Researchers at The University of Tokyo show how including the effects of the surrounding water during the aggregation of charged particles can improve the accuracy of simulations, which may help elucidate biological self-assembly
Researchers at The University of Tokyo show how including the effects of the surrounding water during the aggregation of charged particles can improve the accuracy of simulations, which may help elucidate biological self-assembly
Tyler O'Neal, Staff Editor BIOLOGY

Japanese prof Tanaka improves the predictions of structures by adding the influence of hydrodynamics to supercomputer simulations of suspended charged particles in solutions including cells

In Tokyo, Japan, investigators from the Institute of Industrial Science at The University of Tokyo added the influence of hydrodynamics, which includes water's flow and compressibility properties, to supercomputer simulations of suspended charged particles in an electric field. They found that this greatly improved the predictions of the final structures compared with conventional computational models. This work may help explain how hydrodynamic interactions impact the self-organization of particles suspended in a solution, including in biological systems like cells.

Brownian dynamics (BD) simulations, in which a computer predicts the motion of randomly diffusing particles based on the forces they exert on each other, have greatly improved our understanding of how the material can self-assemble out of smaller parts. However, for the sake of keeping the computational cost manageable, the calculations must usually be simplified. Unfortunately, these approximations sometimes give rise to misleading results.

Now, a team of researchers at The University of Tokyo has demonstrated that simplifying calculations by neglecting the effects of the water hydrodynamics for particles in an aqueous solvent can give rise to inaccurate results. In particular, they show that if the particles are charged and experiencing an external electric field, the final arrangement of self-assembled structures depends on the ability of the solvent water to flow. This is an example of a colloid, a type of mixture in which insoluble particles are suspended in a liquid. This system can assume a semisolid gel state if the particles aggregate to form tendrils that span the entire volume of the sample. “Colloidal self-assembly is a promising bottom-up strategy to create higher-order structures from the elementary building blocks,” says first author Jiaxing Yuan.

The fact can explain the importance of accounting for hydrodynamics that the solvent must flow into the gap between the particles to allow them to separate. The team termed this effect the “inverse squeezing flow” effect because it is the opposite of the squeezing out of the solvent that occurs during colloidal aggregation. The result is that the colloidal particles form clusters with branching tendrils that can form a gel. Conversely, simple BD simulations incorrectly predicted that bundle-like linear aggregates of linear chains would be formed. “Our findings indicate that including hydrodynamics allows us to better predict the pathway of self-assembly, which may lead to the production of soft materials with properties, such as gel stiffness, that can be controlled with an external electric field,” explains the senior author, Hajime Tanaka. This work may lead to the development of smart materials that respond to external conditions, either during manufacturing or in response to changing environments, such as a soft gel that hardens when desired.

Amazonian river winds unraveled by air pollution observations

Tyler O'Neal, Staff Editor BIOLOGY

River winds are induced by the daily thermal contrast between the land and the river. During the daytime, warmer temperatures over the land lead to lighter air masses that are lifted. The air masses in turn drive onshore air movement from the river toward the land. Subsequently, the air subsides over the river. The result is a closed local air circulation cell in the vertical plane. At night, the land cools more rapidly, and the air circulation reverses because the river is warmer. Because these driving forces combine with larger and smaller atmospheric flows of trade winds and local topography, the combined river winds remain elusive and difficult to understand, measure, and simulate. A key question then arises: How to obtain accurate observational evidence of these river wind circulations? 

Traditional meteorology and pollution measurement platforms are unable to measure how wind, temperature, moisture, and air pollutants change with height above the river. Therefore medium-sized unmanned aerial vehicles (UAV – see photo) were used. They have a potential advance in atmospheric studies due to extreme maneuverability in collecting data at high horizontal and vertical resolutions. Sensor-equipped UAVs were used to collect in situ vertical information of meteorological and chemical data in the lower atmosphere during the daytime over the Rio Negro river in the central Amazon. The impacts of atmospheric recirculation tied to the river winds on the air quality of nearby human populations were considered.

Supercomputer modeling component

To support the interpretation of these observations, this study includes a modeling component to couple field observations of river winds and chemistry with fine-scale modeling analyses using a large eddy simulation (LES). This model is developed at the Meteorology and Air Quality Group of Wageningen University & Research. It is also an important component of the Ruisdael observatory.
The LES simulations examined the effects of river winds on air pollution dispersion. The LES simulation explicitly reproduces the turbulence and atmospheric circulations of the Amazon river winds. The simulations captured the main features of river winds observed by UAV sensing. Figure 1 Conceptual representation of thermally driven recirculatory flow of river winds and potential impacts on the dispersion of urban pollution over the river-city landscape and river-forest landscape.

