The impact of pH on proteins is significant but often neglected in molecular dynamics simulations. Constant-pH Molecular Dynamics (CpHMD) is the state-of-the-art method-ology to deal with these effects. However, it still lacks widespread adoption by the scientific community. The stochastic titration CpHMD is one of such methods that, until now, only supported the GROMOS force field family. Here, we extend this method's implementation to include the CHARMM36m force field available in the GROMACS software package. We test this new implementation with a diverse group of proteins, namely, lysozyme, Staphylococcal nuclease, and human and E. coli thioredoxins. All proteins were conformationally stable in the simulations, even at extreme pH values. The RMSE values (pKa prediction vs experimental) obtained were very encouraging, in particular for lysozyme and human thioredoxin. We have also identified a few residues that challenged the CpHMD simulations, highlighting scenarios where the method still needs improvement independently of the force field. The CHARMM36m all-atom implementation was more computationally efficient when compared with the GROMOS 54A7, taking advantage of a shorter nonbonded interaction cutoff and a less frequent neighboring list update. The new extension will allow the study of pH effects in many systems for which this force field is particularly suited, i.e., proteins, membrane proteins, lipid bilayers, and nucleic acids.

The search for more effective and selective drugs to overcome cancer multidrug resistance is urgent. As such, a new series of ruthenium-cyclopentadienyl ("RuCp") compounds with the general formula [Ru(η⁵-C₅H₄R)(4,4'-R'-2,2'-bipy)(PPh₃)] were prepared and fully characterized. All compounds were evaluated toward non-small cell lung cancer cells with different degrees of cisplatin sensitivity (A549, NCI-H2228, Calu-3, and NCI-H1975), showing better cytotoxicity than the first-line chemotherapeutic drug cisplatin. Compounds 2 and 3 (R' = -OCH₃; R = CHO (2) or CH₂OH (3)) further inhibited the activity of P-gp and MRP1 efflux pumps by impairing their catalytic activity. Molecular docking calculations identified the R-site P-gp pocket as the preferred one, which was further validated using site-directed mutagenesis experiments in P-gp. Altogether, our results unveil the first direct evidence of the interaction between P-gp and "RuCp" compounds in the modulation of P-gp activity and establish them as valuable candidates to circumvent cancer MDR.

Incorporating pH into molecular dynamics simulations is vital for accurately capturing the fully coupled conformational, energetic, and protonation landscape of many systems. The constant-pH molecular dynamics (CpHMD) methodologies represent state-of-the-art approaches to achieve this, with stochastic titration CpHMD (st-CpHMD) currently being one of the most well-developed and validated methods. St-CpHMD is already compatible with both the GROMOS 54A7 and CHARMM 36m force fields, and we extend it here to support the AMBER 14SB force field available in the GROMACS software package. We introduce and validate a minor modification to the official atomic partial charges of ff14SB (to achieve neutralization of the main chain) to render them compatible with st-CpHMD, and we benchmark the final implementation using lysozyme and Staphylococcal nuclease proteins. Although the root-mean-square error (RMSE) values of the predictions for pK_a versus experimental data align closely with those obtained using the other supported force fields, we also identified several challenging cases where the method requires further improvement. AMBER 14SB simulations showed a lower computational cost compared to CHARMM 36m, despite being slightly higher than the GROMOS 54A7 simulations. Our findings also indicate that to further enhance computational speed, future efforts should concentrate on accelerating the PB/MC step. With this extension, we have developed the first CpHMD method implementation compatible with the three most widely used protein force fields, enabling, for the first time, a direct performance comparison among them.

Chromosomal region maintenance 1 (CRM1 also known as Xpo1 and exportin‐1) is the receptor for the nuclear export controlling the intracellular localization and function of many cellular and viral proteins that play a crucial role in viral infections and cancer. The inhibition of CRM1 has emerged as a promising therapeutic approach to interfere with the lifecycle of many viruses, for the treatment of cancer, and to overcome therapy resistance. Recently, selinexor has been approved as the first CRM1 inhibitor for the treatment of multiple myeloma, providing proof of concept for this therapeutic option with a new mode of action. However, selinexor is associated with dose‐limiting toxicity and hence, the discovery of alternative small molecule leads that could be developed as less toxic anticancer and antiviral therapeutics will have a significant impact in the clinic. Here, we report a CRM1 inhibitor discovery platform. The development of this platform includes reporter cell lines that monitor CRM1 activity by using red fluorescent protein or green fluorescent protein‐labeled HIV‐1 Rev protein with a strong heterologous nuclear export signal. Simultaneously, the intracellular localization of other proteins, to be interrogated for their capacity to undergo CRM1‐mediated export, can be followed by co‐culturing stable cell lines expressing fluorescent fusion proteins. We used this platform to interrogate the mode of nuclear export of several proteins, including PDK1, p110α, STAT5A, FOXO1, 3, 4 and TRIB2, and to screen a compound collection. We show that while p110α partially relies on CRM1‐dependent nuclear export, TRIB2 is exported from the nucleus in a CRM1‐independent manner. Compound screening revealed the striking activity of an organoselenium compound on the CRM1 nuclear export receptor.

Over the past few decades, species distribution modelling has been increasingly used to monitor invasive species. Studies herein propose to use Cellular Automata (CA), not only to model the distribution of a potentially invasive species but also to infer the potential of the method in risk prediction of Reticulitermes grassei infestation. The test area was mainland Portugal, for which an available presence-only dataset was used. This is a typical dataset type, resulting from either distribution studies or infestation reports. Subterranean termite urban distributions in Portugal from 1970 to 2001 were simulated, and the results were compared with known records from both 2001 (the publication date of the distribution models for R. grassei in Portugal) and 2020. The reported model was able to predict the widespread presence of R. grassei, showing its potential as a viable prediction tool for R. grassei infestation risk in wooden structures, providing the collection of appropriate variables. Such a robust simulation tool can prove to be highly valuable in the decision-making process concerning pest management.