CRII: III: Deep Learning Methods for Protein Inter-residue Distance Prediction

University Of Missouri-Saint Louis, Saint Louis MO

Investigators

Abstract

Proteins are the gears of life. They do most of the work in our cells. Insulin and hemoglobin are two examples. Similar to mechanical gears, proteins function because of their unique and precise three-dimensional structure. For example, hemoglobin carries oxygen in our body because of its precise structure. Learning the precise structures of proteins helps us to better understand life processes and disease mechanisms. This knowledge also enables the design of novel drugs. Determining the structures of a protein in a lab can take months to years. These experiments sometimes fail with no results. As an alternative, artificial intelligence (AI) based computer algorithms are often used to predict structures. Computationally predicting protein structures has been one of the most significant and fundamental challenges during the past 50+ years. Recently, specific types of AI methods, known as convolutional neural networks, have been found to be most effective for solving this problem. However, even the best AI methods are inadequate for many proteins. It is currently an open question which types of AI algorithms are best suited for solving this problem. This project will push forward the current advances in AI-based methods for predicting structures of proteins. The main novelty of this project will be in developing AI-based methods particularly suitable for protein structure-related tasks and investigating what input variables drive the predictability. The project will increase the understanding of which kinds of AI algorithms are effective for solving fundamental biological problems, beginning with protein structure prediction. Ultimately, this will lead to a better understanding of many incurable diseases and enable a much faster development of therapeutics that will positively contribute to health and welfare of individuals, communities, and the nation. Current progress in the field of protein structure prediction comes from deep learning methods and the availability of a large number of protein sequences obtained from high throughput sequencing. Deep learning methods can predict inter-residue contacts and distances, the key information for structure prediction, much more accurately. Despite recent advances, the overall problem remains far from being solved. While many variants of convolutional neural networks and residual networks have been investigated for solving the problem of contact and distance prediction, the potential of many other types of deep learning methods such as capsule networks have not been explored. The work scope is set to achieve high accuracy protein structure prediction using novel methods for protein inter-residue distance prediction, improved feature engineering, and improved selection of high-quality multiple sequence alignments. This work will use datasets at various scales - small, medium, and large - that are representative of the protein structure database. The research will develop novel methods for protein distance prediction using existing and novel variants of capsule networks, deep learning methods for predicting the quality of multiple sequence alignments, and investigate what variables drive the accuracy of deep learning models. These research efforts will increase our understanding of the applicability and limitations of diverse deep learning algorithms in solving the protein folding problem. The findings will also be helpful in solving similar problems in computational biology. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.