PDBminer to Find and Annotate Protein Structures for Computational Analysis

Computational methods relying on protein structure strongly depend on the structure selected for investigation. Typical sources of protein structures include experimental structures available at the Protein Data Bank (PDB) and high-quality in silico model structures, such as those available at the AlphaFold Protein Structure Database. Either option has significant advantages and drawbacks, and exploring the wealth of available structures to identify the most suitable ones for specific applications can be a daunting task. We provide an open-source software package, PDBminer, with the purpose of making structure identification and selection easier, faster, and less error prone. PDBminer searches the AlphaFold Database and the PDB for available structures of interest and provides an up-to-date, quality-ranked table of structures applicable for further use. PDBminer provides an overview of the available protein structures to one or more input proteins, parallelizing the runs if multiple cores are specified. The output table reports the coverage of the protein structures aligned to the UniProt sequence, overcoming numbering differences in PDB structures and providing information regarding model quality, protein complexes, ligands, and nucleic acid chain binding. The PDBminer2coverage and PDBminer2network tools assist in visualizing the results. PDBminer can be applied to overcome the tedious task of choosing a PDB structure without losing the wealth of additional information available in the PDB. Here, we showcase the main functionalities of the package on the p53 tumor suppressor protein. The package is available at http://github.com/ELELAB/PDBminer.

PyInteraph2 and PyInKnife2 to Analyze Networks in Protein Structural Ensembles

Due to the complex nature of noncovalent interactions and their long-range effects, analyzing protein conformations using network theory can be enlightening. Protein Structure Networks (PSNs) provide a convenient formalism to study protein structures in relation to essential properties such as key residues for structural stability, allosteric communication, and the effects of modifications of the protein. PSNs can be defined according to very different principles, and the available tools have limitations in input formats, supported models, and version control. Other outstanding problems are related to the definition of network cutoffs and the assessment of the stability of the network properties. The protein science community could benefit from a common framework to carry out these analyses and make them easier to reproduce, reuse, and evaluate. We here provide two open-source software packages, PyInteraph2 and PyInKnife2, to implement and analyze PSNs in a reproducible and documented manner. PyInteraph2 interfaces with multiple formats for protein ensembles and incorporates different network models with the possibility of integrating them into a macronetwork and performing various downstream analyses, including hubs, connected components, and several other centrality measures, and visualizes the networks or further analyzes them thanks to compatibility with Cytoscape.PyInKnife2 that supports the network models implemented in PyInteraph2. It employs a jackknife resampling approach to estimate the convergence of network properties and streamline the selection of distance cutoffs. We foresee that the modular structure of the code and the supported version control system will promote the transition to a community-driven effort, boost reproducibility, and establish common protocols in the PSN field. As developers, we will guarantee the introduction of new functionalities and maintenance, assistance, and training of new contributors.

RosettaDDGPrediction for high-throughput mutational scans: From stability to binding

Reliable prediction of free energy changes upon amino acid substitutions (ΔΔGs) is crucial to investigate their impact on protein stability and protein-protein interaction. Advances in experimental mutational scans allow high-throughput studies thanks to multiplex techniques. On the other hand, genomics initiatives provide a large amount of data on disease-related variants that can benefit from analyses with structure-based methods. Therefore, the computational field should keep the same pace and provide new tools for fast and accurate high-throughput ΔΔG calculations. In this context, the Rosetta modeling suite implements effective approaches to predict folding/unfolding ΔΔGs in a protein monomer upon amino acid substitutions and calculate the changes in binding free energy in protein complexes. However, their application can be challenging to users without extensive experience with Rosetta. Furthermore, Rosetta protocols for ΔΔG prediction are designed considering one variant at a time, making the setup of high-throughput screenings cumbersome. For these reasons, we devised RosettaDDGPrediction, a customizable Python wrapper designed to run free energy calculations on a set of amino acid substitutions using Rosetta protocols with little intervention from the user. Moreover, RosettaDDGPrediction assists with checking completed runs and aggregates raw data for multiple variants, as well as generates publication-ready graphics. We showed the potential of the tool in four case studies, including variants of uncertain significance in childhood cancer, proteins with known experimental unfolding ΔΔGs values, interactions between target proteins and disordered motifs, and phosphomimetics. RosettaDDGPrediction is available, free of charge and under GNU General Public License v3.0, at https://github.com/ELELAB/RosettaDDGPrediction.

