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Structures Controlled by Entropy: The Flexibility of Strychnine as Example.

ABSTRACT: To study the flexibility of strychnine, we performed molecular dynamics simulations with orientational tensorial constraints (MDOC). Tensorial constraints are derived from nuclear magnetic resonance (NMR) interaction tensors, for instance, from residual dipolar couplings (RDCs). Used as orientational constraints, they rotate the whole molecule and molecular parts with low rotational barriers. Since the NMR parameters are measured at ambient temperatures, orientational constraints generate conformers that populate the whole landscape of Gibbs free energy. In MDOC, structures are populated that are not only controlled by energy but by the entropy term TΔS of the Gibbs free energy. In the case of strychnine, it is shown that ring conformers are populated, which has not been discussed in former investigations. These conformer populations are not only in accordance with RDCs but fulfill nuclear Overhauser effect (NOE)-derived distance constraints and ³J_HH couplings as well.

SUBMITTER: Sternberg U

PROVIDER: S-EPMC9692940 | biostudies-literature | 2022 Nov

REPOSITORIES: biostudies-literature

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Structures Controlled by Entropy: The Flexibility of Strychnine as Example.

Sternberg Ulrich U Witter Raiker R

Molecules (Basel, Switzerland) 20221117 22

To study the flexibility of strychnine, we performed molecular dynamics simulations with orientational tensorial constraints (MDOC). Tensorial constraints are derived from nuclear magnetic resonance (NMR) interaction tensors, for instance, from residual dipolar couplings (RDCs). Used as orientational constraints, they rotate the whole molecule and molecular parts with low rotational barriers. Since the NMR parameters are measured at ambient temperatures, orientational constraints generate confor ...[more]

PMID: 36432085

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Project description:Background: Global efforts to strengthen health research capacity in low- and middle-income countries (LMICs) have intensified in the past few decades, and these efforts are often implemented by consortia. Our review of the literature indicated that reports on health research capacity strengthening (HRCS) consortia have primarily focused on programme outputs and outcomes while management processes and their contributions to consortia goals have received little attention. This qualitative study sought to identify the consortium management processes employed by 10 DELTAS Africa consortia, factors influencing these processes, and leaders' consortium management experiences. Methods: We conducted 24 key informant interviews with the directors and programme managers of all the 10 DELTAS Africa consortia, and funding actors who worked closely with the consortia. The interviews were supplemented by reviews of DELTAS and consortium-specific documents. Data were analysed using the content analysis approach. Results: The consortia studied employed similar management processes but adopted different strategies in executing these processes. Study results indicate that decision-making in consortia is not always a straightforward process as leaders were often faced with dilemmas when determining management strategies to adopt, and often tried to balance multiple factors which were not always aligned. This was demonstrated as consortia selected partners, determined goals and activities, assigned roles and responsibilities, allocated resources, established governance and partner management systems, and coordinated and monitored consortia activities. Factors that influenced the choice of processes and approaches included previous experiences, funders expectations, and the pressure to deliver research outputs. Consortia's unique approaches to management were due to varying contexts and influences and indicate that management decisions are nuanced and cannot easily be formularized. Conclusion: The study has highlighted the importance of flexibility in consortium management and the need to generate research capacity strengthening (RCS)-specific guidance that can assist consortia in resolving dilemmas and making appropriate management decisions.

Dataset Information

Structures Controlled by Entropy: The Flexibility of Strychnine as Example.

Publications

Structures Controlled by Entropy: The Flexibility of Strychnine as Example.

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