Some intriguing high-throughput DNA sequence variants prediction over protein functionality

This paper intends to review computational methods and high throughput automated tools for precisely prediction various functionalities of uncharacterized proteins based on their desired DNA sequence information alone. Then proposes a hybrid weighted network and Genetic Algorithm to improve prediction purpose. The main advantage of the method is the protein function and DNA sequence prediction can be computed precisely using best fitness parent in genetic algorithm. With the accomplishment of human genome sequencing, the number of sequence-known proteins has increased exponentially and the pace is much slower in determining their biological attributes. The gap between DNA sequence variants and their functionalities has become increasingly large. However, detection of sequences based on protein data bank has become benchmark for many researchers. As amount of DNA sequence data continues to increase, the fundamental problem stay at the front of genome analysis. In the course of developing these methods, the following matters were often needed to consider: benchmark dataset construction, gene sequence prediction, operating algorithm, anticipated accuracy, gene recommender and functional integrations. In this review, we are to discuss each of them, with a different focus on operational algorithms and how to increase the accuracy of DNA sequence variants predictio

Prediction of parallel in-register amyloidogenic beta-structures In highly beta-rich protein sequences by pairwise propensity analysis

Thesis (Ph. D.)--Harvard-MIT Division of Health Sciences and Technology, 2009.Cataloged from PDF version of thesis.Includes bibliographical references (p. 123-133).Amyloids and prion proteins are clinically and biologically important beta-structures, whose supersecondary structures are difficult to determine by standard experimental or computational means. In addition, significant conformational heterogeneity is known or suspected to exist in many amyloid fibrils. Recent work has indicated the utility of templates and pairwise probabilistic statistics in betastructure prediction. A new suite of programs, BETASCAN, STITCHER, and HELIXCAP, are presented to address the problem of amyloid structure prediction. BETASCAN calculates likelihood scores for potential beta-strands and strand-pairs based on correlations observed in parallel beta-sheets. The program then determines the strands and pairs with the greatest local likelihood for all of the sequence's potential beta-structures. BETASCAN suggests multiple alternate folding patterns and assigns relative ab initio probabilities based solely on amino acid sequence, probability tables, and pre-chosen parameters. STITCHER processes the output of BETASCAN and uses dynamic programming to 'stitch' structures from flexible abstract templates defined by constraints for amyloid-like all-beta structures. The 'stitched' structures are evaluated by a free-energy-based scoring algorithm incorporating BETASCAN scores, bonuses for favorable side-chain stacking, and penalties for linker entropy. The analyses of STITCHER structures emphasize the importance of side-chain stacking ladders in amyloid formation. HELIXCAP detects a class of end-caps, called beta-helix caps, which stabilize known beta-helix structures. These structures are known to stabilize globular beta-helix proteins and prevent their amyloidogenesis; their presence in a sequence is a powerful negative predictor of amyloid potential. Together, these algorithms permit detection and structural analysis of protein amyloidogenicity from sequence data, enhancing the experimental investigation of amyloids and prion proteins.by Allen Wayne Bryan, Jr.Ph.D

Structural studies of glutathione S-transferases from plants

The aim of this thesis was to structurally characterise a range of plant GSTs to increase the understanding as to which structural features determine substrate specificity. During the course of this work, a total of ten plant GSTs from species including Maize, Wheat, Carnation, Petunia and Arabidopsis thaliana were overexpressed in E. coli. Nine of these overexpressed GSTs were purified the produce protein for crystallisation using a range of matrices such as Orange A agarose, S-hexyl glutathione and metal chelate followed by ion-exchange and/or gel filtration chromatography depending on the particular GST. Initial crystallisation conditions for seven of these purified GSTs were found by sparse matrix screening. Crystallisation conditions providing crystals suitable for X-ray diffraction experiments were determined for four of the GSTs under study. The data gained from these diffraction experiments enabled the solution of four different plant GST three-dimensional structures. The first structure to be solved was ZmGSTF1, a GST isoenzyme constitutively expressed in Maize. The second structure to be solved was AtGSTZ1 from Arabidopsis thaliana. The third GST structure determined (AtGSTT1), again from Arabidopsis thaliana, was found to share significant homology with the mammalian Theta class GSTs. The fourth structure determined was a GST isoenzyme from wheat, able to metabolise the commercially important herbicide fenoxaprop. These structural models provide a detailed understanding of the structure determinants of a variety of GSTs which dictate the different substrate specificities of GSTs and provide suggestions for the rational design of GSTs to improve herbicide selectivity in crops. In addition, this study presents material relating to the in-vivo role of plant GSTs and their binding to endogenous substrates

1999 Eleventh Annual IMSA Presentation Day

Abstracts can be found attached in alphabetical order under the first presenter.https://digitalcommons.imsa.edu/archives_sir/1025/thumbnail.jp