Making lipids very unhappy to discover how they bind to proteins

Membrane lipid composition is maintained by conserved lipid transfer proteins, but computational approaches to study their lipid-binding mechanisms are limiting. Srinivasan et al. (https://doi.org/10.1083/jcb.202312055) develop a clever molecular dynamics simulations assay to accurately model lipid-binding poses in lipid transfer proteins

Sampling a rare protein transition with a hybrid classical-quantum computing algorithm

Simulating spontaneous structural rearrangements in macromolecules with classical Molecular Dynamics (MD) is an outstanding challenge. Conventional supercomputers can access time intervals up to tens of $\mu$s, while many key events occur on exponentially longer time scales. Transition path sampling techniques have the advantage of focusing the computational power on barrier-crossing trajectories, but generating uncorrelated transition paths that explore diverse conformational regions remains an unsolved problem. We employ a path-sampling paradigm combining machine learning (ML) with quantum computing (QC) to address this issue. We use ML on a classical computer to perform a preliminary uncharted exploration of the conformational space. The data set generated in this exploration is then post-processed to obtain a network representation of the reactive kinetics. Quantum annealing machines can exploit quantum superposition to encode all the transition pathways in this network in the initial quantum state and ensure the generation of completely uncorrelated transition paths. In particular, we resort to the DWAVE quantum computer to perform an all-atom simulation of a protein conformational transition that occurs on the ms timescale. Our results match those of a special purpose supercomputer designed to perform MD simulations. These results highlight the role of biomolecular simulation as a ground for applying, testing, and advancing quantum technologies

iMapD: intrinsic Map Dynamics exploration for uncharted effective free energy landscapes

We describe and implement iMapD, a computer-assisted approach for accelerating the exploration of uncharted effective Free Energy Surfaces (FES), and more generally for the extraction of coarse-grained, macroscopic information from atomistic or stochastic (here Molecular Dynamics, MD) simulations. The approach functionally links the MD simulator with nonlinear manifold learning techniques. The added value comes from biasing the simulator towards new, unexplored phase space regions by exploiting the smoothness of the (gradually, as the exploration progresses) revealed intrinsic low-dimensional geometry of the FES

Autonomous artificial intelligence discovers mechanisms of molecular self-organization in virtual experiments

Molecular self-organization driven by concerted many-body interactions produces the ordered structures that define both inanimate and living matter. Understanding the physical mechanisms that govern the formation of molecular complexes and crystals is key to controlling the assembly of nanomachines and new materials. We present an artificial intelligence (AI) agent that uses deep reinforcement learning and transition path theory to discover the mechanism of molecular self-organization phenomena from computer simulations. The agent adaptively learns how to sample complex molecular events and, on the fly, constructs quantitative mechanistic models. By using the mechanistic understanding for AI-driven sampling, the agent closes the learning cycle and overcomes time-scale gaps of many orders of magnitude. Symbolic regression condenses the mechanism into a human-interpretable form. Applied to ion association in solution, gas-hydrate crystal formation, and membrane-protein assembly, the AI agent identifies the many-body solvent motions governing the assembly process, discovers the variables of classical nucleation theory, and reveals competing assembly pathways. The mechanistic descriptions produced by the agent are predictive and transferable to close thermodynamic states and similar systems. Autonomous AI sampling has the power to discover assembly and reaction mechanisms from materials science to biology

Intrinsic map dynamics exploration for uncharted effective free-energy landscapes

We describe and implement a computer-assisted approach for accelerating the exploration of uncharted effective free-energy surfaces (FESs). More generally, the aim is the extraction of coarse-grained, macroscopic information from stochastic or atomistic simulations, such as molecular dynamics (MD). The approach functionally links the MD simulator with nonlinear manifold learning techniques. The added value comes from biasing the simulator toward unexplored phase-space regions by exploiting the smoothness of the gradually revealed intrinsic low-dimensional geometry of the FES

Water Extract from Inflorescences of Industrial Hemp Futura 75 Variety as a Source of Anti-Inflammatory, Anti-Proliferative and Antimycotic Agents: Results from In Silico, In Vitro and Ex Vivo Studies

