Journal of the American Chemical SocietyDOI: 10.1021/jacs.5b08588

Journal of the American Chemical SocietyDOI: 10.1021/jacs.5b07996

Journal of the American Chemical SocietyDOI: 10.1021/jacs.5b04590

Journal of the American Chemical SocietyDOI: 10.1021/jacs.5b08632

Journal of the American Chemical SocietyDOI: 10.1021/jacs.5b07859

ABSTRACT

We tested two pipelines developed for template-free protein structure prediction in the CASP11 experiment. First, the QUARK pipeline constructs structure models by reassembling fragments of continuously distributed lengths excised from unrelated proteins. Five free-modeling (FM) targets have the model successfully constructed by QUARK with a TM-score above 0.4, including the first model of T0837-D1, which has a TM-score = 0.736 and RMSD = 2.9 Å to the native. Detailed analysis showed that the success is partly attributed to the high-resolution contact map prediction derived from fragment-based distance-profiles, which are mainly located between regular secondary structure elements and loops/turns and help guide the orientation of secondary structure assembly. In the Zhang-Server pipeline, weakly scoring threading templates are re-ordered by the structural similarity to the ab initio folding models, which are then reassembled by I-TASSER based structure assembly simulations; 60% more domains with length up to 204 residues, compared to the QUARK pipeline, were successfully modeled by the I-TASSER pipeline with a TM-score above 0.4. The robustness of the I-TASSER pipeline can stem from the composite fragment-assembly simulations that combine structures from both ab initio folding and threading template refinements. Despite the promising cases, challenges still exist in long-range beta-strand folding, domain parsing, and the uncertainty of secondary structure prediction; the latter of which was found to affect nearly all aspects of FM structure predictions, from fragment identification, target classification, structure assembly, to final model selection. Significant efforts are needed to solve these problems before real progress on FM could be made. Proteins 2015. © 2015 Wiley Periodicals, Inc.

ABSTRACT

Interactions between proteins and other molecules play essential roles in all biological processes. Although it is widely held that a protein's ligand specificity is determined primarily by its three-dimensional structure, the general principles by which structure determines ligand binding remain poorly understood. Here we use statistical analyses of a large number of protein−ligand complexes with associated binding-affinity measurements to quantitatively characterize how combinations of atomic interactions contribute to ligand affinity. We find that there are significant differences in how atomic interactions determine ligand affinity for proteins that bind small chemical ligands, those that bind DNA/RNA and those that interact with other proteins. Although protein-small molecule and protein-DNA/RNA binding affinities can be accurately predicted from structural data, models predicting one type of interaction perform poorly on the others. Additionally, the particular combinations of atomic interactions required to predict binding affinity differed between small-molecule and DNA/RNA data sets, consistent with the conclusion that the structural bases determining ligand affinity differ among interaction types. In contrast to what we observed for small-molecule and DNA/RNA interactions, no statistical models were capable of predicting protein−protein affinity with >60% correlation. We demonstrate the potential usefulness of protein-DNA/RNA binding prediction as a possible tool for high-throughput virtual screening to guide laboratory investigations, suggesting that quantitative characterization of diverse molecular interactions may have practical applications as well as fundamentally advancing our understanding of how molecular structure translates into function. Proteins 2015; 83:2100–2114. © 2015 The Authors. Proteins: Structure, Function, and Bioinformatics Published by Wiley Periodicals, Inc.

