMAT • PDB files contains atomic coordinates and associated information.

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KEY CONCEPT: POTENTIAL FUNCTIONS DESCRIBE A SYSTEMS ENERGY AS A FUNCTION OF ITS STRUCTURE Two%main%approaches:% (1).%Physics?Based% (2).%Knowledge?Based%

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Two%main%approaches:% (1).%Physics?Based% (2).%Knowledge?Based% KEY CONCEPT: POTENTIAL FUNCTIONS DESCRIBE A SYSTEMS ENERGY AS A FUNCTION OF ITS STRUCTURE

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PHYSICS-BASED POTENTIALS
 ENERGY TERMS FROM PHYSICAL THEORY The Potential Energy Function Ubond = oscillations about the equilibrium bond length Uangle = oscillations of 3 atoms about an equilibrium bond angle Udihedral = torsional rotation of 4 atoms about a central bond Unonbond = non-bonded energy terms (electrostatics and Lenard-Jones) CHARMM P.E. function, see: http://www.charmm.org/

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img044.jpg (400x300x24b jpeg) Slide Credit: Michael Levitt

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2223234 img054.jpg (400x300x24b jpeg) Slide Credit: Michael Levitt

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PHYSICS-ORIENTED APPROACHES Weaknesses% Fully%physical%detail%becomes%computa`onally%intractable% Approxima`ons%are%unavoidable% (Quantum%effects%approximated%classically,%water%may%be%treated%crudely)% Parameteriza`on%s`ll%required% Strengths% Interpretable,%provides%guides%to%design% Broadly%applicable,%in%principle%at%least% Clear%pathways%to%improving%accuracy% Status% Useful,%widely%adopted%but%far%from%perfect% % Mul`ple%groups%working%on%fewer,%beier%approxs% Force%fields,%quantum% entropy,%water%effects% Moore’s%law:%hardware%improving

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–Johnny Appleseed Put Levit’s Slide here on Computer Power Increases!

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SIDE-NOTE: GPUS AND ANTON SUPERCOMPUTER

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SIDE-NOTE: GPUS AND ANTON SUPERCOMPUTER

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Two%main%approaches:% (1).%Physics?Based% (2).%Knowledge?Based% KEY CONCEPT: POTENTIAL FUNCTIONS DESCRIBE A SYSTEMS ENERGY AS A FUNCTION OF ITS STRUCTURE

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KNOWLEDGE-BASED DOCKING POTENTIALS His`dine% Ligand
 carboxylate Aroma`c
 stacking

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Example:%ligand%carboxylate%O%to%protein%his`dine%N% Find%all%protein?ligand%structures%in%the%PDB%with%a%ligand%carboxylate%O% 1. %%For%each%structure,%histogram%the%distances%from%O%to%every%his`dine%N% 2. %%Sum%the%histograms%over%all%structures%to%obtain%p(rO?N )% 3. %%Compute%E(rO?N )%from%p(rO?N ) ENERGY DETERMINES PROBABILITY (STABILITY) Energy Probability x Boltzmann: Inverse%Boltzmann: Basic idea: Use probability as a proxy for energy

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KNOWLEDGE-BASED DOCKING POTENTIALS A%few%types%of%atom%pairs,%out%of%several%hundred%total Atom?atom%distance%(Angstroms) Nitrogen+/Oxygen? Aroma`c%carbons Alipha`c%carbons “PMF”, Muegge & Martin, J. Med. Chem. (1999) 42:791

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KNOWLEDGE-BASED POTENTIALS Weaknesses% Accuracy%limited%by%availability%of%data% Strengths% Rela`vely%easy%to%implement% Computa`onally%fast% Status% Useful,%far%from%perfect% % May%be%at%point%of%diminishing%returns% (not%always%clear%how%to%make%improvements)

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‣ Overview of structural bioinformatics • Major motivations, goals and challenges ‣ Fundamentals of protein structure • Composition, form, forces and dynamics ‣ Representing and interpreting protein structure • Modeling energy as a function of structure ‣ Example application areas • Predicting functional dynamics & drug discovery NEXT UP:

