In this work, we propose a deep learning approach to improve docking-based virtual screening. The introduced deep neural network, DeepVS, uses the output of a docking program and learns how to extract relevant features from basic data such as atom and residues types obtained from protein-ligand complexes. Our approach introduces the use of atom and amino acid embeddings and implements an effective way of creating distributed vector representations of protein-ligand complexes by modeling the compound as a set of atom contexts that is further processed by a convolutional layer. One of the main advantages of the proposed method is that it does not require feature engineering. We evaluate DeepVS on the Directory of Useful Decoys (DUD), using the output of two docking programs: AutodockVina1.1.2 and Dock6.6. Using a strict evaluation with leaveone-out cross-validation, DeepVS outperforms the docking programs in both AUC ROC and enrichment factor. Moreover, using the output of AutodockVina1.1.2, DeepVS achieves an AUC ROC of 0.81, which, to the best of our knowledge, is the best AUC reported so far for virtual screening using the 40 receptors from DUD.

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Brain-derived neurotrophic factor (BDNF) plays an important role in neurogenesis and synapse formation. The V66M is the most prevalent BDNF mutation in humans and impairs the function and distribution of BDNF. This mutation is related to several psychiatric disorders. The pro-region of BDNF, particularly position 66 and its adjacent residues, are determinant for the intracellular sorting and activity-dependent secretion of BDNF. However, it has not yet been fully elucidated. The present study aims to analyze the effects of the V66M mutation on BDNF structure and function. Here, we applied nine algorithms, including SIFT and PolyPhen-2, for functional and stability prediction of the V66M mutation. The complete theoretical model of BNDF was generated by Rosetta and validated by PROCHECK, RAMPAGE, ProSa, QMEAN and Verify-3D algorithms. Structural alignment was performed using TM-align. Phylogenetic analysis was performed using the ConSurf server. Molecular dynamics (MD) simulations were performed and analyzed using the GROMACS 2018.2 package. The V66M mutation was predicted as deleterious by PolyPhen-2 and SIFT in addition to being predicted as destabilizing by I-Mutant. According to SNPeffect, the V66M mutation does not affect protein aggregation, amyloid propensity, and chaperone binding. The complete theoretical structure of BDNF proved to be a reliable model. Phylogenetic analysis indicated that the V66M mutation of BDNF occurs at a non-conserved position of the protein. MD analyses indicated that the V66M mutation does not affect the BDNF flexibility and surface-to-volume ratio, but affects the BDNF essential motions, hydrogen-bonding and secondary structure particularly at its pre and pro-domain, which are crucial for its activity and distribution. Thus, considering that these parameters are determinant for protein interactions and, consequently, protein function; the alterations observed throughout the MD analyses may be related to the functional impairment of BDNF upon V66M mutation, as well as its involvement in psychiatric disorders.

Conflicting findings about the association between leprosy and TLR1 variants N248S and I602S have been reported. Here, we performed case-control and family based studies, followed by replication in 2 case-control populations from Brazil, involving 3162 individuals. Results indicated an association between TLR1 248S and leprosy in the case-control study (SS genotype odds ratio [OR], 1.81; P = .004) and the family based study (z = 2.02; P = .05). This association was consistently replicated in other populations (combined OR, 1.51; P < .001), corroborating the finding that 248S is a susceptibility factor for leprosy. Additionally, we demonstrated that peripheral blood mononuclear cells (PBMCs) carrying 248S produce a lower tumor necrosis factor/interleukin-10 ratio when stimulated with Mycobacterium leprae but not with lipopolysaccharide or PAM3cysK4. The same effect was observed after infection of PBMCs with the Moreau strain of bacillus Calmette-Guerin but not after infection with other strains. Finally, molecular dynamics simulations indicated that the Toll-like receptor 1 structure containing 248S amino acid is different from the structure containing 248N. Our results suggest that TLR1 248S is associated with an increased risk for leprosy, consistent with its hypoimmune regulatory function.

The standard parameterization of the Linear Interaction Energy (LIE) method has been applied with quite good results to reproduce the experimental absolute binding free energies for several protein-ligand systems. However, we found that this parameterization failed to reproduce the experimental binding free energy of Plasmepsin II (PlmII) in complexes with inhibitors belonging to four dissimilar scaffolds. To overcome this fact, we developed three approaches of LIE, which combine systematic approaches to predict the inhibitor-specific values of α, β, and γ parameters, to gauge their ability to calculate the absolute binding free energies for these PlmII-Inhibitor complexes. Specifically: (i) we modified the linear relationship between the weighted nonpolar desolvation ratio (WNDR) and the α parameter, by introducing two models of the β parameter determined by the free energy perturbation (FEP) method in the absence of the constant term γ, and (ii) we developed a new parameterization model to investigate the linear correlation between WNDR and the correction term γ. Using these parameterizations, we were able to reproduce the experimental binding free energy from these systems with mean absolute errors lower than 1.5 kcal/mol.

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