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Background: Effective follow-up of brain metastasis (BM) patients post-treatment is crucial for adapting therapies and detecting new lesions. Current guidelines (Response Assessment in Neuro-Oncology-BM) have limitations, such as patient-level assessments and arbitrary lesion selection, which may not reflect outcomes in high tumor burden cases. Accurate, reproducible, and automated response assessments can improve follow-up decisions, including (1) optimizing re-treatment timing to avoid treating responding lesions or delaying treatment of progressive ones, and (2) enhancing precision in evaluating responses during clinical trials., Methods: We compared manual and automatic (deep learning-based) lesion contouring using unidimensional and volumetric criteria. Analysis focused on (1) agreement in size and RANO-BM categories, (2) stability of measurements under scanner rotations and over time, and (3) predictability of 1-year outcomes. The study included 49 BM patients, with 184 MRI studies and 448 lesions, retrospectively assessed by radiologists., Results: Automatic contouring and volumetric criteria demonstrated superior stability ( P  < .001 for rotation; P  < .05 over time) and better outcome predictability compared to manual methods. These approaches reduced observer variability, offering reliable and efficient response assessments. The best outcome predictability, defined as 1-year response, was achieved using automatic contours and volumetric measurements. These findings highlight the potential of automated tools to streamline clinical workflows and provide consistency across evaluators, regardless of expertise., Conclusion: Automatic BM contouring and volumetric measurements provide promising tools to improve follow-up and treatment decisions in BM management. By enhancing precision and reproducibility, these methods can streamline clinical workflows and improve the evaluation of response in trials and practice., Competing Interests: None declared., (© The Author(s) 2025. Published by Oxford University Press, the Society for Neuro-Oncology and the European Association of Neuro-Oncology.)

The development of immune checkpoint inhibitors (ICIs) has revolutionized cancer therapy but only a fraction of patients benefits from this therapy. Model-informed drug development can be used to assess prognostic and predictive clinical factors or biomarkers associated with treatment response. Most pharmacometric models have thus far been developed using data from randomized clinical trials, and further studies are needed to translate their findings into the real-world setting. We developed a tumor growth inhibition model based on real-world clinical and imaging data in a population of 91 advanced melanoma patients receiving ICIs (i.e., ipilimumab, nivolumab, and pembrolizumab). Drug effect was modeled as an ON/OFF treatment effect, with a tumor killing rate constant identical for the three drugs. Significant and clinically relevant covariate effects of albumin, neutrophil to lymphocyte ratio, and Eastern Cooperative Oncology Group (ECOG) performance status were identified on the baseline tumor volume parameter, as well as NRAS mutation on tumor growth rate constant using standard pharmacometric approaches. In a population subgroup (n = 38), we had the opportunity to conduct an exploratory analysis of image-based covariates (i.e., radiomics features), by combining machine learning and conventional pharmacometric covariate selection approaches. Overall, we demonstrated an innovative pipeline for longitudinal analyses of clinical and imaging RWD with a high-dimensional covariate selection method that enabled the identification of factors associated with tumor dynamics. This study also provides a proof of concept for using radiomics features as model covariates., (© 2023 The Authors. CPT: Pharmacometrics & Systems Pharmacology published by Wiley Periodicals LLC on behalf of American Society for Clinical Pharmacology and Therapeutics.)

Purpose: A semiautomated pipeline for the collection and curation of free-text and imaging real-world data (RWD) was developed to quantify cancer treatment outcomes in large-scale retrospective real-world studies. The objectives of this article are to illustrate the challenges of RWD extraction, to demonstrate approaches for quality assurance, and to showcase the potential of RWD for precision oncology., Methods: We collected data from patients with advanced melanoma receiving immune checkpoint inhibitors at the Lausanne University Hospital. Cohort selection relied on semantically annotated electronic health records and was validated using process mining. The selected imaging examinations were segmented using an automatic commercial software prototype. A postprocessing algorithm enabled longitudinal lesion identification across imaging time points and consensus malignancy status prediction. Resulting data quality was evaluated against expert-annotated ground-truth and clinical outcomes obtained from radiology reports., Results: The cohort included 108 patients with melanoma and 465 imaging examinations (median, 3; range, 1-15 per patient). Process mining was used to assess clinical data quality and revealed the diversity of care pathways encountered in a real-world setting. Longitudinal postprocessing greatly improved the consistency of image-derived data compared with single time point segmentation results (classification precision increased from 53% to 86%). Image-derived progression-free survival resulting from postprocessing was comparable with the manually curated clinical reference (median survival of 286 v 336 days, P = .89)., Conclusion: We presented a general pipeline for the collection and curation of text- and image-based RWD, together with specific strategies to improve reliability. We showed that the resulting disease progression measures match reference clinical assessments at the cohort level, indicating that this strategy has the potential to unlock large amounts of actionable retrospective real-world evidence from clinical records.

The growing availability of clinical real-world data (RWD) represents a formidable opportunity to complement evidence from randomized clinical trials and observe how oncological treatments perform in real-life conditions. In particular, RWD can provide insights on questions for which no clinical trials exist, such as comparing outcomes from different sequences of treatments. To this end, process mining is a particularly suitable methodology for analyzing different treatment paths and their associated outcomes. Here, we describe an implementation of process mining algorithms directly within our hospital information system with an interactive application that allows oncologists to compare sequences of treatments in terms of overall survival, progression-free survival and best overall response. As an application example, we first performed a RWD descriptive analysis of 303 patients with advanced melanoma and reproduced findings observed in two notorious clinical trials: CheckMate-067 and DREAMseq. Then, we explored the outcomes of an immune-checkpoint inhibitor rechallenge after a first progression on immunotherapy versus switching to a BRAF targeted treatment. By using interactive process-oriented RWD analysis, we observed that patients still derive long-term survival benefits from immune-checkpoint inhibitors rechallenge, which could have direct implications on treatment guidelines for patients able to carry on immune-checkpoint therapy, if confirmed by external RWD and randomized clinical trials. Overall, our results highlight how an interactive implementation of process mining can lead to clinically relevant insights from RWD with a framework that can be ported to other centers or networks of centers., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Wicky, Gatta, Latifyan, Micheli, Gerard, Pradervand, Michielin and Cuendet.)

