Deep learning is an important branch of artificial intelligence that has been successfully applied into medicine and two-dimensional ligand design. The three-dimensional (3D) ligand generation in the 3D pocket of protein target is an interesting and challenging issue for drug design by deep learning. Here, the MolAICal software is introduced to supply a way for generating 3D drugs in the 3D pocket of protein targets by combining with merits of deep learning model and classical algorithm. The MolAICal software mainly contains two modules for 3D drug design. In the first module of MolAICal, it employs the genetic algorithm, deep learning model trained by FDA-approved drug fragments and Vinardo score fitting on the basis of PDBbind database for drug design. In the second module, it uses deep learning generative model trained by drug-like molecules of ZINC database and molecular docking invoked by Autodock Vina automatically. Besides, the Lipinski’s rule of five, Pan-assay interference compounds (PAINS), synthetic accessibility (SA) and other user-defined rules are introduced for filtering out unwanted ligands in MolAICal. To show the drug design modules of MolAICal, the membrane protein glucagon receptor and non-membrane protein SARS-CoV-2 main protease are chosen as the investigative drug targets. The results show MolAICal can generate the various and novel ligands with good binding scores and appropriate XLOGP values. We believe that MolAICal can use the advantages of deep learning model and classical programming for designing 3D drugs in protein pocket. MolAICal is freely for any nonprofit purpose and accessible at https://molaical.github.io.

The microtubule-associated protein tau is critical for the development and maintenance of the nervous system. Tau dysfunction is associated with a variety of neurodegenerative diseases called tauopathies, which are characterized by neurofibrillary tangles formed by abnormally aggregated tau protein. Studying the aggregation mechanism of tau protein is of great significance for elucidating the etiology of tauopathies. The hexapeptide 306VQIVYK311 (PHF6) of R3 has been shown to play a vital role in promoting tau aggregation. In this study, long-term all-atom molecular dynamics simulations in explicit solvent were performed to investigate the mechanisms of spontaneous aggregation and template-induced misfolding of PHF6, and the dimerization at the early stage of nucleation was further specifically analyzed by the Markov state model (MSM). Our results show that PHF6 can spontaneously aggregate to form multimers enriched with β-sheet structure and the β-sheets in multimers prefer to exist in a parallel way. It is observed that PHF6 monomer can be induced to form a β-sheet structure on either side of the template but in a different way. In detail, the β-sheet structure is easier to form on the left side but does not extend well, but on the right side, the monomer can form the extended β-sheet structure. Furthermore, MSM analysis shows that the formation of dimer mainly occurs in three steps. First, the separated monomers collide with each other at random orientations, and then a dimer with short β-sheet structure at the N-terminal forms; finally, β-sheets elongate to form an extended parallel β-sheet dimer. During these processes, multiple intermediate states are identified and multiple paths can form a parallel β-sheet dimer from the disordered coil structure. Moreover, the residues I308, V309, and Y310 play an essential role in the dimerization. In a word, our results uncover the aggregation and misfolding mechanism of PHF6 from the atomic level, which can provide useful theoretical guidance for rational design of effective therapeutic drugs against tauopathies.

Leucine-rich repeat kinase 2 (LRRK2) has been reported in the pathogenesis of Parkinson's disease (PD). G2019S mutant is the most common pathogenic mutation in LRRK2-related PD patients. Inhibition of LRRK2 kinase activity is proposed to be a new therapeutic approach for PD treatment. Therefore, understanding the molecular basis of the interaction between LRRK2 and its inhibitors will be valuable for the discovery and design of LRRK2 inhibitors. However, the structure of human LRRK2 in complex with the inhibitor has not been determined, and the inhibitory mechanism underlying LRRK2 still needs to be further investigated. In this study, molecular dynamics (MD) simulation combined with the molecular mechanics generalized born surface area (MM-GBSA) binding free energy calculation and pharmacophore modeling methods was employed to explore the critical residues in LRRK2 for binding of inhibitors and to investigate the general structural features of the inhibitors with diverse scaffolds. The results from MD simulations suggest that the hinge region residues Glu1948 and Ala1950 play a significant role in maintaining the intermolecular hydrogen bond interaction with the G2019S LRRK2 protein and inhibitor. The strong hinge hydrogen bond with an occupancy rate of more than 95% represents the high activity of LRRK2 inhibitors, and the hydrogen bond interaction with the kinase catalytic loop region could compromise selectivity. Further pharmacophore modeling reveals that the high activity LRRK2 inhibitor should have one aromatic ring, one hydrogen bond acceptor, and one hydrogen bond donor. Hence, the obtained results can provide valuable information to understand the interactions of LRRK2 inhibitors at the atomic level that will be helpful in designing potent inhibitors of LRRK2.

