Proteomic identification of a marker signature for MAPKi resistance in melanoma.

MAPK inhibitors (MAPKi) show outstanding clinical response rates in melanoma patients harbouring BRAF mutations, but resistance is common. The ability of melanoma cells to switch from melanocytic to mesenchymal phenotypes appears to be associated with therapeutic resistance. High-throughput, subcellular proteome analyses and RNAseq on two panels of primary melanoma cells that were either sensitive or resistant to MAPKi revealed that only 15 proteins were sufficient to distinguish between these phenotypes. The two proteins with the highest discriminatory power were PTRF and IGFBP7, which were both highly upregulated in the mesenchymal-resistant cells. Proteomic analysis of CRISPR/Cas-derived PTRF knockouts revealed targets involved in lysosomal activation, endocytosis, pH regulation, EMT, TGFß signalling and cell migration and adhesion, as well as a significantly reduced invasive index and ability to form spheres in 3D culture. Overexpression of PTRF led to MAPKi resistance, increased cell adhesion and sphere formation. In addition, immunohistochemistry of patient samples showed that PTRF expression levels were a significant biomarker of poor progression-free survival, and IGFBP7 levels in patient sera were shown to be higher after relapse.

MHC binding affects the dynamics of different T-cell receptors in different ways.

T cells use their T-cell receptors (TCRs) to scan other cells for antigenic peptides presented by MHC molecules (pMHC). If a TCR encounters a pMHC, it can trigger a signalling pathway that could lead to the activation of the T cell and the initiation of an immune response. It is currently not clear how the binding of pMHC to the TCR initiates signalling within the T cell. One hypothesis is that conformational changes in the TCR lead to further downstream signalling. Here we investigate four different TCRs in their free state as well as in their pMHC bound state using large scale molecular simulations totalling 26 000 ns. We find that the dynamical features within TCRs differ significantly between unbound TCR and TCR/pMHC simulations. However, apart from expected results such as reduced solvent accessibility and flexibility of the interface residues, these features are not conserved among different TCR types. The presence of a pMHC alone is not sufficient to cause cross-TCR-conserved dynamical features within a TCR. Our results argue against models of TCR triggering involving conserved allosteric conformational changes.

Diagnostic Accuracy of Web-Based COVID-19 Symptom Checkers: Comparison Study.

BACKGROUND: A large number of web-based COVID-19 symptom checkers and chatbots have been developed; however, anecdotal evidence suggests that their conclusions are highly variable. To our knowledge, no study has evaluated the accuracy of COVID-19 symptom checkers in a statistically rigorous manner. OBJECTIVE: The aim of this study is to evaluate and compare the diagnostic accuracies of web-based COVID-19 symptom checkers. METHODS: We identified 10 web-based COVID-19 symptom checkers, all of which were included in the study. We evaluated the COVID-19 symptom checkers by assessing 50 COVID-19 case reports alongside 410 non-COVID-19 control cases. A bootstrapping method was used to counter the unbalanced sample sizes and obtain confidence intervals (CIs). Results are reported as sensitivity, specificity, F1 score, and Matthews correlation coefficient (MCC). RESULTS: The classification task between COVID-19-positive and COVID-19-negative for "high risk" cases among the 460 test cases yielded (sorted by F1 score): Symptoma (F1=0.92, MCC=0.85), Infermedica (F1=0.80, MCC=0.61), US Centers for Disease Control and Prevention (CDC) (F1=0.71, MCC=0.30), Babylon (F1=0.70, MCC=0.29), Cleveland Clinic (F1=0.40, MCC=0.07), Providence (F1=0.40, MCC=0.05), Apple (F1=0.29, MCC=-0.10), Docyet (F1=0.27, MCC=0.29), Ada (F1=0.24, MCC=0.27) and Your.MD (F1=0.24, MCC=0.27). For "high risk" and "medium risk" combined the performance was: Symptoma (F1=0.91, MCC=0.83) Infermedica (F1=0.80, MCC=0.61), Cleveland Clinic (F1=0.76, MCC=0.47), Providence (F1=0.75, MCC=0.45), Your.MD (F1=0.72, MCC=0.33), CDC (F1=0.71, MCC=0.30), Babylon (F1=0.70, MCC=0.29), Apple (F1=0.70, MCC=0.25), Ada (F1=0.42, MCC=0.03), and Docyet (F1=0.27, MCC=0.29). CONCLUSIONS: We found that the number of correctly assessed COVID-19 and control cases varies considerably between symptom checkers, with different symptom checkers showing different strengths with respect to sensitivity and specificity. A good balance between sensitivity and specificity was only achieved by two symptom checkers.

