This document discusses the structure of proteins at various levels:
1) Primary structure is the amino acid sequence of a polypeptide chain.
2) Secondary structure includes alpha helices and beta pleated sheets formed by hydrogen bonding between amino acids in the backbone.
3) Tertiary structure is the three-dimensional folding of the entire polypeptide chain, stabilized by interactions between amino acid side chains.
4) Quaternary structure refers to the association of multiple polypeptide subunits in a protein.
The document outlines techniques like X-ray crystallography and NMR that are used to determine protein structures at high resolution.
Protein Structural Prediction
1. Molecular Structure prediction
2. Sequence
3. Protein Folding
4. The Leventhal Paradox
5. Energy (Minimization )
6. The Hydrophobic Effect
7. Protein Structure Determination ( X-ray,NMR)
8. Ab initio Prediction
9. Lattice String Folding
10. Rosetta (Monte Carlo based method)
11. Homology-based Prediction
Protein structure Lecture for M Sc biology students Anuj Kumar
Presentation on Protein Structure for MSc class by Dr Anuj Kumar Scientist at National Institute of Virology, Indian Council of Medical Research (ICMR)
Also useful for Students preparing of CSIR/JRF NET and LS
The document discusses protein structure prediction. It describes that a protein's amino acid sequence determines its 3-dimensional structure, which in turn determines its function. There are four levels of protein structure: primary, secondary, tertiary, and quaternary. Computational methods for predicting structure include homology modeling, which predicts structure based on similarity to proteins with known structures, and ab initio modeling, which predicts structure directly from physical principles. Current ab initio methods struggle with the vast number of possible protein conformations.
This document discusses protein structure prediction and molecular modeling. It begins with an overview of the druggable genome and protein structure prediction approaches such as ab initio modeling, threading, and homology modeling. It then provides details on homology modeling steps including searching databases, selecting templates, aligning sequences, building models, and model evaluation. The document also discusses protein-ligand docking, scoring functions, assessing docking performance, and practical aspects of docking such as protein and ligand preparation.
This presentation discusses protein structure prediction using Rosetta. It begins with an overview of the Critical Assessment of Protein Structure Prediction (CASP) experiments and notes that Rosetta is one of the top performing free-modeling servers. The presentation then describes the basic ab initio protocol used by Rosetta, which involves fragment insertion, scoring, and refinement. It also discusses limitations and success rates. Key aspects of the Rosetta energy functions and sampling algorithms are presented. Examples of specific Rosetta applications including low-resolution modeling and refinement are provided.
The document discusses electrostatic interactions and methods for predicting protein-nucleic acid interactions. It covers the role of electrostatics in determining biomolecular structure and interactions. It also describes Poisson-Boltzmann theory as a framework for modeling electrostatics and various software tools that solve the Poisson-Boltzmann equation. Finally, it outlines different approaches for modeling and predicting protein-nucleic acid interactions, including molecular dynamics simulations and statistical and knowledge-based potential functions.
This document provides an overview of protein structure and function. It discusses the central dogma of life, the 20 common amino acids that make up proteins, and how they fold into defined structures like alpha helices and beta sheets. Key concepts covered include the hydrophobic effect that drives protein folding, domains as fundamental units of structure, and the three main classes of protein structures - alpha, beta, and alpha/beta domains. Real-world protein examples are also briefly mentioned.
This document discusses the structure of proteins at various levels:
1) Primary structure is the amino acid sequence of a polypeptide chain.
2) Secondary structure includes alpha helices and beta pleated sheets formed by hydrogen bonding between amino acids in the backbone.
3) Tertiary structure is the three-dimensional folding of the entire polypeptide chain, stabilized by interactions between amino acid side chains.
4) Quaternary structure refers to the association of multiple polypeptide subunits in a protein.
The document outlines techniques like X-ray crystallography and NMR that are used to determine protein structures at high resolution.
Protein Structural Prediction
1. Molecular Structure prediction
2. Sequence
3. Protein Folding
4. The Leventhal Paradox
5. Energy (Minimization )
6. The Hydrophobic Effect
7. Protein Structure Determination ( X-ray,NMR)
8. Ab initio Prediction
9. Lattice String Folding
10. Rosetta (Monte Carlo based method)
11. Homology-based Prediction
Protein structure Lecture for M Sc biology students Anuj Kumar
Presentation on Protein Structure for MSc class by Dr Anuj Kumar Scientist at National Institute of Virology, Indian Council of Medical Research (ICMR)
Also useful for Students preparing of CSIR/JRF NET and LS
The document discusses protein structure prediction. It describes that a protein's amino acid sequence determines its 3-dimensional structure, which in turn determines its function. There are four levels of protein structure: primary, secondary, tertiary, and quaternary. Computational methods for predicting structure include homology modeling, which predicts structure based on similarity to proteins with known structures, and ab initio modeling, which predicts structure directly from physical principles. Current ab initio methods struggle with the vast number of possible protein conformations.
