The structure assessment web server: for proteins, complexes and more.

The 'structure assessment' web server is a one-stop shop for interactive evaluation and benchmarking of structural models of macromolecular complexes including proteins and nucleic acids. A user-friendly web dashboard links sequence with structure information and results from a variety of state-of-the-art tools, which facilitates the visual exploration and evaluation of structure models. The dashboard integrates stereochemistry information, secondary structure information, global and local model quality assessment of the tertiary structure of comparative protein models, as well as prediction of membrane location. In addition, a benchmarking mode is available where a model can be compared to a reference structure, providing easy access to scores that have been used in recent CASP experiments and CAMEO. The structure assessment web server is available at https://swissmodel.expasy.org/assess.

Automated benchmarking of combined protein structure and ligand conformation prediction.

The prediction of protein-ligand complexes (PLC), using both experimental and predicted structures, is an active and important area of research, underscored by the inclusion of the Protein-Ligand Interaction category in the latest round of the Critical Assessment of Protein Structure Prediction experiment CASP15. The prediction task in CASP15 consisted of predicting both the three-dimensional structure of the receptor protein as well as the position and conformation of the ligand. This paper addresses the challenges and proposed solutions for devising automated benchmarking techniques for PLC prediction. The reliability of experimentally solved PLC as ground truth reference structures is assessed using various validation criteria. Similarity of PLC to previously released complexes are employed to judge PLC diversity and the difficulty of a PLC as a prediction target. We show that the commonly used PDBBind time-split test-set is inappropriate for comprehensive PLC evaluation, with state-of-the-art tools showing conflicting results on a more representative and high quality dataset constructed for benchmarking purposes. We also show that redocking on crystal structures is a much simpler task than docking into predicted protein models, demonstrated by the two PLC-prediction-specific scoring metrics created. Finally, we introduce a fully automated pipeline that predicts PLC and evaluates the accuracy of the protein structure, ligand pose, and protein-ligand interactions.

Assessment of protein-ligand complexes in CASP15.

CASP15 introduced a new category, ligand prediction, where participants were provided with a protein or nucleic acid sequence, SMILES line notation, and stoichiometry for ligands and tasked with generating computational models for the three-dimensional structure of the corresponding protein-ligand complex. These models were subsequently compared with experimental structures determined by x-ray crystallography or cryoEM. To assess these predictions, two novel scores were developed. The Binding-Site Superposed, Symmetry-Corrected Pose Root Mean Square Deviation (BiSyRMSD) evaluated the absolute deviations of the models from the experimental structures. At the same time, the Local Distance Difference Test for Protein-Ligand Interactions (lDDT-PLI) assessed the ability of models to reproduce the protein-ligand interactions in the experimental structures. The ligands evaluated in this challenge range from single-atom ions to large flexible organic molecules. More than 1800 submissions were evaluated for their ability to predict 23 different protein-ligand complexes. Overall, the best models could faithfully reproduce the geometries of more than half of the prediction targets. The ligands' size and flexibility were the primary factors influencing the predictions' quality. Small ions and organic molecules with limited flexibility were predicted with high fidelity, while reproducing the binding poses of larger, flexible ligands proved more challenging.

New prediction categories in CASP15.

Prediction categories in the Critical Assessment of Structure Prediction (CASP) experiments change with the need to address specific problems in structure modeling. In CASP15, four new prediction categories were introduced: RNA structure, ligand-protein complexes, accuracy of oligomeric structures and their interfaces, and ensembles of alternative conformations. This paper lists technical specifications for these categories and describes their integration in the CASP data management system.

Positive correlation between transcriptomic stemness and PI3K/AKT/mTOR signaling scores in breast cancer, and a counterintuitive relationship with PIK3CA genotype.