In silico structural modeling of multiple epigenetic marks on DNA.

Availability and implementation: The code together with examples and tutorials are available from http://www.cs.ox.ac.uk/mosaics. Contact: peter.minary@cs.ox.ac.uk. Supplementary information: Supplementary data are available at Bioinformatics online.

pyHVis3D: visualising molecular simulation deduced H-bond networks in 3D: application to T-cell receptor interactions.

Motivation: Hydrogen bonds (H-bonds) play an essential role for many molecular interactions but are also often transient, making visualising them in a flexible system challenging. Results: We provide pyHVis3D which allows for an easy to interpret 3D visualisation of H-bonds resulting from molecular simulations. We demonstrate the power of pyHVis3D by using it to explain the changes in experimentally measured binding affinities for three T-cell receptor/peptide/MHC complexes and mutants of each of these complexes. Availability and implementation: pyHVis3D can be downloaded for free from http://opig.stats.ox.ac.uk/resources. Contact: science.bernhard.knapp@gmail.com. Supplementary information: Supplementary data are available at Bioinformatics online.

HLA-DM Stabilizes the Empty MHCII Binding Groove: A Model Using Customized Natural Move Monte Carlo.

MHC class II molecules bind peptides derived from extracellular proteins that have been ingested by antigen-presenting cells and display them to the immune system. Peptide loading occurs within the antigen-presenting cell and is facilitated by HLA-DM. HLA-DM stabilizes the open conformation of the MHCII binding groove when no peptide is bound. While a structure of the MHCII/HLA-DM complex exists, the mechanism of stabilization is still largely unknown. Here, we applied customized Natural Move Monte Carlo to investigate this interaction. We found a possible long-range mechanism that implicates the configuration of the membrane-proximal globular domains in stabilizing the open state of the empty MHCII binding groove.

Progress and challenges in predicting protein interfaces.

The majority of biological processes are mediated via protein-protein interactions. Determination of residues participating in such interactions improves our understanding of molecular mechanisms and facilitates the development of therapeutics. Experimental approaches to identifying interacting residues, such as mutagenesis, are costly and time-consuming and thus, computational methods for this purpose could streamline conventional pipelines. Here we review the field of computational protein interface prediction. We make a distinction between methods which address proteins in general and those targeted at antibodies, owing to the radically different binding mechanism of antibodies. We organize the multitude of currently available methods hierarchically based on required input and prediction principles to provide an overview of the field.

Current status and future challenges in T-cell receptor/peptide/MHC molecular dynamics simulations.

The interaction between T-cell receptors (TCRs) and major histocompatibility complex (MHC)-bound epitopes is one of the most important processes in the adaptive human immune response. Several hypotheses on TCR triggering have been proposed. Many of them involve structural and dynamical adjustments in the TCR/peptide/MHC interface. Molecular Dynamics (MD) simulations are a computational technique that is used to investigate structural dynamics at atomic resolution. Such simulations are used to improve understanding of signalling on a structural level. Here we review how MD simulations of the TCR/peptide/MHC complex have given insight into immune system reactions not achievable with current experimental methods. Firstly, we summarize methods of TCR/peptide/MHC complex modelling and TCR/peptide/MHC MD trajectory analysis methods. Then we classify recently published simulations into categories and give an overview of approaches and results. We show that current studies do not come to the same conclusions about TCR/peptide/MHC interactions. This discrepancy might be caused by too small sample sizes or intrinsic differences between each interaction process. As computational power increases future studies will be able to and should have larger sample sizes, longer runtimes and additional parts of the immunological synapse included.

Exploring peptide/MHC detachment processes using hierarchical natural move Monte Carlo.