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PREDICTING FUNCTIONAL DYNAMICS • Proteins"are"intrinsically"flexible"molecules"with"internal" moCons"that"are"oDen"inCmately"coupled"to"their" biochemical"funcCon" – E.g.%%ligand%and%substrate%binding,%conforma`onal%ac`va`on,% allosteric%regula`on,%etc.% • Thus"knowledge"of"dynamics"can"provide"a"deeper" understanding"of"the"mapping"of"structure"to"funcCon"" – Molecular"dynamics%(MD)%and%normal"mode"analysis%(NMA)%are% two%major%methods%for%predic`ng%and%characterizing%molecular% mo`ons%and%their%proper`es

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McCammon, Gelin & Karplus, Nature (1977) [ See: https://www.youtube.com/watch?v=ui1ZysMFcKk ] • Use force-field to find Potential energy between all atom pairs • Move atoms to next state • Repeat to generate trajectory MOLECULAR DYNAMICS SIMULATION

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Divide%Cme%into%discrete%(~1fs)%Cme"steps"(∆t)" (for%integra`ng%equa`ons%of%mo`on,%see%below) t

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Divide%Cme%into%discrete%(~1fs)%Cme"steps"(∆t)" (for%integra`ng%equa`ons%of%mo`on,%see%below) At%each%`me%step%calculate%pair?wise%atomic%forces%(F(t))%% (by%evalua`ng%force4field"gradient) Nucleic motion described classically Empirical force field t

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Divide%Cme%into%discrete%(~1fs)%Cme"steps"(∆t)" (for%integra`ng%equa`ons%of%mo`on,%see%below) At%each%`me%step%calculate%pair?wise%atomic%forces%(F(t))%% (by%evalua`ng%force4field"gradient) Nucleic motion described classically Empirical force field Use%the%forces%to%calculate%velociCes%and%move%atoms%to%new%posiCons% (by%integra`ng%numerically%via%the%“leapfrog”%scheme)" t

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BASIC ANATOMY OF A MD SIMULATION Divide%Cme%into%discrete%(~1fs)%Cme"steps"(∆t)" (for%integra`ng%equa`ons%of%mo`on,%see%below) At%each%`me%step%calculate%pair?wise%atomic%forces%(F(t))%% (by%evalua`ng%force4field"gradient) Nucleic motion described classically Empirical force field Use%the%forces%to%calculate%velociCes%and%move%atoms%to%new%posiCons% (by%integra`ng%numerically%via%the%“leapfrog”%scheme)" REPEAT,""(iterate"many,"many"Cmes…"1ms"="1012"Cme"steps)" t

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MD%Predic`on%of%Func`onal%Mo`ons% “close” “open” Yao%and%Grant,%Biophys%J.%(2013)

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Simula`ons%Iden`fy%Key%Residues% Media`ng%Dynamic%Ac`va`on% Yao%…%Grant,%Journal%of%Biological%Chemistry%(2016)

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EXAMPLE APPLICATION OF MOLECULAR SIMULATIONS TO GPCRS Cell$ membrane Binding GPCR Activation G-protein- coupling G$protein Structure determines function • Example: G protein-coupled receptors (GPCRs) • Largest class of human drug targets • Function: allow the cell to sense and respond to molecules outside it Binding GPCR G protein Cell Membrane

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PROTEINS JUMP BETWEEN MANY, HIERARCHICALLY ORDERED “CONFORMATIONAL SUBSTATES” t H. Frauenfelder et al., Science 229 (1985) 337

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MOLECULAR DYNAMICS IS VERY EXPENSIVE %Example:%F1 ?ATPase%in%water%(183,674%atoms)%for%1%nanosecond:%% %%=>%106%integration%steps%% %%=>%8.4%*%1011%floating%point%operations/step%%% %%%%%%%[n(n?1)/2%interactions]% %%%%%%%Total:% 8.4%*%1017%flop% %%%%%%(on%a%100%Gflop/s%cpu:% ca"25"years!)% …"but"performance"has"been"improved"by"use"of:" %%%%%%multiple%time%stepping% % ca.%%2.5%years% %%%%%%fast%multipole%methods%% ca.%%%1%year%% %%%%%%parallel%computers%% % %%%%%%%%ca.%%5%days% modern%GPUs%%% % %%%%%%%%ca.""1"day" (Anton"supercomputer%%%%%%%%%ca.""minutes) Improve this slide

