Alternative splicing introduces an additional layer of protein diversity and complexity in regulating cellular functions that can be specific to the tissue and cell type, physiological state of a cell, or disease phenotype. Recent high-throughput experimental studies have illuminated the functional role of splicing events through rewiring protein-protein interactions; however, the extent to which the macromolecular interactions are affected by alternative splicing has yet to be fully understood. In silico methods provide a fast and cheap alternative to interrogating functional characteristics of thousands of alternatively spliced isoforms. Here, we develop an accurate feature-based machine learning approach that predicts whether a protein-protein interaction carried out by a reference isoform is perturbed by an alternatively spliced isoform. Our method, called the alternatively spliced interactions prediction (ALT-IN) tool, is compared with the state-of-the-art PPI prediction tools and shows superior performance, achieving 0.92 in precision and recall values.

Despite tremendous efforts by research community during the COVID-19 pandemic, the exact structure of SARS-CoV-2 and related betacoronaviruses remains elusive. Here, we developed and applied an integrative multi-scale computational approach to model the envelope structure of SARS-CoV-2, focusing on studying the dynamic nature and molecular interactions of its most abundant, but largely understudied, M (membrane) protein. The molecular dynamics simulations allowed us to test the envelop stability under different configurations and revealed that M dimers agglomerated into large, filament-like, macromolecular assemblies with distinct molecular patterns formed by M's transmembrane and intra-virion (endo) domains. These results were in agreement with the experimental data, demonstrating a generic and versatile integrative approach to model the structure of a virus de novo, providing insights into critical roles of structural proteins in the viral assembly and integration, and proposing new targets for the antiviral therapies.

Computational protein modeling rapidly advances structural knowledge of viral proteins, but methods for modeling protein complexes still need improvement.

During its first month, the recently emerged 2019 Wuhan novel coronavirus (2019-nCoV) has already infected many thousands of people in mainland China and worldwide and took hundreds of lives. However, the swiftly spreading virus also caused an unprecedentedly rapid response from the research community facing the unknown health challenge of potentially enormous proportions. Unfortunately, the experimental research to understand the molecular mechanisms behind the viral infection and to design a vaccine or antivirals is costly and takes months to develop. To expedite the advancement of our knowledge we leverage the data about the related coronaviruses that is readily available in public databases, and integrate these data into a single computational pipeline. As a result, we provide a comprehensive structural genomics and interactomics road-maps of 2019-nCoV and use these information to infer the possible functional differences and similarities with the related SARS coronavirus. All data are made publicly available to the research community at http://korkinlab.org/wuhan