A streamlined molecular-dynamics workflow for computing solubilities of molecular and ionic crystals

Computing the solubility of crystals in a solvent using atomistic simulations is notoriously challenging due to the complexities and convergence issues associated with free-energy methods, as well as the slow equilibration in direct-coexistence simulations. This paper introduces a molecular-dynamics workflow that simplifies and robustly computes the solubility of molecular or ionic crystals. This method is considerably more straightforward than the state-of-the-art, as we have streamlined and optimised each step of the process. Specifically, we calculate the chemical potential of the crystal using the gas-phase molecule as a reference state, and employ the S0 method to determine the concentration dependence of the chemical potential of the solute. We use this workflow to predict the solubilities of sodium chloride in water, urea polymorphs in water, and paracetamol polymorphs in both water and ethanol. Our findings indicate that the predicted solubility is sensitive to the chosen potential energy surface. Furthermore, we note that the harmonic approximation often fails for both molecular crystals and gas molecules at or above room temperature, and that the assumption of an ideal solution becomes less valid for highly soluble substances

Successful aging, cognitive function, socioeconomic status, and leukocyte telomere length

In a rapidly greying world, the notion that some individuals maintain successful aging trajectories, viz. high physical, cognitive, emotional, and social functioning in older age, is increasingly germane. Biomarkers of such successful aging are increasingly sought. Leukocyte telomere length (LTL), an emerging yardstick of cellular aging that is influenced by but distinct from chronological age, may also be associated to successful aging. Furthermore, given that socio-economic status (SES) influences successful aging trajectories, socioeconomic status may also moderate the association between chronological age and LTL. The goals of this study are to examine 1) whether successful aging is associated with LTL; 2) whether successful aging accounts for age-related LTL and 3) whether low SES moderates the effect of age on LTL. Singaporean Chinese (n = 353) aged 65-80 completed a multidimensional assessment of successful aging and provided blood samples for LTL analysis. Results show that LTL negatively correlates with chronological age and positively correlates with successful aging. Successful aging mediates the association between chronological age and LTL. Moderated mediation analyses show that lower SES is associated with stronger negative associations of chronological age with successful aging and LTL. Moreover, the cognitive functioning dimension of successful aging is uniquely associated with LTL and its association with chronological age is moderated by SES. This study provides evidence that among older Singaporean Chinese with lower SES, declines in successful aging and in cognitive functioning are linked to age-related LTL shortening and hence to accelerated aging at the cellular level

Implementing an Inclusive, Multidisciplinary Supportive Care Model to Provide Integrated Care to Breast and Gynaecological Cancer Survivors: A Case Study at an Asian Ambulatory Cancer Centre

Introduction: Supportive care models considering inclusivity and community services to improve integrated care for cancer survivors are limited. In this case study, we described the implementation of a multidisciplinary care model employing routine distress screening and embedded integrated care pathways to integrate care across disciplines and care sectors, while remaining inclusive of the multi-ethnic and multilingual population in Singapore. We reported implementation outcomes after 18 months of implementation. Description: We reviewed the model’s process indicators from September 2019 to February 2021 at the largest public ambulatory cancer centre. Outcomes assessed included penetration, fidelity to screening protocol, and feasibility in three aspects – inclusiveness of different ethnic and language groups, responsiveness to survivors reporting high distress, and types of community service referrals. Discussion/conclusion: We elucidated opportunities to promote access to community services and inclusivity. Integration of community services from tertiary settings should be systematic through mutually beneficial educational and outreach initiatives, complemented by their inclusion in integrated care pathways to encourage systematic referrals and care coordination. A hybrid approach to service delivery is crucial in ensuring inclusivity while providing flexibility towards external changes such as the COVID-19 pandemic. Future work should explore using telehealth to bolster inclusiveness and advance community care integration

Phase diagrams-Why they matter and how to predict them.

Understanding the thermodynamic stability and metastability of materials can help us to, for example, gauge whether crystalline polymorphs in pharmaceutical formulations are likely to be durable. It can also help us to design experimental routes to novel phases with potentially interesting properties. In this Perspective, we provide an overview of how thermodynamic phase behavior can be quantified both in computer simulations and machine-learning approaches to determine phase diagrams, as well as combinations of the two. We review the basic workflow of free-energy computations for condensed phases, including some practical implementation advice, ranging from the Frenkel-Ladd approach to thermodynamic integration and to direct-coexistence simulations. We illustrate the applications of such methods on a range of systems from materials chemistry to biological phase separation. Finally, we outline some challenges, questions, and practical applications of phase-diagram determination which we believe are likely to be possible to address in the near future using such state-of-the-art free-energy calculations, which may provide fundamental insight into separation processes using multicomponent solvents.University of Cambridge Ernest Oppenheimer Fund Winton Programme for the Physics of Sustainabilit

A streamlined molecular-dynamics workflow for computing solubilities of molecular and ionic crystals

Computing the solubility of crystals in a solvent using atomistic simulations is notoriously challenging due to the complexities and convergence issues associated with free-energy methods, as well as the slow equilibration in direct-coexistence simulations. This paper introduces a molecular-dynamics workflow that simplifies and robustly computes the solubility of molecular or ionic crystals. This method is considerably more straightforward than the state-of-the-art, as we have streamlined and optimised each step of the process. Specifically, we calculate the chemical potential of the crystal using the gas-phase molecule as a reference state, and employ the S0 method to determine the concentration dependence of the chemical potential of the solute. We use this workflow to predict the solubilities of sodium chloride in water, urea polymorphs in water, and paracetamol polymorphs in both water and ethanol. Our findings indicate that the predicted solubility is sensitive to the chosen potential energy surface. Furthermore, we note that the harmonic approximation often fails for both molecular crystals and gas molecules at or above room temperature, and that the assumption of an ideal solution becomes less valid for highly soluble substances

