Discovery of binding proteins for a protein target using protein-protein docking-based virtual screening

PPM1H is down-regulated by ATF6 and dephosphorylates p-RPS6KB1 to inhibit progression of hepatocellular carcinoma

We have shown previously that polymorphism of activating transcription factor 6 (ATF6) is associated with susceptibility to hepatocellular carcinoma (HCC). Therefore, genes down-regulated by ATF6 might play a tumor-suppressing role. In the present study, we identified that expression of protein phosphatase magnesium- or manganous-dependent 1H (PPM1H) mRNA and protein can be inhibited by ATF6 in hepatoma cells and mice with liver Atf6 knockdown. Tumor tissues from 134 HCC patients were analyzed by immunohistochemistry, and PPM1H exhibited higher expression levels in adjacent para-cancer tissues than in HCC tissues. Therefore, patients with higher expression of PPM1H had a better prognosis. PPM1H inhibited proliferation, migration, and invasion of hepatoma cells. In addition, PPM1H inhibited induced HCC nodule formation as well as tumor xenograft growth in diethylnitrosamine/CCl₄-induced HCC mouse model and nude mouse tumorigenicity assay, respectively. A 3D model of PPM1H was obtained by homology multi-template modeling, and ribosomal protein S6 kinase B1 (RPS6KB1) in the bone morphogenetic protein (BMP)/transforming growth factor β (TGF-β) pathway was screened out as the potential substrate of PPM1H by Rosetta. PPM1H could directly dephosphorylate p-RPS6KB1. To conclude, we discovered RPS6KB1 as a new PPM1H dephosphorylation substrate. PPM1H exhibited a suppressive effect on HCC progression by dephosphorylating p-RPS6KB1.

Tertiary motifs as building blocks for the design of protein-binding peptides

Despite advances in protein engineering, the de novo design of small proteins or peptides that bind to a desired target remains a difficult task. Most computational methods search for binder structures in a library of candidate scaffolds, which can lead to designs with poor target complementarity and low success rates. Instead of choosing from pre-defined scaffolds, we propose that custom peptide structures can be constructed to complement a target surface. Our method mines tertiary motifs (TERMs) from known structures to identify surface-complementing fragments or "seeds." We combine seeds that satisfy geometric overlap criteria to generate peptide backbones and score the backbones to identify the most likely binding structures. We found that TERM-based seeds can describe known binding structures with high resolution: the vast majority of peptide binders from 486 peptide-protein complexes can be covered by seeds generated from single-chain structures. Furthermore, we demonstrate that known peptide structures can be reconstructed with high accuracy from peptide-covering seeds. As a proof of concept, we used our method to design 100 peptide binders of TRAF6, seven of which were predicted by Rosetta to form higher-quality interfaces than a native binder. The designed peptides interact with distinct sites on TRAF6, including the native peptide-binding site. These results demonstrate that known peptide-binding structures can be constructed from TERMs in single-chain structures and suggest that TERM information can be applied to efficiently design novel target-complementing binders.

Discovery and characterization of a novel peptide inhibitor against influenza neuraminidase

Neuraminidase, an abundant glycoprotein on the influenza virus surface, plays crucial roles in virus replication. Targeting neuraminidase could be a splendid way for the prevention of the spread of influenza infections. Herein, we have identified an octapeptide (errKPAQP) from a synthesized peptide library, originating from mimicking the binding pocket of oseltamivir in neuraminidase, as a potent peptide neuraminidase inhibitor. The docking-based virtual studies showed that errKPAQP exhibited a strong binding affinity (a docking score of -20.03) and nanomolar affinity (11 nM) to influenza neuraminidase, and can inhibit neuraminidase activity at a concentration as low as 4.25 μM, leading to effective protection of MDCK cells from influenza virus-induced death and replication. Furthermore, errKPAQP presented low hemolytic activity, minimal cytotoxicity, and good pharmacokinetic characteristics, which are imperative for an anti-influenza drug. Importantly, errKPAQP was capable of reducing influenza virus-induced inflammation, the serious damage to the lung tissues, and mortality rates in infected mice, indicating that it could protect against the lethal challenge of influenza viruses in vivo. Therefore, we have developed a novel neuraminidase peptide inhibitor with advantageous biological properties and high inhibitory activity towards neuraminidase, and it can serve as a promising anti-influenza drug.

Rigid-Docking Approaches to Explore Protein-Protein Interaction Space

Protein-protein interactions play core roles in living cells, especially in the regulatory systems. As information on proteins has rapidly accumulated on publicly available databases, much effort has been made to obtain a better picture of protein-protein interaction networks using protein tertiary structure data. Predicting relevant interacting partners from their tertiary structure is a challenging task and computer science methods have the potential to assist with this. Protein-protein rigid docking has been utilized by several projects, docking-based approaches having the advantages that they can suggest binding poses of predicted binding partners which would help in understanding the interaction mechanisms and that comparing docking results of both non-binders and binders can lead to understanding the specificity of protein-protein interactions from structural viewpoints. In this review we focus on explaining current computational prediction methods to predict pairwise direct protein-protein interactions that form protein complexes.

MEGADOCK 4.0: an ultra-high-performance protein-protein docking software for heterogeneous supercomputers

Summary: The application of protein–protein docking in large-scale interactome analysis is a major challenge in structural bioinformatics and requires huge computing resources. In this work, we present MEGADOCK 4.0, an FFT-based docking software that makes extensive use of recent heterogeneous supercomputers and shows powerful, scalable performance of >97% strong scaling.

Availability and Implementation: MEGADOCK 4.0 is written in C++ with OpenMPI and NVIDIA CUDA 5.0 (or later) and is freely available to all academic and non-profit users at: http://www.bi.cs.titech.ac.jp/megadock.

Contact:akiyama@cs.titech.ac.jp

Supplementary information:Supplementary data are available at Bioinformatics online