A major bottleneck in our understanding of the molecular underpinnings of life is the assignment of function to proteins. While molecular experiments provide the most reliable annotation of proteins, their relatively low throughput and restricted purview have led to an increasing role for computational function prediction. However, assessing methods for protein function prediction and tracking progress in the field remain challenging. We conducted the second critical assessment of functional annotation (CAFA), a timed challenge to assess computational methods that automatically assign protein function. We evaluated 126 methods from 56 research groups for their ability to predict biological functions using Gene Ontology and gene-disease associations using Human Phenotype Ontology on a set of 3681 proteins from 18 species. CAFA2 featured expanded analysis compared with CAFA1, with regards to data set size, variety, and assessment metrics. To review progress in the field, the analysis compared the best methods from CAFA1 to those of CAFA2. The top-performing methods in CAFA2 outperformed those from CAFA1. This increased accuracy can be attributed to a combination of the growing number of experimental annotations and improved methods for function prediction. The assessment also revealed that the definition of top-performing algorithms is ontology specific, that different performance metrics can be used to probe the nature of accurate predictions, and the relative diversity of predictions in the biological process and human phenotype ontologies. While there was methodological improvement between CAFA1 and CAFA2, the interpretation of results and usefulness of individual methods remain context-dependent.

Epidemics caused by highly pathogenic avian influenza virus (HPAIV) are a continuing threat to human health and to the world's economy. The development of approaches, which help to understand the significance of structural changes resulting from the alarming mutational propensity for human-to-human transmission of HPAIV, is of particularly interest. Here we compare informational and structural properties of the hemagglutinin (HA) of H5N1 virus and human influenza virus subtypes, which are important for the receptor/virus interaction. Presented results revealed that HA proteins encode highly conserved information that differ between influenza virus subtypes H5N1, H1N1, H3N2, H7N7 and defined an HA domain which may modulate interaction with receptor. We also found that about one third of H5N1 viruses which are isolated during the 2006/07 influenza outbreak in Egypt possibly evolve towards receptor usage similar to that of seasonal H1N1. The presented results may help to better understand the interaction of influenza virus with its receptor(s) and to identify new therapeutic targets for drug development.

The SARS-CoV-2 outbreak caused an unprecedented global public health threat, having a high transmission rate with currently no drugs or vaccines approved. An alternative powerful additional approach to counteract COVID-19 is in silico drug repurposing. The SARS-CoV-2 main protease is essential for viral replication and an attractive drug target. In this study, we used the virtual screening protocol with both long-range and short-range interactions to select candidate SARS-CoV-2 main protease inhibitors. First, the Informational spectrum method applied for small molecules was used for searching the Drugbank database and further followed by molecular docking. After in silico screening of drug space, we identified 57 drugs as potential SARS-CoV-2 main protease inhibitors that we propose for further experimental testing.

The novel A/H1N1 influenza virus, which recently emerged in North America is most closely related to North American H1N1/N2 swine viruses. Until the beginning of 2009, North American swine H1N1/N2 viruses have only sporadically infected humans as dead-end hosts. In 2009 the A/H1N1 virus acquired the capacity to spread efficiently by human to human transmission. The novel A/H1N1 influenza virus has struck thousands of people in more than 70 countries and killed more than 140, representing a public health emergency of international concern. Here we have studied properties of hemagglutinin of A/H1N1 which may modulate virus/receptor interaction.

Introduction: Long-range intermolecular interactions (interactions at distances between 100 and 1000 Å) play an important role in the interaction between drugs and therapeutic targets, and design techniques based on this concept could significantly improve and accelerate new drug discovery. Understanding these long-range intermolecular interactions will also help further our understanding of the molecular mechanisms and the underlying basic biological processes. Areas covered: This article looks at the physical bases of long-range intermolecular interactions in biological systems with a brief review of the literature data to support this concept. The article also gives some examples of techniques used in drug discovery that were based on the long-range intermolecular interaction concept. Expert opinion: The electron–ion interaction potential (EIIP) and average quasivalence number (AQVN) concepts shed new light on the role of long-range intermolecular interactions in biological systems. Further research of...