Combined ipilimumab and nivolumab significantly improve outcomes in metastatic melanoma patients but bear an important financial impact on the healthcare system. Here, we analyze the treatment costs, focusing on irAE. We conducted a retrospective analysis of 62 melanoma patients treated with ipilimumab-nivolumab at the Lausanne University Hospital between 1 June 2016 and 31 August 2019. The frequency of irAEs and outcomes were evaluated. All melanoma-specific costs were analyzed from the first ipilimumab-nivolumab dose until the therapy given subsequently or death. A total of 54/62 (87%) patients presented at least one irAE, and 31/62 (50%) presented a grade 3-4 irAE. The majority of patients who had a complete response 12/14 (86%) and 21/28 (75%) of overall responders presented a grade 3-4 toxicity, and there were no responses in patients without toxicity. Toxicity costs represented only 3% of the total expenses per patient. The most significant contributions were medication costs (44%) and disease costs (39%), mainly disease-related hospitalization costs, not toxicity-related. Patients with a complete response had the lowest global median cost per week of follow up (EUR 2425) and patients who had progressive disease (PD), the highest one (EUR 8325). Except for one patient who had a Grade 5 toxicity (EUR 6043/week), we observe that less severe toxicity grades (EUR 9383/week for Grade 1), or even the absence of toxicity (EUR 9922/week), are associated with higher median costs per week (vs. EUR 3266/week for Grade 4 and EUR 2850/week for Grade 3). The cost of toxicities was unexpectedly low compared to the total costs, especially medication costs. Patients with higher toxicity grades had better outcomes and lower total costs due to treatment discontinuation.

During the acute phase of the COVID-19 pandemic, hospitals faced a challenge to manage patients, especially those with other comorbidities and medical needs, such as cancer patients. Here, we use Process Mining to analyze real-world therapeutic pathways in a cohort of 1182 cancer patients of the Lausanne University Hospital following COVID-19 infection. The algorithm builds trees representing sequences of coarse-grained events such as Home, Hospitalization, Intensive Care and Death. The same trees can also show probability of death or time-to-event statistics in each node. We introduce a new tool, called Differential Process Mining, which enables comparison of two patient strata in each node of the tree, in terms of hits and death rate, together with a statistical significance test. We thus compare management of COVID-19 patients with an active cancer in the first vs. second COVID-19 waves to quantify hospital adaptation to the pandemic. We also compare patients having undergone systemic therapy within 1 year to the rest of the cohort to understand the impact of an active cancer and/or its treatment on COVID-19 outcome. This study demonstrates the value of Process Mining to analyze complex event-based real-world data and generate hypotheses on hospital resource management or on clinical patient care., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Cuendet, Gatta, Wicky, Gerard, Dalla-Vale, Tavazzi, Michielin, Delyon, Ferahta, Cesbron, Lofek, Huber, Jankovic, Demicheli, Bouchaab, Digklia, Obeid, Peters, Eicher, Pradervand and Michielin.)

To assess the safety and efficacy of ipilimumab plus nivolumab around selective internal radiation therapy (SIRT) in patients with metastatic uveal melanoma (mUM). We present a retrospective, single center study of 32 patients with mUM divided into two groups based on the treatment received between April 2013 and April 2021. The SIRT&#95;IpiNivo cohort was treated with Yttrium-90 microspheres and ipilimumab plus nivolumab before or after the SIRT ( n = 18). The SIRT cohort underwent SIRT but did not receive combined immunotherapy with ipilimumab plus nivolumab ( n = 14). Twelve patients (66.7%) of the SIRT&#95;IpiNivo arm received SIRT as first-line treatment and six patients (33.3%) received ipilimumab plus nivolumab prior to SIRT. In the SIRT group, seven patients (50.0%) received single-agent immunotherapy. One patient treated with combined immunotherapy 68 months after the SIRT was included in this group. At the start of ipilimumab plus nivolumab, 94.4% ( n = 17) presented hepatic metastases and 72.2% ( n = 13) had extra liver disease. Eight patients (44.4%) of the SIRT&#95;IpiNivo group experienced grade 3 or 4 immune related adverse events, mainly colitis and hepatitis. Median overall survival from the diagnosis of metastases was 49.6 months (95% confidence interval (CI); 24.1-not available (NA)) in the SIRT&#95;IpiNivo group compared with 13.6 months (95% CI; 11.5-NA) in the SIRT group (log-rank p -value 0.027). The presence of extra liver metastases at the time of SIRT, largest liver lesion more than 8 cm (M1c) and liver tumor volume negatively impacted the survival. This real-world cohort suggests that a sequential treatment of ipilimumab plus nivolumab and SIRT is a well-tolerated therapeutic approach with promising survival rates.

The immune system is constantly protecting its host from the invasion of pathogens and the development of cancer cells. The specific CD8 + T-cell immune response against virus-infected cells and tumor cells is based on the T-cell receptor recognition of antigenic peptides bound to class I major histocompatibility complexes (MHC) at the surface of antigen presenting cells. Consequently, the peptide binding specificities of the highly polymorphic MHC have important implications for the design of vaccines, for the treatment of autoimmune diseases, and for personalized cancer immunotherapy. Evidence-based machine-learning approaches have been successfully used for the prediction of peptide binders and are currently being developed for the prediction of peptide immunogenicity. However, understanding and modeling the structural details of peptide/MHC binding is crucial for a better understanding of the molecular mechanisms triggering the immunological processes, estimating peptide/MHC affinity using universal physics-based approaches, and driving the design of novel peptide ligands. Unfortunately, due to the large diversity of MHC allotypes and possible peptides, the growing number of 3D structures of peptide/MHC (pMHC) complexes in the Protein Data Bank only covers a small fraction of the possibilities. Consequently, there is a growing need for rapid and efficient approaches to predict 3D structures of pMHC complexes. Here, we review the key characteristics of the 3D structure of pMHC complexes before listing databases and other sources of information on pMHC structures and MHC specificities. Finally, we discuss some of the most prominent pMHC docking software., (© 2022. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.)