MOTIVATION: The binding between a peptide and a major histocompatibility complex (MHC) is one of the most important processes for the induction of an adaptive immune response. Many algorithms have been developed to predict peptide/MHC (pMHC) binding. However, no approach has yet been able to give structural insight into how peptides detach from the MHC. RESULTS: In this study, we used a combination of coarse graining, hierarchical natural move Monte Carlo and stochastic conformational optimization to explore the detachment processes of 32 different peptides from HLA-A*02:01. We performed 100 independent repeats of each stochastic simulation and found that the presence of experimentally known anchor amino acids affects the detachment trajectories of our peptides. Comparison with experimental binding affinity data indicates the reliability of our approach (area under the receiver operating characteristic curve 0.85). We also compared to a 1000 ns molecular dynamics simulation of a non-binding peptide (AAAKTPVIV) and HLA-A*02:01. Even in this simulation, the longest published for pMHC, the peptide does not fully detach. Our approach is orders of magnitude faster and as such allows us to explore pMHC detachment processes in a way not possible with all-atom molecular dynamics simulations. AVAILABILITY AND IMPLEMENTATION: The source code is freely available for download at http://www.cs.ox.ac.uk/mosaics/. CONTACT: bernhard.knapp@stats.ox.ac.uk SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Modeling Functional Motions of Biological Systems by Customized Natural Moves.

Simulating the functional motions of biomolecular systems requires large computational resources. We introduce a computationally inexpensive protocol for the systematic testing of hypotheses regarding the dynamic behavior of proteins and nucleic acids. The protocol is based on natural move Monte Carlo, a highly efficient conformational sampling method with built-in customization capabilities that allows researchers to design and perform a large number of simulations to investigate functional motions in biological systems. We demonstrate the use of this protocol on both a protein and a DNA case study. Firstly, we investigate the plasticity of a class II major histocompatibility complex in the absence of a bound peptide. Secondly, we study the effects of the epigenetic mark 5-hydroxymethyl on cytosine on the structure of the Dickerson-Drew dodecamer. We show how our customized natural moves protocol can be used to investigate causal relationships of functional motions in biological systems.

T-Cell Receptor Binding Affects the Dynamics of the Peptide/MHC-I Complex.

The recognition of peptide/MHC by T-cell receptors is one of the most important interactions in the adaptive immune system. A large number of computational studies have investigated the structural dynamics of this interaction. However, to date only limited attention has been paid to differences between the dynamics of peptide/MHC with the T-cell receptor bound and unbound. Here we present the first large-scale molecular dynamics simulation study of this type investigating HLA-B*08:01 in complex with the Epstein-Barr virus peptide FLRGRAYGL and all possible single-point mutations (n = 172). All of the simulations were performed with and without the LC 13 T-cell receptor for a simulation time of 100 ns, yielding 344 simulations and a total simulation time of 34 400 ns. Our study is 2 orders of magnitude larger than the average T-cell receptor/peptide/MHC molecular dynamics simulation study. This data set provides reliable insights into alterations of the peptide/MHC-I dynamics caused by the presence of the T-cell receptor. We found that simulations in the presence of T-cell receptors have more hydrogen bonds between the peptide and MHC, altered flexibility patterns in the MHC helices and the peptide, a lower MHC groove width range, and altered solvent-accessible surface areas. This indicates that without a T-cell receptor the MHC binding groove can open and close, while the presence of the T-cell receptor inhibits these breathing-like motions.

Tertiary Element Interaction in HIV-1 TAR.

HIV-1 replication requires binding to occur between Trans-activation Response Element (TAR) RNA and the TAT protein. This TAR-TAT binding depends on the conformation of TAR, and therapeutic development has attempted to exploit this dynamic behavior. Here we simulate TAR dynamics in the context of mutations inhibiting TAR binding. We find that two tertiary elements, the apical loop and the bulge, can interact directly, and this interaction may be linked to the affinity of TAR for TAT.

Specificities of human CD4+ T cell responses to an inactivated flavivirus vaccine and infection: correlation with structure and epitope prediction.

