Neurodegeneration, the progressive loss of function in neurons that eventually leads to their death, is the cause of many neurodegenerative disorders including Alzheimer’s, Parkinson’s, and Huntington’s diseases. Protein aggregation is a hallmark of most neurodegenerative diseases, where unfolded proteins form intranuclear, cytosolic, and extracellular insoluble aggregates in neurons. Mounting evidence from studies in neurodegenerative disease models shows that molecular chaperones, key regulators of protein aggregation and degradation, play critical roles in the progression of neurodegeneration. Although chaperones exhibit promiscuity in their substrate specificity, specific molecular features are required for substrate recognition. Understanding the basis for substrate recognition by chaperones will aid in the development of therapeutic strategies that regulate chaperone expression levels in order to combat neurodegeneration. Many experimental techniques, including alanine scanning mutagenesis and phage display library screening, have been developed and applied to understand the basis of substrate recognition by chaperones. Here, we present computational algorithms that can be applied to rapidly screen the sequence space of potential substrates to determine the sequence and structural requirements for substrate recognition by chaperones
