Autonomous use of electrical energy by an artificial molecular machine

The ability to exploit energy autonomously is one of the hallmarks of Life. Mastering such processes in artificial nanosystems can open unforeseen technological opportunities. In the last decades, light- and chemically-driven autonomous systems have been developed in relation to conformational motion and self-assembly. On the contrary, the autonomous exploitation of electrical energy remains essentially unexplored, despite being an attractive energy source. Herein we demonstrate the autonomous operation of an electrochemically-powered self-assembling nanomachine. Threading and dethreading motions of a pseudorotaxane take place autonomously in solution, between the electrodes of a scanning electrochemical microscope. This innovative actuation mode allows operating a molecular machine with an energy efficiency of 9%, unprecedented in autonomous systems. The strategy is general and can be applied to any redox-driven system, including molecular pumps that perform work repetitively. Ultimately, our study brings molecular nanoscience one step closer to everyday technology.

MolData, A Molecular Benchmark for Disease and Target Based Machine Learning

Deep learning’s automatic feature extraction has been a revolutionary addition to computational drug discovery, infusing both the capabilities of learning abstract features and discovering complex molecular patterns via learning from molecular data. Since biological and chemical knowledge is necessary for overcoming the challenges of data curation, balancing, training, and evaluation, it is important for databases to contain meaningful information regarding the exact target and disease of each bioassay. The existing depositories such as PubChem or ChMBL offer the screening data of millions of molecules against a variety of cells and targets, however, their bioassays contain complex biological information which can hinder their usage by the machine learning community. In this work, a comprehensive disease and target-based dataset are collected from PubChem in order to facilitate and accelerate molecular machine learning for better drug discovery. MolData is one the largest efforts to date for democratizing the molecular machine learning, with roughly 170 million drug screening results from 1.4 million unique molecules assigned to specific diseases and targets. It also provides 30 unique categories of targets and diseases. Correlation analysis of the MolData bioassays unveils valuable information for drug repurposing for multiple diseases including cancer, metabolic disorders, and infectious diseases. Finally, we provide a benchmark of more than 30 models trained on each category using multitask learning. MolData aims to pave the way for computational drug discovery and accelerate the advancement of molecular artificial intelligence in a practical manner. The MolData benchmark data is available at https:// github.com/Transilico/MolData as well as within the supplementary materials.

MolData, A Molecular Benchmark for Disease and Target Based Machine Learning

Deep learning’s automatic feature extraction has been a revolutionary addition to computational drug discovery, infusing both the capabilities of learning abstract features and discovering complex molecular patterns via learning from molecular data. Since biological and chemical knowledge are necessary for overcoming the challenges of data curation, balancing, training, and evaluation, it is important for databases to contain meaningful information regarding the exact target and disease of each bioassay. The existing depositories such as PubChem or ChemBL offer the screening data of millions of molecules against a variety of cells and targets, however, their bioassays contain complex biological information which can hinder their usage by the machine learning community. In this work, a comprehensive disease and target-based dataset is collected from PubChem in order to facilitate and accelerate molecular machine learning for better drug discovery. MolData is one the largest efforts to date for democratizing the molecular machine learning, with roughly 170 million drug screening results from 1.4 million unique molecules assigned to specific diseases and targets. It also provides 30 unique categories of targets and diseases. Correlation analysis of the MolData bioassays unveil valuable information for drug repurposing for multiple diseases including cancer, metabolic disorders, and infectious diseases. Finally, we provide a benchmark of more than 30 models trained on each category using multitask learning. MolData aims to pave the way for computational drug discovery and accelerate the advancement of molecular artificial intelligence in a practical manner. The MolData benchmark data is available at https://github.com/Transilico/MolData as well as within the supplementary materials.

New insights into the architecture and dynamics of archaella

Archaea swim by means of a unique molecular machine called the archaellum. The archaellum consists of an ATP-powered intracellular motor that drives the rotation of an extracellular filament, allowing the cell to rapidly propel itself through liquid media.The archaellum filament comprises multiple copies of helically organised subunits named archaellins. While in many species several archaellin homologs are encoded in the same operon, structural studies conducted to date have suggested that archaella consist of only one protein species. Thus, the role of the remaining archaellin genes remains elusive.Here we present the structure of the Methanocaldococcus villosus archaellum filament at 3.08 Å resolution. We find that the filament is composed of two alternating archaellins - ArlB1 and ArlB2, suggesting that the architecture and assembly of archaella is more complex than previously thought. Moreover, we identify two major structural elements that enable the archaellum filament to move.Our findings provide new insights into archaeal motility and challenge the current view on the archaellum architecture and assembly.

The Fate of Two Unstoppable Trains After Arriving Destination: Replisome Disassembly During DNA Replication Termination

In eukaryotes, the perfect duplication of the chromosomes is executed by a dynamic molecular machine called the replisome. As a key step to finishing DNA replication, replisome disassembly is triggered by ubiquitylation of the MCM7 subunit of the helicase complex CMG. Afterwards, the CDC48/p97 “unfoldase” is recruited to the ubiquitylated helicase to unfold MCM7 and disassemble the replisome. Here we summarise recently discovered mechanisms of replisome disassembly that are likely to be broadly conserved in eukaryotes. We also discuss two crucial questions that remain to be explored further in the future. Firstly, how is CMG ubiquitylation repressed by the replication fork throughout elongation? Secondly, what is the biological significance of replisome disassembly and what are the consequences of failing to ubiquitylate and disassemble the CMG helicase?

The Mechanism of Rubisco Catalyzed Carboxylation Reaction: Chemical Aspects Involving Acid-Base Chemistry and Functioning of the Molecular Machine

In recent years, a great deal of attention has been paid by the scientific community to improving the efficiency of photosynthetic carbon assimilation, plant growth and biomass production in order to achieve a higher crop productivity. Therefore, the primary carboxylase enzyme of the photosynthetic process Rubisco has received considerable attention focused on many aspects of the enzyme function including protein structure, protein engineering and assembly, enzyme activation and kinetics. Based on its fundamental role in carbon assimilation Rubisco is also targeted by the CO2-fertilization effect, which is the increased rate of photosynthesis due to increasing atmospheric CO2-concentration. The aim of this review is to provide a framework, as complete as possible, of the mechanism of the RuBP carboxylation/hydration reaction including description of chemical events occurring at the enzyme “activating” and “catalytic” sites (which involve Broensted acid-base reactions) and the functioning of the complex molecular machine. Important research results achieved over the last few years providing substantial advancement in understanding the enzyme functioning will be discussed.