Developing new algorithms and concepts towards understanding protein folding, misfolding, and aggregation

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Proteins are essential in performing tasks that are necessary for life, though their proper function can only be performed when folded into their native state. While the native state is stable under physiological conditions, environmental factors such as pH, salt concentration, or interaction with cell membranes may lead to spatial rearrangement of segments within the protein and its subsequent refolding and aggregation into nonnative and often undesirable structures. Protein folding, misfolding, and aggregation plays a critical role in a wide spectrum of fields and applications: Towards advancing protein engineering (a rising multibillion dollar industry); in diminishing protein aggregation that impacts biopharmaceutical development in every stage and is linked to high costs; and in furthering our understanding of protein misfolding disease that afflict over half a million Canadians. Our understanding of the fundamental processes that drive protein behavior has evolved significantly over the past 50 years. Nonetheless, surprisingly little advancements have been made in models that can predict the folding (and misfolding) process de novo, from a different initial conformation. State-of-the art models require substantial computational power and are currently limited to short protein chains, and hence cannot capture all the mechanisms involved. The goal of my lab is to develop new conceptual approaches to understand the mechanisms of protein folding, misfolding, and aggregation that will allow in silico studies of large proteins within a reasonable timeframe using a desktop computer. In the longer run, these methods will be used to understand and predict more complex biomolecular phenomena (such as docking, protein-DNA interactions, and interactions with nanoparticles for biosensor applications). In the proposed program I outline novel approaches for modeling protein behavior using various computational tools and methods. My preliminary work shows that computation time required for protein folding prediction can be significantly reduced if simulated in non-Cartesian coordinates. Combining this work with data mining tools, I recently revealed a relation between helical protein structures and their tendency to form malignant misfolded aggregates. Building on this research, the proposed program will form the basis of 3 PhD theses. The students will be trained in the development of molecular models, data mining, and machine learning in the longer run. Data mining algorithms will be used to recognize patterns within protein databases to reveal sequences that are liable to misfolding and aggregation under environmental stress. Machine learning algorithms will be used to identify reliable folding pathways. This research will advance basic understanding of protein behavior while fostering important computational skills in engineering students.