DLiP-PPI library: An integrated chemical database of small-to-medium-sized molecules targeting protein-protein interactions.

Protein-protein interactions (PPIs) are recognized as important targets in drug discovery. The characteristics of molecules that inhibit PPIs differ from those of small-molecule compounds. We developed a novel chemical library database system (DLiP) to design PPI inhibitors. A total of 32,647 PPI-related compounds are registered in the DLiP. It contains 15,214 newly synthesized compounds, with molecular weight ranging from 450 to 650, and 17,433 active and inactive compounds registered by extracting and integrating known compound data related to 105 PPI targets from public databases and published literature. Our analysis revealed that the compounds in this database contain unique chemical structures and have physicochemical properties suitable for binding to the protein-protein interface. In addition, advanced functions have been integrated with the web interface, which allows users to search for potential PPI inhibitor compounds based on types of protein-protein interfaces, filter results by drug-likeness indicators important for PPI targeting such as rule-of-4, and display known active and inactive compounds for each PPI target. The DLiP aids the search for new candidate molecules for PPI drug discovery and is available online (https://skb-insilico.com/dlip).

A combination of 19F NMR and surface plasmon resonance for site-specific hit selection and validation of fragment molecules that bind to the ATP-binding site of a kinase.

19F NMR has recently emerged as an efficient, sensitive tool for analyzing protein binding to small molecules, and surface plasmon resonance (SPR) is also a popular tool for this purpose. Herein a combination of 19F NMR and SPR was used to find novel binders to the ATP-binding pocket of MAP kinase extracellular regulated kinase 2 (ERK2) by fragment screening with an original fluorinated-fragment library. The 19F NMR screening yielded a high primary hit rate of binders to the ERK2 ATP-binding pocket compared with the rate for the SPR screening. Hit compounds were evaluated and categorized according to their ability to bind to different binding sites in the ATP-binding pocket. The binding manner was characterized by using isothermal titration calorimetry and docking simulation. Combining 19F NMR with other biophysical methods allows the identification of multiple types of hit compounds, thereby increasing opportunities for drug design using preferred fragments.

Dr. PIAS 2.0: an update of a database of predicted druggable protein-protein interactions.

Druggable Protein-protein Interaction Assessment System (Dr. PIAS) is a database of druggable protein-protein interactions (PPIs) predicted by our support vector machine (SVM)-based method. Since the first publication of this database, Dr. PIAS has been updated to version 2.0. PPI data have been increased considerably, from 71,500 to 83,324 entries. As the new positive instances in our method, 4 PPIs and 10 tertiary structures have been added. This addition increases the prediction accuracy of our SVM classifier in comparison with the previous classifier, despite the number of added PPIs and structures is small. We have introduced the novel concept of 'similar positives' of druggable PPIs, which will help researchers discover small compounds that can inhibit predicted druggable PPIs. Dr. PIAS will aid the effective search for druggable PPIs from a mine of interactome data being rapidly accumulated. Dr. PIAS 2.0 is available at http://www.drpias.net.

Significance of the conformation of building blocks in curing of barnacle underwater adhesive.

Barnacles are a unique sessile crustacean that attach irreversibly and firmly to foreign underwater surfaces. Its biological underwater adhesive is a peculiar extracellular multi-protein complex. Here we characterize one of the two major proteins, a 52 kDa protein found in the barnacle cement complex. Cloning of the cDNA revealed that the protein has no homolog in the nonredundant database. The primary structure consists of four long sequence repeats. The process of dissolving the protein at the adhesive joint of the animal by various treatments was monitored in order to obtain insight into the molecular mechanism involved in curing of the adhesive bulk. Treatments with protein denaturant, reducing agents and/or chemical-specific proteolysis in combination with 2D diagonal PAGE indicated no involvement of the protein in intermolecular cross-linkage/polymerization, including formation of intermolecular disulfide bonds. As solubilization of the proteins required high concentrations of denaturing agents, it appears that both the conformation of the protein as building blocks and non-covalent molecular interactions between the building blocks, possibly hydrophobic interactions and hydrogen bonds, are crucial for curing of the cement. It was also suggested that the protein contributes to surface coupling by an anchoring effect to micro- to nanoscopic roughness of surfaces.

