Structure of the human TSC:WIPI3 lysosomal recruitment complex.

Tuberous sclerosis complex (TSC) is targeted to the lysosomal membrane, where it hydrolyzes RAS homolog-mTORC1 binding (RHEB) from its GTP-bound to GDP-bound state, inhibiting mechanistic target of rapamycin complex 1 (mTORC1). Loss-of-function mutations in TSC cause TSC disease, marked by excessive tumor growth. Here, we overcome a high degree of continuous conformational heterogeneity to determine the 2.

LYCHOS is a human hybrid of a plant-like PIN transporter and a GPCR.

Lysosomes have crucial roles in regulating eukaryotic metabolism and cell growth by acting as signalling platforms to sense and respond to changes in nutrient and energy availability. LYCHOS (GPR155) is a lysosomal transmembrane protein that functions as a cholesterol sensor, facilitating the cholesterol-dependent activation of the master protein kinase mechanistic target of rapamycin complex 1 (mTORC1). However, the structural basis of LYCHOS assembly and activity remains unclear.

Enantioselective Transfer Hydrogenation of α-Methoxyimino-β-keto Esters.

α-Methoxyimino-β-keto esters are reported to undergo highly enantioselective catalytic transfer hydrogenation using the Noyori-Ikariya complex RuCl(-cymene)[(,)-Ts-DPEN] in a mixture of formic acid-triethylamine and dimethylformamide at 25 °C. The experimental study performed on over 25 substrates combined with computational analysis revealed that a -configured methoxyimino group positioned alpha to a ketone carbonyl leads to higher reactivity and mostly excellent enantioselectivity within this substrate class. Density functional theory calculations of competing transition states were used in rationalizing the origins of enantioselectivity and the possible role of the methoxyimino group in the reaction outcome.

Quantum chemical package Jaguar: A survey of recent developments and unique features.

This paper is dedicated to the quantum chemical package Jaguar, which is commercial software developed and distributed by Schrödinger, Inc. We discuss Jaguar's scientific features that are relevant to chemical research as well as describe those aspects of the program that are pertinent to the user interface, the organization of the computer code, and its maintenance and testing. Among the scientific topics that feature prominently in this paper are the quantum chemical methods grounded in the pseudospectral approach.

Nanoscale Simulation of Plastic Contaminants Migration in Packaging Materials and Potential Leaching into Model Food Fluids.

Polymers are the most commonly used packaging materials for nutrition and consumer products. The ever-growing concern over pollution and potential environmental contamination generated from single-use packaging materials has raised safety questions. Polymers used in these materials often contain impurities, including unreacted monomers and small oligomers.

Advancing material property prediction: using physics-informed machine learning models for viscosity.

In materials science, accurately computing properties like viscosity, melting point, and glass transition temperatures solely through physics-based models is challenging. Data-driven machine learning (ML) also poses challenges in constructing ML models, especially in the material science domain where data is limited. To address this, we integrate physics-informed descriptors from molecular dynamics (MD) simulations to enhance the accuracy and interpretability of ML models.

Highly efficient implementation of analytic nonadiabatic derivative couplings within the pseudospectral method.

A pseudospectral implementation of nonadiabatic derivative couplings in the Tamm-Dancoff approximation is reported, and the accuracy and efficiency of the pseudospectral nonadiabatic derivative couplings are studied. Our results demonstrate that the pseudospectral method provides mean absolute errors of 0.2%-1.

Signalling of Adrenoceptors: Canonical Pathways and New Paradigms.

The concept of G protein-coupled receptors initially arose from studies of the β-adrenoceptor, adenylyl cyclase, and cAMP signalling pathway. Since then both canonical G protein-coupled receptor signalling pathways and emerging paradigms in receptor signalling have been defined by experiments focused on adrenoceptors. Here, we discuss the evidence for G protein coupling specificity of the nine adrenoceptor subtypes.

Molecular-Scale Exploration of Mechanical Properties and Interactions of Poly(lactic acid) with Cellulose and Chitin.

Poly(lactic acid) (PLA), one of the pillars of the current overarching displacement trend switching from fossil- to natural-based polymers, is often used in association with polysaccharides to increase its mechanical properties. However, the use of PLA/polysaccharide composites is greatly hampered by their poor miscibility, whose underlying nature is still vastly unexplored. This work aims to shed light on the interactions of PLA and two representative polysaccharide molecules (cellulose and chitin) and reveal structure-property relationships from a fundamental perspective using atomistic molecular dynamics.

Development of scalable and generalizable machine learned force field for polymers.

Understanding and predicting the properties of polymers is vital to developing tailored polymer molecules for desired applications. Classical force fields may fail to capture key properties, for example, the transport properties of certain polymer systems such as polyethylene glycol. As a solution, we present an alternative potential energy surface, a charge recursive neural network (QRNN) model trained on DFT calculations made on smaller atomic clusters that generalizes well to oligomers comprising larger atomic clusters or longer chains.

The application of artificial intelligence to accelerate G protein-coupled receptor drug discovery.

The application of artificial intelligence (AI) approaches to drug discovery for G protein-coupled receptors (GPCRs) is a rapidly expanding area. Artificial intelligence can be used at multiple stages during the drug discovery process, from aiding our understanding of the fundamental actions of GPCRs to the discovery of new ligand-GPCR interactions or the prediction of clinical responses. Here, we provide an overview of the concepts behind artificial intelligence, including the subfields of machine learning and deep learning.

Exploring the Effects of Wetting and Free Fatty Acid Deposition on an Atomistic Hair Fiber Surface Model Incorporating Keratin-Associated Protein 5-1.

