Effects of gender and socio-environmental factors on health-care access in oncology: a comprehensive, nationwide study in France.

Background: Gender-based disparities in health-care are common and can affect access to care. We aimed to investigate the impact of gender and socio-environmental indicators on health-care access in oncology in France. Methods: Using the national health insurance system database in France, we identified patients (aged ≥18 years) who were diagnosed with solid invasive cancers between the 1st of January 2018 and the 31st of December 2019. We ensured that only incident cases were identified by excluding patients with an existing cancer diagnosis in 2016 and 2017; skin cancers other than melanoma were also excluded. We extracted 71 socio-environmental variables related to patients' living environment and divided these into eight categories: inaccessibility to public transport, economic deprivation, unemployment, gender-related wage disparities, social isolation, educational barriers, familial hardship, and insecurity. We employed a mixed linear regression model to assess the influence of age, comorbidities, and all eight socio-environmental indices on health-care access, while evaluating the interaction with gender. Health-care access was measured using absolute and relative cancer care expertise indexes. Findings: In total, 594,372 patients were included: 290,658 (49%) women and 303,714 (51%) men. With the exception of unemployment, all socio-environmental indices, age, and comorbidities were inversely correlated with health-care access. However, notable interactions with gender were observed, with a stronger association between socio-environmental factors and health-care access in women than in men. In particular, inaccessibility to public transport (coefficient for absolute cancer care expertise index = -1.10 [-1.22, -0.99], p < 0.0001), familial hardship (-0.64 [-0.72, -0.55], p < 0.0001), social isolation (-0.38 [-0.46, -0.30], p < 0.0001), insecurity (-0.29 [-0.37, -0.21], p < 0.0001), and economic deprivation (-0.13 [-0.19, -0.07], p < 0.0001) had a strong negative impact on health-care access in women. Interpretation: Access to cancer care is determined by a complex interplay of gender and various socio-environmental factors. While gender is a significant component, it operates within the context of multiple socio-environmental influences. Future work should focus on developing targeted interventions to address these multifaceted barriers and promote equitable health-care access for both genders. Funding: None.

Physics-Based Computational Protein Design: An Update.

We describe methods for physics-based protein design and some recent applications from our work. We present the physical interpretation of a MC simulation in sequence space and show that sequences and conformations form a well-defined statistical ensemble, explored with Monte Carlo and Boltzmann sampling. The folded state energy combines molecular mechanics for solutes with continuum electrostatics for solvent. We usually assume one or a few fixed protein backbone structures and discrete side chain rotamers. Methods based on molecular dynamics, which introduce additional backbone and side chain flexibility, are under development. The redesign of a PDZ domain and an aminoacyl-tRNA synthetase enzyme were successful. We describe a versatile, adaptive, Wang-Landau MC method that can be used to design for substrate affinity, catalytic rate, catalytic efficiency, or the specificity of these properties. The methods are transferable to all biomolecules, can be systematically improved, and give physical insights.

Adaptive landscape flattening allows the design of both enzyme: Substrate binding and catalytic power.

Designed enzymes are of fundamental and technological interest. Experimental directed evolution still has significant limitations, and computational approaches are a complementary route. A designed enzyme should satisfy multiple criteria: stability, substrate binding, transition state binding. Such multi-objective design is computationally challenging. Two recent studies used adaptive importance sampling Monte Carlo to redesign proteins for ligand binding. By first flattening the energy landscape of the apo protein, they obtained positive design for the bound state and negative design for the unbound. We have now extended the method to design an enzyme for specific transition state binding, i.e., for its catalytic power. We considered methionyl-tRNA synthetase (MetRS), which attaches methionine (Met) to its cognate tRNA, establishing codon identity. Previously, MetRS and other synthetases have been redesigned by experimental directed evolution to accept noncanonical amino acids as substrates, leading to genetic code expansion. Here, we have redesigned MetRS computationally to bind several ligands: the Met analog azidonorleucine, methionyl-adenylate (MetAMP), and the activated ligands that form the transition state for MetAMP production. Enzyme mutants known to have azidonorleucine activity were recovered by the design calculations, and 17 mutants predicted to bind MetAMP were characterized experimentally and all found to be active. Mutants predicted to have low activation free energies for MetAMP production were found to be active and the predicted reaction rates agreed well with the experimental values. We suggest the present method should become the paradigm for computational enzyme design.

Computational protein design: the Proteus software and selected applications.

We describe an automated procedure for protein design, implemented in a flexible software package, called Proteus. System setup and calculation of an energy matrix are done with the XPLOR modeling program and its sophisticated command language, supporting several force fields and solvent models. A second program provides algorithms to search sequence space. It allows a decomposition of the system into groups, which can be combined in different ways in the energy function, for both positive and negative design. The whole procedure can be controlled by editing 2-4 scripts. Two applications consider the tyrosyl-tRNA synthetase enzyme and its successful redesign to bind both O-methyl-tyrosine and D-tyrosine. For the latter, we present Monte Carlo simulations where the D-tyrosine concentration is gradually increased, displacing L-tyrosine from the binding pocket and yielding the binding free energy difference, in good agreement with experiment. Complete redesign of the Crk SH3 domain is presented. The top 10000 sequences are all assigned to the correct fold by the SUPERFAMILY library of Hidden Markov Models. Finally, we report the acid/base behavior of the SNase protein. Sidechain protonation is treated as a form of mutation; it is then straightforward to perform constant-pH Monte Carlo simulations, which yield good agreement with experiment. Overall, the software can be used for a wide range of application, producing not only native-like sequences but also thermodynamic properties with errors that appear comparable to other current software packages.