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• MD%is%s`ll%`me?consuming%for%large%systems% • Elas`c%network%model%NMA%(ENM?NMA)%is%an%example%of%a% lower%resolu`on%approach%that%finishes%in%seconds%even%for% large%systems. Atomis`c C.%G. • 1%bead%/
 1%amino%acid% • Connected%by% springs Coarse%Grained i j r ij COARSE GRAINING: NORMAL MODE ANALYSIS (NMA)

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NMA models the protein as a network of elastic strings Proteinase K

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‣ Overview of structural bioinformatics • Major motivations, goals and challenges ‣ Fundamentals of protein structure • Composition, form, forces and dynamics ‣ Representing and interpreting protein structure • Modeling energy as a function of structure ‣ Example application areas • Predicting functional dynamics & drug discovery NEXT UP:

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THE TRADITIONAL EMPIRICAL PATH TO DRUG DISCOVERY Compound"library
 (commercial,"in4house,
 syntheCc,"natural) High"throughput"screening
 (HTS) Hit"confirmaCon Lead"compounds
 (e.g.,"µM"Kd ) Lead"opCmizaCon" (Medicinal"chemistry) Potent"drug"candidates
 (nM"Kd )" Animal"and"clinical"
 evaluaCon

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COMPUTER-AIDED LIGAND DESIGN Aims%to%reduce%number%of%compounds%synthesized%and%assayed% Lower%costs% Reduce%chemical%waste% Facilitate%faster%progress

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Two%main%approaches:% (1).%Receptor/Target?Based" (2).%Ligand/Drug4Based%

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Two%main%approaches:% (1).%Receptor/Target?Based" (2).%Ligand/Drug4Based%

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SCENARIO 1: RECEPTOR-BASED DRUG DISCOVERY HIV%Protease/KNI?272%complex Structure%of%Targeted%Protein%Known:%Structure?Based%Drug%Discovery

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PROTEIN-LIGAND DOCKING VDW Dihedral Screened%Coulombic + 4 Poten`al%func`on
 Energy%as%func`on%of%structure Docking%soware 
 Search%for%structure%of%lowest%energy Structure-Based Ligand Design

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STRUCTURE-BASED VIRTUAL SCREENING Candidate%ligands Experimental%assay Compound% database 3D"structure"of"target
 (crystallography,%NMR,% modeling) Virtual"screening
 (e.g.,%computaConal"docking) Ligands Ligand%op`miza`on
 Med%chem,%crystallography,% modeling Drug"candidates

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COMPOUND LIBRARIES Commercial%% (in?house%pharma) Government%(NIH) Academia

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FRAGMENTAL STRUCTURE-BASED SCREENING “Fragment”"library 3D"structure"of"target
 Fragment"docking Compound%design hip://www.beilstein?ins`tut.de/bozen2002/proceedings/Jho`/jho`.html Experimental%assay%and%ligand%op`miza`on
 Med%chem,%crystallography,%modeling Drug"candidates

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Small organic probe fragment affinities map multiple potential binding sites across the structural ensemble. Multiple non active-site pockets identified * * GDP GTP Residue No. Probe Occupancy ethanol isopropanol acetone cyclohexane phenol methylamine benzene acetamide

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Ensemble docking & candidate inhibitor testing 1321N1 U138 U251 U373 U343 Ras-GTP Total Ras Compound effect on U251 cell line Ras activity in different cell lines 3) NCI ligands that target the C1 pocket of K-ras 13616 23895 36818 99660 117028 121182 99660 Top hits from ensemble docking against distal pockets were tested for inhibitory effects on basal ERK activity in glioblastoma cell lines. Ensemble computational docking PLoS One (2011, 2012) DMSO 662796 36818 643000 117028 P-ERK1/2 Total ERK1/2 10 µM Compound testing in cancer cell lines

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Proteins%and%Ligand%are%Flexible + Ligand Protein Complex ΔGo

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COMMON SIMPLIFICATIONS USED IN 
 PHYSICS-BASED DOCKING Quantum%effects%approximated%classically% Protein%oen%held%rigid% Configura`onal%entropy%neglected% Influence%of%water%treated%crudely

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Two%main%approaches:% (1).%Receptor/Target?Based" (2).%Ligand/Drug4Based%

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e.g.%MAP%Kinase%Inhibitors Using%knowledge%of% exis`ng%inhibitors%to% discover%more Scenario"2% Structure%of%Targeted%Protein%Unknown:%Ligand?Based%Drug%Discovery

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Why%Look%for%Another%Ligand%if%You%Already%Have%Some? Experimental%screening%generated%some%ligands,%but%they%don’t%bind%`ghtly% A%company%wants%to%work%around%another%company’s%chemical%patents% An%high?affinity%ligand%is%toxic,%is%not%well?absorbed,%etc.