A PI3Kα-selective inhibitor has recently been approved for use in breast tumors harboring mutations in PIK3CA, the gene encoding p110α. Preclinical studies have suggested that the PI3K/AKT/mTOR signaling pathway influences stemness, a dedifferentiation-related cellular phenotype associated with aggressive cancer. However, to date, no direct evidence for such a correlation has been demonstrated in human tumors. In two independent human breast cancer cohorts, encompassing nearly 3,000 tumor samples, transcriptional footprint-based analysis uncovered a positive linear association between transcriptionally-inferred PI3K/AKT/mTOR signaling scores and stemness scores. Unexpectedly, stratification of tumors according to PIK3CA genotype revealed a "biphasic" relationship of mutant PIK3CA allele dosage with these scores. Relative to tumor samples without PIK3CA mutations, the presence of a single copy of a hotspot PIK3CA variant was associated with lower PI3K/AKT/mTOR signaling and stemness scores, whereas the presence of multiple copies of PIK3CA hotspot mutations correlated with higher PI3K/AKT/mTOR signaling and stemness scores. This observation was recapitulated in a human cell model of heterozygous and homozygous PIK3CAH1047R expression. Collectively, our analysis (1) provides evidence for a signaling strength-dependent PI3K-stemness relationship in human breast cancer; (2) supports evaluation of the potential benefit of patient stratification based on a combination of conventional PI3K pathway genetic information with transcriptomic indices of PI3K signaling activation.

Continuous Automated Model EvaluatiOn (CAMEO)-Perspectives on the future of fully automated evaluation of structure prediction methods.

The Continuous Automated Model EvaluatiOn (CAMEO) platform complements the biennial CASP experiment by conducting fully automated blind evaluations of three-dimensional protein prediction servers based on the weekly prerelease of sequences of those structures, which are going to be published in the upcoming release of the Protein Data Bank. While in CASP14, significant success was observed in predicting the structures of individual protein chains with high accuracy, significant challenges remain in correctly predicting the structures of complexes. By implementing fully automated evaluation of predictions for protein-protein complexes, as well as for proteins in complex with ligands, peptides, nucleic acids, or proteins containing noncanonical amino acid residues, CAMEO will assist new developments in those challenging areas of active research.

NODAL/TGFß signalling mediates the self-sustained stemness induced by PIK3CAH1047R homozygosity in pluripotent stem cells.

Activating PIK3CA mutations are known 'drivers' of human cancer and developmental overgrowth syndromes. We recently demonstrated that the 'hotspot' PIK3CAH1047R variant exerts unexpected allele dose-dependent effects on stemness in human pluripotent stem cells (hPSCs). In this study, we combine high-depth transcriptomics, total proteomics and reverse-phase protein arrays to reveal potentially disease-related alterations in heterozygous cells, and to assess the contribution of activated TGFß signalling to the stemness phenotype of homozygous PIK3CAH1047R cells. We demonstrate signalling rewiring as a function of oncogenic PI3K signalling strength, and provide experimental evidence that self-sustained stemness is causally related to enhanced autocrine NODAL/TGFß signalling. A significant transcriptomic signature of TGFß pathway activation in heterozygous PIK3CAH1047R was observed but was modest and was not associated with the stemness phenotype seen in homozygous mutants. Notably, the stemness gene expression in homozygous PIK3CAH1047R hPSCs was reversed by pharmacological inhibition of NODAL/TGFß signalling, but not by pharmacological PI3Kα pathway inhibition. Altogether, this provides the first in-depth analysis of PI3K signalling in hPSCs and directly links strong PI3K activation to developmental NODAL/TGFß signalling. This work illustrates the importance of allele dosage and expression when artificial systems are used to model human genetic disease caused by activating PIK3CA mutations. This article has an associated First Person interview with the first author of the paper.

Deep neural networks identify signaling mechanisms of ErbB-family drug resistance from a continuous cell morphology space.

It is well known that the development of drug resistance in cancer cells can lead to changes in cell morphology. Here, we describe the use of deep neural networks to analyze this relationship, demonstrating that complex cell morphologies can encode states of signaling networks and unravel cellular mechanisms hidden to conventional approaches. We perform high-content screening of 17 cancer cell lines, generating more than 500 billion data points from ∼850 million cells. We analyze these data using a deep learning model, resulting in the identification of a continuous 27-dimension space describing all of the observed cell morphologies. From its morphology alone, we could thus predict whether a cell was resistant to ErbB-family drugs, with an accuracy of 74%, and predict the potential mechanism of resistance, subsequently validating the role of MET and insulin-like growth factor 1 receptor (IGF1R) as drivers of cetuximab resistance in in vitro models of lung and head/neck cancer.

