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Abstract Estimating the accuracy of protein structural models is a critical task in protein bioinformatics. The need for robust methods in the estimation of protein model accuracy (EMA) is prevalent in the field of protein structure prediction, where computationally‐predicted structures need to be screened rapidly for the reliability of the positions predicted for each of their amino acid residues and their overall quality. Current methods proposed for EMA are either coupled tightly to existing protein structure prediction methods or evaluate protein structures without sufficiently leveraging the rich, geometric information available in such structures to guide accuracy estimation. In this work, we propose a geometric message passing neural network referred to as the geometry‐complete perceptron network for protein structure EMA (GCPNet‐EMA), where we demonstrate through rigorous computational benchmarks that GCPNet‐EMA's accuracy estimations are 47% faster and more than 10% (6%) more correlated with ground‐truth measures of per‐residue (per‐target) structural accuracy compared to baseline state‐of‐the‐art methods for tertiary (multimer) structure EMA including AlphaFold 2. The source code and data for GCPNet‐EMA are available on GitHub, and a public web server implementation is freely available.
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Reconstructing Sparse Multiplex Networks with Application to Covert Networks
Network structure provides critical information for understanding the dynamic behavior of complex systems. However, the complete structure of real-world networks is often unavailable, thus it is crucially important to develop approaches to infer a more complete structure of networks. In this paper, we integrate the configuration model for generating random networks into an Expectation–Maximization–Aggregation (EMA) framework to reconstruct the complete structure of multiplex networks. We validate the proposed EMA framework against the Expectation–Maximization (EM) framework and random model on several real-world multiplex networks, including both covert and overt ones. It is found that the EMA framework generally achieves the best predictive accuracy compared to the EM framework and the random model. As the number of layers increases, the performance improvement of EMA over EM decreases. The inferred multiplex networks can be leveraged to inform the decision-making on monitoring covert networks as well as allocating limited resources for collecting additional information to improve reconstruction accuracy. For law enforcement agencies, the inferred complete network structure can be used to develop more effective strategies for covert network interdiction.
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- Award ID(s):
- 2047488
- PAR ID:
- 10405437
- Date Published:
- Journal Name:
- Entropy
- Volume:
- 25
- Issue:
- 1
- ISSN:
- 1099-4300
- Page Range / eLocation ID:
- 142
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.3390/e25010142