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LIGAND-BASED VIRTUAL SCREENING Compound%Library Known"Ligands Molecular"similarity" Machine?learning% Etc. Candidate%ligands Assay Ac`ves Op`miza`on
 Med%chem,%crystallography,%% modeling Potent"drug"candidates

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CHEMICAL SIMILARITY
 LIGAND-BASED DRUG-DISCOVERY Compounds
 (available/synthesizable) Compare%with%known%ligands Different Test%experimentally Sim ilar Don’t%bother

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CHEMICAL FINGERPRINTS
 BINARY STRUCTURE KEYS Molecule%1 Molecule%2 phenyl m ethyl ketone carboxylate am ide aldehyde chlorine fluorine ethyl naphthyl S?S%bond alc ohol …

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CHEMICAL SIMILARITY FROM FINGERPRINTS
 NI =2 Intersec`on NU =8 Union Molecule%1 Molecule%2 Tanimoto Similarity or Jaccard Index, T

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POTENTIAL DRAWBACKS OF PLAIN CHEMICAL SIMILARITY May"miss"good"ligands"by"being"overly"conservaCve" May"put"too"much"weight"on"irrelevant"details" %%?%Examine%ligand%shape%and%common%substructures% %%?%Build%pharmacophore%models% %%?%Sta`s`cs%and%machine%learning%%on%chemical%descriptors

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Maximum%Common%Substructure Ncommon =34

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+"1 Bulky"hydrophobe AromaCc 5.0%±0.3%Å 3.2%±0.4%Å 2.8%±0.3%Å Pharmacophore%Models% Φάρμακο%(drug)%+%Φορά%(carry) A%3?point%pharmacophore

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Molecular%Descriptors
 More%abstract%than%chemical%fingerprints Physical%descriptors% % molecular%weight% % charge% % dipole%moment% % number%of%H?bond%donors/acceptors% % number%of%rotatable%bonds% % hydrophobicity%(log%P%and%clogP)% Topological% % branching%index% % measures%of%linearity%vs%interconnectedness% Etc.%etc. Rotatable%bonds

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A%High?Dimensional%“Chemical%Space”% Each%compound%is%at%a%point%in%an%n?dimensional%space% Compounds%with%similar%proper`es%are%near%each%other Descriptor%1 Descriptor%2 Descriptor%3 Point%represen`ng%a% compound%in%descriptor% space Apply%mulCvariate"staCsCcs%and%machine"learning%for%descriptor?selec`on.% (e.g.%par`al%least%squares,%support%vector%machines,%random%forest,%etc.)

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• “Everything"should"be"made"as"simple"as"it"can"be"but"not"simpler”"% A%model%is%never"perfect.%%A%model%that%is%not%quan`ta`vely%accurate%in% every%respect%does%not%preclude%one%from%establishing%results%relevant% to%our%understanding%of%biomolecules%as%long%as%the%biophysics%of%the% model%are%properly%understood%and%explored.%% • CalibraCon"of"the"parameters"is"an"ongoing"and"imperfect"process" Ques`ons%and%hypotheses%should%always%be%designed%such%that%they%do% not%depend%crucially%on%the%precise%numbers%used%for%the%various% parameters.%% • A"computaConal"model"is"rarely"universally"right"or"wrong" A%model%may%be%accurate%in%some%regards,%inaccurate%in%others.%%These% subtle`es%can%only%be%uncovered%by%comparing%to%all%available% experimental%data. CAUTIONARY NOTES

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• Structural bioinformatics is computer aided structural biology • Described major motivations, goals and challenges of structural bioinformatics • Reviewed the fundamentals of protein structure • Introduced both physics and knowledge based modeling approaches for describing the structure, energetics and dynamics of proteins computationally SUMMARY

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Ilan Samish et al. Bioinformatics 2015;31:146-150

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INFORMING SYSTEMS BIOLOGY? Genomes DNA & RNA sequence DNA & RNA structure Protein sequence Protein families, motifs and domains Protein structure Protein interactions Chemical entities Pathways Systems Gene expression Literature and ontologies