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Computational protein modeling and the next viral pandemic

Oleksandr Narykov , Suhas Srinivasan , Dmitry Korkin
Letter to the editor Nature Methods | 18: 444-445, 2021. DOI:


It has been one year since the release of the first SARS-CoV-2 genome1, which provided scientists with critical knowledge about its proteins. Thanks to the unprecedented experimental efforts by scientists worldwide, we have now obtained structural knowledge about most SARS-CoV-2 proteins, determining their three-dimensional (3D) shapes. Perhaps even more critical is the structural knowledge of the protein complexes that underlie the basics of viral functioning. Months before the experimental protein structures were solved, computational efforts by several groups provided researchers with accurate 3D models of the viral proteins and their physical interactions with each other and with host proteins. This 3D molecular information is instrumental in basic research, to understand mechanisms behind the viral entry and replication, as well as in structure-based drug design, to determine new antiviral targets, or in vaccine development, to study effects of novel mutations on antigen–antibody binding. Given that it is not ‘if’, but ‘when’ a new viral pandemic will emerge2, it is crucial to know whether computational modeling methods can facilitate structural characterization of viral proteins and their essential complexes. After one year of intensive research by the structural biology community, we have accumulated enough data to evaluate the impact of computational modeling efforts toward understanding the structural nature of the virus.

Assessment of network module identification across complex diseases

Sarvenaz Choobdar , Mehmet E Ahsen , Jake Crawford , et al. (including Oleksand Narykov)
Journal paper Nature Methods | 16: 843-852, 2019. DOI:


Many bioinformatics methods have been proposed for reducing the complexity of large gene or protein networks into relevant subnetworks or modules. Yet, how such methods compare to each other in terms of their ability to identify disease-relevant modules in different types of network remains poorly understood. We launched the ‘Disease Module Identification DREAM Challenge’, an open competition to comprehensively assess module identification methods across diverse protein–protein interaction, signaling, gene co-expression, homology and cancer-gene networks. Predicted network modules were tested for association with complex traits and diseases using a unique collection of 180 genome-wide association studies. Our robust assessment of 75 module identification methods reveals top-performing algorithms, which recover complementary trait-associated modules. We find that most of these modules correspond to core disease-relevant pathways, which often comprise therapeutic targets. This community challenge establishes biologically interpretable benchmarks, tools and guidelines for molecular network analysis to study human disease biology.