This document discusses protein structure prediction and molecular modeling. It begins with an overview of the druggable genome and protein structure prediction approaches such as ab initio modeling, threading, and homology modeling. It then provides details on homology modeling steps including searching databases, selecting templates, aligning sequences, building models, and model evaluation. The document also discusses protein-ligand docking, scoring functions, assessing docking performance, and practical aspects of docking such as protein and ligand preparation.
This presentation discusses protein structure prediction using Rosetta. It begins with an overview of the Critical Assessment of Protein Structure Prediction (CASP) experiments and notes that Rosetta is one of the top performing free-modeling servers. The presentation then describes the basic ab initio protocol used by Rosetta, which involves fragment insertion, scoring, and refinement. It also discusses limitations and success rates. Key aspects of the Rosetta energy functions and sampling algorithms are presented. Examples of specific Rosetta applications including low-resolution modeling and refinement are provided.
The document discusses electrostatic interactions and methods for predicting protein-nucleic acid interactions. It covers the role of electrostatics in determining biomolecular structure and interactions. It also describes Poisson-Boltzmann theory as a framework for modeling electrostatics and various software tools that solve the Poisson-Boltzmann equation. Finally, it outlines different approaches for modeling and predicting protein-nucleic acid interactions, including molecular dynamics simulations and statistical and knowledge-based potential functions.
This document provides an overview of protein structure and function. It discusses the central dogma of life, the 20 common amino acids that make up proteins, and how they fold into defined structures like alpha helices and beta sheets. Key concepts covered include the hydrophobic effect that drives protein folding, domains as fundamental units of structure, and the three main classes of protein structures - alpha, beta, and alpha/beta domains. Real-world protein examples are also briefly mentioned.
1) The document discusses various methods for determining the 3D structure of proteins, including x-ray crystallography, NMR spectroscopy, and cryo-electron microscopy.
2) X-ray crystallography involves purifying the protein, crystallizing it, collecting diffraction data from x-rays hitting the crystal, using this data to determine phases and calculate an electron density map, and building an atomic model through refinement.
3) NMR spectroscopy involves dissolving the purified protein and using nuclear magnetic resonance to measure distances between atomic nuclei, allowing the structure to be calculated.
1. Proteins are made up of amino acids and take on specific three-dimensional structures that dictate their function. Determining a protein's structure is important for understanding its role in biological processes.
2. There are several methods for determining and predicting protein structure, including X-ray crystallography, NMR, and computational methods like homology modeling or ab initio structure prediction.
3. Protein structure is hierarchical, ranging from secondary structure like alpha helices and beta sheets to the overall fold classified in databases like SCOP and CATH. Predicting secondary structure is easier than predicting a protein's full three-dimensional structure.
Protein folding is the process by which a protein goes from an unfolded state to its biologically active three-dimensional structure. It is important to understand protein folding to help predict protein structures from sequence alone and to understand diseases caused by protein misfolding. Proteins typically fold through progressive formation of native-like structures rather than through a random search. Molecular chaperones help other proteins fold within cells. Misfolded proteins can form amyloid fibrils associated with diseases. Computational methods aim to predict protein structures from sequence using fragment libraries and modeling protein energy landscapes. Protein design techniques aim to computationally modify protein sequences to achieve desired stabilities, functions, and binding properties.
This document discusses different approaches to assessing chemical similarity, including philosophers' and chemists' views, structural similarity using substructures and fingerprints, 3D similarity using fields and shapes, physicochemical properties, quantum chemistry methods, and quantified similarity using numerical representations and comparisons. It notes that while similar structures often have similar activities, there are also many exceptions, and similarity needs to be defined with respect to a particular endpoint.
This document discusses different methods for predicting the secondary structure of proteins, including statistical methods like Chou-Fasman and GOR that use amino acid frequencies, and neural network methods like PHD that use multiple sequence alignments and training sets of known structures. It also briefly outlines experimental methods for determining protein structure like X-ray crystallography, NMR spectroscopy, and cryo-electron microscopy.