Immune checkpoint inhibitors have revolutionized the treatment landscape for a number of cancers over the last few decades. Nevertheless, a majority of patients still do not benefit from these treatments. Such patient-specific lack of response can be predicted, in part, from the immune phenotypes present in the tumor microenvironment. We provide a perspective on options to reprogram the tumors and their microenvironment to increase the therapeutic efficacy of immunotherapies and expand their efficacy against cold tumors. Additionally, we review data from current preclinical and clinical trials aimed at testing the different therapeutic options in monotherapy or preferably in combination with checkpoint inhibitors., (Copyright © 2021. Published by Elsevier Ltd.)

Using real-world evidence in biomedical research, an indispensable complement to clinical trials, requires access to large quantities of patient data that are typically held separately by multiple healthcare institutions. We propose FAMHE, a novel federated analytics system that, based on multiparty homomorphic encryption (MHE), enables privacy-preserving analyses of distributed datasets by yielding highly accurate results without revealing any intermediate data. We demonstrate the applicability of FAMHE to essential biomedical analysis tasks, including Kaplan-Meier survival analysis in oncology and genome-wide association studies in medical genetics. Using our system, we accurately and efficiently reproduce two published centralized studies in a federated setting, enabling biomedical insights that are not possible from individual institutions alone. Our work represents a necessary key step towards overcoming the privacy hurdle in enabling multi-centric scientific collaborations., (© 2021. The Author(s).)

How targeted therapies and immunotherapies shape tumors, and thereby influence subsequent therapeutic responses, is poorly understood. In the present study, we show, in melanoma patients and mouse models, that when tumors relapse after targeted therapy with MAPK pathway inhibitors, they are cross-resistant to immunotherapies, despite the different modes of action of these therapies. We find that cross-resistance is mediated by a cancer cell-instructed, immunosuppressive tumor microenvironment that lacks functional CD103 + dendritic cells, precluding an effective T cell response. Restoring the numbers and functionality of CD103 + dendritic cells can re-sensitize cross-resistant tumors to immunotherapy. Cross-resistance does not arise from selective pressure of an immune response during evolution of resistance, but from the MAPK pathway, which not only is reactivated, but also exhibits an increased transcriptional output that drives immune evasion. Our work provides mechanistic evidence for cross-resistance between two unrelated therapies, and a scientific rationale for treating patients with immunotherapy before they acquire resistance to targeted therapy., (© 2021. The Author(s), under exclusive licence to Springer Nature America, Inc.)

In order to successfully predict a proteins function throughout its trajectory, in addition to uncovering changes in its conformational state, it is necessary to employ techniques that maintain its 3D information while performing at scale. We extend a protein representation that encodes secondary and tertiary structure into fix-sized, color images, and a neural network architecture (called GEM-net) that leverages our encoded representation. We show the applicability of our method in two ways: (1) performing protein function prediction, hitting accuracy between 78 and 83 percent, and (2) visualizing and detecting conformational changes in protein trajectories during molecular dynamics simulations.

Mutations in the G protein-coupled receptor (GPCR) rhodopsin are a common cause of autosomal dominant retinitis pigmentosa, a blinding disease. Rhodopsin self-associates in the membrane, and the purified monomeric apo-protein opsin dimerizes in vitro as it transitions from detergent micelles to reconstitute into a lipid bilayer. We previously reported that the retinitis pigmentosa-linked F220C opsin mutant fails to dimerize in vitro, reconstituting as a monomer. Using fluorescence-based assays and molecular dynamics simulations we now report that whereas wild-type and F220C opsin display distinct dimerization propensities in vitro as previously shown, they both dimerize in the plasma membrane of HEK293 cells. Unexpectedly, molecular dynamics simulations show that F220C opsin forms an energetically favored dimer in the membrane when compared with the wild-type protein. The conformation of the F220C dimer is unique, with transmembrane helices 5 and 6 splayed apart, promoting widening of the intracellular vestibule of each protomer and influx of water into the protein interior. FRET experiments with SNAP-tagged wild-type and F220C opsin expressed in HEK293 cells are consistent with this conformational difference. We speculate that the unusual mode of dimerization of F220C opsin in the membrane may have physiological consequences.

Background: Immune checkpoint inhibitors (ICIs) have revolutionised cancer therapy but frequently cause immune-related adverse events (irAEs). Description of late-onset and duration of irAEs in the literature is often incomplete., Methods: To investigate reporting and incidence of late-onset and long-lasting irAEs, we reviewed all registration trials leading to ICI's approval by the US FDA and/or EMA up to December 2019. We analysed real-world data from all lung cancer (LC) and melanoma (Mel) patients treated with approved ICIs at the University Hospital of Lausanne (CHUV) from 2011 to 2019. To account for the immortal time bias, we used a time-dependent analysis to assess the potential association between irAEs and overall survival (OS)., Results: Duration of irAEs and proportion of patients with ongoing toxicities at data cut-off were not specified in 56/62 (90%) publications of ICIs registration trials. In our real-world analysis, including 437 patients (217 LC, 220 Mel), 229 (52.4%) experienced at least one grade ≥2 toxicity, for a total of 318 reported irAEs, of which 112 (35.2%) were long-lasting (≥6 months) and about 40% were ongoing at a median follow-up of 369 days [194-695] or patient death. The cumulative probability of irAE onset from treatment initiation was 42.8%, 51.0% and 57.3% at 6, 12 and 24 months, respectively. The rate of ongoing toxicity from the time of first toxicity onset was 42.8%, 38.4% and 35.7% at 6, 12 and 24 months. Time-dependent analysis showed no significant association between the incidence of irAEs and OS in both cohorts (log Rank p = 0.67 and 0.19 for LC and Mel, respectively)., Conclusions: Late-onset and long-lasting irAEs are underreported but common events during ICIs therapy. Time-dependent survival analysis is advocated to assess their impact on OS. Real-world evidence is warranted to fully capture and characterise late-onset and long-lasting irAEs in order to implement appropriate strategies for patient surveillance and follow-up., Competing Interests: Conflict of interest statement EG, AW, MI, BGM, CLG, SL, BCO, MC, SP, ET, RG, LM, MC declare no conflicts of interest. HB received advisory board or consultant fees from MSD, BMS, Astrazeneca, Roche. JD declares research funding from MSD. GV received advisory board or consultant fees from GSK-Tesaro, Amgen, PharmaMar, Roche, Astrazeneca, Clovis. MA received advisory board or consultant fees from Bayer, Novartis, BMS, Merk and institutional research grant from GSK-Tesaro and PharmaMar. MO and HZ declare research support and honoraria from Roche, BMS, MSD, Astra-Zeneca, GSK and Novartis. NNMA declared an advisory role for Astrazeneca. SZ received travel grants from AstraZeneca. GC has received grants, research support and/or is coinvestigator in clinical trials by BMS, Celgene, Boehringer Ingelheim, Roche, Iovance and Kite; has received honoraria for consultations or presentations by Roche, Genentech, BMS, AstraZeneca, Sanofi-Aventis, Nextcure and GeneosTx. SP has received education grants and received honoraria for providing consultations, attending advisory boards, and/or providing lectures for AbbVie, Amgen, AstraZeneca, Bayer, Biocartis, Bioinvent, Blueprint Medicines, Boehringer Ingelheim, Bristol MyersSquibb, Clovis, Daiichi Sankyo, Debiopharm, Eli Lilly, F. Hoffmann-LaRoche, Foundation Medicine, Illumina, Incyte, Janssen, Merck Sharp &Dohme, Merck Serono, Merrimack, Novartis, PharmaMar, Pfizer, Regeneron, Sanofi, Seattle Genetics and Takeda. MDM received advisory board or consultant fees from Merck Sharp & Dohme, Bristol Myers Squibb, Eisai, Janssen, Astellas, AstraZeneca, Pfizer, Takeda and an institutional research grant from GSK-Tesaro. OM received advisory board or consultant fees from BMS, Roche, Amgen, MSD, Novartis, GSK, Pierre-Fabre and an institutional research grant from BMS, MSD and Amgen., (Copyright © 2021 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

