Multiomics approaches to studying systems biology are very powerful tools that can elucidate changes in the genomic, transcriptomic, proteomic, and metabolomic levels within a particular cell type in response to an infection. These approaches are valuable for understanding the mechanisms behind disease pathogenesis, and specifically how the immune system responds to being challenged. With the emergence of the COVID-019 pandemic, now more than ever, the importance and utility of these tools has become evident in garnering a better understanding of the systems biology within the innate and adaptive immune response and for developing treatments and preventative measures for new and emerging pathogens that pose a threat to human health. In this review we focus on the various state of the art “omics” technologies used within the scope of innate immunity.

Reliably scoring and ranking candidate models of protein complexes and assigning their oligomeric state from the structure of the crystal lattice represent outstanding challenges. A community-wide effort was launched to tackle these challenges. The latest resources on protein complexes and interfaces were exploited to derive a benchmark dataset consisting of 1677 homodimer protein crystal structures, including a balanced mix of physiological and non-physiological complexes. The non-physiological complexes in the benchmark were selected to bury a similar or larger interface area than their physiological counterparts, making it more difficult for scoring functions to differentiate between them. Next, 252 functions for scoring protein-protein interfaces previously developed by 13 groups were collected and evaluated for their ability to discriminate between physiological and non-physiological complexes. A simple consensus score generated using the best performing score of each of the 13 groups, and a cross-validated Random Forest (RF) classifier were created. Both approaches showed excellent performance, with an area under the Receiver Operating Characteristic (ROC) curve of 0.93 and 0.94 respectively, outperforming individual scores developed by different groups. Additionally, AlphaFold2 engines were shown to recall the physiological dimers with significantly higher accuracy than the non-physiological set, lending support for the pertinence of our benchmark dataset. Optimizing the combined power of interface scoring functions and evaluating it on challenging benchmark datasets appears to be a promising strategy.

HNF4α is a master regulator gene belonging to the nuclear receptor superfamily involved in regulating a wide range of critical biological processes in different organs. Structurally, the HNF4A locus is organized with two independent promoters and is subjected to alternative splicing with the production of twelve distinct isoforms. Little is known about the mechanisms each isoform uses to regulate transcription and their biological impact, with some reports addressing these aspects. Proteomic analyses have led to identifying proteins that interact with specific HNF4α isoforms. The identification and validation of these interactions and their role in co-regulating targeted gene expression are essential to understand better the role of this transcription factor in different biological processes and pathologies. This review addresses the historical origin of HNF4α isoforms, some of the main functions of the P1 and P2 isoform subgroups and provide information on the most recent hot topic research on the nature and function of proteins associated with each of the isoforms in some biological contexts.