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%% This BibTeX bibliography file in UTF-8 format was created using Papers.
%% http://mekentosj.com/papers/
@article{Meirovitch:2007p317,
author = {Hagai Meirovitch},
journal = {Curr Opin Struct Biol},
title = {Recent developments in methodologies for calculating the entropy and free energy of biological systems by computer simulation},
abstract = {The Helmholtz free energy, F, plays an important role in proteins because of their rugged potential energy surface, which is 'decorated' with a tremendous number of local wells (denoted microstates, m). F governs protein folding, whereas differences DeltaF(mn) determine the relative populations of microstates that are visited by a flexible cyclic peptide or a flexible protein segment (e.g. a surface loop). Recently developed methodologies for calculating DeltaF(mn) (and entropy differences, DeltaS(mn)) mainly use thermodynamic integration and calculation of the absolute F; interesting new approaches in these categories are the adaptive integration method and the hypothetical scanning molecular dynamics method, respectively.},
affiliation = {Department of Computational Biology, University of Pittsburgh School of Medicine, Pittsburgh, PA 15260, USA. hagaim@pitt.edu},
annote = {Entropy depends on entire ensemble through partition function
Microstates - wider wells on the funnel
- calculate over the microstate is easier for entropy and free energy
- relative populations of microstates is determined by their free energy
Counting method
- count relative populations of microstates directly
- might be difficult to get accurate sample, as some folding slower - incomplete sampling
- principal component analysis can reveal microstates
Thermodynamic integration
- Integration of free energy between two ligand bound states is possible
- Entropy is still difficult; require all interactions be known
- Adaptive integration methods
- Jarzynski's identity...average of work over all non-reversible paths
- Difficult to evaluate methods
Calculation methods
Quasi-harmonic - based on probability density of structures defining a microstate
- neglects correlations higher than quadratic
- anharmonic contributions ignored
- not suitable for water
...to be continued...},
number = {2},
pages = {181--6},
volume = {17},
year = {2007},
month = {Apr},
language = {eng},
keywords = {Multiprotein Complexes, Entropy, Models: Biological, Computer Simulation, Systems Biology, Protein Folding, Thermodynamics},
date-added = {2008-10-15 17:16:35 -0400},
date-modified = {2008-11-11 08:45:33 -0500},
doi = {10.1016/j.sbi.2007.03.016},
pii = {S0959-440X(07)00046-2},
pmid = {17395451},
local-url = {file://localhost/Users/jballanc/Documents/Papers/2007/10.1016j.sbi.2007.03.016_Meirovitch.pdf},
uri = {papers://7ECC1CD1-BFE8-400E-9531-FD0D6B19EBE0/Paper/p317},
read = {Yes},
rating = {0}
}
@article{Galzitskaya:2006p35,
author = {Oxana V Galzitskaya and Sergiy O Garbuzynskiy},
journal = {Proteins},
title = {Entropy capacity determines protein folding},
abstract = {Search and study of the general principles that govern kinetics and thermodynamics of protein folding generate a new insight into the factors controlling this process. Here, based on the known experimental data and using theoretical modeling of protein folding, we demonstrate that there exists an optimal relationship between the average conformational entropy and the average energy of contacts per residue-that is, an entropy capacity-for fast protein folding. Statistical analysis of conformational entropy and number of contacts per residue for 5829 protein structures from four general structural classes (all-alpha, all-beta, alpha/beta, alpha+beta) demonstrates that each class of proteins has its own class-specific average number of contacts (class alpha/beta has the largest number of contacts) and average conformational entropy per residue (class all-alpha has the largest number of rotatable angles phi, psi, and chi per residue). These class-specific features determine the folding rates: alpha proteins are the fastest folding proteins, then follow beta and alpha+beta proteins, and finally alpha/beta proteins are the slowest ones. Our result is in agreement with the experimental folding rates for 60 proteins. This suggests that structural and sequence properties are important determinants of protein folding rates.},
affiliation = {Institute of Protein Research, Russian Academy of Sciences, Pushchino, Moscow Region, Russia. ogalzit@vega.protres.ru},
number = {1},
pages = {144--54},
volume = {63},
year = {2006},
month = {Apr},
keywords = {},
date-added = {2007-11-05 13:57:20 -0500},
date-modified = {2007-11-05 14:34:43 -0500},