Binding of the T cell receptor (TCR) to its cognate, peptide antigen-loaded major histocompatibility complex (pMHC) is a key interaction for triggering T cell activation and ultimately elimination of the target cell. Despite the importance of this interaction for cellular immunity, a comprehensive molecular understanding of TCR specificity and affinity is lacking. We conducted hydrogen/deuterium exchange mass spectrometry (HDX-MS) analyses of individual affinity-enhanced TCR variants and clinically relevant pMHC class I molecules (HLA-A*0201/NY-ESO-1 157-165 ) to investigate the causality between increased binding affinity and conformational dynamics in TCR-pMHC complexes. Differential HDX-MS analyses of TCR variants revealed that mutations for affinity enhancement in TCR CDRs altered the conformational response of TCR to pMHC ligation. Improved pMHC binding affinity was in general observed to correlate with greater differences in HDX upon pMHC binding in modified TCR CDR loops, thereby providing new insights into the TCR-pMHC interaction. Furthermore, a specific point mutation in the β-CDR3 loop of the NY-ESO-1 TCR associated with a substantial increase in binding affinity resulted in a substantial change in pMHC binding kinetics (i.e., very slow k on , revealed by the detection of EX1 HDX kinetics), thus providing experimental evidence for a slow induced-fit binding mode. We also examined the conformational impact of pMHC binding on an unrelated TRAV12-2 gene-encoded TCR directed against the immunodominant MART-1 26-35 cancer antigen restricted by HLA-A*0201. Our findings provide a molecular basis for the observed TRAV12-2 gene bias in natural CD8 + T cell-based immune responses against the MART-1 antigen, with potential implications for general ligand discrimination and TCR cross-reactivity processes.

In the age of Evidence-Based Medicine, Clinical Guidelines (CGs) are recognized to be an indispensable tool to support physicians in their daily clinical practice. Medical Informatics is expected to play a relevant role in facilitating diffusion and adoption of CGs. However, the past pioneering approaches, often fragmented in many disciplines, did not lead to solutions that are actually exploited in hospitals. Process Mining for Healthcare (PM4HC) is an emerging discipline gaining the interest of healthcare experts, and seems able to deal with many important issues in representing CGs. In this position paper, we briefly describe the story and the state-of-the-art of CGs, and the efforts and results of the past approaches of medical informatics. Then, we describe PM4HC, and we answer questions like how can PM4HC cope with this challenge? Which role does PM4HC play and which rules should be employed for the PM4HC scientific community?

Background: Evidence pointing to a synergistic effect of stereotactic radiosurgery (SRS) with concurrent immunotherapy or targeted therapy in patients with melanoma brain metastases (BM) is increasing. We aimed to analyze the effect on overall survival (OS) of immune checkpoint inhibitors (ICI) or BRAF/MEK inhibitors initiated during the 9 weeks before or after SRS. We also evaluated the prognostic value of patients' and disease characteristics as predictors of OS in patients treated with SRS., Methods: We identified patients with BM from cutaneous or unknown primary origin melanoma treated with SRS between 2011 and 2018., Results: We included 84 patients. The median OS was 12 months (95% CI 9-20 months). The median follow-up was 30 months (95% CI 28-49). Twenty-eight patients with newly diagnosed BM initiated anti-PD-1 +/-CTLA-4 therapy (n = 18), ipilimumab monotherapy (n = 10) or BRAF+/- MEK inhibitors (n = 11), during the 9 weeks before or after SRS. Patients who received anti-PD-1 +/-CTLA-4 mAb showed an improved survival in comparison to ipilimumab monotherapy (OS 24 vs. 7.5 months; HR 0.32, 95% 0.12-0.83, p = 0.02) and BRAF +/-MEK inhibitors (OS 24 vs. 7 months, respectively; HR 0.11, 95% 0.04-0.34, p = 0.0001). This benefit remained significant when compared to the subgroup of patients treated with dual BRAF/MEK inhibition (BMi) (n = 5). In a multivariate Cox regression analysis an age > 65, synchronous BM, > 2 metastatic sites, > 4 BM, and an ECOG > 1 were correlated with poorer prognosis. A treatment with anti-PD-1+/-CTLA-4 mAbs within 9 weeks of SRS was associated with better outcomes. The presence of serum lactate dehydrogenase (LDH) levels ≥ 2xULN at BM diagnosis was associated with lower OS (HR 1.60, 95% CI 1.03-2.50; p = 0.04)., Conclusions: The concurrent administration of anti-PD-1+/-CTLA-4 mAbs with SRS was associated with improved survival in melanoma patients with newly diagnosed BM. In addition to CNS tumor burden, the extension of systemic disease retains its prognostic value in patients treated with SRS. Elevated serum LDH levels are predictors of poor outcome in these patients.