PanelomiX for the Combination of Biomarkers.

Proteomics has allowed the discovery and validation of a massive number of biomarkers. However most of them suffer from insufficient specificity and sensitivity and therefore didn't translate to clinical practice. Combining biomarkers with different properties into panels can be an efficient way to bypass these limitations and facilitate the translation of biomarkers into clinical practice.

UniLectin3D, a database of carbohydrate binding proteins with curated information on 3D structures and interacting ligands.

Lectins, and related receptors such as adhesins and toxins, are glycan-binding proteins from all origins that decipher the glycocode, i.e. the structural information encoded in the conformation of complex carbohydrates present on the surface of all cells. Lectins are still poorly classified and annotated, but since their functions are based on ligand recognition, their 3D-structures provide a solid foundation for characterization. UniLectin3D is a curated database that classifies lectins on origin and fold, with cross-links to literature, other databases in glycosciences and functional data such as known specificity. The database provides detailed information on lectins, their bound glycan ligands, and features their interactions using the Protein-Ligand Interaction Profiler (PLIP) server. Special care was devoted to the description of the bound glycan ligands with the use of simple graphical representation and numerical format for cross-linking to other databases in glycoscience. We conceived the design of the database architecture and the navigation tools to account for all organisms, as well as to search for oligosaccharide epitopes complexed within specified binding sites. UniLectin3D is accessible at https://www.unilectin.eu/unilectin3D.

Bowhead: Bayesian modelling of cell velocity during concerted cell migration.

Cell migration is a central biological process that requires fine coordination of molecular events in time and space. A deregulation of the migratory phenotype is also associated with pathological conditions including cancer where cell motility has a causal role in tumor spreading and metastasis formation. Thus cell migration is of critical and strategic importance across the complex disease spectrum as well as for the basic understanding of cell phenotype. Experimental studies of the migration of cells in monolayers are often conducted with 'wound healing' assays. Analysis of these assays has traditionally relied on how the wound area changes over time. However this method does not take into account the shape of the wound. Given the many options for creating a wound healing assay and the fact that wound shape invariably changes as cells migrate this is a significant flaw. Here we present a novel software package for analyzing concerted cell velocity in wound healing assays. Our method encompasses a wound detection algorithm based on cell confluency thresholding and employs a Bayesian approach in order to estimate concerted cell velocity with an associated likelihood. We have applied this method to study the effect of siRNA knockdown on the migration of a breast cancer cell line and demonstrate that cell velocity can track wound healing independently of wound shape and provides a more robust quantification with significantly higher signal to noise ratios than conventional analyses of wound area. The software presented here will enable other researchers in any field of cell biology to quantitatively analyze and track live cell migratory processes and is therefore expected to have a significant impact on the study of cell migration, including cancer relevant processes. Installation instructions, documentation and source code can be found at http://bowhead.lindinglab.science licensed under GPLv3.

Extracellular Vesicles in Bile as Markers of Malignant Biliary Stenoses.