This course provides an in-depth understanding of three-dimensional macromolecular structure and the relationship between the conformation of proteins and nucleic acids and their biological functions. Students will learn to visualize and analyze macromolecular structures using molecular graphics software and assess the structural basis of biological activity. The course covers topics related to multi-molecular assemblies, catalytic machines, and membrane proteins. Students will be assessed through a final exam and computer graphics exercises completed in a lab notebook.
Proteins : is made of chain of amino acids ( amino acid= monomers) therefor the protein is polymers .
The proteins are made up of carbon, hydrogen, oxygen, and nitrogen.
Amino acid :
The document discusses the four levels of protein structure: primary, secondary, tertiary, and quaternary. It provides examples of common secondary structures like alpha helices and beta sheets. Tertiary structure describes the 3D arrangement of all atoms in the protein. Quaternary structure refers to the association of multiple polypeptide chains. The document outlines various experimental techniques used to determine protein structure like X-ray crystallography and NMR.
This document presents an overview of molecular modeling techniques. It discusses the history of molecular modeling and some common computational methods like molecular mechanics, quantum mechanics and molecular dynamics. It also describes different modeling approaches like template modeling techniques such as homology modeling and threading as well as template-free modeling methods including ab initio and knowledge-based modeling. The document concludes that molecular modeling can provide useful insights for research if used carefully while also noting current limitations, especially for modeling larger protein structures.
Molecular docking is a method that predicts the preferred orientation of one molecule to another when bound to form a stable complex. It involves finding the best "fit" between a small molecule ligand and a protein receptor binding site. The key stages are target selection and preparation, ligand selection and preparation, docking, and evaluation. Docking software uses scoring functions to evaluate the strength of interaction and identify the best binding orientation. Applications include virtual screening in drug discovery and predicting enzyme-substrate interactions in bioremediation.
This document discusses various topics related to protein structure and folding. It provides details on methods for determining protein tertiary and quaternary structure using X-ray crystallography and NMR. It also discusses the protein folding problem, factors that influence folding, and diseases associated with misfolding. Molecular chaperones that assist with protein folding, such as GroEL, are described. Prions and amyloids, which form when proteins misfold, are also mentioned.
The document discusses protein modeling, which involves predicting the 3D structure of a protein from its amino acid sequence using computational methods. It describes why computational modeling is necessary, as experimental techniques like X-ray crystallography and NMR are often slow and many proteins do not crystallize well. The main methods covered are homology modeling, threading, and ab initio modeling. Key steps in homology modeling include template recognition, alignment, backbone generation, loop modeling, side chain modeling, and model refinement. Validation tools like Ramachandran plots, Verify3D, and ERRAT are also summarized.
Computational Prediction Of Protein-1.pptxashharnomani
This document discusses computational methods for predicting protein structure, including homology modeling, fold recognition/threading, and ab initio prediction. Homology modeling predicts structure based on sequence similarity to proteins with known structures. It involves aligning the target sequence to template structures, then modeling secondary structure, loops, and side chains. Accuracy depends on template quality and sequence identity above 30%. Fold recognition matches sequences to structure folds without clear homology. Ab initio prediction predicts structure from sequence alone using physics-based forces.
This document discusses protein production and structure determination. It covers expressing recombinant proteins in common systems like E. coli, insect, and mammalian cells. Purification methods like affinity and size-exclusion chromatography are described. Quality control steps involving SDS-PAGE, mass spectrometry, and functional assays are outlined. Protein crystallography and X-ray crystal structure determination are summarized, including crystallization screening, diffraction data collection, phasing methods, and model building and refinement. The importance of protein structures for drug design is also briefly mentioned.
HERE IN THIS PRESENTATION HY HOMOLOGY MODELING IS EXPLAIN , WITH EXAMPLES OF PROTEIN PRIMARY AND SECONDARY, SHOWING THE IMAGES FORM WHICH MAKES EASY TO UNDERSTAND
Design of fragment screening libraries (IQPC 2008)Peter Kenny
This document discusses the design of fragment screening libraries for fragment-based drug discovery. It describes how fragment hits have high ligand efficiency due to their low molecular weight. The document outlines criteria for selecting fragments, including molecular complexity, solubility, and availability of chemical neighbors. It presents details on the design of the GFSL05 20,000 compound screening library from AstraZeneca, including controlling molecular properties like size, lipophilicity, and structural diversity. Literature on fragment-based screening and library design is also cited.