The coreceptor CD8αβ can greatly promote activation of T cells by strengthening T-cell receptor (TCR) binding to cognate peptide-MHC complexes (pMHC) on antigen presenting cells and by bringing p56 Lck to TCR/CD3. Here, we demonstrate that CD8 can also bind to pMHC on the T cell (in cis) and that this inhibits their activation. Using molecular modeling, fluorescence resonance energy transfer experiments on living cells, biochemical and mutational analysis, we show that CD8 binding to pMHC in cis involves a different docking mode and is regulated by posttranslational modifications including a membrane-distal interchain disulfide bond and negatively charged O-linked glycans near positively charged sequences on the CD8β stalk. These modifications distort the stalk, thus favoring CD8 binding to pMHC in cis. Differential binding of CD8 to pMHC in cis or trans is a means to regulate CD8 + T-cell responses and provides new translational opportunities., (Copyright © 2019 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

A method for calculating the free energy difference between two structurally defined conformational states of a chemical system is developed. A path is defined using a previously reported collective variable that interpolates between two or more conformations, and a restraint is introduced in order to keep the system close to the path. The evolution of the system along the path, which typically presents a high free energy barrier, is generated using enhanced sampling schemes. Although the formulation of the method in terms of a path is quite general, an important advance in this work is the demonstration that prior knowledge of the path is, in fact, not needed and that the free energy difference can be obtained using a simplified definition of the path collective variable that only involves the endpoints. We first validate this method on cyclohexane isomerization. The method is then tested for an extensive conformational change in a realistic molecular system by calculating the free energy difference between the α -helix and β -hairpin conformations of deca-alanine in solution. Finally, the method is applied to a biologically relevant system to calculate the free energy difference of an observed and a hypothetical conformation of an antigenic peptide bound to a major histocompatibility complex.

LeuT, a prokaryotic member of the neurotransmitter:sodium symporter (NSS) family, is an established structural model for mammalian NSS counterparts. We investigate the substrate translocation mechanism of LeuT by measuring the solution-phase structural dynamics of the transporter in distinct functional states by hydrogen/deuterium exchange mass spectrometry (HDX-MS). Our HDX-MS data pinpoint LeuT segments involved in substrate transport and reveal for the first time a comprehensive and detailed view of the dynamics associated with transition of the transporter between outward- and inward-facing configurations in a Na + - and K + -dependent manner. The results suggest that partial unwinding of transmembrane helices 1/5/6/7 drives LeuT from a substrate-bound, outward-facing occluded conformation toward an inward-facing open state. These hitherto unknown, large-scale conformational changes in functionally important transmembrane segments, observed for LeuT in detergent-solubilized form and when embedded in a native-like phospholipid bilayer, could be of physiological relevance for the translocation process.

Allostery plays a crucial role in the mechanism of neurotransmitter-sodium symporters, such as the human dopamine transporter. To investigate the molecular mechanism that couples the transport-associated inward release of the Na + ion from the Na2 site to intracellular gating, we applied a combination of the thermodynamic coupling function (TCF) formalism and Markov state model analysis to a 50-μs data set of molecular dynamics trajectories of the human dopamine transporter, in which multiple spontaneous Na + release events were observed. Our TCF approach reveals a complex landscape of thermodynamic coupling between Na + release and inward-opening, and identifies diverse, yet well-defined roles for different Na + -coordinating residues. In particular, we identify a prominent role in the allosteric coupling for the Na + -coordinating residue D421, where mutation has previously been associated with neurological disorders. Our results highlight the power of the TCF analysis to elucidate the molecular mechanism of complex allosteric processes in large biomolecular systems., (Copyright © 2017 Biophysical Society. Published by Elsevier Inc. All rights reserved.)

Binding of the retroviral structural protein Gag to the cellular plasma membrane is mediated by the protein's matrix (MA) domain. Prominent among MA-PM interactions is electrostatic attraction between the positively charged MA domain and the negatively charged plasma membrane inner leaflet. Previously, we reported that membrane association of HIV-1 Gag, as well as purified Rous sarcoma virus (RSV) MA and Gag, depends strongly on the presence of acidic lipids and is enhanced by cholesterol (Chol). The mechanism underlying this enhancement was unclear. Here, using a broad set of in vitro and in silico techniques we addressed molecular mechanisms of association between RSV MA and model membranes, and investigated how Chol enhances this association. In neutron scattering experiments with liposomes in the presence or absence of Chol, MA preferentially interacted with preexisting POPS-rich clusters formed by nonideal lipid mixing, binding peripherally to the lipid headgroups with minimal perturbation to the bilayer structure. Molecular dynamics simulations showed a stronger MA-bilayer interaction in the presence of Chol, and a large Chol-driven increase in lipid packing and membrane surface charge density. Although in vitro MA-liposome association is influenced by disparate variables, including ionic strength and concentrations of Chol and charged lipids, continuum electrostatic theory revealed an underlying dependence on membrane surface potential. Together, these results conclusively show that Chol affects RSV MA-membrane association by making the electrostatic potential at the membrane surface more negative, while decreasing the penalty for lipid headgroup desolvation. The presented approach can be applied to other viral and nonviral proteins., (Copyright © 2017 Biophysical Society. Published by Elsevier Inc. All rights reserved.)