doi = {10.1002/prot.20851},
pmid = {16400647},
local-url = {file://localhost/Users/jballanc/Documents/Papers/2006/10.1002prot.20851_Galzitskaya.pdf},
uri = {papers://7ECC1CD1-BFE8-400E-9531-FD0D6B19EBE0/Paper/p35},
read = {Yes},
rating = {0}
}
@article{Zhang:2006p11,
author = {Jinfeng Zhang and Jun S Liu},
journal = {PLoS Comput Biol},
title = {On side-chain conformational entropy of proteins},
abstract = {The role of side-chain entropy (SCE) in protein folding has long been speculated about but is still not fully understood. Utilizing a newly developed Monte Carlo method, we conducted a systematic investigation of how the SCE relates to the size of the protein and how it differs among a protein's X-ray, NMR, and decoy structures. We estimated the SCE for a set of 675 nonhomologous proteins, and observed that there is a significant SCE for both exposed and buried residues for all these proteins-the contribution of buried residues approaches approximately 40% of the overall SCE. Furthermore, the SCE can be quite different for structures with similar compactness or even similar conformations. As a striking example, we found that proteins' X-ray structures appear to pack more "cleverly" than their NMR or decoy counterparts in the sense of retaining higher SCE while achieving comparable compactness, which suggests that the SCE plays an important role in favouring native protein structures. By including a SCE term in a simple free energy function, we can significantly improve the discrimination of native protein structures from decoys.},
affiliation = {Department of Statistics, Harvard University, Cambridge, Massachusetts, United States of America.},
number = {12},
pages = {e168},
volume = {2},
year = {2006},
month = {Dec},
keywords = {},
date-added = {2007-11-05 13:57:20 -0500},
date-modified = {2007-11-05 22:32:34 -0500},
doi = {10.1371/journal.pcbi.0020168},
pii = {06-PLCB-RA-0310R3},
pmid = {17154716},
local-url = {file://localhost/Users/jballanc/Documents/Papers/2006/10.1371journal.pcbi.0020168_Zhang.pdf},
uri = {papers://7ECC1CD1-BFE8-400E-9531-FD0D6B19EBE0/Paper/p11},
read = {Yes},
rating = {0}
}
@article{Brady:1996p394,
author = {G P Brady and A Szabo and K A Sharp},
journal = {J Mol Biol},
title = {On the decomposition of free energies},
abstract = {The decomposition of free energies and entropies into components has recently been discussed within the framework of the free energy perturbation (FEP) and thermodynamic integration (TI) methods. In FEP, the cumulant expansion of the excess free energy contains coupling terms in second and higher orders. It is shown here that this expansion can be expressed in terms of temperature derivatives of the mean energy, suggesting a natural decomposition of the free energy into components corresponding to each term in the Hamiltonian. This result is derived in such a way that it establishes the equivalence to a particular form of component analysis based on TI in which all terms in the interaction energy are turned on simultaneously using 1/kT as the coupling parameter.},
affiliation = {Dept. of Biochemistry and Biophysics, University of Pennsylvania, Philadelphia 19104-6059, USA.},
annote = {Mean configurational energy is an ensemble average of potential
- Each component of potential contributes a corresponding mean configurational energy
- Thus, can be separated...
Excess free energy depends on quadratic terms...
- seemingly unable to separate terms
- quadratic terms are related to temperature derivatives of potential
- using thermodynamic integration (integrate potential between 0 and T), potential terms are seperable
- technique depends on path chosen for integration; must pick the most informative path},
number = {2},
pages = {123--5},
volume = {263},
year = {1996},
month = {Oct},
language = {eng},
keywords = {Energy Metabolism, Entropy, Models: Theoretical, Computer Simulation, Thermodynamics},
date-added = {2008-11-11 20:21:48 -0500},
date-modified = {2008-11-11 20:49:25 -0500},
doi = {10.1006/jmbi.1996.0563},
pii = {S0022-2836(96)90563-X},
pmid = {8913295},
local-url = {file://localhost/Users/jballanc/Documents/Papers/1996/10.1006jmbi.1996.0563_Brady.pdf},
uri = {papers://7ECC1CD1-BFE8-400E-9531-FD0D6B19EBE0/Paper/p394},
read = {Yes},
rating = {0}
}
@article{Clark:2008p141,
author = {A Clay Clark},
journal = {Arch Biochem Biophys},
title = {Protein folding: are we there yet?},
annote = {Currently cannot predict structure from sequence
Anfinsen kicked off folding problem
Mirsky {\&} Pauling - internal H-bonds hold together protein
Brandts (1964) - proposed two-state model
Two-state model leads to Levinthal contemplating his paradox
- Still today: do fold intermediates lie on folding pathway or off it?