Tick-borne encephalitis (TBE) virus is endemic in large parts of Europe and Central and Eastern Asia and causes more than 10,000 annual cases of neurological disease in humans. It is closely related to the mosquito-borne yellow fever, dengue, Japanese encephalitis, and West Nile viruses, and vaccination with an inactivated whole-virus vaccine can effectively prevent clinical disease. Neutralizing antibodies are directed to the viral envelope protein (E) and an accepted correlate of immunity. However, data on the specificities of CD4(+) T cells that recognize epitopes in the viral structural proteins and thus can provide direct help to the B cells producing E-specific antibodies are lacking. We therefore conducted a study on the CD4(+) T cell response against the virion proteins in vaccinated people in comparison to TBE patients. The data obtained with overlapping peptides in interleukin-2 (IL-2) enzyme-linked immunosorbent spot (ELISpot) assays were analyzed in relation to the three-dimensional structures of the capsid (C) and E proteins as well as to epitope predictions based on major histocompatibility complex (MHC) class II peptide affinities. In the C protein, peptides corresponding to two out of four alpha helices dominated the response in both vaccinees and patients, whereas in the E protein concordance of immunodominance was restricted to peptides of a single domain (domain III). Epitope predictions were much better for C than for E and were especially erroneous for the transmembrane regions. Our data provide evidence for a strong impact of protein structural features that influence peptide processing, contributing to the discrepancies observed between experimentally determined and computer-predicted CD4(+) T cell epitopes. Importance: Tick-borne encephalitis virus is endemic in large parts of Europe and Asia and causes more than 10,000 annual cases of neurological disease in humans. It is closely related to yellow fever, dengue, Japanese encephalitis, and West Nile viruses, and vaccination with an inactivated vaccine can effectively prevent disease. Both vaccination and natural infection induce the formation of antibodies to a viral surface protein that neutralize the infectivity of the virus and mediate protection. B lymphocytes synthesizing these antibodies require help from other lymphocytes (helper T cells) which recognize small peptides derived from proteins contained in the viral particle. Which of these peptides dominate immune responses to vaccination and infection, however, was unknown. In our study we demonstrate which parts of the proteins contribute most strongly to the helper T cell response, highlight specific weaknesses of currently available approaches for their prediction, and demonstrate similarities and differences between vaccination and infection.

Large scale characterization of the LC13 TCR and HLA-B8 structural landscape in reaction to 172 altered peptide ligands: a molecular dynamics simulation study.

The interplay between T cell receptors (TCRs) and peptides bound by major histocompatibility complexes (MHCs) is one of the most important interactions in the adaptive immune system. Several previous studies have computationally investigated their structural dynamics. On the basis of these simulations several structural and dynamical properties have been proposed as effectors of the immunogenicity. Here we present the results of a large scale Molecular Dynamics simulation study consisting of 100 ns simulations of 172 different complexes. These complexes consisted of all possible point mutations of the Epstein Barr Virus peptide FLRGRAYGL bound by HLA-B*08:01 and presented to the LC13 TCR. We compare the results of these 172 structural simulations with experimental immunogenicity data. We found that simulations with more immunogenic peptides and those with less immunogenic peptides are in fact highly similar and on average only minor differences in the hydrogen binding footprints, interface distances, and the relative orientation between the TCR chains are present. Thus our large scale data analysis shows that many previously suggested dynamical and structural properties of the TCR/peptide/MHC interface are unlikely to be conserved causal factors for peptide immunogenicity.

Examining variable domain orientations in antigen receptors gives insight into TCR-like antibody design.

The variable domains of antibodies and T-Cell receptors (TCRs) share similar structures. Both molecules act as sensors for the immune system but recognise their respective antigens in different ways. Antibodies bind to a diverse set of antigenic shapes whilst TCRs only recognise linear peptides presented by a major histocompatibility complex (MHC). The antigen specificity and affinity of both receptors is determined primarily by the sequence and structure of their complementarity determining regions (CDRs). In antibodies the binding site is also known to be affected by the relative orientation of the variable domains, VH and VL. Here, the corresponding property for TCRs, the Vß-Vα orientation, is investigated and compared with that of antibodies. We find that TCR and antibody orientations are distinct. General antibody orientations are found to be incompatible with binding to the MHC in a canonical TCR-like mode. Finally, factors that cause the orientation of TCRs and antibodies to be different are investigated. Packing of the long Vα CDR3 in the domain-domain interface is found to be influential. In antibodies, a similar packing affect can be achieved using a bulky residue at IMGT position 50 on the VH domain. Along with IMGT VH 50, other positions are identified that may help to promote a TCR-like orientation in antibodies. These positions should provide useful considerations in the engineering of therapeutic TCR-like antibodies.

Gro2mat: a package to efficiently read gromacs output in MATLAB.