The document discusses protein structure modeling through homology modeling. It describes the key steps in homology modeling which include: (1) finding a suitable template through database searches, (2) aligning the target sequence to the template, (3) assigning coordinates from conserved regions of the template, (4) building loops and variable regions either from other structures or de novo, (5) searching for optimal side chain conformations, and (6) refining the model through molecular mechanics. The document emphasizes validating the final model to identify any inherent errors from the template or modeling process.
This document discusses quantitative structure-activity relationships (QSAR) modeling for drug discovery. It describes how QSAR has evolved from using 2D descriptors to 3D modeling that accounts for molecular shape and conformation. Accurately positioning molecules in 3D space relative to a reference molecule is important. Various algorithms are used to measure conformational and shape similarities between molecules. Database searching involves fitting candidate molecules to a template that represents the dimensions and physicochemical properties of a drug target's active site. High-throughput screening and virtual screening are approaches to evaluating large numbers of molecules from databases. The concept of isosterism, where structural components impart similar properties, is also important for database searching and analog design.
This document outlines the goals, work plan, and parameters for a project aimed at assessing the ability of the MDmix simulation method to quantify the contribution of individual residues to the binding free energy of protein-protein complexes. The project will involve assembling databases of protein complexes with known binding interfaces, preparing the complexes for simulation, running MDmix simulations under optimized conditions, analyzing the energy distributions from the simulations to assign binding free energies to residues, and comparing these results to experimental alanine scanning mutagenesis data and other computational methods. The document discusses selecting appropriate simulation conditions and probes to model protein flexibility and solvent interactions accurately while optimizing for computational efficiency.
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1) The document discusses various methods for determining the 3D structure of proteins, including x-ray crystallography, NMR spectroscopy, and cryo-electron microscopy.
2) X-ray crystallography involves purifying the protein, crystallizing it, collecting diffraction data from x-rays hitting the crystal, using this data to determine phases and calculate an electron density map, and building an atomic model through refinement.
3) NMR spectroscopy involves dissolving the purified protein and using nuclear magnetic resonance to measure distances between atomic nuclei, allowing the structure to be calculated.
1. Proteins are made up of amino acids and take on specific three-dimensional structures that dictate their function. Determining a protein's structure is important for understanding its role in biological processes.
2. There are several methods for determining and predicting protein structure, including X-ray crystallography, NMR, and computational methods like homology modeling or ab initio structure prediction.
3. Protein structure is hierarchical, ranging from secondary structure like alpha helices and beta sheets to the overall fold classified in databases like SCOP and CATH. Predicting secondary structure is easier than predicting a protein's full three-dimensional structure.
Protein folding is the process by which a protein goes from an unfolded state to its biologically active three-dimensional structure. It is important to understand protein folding to help predict protein structures from sequence alone and to understand diseases caused by protein misfolding. Proteins typically fold through progressive formation of native-like structures rather than through a random search. Molecular chaperones help other proteins fold within cells. Misfolded proteins can form amyloid fibrils associated with diseases. Computational methods aim to predict protein structures from sequence using fragment libraries and modeling protein energy landscapes. Protein design techniques aim to computationally modify protein sequences to achieve desired stabilities, functions, and binding properties.
This document discusses different approaches to assessing chemical similarity, including philosophers' and chemists' views, structural similarity using substructures and fingerprints, 3D similarity using fields and shapes, physicochemical properties, quantum chemistry methods, and quantified similarity using numerical representations and comparisons. It notes that while similar structures often have similar activities, there are also many exceptions, and similarity needs to be defined with respect to a particular endpoint.
This document discusses different methods for predicting the secondary structure of proteins, including statistical methods like Chou-Fasman and GOR that use amino acid frequencies, and neural network methods like PHD that use multiple sequence alignments and training sets of known structures. It also briefly outlines experimental methods for determining protein structure like X-ray crystallography, NMR spectroscopy, and cryo-electron microscopy.
This course provides an in-depth understanding of three-dimensional macromolecular structure and the relationship between the conformation of proteins and nucleic acids and their biological functions. Students will learn to visualize and analyze macromolecular structures using molecular graphics software and assess the structural basis of biological activity. The course covers topics related to multi-molecular assemblies, catalytic machines, and membrane proteins. Students will be assessed through a final exam and computer graphics exercises completed in a lab notebook.
Proteins : is made of chain of amino acids ( amino acid= monomers) therefor the protein is polymers .
The proteins are made up of carbon, hydrogen, oxygen, and nitrogen.
Amino acid :
The document discusses the four levels of protein structure: primary, secondary, tertiary, and quaternary. It provides examples of common secondary structures like alpha helices and beta sheets. Tertiary structure describes the 3D arrangement of all atoms in the protein. Quaternary structure refers to the association of multiple polypeptide chains. The document outlines various experimental techniques used to determine protein structure like X-ray crystallography and NMR.