BACKGROUND & AIMS: Algorithms for diagnosis of malignant common bile duct (CBD) stenoses are complex and lack accuracy. Malignant tumors secrete large numbers of extracellular vesicles (EVs) into surrounding fluids; EVs might therefore serve as biomarkers for diagnosis. We investigated whether concentrations of EVs in bile could discriminate malignant from nonmalignant CBD stenoses. METHODS: We collected bile and blood samples from 50 patients undergoing therapeutic endoscopic retrograde cholangiopancreatography at university hospitals in Europe for CBD stenosis of malignant (pancreatic cancer, n = 20 or cholangiocarcinoma, n = 5) or nonmalignant (chronic pancreatitis [CP], n = 15) origin. Ten patients with CBD obstruction due to biliary stones were included as controls. EV concentrations in samples were determined by nanoparticle tracking analyses. The discovery cohort comprised the first 10 patients with a diagnosis of pancreatic cancer, based on tissue analysis, and 10 consecutive controls. Using samples from these subjects, we identified a threshold concentration of bile EVs that could best discriminate between patients with pancreatic cancer from controls. We verified the diagnostic performance of bile EV concentration by analyzing samples from the 30 consecutive patients with a diagnosis of malignant (pancreatic cancer or cholangiocarcinoma, n = 15) or nonmalignant (CP, n = 15) CBD stenosis. Samples were compared using the Mann-Whitney test and nonparametric Spearman correlation analysis. Receiver operating characteristic area under the curve was used to determine diagnostic accuracy. RESULTS: In both cohorts, the median concentration of EVs was significantly higher in bile samples from patients with malignant CBD stenoses than controls or nonmalignant CBD stenoses (2.41 × 1015 vs 1.60 × 1014 nanoparticles/L in the discovery cohort; P < .0001 and 4.00 × 1015 vs 1.26 × 1014 nanoparticles/L in the verification cohort; P < .0001). A threshold of 9.46 × 1014 nanoparticles/L in bile best distinguished patients with malignant CBD from controls in the discovery cohort. In the verification cohort, this threshold discriminated malignant from nonmalignant CBD stenoses with 100% accuracy. Serum concentration of EVs distinguished patients with malignant vs patients with nonmalignant CBD stenoses with 63.3% diagnostic accuracy. CONCLUSIONS: Concentration of EVs in bile samples discriminates between patients with malignant vs nonmalignant CBD stenosis with 100% accuracy. Further studies are needed to confirm these findings. Clinical Trial registration no: ISRCTN66835592.

Human sweat metabolomics for lung cancer screening.

Sweat is one of the less employed biofluids for discovery of markers in spite of its increased application in medicine for detection of drugs or for diagnostic of cystic fibrosis. In this research, human sweat was used as clinical sample to develop a screening tool for lung cancer, which is the carcinogenic disease with the highest mortality rate owing to the advanced stage at which it is usually detected. In this context, a method based on the metabolite analysis of sweat to discriminate between patients with lung cancer versus smokers as control individuals is proposed. The capability of the metabolites identified in sweat to discriminate between both groups of individuals was studied and, among them, a trisaccharide phosphate presented the best independent performance in terms of the specificity/sensitivity pair (80 and 72.7%, respectively). Additionally, two panels of metabolites were configured using the PanelomiX tool as an attempt to reduce false negatives (at least 80% specificity) and false positives (at least 80% sensitivity). The first panel (80% specificity and 69% sensitivity) was composed by suberic acid, a tetrahexose, and a trihexose, while the second panel (69% specificity and 80% sensitivity) included nonanedioic acid, a trihexose, and the monoglyceride MG(22:2). Thus, the combination of the five metabolites led to a single panel providing 80% specificity and 79% sensitivity, reducing the false positive and negative rates to almost 20%. The method was validated by estimation of within-day and between-days variability of the quantitative analysis of the five metabolites.

The prognostic significance of the serum biomarker heart-fatty acidic binding protein in comparison with s100b in severe traumatic brain injury.

The outcome after severe traumatic brain injury (TBI) is largely unfavorable, with approximately two thirds of patients suffering from severe disabilities or dying during the first 6 months. Existing predictive models displayed only limited utility for outcome prediction in individual patients. Time courses of heart-fatty acidic binding protein (H-FABP) and their association with outcome were investigated and compared with S100b. Forty-nine consecutive patients with severe TBI (sTBI; Head component of the Abbreviated Injury Scale [HAIS] >3) with mono and multiple trauma were enrolled in this study. Enzyme-linked immunosorbent assay measured blood concentrations of H-FABP and S100b at 6, 12, 24, and 48 h after TBI. Outcome measures were conscious state at 14 days (Glasgow Coma Scale), disability (Glasgow Outcome Scale Extended; GOSE), and mortality at 3 months. Univariate logistic regression analysis and receiver operating characteristic curves analysis were carried out. Maximal H-FABP and S100b concentrations were observed at 6 h after TBI (34.4±34.0 and 0.64±0.99 ng/mL, respectively). Patients with multi-trauma had significantly higher H-FABP concentrations at 24 and 48 h (22.6±25.6 and 12.4±18.2 ng/mL, respectively), compared to patients with mono trauma (6.9±5.1 and 3.7±4.2 ng/mL, respectively). In the first 48 h, H-FABP and S100b were inversely correlated with the GOSE at 3 months; H-FABP at 48 h predicted mortality with 75% sensitivity and 93% specificity. Early blood levels of H-FABP after sTBI have prognostic significance for survival and disability.

