Predicting the amyloid fold and the propensity of peptide segments to adopt amyloid-like structures remain a challenge. However, recent progress has facilitated structure-based prediction of steric zipper propensity and the use of machine learning to accelerate the calculation of predictive models across many scientific areas. Leveraging these advances, we have developed a new approach for rapid proteome-wide assessment of zipper profiles that is informed by four million steric zipper predictions collected over ten years. This collection is used to build a machine learning model capable of rapidly predicting steric zipper propensity, and allowing for the assessment of zippers at both the protein and proteome level. Our predictions show enrichment for zipper forming segments in proteins involved in cell wall reorganization in yeast, highlighting a potential category of interest for experimental characterization. Overall, our predictive model allows for the exploration of amyloid formation across the tree of life and provides a tool for assessment of both novel and designed sequences for zipper density.

The purpose of this research is to determine how models of human auditory physiology can improve the performance of automatic speech recognition systems.In this study, a series of experiments was undertaken to discover how humans categorize and confuse vowels in natural speech.The recognition task comprised a large number of vowel nuclei isolated from naturally spoken sentences of a large number of talkers.Machine vowel classifiers were trained to match the results of these vowel categorization experiments using two input feature representations: a spectral-energy feature representation, and a representation derived from an auditory model.Classifiers trained to input representations derived from the auditory model match human performance and are more robust in the presence of noise and spectral filtering than classifiers trained to spectral-energy representations.