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Chris A. Kaiser. Eklund, Aron Charles, 1974- Massachusetts Institute of Technology. Dept. of Biology. Massachusetts Institute of Technology. Department of Biology 2005-05-19T14:39:17Z 2005-05-19T14:39:17Z 2001 2002 http://hdl.handle.net/1721.1/16804 50529482 Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Biology, February 2002. Includes bibliographical references (leaves 60-62). This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections. Disulfide bonds play an important role in the structural stability of the proteins that contain them. Yet, little is known about the specificity with which they are formed. To address this, a representative set of disulfide bonds from nonhomologous eukaryotic polypeptides was created. The amino acid sequences flanking these disulfide bonds were searched for conserved patterns that may reflect recognition sites by the disulfide bond forming enzyme protein disulfide isomerase (PDI). Several methods of classifying disulfide bonds were explored, and each class was analyzed for conserved sequence patterns. To maximize the chances of finding a conserved recognition site, a simulated annealing algorithm was implemented to divide a set of disulfide-bonded cysteines into two sets of cysteines with an average sequence environment that is as far from randomly-distributed as possible. No significant conserved patterns were found in the set of disulfide bonds or within any of the classification schemes introduced. Additionally, several methods for predicting disulfide bond connectivity were explored. The most successful methods predicted connectivity based on the sequential distance between cysteines. by Aron Charles Eklund. S.M. 62 leaves 5088275 bytes 5088025 bytes application/pdf application/pdf eng Massachusetts Institute of Technology M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission. http://dspace.mit.edu/handle/1721.1/7582 Biology. Patterns in the sequence context of protein disulfide bonds Thesis application/pdf false Publication unknown unknown <Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="eece7346-740f-4005-84a8-3367cb4cd6f0"> <Type xmlns="https://www.openaire.eu/cerif-profile/vocab/COAR_Publication_Types">http://purl.org/coar/resource_type/c_1843</Type> <Language>eng</Language> <Title>Patterns in the sequence context of protein disulfide bonds</Title> <PublishedIn> <Publication> </Publication> </PublishedIn> <PublicationDate>2002</PublicationDate> <Authors> <Author> <DisplayName>Eklund, Aron Charles, 1974-</DisplayName> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> </Authors> <Editors> </Editors> <Publishers> <Publisher> <DisplayName>Massachusetts Institute of Technology</DisplayName> <OrgUnit /> </Publisher> </Publishers> <License>http://dspace.mit.edu/handle/1721.1/7582</License> <Keyword>Biology.</Keyword> <Abstract>Disulfide bonds play an important role in the structural stability of the proteins that contain them. Yet, little is known about the specificity with which they are formed. To address this, a representative set of disulfide bonds from nonhomologous eukaryotic polypeptides was created. The amino acid sequences flanking these disulfide bonds were searched for conserved patterns that may reflect recognition sites by the disulfide bond forming enzyme protein disulfide isomerase (PDI). Several methods of classifying disulfide bonds were explored, and each class was analyzed for conserved sequence patterns. To maximize the chances of finding a conserved recognition site, a simulated annealing algorithm was implemented to divide a set of disulfide-bonded cysteines into two sets of cysteines with an average sequence environment that is as far from randomly-distributed as possible. No significant conserved patterns were found in the set of disulfide bonds or within any of the classification schemes introduced. Additionally, several methods for predicting disulfide bond connectivity were explored. The most successful methods predicted connectivity based on the sequential distance between cysteines.</Abstract> <Access xmlns="http://purl.org/coar/access_right" > </Access> </Publication>