This study shows the need to combine methodologies to measure (drones) and high-detailed modeling (LES). The implication of this study is that air recirculation induced by river winds slows the dispersion of air pollution. It also changes the spatial distribution and chemistry of air pollutants and may increase the risk of human exposure to air pollution in the riparian region. The findings emphasize the need to understand the impacts of river winds on air pollution. It highlights that air pollution management strategies and policies in Amazonia should incorporate the effects of river winds for effective pollution mitigation and control. Further research is being conducted with the NWO project Cloudroots.

Is your ML training set biased? How to develop new drugs based on merged datasets

Tyler O'Neal, Staff Editor BIOLOGY

Polymorphs are molecules that have different molecular packing arrangements despite identical chemical compositions. In a recent paper, researchers at GlaxoSmithKline (GSK) and the Cambridge Crystallographic Data Centre (CCDC) combined their proprietary (GSK) and published (CCDC) datasets to better train machine learning (ML) models to predict stable polymorphs to use in new drug candidates. The authors combined proprietary (GSK) and published (CCDC) datasets to better train machine learning (ML) models for drug discovery. CREDIT Image by Alex Moldovan.

What are the key differences between the CCDC and GSK datasets?

CCDC curates and maintains the Cambridge Structural Database (CSD). For the past century, scientists all over the world have contributed published, experimental crystal structures to the CSD, which now has over 1.1 million structures. The paper’s authors used a drug subset from the CSD combined with structures from GSK. The GSK structures were collected at different stages of the pharmaceutical pipeline and are not limited to marketed products. Co-author Dr. Jason Cole, senior research fellow on CCDC’s research and development team, explained why structures gathered at different stages of the drug discovery pipeline are so important.

“In early-stage drug discovery, a crystal structure can help to rationalize conformational effects, for example, or characterize the chemistry of a new chemical entity where other techniques have led to ambiguity,” Cole said. “Later in the process, when a new chemical entity is studied as a candidate molecule, crystal structures are critical as they inform form selection and can later aid in overcoming formulation and tabletting issues.”

This information can help researchers prioritize their efforts—saving time and potentially lives down the road.

“By understanding a range of crystal structures, scientists can also assess the risk of a given form being long-term unstable,” Cole said. “A full characterization of the structural landscape leads to confidence in taking a form forward.”

How do ML models in pharmaceutical science benefit from multiple datasets?

Industrial data sets reflect more than just science; they reflect cultural choices within a given organization.

“You will only find co-crystals if you look for co-crystals,” Cole said, as an example. “Most companies prefer to formulate a free, or unbound, drug. One can assume that the types of structures in an industrial set reflect conscious decisions to search for forms of given types, whereas fewer bounds are placed on the researchers who contribute to the CSD.”

ML models benefit from two key things: data volume and data specificity. That’s why coupling the volume and variety of data in the CSD with proprietary data sets is so helpful.

“Large amounts of data lead to more confident predictions,” Cole said. “Data that are most directly relevant to the problem lead to more accurate predictions. In the predictions that use CCDC software, we select a subset of the most relevant entries that is large enough to give confidence. The GSK set is bound to have highly relevant compounds to other compounds in their commercial portfolio. So the model-building software can use these.”

Industrial researchers working with highly relevant data can run into issues when they don’t have enough to generate confident models.

“Consider that CSD software typically picks around two thousand structures from the 1.1 million in the CSD,” Cole said. “The industrial set is tiny by comparison, but you could pick, say, 40 or 50 highly relevant structures. You'd have insufficient data to build a good model with that alone, but the added compounds from the CSD supplement the data set. In essence, by including the GSK and CSD sets we get the best of both worlds: all the highly relevant industrial structures and a set of quite relevant CSD structures together to build a high-quality model.”

Why do polymorphs present a risk to the pharmaceutical industry?

The different packing arrangements mean that one polymorph might be more suited for therapeutic delivery, while another form of the same compound might not. Researchers use crystal structure databases to make knowledge-based predictions about whether a potential new drug is comprised of a good, stable form that manufacturers can make, store, and deliver in a therapeutic manner. The authors at GSK and CCDC completed a robust analysis of the small molecule crystal structures containing X-ray diffraction results from GSK and its heritage companies for the past 40 years. They then combined those results with a drug subset of structures from CCDC’s CSD, which contains over 1.1 million small-molecule organic and metal-organic crystal structures from researchers all over the world.