Industrial hemp (Cannabis sativa) is traditionally cultivated as a valuable source of fibers and nutrients. Multiple studies also demonstrated antimicrobial, anti-proliferative, phytotoxic and insecticide effects of the essential oil from hemp female inflorescences. On the other side, only a few studies explored the potential pharmacological application of polar extracts from inflorescences. In the present study, we investigated the water extract from inflorescences of industrial hemp Futura 75 variety, from phytochemical and pharmacological point of view. The water extract was assayed for phenolic compound content, radical scavenger/reducing, chelating and anti-tyrosinase effects. Through an ex vivo model of toxicity induced by lipopolysaccharide (LPS) on isolated rat colon and liver, we explored the extract effects on serotonin, dopamine and kynurenine pathways and the production of prostaglandin (PG)E2. Anti-proliferative effects were also evaluated against human colon cancer HCT116 cell line. Additionally, antimycotic effects were investigated against Trichophyton rubrum, Trichophyton interdigitale, Microsporum gypseum. Finally, in silico studies, including bioinformatics, network pharmacology and docking approaches were conducted in order to predict the putative targets underlying the observed pharmacological and microbiological effects. Futura 75 water extract was able to blunt LPS-induced reduction of serotonin and increase of dopamine and kynurenine turnover, in rat colon. Additionally, the reduction of PGE2 levels was observed in both colon and liver specimens, as well. The extract inhibited the HCT116 cell viability, the growth of T. rubrum and T. interdigitale and the activity of tyrosinase, in vitro, whereas in silico studies highlighting the inhibitions of cyclooxygenase-1 (induced by carvacrol), carbonic anhydrase IX (induced by chlorogenic acid and gallic acid) and lanosterol 14-α-demethylase (induced by rutin) further support the observed pharmacological and antimycotic effects. The present findings suggest female inflorescences from industrial hemp as high quality by-products, thus representing promising sources of nutraceuticals and cosmeceuticals against inflammatory and infectious diseases.Fil: Orlando, Giustino. University “G. d’Annunzio”. Department of Pharmacy; ItaliaFil: Recinella, Lucia. University “G. d’Annunzio”. Department of Pharmacy; ItaliaFil: Chiavaroli, Annalisa. University “G. d’Annunzio”. Department of Pharmacy; ItaliaFil: Brunetti, Luigi. University “G. d’Annunzio”. Department of Pharmacy; ItaliaFil: Leone, Sheila. University “G. d’Annunzio”. Department of Pharmacy; ItaliaFil: Carradori, Simone. University “G. d’Annunzio”. Department of Pharmacy; ItaliaFil: Di Simone, Simonetta. University “G. d’Annunzio”. Department of Pharmacy; ItaliaFil: Ciferri, Maria Chiara. University “G. d’Annunzio”. Department of Pharmacy; ItaliaFil: Zengin, Gokhan. Universidad de Selcuk; TurquíaFil: Ak, Gunes. Universidad de Selcuk; TurquíaFil: Abdullah, Hassan H.. Salahaddin University-Erbil; Iraq. Universiti Sains Malaysia; MalasiaFil: Cordisco, Estefanía. Universidad Nacional de Rosario. Facultad de Ciencias Bioquímicas y Farmacéuticas. Departamento de Química Orgánica. Área Farmacognosia; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Rosario; ArgentinaFil: Sortino, Maximiliano Andrés. Universidad Nacional de Rosario. Facultad de Ciencias Bioquímicas y Farmacéuticas. Departamento de Química Orgánica. Área Farmacognosia; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Rosario; ArgentinaFil: Svetaz, Laura Andrea. Universidad Nacional de Rosario. Facultad de Ciencias Bioquímicas y Farmacéuticas. Departamento de Química Orgánica. Área Farmacognosia; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Rosario; ArgentinaFil: Politi, Matteo. University “G. d’Annunzio”. Department of Pharmacy; ItaliaFil: Angelini, Paola. Università di Perugia; ItaliaFil: Covino, Stefano. Università di Perugia; ItaliaFil: Venanzoni, Roberto. Università di Perugia; ItaliaFil: Cesa, Stefania. Università degli Studi di Roma "La Sapienza"; ItaliaFil: Menghini, Luigi. University “G. d’Annunzio”. Department of Pharmacy; ItaliaFil: Ferrante, Claudio. University “G. d’Annunzio”. Department of Pharmacy; Itali