Successful cases have been reported in the literature, demonstrating the efficiency of ML techniques combined with traditional approaches to study medicinal chemistry problems. Some ML techniques used in drug design are: support vector machine, random forest, decision trees and artificial neural networks. Currently, an important application of ML techniques is related to the calculation of scoring functions used in docking and virtual screening assays from a consensus, combining traditional and ML techniques in order to improve the prediction of binding sites and docking solutions.

The stochastic drift-diffusion (DrDiff) theory is an approach used to characterize the dynamical properties of simulation data. With new features in transition times analyses, the framework characterized the thermodynamic free-energy profile [F(Q)], the folding time (τf), and transition path time (τTP) by determining the coordinate-dependent drift-velocity [v(Q)] and diffusion [D(Q)] coefficients from trajectory time traces. In order to explore the DrDiff approach and to tune it with two other methods (Bayesian analysis and fep1D algorithm), a numerical integration of the Langevin equation with known D(Q) and F(Q) was performed and the inputted coefficients were recovered with success by the diffusion models. DrDiff was also applied to investigate the prion protein (PrP) kinetics and thermodynamics by analyzing folding/unfolding simulations. The protein structure-based model, the well-known Go¯-model, was employed in a coarse-grained Cα level to generate long constant-temperature time series. PrP was chosen due to recent experimental single-molecule studies in D and τTP that stressed the importance and the difficulty of probing these quantities and the rare transition state events related to prion misfolding and aggregation. The PrP thermodynamic double-well F(Q) profile, the “X” shape of τf(T), and the linear shape of τTP(T) were predicted with v(Q) and D(Q) obtained by the DrDiff algorithm. With the advance of single-molecule techniques, the DrDiff framework might be a useful ally for determining kinetic and thermodynamic properties by analyzing time observables of biomolecular systems. The code is freely available at https://github.com/ronaldolab/DrDiff.

Dipeptidyl peptidase-4 (DPP-4) is a target to treat type II diabetes mellitus. Therefore, it is important to understand the structural aspects of this enzyme and its interaction with drug candidates. This study involved molecular dynamics simulations, normal mode analysis, binding site detection and analysis of molecular interactions to understand the protein dynamics. We identified some DPP-4 functional motions contributing to the exposure of the binding sites and twist movements revealing how the two enzyme chains are interconnected in their bioactive form, which are defined as chains A (residues 40–767) and B (residues 40–767). By understanding the enzyme structure, its motions and the regions of its binding sites, it will be possible to contribute to the design of new DPP-4 inhibitors as drug candidates to treat diabetes.

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There are two different prion conformations: (1) the cellular natural (PrP) and (2) the scrapie (PrP), an infectious form that tends to aggregate under specific conditions. PrP and PrP are widely different regarding secondary and tertiary structures. PrP contains more and longer β-strands compared to PrP. The lack of solved PrP structures precludes a proper understanding of the mechanisms related to the transition between cellular and scrapie forms, as well as the aggregation process. In order to investigate the conformational transition between PrP and PrP, we applied MDeNM (molecular dynamics with excited normal modes), an enhanced sampling simulation technique that has been recently developed to probe large structural changes. These simulations yielded new structural rearrangements of the cellular prion that would have been difficult to obtain with standard MD simulations. We observed an increase in β-sheet formation under low pH (≤ 4) and upon oligomerization, whose relevance was discussed on the basis of the energy landscape theory for protein folding. The characterization of intermediate structures corresponding to transition states allowed us to propose a conversion model from the cellular to the scrapie prion, which possibly ignites the fibril formation. This model can assist the design of new drugs to prevent neurological disorders related to the prion aggregation mechanism.

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