T-Cell receptor (TCR)-mediated recognition of the peptide-bound major histocompatibility complex (pMHC) initiates an adaptive immune response against antigen-presenting target cells. The recognition events take place at the TCR-pMHC interface, and their effects on TCR conformation and dynamics are controversial. Here, we have measured the time-resolved hydrogen/deuterium exchange (HDX) of a soluble TCR in the presence and absence of its cognate pMHC by mass spectrometry to delineate the impact of pMHC binding on solution-phase structural dynamics in the TCR. Our results demonstrate that while TCR-pMHC complex formation significantly stabilizes distinct CDR loops of the TCR, it does not trigger structural changes in receptor segments remote from the binding interface. Intriguingly, our HDX measurements reveal that the TCR α-constant domain (C- and F-strand) directly interacts with the unbound MHC light chain, β 2 -microglobulin (β 2 m). Surface plasmon resonance measurements corroborated a binding event between TCR and β 2 m with a dissociation constant of 167 ± 20 μM. We propose a model structure for the TCR-β 2 m complex based on a refined protein-protein docking approach driven by HDX data and information from molecular dynamics simulations. Using a biological assay based on TCR gene-engineered primary human T cells, we did not observe a significant effect of β 2 m on T-cell cytotoxicity, suggesting an alternate role for β 2 m binding. Overall, we show that binding of β 2 m to the TCR occurs in vitro and, as such, not only should be considered in structure-function studies of the TCR-pMHC complex but also could play a hitherto unidentified role in T-cell function in vivo.

Allostery plays a fundamental role in most biological processes. However, little theory is available to describe it outside of two-state models. Here we use a statistical mechanical approach to show that the allosteric coupling between two collective variables is not a single number, but instead a two-dimensional thermodynamic coupling function that is directly related to the mutual information from information theory and the copula density function from probability theory. On this basis, we demonstrate how to quantify the contribution of specific energy terms to this thermodynamic coupling function, enabling an approximate decomposition that reveals the mechanism of allostery. We illustrate the thermodynamic coupling function and its use by showing how allosteric coupling in the alanine dipeptide molecule contributes to the overall shape of the Φ/Ψ free energy surface, and by identifying the interactions that are necessary for this coupling.

Solute transport across cell membranes is ubiquitous in biology as an essential physiological process. Secondary active transporters couple the unfavorable process of solute transport against its concentration gradient to the energetically favorable transport of one or several ions. The study of such transporters over several decades indicates that their function involves complex allosteric mechanisms that are progressively being revealed in atomistic detail. We focus on two well-characterized sodium-coupled symporters: the bacterial amino acid transporter LeuT, which is the prototype for the "gated pore" mechanism in the mammalian synaptic monoamine transporters, and the archaeal GltPh, which is the prototype for the "elevator" mechanism in the mammalian excitatory amino acid transporters. We present the evidence for the role of allostery in the context of a quantitative formalism that can reconcile biochemical and biophysical data and thereby connects directly to recent insights into the molecular structure and dynamics of these proteins. We demonstrate that, while the structures and mechanisms of these transporters are very different, the available data suggest a common role of specific models of allostery in their functions. We argue that such allosteric mechanisms appear essential not only for sodium-coupled symport in general but also for the function of other types of molecular machines in the membrane.

Glutamate transporters terminate neurotransmission by clearing synaptically released glutamate from the extracellular space, allowing repeated rounds of signalling and preventing glutamate-mediated excitotoxicity. Crystallographic studies of a glutamate transporter homologue from the archaeon Pyrococcus horikoshii, GltPh, showed that distinct transport domains translocate substrates into the cytoplasm by moving across the membrane within a central trimerization scaffold. Here we report direct observations of these 'elevator-like' transport domain motions in the context of reconstituted proteoliposomes and physiological ion gradients using single-molecule fluorescence resonance energy transfer (smFRET) imaging. We show that GltPh bearing two mutations introduced to impart characteristics of the human transporter exhibits markedly increased transport domain dynamics, which parallels an increased rate of substrate transport, thereby establishing a direct temporal relationship between transport domain motion and substrate uptake. Crystallographic and computational investigations corroborated these findings by revealing that the 'humanizing' mutations favour structurally 'unlocked' intermediate states in the transport cycle exhibiting increased solvent occupancy at the interface between the transport domain and the trimeric scaffold.

In molecular dynamics simulations, most enhanced sampling methods are traditionally associated with one particular estimator to calculate the free energy surface (FES), such as the histogram, the mean force, or the bias potential. Here, we start from the realization that four enhanced sampling methods, metadynamics and well-tempered metadynamics (in their extended Lagrangian form), as well as driven adiabatic free energy dynamics (dAFED) and unified free energy dynamics (UFED), can be used in combination with any of the three above-mentioned FES estimators. We compare the convergence properties of these estimators on the alanine dipeptide and a sodium ion solvation shell. We find that the mean force estimator is superior in all cases. We also show that it can be marginally beneficial to combine information from the histogram and the force, provided that both are of comparable accuracy.

The quality of classical biomolecular simulations is inevitably limited by two problems: the accuracy of the force field used and the comprehensiveness of configuration space sampling. In this work we tackle the sampling problem by carrying out driven adiabatic free energy dynamics to obtain converged free energy surfaces of dipeptides in the gas phase and in solution using selected dihedral angles as collective variables. To calculate populations of conformational macrostates observed in experiment, we introduce a fuzzy clustering algorithm in collective-variable space, which delineates macrostates without prior definition of arbitrary boundaries. With this approach, we calculate the conformational preferences of small peptides with six biomolecular force fields chosen from among the most recent and widely used. We assess the accuracy of each force field against recently published Raman or IR-UV spectroscopy measurements of conformer populations for the dipeptides in solution or in the gas phase.

Adoptive cell transfer using engineered T cells is emerging as a promising treatment for metastatic melanoma. Such an approach allows one to introduce T cell receptor (TCR) modifications that, while maintaining the specificity for the targeted antigen, can enhance the binding and kinetic parameters for the interaction with peptides (p) bound to major histocompatibility complexes (MHC). Using the well-characterized 2C TCR/SIYR/H-2K(b) structure as a model system, we demonstrated that a binding free energy decomposition based on the MM-GBSA approach provides a detailed and reliable description of the TCR/pMHC interactions at the structural and thermodynamic levels. Starting from this result, we developed a new structure-based approach, to rationally design new TCR sequences, and applied it to the BC1 TCR targeting the HLA-A2 restricted NY-ESO-1157-165 cancer-testis epitope. Fifty-four percent of the designed sequence replacements exhibited improved pMHC binding as compared to the native TCR, with up to 150-fold increase in affinity, while preserving specificity. Genetically engineered CD8(+) T cells expressing these modified TCRs showed an improved functional activity compared to those expressing BC1 TCR. We measured maximum levels of activities for TCRs within the upper limit of natural affinity, K D = ∼1 - 5 μM. Beyond the affinity threshold at K D < 1 μM we observed an attenuation in cellular function, in line with the "half-life" model of T cell activation. Our computer-aided protein-engineering approach requires the 3D-structure of the TCR-pMHC complex of interest, which can be obtained from X-ray crystallography. We have also developed a homology modeling-based approach, TCRep 3D, to obtain accurate structural models of any TCR-pMHC complexes when experimental data is not available. Since the accuracy of the models depends on the prediction of the TCR orientation over pMHC, we have complemented the approach with a simplified rigid method to predict this orientation and successfully assessed it using all non-redundant TCR-pMHC crystal structures available. These methods potentially extend the use of our TCR engineering method to entire TCR repertoires for which no X-ray structure is available. We have also performed a steered molecular dynamics study of the unbinding of the TCR-pMHC complex to get a better understanding of how TCRs interact with pMHCs. This entire rational TCR design pipeline is now being used to produce rationally optimized TCRs for adoptive cell therapies of stage IV melanoma.

