A phenomenon as complex as protein folding requires a complex model to approximate it. This thesis presents a bottom-up approach for building complex probabilistic models of protein secondary structure by incorporating the multiple information sources which we call experts. Expert opinions are represented by probability distributions over the set of possible structures. Bayesian treatment of a group of experts results in a consensus opinion that combines the experts' probability distributions using the operators of normalized product, quotient and exponentiation. The expression of this consensus opinion simplifiesto a product of the expert opinions with two assumptions: (1) balanced training of experts, i. e. , uniform prior probability over all structures, and (2) conditional independence between expert opinions,given the structure. This research also studies how Markov chains and hidden Markov models may be used to represent expert opinion. Closure properties areproven, and construction algorithms are given for product of hidden Markov models, and product, quotient and exponentiation of Markovchains. Algorithms for extracting single-structure predictions from these models are also given. Current product-of-experts approaches in machine learning are top-down modeling strategies that assume expert independence, and require simultaneous training of all experts. This research describes a bottom-up modeling strategy that can incorporate conditionally dependent experts, and assumes separately trained experts.

A phenomenon as complex as protein folding requires a complex model to approximate it. This thesis presents a bottom-up approach for building complex probabilistic models of protein secondary structure by incorporating the multiple information sources which we call experts. Expert opinions are represented by probability distributions over the set of possible structures. Bayesian treatment of a group of experts results in a consensus opinion that combines the experts' probability distributions using the operators of normalized product, quotient and exponentiation. The expression of this consensus opinion simplifiesto a product of the expert opinions with two assumptions: (1) balanced training of experts, i. e. , uniform prior probability over all structures, and (2) conditional independence between expert opinions,given the structure. This research also studies how Markov chains and hidden Markov models may be used to represent expert opinion. Closure properties areproven, and construction algorithms are given for product of hidden Markov models, and product, quotient and exponentiation of Markovchains. Algorithms for extracting single-structure predictions from these models are also given. Current product-of-experts approaches in machine learning are top-down modeling strategies that assume expert independence, and require simultaneous training of all experts. This research describes a bottom-up modeling strategy that can incorporate conditionally dependent experts, and assumes separately trained experts.

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Prediction of protein secondary structure from primary sequence of amino acids is a very challenging task, and the problem has been approached from several angles. Proteins have many different biological functions; they may act as enzymes or as building blocks (muscle fibers) or may have transport function (e.g., transport of oxygen). The three-dimensional protein structure determines the functional properties of the protein. A lot of interesting work has been done on this problem, and over the last 10 to 20 years the methods have gradually improved in accuracy. In this dissertation we investigate several techniques for predicting the protein secondary structure. The prediction is carried out mainly using pattern classification techniques such as neural networks, genetic algorithms, simulated annealing. Each individual algorithm may work well in certain situations but fails in others. Capitalizing on the positive decisions can be achieved by forcing the various methods to collaborate to reach a unified consensus based on their previous performances. The process of combining classifiers is called decision fusion. The various decision fusion techniques such as the committee method, correlation method and the Bayesian inference methods to fuse the solutions from various approaches and to get better prediction accuracy are thoroughly explored in this dissertation. The RS126 data set was used for training and testing purposes. The results of applying pattern classification algorithms along with decision fusion techniques showed improvement in the prediction accuracy compared to that of prediction by neural networks or pattern classification algorithms individually or combined with neural networks. This research has shown that decision fusion techniques can be used to obtain better protein secondary structure prediction accuracy.

In this thesis the relative importance of input encoding and learning algorithm on protein secondary structure prediction is explored. A novel input encoding, based on multidimensional scaling applied to a recently published amino acid substitution matrix, is developed and shown to be superior to an arbitrary input encoding. Both decimal valued and binary input encodings are compared. Two neural network learning algorithms, Resilient Propagation and Learning Vector Quantization, which have not previously been applied to the problem of protein secondary structure prediction, are examined. Input encoding is shown to have a greater impact on prediction accuracy than learning methodology with a binary input encoding providing the highest training and test set prediction accuracy.

Master of Science / Department of Grain Science and Industry / David L. Wetzel / Fourier transform infrared microspectroscopy was used to determine protein secondary structure in hard wheat breeding lines in situ, providing a molecular means to rank endosperm hardness for the selection of wheat cultivars for a specific end-use. Mapping with a single masked spot size diameter of 4.5 [Mu]m or confocal 5 [Mu]μm on beamlines U10B and U2B, respectively, produced spectra from the subaleurone layer within each wheat kernel using the high spatial resolution available with synchrotron infrared microspectroscopy. This procedure was used for the first four crop years. A focal plane array instrument was adapted for use for the remaining two crop years with a slight reduction of spatial resolution. Deconvolution and curve fitting were applied to the amide I region of spectra selected from the interstitial protein between the starch granules, and the relative amount of [Alpha]-helix to other protein secondary structures was revealed. Over six crop years, the [Alpha]-helix to [Beta]-sheet ratio of experimental wheat varieties were compared to those of released varieties in 143 mapping experiments. The highest measurable value was 2.50 while the lowest was 1.11, a range consistent with hard wheat secondary structure determination found in previous studies (13, 16). The determination of protein secondary structure provides a means of ranking experimental breeding lines for selection in specific end-use applications. FT-IR microspectroscopic imaging was used to develop a method, using myoglobin as the model protein, to study the effects of thermal processing to 100 [degrees]C on protein secondary structure. Films cast onto highly polished stainless steel plates allowed the study of the exact same film before and after heating. Analyzing the shift in the amide I peak maxima of reflection absorption spectra for 280 pixels from myoglobin films revealed the depletion of [Alpha]-helix at the expense of other protein secondary structures. Deconvolution and curve fitting techniques were applied to the amide I region of each spectral average to model protein secondary structure components found within the region. The method developed was applied to another animal source, gelatin, and a plant source, wheat gluten.

