Congratulations to the winners of the Best Paper of 2018 for the Environmental Science family of journals:
Matthew R. Findlay, Daniel N. Freitas, Maryam Mobed-Miremadi and Korin E. Wheeler,
Machine learning provides predictive analysis into silver nanoparticle protein corona formation
from physicochemical properties, Environmental Science: Nano, 2018, 5, 64–71
To find out more about this exceptional research work, we conducted an interview with the paper’s principal investigator, Dr Korin E. Wheeler, an Associate Professor in the Department of Chemistry & Biochemistry at Santa Clara University. She holds a Bachelor’s degree in chemistry from New College of Florida, a PhD in bioinorganic chemistry from Northwestern University, and worked in proteogenomics as a post-doc at Lawrence Livermore National Laboratory. Her independent research at Santa Clara University focuses upon assessing the diversity of nanomaterial transformations from a biochemical perspective and developing new approaches for characterization at the nano-bio interface. Her lab’s work has been funded by the National Science Foundation, National Institutes of Health, and Research Corporation for Science advancement. In addition to the Royal Society of Chemistry’s Environmental Science Journal’s best paper award (2018), she has recently been awarded the Henry Dreyfus Teacher-Scholar award (2018).
What is the story behind this work?
Initially, we worked to establish a robust method of proteomic characterization of the protein corona around engineered nanoparticles (ES:Nano in 2014). As we worked through the initial data, it was clear that the proteomics database held more information than we were able to digest and deserved further analysis. Here, we’ve applied machine learning tools, which are particularly useful when making predictions with a lot of data collected on complex systems. Now, we can begin to parse which features of the engineered particle, reaction conditions, and biophysical features of a protein lead to the formation of the protein corona population. With machine learning tools it is feasible to imagine tackling the diversity of coronas that could form around nanomaterials in the environment. Moreover, we may be able to gain universal insights into features that mediate formation of corona populations.
Why is it important to understand protein corona formation on ENMs?
The protein corona alters the engineered properties of ENMs, giving them a new biological identity and biochemical reactivity. With an understanding of the protein corona on ENMs, we can better predict toxicity, improve targeting of nano-drugs, and ease design of nano-enabled diagnostics. More broadly, in environmental applications, protein corona studies can inform the use of nano-enabled remediation strategies, design of nanomaterials for agriculture, and prediction of nanoparticle fate in an ecosystem.
What led you to pursue this field of research?
I really enjoy the combination of basic scientific inquiry that leads to environmental impacts. Research in a very interdisciplinary field like environmental nanoscience is also really satisfying because there are so many paths to solving a problem and so much to learn.
What aspect of your work are you most excited about at the moment?
We are excited to bring this to the next level with a large scale proteomics data set to strengthen the model and apply it to a breadth of materials and conditions. With a strong model, we can increase the relevance of protein corona work for environmental systems, where the variations in proteins and conditions are seemingly infinite. If modeling is successful, then we could possibly eliminate the need for experimental characterization in every condition.
What are the most important questions to be asked/answered in this field of research?
The field is in its nascent stages. We have established the landscape of transformations that engineered nanomaterials undergo in the environment, but are only beginning to establish a molecular level picture. One of the next phases of investigation includes insights into the transformations over a material’s lifetime in the environment. Currently, we’ve snapshots of nanomaterials as age and transform in various systems. There are some really interesting studies coming out that look at multiple environmental variables, or exposure scenarios where we can begin to build a moving picture to assess the important features that contribute to nanomaterial transformations and fate in the environment. These insights can help lead to some next generation materials for the agri-sector, which is really exciting.
What do you find most challenging about your research?
The obvious challenge we face is the complexity of environmental systems and the fact that the research sits between multiple disciplines. Given the many variables and interdisciplinarity, we are constantly pushed to communicate better and design new methods to tackle complex problems. I find that the most difficult aspects of a project can also bring the most joy. For example, I chose to work at an institution with primarily undergraduate researchers. Sure, it likely slows us down a bit, but at this stage in their career, they are fearless! No one told them that interdisciplinary work is hard, they simply see it as an opportunity. When I become overwhelmed, I just walk into my lab and the students remind me to approach it all with an open mind and to focus on learning.
What are the next steps for developing this work further?
As a communication, this paper highlights the utility of a random forest classification to tackle the prediction of protein corona populations. The predictive power of this approach, however, depends upon the quality, breadth, and depth of our database used in modeling. We are working with others to develop guidelines for data reporting in an attempt to enable interrogation of datasets across manuscripts (for example, see this recent piece). We are also expanding the dataset to include other organisms, particles, and conditions. We can’t do it all alone though. If others have data on protein enrichment within the corona, we’d be delighted to connect, expand the dataset, and improve this tool!
Read the Best Paper of 2018 for the Environmental Science family of journals by Korin E. Wheeler et al. here.
To learn more about the Best Papers of 2018 in the Environmental Science family of journals, check out the Editorial here and view the nominees collection by clicking the button below.