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Iowa State University

1. Sundararajan, Saraswathi. Fast learning optimized prediction methodology for protein secondary structure prediction, relative solvent accessibility prediction and phosphorylation prediction.

Degree: 2011, Iowa State University

Computational methods are rapidly gaining importance in the field of structural biology, mostly due to the explosive progress in genome sequencing projects and the large disparity between the number of sequences and the number of structures. There has been an exponential growth in the number of available protein sequences and a slower growth in the number of structures. There is therefore an urgent need to develop computed structures and identify the functions of these sequences. Developing methods that will satisfy these needs both efficiently and accurately is of paramount importance for advances in many biomedical fields, for a better basic understanding of aberrant states of stress and disease, including drug discovery and discovery of biomarkers. Several aspects of secondary structure predictions and other protein structure-related predictions are investigated using different types of information such as data obtained from knowledge-based potentials derived from amino acids in protein sequences, physicochemical properties of amino acids and propensities of amino acids to appear at the ends of secondary structures. Investigating the performance of these secondary structure predictions by type of amino acid highlights some interesting aspects relating to the influences of the individual amino acid types on formation of secondary structures and points toward ways to make further gains. Other research areas include Relative Solvent Accessibility (RSA) predictions and predictions of phosphorylation sites, which is one of the Post-Translational Modification (PTM) sites in proteins. Protein secondary structures and other features of proteins are predicted efficiently, reliably, less expensively and more accurately. A novel method called Fast Learning Optimized PREDiction (FLOPRED) Methodology is proposed for predicting protein secondary structures and other features, using knowledge-based potentials, a Neural Network based Extreme Learning Machine (ELM) and advanced Particle Swarm Optimization (PSO) techniques that yield better and faster convergence to produce more accurate results. These techniques yield superior classification of secondary structures, with a training accuracy of 93.33% and a testing accuracy of 92.24% with a standard deviation of 0.48% obtained for a small group of 84 proteins. We have a Matthew's correlation-coefficient ranging between 80.58% and 84.30% for these secondary structures. Accuracies for individual amino acids range between 83% and 92% with an average standard deviation between 0.3% and 2.9% for the 20 amino acids. On a larger set of 415 proteins, we obtain a testing accuracy of 86.5% with a standard deviation of 1.38%. These results are significantly higher than those found in the literature. Prediction of protein secondary structure based on amino acid sequence is a common technique used to predict its 3-D structure. Additional information such as the biophysical properties of the amino acids can help improve the results of secondary structure prediction. A database of protein…

Subjects/Keywords: Machine Learning; Neural Network; Particle Swarm Optimization; Phosphrylation; Protein; Secondary Structure Prediction; Biochemistry, Biophysics, and Structural Biology

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APA (6th Edition):

Sundararajan, S. (2011). Fast learning optimized prediction methodology for protein secondary structure prediction, relative solvent accessibility prediction and phosphorylation prediction. (Thesis). Iowa State University. Retrieved from https://lib.dr.iastate.edu/etd/11986

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Sundararajan, Saraswathi. “Fast learning optimized prediction methodology for protein secondary structure prediction, relative solvent accessibility prediction and phosphorylation prediction.” 2011. Thesis, Iowa State University. Accessed December 05, 2020. https://lib.dr.iastate.edu/etd/11986.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Sundararajan, Saraswathi. “Fast learning optimized prediction methodology for protein secondary structure prediction, relative solvent accessibility prediction and phosphorylation prediction.” 2011. Web. 05 Dec 2020.

Vancouver:

Sundararajan S. Fast learning optimized prediction methodology for protein secondary structure prediction, relative solvent accessibility prediction and phosphorylation prediction. [Internet] [Thesis]. Iowa State University; 2011. [cited 2020 Dec 05]. Available from: https://lib.dr.iastate.edu/etd/11986.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Sundararajan S. Fast learning optimized prediction methodology for protein secondary structure prediction, relative solvent accessibility prediction and phosphorylation prediction. [Thesis]. Iowa State University; 2011. Available from: https://lib.dr.iastate.edu/etd/11986

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

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