SNPeffect 4.0: on-line prediction of molecular and structural effects of protein-coding variants

Single nucleotide variants (SNVs) are, together with copy number variation, the primary source of variation in the human genome and are associated with phenotypic variation such as altered response to drug treatment and susceptibility to disease. Linking structural effects of non-synonymous SNVs to functional outcomes is a major issue in structural bioinformatics. The SNPeffect database (http://snpeffect.switchlab.org) uses sequence- and structure-based bioinformatics tools to predict the effect of protein-coding SNVs on the structural phenotype of proteins. It integrates aggregation prediction (TANGO), amyloid prediction (WALTZ), chaperone-binding prediction (LIMBO) and protein stability analysis (FoldX) for structural phenotyping. Additionally, SNPeffect holds information on affected catalytic sites and a number of post-translational modifications. The database contains all known human protein variants from UniProt, but users can now also submit custom protein variants for a SNPeffect analysis, including automated structure modeling. The new meta-analysis application allows plotting correlations between phenotypic features for a user-selected set of variants

Accurate Prediction of DnaK-Peptide Binding via Homology Modelling and Experimental Data

Molecular chaperones are essential elements of the protein quality control machinery that governs translocation and folding of nascent polypeptides, refolding and degradation of misfolded proteins, and activation of a wide range of client proteins. The prokaryotic heat-shock protein DnaK is the E. coli representative of the ubiquitous Hsp70 family, which specializes in the binding of exposed hydrophobic regions in unfolded polypeptides. Accurate prediction of DnaK binding sites in E. coli proteins is an essential prerequisite to understand the precise function of this chaperone and the properties of its substrate proteins. In order to map DnaK binding sites in protein sequences, we have developed an algorithm that combines sequence information from peptide binding experiments and structural parameters from homology modelling. We show that this combination significantly outperforms either single approach. The final predictor had a Matthews correlation coefficient (MCC) of 0.819 when assessed over the 144 tested peptide sequences to detect true positives and true negatives. To test the robustness of the learning set, we have conducted a simulated cross-validation, where we omit sequences from the learning sets and calculate the rate of repredicting them. This resulted in a surprisingly good MCC of 0.703. The algorithm was also able to perform equally well on a blind test set of binders and non-binders, of which there was no prior knowledge in the learning sets. The algorithm is freely available at http://limbo.vib.be

Variable glutamine-rich repeats modulate transcription factor activity

Excessive expansions of glutamine (Q)-rich repeats in various human proteins are known to result in severe neurodegenerative disorders such as Huntington's disease and several ataxias. However, the physiological role of these repeats and the consequences of more moderate repeat variation remain unknown. Here, we demonstrate that Q-rich domains are highly enriched in eukaryotic transcription factors where they act as functional modulators. Incremental changes in the number of repeats in the yeast transcriptional regulator Ssn6 (Cyc8) result in systematic, repeat-length-dependent variation in expression of target genes that result in direct phenotypic changes. The function of Ssn6 increases with its repeat number until a certain threshold where further expansion leads to aggregation. Quantitative proteomic analysis reveals that the Ssn6 repeats affect its solubility and interactions with Tup1 and other regulators. Thus, Q-rich repeats are dynamic functional domains that modulate a regulator's innate function, with the inherent risk of pathogenic repeat expansions

Delivery of sTRAIL variants by MSCs in combination with cytotoxic drug treatment leads to p53-independent enhanced antitumor effects