Mutations affect folded and unfolded state...must account for both
Folding start - hydrophobic collapse? molten globule? something else?
},
number = {1},
pages = {1--3},
volume = {469},
year = {2008},
month = {Jan},
language = {eng},
keywords = {Models: Theoretical, Nuclear Magnetic Resonance: Biomolecular, Entropy, Kinetics, Protein Folding, Hydrogen Bonding, Protein Conformation},
date-added = {2008-01-10 14:43:45 -0500},
date-modified = {2008-11-10 09:28:23 -0500},
doi = {10.1016/j.abb.2007.10.007},
pii = {S0003-9861(07)00509-7},
pmid = {18068782},
local-url = {file://localhost/Users/jballanc/Documents/Papers/2008/10.1016j.abb.2007.10.007_Clark.pdf},
uri = {papers://7ECC1CD1-BFE8-400E-9531-FD0D6B19EBE0/Paper/p141},
read = {Yes},
rating = {0}
}
@article{Dill:2008p283,
author = {Ken A Dill and S Banu Ozkan and M Scott Shell and Thomas R Weikl},
journal = {Annual review of biophysics},
title = {The protein folding problem},
abstract = {The "protein folding problem" consists of three closely related puzzles: (a) What is the folding code? (b) What is the folding mechanism? (c) Can we predict the native structure of a protein from its amino acid sequence? Once regarded as a grand challenge, protein folding has seen great progress in recent years. Now, foldable proteins and nonbiological polymers are being designed routinely and moving toward successful applications. The structures of small proteins are now often well predicted by computer methods. And, there is now a testable explanation for how a protein can fold so quickly: A protein solves its large global optimization problem as a series of smaller local optimization problems, growing and assembling the native structure from peptide fragments, local structures first.},
affiliation = {Department of Pharmaceutical Chemistry, University of California, San Francisco, California 94143, USA. dill@maxwell.ucsf.edu},
annote = {Folding is 3 problems:
1. Folding code - what forces?
2. Prediction - how?
3. Folding process - what kinetics?
Anfinsen - folding is thermodynamic, not kinetic
Original idea - lot's of little forces, heirarchical
Mid-1980's idea - hydrophobic forces drive, all-at-once
-- Marginal stability means no force can be neglected
Secondary structure stabilized by H-bonding, but requires compactness
- Hydrophobic force pulls toward collapse
Prediction increasingly successful
- Better simulations...
- ...but mostly homology modeling and meta analysis
Levinthal's paradox (1968)
- Extremely large conformational space, but best structure found quickly
- Many models: heirarchical, nucleation, native-like unfolded topologies, etc.
PSB Plot
- Folding speed depends on localness
- Fast folders have more local interactions
Funnels
- {\#} of conformations at given energy correspond to entropy
- funnel because unfolded protein can be many states
- proteins unfold w/heat because funnel is wider at top
- cold unfolding is a consequence of solvent
Folding can happen serially and in parallel simultaneously
Rate limiting step does not necessarily represent energy barrier...
Downhill folding
- no energy barrier
- very fast
- may or may not exist
- display anti-Arrhenius kinetics (heat up => fold slower)
Zipping and Assembly
- divide-and-conquer strategy for folding
- each part of the chain looks for metastable folds
- can be verified by circular permutation: join ends of protein, break in middle, refold?