This document presents an overview of molecular modeling techniques. It discusses the history of molecular modeling and some common computational methods like molecular mechanics, quantum mechanics and molecular dynamics. It also describes different modeling approaches like template modeling techniques such as homology modeling and threading as well as template-free modeling methods including ab initio and knowledge-based modeling. The document concludes that molecular modeling can provide useful insights for research if used carefully while also noting current limitations, especially for modeling larger protein structures.
Molecular docking is a method that predicts the preferred orientation of one molecule to another when bound to form a stable complex. It involves finding the best "fit" between a small molecule ligand and a protein receptor binding site. The key stages are target selection and preparation, ligand selection and preparation, docking, and evaluation. Docking software uses scoring functions to evaluate the strength of interaction and identify the best binding orientation. Applications include virtual screening in drug discovery and predicting enzyme-substrate interactions in bioremediation.
This document discusses various topics related to protein structure and folding. It provides details on methods for determining protein tertiary and quaternary structure using X-ray crystallography and NMR. It also discusses the protein folding problem, factors that influence folding, and diseases associated with misfolding. Molecular chaperones that assist with protein folding, such as GroEL, are described. Prions and amyloids, which form when proteins misfold, are also mentioned.
The document discusses protein modeling, which involves predicting the 3D structure of a protein from its amino acid sequence using computational methods. It describes why computational modeling is necessary, as experimental techniques like X-ray crystallography and NMR are often slow and many proteins do not crystallize well. The main methods covered are homology modeling, threading, and ab initio modeling. Key steps in homology modeling include template recognition, alignment, backbone generation, loop modeling, side chain modeling, and model refinement. Validation tools like Ramachandran plots, Verify3D, and ERRAT are also summarized.
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This document discusses computational methods for predicting protein structure, including homology modeling, fold recognition/threading, and ab initio prediction. Homology modeling predicts structure based on sequence similarity to proteins with known structures. It involves aligning the target sequence to template structures, then modeling secondary structure, loops, and side chains. Accuracy depends on template quality and sequence identity above 30%. Fold recognition matches sequences to structure folds without clear homology. Ab initio prediction predicts structure from sequence alone using physics-based forces.
This document discusses protein production and structure determination. It covers expressing recombinant proteins in common systems like E. coli, insect, and mammalian cells. Purification methods like affinity and size-exclusion chromatography are described. Quality control steps involving SDS-PAGE, mass spectrometry, and functional assays are outlined. Protein crystallography and X-ray crystal structure determination are summarized, including crystallization screening, diffraction data collection, phasing methods, and model building and refinement. The importance of protein structures for drug design is also briefly mentioned.
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This document discusses the design of fragment screening libraries for fragment-based drug discovery. It describes how fragment hits have high ligand efficiency due to their low molecular weight. The document outlines criteria for selecting fragments, including molecular complexity, solubility, and availability of chemical neighbors. It presents details on the design of the GFSL05 20,000 compound screening library from AstraZeneca, including controlling molecular properties like size, lipophilicity, and structural diversity. Literature on fragment-based screening and library design is also cited.
The document discusses protein structure modeling through homology modeling. It describes the key steps in homology modeling which include: (1) finding a suitable template through database searches, (2) aligning the target sequence to the template, (3) assigning coordinates from conserved regions of the template, (4) building loops and variable regions either from other structures or de novo, (5) searching for optimal side chain conformations, and (6) refining the model through molecular mechanics. The document emphasizes validating the final model to identify any inherent errors from the template or modeling process.
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protein structure from genomic and computational biology
1. Biophysics 101:
Genomics & Computational Biology
Section 8: Protein Structure
Faisal Reza
Nov. 11th, 2003
B101.pdb from PS5 shown at left with:
• animated ball and stick model, colored CPK
• H-bonds on, colored green
• van der Waals radii on, also colored CPK
Based on the backbone and H-bond configuration shown,
what secondary structure might this be?
2. Outline
• Course Projects
• Biology/Chemistry of Protein Structure
– Protein Assembly, Folding, Packing and Interaction
– Primary, Secondary, Tertiary and Quaternary
structures
– Class, Fold, Topology
• CS/Math/Physics of Protein Structure
– Experimental Determination and Analysis
– Computational Determination and Analysis
• Proteomics
• Mass Spectrometry
3. • Videotaping authorization form
• Submission Parameters (via email)
– when: December 2, 2003 12noon EST.