Prediction of protein motions from amino acid sequence and its application to protein-protein interaction.

BACKGROUND: Structural flexibility is an important characteristic of proteins because it is often associated with their function. The movement of a polypeptide segment in a protein can be broken down into two types of motions: internal and external ones. The former is deformation of the segment itself, but the latter involves only rotational and translational motions as a rigid body. Normal Model Analysis (NMA) can derive these two motions, but its application remains limited because it necessitates the gathering of complete structural information. RESULTS: In this work, we present a novel method for predicting two kinds of protein motions in ordered structures. The prediction uses only information from the amino acid sequence. We prepared a dataset of the internal and external motions of segments in many proteins by application of NMA. Subsequently, we analyzed the relation between thermal motion assessed from X-ray crystallographic B-factor and internal/external motions calculated by NMA. Results show that attributes of amino acids related to the internal motion have different features from those related to the B-factors, although those related to the external motion are correlated strongly with the B-factors. Next, we developed a method to predict internal and external motions from amino acid sequences based on the Random Forest algorithm. The proposed method uses information associated with adjacent amino acid residues and secondary structures predicted from the amino acid sequence. The proposed method exhibited moderate correlation between predicted internal and external motions with those calculated by NMA. It has the highest prediction accuracy compared to a naïve model and three published predictors. CONCLUSIONS: Finally, we applied the proposed method predicting the internal motion to a set of 20 proteins that undergo large conformational change upon protein-protein interaction. Results show significant overlaps between the predicted high internal motion regions and the observed conformational change regions.

Identification and functional characterization of a novel barnacle cement protein.

Barnacle attachment to various foreign materials in water is guided by an extracellular multiprotein complex. A 19 kDa cement protein was purified from the Megabalanus rosa cement, and its cDNA was cloned and sequenced. The gene was expressed only in the basal portion of the animal, where the histologically identified cement gland is located. The sequence of the protein showed no homology to other known proteins in the databases, indicating that it is a novel protein. Agreement between the molecular mass determined by MS and the molecular weight estimated from the cDNA indicated that the protein bears no post-translational modifications. The bacterial recombinant was prepared in soluble form under physiologic conditions, and was demonstrated to have underwater irreversible adsorption activity to a variety of surface materials, including positively charged, negatively charged and hydrophobic ones. Thus, the function of the protein was suggested to be coupling to foreign material surfaces during underwater attachment. Homologous genes were isolated from Balanus albicostatus and B. improvisus, and their amino acid compositions showed strong resemblance to that of M. rosa, with six amino acids, Ser, Thr, Ala, Gly, Val and Lys, comprising 66-70% of the total, suggesting that such a biased amino acid composition may be important for the function of this protein.

POODLE-L: a two-level SVM prediction system for reliably predicting long disordered regions.

MOTIVATION: Recent experimental and theoretical studies have revealed several proteins containing sequence segments that are unfolded under physiological conditions. These segments are called disordered regions. They are actively investigated because of their possible involvement in various biological processes, such as cell signaling, transcriptional and translational regulation. Additionally, disordered regions can represent a major obstacle to high-throughput proteome analysis and often need to be removed from experimental targets. The accurate prediction of long disordered regions is thus expected to provide annotations that are useful for a wide range of applications. RESULTS: We developed Prediction Of Order and Disorder by machine LEarning (POODLE-L; L stands for long), the Support Vector Machines (SVMs) based method for predicting long disordered regions using 10 kinds of simple physico-chemical properties of amino acid. POODLE-L assembles the output of 10 two-level SVM predictors into a final prediction of disordered regions. The performance of POODLE-L for predicting long disordered regions, which exhibited a Matthew's correlation coefficient of 0.658, was the highest when compared with eight well-established publicly available disordered region predictors. AVAILABILITY: POODLE-L is freely available at http://mbs.cbrc.jp/poodle/poodle-l.html. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.