Physics based models
- take long but can get reasonably close
- vital for understanding beyond native structure
- ZAM: model small segments using Replica Exchange and "grow" metastable bits},
pages = {289--316},
volume = {37},
year = {2008},
month = {Jan},
language = {eng},
keywords = {Models: Chemical, Proteins, Protein Folding, Protein Conformation, Computer Simulation, Models: Molecular},
date-added = {2008-08-03 00:25:26 -0400},
date-modified = {2008-11-10 09:15:21 -0500},
doi = {10.1146/annurev.biophys.37.092707.153558},
pmid = {18573083},
local-url = {file://localhost/Users/jballanc/Documents/Papers/2008/10.1146annurev.biophys.37.092707.153558_Dill.pdf},
uri = {papers://7ECC1CD1-BFE8-400E-9531-FD0D6B19EBE0/Paper/p283},
read = {Yes},
rating = {0}
}
@article{Brady:1997p318,
author = {G P Brady and K A Sharp},
journal = {Curr Opin Struct Biol},
title = {Entropy in protein folding and in protein-protein interactions},
abstract = {The reduction of conformational entropy is a major barrier that has to be overcome in protein folding and binding. Changes in solvent entropy are also a major factor. Recent advances include clarification of the fundamental issues concerning the separation of entropy into components, the treatment of association entropy in binding, and the role of size and shape effects in solvation entropy. Advances in the application of entropy calculations include an emerging consensus for estimates of backbone and sidechain entropy loss in protein folding via use of numerically intensive methods for sampling, and use of the expanding protein-structure database.},
affiliation = {Johnson Research Foundation, Department of Biochemistry and Biophysics, University of Pennsylvania, Philadelphia, PA 19104, USA. brady@crystal.med.upenn.edu},
annote = {Protein folding = lower entropy
- Offset by specific interactions
- Hydration may give positive "solvent release" entropy
- Many debates remain...
Entropy calculations based off Boltzmann expression
- ratio of states for finite, well enumerated states
- ratio of frequecies, force constants, or amplitudes for harmonic oscilators
- can be modified to work with anharmonic oscillators
- for square well potentials: related to well length
Separability
- Mean energy and enthalpy can be separated into terms
- Uncertain if entropy can be likewise decomposed
- Coarse separation should, at least, be possible:
Molecule vs Solvent
Intramolecular vs External (rot, trans) vs Intermolecular
Conformational entropy
- Separated into backbone and sidechain
- Backbone deals with phi, psi angles; important for 2º structure
- Side-chain etropy estimated by rotamer counting
Solvent entropy - most solutes gain entropy when buried due to release of solvent...
Association entropy - related to translational and rotational losses on capture of ligand...},
number = {2},
pages = {215--21},
volume = {7},
year = {1997},
month = {Apr},
language = {eng},
keywords = {Entropy, Protein Conformation, Databases: Factual, Solvents, Algorithms, Protein Binding},
date-added = {2008-10-15 17:16:35 -0400},
date-modified = {2008-11-11 20:26:21 -0500},
pii = {S0959-440X(97)80028-0},
pmid = {9094326},
local-url = {file://localhost/Users/jballanc/Documents/Papers/1997/Brady.pdf},
uri = {papers://7ECC1CD1-BFE8-400E-9531-FD0D6B19EBE0/Paper/p318},
read = {Yes},
rating = {0}
}
@article{Cao:2008p142,
author = {Yi Cao and Hongbin Li},
journal = {J Mol Biol},
title = {How do chemical denaturants affect the mechanical folding and unfolding of proteins?},
abstract = {We present the first single-molecule atomic force microscopy study on the effect of chemical denaturants on the mechanical folding/unfolding kinetics of a small protein GB1 (the B1 immunoglobulin-binding domain of protein G from Streptococcus). Upon increasing the concentration of the chemical denaturant guanidinium chloride (GdmCl), we observed a systematic decrease in the mechanical stability of GB1, indicating the softening effect of the chemical denaturant on the mechanical stability of proteins. This mechanical softening effect originates from the reduced free-energy barrier between the folded state and the unfolding transition state, which decreases linearly as a function of the denaturant concentration. Chemical denaturants, however, do not alter the mechanical unfolding pathway or shift the position of the transition state for mechanical unfolding. We also found that the folding rate constant of GB1 is slowed down by GdmCl in mechanical folding experiments. By combining the mechanical folding/unfolding kinetics of GB1 in GdmCl solution, we developed the "mechanical chevron plot" as a general tool to understand how chemical denaturants influence the mechanical folding/unfolding kinetics and free-energy diagram in a quantitative fashion. This study demonstrates great potential in combining chemical denaturation with single-molecule atomic force microscopy techniques to reveal invaluable information on the energy landscape underlying protein folding/unfolding reactions.},
