**Gustav Arvidsson and Anthony P. H. Wright \***

Clinical Research Center, Department of Laboratory Medicine, Karolinska Institutet, Huddinge SE 141 57, Sweden; gustav.arvidsson@ki.se

**\*** Correspondence: anthony.wright@ki.se

Received: 30 August 2018; Accepted: 4 October 2018; Published: 10 October 2018

**Abstract:** Conformational protein properties are coupled to protein functionality and could provide a useful parameter for functional annotation of differentially expressed genes in transcriptome studies. The aim was to determine whether predicted intrinsic protein disorder was differentially associated with proteins encoded by genes that are differentially regulated in lymphoma cells upon interaction with stromal cells, an interaction that occurs in microenvironments, such as lymph nodes that are protective for lymphoma cells during chemotherapy. Intrinsic disorder protein properties were extracted from the Database of Disordered Protein Prediction (D2P2), which contains data from nine intrinsic disorder predictors. Proteins encoded by differentially regulated cell-adhesion regulated genes were enriched in intrinsically disordered regions (IDRs) compared to other genes both with regard to IDR number and length. The enrichment was further ascribed to down-regulated genes. Consistently, a higher proportion of proteins encoded by down-regulated genes contained at least one IDR or were completely disordered. We conclude that down-regulated genes in stromal cell-adherent lymphoma cells encode proteins that are characterized by elevated levels of intrinsically disordered conformation, indicating the importance of down-regulating functional mechanisms associated with intrinsically disordered proteins in these cells. Further, the approach provides a generally applicable and complementary alternative to classification of differentially regulated genes using gene ontology or pathway enrichment analysis.

**Keywords:** intrinsic disorder; intrinsic disorder prediction; intrinsically disordered region; protein conformation; transcriptome; RNA sequencing; Microarray; differentially regulated genes; gene ontology analysis; functional analysis