Journal of the American Chemical SocietyDOI: 10.1021/jacs.5b08232

Journal of the American Chemical SocietyDOI: 10.1021/jacs.5b07146

Journal of the American Chemical SocietyDOI: 10.1021/jacs.5b07107

Background: One of the most important application spectrums of transcriptomic data is cancer phenotype classification. Many characteristics of transcriptomic data, such as redundant features and technical artifacts, make over-fitting commonplace. Promising classification results often fail to generalize across datasets with different sources, platforms, or preprocessing. Recently a novel differential network rank conservation (DIRAC) algorithm to characterize cancer phenotypes using transcriptomic data. DIRAC is a member of a family of algorithms that have shown useful for disease classification based on the relative expression of genes. Combining the robustness of this family’s simple decision rules with known biological relationships, this systems approach identifies interpretable, yet highly discriminate networks. While DIRAC has been briefly employed for several classification problems in the original paper, the potentials of DIRAC in cancer phenotype classification, and especially robustness against artifacts in transcriptomic data have not been fully characterized yet. Results: In this study we thoroughly investigate the potentials of DIRAC by applying it to multiple datasets, and examine the variations in classification performances when datasets are (i) treated and untreated for batch effect; (ii) preprocessed with different techniques. We also propose the first DIRAC-based classifier to integrate multiple networks. We show that the DIRAC-based classifier is very robust in the examined scenarios. To our surprise, the trained DIRAC-based classifier even translated well to a dataset with different biological characteristics in the presence of substantial batch effects that, as shown here, plagued the standard expression value based classifier. In addition, the DIRAC-based classifier, because of the integrated biological information, also suggests pathways to target in specific subtypes, which may enhance the establishment of personalized therapy in diseases such as pediatric AML. In order to better comprehend the prediction power of the DIRAC-based classifier in general, we also performed classifications using publicly available datasets from breast and lung cancer. Furthermore, multiple well-known classification algorithms were utilized to create an ideal test bed for comparing the DIRAC-based classifier with the standard gene expression value based classifier. We observed that the DIRAC-based classifier greatly outperforms its rival. Conclusions: Based on our experiments with multiple datasets, we propose that DIRAC is a promising solution to the lack of generalizability in classification efforts that uses transcriptomic data. We believe that superior performances presented in this study may motivate other to initiate a new aline of research to explore the untapped power of DIRAC in a broad range of cancer types.

Journal of the American Chemical SocietyDOI: 10.1021/jacs.5b05032

Journal of the American Chemical SocietyDOI: 10.1021/jacs.5b07009

Erratum: Mechanism of phospho-ubiquitin-induced PARKIN activation

Nature 526, 7575 (2015). doi:10.1038/nature15531

Authors: Tobias Wauer, Michal Simicek, Alexander Schubert & David Komander

Nature524, 370–374 (2015); doi:10.1038/nature14879The print and PDF versions of this Letter are correct, but the wrong HTML versions of Figs 1–4 and ED Figs 1–10 were used initially, owing to an in-house error; these have been

Corrigendum: Cleavage of CAD inhibitor in CAD activation and DNA degradation during apoptosis

Nature 526, 7575 (2015). doi:10.1038/nature15532

Authors: Hideki Sakahira, Masato Enari & Shigekazu Nagata

Nature391, 96–99 (1998); doi:10.1038/34214Recently, it has come to our attention that in Fig. 1a of this Letter, lanes 1 and 5 appear to be duplicated and lanes 6 and 10 appear to be duplicated. It is

Erratum: IgG1 protects against renal disease in a mouse model of cryoglobulinaemia

Nature 526, 7575 (2015). doi:10.1038/nature15534

Authors: Richard T. Strait, Monica T. Posgai, Ashley Mahler, Nathaniel Barasa, Chaim O. Jacob, Jörg Köhl, Marc Ehlers, Keith Stringer, Shiva Kumar Shanmukhappa, David Witte, Md Monir Hossain, Marat Khodoun, Andrew B. Herr & Fred D. Finkelman

Nature517, 501–504 (2015); doi:10.1038/nature13868Owing to a production error, in Fig. 1b of this Letter, the key should have shown that the black bars corresponded to ‘WT’ and the red bars to ‘γ1−’, instead of

Power play

Nature 525, 7570 (2015). doi:10.1038/525425b

The replacement of mitochondria does not signal ethical problems.

STAP revisited

Nature 525, 7570 (2015). doi:10.1038/525426a

Reanalysis of the controversy provides a strong example of the self-correcting nature of science.

Make academic job advertisements fair to all

Nature 525, 7570 (2015). http://www.nature.com/doifinder/10.1038/525427a

Author: Mathias Wullum Nielsen

Too many university posts are given to men without proper competition, says Mathias Wullum Nielsen.

Planetary science: Global ocean on Enceladus

Nature 525, 7570 (2015). doi:10.1038/525428a

Beneath an icy crust, Saturn's moon Enceladus (pictured) has an ocean that covers its entire globe.NASA's Cassini spacecraft measured wobbles in Enceladus's rotation over more than seven years. The data confirm that the crust is moving separately from the rocky core, meaning that there