The Intrinsic Pepsin Resistance of Interleukin-8 Can Be Explained from a Combined Bioinformatical and Experimental Approach.

Interleukin-8 (IL-8, CXCL8) is a neutrophil chemotactic factor belonging to the family of chemokines. IL-8 was shown to resist pepsin cleavage displaying its high resistance to this protease. However, the molecular mechanisms underlying this resistance are not fully understood. Using our in-house database containing the data on three-dimensional arrangements of secondary structure elements from the whole Protein Data Bank, we found a striking structural similarity between IL-8 and pepsin inhibitor-3. Such similarity could play a key role in understanding IL-8 resistance to the protease pepsin. To support this hypothesis, we applied pepsin assays confirming that intact IL-8 is not degraded by pepsin in comparison to IL-8 in a denaturated state. Applying 1H-15N Heteronuclear Single Quantum Coherence NMR measurements, we determined the putative regions at IL-8 that are potentially responsible for interactions with the pepsin. The results obtained in this work contribute to the understanding of the resistance of IL-8 to pepsin proteolysis in terms of its structural properties.

doRiNA: a database of RNA interactions in post-transcriptional regulation.

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.

Fluorine in protein environments: a QM and MD study.

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.

Analysis of the impact of solvent on contacts prediction in proteins.

BACKGROUND: The 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. RESULTS: The 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. CONCLUSION: Our 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.

SCOWLP classification: structural comparison and analysis of protein binding regions.

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. DESCRIPTION: Protein binding regions (PBRs) might be ideally described as well-defined separated regions that share no interacting residues one another. However, PBRs are often irregular, discontinuous and can share a wide range of interacting residues among them. The criteria to define an individual binding region can be often arbitrary and may differ from other binding regions within a protein family. Therefore, the rational behind protein interface classification should aim to fulfil the requirements of the analysis to be performed.We extract detailed interaction information of protein domains, peptides and interfacial solvent from the SCOWLP database and we classify the PBRs of each domain family. For this purpose, we define a similarity index based on the overlapping of interacting residues mapped in pair-wise structural alignments. We perform our classification with agglomerative hierarchical clustering using the complete-linkage method. Our classification is calculated at different similarity cut-offs to allow flexibility in the analysis of PBRs, feature especially interesting for those protein families with conflictive binding regions. The hierarchical classification of PBRs is implemented into the SCOWLP database and extends the SCOP classification with three additional family sub-levels: Binding Region, Interface and Contacting Domains. SCOWLP contains 9,334 binding regions distributed within 2,561 families. In 65% of the cases we observe families containing more than one binding region. Besides, 22% of the regions are forming complex with more than one different protein family. CONCLUSION: The current SCOWLP classification and its web application represent a framework for the study of protein interfaces and comparative analysis of protein family binding regions. This comparison can be performed at atomic level and allows the user to study interactome conservation and variability. The new SCOWLP classification may be of great utility for reconstruction of protein complexes, understanding protein networks and ligand design. SCOWLP will be updated with every SCOP release. The web application is available at http://www.scowlp.org.