Neopterin is a cerebrospinal fluid marker for treatment outcome evaluation in patients affected by Trypanosoma brucei gambiense sleeping sickness.

BACKGROUND: Post-therapeutic follow-up is essential to confirm cure and to detect early treatment failures in patients affected by sleeping sickness (HAT). Current methods, based on finding of parasites in blood and cerebrospinal fluid (CSF) and counting of white blood cells (WBC) in CSF, are imperfect. New markers for treatment outcome evaluation are needed. We hypothesized that alternative CSF markers, able to diagnose the meningo-encephalitic stage of the disease, could also be useful for the evaluation of treatment outcome. METHODOLOGY/PRINCIPAL FINDINGS: Cerebrospinal fluid from patients affected by Trypanosoma brucei gambiense HAT and followed for two years after treatment was investigated. The population comprised stage 2 (S2) patients either cured or experiencing treatment failure during the follow-up. IgM, neopterin, B2MG, MMP-9, ICAM-1, VCAM-1, CXCL10 and CXCL13 were first screened on a small number of HAT patients (n = 97). Neopterin and CXCL13 showed the highest accuracy in discriminating between S2 cured and S2 relapsed patients (AUC 99% and 94%, respectively). When verified on a larger cohort (n = 242), neopterin resulted to be the most efficient predictor of outcome. High levels of this molecule before treatment were already associated with an increased risk of treatment failure. At six months after treatment, neopterin discriminated between cured and relapsed S2 patients with 87% specificity and 92% sensitivity, showing a higher accuracy than white blood cell numbers. CONCLUSIONS/SIGNIFICANCE: In the present study, neopterin was highlighted as a useful marker for the evaluation of the post-therapeutic outcome in patients suffering from sleeping sickness. Detectable levels of this marker in the CSF have the potential to shorten the follow-up for HAT patients to six months after the end of the treatment.

New biomarkers for stage determination in Trypanosoma brucei rhodesiense sleeping sickness patients.

Accurate stage determination is crucial in the choice of treatment for patients suffering from sleeping sickness, also known as human African trypanosomiasis (HAT). Current staging methods, based on the counting of white blood cells (WBC) and the detection of parasites in the cerebrospinal fluid (CSF) have limited accuracy. We hypothesized that immune mediators reliable for staging T. b. gambiense HAT could also be used to stratify T. b. rhodesiense patients, the less common form of HAT.A population comprising 85 T. b. rhodesiense patients, 14 stage 1 (S1) and 71 stage 2 (S2) enrolled in Malawi and Uganda, was investigated. The CSF levels of IgM, MMP-9, CXCL13, CXCL10, ICAM-1, VCAM-1, neopterin and B2MG were measured and their staging performances evaluated using receiver operating characteristic (ROC) analyses.IgM, MMP-9 and CXCL13 were the most accurate markers for stage determination (partial AUC 88%, 86% and 85%, respectively). The combination in panels of three molecules comprising CXCL13-CXCL10-MMP-9 or CXCL13-CXCL10-IgM significantly increased their staging ability to partial AUC 94% (p value < 0.01).The present study highlighted new potential markers for stage determination of T. b. rhodesiense patients. Further investigations are needed to better evaluate these molecules, alone or in panels, as alternatives to WBC to make reliable choice of treatment.

EasyProt--an easy-to-use graphical platform for proteomics data analysis.

High throughput protein identification and quantification analysis based on mass spectrometry are fundamental steps in most proteomics projects. Here, we present EasyProt (available at http://easyprot.unige.ch), a new platform for mass spectrometry data processing, protein identification, quantification and unexpected post-translational modification characterization. EasyProt provides a fully integrated graphical experience to perform a large part of the proteomic data analysis workflow. Our goal was to develop a software platform that would fulfill the needs of scientists in the field, while emphasizing ease-of-use for non-bioinformatician users. Protein identification is based on OLAV scoring schemes and protein quantification is implemented for both, isobaric labeling and label-free methods. Additional features are available, such as peak list processing, isotopic correction, spectra filtering, charge-state deconvolution and spectra merging. To illustrate the EasyProt platform, we present two identification and quantification workflows based on isobaric tagging and label-free methods.

