Temperature-induced conformational transition of a model elastin-like peptide GVG(VPGVG)(3) in water

The conformation of a single elastin-like peptide GVG(VPGVG)3 in liquid water is studied by computer simulations in the temperature interval between 280 and 440 K. Two main conformational states of the peptide can be distinguished: a rigid conformational state, dominating at low temperatures, and a flexible conformational state, dominating at high temperatures. A temperature-induced transition between these states occurs at about 310 K, rather close to a transition temperature seen in experiments. This transition is accompanied by the thermal breaking of the hydrogen-bonded spanning network of the hydration water via a percolation transition upon heating. This finding indicates that the H-bond clustering structure of the hydration water plays an important role in the conformational stability of biomolecules. A second important observation is the Gaussian distribution of the end-to-end distance in the high-temperature state, which supports the idea of a rubber-like elasticity of the studied elastin-like peptide. Finally our results challenge the idea of the folding of elastin-like peptides upon heating.     

Degradation of dibutyl phthalate by Paenarthrobacter sp. Shss isolated from Saravan landfill,Hyrcanian Forests,Iran

Biodegradation - Phthalic acid esters are predominantly used as plasticizers and are industrially produced on the million ton scale per year. They exhibit endocrine-disrupting, carcinogenic,...     

Correction to: Who is the ocean? Preface to the future seas 2030 special issue

Reviews in Fish Biology and Fisheries - A correction to this paper has been published: https://doi.org/10.1007/s11160-021-09657-9     

Safeguarding marine life: conservation of biodiversity and ecosystems

Reviews in Fish Biology and Fisheries - Marine ecosystems and their associated biodiversity sustain life on Earth and hold intrinsic value. Critical marine ecosystem services include maintenance of...     

A+-Helix of Protein C Inhibitor (PCI) Is a Cell-penetrating Peptide That Mediates Cell Membrane Permeation of PCI

Protein C inhibitor (PCI) is a serpin with broad protease reactivity. It binds glycosaminoglycans and certain phospholipids that can modulate its inhibitory activity. PCI can penetrate through cellular membranes via binding to phosphatidylethanolamine. The exact mechanism of PCI internalization and the intracellular role of the serpin are not well understood. Here we showed that testisin, a glycosylphosphatidylinositol-anchored serine protease, cleaved human PCI and mouse PCI (mPCI) at their reactive sites as well as at sites close to their N terminus. This cleavage was observed not only with testisin in solution but also with cell membrane-anchored testisin on U937 cells. The cleavage close to the N terminus released peptides rich in basic amino acids. Synthetic peptides corresponding to the released peptides of human PCI (His¹–Arg¹¹) and mPCI (Arg¹–Ala¹⁸) functioned as cell-penetrating peptides. Because intact mPCI but not testisin-cleaved mPCI was internalized by Jurkat T cells, a truncated mPCI mimicking testisin-cleaved mPCI was created. The truncated mPCI lacking 18 amino acids at the N terminus was not taken up by Jurkat T cells. Therefore our model suggests that testisin or other proteases could regulate the internalization of PCI by removing its N terminus. This may represent one of the mechanisms regulating the intracellular functions of PCI.     

A new disease-specific machine learning approach for the prediction of cancer-causing missense variants

High-throughput genotyping and sequencing techniques are rapidly and inexpensively providing large amounts of human genetic variation data. Single Nucleotide Polymorphisms (SNPs) are an important source of human genome variability and have been implicated in several human diseases, including cancer. Amino acid mutations resulting from non-synonymous SNPs in coding regions may generate protein functional changes that affect cell proliferation. In this study, we developed a machine learning approach to predict cancer-causing missense variants. We present a Support Vector Machine (SVM) classifier trained on a set of 3163 cancer-causing variants and an equal number of neutral polymorphisms. The method achieve 93% overall accuracy, a correlation coefficient of 0.86, and area under ROC curve of 0.98. When compared with other previously developed algorithms such as SIFT and CHASM our method results in higher prediction accuracy and correlation coefficient in identifying cancer-causing variants.     