(9AM EST if presenting on December 2, 2003)
– where: bphys101@fas.harvard.edu
– what: (1) written project (.doc, ~1000-3000 words)
(2) presentation slides (.ppt, 1-2 MB)
• Presentation Parameters (in person)
– when: December {2, 9, 16}, 2003 {12-2PM, 5:30-7:30PM} EST.
– where: HMS Cannon Seminar Room for 12-2PM
Science Ctr. Lecture Hall A for 5:30-7:30PM
– what: (1) oral presentations (6 min/person + 2 min/person Q/A)
(2) grading rubric and further information:
http://www.courses.fas.harvard.edu/~bphys101/projects/index.html
Course Projects
4. Biology/Chemistry of Protein Structure
Primary
Secondary
Tertiary
Quaternary
Assembly
Folding
Packing
Interaction
S
T
R
U
C
T
U
R
E
P
R
O
C
E
S
S
5. Protein Assembly
• occurs at the ribosome
• involves dehydration
synthesis and
polymerization of amino
acids attached to tRNA:
NH - {A + B A-B + H O} -COO
• thermodynamically
unfavorable, with E =
+10kJ/mol, thus coupled
to reactions that act as
sources of free energy
• yields primary structure
2 n
3
+ -
6. Primary Structure
• linear
• ordered
• 1 dimensional
• sequence of amino
acid polymer
• by convention, written
from amino end to
carboxyl end
• a perfectly linear
amino acid polymer is
neither functional nor
energetically
favorable folding!
primary structure of human insulin
CHAIN 1: GIVEQ CCTSI CSLYQ LENYC N
CHAIN 2: FVNQH LCGSH LVEAL YLVCG ERGFF YTPKT
7. Protein Folding
• tumbles towards
conformations that reduce
E (this process is thermo-
dynamically favorable)
• yields secondary structure
• occurs in the cytosol
• involves localized spatial
interaction among primary
structure elements, i.e. the
amino acids
• may or may not involve
chaperone proteins
8. Secondary Structure
• non-linear
• 3 dimensional
• localized to regions of an
amino acid chain
• formed and stabilized by
hydrogen bonding,
electrostatic and van der
Waals interactions
9. Ramachandran Plot
• Pauling built models based on the following
principles, codified by Ramachandran:
(1) bond lengths and angles – should be
similar to those found in individual
amino acids and small peptides
(2) peptide bond – should be planer
(3) overlaps – not permitted, pairs of atoms
no closer than sum of their covalent radii
(4) stabilization – have sterics that permit
hydrogen bonding
• Two degrees of freedom:
(1) (phi) angle = rotation about N – C
(2) (psi) angle = rotation about C – C
• A linear amino acid polymer with some folds
is better but still not functional nor
completely energetically favorable
packing!
10. Protein Packing
• occurs in the cytosol (~60% bulk
water, ~40% water of hydration)
• involves interaction between
secondary structure elements
and solvent
• may be promoted by
chaperones, membrane proteins
• tumbles into molten globule
states
• overall entropy loss is small
enough so enthalpy determines
sign of E, which decreases
(loss in entropy from packing
counteracted by gain from
desolvation and reorganization
of water, i.e. hydrophobic effect)
• yields tertiary structure
11. Tertiary Structure
• non-linear
• 3 dimensional
• global but restricted to the
amino acid polymer
• formed and stabilized by
hydrogen bonding, covalent
(e.g. disulfide) bonding,
hydrophobic packing toward
core and hydrophilic
exposure to solvent
• A globular amino acid
polymer folded and
compacted is somewhat
functional (catalytic) and
energetically favorable
interaction!
