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.

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: [email protected]. Supplementary information: Supplementary data are available at Bioinformatics online.

Abstract There are four known epigenetic cytosine modifications in mammals: methylation (5mC), hydroxymethylation (5hmC), formylation (5fC) and carboxylation (5caC). The biological effects of 5mC are well understood but the roles of the remaining modifications remain elusive. Experimental and computational studies suggest that a single epigenetic mark has little structural effect but six of them can radically change the structure of DNA to a new form, F-DNA. Investigating the collective effect of multiple epigenetic marks requires the ability to interrogate all possible combinations of epigenetic states (e.g. methylated/non-methylated) along a stretch of DNA. Experiments on such complex systems are only feasible on small, isolated examples and there currently exist no systematic computational solutions to this problem. We address this issue by extending the use of Natural Move Monte Carlo to simulate the conformations of epigenetic marks. We validate our protocol by reproducing in silico experimental observations from two recently published high-resolution crystal structures that contain epigenetic marks 5hmC and 5fC. We further demonstrate that our protocol correctly finds either the F-DNA or the B-DNA states more energetically favorable depending on the configuration of the epigenetic marks. We hope that the computational efficiency and ease of use of this novel simulation framework would form the basis for future protocols and facilitate our ability to rapidly interrogate diverse epigenetic systems. Availability and implementation The code together with examples and tutorials are available from http://www.cs.ox.ac.uk/mosaics Supplementary information Supplementary data are available at Bioinformatics online.

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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.

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: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

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.

Cross-disciplinary collaborations have become an increasingly important part of science. They are seen as a key factor for finding solutions to pressing societal challenges on a global scale including green technologies, sustainable food production and drug development. This has also been realized by regulators and policy-makers, as it is reflected in the 80 billion Euro "Horizon 2020" EU Framework Programme for Research and Innovation. This programme puts special emphasis at breaking down barriers between fields to create a path breaking environment for knowledge, research and innovation. However, igniting and successfully maintaining cross-disciplinary collaborations can be a delicate task. In this article we focus on the specific challenges associated with cross-disciplinary research in particular from the perspective of the theoretician. As research fellows of the 2020 Science project (http://www.2020science.net) and collaboration partners, we bring broad experience of developing interdisciplinary collaborations [2–12]. We intend this guide for early career computational researchers as well as more senior scientists who are entering a cross disciplinary setting for the first time. We describe the key benefits, as well as some possible pitfalls, arising from collaborations between scientists with backgrounds in very different fields. This paper has inter alia been cited by Times Higher education: http://www.timeshighereducation.co.uk/news/people/the-secrets-to-successful-interdisciplinary-work/2020267.article .

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 Vβ-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.

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.

In order to attempt to understand the complexity inherent in nature, mathematical, statistical and computational techniques are increasingly being employed in the life sciences. In particular, the use and development of software tools is becoming vital for investigating scientific hypotheses, and a wide range of scientists are finding software development playing a more central role in their day-to-day research. In fields such as biology and ecology, there has been a noticeable trend towards the use of quantitative methods for both making sense of ever-increasing amounts of data [1] and building or selecting models [2]. As Research Fellows of the “2020 Science” project (http://www.2020science.net), funded jointly by the EPSRC (Engineering and Physical Sciences Research Council) and Microsoft Research, we have firsthand experience of the challenges associated with carrying out multidisciplinary computation-based science [3]–[5]. In this paper we offer a jargon-free guide to best practice when developing and using software for scientific research. While many guides to software development exist, they are often aimed at computer scientists [6] or concentrate on large open-source projects [7]; the present guide is aimed specifically at the vast majority of scientific researchers: those without formal training in computer science. We present our ten simple rules with the aim of enabling scientists to be more effective in undertaking research and therefore maximise the impact of this research within the scientific community. While these rules are described individually, collectively they form a single vision for how to approach the practical side of computational science. Our rules are presented in roughly the chronological order in which they should be undertaken, beginning with things that, as a computational scientist, you should do before you even think about writing any code. For each rule, guides on getting started, links to relevant tutorials, and further reading are provided in the supplementary material (Text S1).

BACKGROUND: The binding between the major histocompatibility complex and the presented peptide is an indispensable prerequisite for the adaptive immune response. There is a plethora of different in silico techniques for the prediction of the peptide binding affinity to major histocompatibility complexes. Most studies screen a set of peptides for promising candidates to predict possible T cell epitopes. In this study we ask the question vice versa: Which peptides do have highest binding affinities to a given major histocompatibility complex according to certain in silico scoring functions? RESULTS: Since a full screening of all possible peptides is not feasible in reasonable runtime, we introduce a heuristic approach. We developed a framework for Genetic Algorithms to optimize peptides for the binding to major histocompatibility complexes. In an extensive benchmark we tested various operator combinations. We found that (1) selection operators have a strong influence on the convergence of the population while recombination operators have minor influence and (2) that five different binding prediction methods lead to five different sets of "optimal" peptides for the same major histocompatibility complex. The consensus peptides were experimentally verified as high affinity binders. CONCLUSION: We provide a generalized framework to calculate sets of high affinity binders based on different previously published scoring functions in reasonable runtime. Furthermore we give insight into the different behaviours of operators and scoring functions of the Genetic Algorithm.