Molecular structures of biomaterials have close relation to their functions. We are interested in how biological building blocks assemble into the structures of native biomaterials and the hierarchy of those structures. We tackled the problem mainly with X-ray diffraction experiments and developed a thorough analysis technique to assign the X-ray signals to protein secondary structures and chitin. Three different types of biomaterials were examined: vimentin fibres, squid pens, and human hair. In vimentin fibres, we found that the secondary protein structures play an important role in the strength of the fibres. In native squid pens, we found a self-similar, hierarchical structure from millimetres down to nanometres. In human hair, we compared the signals corresponding to keratin proteins, intermediate filaments, and lipids between different subjects, and found small deviations. The structures of these three biomaterials, which encompass different orders of length scales, were described both quantitatively and graphically. We hope that this work will eventually allow us to understand how and why nature builds biomaterials this way. / Thesis / Master of Science (MSc)

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Biomaterials requirements nowadays are becoming more and more specialized to meet increasingly demanding needs for biomedical applications such as matrices for tissue scaffolds. Among various useful classes of biomaterials, protein-based materials have been extensively pursued as they can offer a wide range of material properties to accommodate a broader spectrum of functional and performance requirements. The advent of genetic engineering and recombinant DNA technology has enabled the production of new protein-based biopolymers with precisely controlled amino acid sequence. As an example, silk-elastinlike protein (SELP) polymers consisting of polypeptide sequences from native silk of remarkable mechanical strength and polypeptide sequences from native elastin that is extremely durable and resilient have been produced. In this dissertation, a particular silk-elastinlike protein copolymer, SELP-47K, was cast into film form, and fully characterized for its material properties, including the mechanical property, secondary structure transition, optical transparency, surface, and other physical, chemical properties. The relationship between mechanical property and protein secondary structure was investigated as well. In addition, the material property tunability which can be induced by physical, mechanical, and chemical treatments has been explored. It is worth noting that the physically crosslinked SELP-47K films displayed mechanical properties comparable to those of native elastin obtained from bovine ligament. Secondary structure study through Raman and FTIR spectra showed that methanol treatment is capable of inducing theβ-sheet crystallization of silklike blocks, which act as physical crosslinks in the protein polymer chain network, thus stabilizing the protein structure and conferring the improved material integrity. The SELP-47K protein polymer thin films displayed excellent optical transparency. In particular, its excellent optical transmittance (over 90%) in visible light range may indicate SELPs can be a family of promising biomaterial candidate for ocular applications. Besides material property characterization, SELP-47K protein polymer has been fabricated into a variety of drug delivery devices to sustainably release a common ocular antibiotic, ciprofloxacin over a period of up to 220 h, with near-first order kinetics.

In this thesis, we developed Bayesian models and machine learning algorithms for protein secondary structure and beta-sheet prediction problems. In protein secondary structure prediction, we developed hidden semi-Markov models, N-best algorithms and training set reduction procedures for proteins in the single-sequence category. We introduced three residue dependency models (both probabilistic and heuristic) incorporating the statistically significant amino acid correlation patterns at structural segment borders. We allowed dependencies to positions outside the segments to relax the condition of segment independence. Another novelty of the models is the dependency to downstream positions, which is important due to asymmetric correlation patterns observed uniformly in structural segments. Among the dataset reduction methods, we showed that the composition based reduction generated the most accurate results. To incorporate non-local interactions characteristic of beta-sheets, we developed two N-best algorithms and a Bayesian beta-sheet model. In beta-sheet prediction, we developed a Bayesian model to characterize the conformational organization of beta-sheets and efficient algorithms to compute the optimum architecture, which includes beta-strand pairings, interaction types (parallel or anti-parallel) and residue-residue interactions (contact maps). We introduced a Bayesian model for proteins with six or less beta-strands, in which we model the conformational features in a probabilistic framework by combining the amino acid pairing potentials with a priori knowledge of beta-strand organizations. To select the optimum beta-sheet architecture, we analyzed the space of possible conformations by efficient heuristics, in which we significantly reduce the search space by enforcing the amino acid pairs that have strong interaction potentials. For proteins with more than six beta-strands, we first computed beta-strand pairings using the BetaPro method. Then, we computed gapped alignments of the paired beta-strands in parallel and anti-parallel directions and chose the interaction types and beta-residue pairings with maximum alignment scores. Accurate prediction of secondary structure, beta-sheets and non-local contacts should improve the accuracy and quality of the three-dimensional structure prediction.