Wolynes, Peter GuyLevine, Herbert2019-07-152020-02-012019-082019-07-02August 201Chen, Mingchen. "Protein Aggregation in the Formation of Long Term Memory and Neurodegenerative Diseases." (2019) Diss., Rice University. <a href="https://hdl.handle.net/1911/106140">https://hdl.handle.net/1911/106140</a>.https://hdl.handle.net/1911/106140The formation of intra- and extra-cellular amyloid fibres through protein aggregation are traditionally coupled to a series of devastating and incurable neurodegenerative disorders, as well as functional implications. Computational simulations of protein aggregation has been challenging due to the relative long time scale and the involvement of larger systems. In this thesis, we summarized the efforts of using a coarse-grained model, the associative memory, water-mediated interactions, structure and energy model (AWSEM), in the study of protein aggregation and structures of amyloids. In the first part, we explored the aggregation free energy landscapes of a mechanical prion, CPEB protein. While CPEB is aggregation prone, the aggregation process is only favorable after exerting mechanical forces. We propose that an active cytoskeleton can be the origin of this mechanical force, and this mechanical catalysis makes possible a positive feedback loop that would localize the formation of CPEB fibres to active synapse areas and mark the formation of long-term memory. In the second part, we explored the aggregation landscapes of polyglutamine repeats, which are involved in over 9 neurodegenerative diseases. Free energy analyses show the length dependence of the aggregation of polyglutamine repeats, and this length-dependence property arises from the intrinsic properties of polyglutamine repeats to form β-hairpins. Following this the aggregation of full-length Huntington Exon1 encoded protein fragments, which is the culprit in Huntington’s Diseases is explored. The simulations show that the addition of the N-terminal 17-residue sequence (NT17) facilitates polyQ aggregation by encouraging the formation of prefibrillar oligomers, while adding the C-terminal polyproline sequence (P10) inhibits aggregation. The combination of both terminal additions in HTT exon 1 fragment leads to a complex aggregation mechanism, whose basic core resembles that found for the aggregation of pure polyQ repeats. At the extrapolated physiological concentration, while the grand canonical free energy profiles are uphill for HTT exon1 fragments having 20 or 30 glutamines, the aggregation landscape for fragments with 40 repeats has become downhill, thus explaining the correlation between length and Huntington disease onset age seen clinically. After elucidating the mechanisms of protein aggregation in the above cases, the structure of amyloidogenic peptides is examined under the framework of energy landscape theory. A predictive tool, the "AWSEM-Amylometer" was developed to predict the topology and aggregation propensity of peptides. The AWSEM-Amylometer notably performs better than other software in terms of the prediction of amyloidogenic sequences, and it also predicts the amyloid topology of existing peptide amyloids accurately. Nevertheless, the tertiary assembly of those amyloidogenic segments in full-length proteins is still largely unknown. So in the final part of this thesis, another tool, the AWSEM-Ribbon model, with layered constraints on each protein monomers, in the study of amyloid structures is introduced. Adopting this view of fiber architecture leads to a practical method of predicting stable protofilament structures for arbitrary peptide sequences. We apply this scheme to variants of Aβ, the amyloid forming peptide that is characteristically associated with Alzheimer’s disease. Consistent with what is known from experiment, Aβ protofibrils are found to be polymorphic. The polymorph landscape of Aβ also suggests some evolutionary aspects of amyloid protein assembly. Overall, the simulations presented here not only provide novel indications of detailed molecular mechanisms for the formation of long-term memory and the progress of neurodegenerative diseases, but also indicate the capability of energy landscape analyses to address protein misfolding/aggregation.application/pdfengCopyright is held by the author, unless otherwise indicated. Permission to reuse, publish, or reproduce the work beyond the bounds of fair use or other exemptions to copyright law must be obtained from the copyright holder.Protein AggregationLong-term Memory FormationHuntington's DiseaseEnergy Landscape TheoryFunnel TheoryAmyloidThesis2019-07-15