1,721,089 research outputs found
Fuzziness and Frustration in the Energy Landscape of Protein Folding, Function, and Assembly
Get PDFConspectusAre all protein interactions fully optimized? Do suboptimal interactions compromise specificity? What is the functional impact of frustration? Why does evolution not optimize some contacts? Proteins and their complexes are best described as ensembles of states populating an energy landscape. These ensembles vary in breadth from narrow ensembles clustered around a single average X-ray structure to broader ensembles encompassing a few different functional "taxonomic" states on to near continua of rapidly interconverting conformations, which are called "fuzzy" or even "intrinsically disordered". Here we aim to provide a comprehensive framework for confronting the structural and dynamical continuum of protein assemblies by combining the concepts of energetic frustration and interaction fuzziness. The diversity of the protein structural ensemble arises from the frustrated conflicts between the interactions that create the energy landscape. When frustration is minimal after folding, it results in a narrow ensemble, but residual frustrated interactions result in fuzzy ensembles, and this fuzziness allows a versatile repertoire of biological interactions. Here we discuss how fuzziness and frustration play off each other as proteins fold and assemble, viewing their significance from energetic, functional, and evolutionary perspectives.We demonstrate, in particular, that the common physical origin of both concepts is related to the ruggedness of the energy landscapes, intramolecular in the case of frustration and intermolecular in the case of fuzziness. Within this framework, we show that alternative sets of suboptimal contacts may encode specificity without achieving a single structural optimum. Thus, we demonstrate that structured complexes may not be optimized, and energetic frustration is realized via different sets of contacts leading to multiplicity of specific complexes. Furthermore, we propose that these suboptimal, frustrated, or fuzzy interactions are under evolutionary selection and expand the biological repertoire by providing a multiplicity of biological activities. In accord, we show that non-native interactions in folding or interaction landscapes can cooperate to generate diverse functional states, which are essential to facilitate adaptation to different cellular conditions. Thus, we propose that not fully optimized structures may actually be beneficial for biological activities of proteins via an alternative set of suboptimal interactions. The importance of such variability has not been recognized across different areas of biology.This account provides a modern view on folding, function, and assembly across the protein universe. The physical framework presented here is applicable to the structure and dynamics continuum of proteins and opens up new perspectives for drug design involving not fully structured, highly dynamic protein assemblies
The Energy Landscape of Associative Memory Energy Functions and Their Application to Ab Initio Protein Structure Prediction
No full textThe ability to predict the structure of a protein from its amino acid sequence is one of the major practical benefits that it is hoped will result from a greater understanding of the physics of protein folding. For sequences for which a highly homologous sequence with known structure is available, this hope has largely been realized. For the so-called ab initio case, where such evolutionary information is lacking, prediction becomes less reliable. Analytical theories of the protein folding reaction reveal that the ability to fold is largely dependent on a few statistical properties of the energy landscape. These include the gap in energy between the native basin and the ensemble of misfolded states and the variance in energy of the unfolded states. This insight may be used to design model energy functions for ab initio prediction. In particular the class of associative memory models are explicitly based on energy landscape ideas. In an associative memory model the energy of a particular conformation is a function of a set of sequence-structure correlations learned from a database of known structures. By varying the content of the database it is possible to tune the energy landscape between extremes consisting of a smooth funnel to the native state on the one hand, and a rough energy surface pock-marked by local traps on the other. In addition, associative memory models based on databases which contain no globally related structures may be used to obtain ab initio predictions. The quality of predicted structures built up from purely local structural relationships in this way is sufficiently high, and the computational cost of obtaining them sufficiently low, that the technique is expected to be useful in such large scale applications as genome annotation.Made available in DSpace on 2015-09-25T22:46:03Z (GMT). No. of bitstreams: 2