Alternative academic approaches for testing homologous recombination deficiency in ovarian cancer in the MITO16A/MaNGO-OV2 trial

Background

The detection of homologous recombination deficiency (HRD) can identify patients who are more responsive to platinum and poly ADP ribose polymerase inhibitors (PARPi). MyChoice CDx (Myriad) is the most used HRD test in ovarian cancer (OC). However, some limitations of commercial tests exist, because of the high rate of inconclusive results, costs, and the impossibility of evaluating functional resistance mechanisms.

Patients and methods

Two academic genomic tests and a functional assay, the RAD51 foci, were evaluated to detect HRD. One hundred patients with high-grade OC enrolled in the MITO16A/MaNGO-OV2 trial and treated with first-line therapy with carboplatin, paclitaxel, and bevacizumab were analyzed.

Results

The failure rate of the two genomic assays was 2%. The sensitivity in detecting HRD when compared with Myriad was 98.1% and 90.6%, respectively. The agreement rate with Myriad was 0.92 and 0.87, with a Cohen’s κ coefficient corresponding to 0.84 and 0.74, respectively. For the RAD51 foci assay, the failure rate was 30%. When the test was successful, discordant results for deficient and proficient tumors were observed, and additional HRD patients were identified compared to Myriad; sensitivity was 82.9%, agreement rate was 0.65, and Cohen’s κ coefficient was 0.18. The HRD detected by genomic assays and residual tumor at primary surgery and stage was correlated with progression-free survival at multivariate analysis.

Conclusions

Results suggest the feasibility of academic tests for assessing HRD status that show robust concordance with Myriad and correlation with clinical outcome. The contribution of the functional information related to the RAD51 foci test to the genomic data needs further investigation.

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Deficiency in homologous recombination repair of DNA generates genomic instability and permanent genomic changes.

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HRD status is fundamental for identifying OC patients suitable for platinum and PARPi treatment.

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HRD testing is considered a topic with urgent need for improvement, going beyond those available commercially.

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Within this study, two academic genomic tests and a functional assay, the RAD51 foci, were evaluated to detect HRD.

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Our tests compare favorably with the reference Myriad assay and correlate with the outcome of high-grade OC patients.

MutateX: an automated pipeline for in silico saturation mutagenesis of protein structures and structural ensembles

Mutations, which result in amino acid substitutions, influence the stability of proteins and their binding to biomolecules. A molecular understanding of the effects of protein mutations is both of biotechnological and medical relevance. Empirical free energy functions that quickly estimate the free energy change upon mutation (ΔΔG) can be exploited for systematic screenings of proteins and protein complexes. In silico saturation mutagenesis can guide the design of new experiments or rationalize the consequences of known mutations. Often software such as FoldX, while fast and reliable, lack the necessary automation features to apply them in a high-throughput manner. We introduce MutateX, a software to automate the prediction of ΔΔGs associated with the systematic mutation of each residue within a protein, or protein complex to all other possible residue types, using the FoldX energy function. MutateX also supports ΔΔG calculations over protein ensembles, upon post-translational modifications and in multimeric assemblies. At the heart of MutateX lies an automated pipeline engine that handles input preparation, parallelization and outputs publication-ready figures. We illustrate the MutateX protocol applied to different case studies. The results of the high-throughput scan provided by our tools can help in different applications, such as the analysis of disease-associated mutations, to complement experimental deep mutational scans, or assist the design of variants for industrial applications. MutateX is a collection of Python tools that relies on open-source libraries. It is available free of charge under the GNU General Public License from https://github.com/ELELAB/mutatex.

Discovering environmental management opportunities for infectious disease control

Climate change and emerging drug resistance make the control of many infectious diseases increasingly challenging and diminish the exclusive reliance on drug treatment as sole solution to the problem. As disease transmission often depends on environmental conditions that can be modified, such modifications may become crucial to risk reduction if we can assess their potential benefit at policy-relevant scales. However, so far, the value of environmental management for this purpose has received little attention. Here, using the parasitic disease of fasciolosis in livestock in the UK as a case study, we demonstrate how mechanistic hydro-epidemiological modelling can be applied to understand disease risk drivers and the efficacy of environmental management across a large heterogeneous domain. Our results show how weather and other environmental characteristics interact to define disease transmission potential and reveal that environmental interventions such as risk avoidance management strategies can provide a valuable alternative or complement to current treatment-based control practice.

