Protein structure prediction and conformational transitions

dc.contributor.advisor Robert Jernigan
dc.contributor.advisor Guang Song
dc.contributor.author Cheng, Haitao
dc.contributor.department Biochemistry, Biophysics and Molecular Biology
dc.date 2018-08-11T13:29:22.000
dc.date.accessioned 2020-06-30T02:28:49Z
dc.date.available 2020-06-30T02:28:49Z
dc.date.copyright Thu Jan 01 00:00:00 UTC 2009
dc.date.embargo 2013-06-05
dc.date.issued 2009-01-01
dc.description.abstract <p>There is a critical need for protein structure and function prediction. Accurate protein secondary structure prediction is essential for many bioinformatics applications, including protein tertiary structure prediction. We developed an algorithm (Fragment Data Mining, FDM) for protein secondary structure prediction using fragments of known structures obtained by multiple sequence alignment (MSA). Its performance is excellent where high-score MSA matches are available. By combing it with GOR V, a new Consensus Database Mining (CDM) method was developed, which surpasses the performances of both FDM and GOR V. For each residue, it chooses to use either the result of FDM or GOR V depending upon the availability of high-score matches of MSA. A server has been set up to make CDM publicly accessible. It becomes more popular due to the reliability and efficiency of its performance, the simplicity of its use, and its potential for improvement with the rapidly growing number of determined structrues.</p> <p>Phosphorylation is the most important post translational modifications for cellular regulation and signal transduction. Upon phosphorylation, proteins can undergo obvious conformational changes. It is challenging to characterize these changes because of the high flexibility of phosphorylation regions and the difficulties in obtaining diffraction quality crystals. In the current study, we focused on the conformational changes of CDK2 due to phosphorylation at Thr160. We use CA-CB-side chain (CABS) modeling, Targeted Molecular Dynamics (TMD) and conventional molecular dynamics (MD) to simulate the structural transition and create transition pathways. Principal component analysis (PCA) of the trajectories and normal mode analysis (NMA) with anisotropic network model (ANM - an elastic network model) were used for trajectory analysis and performance comparisons. The CABS with appropriate constraint weights and TMD with proper force constants successfully simulated the conformational changes of CDK2 phosphorylation, including the formation of the arginine cluster, maintaining the geometrical relationships of the conserved residues, and the characteristic movement of the active loop (T loop). For conventional MD, we use the CABS modeling and energy optimization to contruct the missing segments lin the structure. CABS is, for the first time, used also to create transition pathways as well as to patch in the segment without determined coordinates. It proved especially valuable in the study of small localized conformational changes. The results show that CABS and TMD are both effective approaches for creating pathways of transitions due to phosphorylation. PCA showed significant overlaps with set of low frequency ANM normal modes. It is possible to explore the mechanisms of phosphorylation-induced conformational changes with these simulation methods and analysis methods.</p>
dc.format.mimetype application/pdf
dc.identifier archive/lib.dr.iastate.edu/etd/10488/
dc.identifier.articleid 1494
dc.identifier.contextkey 2802522
dc.identifier.doi https://doi.org/10.31274/etd-180810-863
dc.identifier.s3bucket isulib-bepress-aws-west
dc.identifier.submissionpath etd/10488
dc.identifier.uri https://dr.lib.iastate.edu/handle/20.500.12876/24694
dc.language.iso en
dc.source.bitstream archive/lib.dr.iastate.edu/etd/10488/Cheng_iastate_0097E_10313.pdf|||Fri Jan 14 18:21:43 UTC 2022
dc.subject.disciplines Biochemistry, Biophysics, and Structural Biology
dc.title Protein structure prediction and conformational transitions
dc.type article
dc.type.genre dissertation
dspace.entity.type Publication
relation.isOrgUnitOfPublication faf0a6cb-16ca-421c-8f48-9fbbd7bc3747
thesis.degree.discipline Bioinformatics and Computational Biology
thesis.degree.level dissertation
thesis.degree.name Doctor of Philosophy
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