ATPase Subdomain IA Is a Mediator of Interdomain Allostery in Hsp70 Molecular Chaperones

The versatile functions of the heat shock protein 70 (Hsp70) family of molecular chaperones rely on allosteric interactions between their nucleotide-binding and substrate-binding domains, NBD and SBD. Understanding the mechanism of interdomain allostery is essential to rational design of Hsp70 modulators. Yet, despite significant progress in recent years, how the two Hsp70 domains regulate each other's activity remains elusive. Covariance data from experiments and computations emerged in recent years as valuable sources of information towards gaining insights into the molecular events that mediate allostery. In the present study, conservation and covariance properties derived from both sequence and structural dynamics data are integrated with results from Perturbation Response Scanning and in vivo functional assays, so as to establish the dynamical basis of interdomain signal transduction in Hsp70s. Our study highlights the critical roles of SBD residues D481 and T417 in mediating the coupled motions of the two domains, as well as that of G506 in enabling the movements of the α-helical lid with respect to the β-sandwich. It also draws attention to the distinctive role of the NBD subdomains: Subdomain IA acts as a key mediator of signal transduction between the ATP- and substrate-binding sites, this function being achieved by a cascade of interactions predominantly involving conserved residues such as V139, D148, R167 and K155. Subdomain IIA, on the other hand, is distinguished by strong coevolutionary signals (with the SBD) exhibited by a series of residues (D211, E217, L219, T383) implicated in DnaJ recognition. The occurrence of coevolving residues at the DnaJ recognition region parallels the behavior recently observed at the nucleotide-exchange-factor recognition region of subdomain IIB. These findings suggest that Hsp70 tends to adapt to co-chaperone recognition and activity via coevolving residues, whereas interdomain allostery, critical to chaperoning, is robustly enabled by conserved interactions. © 2014 General et al

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe

Rheological behavior of semi-solid 7075 aluminum alloy at steady state

The further application of semi-solid processing lies in the in-depth fundamental study like rheological behavior. In this research, the apparent viscosity of the semi-solid slurry of 7075 alloy was measured using a Couette type viscometer. The effects of solid fraction and shearing rate on the apparent viscosity of this alloy were investigated under different processing conditions. It can be seen that the apparent viscosity increases with an increase in the solid fraction from 10% to 50% (temperature 620 篊 to 630 篊) at steady state. When the solid fraction was fixed, the apparent viscosity can be decreased by altering the shearing rate from 61.235 s-1 to 489.88 s-1 at steady state. An empirical equation that shows the effects of solid fraction and shearing rate on the apparent viscosity is fitted. The microstructure of quenched samples was examined to understand the alloy抯 rheological behavior

Identification of residue pairing in interacting β-strands from a predicted residue contact map

Abstract Background Despite the rapid progress of protein residue contact prediction, predicted residue contact maps frequently contain many errors. However, information of residue pairing in β strands could be extracted from a noisy contact map, due to the presence of characteristic contact patterns in β-β interactions. This information may benefit the tertiary structure prediction of mainly β proteins. In this work, we propose a novel ridge-detection-based β-β contact predictor to identify residue pairing in β strands from any predicted residue contact map. Results Our algorithm RDb2C adopts ridge detection, a well-developed technique in computer image processing, to capture consecutive residue contacts, and then utilizes a novel multi-stage random forest framework to integrate the ridge information and additional features for prediction. Starting from the predicted contact map of CCMpred, RDb2C remarkably outperforms all state-of-the-art methods on two conventional test sets of β proteins (BetaSheet916 and BetaSheet1452), and achieves F1-scores of ~ 62% and ~ 76% at the residue level and strand level, respectively. Taking the prediction of the more advanced RaptorX-Contact as input, RDb2C achieves impressively higher performance, with F1-scores reaching ~ 76% and ~ 86% at the residue level and strand level, respectively. In a test of structural modeling using the top 1 L predicted contacts as constraints, for 61 mainly β proteins, the average TM-score achieves 0.442 when using the raw RaptorX-Contact prediction, but increases to 0.506 when using the improved prediction by RDb2C. Conclusion Our method can significantly improve the prediction of β-β contacts from any predicted residue contact maps. Prediction results of our algorithm could be directly applied to effectively facilitate the practical structure prediction of mainly β proteins. Availability All source data and codes are available at http://166.111.152.91/Downloads.html or the GitHub address of https://github.com/wzmao/RDb2C

DeepConPred2: An Improved Method for the Prediction of Protein Residue Contacts

Information of residue-residue contacts is essential for understanding the mechanism of protein folding, and has been successfully applied as special topological restraints to simplify the conformational sampling in de novo protein structure prediction. Prediction of protein residue contacts has experienced amazingly rapid progresses recently, with prediction accuracy approaching impressively high levels in the past two years. In this work, we introduce a second version of our residue contact predictor, DeepConPred2, which exhibits substantially improved performance and sufficiently reduced running time after model re-optimization and feature updates. When testing on the CASP12 free modeling targets, our program reaches at least the same level of prediction accuracy as the best contact predictors so far and provides information complementary to other state-of-the-art methods in contact-assisted folding. Keywords: Residue contact prediction, Web server, Protein structure prediction, Contact-assisted folding, Machine learnin

Results from coevolution analysis of Hsp70 family members.

<p>On panel <b>A</b>, the heat map based on PSICOV covariance predictions is displayed. The white rectangular frame encloses the portion corresponding to interdomain co-variances. Residue pairs distinguished by strongest interdomain signals are listed in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003624#pcbi-1003624-t001" target="_blank"><b>Table 1</b></a> and illustrated in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003624#pcbi-1003624-g007" target="_blank"><b>Figure 7</b></a>. Those residues exhibiting high cumulative interdomain coevolutionary propensities are labeled and displayed in space-filling representation (labeled on panel <b>B</b>) and listed in <b><a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003624#pcbi.1003624.s007" target="_blank">Table S1</a></b>. The ribbon diagram is color-coded by the propensity of residues to exhibit coevolutionary patterns. NEF- and DnaJ-binding regions are highlighted. The DnaJ region is located mostly on the back of the area shown.</p

Emerging network of interactions establishing the communication between the DnaJ binding site (near E217 and V389) and the ATP-binding site of DnaK.

<p>(<b>A</b>) Two interconnected pathways, also coupled to each other (via E171-D194 interaction) are shown, belonging to the respective subdomains IA (<i>red</i>) and IIA (<i>green</i>) of the DnaK NBD. (<b>B</b>) Most on-pathway residues are conserved. L177, which plays a central role is distinguished by its coevolution with V389 (<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003624#pcbi-1003624-g008" target="_blank"><b>Figure 8</b></a>) and high influence/sensitivity with respect to the majority of displayed residues (<b><a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003624#pcbi.1003624.s006" target="_blank">Figure S6</a></b>).</p