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Computational Studies in Chemical Quantum Physics
No full textTwo stochastic dynamic models are used to study several aspects of curve crossing phenomena in dissipative systems. A surface hopping model is used to test the qualitative predictions of earlier theories. The simulation results agree well with the qualitative picture. Results are obtained for an alternate semiclassical model based on a vector spin representation which is derived via a variational principle. The vector model shows some differences in behavior as compared to the hopping model. In certain regimes the vector model shows chaotic behavior.A numerical path integral technique based upon a quasiclassical Langevin equation is presented for the calculation of quantum mechanical properties of a system coupled to a dissipative bath. Fully quantum mechanical results are obtained by decorating quasiclassical paths with quantum fluctuations. The use of Diophantine integration has been compared to Monte Carlo sampling of the fluctuation variables. Results are presented for several model systems.Made available in DSpace on 2015-05-13T15:39:29Z (GMT). No. of bitstreams: 2
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Previous issue date: 1987Embargo set by: Seth Robbins for item 78526
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Reason: Restricted to the U of I community idenfinitely during batch ingest of legacy ETDsRestricted to the U of I community idenfinitely during batch ingest of legacy ETDsU of I Only124 p.Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 1987
Multi-scale computational modeling of an RNA-binding prion, CPEB3, reveals its molecular mechanisms underlying the formation of long-term memory
No full textThe growth and stabilization of dendritic spines is thought to be essential for strengthening the connections
between neurons, and thereby memories. Actin cytoskeleton remodeling in spines is the basis of this growth and
stabilization. CPEB proteins were first identified as a group of RNA-binding proteins regulating the translation
of their target mRNAs, like actin mRNAs. Intriguingly, one isoform in CPEB family, CPEB3, has been recently reported
as a functional prion that interacts with actin cytoskeleton. These observations make CPEB3
a seductively plausible candidate as a synaptic tag to strengthen the actin cytoskeleton and long-term memory
by forming stable aggregates, and simultaneously regulate the local translation of synaptic proteins in spines.
Numerous gene knockout experiments have been conducted about CPEB3 and its homologs to investigate CPEB3's
function in memories. However, it is challenging for experimentalists to collect structural information of CPEB3
due to the conformational flexibility of prion-like proteins, and the picture of CPEB3's role
in synaptic plasticity is still unclear at molecular level.
In this thesis, to fill in the missing pieces in the molecular mechanisms of CPEB3,
we utilized multi-scale computational modeling by conducting bioinformatic searches,
setting up reaction-diffusion systems, and mainly by running molecular dynamics simulations
using a coarse-grained protein force field - the Associative memory, Water-mediated, Structure and Energy Model (AWSEM).
In the first part, we studied the interaction between actin and CPEB3 and
proposed a molecular model for the complex structure of CPEB3 bound to an actin filament (F-actin).
Our model gives insights into the molecular details of the F-actin/CPEB3 positive feedback loop
underlying long-term memory which involves CPEB3's binding to F-actin, its aggregation triggered by F-actin,
and its regulation by SUMOylation.
The soluble CPEB3 monomers repress translation, whereas CPEB3 aggregates activate the translation of
its target mRNAs. The CPEB3 aggregates, however, that act as long-lasting prions providing "conformational memory",
may raise the problem of the consequent translational activation being unregulated.
In the second part of the thesis, I propose a computational model of the complex structure between CPEB3 RNA-binding domain
(CPEB3-RBD) and small ubiquitin-like modifier protein 2 (SUMO2).
Free energy calculations suggest that the allosteric binding of CPEB3 with SUMO2 can confine the CPEB3-RBD
to a conformation that favors RNA-binding, and thereby can amplify its RNA-binding affinity.
Combining this model with previous experiments showing that CPEB3 monomers are SUMOylated in basal synapses
but become deSUMOylated and start to aggregate upon stimulation,
we suggest a way in which the translational control of CPEB3 can be switched back to a repressive mode
after a stimulation pulse, through an RNA binding shift from binding to CPEB3 fibers
to binding to SUMOylated CPEB3 monomers in basal synapses.
In the last part, inspired by the specific geometry and polarity of the assembly of mRNAs and CPEB3 aggregates,
a vectorial channeling mechanism is proposed to describe the local translational regulation
by general mRNA/protein assemblies including functional prions and condensates.