Matrix metalloproteinase 9 and cellular fibronectin plasma concentrations are predictors of the composite endpoint of length of stay and death in the intensive care unit after severe traumatic brain injury.

BACKGROUND: The relationship between severe traumatic brain injury (TBI) and blood levels of matrix metalloproteinase-9 (MMP-9) or cellular fibronectin (c-Fn) has never been reported. In this study, we aimed to assess whether plasma concentrations of MMP-9 and c-Fn could have predictive values for the composite endpoint of intensive care unit (ICU) length of stay (LOS) of survivors and mortality after severe TBI. Secondary outcomes were the state of consciousness measured with the Glasgow Coma Scale (GCS) of survivors at 14 days and Glasgow Outcome Scale Extended (GOSE) at 3 months. METHODS: Forty-nine patients with abbreviated injury scores of the head region ≥ 4 were included. Blood was sampled at 6, 12, 24 and 48 hours after injury. MMP-9 and c-Fn concentrations were measured by ELISA. The values of MMP-9 and c-Fn, and, for comparison, the value of the GCS on the field of the accident (fGCS), as predictors of the composite outcome of ICU LOS and death were assessed by logistic regression. RESULTS: There was a linear relationship between maximal MMP-9 concentration, measured during the 6-12-hour period, and maximal c-Fn concentration, measured during the 24-48-hour period. The risk of staying longer than 9 days in the ICU or of dying was increased in patients with a maximal early MMP-9 concentration ≥ 21.6 ng/ml (OR = 5.0; 95% CI: 1.3 to 18.6; p = 0.02) or with a maximal late c-Fn concentration ≥ 7.7 µg/ml (OR = 5.4; 95% CI: 1.4 to 20.8; p = 0.01). A similar risk association was observed with fGCS ≤8 (OR, 4.4; 95% CI, 1.2-15.8; p = 0.02). No relationship was observed between MMP-9, c-Fn concentrations or fGCS and the GCS at 14 days of survivors and GOSE at 3 months. CONCLUSIONS: Plasma MMP-9 and c-Fn concentrations in the first 48 hours after injury are predictive for the composite endpoint of ICU LOS and death after severe TBI but not for consciousness at 14 days and outcome at 3 months.

Blood glutathione S-transferase-π as a time indicator of stroke onset.

BACKGROUND: Ability to accurately determine time of stroke onset remains challenging. We hypothesized that an early biomarker characterized by a rapid increase in blood after stroke onset may help defining better the time window during which an acute stroke patient may be candidate for intravenous thrombolysis or other intravascular procedures. METHODS: The blood level of 29 proteins was measured by immunoassays on a prospective cohort of stroke patients (N = 103) and controls (N = 132). Mann-Whitney U tests, ROC curves and diagnostic odds ratios were applied to evaluate their clinical performances. RESULTS: Among the 29 molecules tested, GST-π concentration was the most significantly elevated marker in the blood of stroke patients (p<0.001). More importantly, GST-π displayed the best area under the curve (AUC, 0.79) and the best diagnostic odds ratios (10.0) for discriminating early (N = 22, <3 h of stroke onset) vs. late stroke patients (N = 81, >3 h after onset). According to goal-oriented distinct cut-offs (sensitivity(Se)-oriented: 17.7 or specificity(Sp)-oriented: 65.2 ug/L), the GST-π test obtained 91%Se/50%Sp and 50%Se/91%Sp, respectively. Moreover, GST-π showed also the highest AUC (0.83) and performances for detecting patients treated with tPA (N = 12) compared to ineligible patients (N = 103). CONCLUSIONS: This study demonstrates that GST-π can accurately predict the time of stroke onset in over 50% of early stroke patients. The GST-π test could therefore complement current guidelines for tPA administration and potentially increase the number of patients accessing thrombolysis.