Alchemical free energy simulations are commonly used to calculate relative binding or solvation free energies in molecular systems. The convergence of alchemical free energy calculations is often hampered by inefficient sampling of the conformational degrees of freedom, which remain trapped in metastable substates. Here, we show that thermodynamic integration (TI) or free energy perturbation (FEP) can be combined with the recent driven adiabatic free energy dynamics (dAFED) method, in order to enhance conformational sampling along a set of chosen collective variables. The resulting TI-dAFED or FEP-dAFED methods are validated on a two-dimensional analytical problem. The ability of these methods to provide accurate free energy differences for realistic molecular systems is demonstrated by calculating the enantiomerization free energy of the alanine dipeptide in explicit solvent.

We propose a general framework for the efficient sampling of conformational equilibria in complex systems and the generation of associated free energy hypersurfaces in terms of a set of collective variables. The method is a strategic synthesis of the adiabatic free energy dynamics approach, previously introduced by us and others, and existing schemes using Gaussian-based adaptive bias potentials to disfavor previously visited regions. In addition, we suggest sampling the thermodynamic force instead of the probability density to reconstruct the free energy hypersurface. All these elements are combined into a robust extended phase-space formalism that can be easily incorporated into existing molecular dynamics packages. The unified scheme is shown to outperform both metadynamics and adiabatic free energy dynamics in generating two-dimensional free energy surfaces for several example cases including the alanine dipeptide in the gas and aqueous phases and the met-enkephalin oligopeptide. In addition, the method can efficiently generate higher dimensional free energy landscapes, which we demonstrate by calculating a four-dimensional surface in the Ramachandran angles of the gas-phase alanine tripeptide.

The T-cell receptor (TCR) interaction with antigenic peptides (p) presented by the major histocompatibility complex (MHC) molecule is a key determinant of immune response. In addition, TCR-pMHC interactions offer examples of features more generally pertaining to protein-protein recognition: subtle specificity and cross-reactivity. Despite their importance, molecular details determining the TCR-pMHC binding remain unsolved. However, molecular simulation provides the opportunity to investigate some of these aspects. In this study, we perform extensive equilibrium and steered molecular dynamics simulations to study the unbinding of three TCR-pMHC complexes. As a function of the dissociation reaction coordinate, we are able to obtain converged H-bond counts and energy decompositions at different levels of detail, ranging from the full proteins, to separate residues and water molecules, down to single atoms at the interface. Many observed features do not support a previously proposed two-step model for TCR recognition. Our results also provide keys to interpret experimental point-mutation results. We highlight the role of water both in terms of interface resolvation and of water molecules trapped in the bound complex. Importantly, we illustrate how two TCRs with similar reactivity and structures can have essentially different binding strategies., (Copyright © 2011 Wiley-Liss, Inc.)

The drug discovery process has been deeply transformed recently by the use of computational ligand-based or structure-based methods, helping the lead compounds identification and optimization, and finally the delivery of new drug candidates more quickly and at lower cost. Structure-based computational methods for drug discovery mainly involve ligand-protein docking and rapid binding free energy estimation, both of which require force field parameterization for many drug candidates. Here, we present a fast force field generation tool, called SwissParam, able to generate, for arbitrary small organic molecule, topologies, and parameters based on the Merck molecular force field, but in a functional form that is compatible with the CHARMM force field. Output files can be used with CHARMM or GROMACS. The topologies and parameters generated by SwissParam are used by the docking software EADock2 and EADock DSS to describe the small molecules to be docked, whereas the protein is described by the CHARMM force field, and allow them to reach success rates ranging from 56 to 78%. We have also developed a rapid binding free energy estimation approach, using SwissParam for ligands and CHARMM22/27 for proteins, which requires only a short minimization to reproduce the experimental binding free energy of 214 ligand-protein complexes involving 62 different proteins, with a standard error of 2.0 kcal mol(-1), and a correlation coefficient of 0.74. Together, these results demonstrate the relevance of using SwissParam topologies and parameters to describe small organic molecules in computer-aided drug design applications, together with a CHARMM22/27 description of the target protein. SwissParam is available free of charge for academic users at www.swissparam.ch., (Copyright © 2011 Wiley Periodicals, Inc.)

CHARMM27 is a widespread and popular force field for biomolecular simulation, and several recent algorithms such as implicit solvent models have been developed specifically for it. We have here implemented the CHARMM force field and all necessary extended functional forms in the GROMACS molecular simulation package, to make CHARMM-specific features available and to test them in combination with techniques for extended time steps, to make all major force fields available for comparison studies in GROMACS, and to test various solvent model optimizations, in particular the effect of Lennard-Jones interactions on hydrogens. The implementation has full support both for CHARMM-specific features such as multiple potentials over the same dihedral angle and the grid-based energy correction map on the ϕ, ψ protein backbone dihedrals, as well as all GROMACS features such as virtual hydrogen interaction sites that enable 5 fs time steps. The medium-to-long time effects of both the correction maps and virtual sites have been tested by performing a series of 100 ns simulations using different models for water representation, including comparisons between CHARMM and traditional TIP3P. Including the correction maps improves sampling of near native-state conformations in our systems, and to some extent it is even able to refine distorted protein conformations. Finally, we show that this accuracy is largely maintained with a new implicit solvent implementation that works with virtual interaction sites, which enables performance in excess of 250 ns/day for a 900-atom protein on a quad-core desktop computer.