Rifampin is the first-line antituberculosis drug, with Mycobacterium tuberculosis RNA polymerase as the molecular target. Unfortunately, M. tuberculosis strains that are resistant to rifampin have been identified in clinical settings, which limits its therapeutic effects. In clinical isolates, S531L and D516V (in Escherichia coli) are two common mutated codons in the gene rpoB, corresponding to S456L and D441V in M. tuberculosis. However, the resistance mechanism at the molecular level is still elusive. In this work, Gaussian accelerated molecular dynamics simulations were performed to uncover the resistance mechanism of rifampin due to S456L and D441V mutations at the atomic level. The binding free energy analysis revealed that the reduction in the ability of two mutants to bind rifampin is mainly due to a decrease in electrostatic interaction, specifically, a decrease in the energy contribution of the R454 residue. R454 acts as an anchor and forms stable hydrogen bond interaction with rifampin, allowing rifampin to be stably incorporated in the center of the binding pocket. However, the disappearance of the hydrogen bond between R454 and the mutated residues increases the flexibility of the side chain of R454. The conformation of R454 changes, and the hydrogen bond interaction between it and rifampin is disrupted. As result, the rifampin molecule moves to the outside of the pocket, and the binding affinity decreases. Overall, these findings can provide useful information for understanding the drug resistance mechanism of rifampin and also can give theoretical guidance for further design of novel inhibitors to overcome the drug resistance.

Leucine rich repeat kinase 2 (LRRK2) has been reported in the pathogenesis of Parkinson’s disease (PD). Inhibition of LRRK2 kinase activity is a therapeutic approach that may provide new treatments for PD. In this study, novel LRRK2 inhibitors were identified by performing a docking-based virtual screening (VS). Due to the absence of a crystal structure of LRRK2, homology modeling was adopted to model human LRRK2 kinase domain that binds the inhibitor. Next, a docking-based virtual screening protocol was applied to identify LRRK2 small molecule inhibitors targeting the ATP binding pocket. A total of 28 compounds were selected and subjected to LRRK2 kinase inhibition assay. As a result, two small molecules with novel skeleton, compounds LY2019-005 and LY2019-006, were identified as potential LRRK2 kinase inhibitors with the IC50 of these two compounds against the wild-type and G2019S mutant LRRK2 kinase being 424.40 ± 1.31 nM, 378.80 ± 1.20 nM and 1526.00 ± 0.87 nM, 1165.00 ± 1.18 nM, respectively. Molecular dynamics (MD) simulation was carried out to reveal the binding mode of the newly identified compound LY2019-005 to the LRRK2 kinase domain. The binding modes indicate that the important hydrogen bond between hinge region (such as Ala1950) and inhibitor is crucial for the inhibition activity. In summary, our study provides a highly efficient way to discover LRRK2 inhibitors, and we find two highly efficient novel LRRK2 inhibitors, which could be helpful for the development of potential drugs targeting LRRK2 in PD therapy.

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Rapid and accurate prediction of drug-target affinity can accelerate and improve the drug discovery process. Recent studies show that deep learning models may have the potential to provide fast and accurate drug-target affinity prediction. However, the existing deep learning models still have their own disadvantages that make it difficult to complete the task satisfactorily. Complex-based models rely heavily on the time-consuming docking process, and complex-free models lacks interpretability. In this study, we introduced a novel knowledge-distillation insights drug-target affinity prediction model with feature fusion inputs to make fast, accurate and explainable predictions. We benchmarked the model on public affinity prediction and virtual screening dataset. The results show that it outperformed previous state-of-the-art models and achieved comparable performance to previous complex-based models. Finally, we study the interpretability of this model through visualization and find it can provide meaningful explanations for pairwise interaction. We believe this model can further improve the drug-target affinity prediction for its higher accuracy and reliable interpretability.

Parkinson's disease (PD) is the second most common neurodegenerative disorder that affects more than ten million people worldwide. However, the current PD treatments are still limited and alternative treatment strategies are urgently required. Leucine-rich repeat kinase 2 (LRRK2) has been recognized as a promising target for PD treatment. However, there are no approved LRRK2 inhibitors on the market. To rapidly identify potential drug repurposing candidates that inhibit LRRK2 kinase, we report a structure-based drug repurposing workflow that combines molecular docking, recursive partitioning model, molecular dynamics (MD) simulation, and molecular mechanics-generalized Born surface area (MM-GBSA) calculation. Thirteen compounds screened from our drug repurposing workflow were further evaluated through the experiment. The experimental results showed six drugs (Abivertinib, Aumolertinib, Encorafenib, Bosutinib, Rilzabrutinib, and Mobocertinib) with IC 50 less than 5 μM that were identified as potential LRRK2 kinase inhibitors. The most potent compound Abivertinib showed potent inhibitions with IC 50 toward G2019S mutation and wild-type LRRK2 of 410.3 nM and 177.0 nM, respectively. Our combination screening strategy had a 53% hit rate in this repurposing task. MD simulations and MM-GBSA free energy analysis further revealed the atomic binding mechanism between the identified drugs and G2019S LRRK2. In summary, the results showed that our drug repurposing workflow could be used to identify potent compounds for LRRK2. The potent inhibitors discovered in our work can be a starting point to develop more effective LRRK2 inhibitors.