Remote thioredoxin recognition using evolutionary conservation and structural dynamics

The thioredoxin family of oxidoreductases plays an important role in redox signaling and control of protein function. Not only are thioredoxins linked to a variety of disorders, but their stable structure has also seen application in protein engineering. Both sequence-based and structure-based tools exist for thioredoxin identification, but remote homolog detection remains a challenge. We developed a thioredoxin predictor using the approach of integrating sequence with structural information. We combined a sequence-based Hidden Markov Model (HMM) with a molecular dynamics enhanced structure-based recognition method (dynamic FEATURE, DF). This hybrid method (HMMDF) has high precision and recall (0.90 and 0.95, respectively) compared with HMM (0.92 and 0.87, respectively) and DF (0.82 and 0.97, respectively). Dynamic FEATURE is sensitive but struggles to resolve closely related protein families, while HMM identifies these evolutionary differences by compromising sensitivity. Our method applied to structural genomics targets makes?a strong prediction of a novel thioredoxin.     

Using multiple microenvironments to find similar ligand-binding sites: application to kinase inhibitor binding

The recognition of cryptic small-molecular binding sites in protein structures is important for understanding off-target side effects and for recognizing potential new indications for existing drugs. Current methods focus on the geometry and detailed chemical interactions within putative binding pockets, but may not recognize distant similarities where dynamics or modified interactions allow one ligand to bind apparently divergent binding pockets. In this paper, we introduce an algorithm that seeks similar microenvironments within two binding sites, and assesses overall binding site similarity by the presence of multiple shared microenvironments. The method has relatively weak geometric requirements (to allow for conformational change or dynamics in both the ligand and the pocket) and uses multiple biophysical and biochemical measures to characterize the microenvironments (to allow for diverse modes of ligand binding). We term the algorithm PocketFEATURE, since it focuses on pockets using the FEATURE system for characterizing microenvironments. We validate PocketFEATURE first by showing that it can better discriminate sites that bind similar ligands from those that do not, and by showing that we can recognize FAD-binding sites on a proteome scale with Area Under the Curve (AUC) of 92%. We then apply PocketFEATURE to evolutionarily distant kinases, for which the method recognizes several proven distant relationships, and predicts unexpected shared ligand binding. Using experimental data from ChEMBL and Ambit, we show that at high significance level, 40 kinase pairs are predicted to share ligands. Some of these pairs offer new opportunities for inhibiting two proteins in a single pathway.     

Functional characterization of human cancer-derived TRKB mutations

Cancer originates from cells that have acquired mutations in genes critical for controlling cell proliferation, survival and differentiation. Often, tumors continue to depend on these so-called driver mutations, providing the rationale for targeted anticancer therapies. To date, large-scale sequencing analyses have revealed hundreds of mutations in human tumors. However, without their functional validation it remains unclear which mutations correspond to driver, or rather bystander, mutations and, therefore, whether the mutated gene represents a target for therapeutic intervention. In human colorectal tumors, the neurotrophic receptor TRKB has been found mutated on two different sites in its kinase domain (TRKB(T695I) and TRKB(D751N)). Another site, in the extracellular part of TRKB, is mutated in a human lung adenocarcinoma cell line (TRKB(L138F)). Lastly, our own analysis has identified one additional TRKB point mutation proximal to the kinase domain (TRKB(P507L)) in a human melanoma cell line. The functional consequences of all these point mutations, however, have so far remained elusive. Previously, we have shown that TRKB is a potent suppressor of anoikis and that TRKB-expressing cells form highly invasive and metastatic tumors in nude mice. To assess the functional consequences of these four TRKB mutations, we determined their potential to suppress anoikis and to form tumors in nude mice. Unexpectedly, both colon cancer-derived mutants, TRKB(T695I) and TRKB(D751N), displayed reduced activity compared to that of wild-type TRKB. Consistently, upon stimulation with the TRKB ligand BDNF, these mutants were impaired in activating TRKB and its downstream effectors AKT and ERK. The two mutants derived from human tumor cell lines (TRKB(L138F) and TRKB(P507L)) were functionally indistinguishable from wild-type TRKB in both in-vitro and in-vivo assays. In conclusion, we fail to detect any gain-of-function of four cancer-derived TRKB point mutations.