The analysis shows that the vectorial processive nature of translation
can couple to transport via diffusion so as to repress or activate translation
depending on the structure of the RNA protein assembly. We find that multiple factors
including diffusivity changes and free energy biases in the
assemblies can regulate the translation rate of mRNA by changing the balance between
substrate recycling and competition between mRNAs
Exploring Amyloid-B Aggregation Pathways and Identifying Key Differences Between AB40, AB42, and Known Mutants
No full textFirst described in 1906 by Alois Alzheimer, who reported the presence of senile plaques and neurofibrillary tangles in the brain tissue of a sufferer, Alzheimer’s disease is now the most common form of dementia. Many years later it was determined that the hallmark senile plaques were composed of aggregated peptide fragments, referred to as amyloid beta. Like other amyloids, these fragments can exist as highly disordered monomers, disordered soluble oligomers, and highly ordered macroscopic fibrils. Despite decades of research, many open questions remain in regard to the role of amyloid beta in the pathology of Alzheimer’s disease. In this dissertation, molecular dynamics simulations, in complement with experimental results, will be employed to address a few of these questions. In particular, a molecular level picture is developed elucidating the differences in aggregation rates between the two most common isoforms of the protein, providing evidence of possible pharmaceutical targets. Data related to the conformational search undertaken by amyloid beta monomers will be analyzed, revealing a possible connection between the secondary structure of wild type and mutated monomers and their observed fibrilization rates. Finally, the fibril elongation behavior of amyloid beta containing a single-point mutation associated with a devastating form of Alzheimer’s disease will be investigated to gain insight into the physicochemical properties driving its distinct clinical presentations. The resulting conclusions of these efforts lay the foundation for further experiments which could address critical factors related to the pathological role of aggregation rates, different early oligomerization behavior, and sources of neurotoxicity
Quantum Theory of Glasses
No full textThe quantum excitations in glasses have long presented a set of puzzles for condensed matter scientists. A common view is that they are largely disordered analogs of elementary excitations in crystals, supplemented by two level systems which are chemically local entities coming from disorder. This thesis suggests a radical revision of this picture. We argue that the excitations in low temperature glasses are deeply connected to the energy landscape of the glass when it vitrifies: the excitations are not low excited states built on a single ground state but locally defined resonances, high in the energy spectrum of a solid. Two level systems involve resonant collective tunneling motions of around two hundred molecular units. The Boson Peak and the plateau in thermal conductuvity, observed at higher temperatures, arise from the same motions but the motions are no longer fully coherent.Made available in DSpace on 2015-09-25T22:12:47Z (GMT). No. of bitstreams: 2
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Reason: Restricted to the U of I community idenfinitely during batch ingest of legacy ETDsRestricted to the U of I community idenfinitely during batch ingest of legacy ETDsU of I Only81 p.Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2002
Local frustration around enzyme active sites
Get PDFConflicting biological goals often meet in the specification of protein sequences for structure and function. Overall, strong energetic conflicts are minimized in folded native states according to the principle of minimal frustration, so that a sequence can spontaneously fold, but local violations of this principle open up the possibility to encode the complex energy landscapes that are required for active biological functions. We survey the local energetic frustration patterns of all protein enzymes with known structures and experimentally annotated catalytic residues. In agreement with previous hypotheses, the catalytic sites themselves are often highly frustrated regardless of the protein oligomeric state, overall topology, and enzymatic class. At the same time a secondary shell of more weakly frustrated interactions surrounds the catalytic site itself. We evaluate the conservation of these energetic signatures in various family members of major enzyme classes, showing that local frustration is evolutionarily more conserved than the primary structure itself.Fil: Freiberger, Maria Ines. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Química Biológica de la Facultad de Ciencias Exactas y Naturales. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Química Biológica de la Facultad de Ciencias Exactas y Naturales; Argentina. Universidad Nacional de Entre Ríos; ArgentinaFil: Guzovsky, Ana Brenda. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Química Biológica de la Facultad de Ciencias Exactas y Naturales. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Química Biológica de la Facultad de Ciencias Exactas y Naturales; ArgentinaFil: Wolynes, Peter G.. Rice University; Estados UnidosFil: Parra, Rodrigo Gonzalo. Universidad Nacional de Entre Ríos; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Ferreiro, Diego. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Química Biológica de la Facultad de Ciencias Exactas y Naturales. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Química Biológica de la Facultad de Ciencias Exactas y Naturales; Argentin
Understanding the folding mechanisms of membrane proteins through molecular simulations and energy landscape analysis
No full textThe folding mechanisms of membrane proteins are notoriously hard to determine, due to the multiple events involved in the folding process.
In recent years, the development of single molecule techniques has opened the door to studying individual folding events experimentally.