A mechanistic hydro-epidemiological model of liver fluke risk

The majority of existing models for predicting disease risk in response to climate change are empirical. These models exploit correlations between historical data, rather than explicitly describing relationships between cause and response variables. Therefore, they are unsuitable for capturing impacts beyond historically observed variability and have limited ability to guide interventions. In this study, we integrate environmental and epidemiological processes into a new mechanistic model, taking the widespread parasitic disease of fasciolosis as an example. The model simulates environmental suitability for disease transmission at a daily time step and 25 m resolution, explicitly linking the parasite life cycle to key weather–water–environment conditions. Using epidemiological data, we show that the model can reproduce observed infection levels in time and space for two case studies in the UK. To overcome data limitations, we propose a calibration approach combining Monte Carlo sampling and expert opinion, which allows constraint of the model in a process-based way, including a quantification of uncertainty. The simulated disease dynamics agree with information from the literature, and comparison with a widely used empirical risk index shows that the new model provides better insight into the time–space patterns of infection, which will be valuable for decision support.

Mechanism of action of trabectedin in desmoplastic small round cell tumor cells

Background

Desmoplastic small round cell tumor (DSRCT) is a rare and highly aggressive disease, that can be described as a member of the family of small round blue cell tumors. The molecular diagnostic marker is the t(11;22)(p13;q12) translocation, which creates an aberrant transcription factor, EWS-WT1, that underlies the oncogenesis of DSRCT. Current treatments are not very effective so new active drugs are needed. Trabectedin, now used as a single agent for the treatment of soft tissue sarcoma, was reported to be active in some pre-treated DSRCT patients. Using JN-DSRCT-1, a cell line derived from DSRCT expressing the EWS-WT1 fusion protein, we investigated the ability of trabectedin to modify the function of the chimeric protein, as in other sarcomas expressing fusion proteins. After detailed characterization of the EWS-WT1 transcripts structure, we investigated the mode of action of trabectedin, looking at the expression and function of the oncogenic chimera.

Methods

We characterized JN-DSRCT-1 cells using cellular approaches (FISH, Clonogenicity assay) and molecular approaches (Sanger sequencing, ChIP, GEP).

Results

JN-DSRCT-1 cells were sensitive to trabectedin at nanomolar concentrations. The cell line expresses different variants of EWS-WT1, some already identified in patients. EWS-WT1 mRNA expression was affected by trabectedin and chimeric protein binding on its target gene promoters was reduced. Expression profiling indicated that trabectedin affects the expression of genes involved in cell proliferation and apoptosis.

Conclusions

The JN-DSRCT-1 cell line, in vitro, is sensitive to trabectedin: after drug exposure, EWS-WT1 chimera expression decreases as well as binding on its target promoters. Probably the heterogeneity of chimera transcripts is an obstacle to precisely defining the molecular mode of action of drugs, calling for further cellular models of DSRCT, possibly growing in vivo too, to mimic the biological complexity of this disease.

Electronic supplementary material

The online version of this article (doi:10.1186/s12885-017-3091-1) contains supplementary material, which is available to authorized users.

A systems biology approach to investigate the mechanism of action of trabectedin in a model of myelomonocytic leukemia

This study was designed to investigate the mode of action of trabectedin in myelomonocytic leukemia cells by applying systems biology approaches to mine gene expression profiling data and pharmacological assessment of the cellular effects. Significant enrichment was found in regulons of target genes inferred for specific transcription factors, among which MAFB was the most upregulated after treatment and was central in the transcriptional network likely to be relevant for the specific therapeutic effects of trabectedin against myelomonocytic cells. Using the Connectivity Map, similarity among transcriptional signatures elicited by treatment with different compounds was investigated, showing a high degree of similarity between transcriptional signatures of trabectedin and those of the topoisomerase I inhibitor, irinotecan, and an anti-dopaminergic antagonist, thioridazine. The study highlights the potential importance of systems biology approaches to generate new hypotheses that are experimentally testable to define the specificity of the mechanism of action of drugs.

