{'fraction_non_alphanumeric': 0.018325903912828134, 'fraction_numerical': 0.0009905894006934125, 'mean_word_length': 6.1886120996441285, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 1, 'xml': 0}, 'pii_count': 1, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Probabilistic Modeling and Alignment of Protein Structure Families

ABSTRACT Motivation: Multiple protein structure alignment is an important tool in bioinformatics. Although several algorithms exist for this purpose, recent publications highlight inconsistencies among alignments from different algorithms and, increasingly, the recognition that alignments by a single algorithm may be highly unstable under small fluctuations of the input protein structures arising from thermal fluctuations, measurement error, and intrinsic flexibility. Lack of robustness to such uncertainties is a barrier to routine use to obtain reliable and reproducible biological insights. Similar issues in biopolymer sequence alignment are routinely handled by probabilistic models and statistical alignment techniques. Extending these principles from 1D sequence to 3D structure requires new statistical models and computational algorithms. Results: We present a fully probabilistic approach to the problem of multiple structure alignment in a protein family. In contrast to existing methods based on optimization of heuristic score functions, our approach is based on an explicit statistical model with testable assumptions. The resulting algorithm produces a Bayesian posterior distribution over possible alignments which accounts for alignment uncertainty arising from evolutionary variability, experimental noise, and thermal fluctuation, as well as the unknown parameters of the alignment algorithm itself. We demonstrate the robustness of this approach on alignments identified previously in the literature as “difficult” for existing algorithms. We also show the potential for significant stabilization of tree reconstruction in structural phylogenetics. We conclude by applying the algorithm deduce an important region responsible for functional diversification in two structurally similar paralogous neuronal guidance proteins. Availability: Program will be available at: http://www.duke.edu/ scs/ Contact: schmidler@stat.duke.edu