Optimization and Machine Learning Applications to Protein Sequence and Structure A DISSERTATION SUBMITTED TO THE FACULTY OF THE GRADUATE SCHOOL OF THE UNIVERSITY OF MINNESOTA BY Kevin W. DeRonne IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF Doctor of Philosophy George Karypis January, 2013 ... which are more accurate and computationally efficient than current state of the art techniques like BLASTp to annotate protein sequences. In such settings, machine learning can be used to explore distant regions of sequence space that may serve as substrates for directed evolution. Context. Proteins can be subdivided into two classes: membrane proteins and globular proteins. Protein engineering through machine-learning-guided directed evolution enables the optimization of protein functions. Bengio et al. Machine-learning approaches predict how sequence maps to function in a data-driven manner without requiring a detailed model of the underlying physics or biol. Mikolov et al. Protein engineers have been sentenced to long treks through sequence … Protein engineering through machine-learning-guided directed evolution enables the optimization of protein functions. The sequence imposes an order on the observations that must be preserved when training models and making predictions. pathways. 2013 link ; Learning protein sequence embeddings using information from structure. How big is the average protein? The authors use machine-learning methods in a novel three-step strategy for protein structure prediction. We propose to learn embedded representations of protein sequences that take advantage of the vast quantity of unmeasured protein sequence data available. Generally, prediction problems that involve sequence data are referred to as sequence prediction problems, although there are a suite of problems that differ based on the input and output sequences. This technique works best for imaging proteins that exist in only one conformation, but MIT researchers have now developed a machine-learning algorithm that helps them identify multiple possible structures that a protein can take. Machine-learning models generally require that their inputs be vectors, and the conversion from a protein sequence to a vector representation affects the model’s ability to learn. link ; XKCD: Machine Learning link ; Neural Probabilistic Language Models. It is a multi class classification problem, for a given sequence of amino acids we need to predict its protein family accession. Support vector machine (SVM) are versatile supervised machine learning methods that can be used for linear or nonlinear classification or regression. The PDB archive is a repository of atomic coordinates and other information describing proteins and other important biological macromolecules. 2006 link ; Distributed Representations of Words and Phrases and their Compositionality. Machine learning-assisted directed evolution with combinatorial libraries provides a tool for understanding the protein sequence–function relationship and for rapidly engineering useful proteins. 12, 13 Support vector machines have found broad application in protein structure prediction, drug design and classification and cancer diagnosis. This is a protein data set retrieved from Research Collaboratory for Structural Bioinformatics (RCSB) Protein Data Bank (PDB). Machine Learning Problem. Cryo-electron microscopy (cryo-EM) allows scientists to produce high-resolution, three-dimensional images of tiny molecules such as proteins. Sequence prediction is different from other types of supervised learning problems. Machine-learning approaches predict how sequence maps to …