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We present an analytical model representation of the electron density ρ(r) in molecules in the form of expansions of a few functions (exponentials and a Gaussian) per atom. Based on a former analytical model of ρ(r) in atoms, we devised its molecular implementation by introducing the anisotropy inherent in the electron distribution of atoms in molecules by means of proper anisotropic functions. The resulting model named A2MD (anisotropic analytical model of density) takes an analytical form highly suitable for obtaining the electron density in large biomolecules as its computational cost scales linearly with the number of atoms. To obtain the parameters of the model, we first devised a fitting procedure to reference electron densities obtained in ab initio correlated quantum calculations. Second, in order to skip costly ab initio calculations, we also developed a machine learning (ML)-based predictor that used neural networks trained on broad molecular datasets to determine the parameters of the model. The resulting ML methodology that we named A2MDnet (A2MD network-trained) was able to provide reliable electron densities as a basis to predict molecular features without requiring quantum calculations. The results presented together with the low computational scaling associated to the A2MD representation of ρ(r) suggest potential applications to obtain reliable electron densities and ρ(r)-based molecular properties in biomacromolecules.Rotavirus group A remains a major cause of diarrhea in infants and young children worldwide. The permanent emergence of new genotypes puts the potential effectiveness of vaccines under serious questions. Thirteen VP1 structures with mutations mapping to the RNA entry site were analyzed using molecular dynamics simulations, and the results were combined with the experimental findings reported previously. The results revealed structural fluctuations in the protein-protein recognition sites and in the bottleneck of the RNA entry site that may affect the interaction of different proteins and delay the initiation of the viral replication, respectively. Altogether, the structural analysis of VP1 in the region crucial for the initiation of the viral replication, mainly the bottleneck site, may boost efforts to develop antivirals, as they might complement the available vaccines.Microbe class I terpene cyclases (TPCs) are responsible for deriving numerous functionally and structurally diverse groups of terpenoid natural products. The conformational change of their active pockets from "open" state to "closed" state upon substrate binding has been clarified. However, the key structural basis relevant to this active pocket dynamics and its detailed molecular mechanism are still unclear. In this work, on the basis of the molecular dynamics (MD) on two microbe class I TPCs (SdS and bCinS), we propose that the active pocket dynamics is highly dependent on the residue orientation of two conserved structural bases R-D dyad and X-R-D triad, rather than the previously suggested flexibility of kink region. Actually, we considered that the flexibility of kink region is synchronous with the R residue orientation of the X-R-D triad, which could regulate the entrance size of active pocket and thus affect the substrate selectivity of active pocket by utilizing the promiscuity of the X-R-D triad. Furthermore, to better understand the function of the two structural bases, two intelligible models of "PPi catcher-locker" and "selector-PPi sensor-orienter" are proposed to, respectively, describe the R-D dyad and X-R-D triad and broadened to more microbe class I TPCs. These findings exhibit the dynamics of active pocket inaccessible in static crystal structures and provide useful structural basis knowledge for further design of microbe class I TPCs with different cyclization ability.Histone methylation reader proteins (HMRPs) regulate gene transcription by recognizing, at their "aromatic cage" domains, various Lys/Arg methylation states on histone tails. Because epigenetic dysregulation underlies a wide range of diseases, HMRPs have become attractive drug targets. However, structure-based efforts in targeting them are still in their infancy. Structural information from functionally unrelated aromatic-cage-containing proteins (ACCPs) and their cocrystallized ligands could be a good starting point. In this light, we mined the Protein Data Bank to retrieve the structures of ACCPs in complex with cationic peptidic/small-molecule ligands. Our analysis revealed that the vast majority of retrieved ACCPs belong to three classes transcription regulators (chiefly HMRPs), signaling proteins, and hydrolases. [https://www.selleckchem.com/products/3-methyladenine.html 3-MA nmr] Although acyclic (and monocyclic) amines and quats are the typical cation-binding functional groups found in HMRP small-molecule inhibitors, numerous atypical cationic groups were identified in non-HMRP inhibitors, which could serve as potential bioisosteres to methylated Lys/Arg on histone tails. Also, as HMRPs are involved in protein-protein interactions, they possess large binding sites, and thus, their selective inhibition might only be achieved by large and more flexible (beyond rule of five) ligands. Hence, the ligands of the collected dataset represent suitable versatile templates for further elaboration into potent and selective HMRP inhibitors.Deep learning has demonstrated significant potential in advancing state of the art in many problem domains, especially those benefiting from automated feature extraction. Yet, the methodology has seen limited adoption in the field of ligand-based virtual screening (LBVS) as traditional approaches typically require large, target-specific training sets, which limits their value in most prospective applications. Here, we report the development of a neural network architecture and a learning framework designed to yield a generally applicable tool for LBVS. Our approach uses the molecular graph as input and involves learning a representation that places compounds of similar biological profiles in close proximity within a hyperdimensional feature space; this is achieved by simultaneously leveraging historical screening data against a multitude of targets during training. Cosine distance between molecules in this space becomes a general similarity metric and can readily be used to rank order database compounds in LBVS workflows.