Thermodynamic origins of two-component multiphase condensates of proteins

Intracellular condensates are highly multi-component systems in which complex phase behaviour can ensue, including the formation of architectures comprising multiple immiscible condensed phases. Relying solely on physical intuition to manipulate such condensates is difficult because of the complexity of their composition, and systematically learning the underlying rules experimentally would be extremely costly. We address this challenge by developing a computational approach to design pairs of protein sequences that result in well-separated multilayered condensates and elucidate the molecular origins of these compartments. Our method couples a genetic algorithm to a residue-resolution coarse-grained protein model. We demonstrate that we can design protein partners to form multiphase condensates containing naturally occurring proteins, such as the low-complexity domain of hnRNPA1 and its mutants, and show how homo- and heterotypic interactions must differ between proteins to result in multiphasicity. We also show that in some cases the specific pattern of amino-acid residues plays an important role. Our findings have wide-ranging implications for understanding and controlling the organisation, functions and material properties of biomolecular condensates.We acknowledge funding from the University of Cambridge Ernest Oppenheimer Fund [PYC], the Winton Programme for the Physics of Sustainability [PYC, RC-G], the European Research Council under the European Union’s Horizon 2020 research and innovation programme [grant 803326; RC-G]. JAJ is a Junior Research Fellow at King’s College. This work was performed using resources provided by the Cambridge Tier-2 system operated by the University of Cambridge Research Computing Service funded by EPSRC Tier-2 capital grant EP/P020259/1 [RC-G, JAJ, AR]

Thermodynamic origins of two-component multiphase condensates of proteins.

Acknowledgements: We acknowledge funding from the University of Cambridge Ernest Oppenheimer Fund [PYC], the Winton Programme for the Physics of Sustainability [PYC, RC-G], the European Research Council under the European Union's Horizon 2020 research and innovation programme [grant 803326; RC-G]. JAJ is a Junior Research Fellow at King's College. This work was performed using resources provided by the Cambridge Tier-2 system operated by the University of Cambridge Research Computing Service funded by EPSRC Tier-2 capital grant EP/P020259/1 [RC-G, JAJ, AR]. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.Intracellular condensates are highly multi-component systems in which complex phase behaviour can ensue, including the formation of architectures comprising multiple immiscible condensed phases. Relying solely on physical intuition to manipulate such condensates is difficult because of the complexity of their composition, and systematically learning the underlying rules experimentally would be extremely costly. We address this challenge by developing a computational approach to design pairs of protein sequences that result in well-separated multilayered condensates and elucidate the molecular origins of these compartments. Our method couples a genetic algorithm to a residue-resolution coarse-grained protein model. We demonstrate that we can design protein partners to form multiphase condensates containing naturally occurring proteins, such as the low-complexity domain of hnRNPA1 and its mutants, and show how homo- and heterotypic interactions must differ between proteins to result in multiphasicity. We also show that in some cases the specific pattern of amino-acid residues plays an important role. Our findings have wide-ranging implications for understanding and controlling the organisation, functions and material properties of biomolecular condensates.We acknowledge funding from the University of Cambridge Ernest Oppenheimer Fund [PYC], the Winton Programme for the Physics of Sustainability [PYC, RC-G], the European Research Council under the European Union’s Horizon 2020 research and innovation programme [grant 803326; RC-G]. JAJ is a Junior Research Fellow at King’s College. This work was performed using resources provided by the Cambridge Tier-2 system operated by the University of Cambridge Research Computing Service funded by EPSRC Tier-2 capital grant EP/P020259/1 [RC-G, JAJ, AR]

Thermodynamic origins of two-component multiphase condensates of proteins.

Intracellular condensates are highly multi-component systems in which complex phase behaviour can ensue, including the formation of architectures comprising multiple immiscible condensed phases. Relying solely on physical intuition to manipulate such condensates is difficult because of the complexity of their composition, and systematically learning the underlying rules experimentally would be extremely costly. We address this challenge by developing a computational approach to design pairs of protein sequences that result in well-separated multilayered condensates and elucidate the molecular origins of these compartments. Our method couples a genetic algorithm to a residue-resolution coarse-grained protein model. We demonstrate that we can design protein partners to form multiphase condensates containing naturally occurring proteins, such as the low-complexity domain of hnRNPA1 and its mutants, and show how homo- and heterotypic interactions must differ between proteins to result in multiphasicity. We also show that in some cases the specific pattern of amino-acid residues plays an important role. Our findings have wide-ranging implications for understanding and controlling the organisation, functions and material properties of biomolecular condensates.We acknowledge funding from the University of Cambridge Ernest Oppenheimer Fund [PYC], the Winton Programme for the Physics of Sustainability [PYC, RC-G], the European Research Council under the European Union’s Horizon 2020 research and innovation programme [grant 803326; RC-G]. JAJ is a Junior Research Fellow at King’s College. This work was performed using resources provided by the Cambridge Tier-2 system operated by the University of Cambridge Research Computing Service funded by EPSRC Tier-2 capital grant EP/P020259/1 [RC-G, JAJ, AR]