A prognostic regulatory pathway in stage I epithelial ovarian cancer: new hints for the poor prognosis assessment

BACKGROUND

Clinical and pathological parameters of patients with epithelial ovarian cancer (EOC) do not thoroughly predict patients' outcome. Despite the good outcome of stage I EOC compared with that of stages III and IV, the risk assessment and treatments are almost the same. However, only 20% of stage I EOC cases relapse and die, meaning that only a proportion of patients need intensive treatment and closer follow-up. Thus, the identification of cell mechanisms that could improve outcome prediction and rationalize therapeutic options is an urgent need in the clinical practice.

PATIENTS AND METHODS

We have gathered together 203 patients with stage I EOC diagnosis, from whom snap-frozen tumor biopsies were available at the time of primary surgery before any treatment. Patients, with a median follow-up of 7 years, were stratified into a training set and a validation set.

RESULTS AND CONCLUSIONS

Integrated analysis of miRNA and gene expression profiles allowed to identify a prognostic cell pathway, composed of 16 miRNAs and 10 genes, wiring the cell cycle, 'Activins/Inhibins' and 'Hedgehog' signaling pathways. Once validated by an independent technique, all the elements of the circuit resulted associated with overall survival (OS) and progression-free survival (PFS), in both univariate and multivariate models. For each patient, the circuit expressions have been translated into an activation state index (integrated signature classifier, ISC), used to stratify patients into classes of risk. This prediction reaches the 89.7% of sensitivity and 96.6% of specificity for the detection of PFS events. The prognostic value was then confirmed in the external independent validation set in which the PFS events are predicted with 75% sensitivity and 94.7% specificity. Moreover, the ISC shows higher classification performance than conventional clinical classifiers. Thus, the identified circuit enhances the understanding of the molecular mechanisms lagging behind stage I EOC and the ISC improves our capabilities to assess, at the time of diagnosis, the patient risk of relapse.

Identification of high-grade serous ovarian cancer miRNA species associated with survival and drug response in patients receiving neoadjuvant chemotherapy: a retrospective longitudinal analysis using matched tumor biopsies

BACKGROUND

Neoadjuvant chemotherapy (NACT) has been recognized as a reliable therapeutic strategy in patients with unresectable advanced epithelial ovarian cancer (EOC). The molecular events leading to platinum (Pt) response in NACT settings have hitherto not been explored. In the present work, longitudinal changes of miRNA expression profile were investigated to identify miRNA families with prognostic role in high-grade serous EOC patients who received the NACT regimen.

PATIENTS AND METHODS

One hundred sixty-four matched tumor biopsies taken at initial laparoscopic evaluation and at interval-debulking surgery (IDS) after four courses of Pt-based therapy were selected from 82 stage IIIC-IV high-grade serous-EOC patients that were judged unsuitable for complete primary debulking and subjected the NACT protocol. miRNA profiling by microarray, real-time PCR and immuno-histochemical staining for Smad2 phosphorylation (P-Smad2) were used for data analysis.

RESULTS

Analysis revealed that 369 miRNAs were differentially expressed in matched biopsies (referred to as DEMs). DEMs were not scattered across the genome, but clustered into families: miR-199, let-7, miR-30, miR-181 and miR-29. Multivariate analysis showed that miR-199a-3p, miR-199a-5p, miR-181a-5p and let-7g-5p associated with overall and progression-free survival (P < 0.05); miR-199a-3p, miR-199a-5p and miR-181a-5p associated with residual tumor volume and Pt-free interval (P < 0.05). Immuno-histochemical staining confirmed an enrichment of P-Smad2, a marker of transforming growth factor-β activation, in tumors from patients with shorter PFS and OS, and with high levels of expression of miR-181a-5p (P < 0.05). Kaplan-Meier curves plotting concomitant expression of P-Smad2 and miR-181a-5p show significant differences in PFS and OS compared with those depicting the expression of each biomarker alone (P < 0.001).

CONCLUSIONS

This study describes several miRNA families with a prognostic role in the NACT setting. It also confirms that concomitant analysis of P-Smad2 and miR-181a-5p in surgical samples may be capable of identifying those ovarian cancer patients with poor outcome and little chance of response to Pt-based NACT.