affiliation = {Department of Chemistry, The University of British Columbia, Vancouver, BC, Canada.},
number = {1},
pages = {316--24},
volume = {375},
year = {2008},
month = {Jan},
language = {eng},
keywords = {Thermodynamics, Microscopy: Atomic Force, Protein Denaturation, Protein Engineering, Bacterial Proteins, Protein Folding, Streptococcus, Guanidine, Kinetics, Monte Carlo Method, Computer Simulation, Protein Structure: Tertiary, Nerve Tissue Proteins, Dose-Response Relationship: Drug, Protein Conformation},
date-added = {2008-01-10 14:43:45 -0500},
date-modified = {2008-01-10 14:45:41 -0500},
doi = {10.1016/j.jmb.2007.10.024},
pii = {S0022-2836(07)01347-2},
pmid = {18021802},
local-url = {file://localhost/Users/jballanc/Documents/Papers/2008/10.1016j.jmb.2007.10.024_Cao.pdf},
uri = {papers://7ECC1CD1-BFE8-400E-9531-FD0D6B19EBE0/Paper/p142},
read = {Yes},
rating = {0}
}
@article{Rose:2006p362,
author = {George D Rose and Patrick J Fleming and Jayanth R Banavar and Amos Maritan},
journal = {Proc Natl Acad Sci USA},
title = {A backbone-based theory of protein folding},
abstract = {Under physiological conditions, a protein undergoes a spontaneous disorder order transition called "folding." The protein polymer is highly flexible when unfolded but adopts its unique native, three-dimensional structure when folded. Current experimental knowledge comes primarily from thermodynamic measurements in solution or the structures of individual molecules, elucidated by either x-ray crystallography or NMR spectroscopy. From the former, we know the enthalpy, entropy, and free energy differences between the folded and unfolded forms of hundreds of proteins under a variety of solvent/cosolvent conditions. From the latter, we know the structures of approximately 35,000 proteins, which are built on scaffolds of hydrogen-bonded structural elements, alpha-helix and beta-sheet. Anfinsen showed that the amino acid sequence alone is sufficient to determine a protein's structure, but the molecular mechanism responsible for self-assembly remains an open question, probably the most fundamental open question in biochemistry. This perspective is a hybrid: partly review, partly proposal. First, we summarize key ideas regarding protein folding developed over the past half-century and culminating in the current mindset. In this view, the energetics of side-chain interactions dominate the folding process, driving the chain to self-organize under folding conditions. Next, having taken stock, we propose an alternative model that inverts the prevailing side-chain/backbone paradigm. Here, the energetics of backbone hydrogen bonds dominate the folding process, with preorganization in the unfolded state. Then, under folding conditions, the resultant fold is selected from a limited repertoire of structural possibilities, each corresponding to a distinct hydrogen-bonded arrangement of alpha-helices and/or strands of beta-sheet.},
affiliation = {T. C. Jenkins Department of Biophysics,The Johns Hopkins University, Jenkins Hall, 3400 North Charles Street, Baltimore, MD 21218, USA. grose@jhu.edu},
number = {45},
pages = {16623--33},
volume = {103},
year = {2006},
month = {Nov},
language = {eng},
keywords = {Hydrogen Bonding, Humans, Protein Denaturation, Animals, Protein Folding, Protein Structure: Secondary, Protein Structure: Tertiary, Models: Molecular, Thermodynamics},
date-added = {2008-11-09 15:56:43 -0500},
date-modified = {2008-11-09 18:39:17 -0500},
doi = {10.1073/pnas.0606843103},
pii = {0606843103},
pmid = {17075053},
local-url = {file://localhost/Users/jballanc/Documents/Papers/2006/10.1073pnas.0606843103_Rose.pdf},
uri = {papers://7ECC1CD1-BFE8-400E-9531-FD0D6B19EBE0/Paper/p362},
read = {Yes},
rating = {0}
}
@article{Lippow:2007p360,
author = {Shaun M Lippow and Bruce Tidor},
journal = {Current Opinion in Biotechnology},
title = {Progress in computational protein design},
