The inability to rapidly generate accurate and robust parameters for novel chemical matter continues to severely limit the application of molecular dynamics (MD) simulations to many biological systems of interest, especially in fields such as drug discovery. Although the release of generalized versions of common classical force fields, e.g., GAFF and CGenFF, have posited guidelines for parameterization of small molecules, many technical challenges remain that have hampered their wide-scale extension. The Force Field Toolkit (ffTK), described herein, minimizes common barriers to ligand parameterization through algorithm and method development, automation of tedious and error-prone tasks, and graphical user interface design. Distributed as a VMD plugin, ffTK facilitates the traversal of a clear and organized workflow resulting in a complete set of CHARMM-compatible parameters. A variety of tools are provided to generate quantum mechanical target data, set up multidimensional optimization routines, and analyze parameter performance. Parameters developed for a small test set of molecules using ffTK were comparable to existing CGenFF parameters in their ability to reproduce experimentally measured values for pure-solvent properties (<15% error from experiment) and free energy of solvation (±0.5 kcal/mol from experiment).

Somatic mutations in the estrogen receptor alpha (ERα) gene (ESR1), especially Y537S and D538G, have been linked to acquired resistance to endocrine therapies. Cell-based studies demonstrated that these mutants confer ERα constitutive activity and antiestrogen resistance and suggest that ligand-binding domain dysfunction leads to endocrine therapy resistance. Here, we integrate biophysical and structural biology data to reveal how these mutations lead to a constitutively active and antiestrogen-resistant ERα. We show that these mutant ERs recruit coactivator in the absence of hormone while their affinities for estrogen agonist (estradiol) and antagonist (4-hydroxytamoxifen) are reduced. Further, they confer antiestrogen resistance by altering the conformational dynamics of the loop connecting Helix 11 and Helix 12 in the ligand-binding domain of ERα, which leads to a stabilized agonist state and an altered antagonist state that resists inhibition.

Estrogen receptor alpha (ERα), a key driver of breast cancer, normally requires estrogen for activation. Mutations that constitutively activate ERα without the need for hormone are frequently found in endocrine therapy-resistant breast cancer metastases and are associated with poor patient outcomes. The location of these mutations in the ER ligand-binding domain and their impact on receptor conformation suggest that they subvert distinct mechanisms that normally maintain the low basal state of wild-type ERα in the absence of hormone. Such mutations provide opportunities to probe fundamental issues underlying ligand-mediated control of ERα activity. Instructive contrasts between these ER mutations and those that arise in androgen receptor (AR) during antiandrogen treatment of prostate cancer highlight differences in how activating functions in ER vs. AR control receptor activity, how hormonal pressures (deprivation vs. antagonism) drive the selection of phenotypically different mutants, and how altered protein conformations can reduce antagonist potency and altered ligand-receptor contacts can invert the response that a receptor has to an agonist vs. an antagonist. A deeper understanding of how ligand regulation of receptor conformation is linked to receptor function offers a conceptual framework for developing new antiestrogens that might be more effective in preventing and treating breast cancer.

Multiple sclerosis (MS) is an incurable autoimmune neurodegenerative disease. Environmental factors may be key to MS prevention and treatment. MS prevalence and severity decrease with increasing sunlight exposure and vitamin D 3 supplies, supporting our hypothesis that the sunlight-dependent hormone, 1,25-dihydroxyvitamin D 3 (1,25-(OH) 2 D 3 ), inhibits autoimmune T-cell responses in MS. Moreover, 1,25-(OH) 2 D 3 inhibits and reverses experimental autoimmune encephalomyelitis (EAE), an MS model. Here, we investigated whether 1,25-(OH) 2 D 3 inhibits EAE via the vitamin D receptor (VDR) in T lymphocytes. Using bone marrow chimeric mice with a disrupted VDR only in radiosensitive hematopoietic cells or radio-resistant non-hematopoietic cells, we found that hematopoietic cell VDR function was necessary for 1,25-(OH) 2 D 3 to inhibit EAE. Furthermore, conditional targeting experiments showed that VDR function in T cells was necessary. Neither 1,25-(OH) 2 D 3 nor T-cell-specific VDR targeting influenced CD4 1 Foxp3 1 T-cell proportions in the periphery or the CNS in these studies. These data support a model wherein 1,25-(OH) 2 D 3 acts directly on pathogenic CD41 T cells to inhibit EAE.

Adaptive Multilevel Summation (AMS) is a rare event sampling method that requires minimal parameter tuning and that allows unbiased sampling of transition pathways of a given rare event. Previous simulation studies have verified the efficiency and accuracy of AMS in the calculation of transition times for simple systems in both Monte Carlo and molecular dynamics (MD) simulations. Now, AMS is applied for the first time to a MD simulation of protein-ligand dissociation, representing a leap in complexity from the previous test cases. Of interest is the dissociation rate, which is typically too low to be accessible to conventional MD. The present study joins other recent efforts to develop advanced sampling techniques in MD to calculate dissociation rates, which are gaining importance in the pharmaceutical field as indicators of drug efficacy. The system investigated here, benzamidine bound to trypsin, is an example common to many of these efforts. The AMS estimate of the dissociation rate was found to be (2.6 ± 2.4) × 102 s−1, which compares well with the experimental value.