The complex development of cosmetic and medical formulations relies on an ever-growing accuracy of predictive models of hair surfaces. Hitherto, modeling efforts have focused on the description of 18-methyl eicosanoic acid (18-MEA), the primary fatty acid covalently attached to the hair surface, without explicit modeling of the protein layer. Herein, the molecular details of the outermost surface of the human hair fiber surface, also called the F-layer, were studied using molecular dynamics (MD) simulations.

The application of targeted protein degradation technologies to G protein-coupled receptors.

The ubiquitin-proteasome system is one of the major pathways for the degradation of cellular proteins. In recent years, methods have been developed to exploit the ubiquitin-proteasome system to artificially degrade target proteins. Targeted protein degraders are extremely useful as biological tools for discovery research.

Imaging in international sporting event: experience from the Birmingham Commonwealth Games 2022.

Aim: To explain the design and delivery of diagnostic imaging and image-guided intervention services for an international games. The authors share their experiences from the Birmingham Commonwealth Games 2022.

Materials And Methods: A retrospective analysis was undertaken of anonymised data from the Zillion, Easyvision (RIS and PACS), and Encounter platforms for image viewing, interpretation and reporting during the Games.

Shearing friction behaviour of synthetic polymers compared to a functionalized polysaccharide on biomimetic surfaces: models for the prediction of performance of eco-designed formulations.

The substitution of natural, bio-based and/or biodegradable polymers for those of petrochemical origin in consumer formulations has become an active area of research and development as the sourcing and destiny of material components becomes a more critical factor in product design. These polymers often differ from their petroleum-based counterparts in topology, raw material composition and solution behaviour. Effective and efficient reformulation that maintains comparable cosmetic performance to existing products requires a deep understanding of the differences in frictional behaviour between polymers as a function of their molecular structure.

Pathological changes in GPCR signal organisation: Opportunities for targeted therapies for triple negative breast cancer.

Triple negative breast cancer (TNBC) has the poorest prognosis compared to other breast cancer subtypes, due to a historical lack of targeted therapies and high rates of relapse. Greater insight into the components of signalling pathways in TNBC tumour cells has led to the clinical evaluation, and in some cases approval, of targeted therapies. In the last decade, G protein-coupled receptors, such as the β-adrenoceptor, have emerged as potential new therapeutic targets.

Design of Molecules with Low Hole Reorganization Energy Based on a Quarter-Million Molecule DFT Screen: Part 2.

Organic semiconductors have many desirable properties including improved manufacturing and flexible mechanical properties. Due to the vastness of chemical space, it is essential to efficiently explore chemical space when designing new materials, including through the use of generative techniques. New generative machine learning methods for molecular design continue to be published in the literature at a significant rate but successfully adapting methods to new chemistry and problem domains remains difficult.

High-Dimensional Neural Network Potential for Liquid Electrolyte Simulations.

Liquid electrolytes are one of the most important components of Li-ion batteries, which are a critical technology of the modern world. However, we still lack the computational tools required to accurately calculate key properties of these materials (viscosity and ionic diffusivity) from first principles necessary to support improved designs. In this work, we report a machine learning-based force field for liquid electrolyte simulations, which bridges the gap between the accuracy of range-separated hybrid density functional theory and the efficiency of classical force fields.

Structure of the metastatic factor P-Rex1 reveals a two-layered autoinhibitory mechanism.

P-Rex (PI(3,4,5)P-dependent Rac exchanger) guanine nucleotide exchange factors potently activate Rho GTPases. P-Rex guanine nucleotide exchange factors are autoinhibited, synergistically activated by Gβγ and PI(3,4,5)P binding and dysregulated in cancer. Here, we use X-ray crystallography, cryogenic electron microscopy and crosslinking mass spectrometry to determine the structural basis of human P-Rex1 autoinhibition.

Organic radical emitters: nature of doublet excitons in emissive layers.

Organic radical emitters have received significant attention as a new route to efficient organic light-emitting diodes (OLEDs). The electronic structure of radical emitters allows bypassing the triplet harvesting issue in current OLED devices. However, the nature of doublet excited states remains elusive due to the complex nature of emissive layers.

Active Learning Accelerates Design and Optimization of Hole-Transporting Materials for Organic Electronics.

Data-driven methods are receiving increasing attention to accelerate materials design and discovery for organic light-emitting diodes (OLEDs). Machine learning (ML) has enabled high-throughput screening of materials properties to suggest new candidates for organic electronics. However, building reliable predictive ML models requires creating and managing a high volume of data that adequately address the complexity of materials' chemical space.

Design of Organic Electronic Materials With a Goal-Directed Generative Model Powered by Deep Neural Networks and High-Throughput Molecular Simulations.

In recent years, generative machine learning approaches have attracted significant attention as an enabling approach for designing novel molecular materials with minimal design bias and thereby realizing more directed design for a specific materials property space. Further, data-driven approaches have emerged as a new tool to accelerate the development of novel organic electronic materials for organic light-emitting diode (OLED) applications. We demonstrate and validate a goal-directed generative machine learning framework based on a recurrent neural network (RNN) deep reinforcement learning approach for the design of hole transporting OLED materials.

The cryo-EM structure of the human neurofibromin dimer reveals the molecular basis for neurofibromatosis type 1.

Neurofibromin (NF1) mutations cause neurofibromatosis type 1 and drive numerous cancers, including breast and brain tumors. NF1 inhibits cellular proliferation through its guanosine triphosphatase-activating protein (GAP) activity against rat sarcoma (RAS). In the present study, cryo-electron microscope studies reveal that the human ~640-kDa NF1 homodimer features a gigantic 30 × 10 nm array of α-helices that form a core lemniscate-shaped scaffold.