However, even in these single molecule experiments the structural details underlying the observed transitions can only be inferred.
Similar to E. coli as a model organism, rhomboid protease GlpG is typically used to study membrane protein.
Previous single molecular experiments have suggested that GlpG has an anomalously low thermodynamic stability.
By performing molecular simulations and energy landscape analysis, we showed that the seemingly low stability was due to the presence of folding intermediates.
Our finding was confirmed by a subsequent experimental study by the same experimental group, where our predicted intermediates were observed.
On the technique side, we developed our next generation simulation package: OpenAWSEM and Open3SPN2.
OpenAWSEM achieves orders of magnitude of speedup with GPU compared with single core CPU, and enables rapid prototyping force fields with automatic derivative calculations
Application of the Statistical Energy Landscape Theory to Protein Structure Prediction
No full textThere has been an exponential increase in sequence information over the past few years due mostly to the large scale genome projects. The corresponding structural information, however, is being determined at a much slower rate. Consequently, the sequence to structure gap is still growing. Many scientists are looking to structure prediction through computational methods to narrow this gap. Using ideas from the energy landscape theory first introduced by Bryngelson and Wolynes, and database analysis, we have designed statistical mechanical energy functions for protein structure prediction by threading and simulated annealing molecular dynamics methods. These optimized energy functions improve upon the previous approximations of Goldstein, Luthey-Schulten and Wolynes by taking into account correlations in the energy landscape. For the self-consistent energy function used in threading, alignments are comparable to those obtain by evolutionary distance-based alignments and consistently appear to be more accurate for sequences that have lower than 20% sequence identity. The self-consistent threading algorithm we developed was used in our participation in the second annual critical assessment of protein structure prediction (CASP2) competition, and our results place us among the top three groups in comparative modeling. Using this energy function, we predicted the structures of four archaeal adenylate kinases that had less than 17% sequence similarity to any known structure. These predicted structures were then used to study the sources of protein thermostablitiy. The energy function used in the molecular dynamics approach or treatment of protein folding was optimized based on a more complete characterization of a protein's energy landscape. This characterization involved a better statistical sampling of the misfolded states and a partial treatment of dominant short-range correlations and interactions determining collapse. This lead to more accurate structure predictions than was possible by the previous treatment.Made available in DSpace on 2015-09-25T22:14:25Z (GMT). No. of bitstreams: 2
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Previous issue date: 1998Embargo set by: Seth Robbins for item 85707
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Reason: Restricted to the U of I community idenfinitely during batch ingest of legacy ETDsRestricted to the U of I community idenfinitely during batch ingest of legacy ETDsU of I Only165 p.Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 1998
Application of the Statistical Energy Landscape Theory to Protein Structure Prediction
No full text165 p.Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 1998.There has been an exponential increase in sequence information over the past few years due mostly to the large scale genome projects. The corresponding structural information, however, is being determined at a much slower rate. Consequently, the sequence to structure gap is still growing. Many scientists are looking to structure prediction through computational methods to narrow this gap. Using ideas from the energy landscape theory first introduced by Bryngelson and Wolynes, and database analysis, we have designed statistical mechanical energy functions for protein structure prediction by threading and simulated annealing molecular dynamics methods. These optimized energy functions improve upon the previous approximations of Goldstein, Luthey-Schulten and Wolynes by taking into account correlations in the energy landscape. For the self-consistent energy function used in threading, alignments are comparable to those obtain by evolutionary distance-based alignments and consistently appear to be more accurate for sequences that have lower than 20% sequence identity. The self-consistent threading algorithm we developed was used in our participation in the second annual critical assessment of protein structure prediction (CASP2) competition, and our results place us among the top three groups in comparative modeling. Using this energy function, we predicted the structures of four archaeal adenylate kinases that had less than 17% sequence similarity to any known structure. These predicted structures were then used to study the sources of protein thermostablitiy. The energy function used in the molecular dynamics approach or treatment of protein folding was optimized based on a more complete characterization of a protein's energy landscape. This characterization involved a better statistical sampling of the misfolded states and a partial treatment of dominant short-range correlations and interactions determining collapse. This lead to more accurate structure predictions than was possible by the previous treatment.U of I OnlyRestricted to the U of I community idenfinitely during batch ingest of legacy ETD
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