We present a novel steered molecular dynamics scheme to induce the dissociation of large protein-protein complexes. We apply this scheme to study the interaction of a T cell receptor (TCR) with a major histocompatibility complex (MHC) presenting a peptide (p). Two TCR-pMHC complexes are considered, which only differ by the mutation of a single amino acid on the peptide; one is a strong agonist that produces T cell activation in vivo, while the other is an antagonist. We investigate the interaction mechanism from a large number of unbinding trajectories by analyzing van der Waals and electrostatic interactions and by computing energy changes in proteins and solvent. In addition, dissociation potentials of mean force are calculated with the Jarzynski identity, using an averaging method developed for our steering scheme. We analyze the convergence of the Jarzynski exponential average, which is hampered by the large amount of dissipative work involved and the complexity of the system. The resulting dissociation free energies largely underestimate experimental values, but the simulations are able to clearly differentiate between wild-type and mutated TCR-pMHC and give insights into the dissociation mechanism.

Widely used programs for molecular dynamics simulation of (bio)molecular systems are the Verlet and leapfrog algorithms. In these algorithms, the particle velocities are less accurately propagated than the positions. Important quantities for the simulation such as the temperature and the pressure involve the squared velocities at full time steps. Here, we derive an expression for the squared particle velocity at full time step in the leapfrog scheme, which is more accurate than the standardly used one. In particular, this allows us to show that the full time step kinetic energy of a particle is more accurately computed as the average of the kinetic energies at previous and following half steps than as the square of the average velocity as implemented in various molecular dynamics codes. Use of the square of the average velocity introduces a systematic bias in the calculation of the instantaneous temperature and pressure of a molecular dynamics system. We show the consequences when the system is coupled to a thermostat and a barostat.

The ground state electronic structure of oxidized azurin from Pseudomonas aeruginosa and its optical response have been investigated by combining hybrid quantum mechanics/molecular mechanics simulations with time-dependent density functional theory. In agreement with experiment, we find that the unpaired electron spin density is mainly localized on the copper ion. The vertical absorption spectrum in the visible range is well reproduced, with the central band centered around 2.1 eV. The anisotropic dipolar field due to the extended alpha-helix polarizes the metal binding site and is responsible for a shift of the absorption bands by +/-0.1-0.2 eV. At 300 K, the bond distances of the copper binding site undergo large fluctuations (approximately 0.3 A). It is crucial to take these thermal fluctuations into account for a faithful description of the optical properties.

The Jarzynski identity (JI) relates nonequilibrium work averages to thermodynamic free energy differences. It was shown in a recent contribution [M. A. Cuendet, Phys. Rev. Lett. 96, 120602 (2006)] that the JI can, in particular, be derived directly from the Nose-Hoover thermostated dynamics. This statistical mechanical derivation is particularly relevant in the framework of molecular dynamics simulation, because it is based solely on the equations of motion considered and is free of any additional assumptions on system size or bath coupling. Here, this result is generalized to a variety of dynamics, along two directions. On the one hand, specific improved thermostating schemes used in practical applications are treated. These include Nose-Hoover chains, higher moment thermostats, as well as an isothermal-isobaric scheme yielding the JI in the NPT ensemble. On the other hand, the theoretical generality of the new derivation is explored. Generic dynamics with arbitrary coupling terms and an arbitrary number of thermostating variables, both non-Hamiltonian and Hamiltonian, are shown to imply the JI. In particular, a nonautonomous formulation of the generalized Nose-Poincare thermostat is proposed. Finally, general conditions required for the JI derivation are briefly discussed.

The recent Jarzynski identity (JI) relates thermodynamic free energy differences to nonequilibrium work averages. Several proofs of the JI have been provided on the thermodynamic level. They rely on assumptions such as equivalence of ensembles in the thermodynamic limit or weakly coupled infinite heat baths. However, the JI is widely applied to computer simulations involving finite numbers of particles, whose equations of motion are strongly coupled to a few extra degrees of freedom modeling a thermostat. In this case, the above assumptions are no longer valid. We propose a statistical mechanical approach to the JI solely based on the specific equations of motion, without any further assumption. We provide a detailed derivation for the non-Hamiltonian Nosé-Hoover dynamics, which is routinely used in computer simulations to produce canonical sampling.

Because the prevailing form of hormone replacement therapy is associated with the development of cancer in breast and endometrial tissues, alternatives are needed for the management of menopausal symptoms. Formulations of Trifolium pratense L. (red clover) are being used to alleviate menopause-associated hot flashes but have shown mixed results in clinical trials. The strobiles of Humulus lupulusL. (hops) have been reported to contain the prenylflavanone, 8-prenylnaringenin (8-PN), as the most estrogenic constituent, and this was confirmed using an estrogen receptor ligand screening assay utilizing ultrafiltration mass spectrometry. Extracts of hops and red clover and their individual constituents including 8-PN, 6-prenylnaringenin (6-PN), isoxanthohumol (IX), and xanthohumol (XN) from hops and daidzein, formononetin, biochanin A, and genistein from red clover were compared using a variety of in vitro estrogenic assays. The IC50 values for the estrogen receptor alpha and beta binding assays were 15 and 27 microg/mL, respectively, for hops and 18.0 and 2.0 microg/mL, respectively, for the red clover extract. Both of the extracts, genistein, and 8-PN activated the estrogen response element (ERE) in Ishikawa cells while the extracts, biochanin A, genistein, and 8-PN, significantly induced ERE-luciferase expression in MCF-7 cells. Hop and red clover extracts as well as 8-PN up-regulated progesterone receptor (PR) mRNA in the Ishikawa cell line. In the MCF-7 cell line, PR mRNA was significantly up-regulated by the extracts, biochanin A, genistein, 8-PN, and IX. The two extracts had EC50 values of 1.1 and 1.9 microg/mL, respectively, in the alkaline phosphatase induction assay. On the basis of these data, hops and red clover could be attractive for the development as herbal dietary supplements to alleviate menopause-associated symptoms.