Lifeboat Foundation

In a scientific first, researchers have used machine learning-powered AI to design de novo enzymes — never-before-existing proteins that accelerate biochemical reactions in living organisms. Enzymes drive a wide range of critical processes, from digestion to building muscle to breathing.

A team led by the University of Washington’s Institute for Protein Design, along with colleagues at UCLA and China’s Xi’an Jiaotong University, used their AI engine to create new enzymes of a kind called luciferases. Luciferases — as their name implies — catalyze chemical reactions that emit light; they’re what give fireflies their flare.

“Living organisms are remarkable chemists,” David Baker, a professor of biochemistry at UW and the study’s senior author, said.

Brain waves act as carriers of information. A recently proposed “Cytoelectric Coupling” hypothesis suggests that these wavering electric fields contribute to the optimization of the brain network’s efficiency and robustness. They do this by influencing the physical configuration of the brain’s molecular framework.

In order to carry out its multifaceted functions, which include thought, the brain operates on various levels. Information like objectives or visuals is depicted through synchronized electrical activity among neuronal networks. Simultaneously, a combination of proteins and other biochemicals within and surrounding each neuron physically execute the mechanics required for participation in these networks.

A new paper by researchers at MIT, City University of London, and Johns Hopkins University posits that the electrical fields of the network influence the physical configuration of neurons’ sub-cellular components to optimize network stability and efficiency, a hypothesis the authors call “Cytoelectric Coupling.”

The chemical origin of life on Earth is a puzzle that scientists have been trying to piece together for decades. Many hypotheses have been proposed to explain how life came to be and what chemical and environmental factors on early Earth could have led to it. A step required in a number of these hypotheses involves the abiotic synthesis of genetic polymers—materials made up of a sequence of repeating chemical units with the ability to store and pass down information through base-pairing interactions.

One such hypothesis is the RNA (ribonucleic acid) world hypothesis, which draws from this concept and suggests that RNA could have been the original biopolymer of life both for genetic information storage and transmission, and for catalysis. However, in the absence of chemical activation of RNA monomers, studies have found that RNA polymerization would have been inefficient under primitive dry-down conditions without specialized circumstances such as lipid or salt-assisted synthesis or mineral templating.

While this does not necessarily make the RNA world hypothesis less plausible, primitive chemical systems were quite diverse and could not have possibly been as clean to just contain RNA and lipids, suggesting that other forms of primitive nucleic acid polymerization may have also taken place.

Eventually, these molecules probably evolved a lipid (fatty) boundary separating the internal environment of the organism from the exterior, forming protocells. Protocells could concentrate and organize better the molecules needed in biochemical reactions, providing a contained and efficient metabolism.

Life on Repeat?

Abiogenesis could have happened more than once. Earth could have birthed self-replicating molecules several times, and maybe early life for thousands or millions of years just consisted of a bunch of different self-replicating RNA molecules, with independent origins, competing for the same building blocks. Alas, due to the ancient and microscopic nature of this process, we may never know.

This study demonstrates that AI can be incredibly effective in helping us identify new drug candidates – particularly at early stages of drug discovery and for diseases with complex biology or few known molecular targets.

A machine learning model has been trained to recognise the key features of chemicals with senolytic activity. It recently found three chemicals able to remove senescent cells without damaging healthy cells.

Molecular structure of oleandrin. Credit: Mplanine, CC BY-SA 4.0, via Wikimedia Commons.

Continue reading “AI finds potential anti-aging molecules” »

For many years, the fields of physics and chemistry have held the belief that the properties of solid materials are fundamentally determined by the atoms and molecules they consist of. For instance, the crystalline nature of salt is credited to the ionic bond formed between sodium and chloride ions. Similarly, metals such as iron or copper owe their robustness to the metallic bonds between their respective atoms, and the elasticity of rubbers stems from the flexible bonds in the polymers that form them. This principle also applies to substances like fungi, bacteria, and wood.

Or so the story goes.

A new paper recently published in Nature upends that paradigm, and argues that the character of many biological materials is actually created by the water that permeates these materials. Water gives rise to a solid and goes on to define the properties of that solid, all the while maintaining its liquid characteristics.

ALBUQUERQUE, N.M. — In people with epilepsy, seizure-alert dogs can smell small changes in body chemistry and warn of an impending seizure an hour or more before it occurs. Inspired by this feat of nature, a team of researchers has sniffed out a way to replicate the ability with technology.

Rocks and minerals contribute essential raw materials for any civilization, and in a technological society minerals (and the rare elements they contain) are especially sought after. In the past, most discoveries of mineral deposits have resulted from perseverance and luck.

In the last 200 years scientists realized that minerals are not distributed randomly. Many of the over 5,000 different minerals occurring on Earth exist in a so-called paragenesis. A paragenesis is a mineral assemblage formed under specific physico-chemical rules, like a certain chemical composition of the host rock or when the right conditions — like temperature and pressure — are met.

A machine learning model can predict the locations of minerals on Earth — and potentially other planets — by taking advantage of patterns in mineral associations.

Continue reading “How AI Can Help Find New Minerals On Earth And Other Planets” »

American chemical society: chemistry for life.