12. Protein Interaction
• occurs in the cytosol, in close proximity to other
folded and packed proteins
• involves interaction among tertiary structure
elements of separate polymer chains
• may be promoted by chaperones, membrane
proteins, cytosolic and extracellular elements as
well as the proteins’ own propensities
• E decreases further due to further
desolvation and reduction of surface area
• globular proteins, e.g. hemoglobin,
largely involved in catalytic roles
• fibrous proteins, e.g. collagen,
largely involved in structural roles
• yields quaternary structure
13. Quaternary Structure
• non-linear
• 3 dimensional
• global, and across
distinct amino acid
polymers
• formed by hydrogen
bonding, covalent
bonding, hydrophobic
packing and hydrophilic
exposure
• favorable, functional
structures occur
frequently and have been
categorized
14. Class/Motif
• class = secondary structure
composition,
e.g. all , all , segregated +,
mixed /
• motif = small, specific
combinations of secondary
structure elements,
e.g. -- loop
• both subset of
fold/architecture/domains
15. Fold/Architecture/Domains
• fold = architecture = the
overall shape and
orientation of the secondary
structures, ignoring
connectivity between the
structures,
e.g. / barrel, TIM barrel
• domain = the
functional property
of such a fold or
architecture,
e.g. binding, cleaving,
spanning sites
• subset of topology/fold
families/superfamilies
16. Topology/Fold families/Superfamilies
• topology = the overall shape
and connectivity of the folds
and domains
• fold families = categorization
that takes into account
topology and previous subsets
as well as empirical/biological
properties, e.g. flavodoxin
• superfamilies = in addition to
fold families, includes
evolutionary/ancestral
properties
CLASS: +
FOLD: sandwich
FOLD FAMILY: flavodoxin
17. CS/Math/Physics of Protein Structure
• Experimental Determination and Analysis
• Computational Determination and Analysis
18. Experimental Determination and Analysis
• Repositories
– Protein Data Bank
– Molecular Modeling DataBase
• Resolution
– X-Ray Crystallography
– NMR Spectroscopy
– Mass Spectroscopy (next week)
– Fluorescence Resonance Energy Transfer
19. Protein Data Bank
• Coordinates database
RCSB Protein Data Bank (PDB)
– has many structures, partly
due to minor differences in
structure resolution and
annotation
– has much fewer fold
families, partly due to
evolved pathways and
mechanisms
– .pdb = data from experiment,
with missing parameters
and multiple conformations
Cumulative increase in the
number of domains
Cumulative increase in the
number of domains
Cumulative increase in the
number of folds and
superfamilies
20. Molecular Modeling DataBase
• Comparative database
NCBI Molecular Modeling DataBase (MMDB)
– subset of PDB, excludes theoretical structures, with
native .asn format
– .asn = single-coordinate per-atom molecules, explicit
bonding and SS remarks
– suited for computation, such as homology modeling
and structure comparison
21. X-Ray Crystallography
• crystallize and
immobilize single,
perfect protein
• bombard with X-rays,
record scattering
diffraction patterns
• determine electron
density map from
scattering and phase
via Fourier transform:
• use electron density
and biochemical
knowledge of the
protein to refine and
determine a model
"All crystallographic models are not equal. ... The brightly colored stereo views
of a protein model, which are in fact more akin to cartoons than to
molecules, endow the model with a concreteness that exceeds the
intentions of the thoughtful crystallographer. It is impossible for the
crystallographer, with vivid recall of the massive labor that produced the
model, to forget its shortcomings. It is all too easy for users of the model to
be unaware of them. It is also all too easy for the user to be unaware that,
through temperature factors, occupancies, undetected parts of the protein,
and unexplained density, crystallography reveals more than a single
molecular model shows.“
- Rhodes, “Crystallography Made Crystal Clear” p. 183.
22. NMR Spectroscopy
• protein in aqueous
solution, motile and
tumbles/vibrates with
thermal motion
• NMR detects chemical
shifts of atomic nuclei
with non-zero spin, shifts
due to electronic
environment nearby
• determine distances
between specific pairs of
atoms based on shifts,
“constraints”
• use constraints and
biochemical knowledge of
the protein to determine
an ensemble of models
determining constraints
using constraints to determine
secondary structure
23. Fluorescence Resonance Energy Transfer
• FRET described as a “molecular ruler”
• segments of a protein are tagged with fluorophores
• energy transfer occurs when donor and acceptor
interact, falls off as 1/d6 where d is separation between
donor and acceptor
• donor and acceptor must be within 50 Å,
acceptor emission sensitive to distance change
• can determine pairs of side chains that are separated
when unfolded and close when folded
24. Computational Determination and Analysis
• Databases
– CATH (Class, Architecture, Topology, Homologous
superfamily)
– SCOP (Structural Classification Of Proteins)
– FSSP (Fold classification based on Structure-Structure
alignment of Proteins)
• Prediction
– Ab-initio, theoretical modeling, and conformation space
search
– Homology modeling and threading
– Energy minimization, simulation and Monte Carlo
• Proteomics (next week)
