In animals, RNA binding proteins (RBPs) and microRNAs (miRNAs) post-transcriptionally regulate the expression of virtually all genes by binding to RNA. Recent advances in experimental and computational methods facilitate transcriptome-wide mapping of these interactions. It is thought that the combinatorial action of RBPs and miRNAs on target mRNAs form a post-transcriptional regulatory code. We provide a database that supports the quest for deciphering this regulatory code. Within doRiNA, we are systematically curating, storing and integrating binding site data for RBPs and miRNAs. Users are free to take a target (mRNA) or regulator (RBP and/or miRNA) centric view on the data. We have implemented a database framework with short query response times for complex searches (e.g. asking for all targets of a particular combination of regulators). All search results can be browsed, inspected and analyzed in conjunction with a huge selection of other genome-wide data, because our database is directly linked to a local copy of the UCSC genome browser. At the time of writing, doRiNA encompasses RBP data for the human, mouse and worm genomes. For computational miRNA target site predictions, we provide an update of PicTar predictions.

Background: Detailed information about protein interactions is critical for our understanding of the principles governing protein recognition mechanisms. The structures of many proteins have been experimentally determined in complex with different ligands bound either in the same or different binding regions. Thus, the structural interactome requires the development of tools to classify protein binding regions. A proper classification may provide a general view of the regions that a protein uses to bind others and also facilitate a detailed comparative analysis of the interacting information for specific protein binding regions at atomic level. Such classification might be of potential use for deciphering protein interaction networks, understanding protein function, rational engineering and design.

Noncanonical amino acids with newly designed side-chain functionalities represent powerful tools to improve structural, biological, and pharmacological properties of peptides and proteins. In this context, fluorinated amino acids have increasingly gained importance. Despite the current wide use of fluorination in protein engineering, the basic properties of fluorine in protein environments are still not completely understood. Our aim has been to characterize the physicochemical properties of fluorinated amino acids by using quantum mechanics (QM) and molecular dynamics (MD) approaches. We have analyzed geometry, charges, and hydrogen bonding abilities of several ethane fluorinated derivatives at different QM theory levels and have used them as simplified models for fluorinated amino acid side chains. We have parametrized four fluorinated L-amino acids for the AMBER ff94/99 force field: 4-monofluoroethylglycine (MfeGly), 4,4-difluoroethylglycine (DfeGly), 4,4,4-trifluoroethylglycine (TfeGly), and 4,4-difluoropropylglycine (DfpGly). We have characterized them in terms of molecular volumes, conformational preferences, and hydration properties. The obtained results illustrate that fluorine and hydrogen atoms of fluoromethyl groups could be potential acceptors or donors of weak hydrogen bonds in protein environments. Hydration of the studied fluorinated amino acids was found to be more favorable than for their nonfluorinated analogues, and hydrophobicity was observed to increase with the number of fluorine atoms, which is in accordance with the experimental retention times we obtain for these amino acids. This study broadens our understanding of the properties of fluorine within protein environments, which is important to exploit the full potential of fluorine's unique properties for applications in the field of protein engineering.

BackgroundThe correlated mutations concept is based on the assumption that interacting protein residues coevolve, so that a mutation in one of the interacting counterparts is compensated by a mutation in the other. Approaches based on this concept have been widely used for protein contacts prediction since the 90s. Previously, we have shown that water-mediated interactions play an important role in protein interfaces. We have observed that current "dry" correlated mutations approaches might not properly predict certain interactions in protein interfaces due to the fact that they are water-mediated.ResultsThe goal of this study has been to analyze the impact of including solvent into the concept of correlated mutations. For this purpose we use linear combinations of the predictions obtained by the application of two different similarity matrices: a standard "dry" similarity matrix (DRY) and a "wet" similarity matrix (WET) derived from all water-mediated protein interfacial interactions in the PDB. We analyze two datasets containing 50 domains and 10 domain pairs from PFAM and compare the results obtained by using a combination of both matrices. We find that for both intra- and interdomain contacts predictions the introduction of a combination of a "wet" and a "dry" similarity matrix improves the predictions in comparison to the "dry" one alone.ConclusionOur analysis, despite the complexity of its possible general applicability, opens up that the consideration of water may have an impact on the improvement of the contact predictions obtained by correlated mutations approaches.