abstract = {Current progress in computational structure-based protein design is reviewed in the areas of methodology and applications. Foundational advances include new potential functions, more efficient ways of computing energetics, flexible treatments of solvent, and useful energy function approximations, as well as ensemble-based approaches to scoring designs for inclusion of entropic effects, improvements to guaranteed and to stochastic search techniques, and methods to design combinatorial libraries for screening and selection. Applications include new approaches and successes in the design of specificity for protein folding, binding, and catalysis, in the redesign of proteins for enhanced binding affinity, and in the application of design technology to study and alter enzyme catalysis. Computational protein design continues to mature and advance.},
affiliation = {Department of Chemical Engineering, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, MA 02139, USA. lippow@alum.mit.edu},
number = {4},
pages = {305--11},
volume = {18},
year = {2007},
month = {Aug},
language = {eng},
keywords = {Computational Biology, Protein Conformation, Algorithms, Protein Engineering, Proteins, Computer-Aided Design},
date-added = {2008-11-09 09:44:52 -0500},
date-modified = {2008-11-09 09:45:14 -0500},
doi = {10.1016/j.copbio.2007.04.009},
pii = {S0958-1669(07)00077-8},
pmid = {17644370},
local-url = {file://localhost/Users/jballanc/Documents/Papers/2007/10.1016j.copbio.2007.04.009_Lippow.pdf},
uri = {papers://7ECC1CD1-BFE8-400E-9531-FD0D6B19EBE0/Paper/p360},
read = {Yes},
rating = {0}
}
@article{Sciretti:2008p361,
author = {D Sciretti and P Bruscolini and A Pelizzola and M Pretti and A Jaramillo},
journal = {Proteins},
title = {Computational protein design with side-chain conformational entropy},
abstract = {Recent advances in modeling protein structures at the atomic level have made it possible to tackle "de novo" computational protein design. Most procedures are based on combinatorial optimization using a scoring function that estimates the folding free energy of a protein sequence on a given main-chain structure. However, the computation of the conformational entropy in the folded state is generally an intractable problem, and its contribution to the free energy is not properly evaluated. In this article, we propose a new automated protein design methodology that incorporates such conformational entropy based on statistical mechanics principles. We define the free energy of a protein sequence by the corresponding partition function over rotamer states. The free energy is written in variational form in a pairwise approximation and minimized using the Belief Propagation algorithm. In this way, a free energy is associated to each amino acid sequence: we use this insight to rescore the results obtained with a standard minimization method, with the energy as the cost function. Then, we set up a design method that directly uses the free energy as a cost function in combination with a stochastic search in the sequence space. We validate the methods on the design of three superficial sites of a small SH3 domain, and then apply them to the complete redesign of 27 proteins. Our results indicate that accounting for entropic contribution in the score function affects the outcome in a highly nontrivial way, and might improve current computational design techniques based on protein stability. Proteins 2008. (c) 2008 Wiley-Liss, Inc.},
affiliation = {Departamento de F{\'\i}sica Te{\'o}rica, Universidad de Zaragoza, c. Pedro Cerbuna 12, Zaragoza 50009, Spain.},
annote = {Summary - Including side-chain entropy through the use of free energy as a scoring function improves protein structure design.
Protein design major goal...
- Computational complexity determined by level of detail
- Should use free energy, which means calculating entropy
- Vibrational entropy a wash; conformational and solvation entropy matter
- Solvation solved by use of solvation free energies in defining energy
Computing conformational entropy is difficult!
- Current design uses single residue terms
- Either mean-field technique or empirical
- Can impact candidate quality: higher energy with large {\#} of conformations might be better
-- Use conformational free energy
...to be continued...},
pages = {},
year = {2008},
month = {Jul},
language = {ENG},
date-added = {2008-11-09 09:45:37 -0500},
date-modified = {2008-11-12 16:21:00 -0500},
doi = {10.1002/prot.22145},
pmid = {18618711},
local-url = {file://localhost/Users/jballanc/Documents/Papers/2008/10.1002prot.22145_Sciretti.pdf},
uri = {papers://7ECC1CD1-BFE8-400E-9531-FD0D6B19EBE0/Paper/p361},
read = {Yes},
rating = {0}
}
@article{Service:2008p294,
author = {Robert F Service},
journal = {Science},
title = {Problem solved* (*sort of)},
annote = {Summary - Overview of progress on the protein folding problem.