Mesenchymal stem cells (MSCs) are able to infiltrate tumor tissues and thereby effectively deliver gene therapeutic payloads. Here, we engineered murine MSCs (mMSCs) to express a secreted form of the TNF-related apoptosis-inducing ligand (TRAIL), which is a potent inducer of apoptosis in tumor cells, and tested these MSCs, termed MSC.sTRAIL, in combination with conventional chemotherapeutic drug treatment in colon cancer models. When we pretreated human colorectal cancer HCT116 cells with low doses of 5-fluorouracil (5-FU) and added MSC.sTRAIL, we found significantly increased apoptosis as compared with single-agent treatment. Moreover, HCT116 xenografts, which were cotreated with 5-FU and systemically delivered MSC.sTRAIL, went into remission. Noteworthy, this effect was protein 53 (p53) independent and was mediated by TRAIL-receptor 2 (TRAIL-R2) upregulation, demonstrating the applicability of this approach in p53-defective tumors. Consequently, when we generated MSCs that secreted TRAIL-R2-specific variants of soluble TRAIL (sTRAIL), we found that such engineered MSCs, labeled MSC.sTRAIL DR5, had enhanced antitumor activity in combination with 5-FU when compared with MSC.sTRAIL. In contrast, TRAIL-resistant pancreatic carcinoma PancTu1 cells responded better to MSC.sTRAIL DR4 when the antiapoptotic protein XIAP (X-linked inhibitor of apoptosis protein) was silenced concomitantly. Taken together, our results demonstrate that TRAIL-receptor selective variants can potentially enhance the therapeutic efficacy of MSC-delivered TRAIL as part of individualized and tumor-specific combination treatments. © 2013 Macmillan Publishers Limited All rights reserved

Autonomous zinc-finger nuclease pairs for targeted chromosomal deletion

Zinc-finger nucleases (ZFNs) have been successfully used for rational genome engineering in a variety of cell types and organisms. ZFNs consist of a non-specific FokI endonuclease domain and a specific zinc-finger DNA-binding domain. Because the catalytic domain must dimerize to become active, two ZFN subunits are typically assembled at the cleavage site. The generation of obligate heterodimeric ZFNs was shown to significantly reduce ZFN-associated cytotoxicity in single-site genome editing strategies. To further expand the application range of ZFNs, we employed a combination of in silico protein modeling, in vitro cleavage assays, and in vivo recombination assays to identify autonomous ZFN pairs that lack cross-reactivity between each other. In the context of ZFNs designed to recognize two adjacent sites in the human HOXB13 locus, we demonstrate that two autonomous ZFN pairs can be directed simultaneously to two different sites to induce a chromosomal deletion in ∼10% of alleles. Notably, the autonomous ZFN pair induced a targeted chromosomal deletion with the same efficacy as previously published obligate heterodimeric ZFNs but with significantly less toxicity. These results demonstrate that autonomous ZFNs will prove useful in targeted genome engineering approaches wherever an application requires the expression of two distinct ZFN pairs

Processing Induced Changes in Food Proteins: Amyloid Formation during Boiling of Hen Egg White

Amyloid fibrils (AFs) are highly ordered protein nanofibers composed of cross β-structure that occur in nature, but that also accumulate in age-related diseases. Amyloid propensity is a generic property of proteins revealed by conditions that destabilize the native state, suggesting that food processing conditions may promote AF formation. This had only been shown for foie gras, but not in common foodstuffs. We here extracted a dense network of fibrillar proteins from commonly consumed boiled hen egg white (EW) using chemical and/or enzymatic treatments. Conversion of EW proteins into AFs during boiling was demonstrated by thioflavin T fluorescence, Congo red staining, and X-ray fiber diffraction measurements. Our data show that cooking converts approximately 1–3% of the protein in EW into AFs, suggesting that they are a common component of the human diet

