Electrostatic interactions play a fundamental role in determining the structure and dynamics of biomolecules in solution. However the accurate representation of electrostatics in classical mechanics based simulation approaches such as molecular dynamics (MD) is a challenging task. Given the growing importance that MD simulation methods are taking on in the study of protein folding, protein stability and dynamics, and in structure prediction and design projects, it is important to evaluate the influence that different electrostatic schemes have on the results of MD simulations. In this paper we performed long timescale simulations (500 ns) of two peptides, beta3 and RN24 forming different secondary structures, using for each peptide four different electrostatic schemes (namely PME, reaction field correction, and cut-off schemes with and without neutralizing counterions) for a total of eight 500 ns long MD runs. The structural and conformational features of each peptide under the different conditions were evaluated in terms of the time dependence of the flexibility, secondary structure evolution, hydrogen-bonding patterns, and several other structural parameters. The degree of sampling for each simulation as a function of the electrostatic scheme was also critically evaluated. Our results suggest that, while in the case of the short peptide RN24 the performances of the four methods are comparable, PME and RF schemes perform better in maintaining the structure close to the native one for the β-sheet peptide beta3, in which long range contacts are mostly responsible for the definition of the native structure.

We present a systematic test of the performance of three popular united-atom force fields-OPLS-UA, GROMOS and TraPPE-at predicting hydrophobic solvation, more precisely at describing the solvation of alkanes in alkanes. Gibbs free energies of solvation were calculated for 52 solute/solvent pairs from Molecular Dynamics simulations and thermodynamic integration making use of the IBERCIVIS volunteer computing platform. Our results show that all force fields yield good predictions when both solute and solvent are small linear or branched alkanes (up to pentane). However, as the size of the alkanes increases, all models tend to increasingly deviate from experimental data in a systematic fashion. Furthermore, our results confirm that specific interaction parameters for cyclic alkanes in the united-atom representation are required to account for the additional excluded volume within the ring. Overall, the TraPPE model performs best for all alkanes, but systematically underpredicts the magnitude of solvation free energies by about 6% (RMSD of 1.2 kJ/mol). Conversely, both GROMOS and OPLS-UA systematically overpredict solvation free energies (by ∼13% and 15%, respectively). The systematic trends suggest that all models can be improved by a slight adjustment of their Lennard-Jones parameters. © 2016 Wiley Periodicals, Inc.

The high burden of malaria and HIV/AIDS prevents economic and social progress in developing countries. A continuing need exists for development of novel drugs and treatment regimens for both diseases in order to address the tolerability and long-term safety concerns associated with current treatment options and the emergence of drug resistance. We describe new spiro-β-lactam derivatives with potent (nM) activity against HIV and Plasmodium and no activity against bacteria and yeast. The best performing molecule of the series, BSS-730A, inhibited both HIV-1 and HIV-2 replication with an IC50 of 13 ± 9.59 nM and P. berghei hepatic infection with an IC50 of 0.55 ± 0.14 μM with a clear impact on parasite development. BSS-730A was also active against the erythrocytic stages of P. falciparum, with an estimated IC50 of 0.43 ± 0.04 μM. Time-of-addition studies showed that BSS-730A potentially affects all stages of the HIV replicative cycle, suggesting a complex mechanism of action. BSS-730A was active against multidrug-resistant HIV isolates, with a median 2.4-fold higher IC50 relative to control isolates. BSS-730A was equally active against R5 and X4 HIV isolates and displayed strong synergism with the entry inhibitor AMD3100. BSS-730A is a promising candidate for development as a potential therapeutic and/or prophylactic agent against HIV and Plasmodium.

