Ligand binding free energy evaluation by Monte Carlo Recursion.

The correct evaluation of ligand binding free energies by computational methods is still a very challenging active area of research. The most employed methods for these calculations can be roughly classified into four groups: (i) the fastest and less accurate methods, such as molecular docking, designed to sample a large number of molecules and rapidly rank them according to the potential binding energy; (ii) the second class of methods use a thermodynamic ensemble, typically generated by molecular dynamics, to analyze the endpoints of the thermodynamic cycle for binding and extract differences, in the so-called 'end-point' methods; (iii) the third class of methods is based on the Zwanzig relationship and computes the free energy difference after a chemical change of the system (alchemical methods); and (iv) methods based on biased simulations, such as metadynamics, for example. These methods require increased computational power and as expected, result in increased accuracy for the determination of the strength of binding. Here, we describe an intermediate approach, based on the Monte Carlo Recursion (MCR) method first developed by Harold Scheraga. In this method, the system is sampled at increasing effective temperatures, and the free energy of the system is assessed from a series of terms W(b,T), computed from Monte Carlo (MC) averages at each iteration. We show the application of the MCR for ligand binding with datasets of guest-hosts systems (N = 75) and we observed that a good correlation is obtained between experimental data and the binding energies computed with MCR. We also compared the experimental data with an end-point calculation from equilibrium Monte Carlo calculations that allowed us to conclude that the lower-energy (lower-temperature) terms in the calculation are the most relevant to the estimation of the binding energies, resulting in similar correlations between MCR and MC data and the experimental values. On the other hand, the MCR method provides a reasonable view of the binding energy funnel, with possible connections with the ligand binding kinetics, as well. The codes developed for this analysis are publicly available on GitHub as a part of the LiBELa/MCLiBELa project (https://github.com/alessandronascimento/LiBELa).

QSAR studies on benzothiophene derivatives as Plasmodium falciparum N-myristoyltransferase inhibitors: Molecular insights into affinity and selectivity.

Malaria is an infectious disease caused by protozoan parasites of the genus Plasmodium and transmitted by Anopheles spp. mosquitos. Due to the emerging resistance to currently available drugs, great efforts must be invested in discovering new molecular targets and drugs. N-myristoyltransferase (NMT) is an essential enzyme to parasites and has been validated as a chemically tractable target for the discovery of new drug candidates against malaria. In this work, 2D and 3D quantitative structure-activity relationship (QSAR) studies were conducted on a series of benzothiophene derivatives as P. falciparum NMT (PfNMT) and human NMT (HsNMT) inhibitors to shed light on the molecular requirements for inhibitor affinity and selectivity. A combination of Quantitative Structure-activity Relationship (QSAR) methods, including the hologram quantitative structure-activity relationship (HQSAR), comparative molecular field analysis (CoMFA), and comparative molecular similarity index analysis (CoMSIA) models, were used, and the impacts of the molecular alignment strategies (maximum common substructure and flexible ligand alignment) and atomic partial charge methods (Gasteiger-Hückel, MMFF94, AM1-BCC, CHELPG, and Mulliken) on the quality and reliability of the models were assessed. The best models exhibited internal consistency and could reasonably predict the inhibitory activity against both PfNMT (HQSAR: q2 /r2 /r2pred = 0.83/0.98/0.81; CoMFA: q2 /r2 /r2pred = 0.78/0.97/0.86; CoMSIA: q2 /r2 /r2pred = 0.74/0.95/0.82) and HsNMT (HQSAR: q2 /r2 /r2pred = 0.79/0.93/0.74; CoMFA: q2 /r2 /r2pred = 0.82/0.98/0.60; CoMSIA: q2 /r2 /r2pred = 0.62/0.95/0.56). The results enabled the identification of the polar interactions (electrostatic and hydrogen-bonding properties) as the major molecular features that affected the inhibitory activity and selectivity. These findings should be useful for the design of PfNMT inhibitors with high affinities and selectivities as antimalarial lead candidates.

Non-Toxic Dimeric Peptides Derived from the Bothropstoxin-I Are Potent SARS-CoV-2 and Papain-like Protease Inhibitors.

The COVID-19 outbreak has rapidly spread on a global scale, affecting the economy and public health systems throughout the world. In recent years, peptide-based therapeutics have been widely studied and developed to treat infectious diseases, including viral infections. Herein, the antiviral effects of the lysine linked dimer des-Cys11, Lys12,Lys13-(pBthTX-I)2K ((pBthTX-I)2K)) and derivatives against SARS-CoV-2 are reported. The lead peptide (pBthTX-I)2K and derivatives showed attractive inhibitory activities against SARS-CoV-2 (EC50 = 28-65 µM) and mostly low cytotoxic effect (CC50 > 100 µM). To shed light on the mechanism of action underlying the peptides' antiviral activity, the Main Protease (Mpro) and Papain-Like protease (PLpro) inhibitory activities of the peptides were assessed. The synthetic peptides showed PLpro inhibition potencies (IC50s = 1.0-3.5 µM) and binding affinities (Kd = 0.9-7 µM) at the low micromolar range but poor inhibitory activity against Mpro (IC50 > 10 µM). The modeled binding mode of a representative peptide of the series indicated that the compound blocked the entry of the PLpro substrate toward the protease catalytic cleft. Our findings indicated that non-toxic dimeric peptides derived from the Bothropstoxin-I have attractive cellular and enzymatic inhibitory activities, thereby suggesting that they are promising prototypes for the discovery and development of new drugs against SARS-CoV-2 infection.