Anfinsen's original experiment denaturing and renaturing RNase.
Solution may be in reach thanks to networks.
Two key questions:
1. How? and How quickly?
2. Shape?
Levinthal's paradox - folding not random
Folding solved in parallel.
Dill's funnel - more complicated proteins fold slower.
- Test by mutating residues to make folding faster
2007 - Gruebele - Folded lambda-repressor faster (200x)
- Faster folding often leads to non-functional proteins
- "They evolved to do a job, not to fold fast"
How is mostly answered, but shape is still tough.
Two general strategies:
1. Ab initio
2. Homology models
New Techniques
- NMR shifts + Prediction
- Folding@home, Rosetta@home, GPU grids, etc.
},
number = {5890},
pages = {784--6},
volume = {321},
year = {2008},
month = {Aug},
language = {eng},
keywords = {Algorithms, Protein Folding, Models: Molecular, Nuclear Magnetic Resonance: Biomolecular, Computer Simulation, Proteins, Protein Conformation},
date-added = {2008-09-01 22:56:05 -0400},
date-modified = {2008-10-30 10:11:35 -0400},
doi = {10.1126/science.321.5890.784},
pii = {321/5890/784},
pmid = {18687949},
local-url = {file://localhost/Users/jballanc/Documents/Papers/2008/10.1126science.321.5890.784_Service.pdf},
uri = {papers://7ECC1CD1-BFE8-400E-9531-FD0D6B19EBE0/Paper/p294},
read = {Yes},
rating = {0}
}
@article{Berezovsky:2005p40,
author = {Igor N Berezovsky and William W Chen and Paul J Choi and Eugene I Shakhnovich},
journal = {PLoS Comput Biol},
title = {Entropic stabilization of proteins and its proteomic consequences},
abstract = {Evolutionary traces of thermophilic adaptation are manifest, on the whole-genome level, in compositional biases toward certain types of amino acids. However, it is sometimes difficult to discern their causes without a clear understanding of underlying physical mechanisms of thermal stabilization of proteins. For example, it is well-known that hyperthermophiles feature a greater proportion of charged residues, but, surprisingly, the excess of positively charged residues is almost entirely due to lysines but not arginines in the majority of hyperthermophilic genomes. All-atom simulations show that lysines have a much greater number of accessible rotamers than arginines of similar degree of burial in folded states of proteins. This finding suggests that lysines would preferentially entropically stabilize the native state. Indeed, we show in computational experiments that arginine-to-lysine amino acid substitutions result in noticeable stabilization of proteins. We then hypothesize that if evolution uses this physical mechanism as a complement to electrostatic stabilization in its strategies of thermophilic adaptation, then hyperthermostable organisms would have much greater content of lysines in their proteomes than comparably sized and similarly charged arginines. Consistent with that, high-throughput comparative analysis of complete proteomes shows extremely strong bias toward arginine-to-lysine replacement in hyperthermophilic organisms and overall much greater content of lysines than arginines in hyperthermophiles. This finding cannot be explained by genomic GC compositional biases or by the universal trend of amino acid gain and loss in protein evolution. We discovered here a novel entropic mechanism of protein thermostability due to residual dynamics of rotamer isomerization in native state and demonstrated its immediate proteomic implications. Our study provides an example of how analysis of a fundamental physical mechanism of thermostability helps to resolve a puzzle in comparative genomics as to why amino acid compositions of hyperthermophilic proteomes are significantly biased toward lysines but not similarly charged arginines.},
affiliation = {Department of Chemistry and Chemical Biology, Harvard University, Cambridge, Massachusetts, United States of America.},
number = {4},
pages = {e47},
volume = {1},
year = {2005},
month = {Sep},
keywords = {},
date-added = {2007-11-05 13:57:20 -0500},
date-modified = {2007-11-05 20:42:25 -0500},
doi = {10.1371/journal.pcbi.0010047},
pmid = {16201009},
local-url = {file://localhost/Users/jballanc/Documents/Papers/2005/10.1371journal.pcbi.0010047_Berezovsky.pdf},
uri = {papers://7ECC1CD1-BFE8-400E-9531-FD0D6B19EBE0/Paper/p40},
read = {Yes},
rating = {0}
}
@article{Hu:2006p68,
author = {Xiaozhen Hu and Brian Kuhlman},
journal = {Proteins},