Protein aggregation into insoluble fibrillar structures known as amyloid characterizes several neurodegenerative diseases, including Alzheimer's, Huntington's and Creutzfeldt-Jakob. Transthyretin (TTR), a homotetrameric plasma protein, is known to be the causative agent of amyloid pathologies such as FAP (familial amyloid polyneuropathy), FAC (familial amyloid cardiomiopathy) and SSA (senile systemic amyloidosis). It is generally accepted that TTR tetramer dissociation and monomer partial unfolding precedes amyloid fibril formation. To explore the TTR unfolding landscape and to identify potential intermediate conformations with high tendency for amyloid formation, we have performed molecular dynamics unfolding simulations of WT-TTR and L55P-TTR, a highly amyloidogenic TTR variant. Our simulations in explicit water allow the identification of events that clearly discriminate the unfolding behavior of WT and L55P-TTR. Analysis of the simulation trajectories show that (i) the L55P monomers unfold earlier and to a larger extent than the WT; (ii) the single a-helix in the TTR monomer completely unfolds in most of the L55P simulations while remain folded in WT simulations; (iii) L55P forms, early in the simulations, aggregation-prone conformations characterized by full displacement of strands C and D from the main b-sandwich core of the monomer; (iv) L55P shows, late in the simulations, severe loss of the H-bond network and consequent destabilization of the CBEF b-sheet of the b-sandwich; (v) WT forms aggregationcompatible conformations only late in the simulations and upon extensive unfolding of the monomer. These results clearly show that, in comparison with WT, L55P-TTR does present a much higher probability of forming transient conformations compatible with aggregation and amyloid formation.

Drug design is an important area of study for pharmaceutical businesses. However, low efficacy, off-target delivery, time consumption, and high cost are challenges and can create barriers that impact this process. Deep Learning models are emerging as a promising solution to perform de novo drug design, i.e., to generate drug-like molecules tailored to specific needs. However, stereochemistry was not explicitly considered in the generated molecules, which is inevitable in targeted-oriented molecules. This paper proposes a framework based on Feedback Generative Adversarial Network (GAN) that includes optimization strategy by incorporating Encoder–Decoder, GAN, and Predictor deep models interconnected with a feedback loop. The Encoder–Decoder converts the string notations of molecules into latent space vectors, effectively creating a new type of molecular representation. At the same time, the GAN can learn and replicate the training data distribution and, therefore, generate new compounds. The feedback loop is designed to incorporate and evaluate the generated molecules according to the multiobjective desired property at every epoch of training to ensure a steady shift of the generated distribution towards the space of the targeted properties. Moreover, to develop a more precise set of molecules, we also incorporate a multiobjective optimization selection technique based on a non-dominated sorting genetic algorithm. The results demonstrate that the proposed framework can generate realistic, novel molecules that span the chemical space. The proposed Encoder–Decoder model correctly reconstructs 99% of the datasets, including stereochemical information. The model’s ability to find uncharted regions of the chemical space was successfully shown by optimizing the unbiased GAN to generate molecules with a high binding affinity to the Kappa Opioid and Adenosine $$A_ {2a}$$ A 2 a receptor. Furthermore, the generated compounds exhibit high internal and external diversity levels 0.88 and 0.94, respectively, and uniqueness.

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Abstract. The rational development of new drugs is a complex and expensive process, comprising several steps. Typically, it starts by screening databases of small organic molecules for chemical structures with potential of binding to a target receptor and prioritizing the most promising ones. Only a few of these will be selected for biological evaluation and further refinement through chemical synthesis. Despite the accumulated knowledge by pharmaceutical companies that continually improve the process of finding new drugs, a myriad of factors affect the activity of putative candidate molecules in vivo and the propensity for causing adverse and toxic effects is recognized as the major hurdle behind the current "target-rich, lead-poor" scenario. In this study we evaluate the use of several Machine Learning algorithms to find useful rules to the elucidation and prediction of toxicity using 1D and 2D molecular descriptors. The results indicate that: i) Machine Learning algorithms can effectively use 1D molecular descriptors to construct accurate and simple models; ii) extending the set of descriptors to include 2D descriptors improve the accuracy of the models.