Machine learning for control over the colors of fluorescent silver nanoclusters

Significance 

Engineered DNA sequence has shown potential to realize self-assembling DNA nanostructures with custom geometries that can direct assembly of molecules and nanoparticles, and even program motion. Further, with advances in nanotechnology, DNA has shown promising attributes that can enable it to be used to template inorganic particles, such as DNA-stabilized silver clusters that comprise of just ~10-30 atoms. So far, the aforementioned silver clusters are the most studied DNA-stabilized metal cluster as they exhibit narrow-band fluorescence colors spanning visible to near-infrared wavelengths. Consequently, they have an intriguing potential as fluorescence reporters for a wide range of nanoscale events. Unfortunately, their applications are inhibited by the unsolved puzzle of the cluster “genome”: How does DNA sequence determine silver cluster color?

Materials informatics is emerging as an efficient route to realizing desired material properties through data-driven prediction of synthesis parameters. Therefore, it is imperative that machine learning and bioinformatics be employed hand-in-hand to subdue the challenges enlisted and also uncover how DNA sequence selects for DNA-stabilized silver clusters.

Recently, a team of researchers including Dr. Stacy M. Copp from the Center for Integrated Nanotechnologies, Los Alamos National Laboratory, in collaboration with Alexander Gorovits ( PhD candidate), Sruthi Gudibandi and Dr. Petko Bogdanov at the University at Albany – SUNY and Dr. Steven M. Swasey and Professor Elisabeth Gwinn at University of California Santa Barbara carried out an investigation on the genomic role of DNA sequence for fluorescent silver clusters using a data-driven approach. Specifically, they sought to engineer DNA templates so as to control the morphology, and thereby the fluorescence color, of clusters of just about 10-20 silver atoms. Their work is currently published in the research journal, ACS Nano.

In brief, their research work entailed the application of high-throughput experimental methods to generate a large training data set that associated many DNA sequences with the fluorescence spectra of the silver clusters they stabilized. Further, they utilized a pattern recognition algorithm to extract DNA base subsequences termed ‘motifs’ that were correlated to specific fluorescence wavelength bands and employed in a machine learning classifier. Lastly, the researchers used the trained classifier to select new templates for the DNA-stabilized silver clusters in specific color bands, and also verify the templates experimentally.

The authors observed that the technique they employed first discovered the DNA base motifs that were most discriminative of color, using a combination of motif mining and feature selection. Moreover, the set of trained pairwise classifiers identified new candidate sequences built from the discovered motifs that fell within a selected color class. Furthermore, they realized that their technique improved selectivity of longer wavelength DNA-stabilized silver clusters, at the boundary of the visible and near-infrared spectrum.

In summary, Copp and colleagues successfully developed a data-driven technique to design DNA templates that select silver clusters of certain sizes, and therefore fluorescence colors, by learning from a large experimental training data set. In general, their technique demonstrated successful design of DNA templates that discriminated for cluster size differences of just a few silver atoms, as well as exploring why the discovered base motifs were selective of color. Altogether, their work portrayed the power of combining new advances in high-throughput experiments with data informatics to achieve molecular design of materials systems.

We believe that these results have implications not just for DNA-templated metal clusters but also for other soft matter systems characterized by extremely large parameter spaces, where techniques such as machine learning are beginning to transform the way we study such systems.” Said Stacy Copp to Advances in Engineering.

Machine learning for control over  colors of fluorescent silver nanoclusters- Advances in Engineering

About the author

Stacy Copp is a Hoffman Distinguished Postdoctoral Fellow and UC President’s Postdoctoral Fellow at Los Alamos National Laboratory, where she works with Gabriel Montaño and Atul Parikh. She is also a 2018 L’Oreal USA for Women in Science Fellow. Stacy’s research focuses on novel photonic materials scaffolded by biological and synthetic polymers.

Much of her work incorporates tools from machine learning and data mining to “learn” the underlying scientific principles that govern self-assembly and to intelligently design new materials. Stacy earned her BS from the University of Arizona in 2011 and her PhD in Physics from UC Santa Barbara in 2016, where she worked in the group of Elisabeth Gwinn.

About the author

Alexander Gorovits is a PhD student in Computer Science at the University at Albany – SUNY. He is interested in machine learning, network mining, and artificial intelligence along with their applications to biological systems and biomaterials. His current work involves elucidating temporal behaviors of clusters within a variety of dynamic networks.

Alexander has a prior background in mathematics and biology, having received a BA in mathematics and biology as well as a Masters of Engineering in biomedical engineering from Cornell University.

About the author

Steven M. Swasey received his B.S. degree in Chemistry from Florida Atlantic University in 2009. He graduated from University of California Santa Barbara with a Ph.D. in Chemistry in early 2018. His work focused on better understanding the unique interactions of silver with DNA for potential applications in nanotechnology, nanophotonics and biomedical uses.

Currently he is employed at Thermochem, a company which provides consulting, analytical and engineering services primarily to the geothermal energy industry. As a part of the analytical services laboratory, he is involved in method development, quality control/assurance and helping oversee laboratory operations.

About the author

Sruthi Gudibandi works as Data Systems Specialist for Health Research Inc. in Healthcare domain. Her focus is in the area of Business Intelligence, she works on SAP BI and .Net projects. She received her Master’s in Computer Science degree from University at Albany – SUNY in 2016 and her Bachelor’s in Computer Science from Jawaharlal Nehru Technological University Hyderabad, India in 2013. Previous work experience include working as Research assistant in the area of Machine Learning with Professor Petko Bogdanov, also worked as Associate Software Engineer for one year in India.

About the author

Petko Bogdanov is an Assistant Professor at the computer science department of University at Albany–SUNY. His research interests include data mining and management and applications to bioinformatics, neuroscience, data-driven nanomaterial design and sociology. Previously, he was a postdoctoral fellow at the department of computer science at University of California, Santa Barbara. He received his PhD and MS in Computer Science from the University of California, Santa Barbara in 2012 and his BE in Computer Engineering from Technical University of Sofia in 2005. Dr. Bogdanov is a member of the IEEE and the ACM and his research has been sponsored by grants from NSF, DARPA and ONR.

About the author

Elisabeth Gwinn is Professor of Physics at UCSB. Her research in experimental condensed matter physics has included collective excitations of electron gases in semiconductor heterostructures, systems of coupled quantum Hall edge states, magnetism in Mn-doped semiconductor heterostructures and pattern formation in nonequilibrium systems. Her current research focuses on DNA-templated silver clusters and use of DNA nanotechnology to form nanoscale assemblies of these clusters.

Reference

Stacy M. Copp, Alexander Gorovits, Steven M. Swasey, Sruthi Gudibandi, Petko Bogdanov, Elisabeth G. Gwinn. Fluorescence Color by Data-Driven Design of Genomic Silver Clusters. ACS Nano 2018, volume 12, page 8240−8247.

Go To ACS Nano

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