Dark Proteome Database: Studies on Dark Proteins

Abstract

The dark proteome as we define it, is the part of the proteome where 3D structure has not been observed either by homology modeling or by experimental characterization in the protein universe. From the 550.116 proteins available in Swiss-Prot (as of July 2016) 43.2% of the Eukarya universe and 49.2% of the Virus universe are part of the dark proteome. In Bacteria and Archaea, the percentage of the dark proteome presence is significantly less, with 12.6% and 13.3% respectively. In this work, we present the map of the dark proteome in Human and in other model organisms. The most significant result is that around 40%-50% of the proteome of these organisms are still in the dark, where the higher percentages belong to higher eukaryotes (mouse and human organisms). Due to the amount of darkness present in the human organism being more than 50%, deeper studies were made, including the identification of 'dark' genes that are responsible for the production of the so-called dark proteins, as well as, the identification of the 'dark' organs where dark proteins are over represented, namely heart, cervical mucosa and natural killer cells. This is a step forward in the direction of the human dark proteome.

Dark Proteome Database: Studies on Disorder

“…The Swiss-Prot dataset was composed of 550,116 proteins divided into four kingdoms: 19,370 protein sequences from Archaea, 332,327 from Bacteria, 181,814 from Eukaryota, and 16,605 from Viruses [ 6 ]. However, to maintain a fair comparison with the previous results [ 2 , 8 , 9 ], in the presented 2D plots and parallel coordinates representations, the used number of proteins was reduced to 18,999 in Archaea, 326,945 in Bacteria, 176,646 in Eukaryota, and 16,316 in Viruses.…”

“…The choice of the used predictors was made based on our previous works on this topic [ 2 , 6 , 8 , 9 ], and on the nature of the methods or programs that they use. Since we wanted to perform a comparison of the results obtained by a predictor that uses a certain type of methods, another one that uses different methods from the first one and, lastly, a predictor that uses different methods from the previous two predictors.…”

“…META-Disorder (MD) [ 10 ] was chosen, due to the usage of a support vector machine classifier for the prediction of disorder. MD was previously applied on data from 2014 [ 8 , 9 ], but in this work, MD was applied to data from 2016 in order to observe the prediction evolution of these two datasets. An updated version of IUPred (IUPred2A) [ 11 ] was also studied.…”

“…VSL2 [ 12 ] was also used as a predictor in this category due to the fact that it incorporates several methods to calculate disorder based on neural networks. Finally, to visualize darkness versus disorder, Venn diagrams, the usual 2D Plots [ 2 , 8 , 9 ], and a Parallel Coordinates [ 13 ] viewer for the Dark Proteome Database (DPD) [ 6 ] was adapted for demonstration.…”

Analysis of Protein Disorder Predictions in the Light of a Protein Structural Alphabet

“…Intrinsically-disordered proteins and regions are complicated, as they do not have unique and simple characteristics. Hence, IDPs represent complete disordered proteins that stay disordered, but they can also adopt one conformation when they bind to their ligands or partners [ 66 , 67 ] or participate in multiple systems [ 68 ], they are essential to functions [ 69 , 70 ], drug design [ 71 ], and protein design [ 72 ].…”