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The adipokine Leptin activates its type I cytokine receptor (LEP-R) in the hypothalamus to regulate body weight and exerts additional pleiotropic functions in immunity, fertility, and cancer. However, the structure and mechanism of Leptin-mediated LEP-R assemblies has remained unclear. Here, we show that Leptin:LEP-R assemblies adopt an unprecedented structure within the type I cytokine receptor family featuring 3:3 stoichiometry. We validate Leptin-induced trimerization of LEP-R in the plasma membrane of living cells via multicolor single molecule microscopy. In mediating such assemblies Leptin undergoes drastic restructuring that activates its site III for binding to the Ig-domain of an adjacent LEP-R molecule in the complex. These interactions are abolished by pathological mutations linked to obesity. Collectively, our study uncovers an evolutionarily conserved Leptin:LEP-R assembly as a new mechanistic blueprint for Leptin-mediated signaling in physiology and disease, including insights into how the lowly abundant signaling-competent isoforms of LEP-R can productively participate in signaling.

The design of proteins that bind to a specific site on the surface of a target protein using no information other than the three-dimensional structure of the target remains a challenge1–5. Here we describe a general solution to this problem that starts with a broad exploration of the vast space of possible binding modes to a selected region of a protein surface, and then intensifies the search in the vicinity of the most promising binding modes. We demonstrate the broad applicability of this approach through the de novo design of binding proteins to 12 diverse protein targets with different shapes and surface properties. Biophysical characterization shows that the binders, which are all smaller than 65 amino acids, are hyperstable and, following experimental optimization, bind their targets with nanomolar to picomolar affinities. We succeeded in solving crystal structures of five of the binder–target complexes, and all five closely match the corresponding computational design models. Experimental data on nearly half a million computational designs and hundreds of thousands of point mutants provide detailed feedback on the strengths and limitations of the method and of our current understanding of protein–protein interactions, and should guide improvements of both. Our approach enables the targeted design of binders to sites of interest on a wide variety of proteins for therapeutic and diagnostic applications.

To investigate the stoichiometry of complexes of Leptin and variants thereof in complex with full-length LEP-RECD, we employedanalytical ultracentrifugation (AUC) sedimentation velocity experiments. &nbsp

RationaleEstimating the causal effect of an intervention at individual level, also called individual treatment effect (ITE), may help in identifying response prior to the intervention.ObjectivesWe aimed to develop machine learning (ML) models which estimate ITE of an intervention using data from randomised controlled trials and illustrate this approach with prediction of ITE on annual chronic obstructive pulmonary disease (COPD) exacerbation rates.MethodsWe used data from 8151 patients with COPD of the Study to Understand Mortality and MorbidITy in COPD (SUMMIT) trial (NCT01313676) to address the ITE of fluticasone furoate/vilanterol (FF/VI) versus control (placebo) on exacerbation rate and developed a novel metric, Q-score, for assessing the power of causal inference models. We then validated the methodology on 5990 subjects from the InforMing the PAthway of COPD Treatment (IMPACT) trial (NCT02164513) to estimate the ITE of FF/umeclidinium/VI (FF/UMEC/VI) versus UMEC/VI on exacerbation rate. We used Causal Forest as causal inference model.ResultsIn SUMMIT, Causal Forest was optimised on the training set (n=5705) and tested on 2446 subjects (Q-score 0.61). In IMPACT, Causal Forest was optimised on 4193 subjects in the training set and tested on 1797 individuals (Q-score 0.21). In both trials, the quantiles of patients with the strongest ITE consistently demonstrated the largest reductions in observed exacerbations rates (0.54 and 0.53, pConclusionsThis study shows that ML models for causal inference can be used to identify individual response to different COPD treatments and highlight treatment traits. Such models could become clinically useful tools for individual treatment decisions in COPD.