title = {Protein design simulations suggest that side-chain conformational entropy is not a strong determinant of amino acid environmental preferences},
abstract = {Loss of side-chain conformational entropy is an important force opposing protein folding and the relative preferences of the amino acids for being buried or solvent exposed may be partially determined by which amino acids lose more side-chain entropy when placed in the core of a protein. To investigate these preferences, we have incorporated explicit modeling of side-chain entropy into the protein design algorithm, RosettaDesign. In the standard version of the program, the energy of a particular sequence for a fixed backbone depends only on the lowest energy side-chain conformations that can be identified for that sequence. In the new model, the free energy of a single amino acid sequence is calculated by evaluating the average energy and entropy of an ensemble of structures generated by Monte Carlo sampling of amino acid side-chain conformations. To evaluate the impact of including explicit side-chain entropy, sequences were designed for 110 native protein backbones with and without the entropy model. In general, the differences between the two sets of sequences are modest, with the largest changes being observed for the longer amino acids: methionine and arginine. Overall, the identity between the designed sequences and the native sequences does not increase with the addition of entropy, unlike what is observed when other key terms are added to the model (hydrogen bonding, Lennard-Jones energies, and solvation energies). These results suggest that side-chain conformational entropy has a relatively small role in determining the preferred amino acid at each residue position in a protein.},
affiliation = {Department of Biochemistry and Biophysics, University of North Carolina, Chapel Hill 27599, USA.},
number = {3},
pages = {739--48},
volume = {62},
year = {2006},
month = {Mar},
keywords = {},
date-added = {2007-11-05 13:57:20 -0500},
date-modified = {2008-01-18 09:50:51 -0500},
doi = {10.1002/prot.20786},
pmid = {16317667},
URL = {http://www3.interscience.wiley.com/cgi-bin/abstract/112161625/ABSTRACT?CRETRY=1&SRETRY=0},
local-url = {file://localhost/Users/jballanc/Documents/Papers/2006/10.1002prot.20786_Hu.pdf},
uri = {papers://7ECC1CD1-BFE8-400E-9531-FD0D6B19EBE0/Paper/p68},
read = {Yes},
rating = {0}
}
@article{Camilloni:2008p276,
author = {C Camilloni and A Guerini Rocco and I Eberini and E Gianazza and R A Broglia and G Tiana},
journal = {Biophys J},
title = {Urea and guanidinium chloride denature protein L in different ways in molecular dynamics simulations},
abstract = {In performing protein-denaturation experiments, it is common to employ different kinds of denaturants interchangeably. We make use of molecular dynamics simulations of Protein L in water, in urea, and in guanidinium chloride (GdmCl) to ascertain if there are any structural differences in the associated unfolding processes. The simulation of proteins in solutions of GdmCl is complicated by the large number of charges involved, making it difficult to set up a realistic force field. Furthermore, at high concentrations of this denaturant, the motion of the solvent slows considerably. The simulations show that the unfolding mechanism depends on the denaturing agent: in urea the beta-sheet is destabilized first, whereas in GdmCl, it is the alpha-helix. Moreover, whereas urea interacts with the protein accumulating in the first solvation shell, GdmCl displays a longer-range electrostatic effect that does not perturb the structure of the solvent close to the protein.},
affiliation = {Department of Physics, University of Milano and INFN, I-20133 Milan, Italy.},
number = {12},
pages = {4654--61},
volume = {94},
year = {2008},
month = {Jun},
language = {eng},
keywords = {Bacterial Proteins, Urea, Binding Sites, Protein Binding, Models: Molecular, Protein Denaturation, Guanidine, Computer Simulation, Protein Conformation, Models: Chemical},
date-added = {2008-07-21 02:09:44 -0400},
date-modified = {2008-07-21 02:10:15 -0400},
doi = {10.1529/biophysj.107.125799},
pii = {biophysj.107.125799},
pmid = {18339753},
local-url = {file://localhost/Users/jballanc/Documents/Papers/2008/10.1529biophysj.107.125799_Camilloni.pdf},
uri = {papers://7ECC1CD1-BFE8-400E-9531-FD0D6B19EBE0/Paper/p276},
rating = {0}
}
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