25. CATH
• a combination of manual and
automated hierarchical classification
• four major levels:
– Class (C) – based on secondary
structure content
– Architecture (A) – based on gross
orientation of secondary
structures
– Topology (T) – based on
connections and numbers of
secondary structures
– Homologous superfamily (H) –
based on structure/function
evolutionary commonalities
• provides useful geometric
information (e.g. architecture)
• partial automation may result in
examples near fixed thresholds
being assigned inaccurately
26. SCOP • a purely manual hierarchical
classification
• three major levels:
– Family – based on clear
evolutionary relationship
(pairwise residue identities
between proteins are >30%)
– Superfamily – based on
probable evolutionary origin
(low sequence identity but
common structure/function
features
– Fold – based on major
structural similarity (major
secondary structures in
same arrangement and
topology
• provides detailed evolutionary
information
• manual process influences
update frequency and equally
exhaustive examination
27. FSSP
• a purely automated
• hierarchical classification
• three major levels:
– representative set – 330
protein chains (less than 30%
sequence identity)
– clustering – based on
structural alignment into fold
families
– convergence – cutting at a
high statistical significance
level increases the number of
distinct families, gradually
approaching one family per
protein chain
• continually updated, presents
data and lets user assess
• Without sufficient knowledge,
user may not assess data
appropriately
list of representative set
clustering dendogram
28. CATH vs. SCOP vs. FSSP
• approximately two-thirds of the protein chains in each
database are common to all three databases
FSSP pairwise matches (Z-score
4.0) compared to CATH and
SCOP matches at the fold level
(a), homology level (b)
FSSP pairwise matches (Z-score
6.0) compared to CATH and
SCOP matches at the fold level
(c), homology level (d)
FSSP pairwise matches (Z-score
8.0) compared to CATH and
SCOP matches at the fold level
(e), homology level (f)
29. Ab-initio, theoretical modeling,
and conformation space search
• Ab-initio = given amino acid primary structure, i.e. sequence,
derive structure from first principles (e.g. treat amino acids as
beads and derive possible structures by rotating through all
possible , angles using a “reliable” energy function, then
optimize globally)
• Theoretical modeling = subset of ab-initio, given amino acid
primary structure and knowledge about characteristic features,
derive structure that has that structure and features
(e.g. protein has an iron binding site
possible heme substructure)
• Conformation space search = subset of ab-initio, but a
stochastic search in which the sample space is reduced by
initial conditions/assumptions (e.g. reduce sample space to
conform to Ramachandran plot)
30. Homology modeling and threading
• Homology modeling = knowledge-based approach, given a
sequence database, use multiple sequence alignment on this
database to identify structurally conserved regions and
construct structure backbone and loops based on these
regions, restore side-chains and refine through energy
minimization (apply to proteins that have high sequence
similarity to those in the database)
• Threading = knowledge-based approach, given a structure
database of interest (e.g. one that provides a limited set of
possible structures per given sequence for fold recognition,
one that provides a one structure per given limited set of
possible sequences for inverse folding) use scoring
functions and correlations from this database to derive
structure that is in agreement (apply to proteins with
moderate sequence similarity to those in the database)
31. Energy minimization, simulation
and Monte Carlo
• Energy minimization = select an appropriate energy function
and derive conformations that yield minimal energies based
on this function
• Simulation = select appropriate molecular conditions and
derive conformations that are suited to these molecular
conditions
• Monte Carlo = subset of molecular simulation, but it is an
iterated search through a Markov chain of conformations
(many iterations canonical distribution, P(particular
conformation)~exp(-E/T)) proposed by N. Metropolis, in which
a new conformation is generated from the current one by a
small ``move'' and is accepted with a probability Pacc = min(1,
exp(-E/kT)), which depends on the corresponding change in
energy, E, and on an external adjustable parameter, kT
33. References
C. Branden, J. Tooze. “Introduction to Protein Structure.” Garland Science Publishing, 1999.
C. Chothia, T. Hubard, S. Brenner, H. Barns, A. Murzin. “Protein Folds in the All-β and ALL-α Classes.”
Annu. Rev. Biophys. Biomol. Struct., 1997, 26:597-627.
G.M. Church. “Proteins 1: Structure and Interactions.” Biophysics 101: Computational Biology and
Genomics, October 28, 2003.
C. Hadley, D.T. Jones. “A systematic comparison of protein structure classifications: SCOP, CATH and
FSSP.” Structure, August 27, 1999, 7:1099-1112.
S. Komili. “Section 8: Protein Structure.” Biophysics 101: Computational Biology and Genomics,
November 12, 2002.
D.L. Nelson, A.L. Lehninger, M.M. Cox. “Principles of Biochemistry, Third Edition.” Worth Publishing,
May 2002.
.pdb animation created with PDB to MultiGif, http://www.dkfz-heidelberg.de/spec/pdb2mgif/expert.html