Profiling cancer gene mutations in longitudinal epithelial ovarian cancer biopsies by targeted next-generation sequencing: a retrospective study

BACKGROUND

The majority of patients with stage III-IV epithelial ovarian cancer (EOC) relapse after initially responding to platinum-based chemotherapy, and develop resistance. The genomic features involved in drug resistance are unknown. To unravel some of these features, we investigated the mutational profile of genes involved in pathways related to drug sensitivity in a cohort of matched tumors obtained at first surgery (Ft-S) and second surgery (Sd-S).

PATIENTS AND METHODS

Matched biopsies (33) taken at Ft-S and Sd-S were selected from the 'Pandora' tumor tissue collection. DNA libraries for 65 genes were generated using the TruSeq Custom Amplicon kit and sequenced on MiSeq (Illumina). Data were analyzed using a high-performance cluster computing platform (Cloud4CARE project) and independently validated.

RESULTS

A total of 2270 somatic mutations were identified (89.85% base substitutions 8.19% indels, and 1.92% unknown). Homologous recombination (HR) genes and TP53 were mutated in the majority of Ft-S, while ATM, ATR, TOP2A and TOP2B were mutated in the entire dataset. Only 2% of mutations were conserved between matched Ft-S and Sd-S. Mutations detected at second surgery clustered patients in two groups characterized by different mutational profiles in genes associated with HR, PI3K, miRNA biogenesis and signal transduction.

CONCLUSIONS

There was a low level of concordance between Ft-S and Sd-S in terms of mutations in genes involved in key processes of tumor growth and drug resistance. This result suggests the importance of future longitudinal analyses to improve the clinical management of relapsed EOC.

Mode of action of trabectedin in myxoid liposarcomas

To elucidate the mechanisms behind the high sensitivity of myxoid/round cell liposarcoma (MRCL) to trabectedin and the suggested selectivity for specific subtypes, we have developed and characterized three MRCL xenografts, namely ML017, ML015 and ML004 differing for the break point of the fusion gene FUS-CHOP, respectively of type I, II and III. FUS-CHOP binding to the promoters of some target genes such as Pentraxin 3 or Fibronectin 1, assessed by chromatin immunoprecipitation, was strongly reduced in the tumor 24 h after the first or the third weekly dose of trabectedin, indicating that the drug at therapeutic doses causes a detachment of the FUS-CHOP chimera from its target promoters as previously shown in vitro. Moreover, the higher sensitivity of MRCL types I and II appears to be related to a more prolonged block of the transactivating activity of the fusion protein. Doxorubicin did not affect the binding of FUS-CHOP to target promoters. Histologically, the response to trabectedin in ML017 and ML015 was associated with a marked depletion of non-lipogenic tumoral cells and vascular component, as well as lipidic maturation as confirmed by PPARγ2 expression in western Blot. By contrast, in ML004 no major changes either in the cellularity or in the amount of mature were found, and consistently PPARγ2 was null. In conclusion, the data support the view that the selective mechanism of action of trabectedin in MRCL is specific and related to its ability to cause a functional inactivation of the oncogenic chimera with consequent derepression of the adypocytic differentiation.

Algorithm for automatic genotype calling of single nucleotide polymorphisms using the full course of TaqMan real-time data

Single nucleotide polymorphisms (SNPs) are often determined using TaqMan real-time PCR assays (Applied Biosystems) and commercial software that assigns genotypes based on reporter probe signals at the end of amplification. Limitations to the large-scale application of this approach include the need for positive controls or operator intervention to set signal thresholds when one allele is rare. In the interest of optimizing real-time PCR genotyping, we developed an algorithm for automatic genotype calling based on the full course of real-time PCR data. Best cycle genotyping algorithm (BCGA), written in the open source language R, is based on the assumptions that classification depends on the time (cycle) of amplification and that it is possible to identify a best discriminating cycle for each SNP assay. The algorithm is unique in that it classifies samples according to the behavior of blanks (no DNA samples), which cluster with heterozygous samples. This method of classification eliminates the need for positive controls and permits accurate genotyping even in the absence of a genotype class, for example when one allele is rare. Here, we describe the algorithm and test its validity, compared to the standard end-point method and to DNA sequencing.