Molecular dynamics (MD) simulations are a state-of-the-art computational method used to investigate molecular interactions at atomic scale. Interaction processes out of experimental reach can be monitored using MD software, such as Gromacs. Here, we present the gro2mat package that allows fast and easy access to Gromacs output files from Matlab. Gro2mat enables direct parsing of the most common Gromacs output formats including the binary xtc-format. No openly available Matlab parser currently exists for this format. The xtc reader is orders of magnitudes faster than other available pdb/ascii workarounds. Gro2mat is especially useful for scientists with an interest in quick prototyping of new mathematical and statistical approaches for Gromacs trajectory analyses. © 2014 Wiley Periodicals, Inc.

Crowdsourcing yields a new standard for kinks in protein helices.

Kinks are functionally important structural features found in the α-helices of proteins. Structurally, they are points at which a helix abruptly changes direction. Current kink definition and identification methods often disagree with one another. Here we describe a crowdsourcing approach to obtain a reliable gold standard set of kinks. Using an online interface, we collected more than 10,000 classifications of 300 helices into straight, curved, or kinked categories. We found that participants were better at discriminating between straight and not-straight helices than between kinked and curved helices. Surprisingly, more obvious kinks were not necessarily identified as more localized within the helix. We present a set of 252 helices where more than 50% of the participants agree on a classification. This set can be used as a reliable gold standard to develop, train, and compare computational methods. An interactive visualization of the results is available online at http://opig.stats.ox.ac.uk/webapps/ahah/php/experiment_results.php .

Differential geometric analysis of alterations in MH α-helices.

Antigen presenting cells present processed peptides via their major histocompatibility (MH) complex to the T cell receptors (TRs) of T cells. If a peptide is immunogenic, a signaling cascade can be triggered within the T cell. However, the binding of different peptides and/or different TRs to MH is also known to influence the spatial arrangement of the MH α-helices which could itself be an additional level of T cell regulation. In this study, we introduce a new methodology based on differential geometric parameters to describe MH deformations in a detailed and comparable way. For this purpose, we represent MH α-helices by curves. On the basis of these curves, we calculate in a first step the curvature and torsion to describe each α-helix independently. In a second step, we calculate the distribution parameter and the conical curvature of the ruled surface to describe the relative orientation of the two α-helices. On the basis of four different test sets, we show how these differential geometric parameters can be used to describe changes in the spatial arrangement of the MH α-helices for different biological challenges. In the first test set, we illustrate on the basis of all available crystal structures for (TR)/pMH complexes how the binding of TRs influences the MH helices. In the second test set, we show a cross evaluation of different MH alleles with the same peptide and the same MH allele with different peptides. In the third test set, we present the spatial effects of different TRs on the same peptide/MH complex. In the fourth test set, we illustrate how a severe conformational change in an α-helix can be described quantitatively. Taken together, we provide a novel structural methodology to numerically describe subtle and severe alterations in MH α-helices for a broad range of applications.

Relationship of pentraxin 3 with insulin sensitivity in gestational diabetes.

BACKGROUND: Pentraxin 3 (PTX3) is a cytokine-inducible molecule expressed in different tissues, the levels of which increase in a response to a variety of inflammatory conditions. Recently, it has been linked to the serum glucose levels and some comorbidities in type 2 diabetes. MATERIALS AND METHODS: Here, we aimed to investigate the role of PTX3 in gestational diabetes mellitus (GDM), which is considered a forerunner of type 2 diabetes. Fasting PTX3 serum levels were measured in 90 women [45 GDM, 45 normal glucose tolerance (NGT)] during pregnancy. In addition, PTX3 was measured during a 2 h, 75 g oral glucose tolerance test (OGTT) in 20 women (10 GDM, 10 NGT) at 24-28 weeks of gestation and in 16 of them after delivery (10GDM, 6 NGT). RESULTS: A continuous increase in PTX3 levels was observed during the OGTT and reached in the GDM group a significant difference after 120 min compared with baseline (P < 0·05). Additionally, a rise in the PTX3 concentration was significantly higher in the GDM- compared with the NGT group, 120 min after glucose challenge (P < 0·01). During pregnancy, serum glucose and C-peptide were positively correlated with the PTX3 levels in the whole study group, whereas a negative association was found with the insulin sensitivity parameters QUICKI and OGIS. CONCLUSIONS: Dependence of PTX3 on serum glucose levels was more pronounced in women with GDM than in the NGT group. This notion together with its inverse relation to the parameters of insulin sensitivity, suggests a potential involvement of PTX3 in GDM pathology.