FlexOracle: predicting flexible hinges by identification of stable domains

<p>Abstract</p> <p>Background</p> <p>Protein motions play an essential role in catalysis and protein-ligand interactions, but are difficult to observe directly. A substantial fraction of protein motions involve hinge bending. For these proteins, the accurate identification of flexible hinges connecting rigid domains would provide significant insight into motion. Programs such as GNM and FIRST have made global flexibility predictions available at low computational cost, but are not designed specifically for finding hinge points.</p> <p>Results</p> <p>Here we present the novel FlexOracle hinge prediction approach based on the ideas that energetic interactions are stronger <it>within </it>structural domains than <it>between </it>them, and that fragments generated by cleaving the protein at the hinge site are independently stable. We implement this as a tool within the Database of Macromolecular Motions, MolMovDB.org. For a given structure, we generate pairs of fragments based on scanning all possible cleavage points on the protein chain, compute the energy of the fragments compared with the undivided protein, and predict hinges where this quantity is minimal. We present three specific implementations of this approach. In the first, we consider only pairs of fragments generated by cutting at a <it>single </it>location on the protein chain and then use a standard molecular mechanics force field to calculate the enthalpies of the two fragments. In the second, we generate fragments in the same way but instead compute their free energies using a knowledge based force field. In the third, we generate fragment pairs by cutting at <it>two </it>points on the protein chain and then calculate their free energies.</p> <p>Conclusion</p> <p>Quantitative results demonstrate our method's ability to predict known hinges from the Database of Macromolecular Motions.</p

An Evolutionary Trade-Off between Protein Turnover Rate and Protein Aggregation Favors a Higher Aggregation Propensity in Fast Degrading Proteins

We previously showed the existence of selective pressure against protein aggregation by the enrichment of aggregation-opposing ‘gatekeeper’ residues at strategic places along the sequence of proteins. Here we analyzed the relationship between protein lifetime and protein aggregation by combining experimentally determined turnover rates, expression data, structural data and chaperone interaction data on a set of more than 500 proteins. We find that selective pressure on protein sequences against aggregation is not homogeneous but that short-living proteins on average have a higher aggregation propensity and fewer chaperone interactions than long-living proteins. We also find that short-living proteins are more often associated to deposition diseases. These findings suggest that the efficient degradation of high-turnover proteins is sufficient to preclude aggregation, but also that factors that inhibit proteasomal activity, such as physiological ageing, will primarily affect the aggregation of short-living proteins

Molecular Binding Mechanism of TtgR Repressor to Antibiotics and Antimicrobials

A disturbing phenomenon in contemporary medicine is the prevalence of multidrug-resistant pathogenic bacteria. Efflux pumps contribute strongly to this antimicrobial drug resistance, which leads to the subsequent failure of clinical treatments. The TtgR protein of Pseudomonas putida is a HTH-type transcriptional repressor that controls expression of the TtgABC efflux pump, which is the main contributor to resistance against several antimicrobials and toxic compounds in this microbe. One of the main strategies to modulate the bacterial resistance is the rational modification of the ligand binding target site. We report the design and characterization of four mutants-TtgRS77A, TtgRE78A, TtgRN110A and TtgRH114A - at the active ligand binding site. The biophysical characterization of the mutants, in the presence and in the absence of different antimicrobials, revealed that TtgRN110A is the variant with highest thermal stability, under any of the experimental conditions tested. EMSA experiments also showed a different dissociation pattern from the operator for TtgRN110A, in the presence of several antimicrobials, making it a key residue in the TtgR protein repression mechanism of the TtgABC efflux pump. We found that TtgRE78A stability is the most affected upon effector binding. We also probe that one mutation at the C-terminal half of helix-α4, TtgRS77A, provokes a severe protein structure distortion, demonstrating the important role of this residue in the overall protein structure and on the ligand binding site. The data provide new information and deepen the understanding of the TtgR-effector binding mechanism and consequently the TtgABC efflux pump regulation mechanism in Pseudomonas putida.This work was supported by Spanish Ministry of Economy and Competitiveness, National programme for Recruitment and Incorporation of Human Resources, Subprogramme: Ramon y Cajal RYC-2009-04570 and grant P11-CVI-7391 from Junta de Andalucía and EFDR (European Regional Development Fund)

Integrating water exclusion theory into βcontacts to predict binding free energy changes and binding hot spots

10.1186/1471-2105-15-57BMC Bioinformatics151-BBMI