Warwick, EPFL team uses phage to discover new antifreeze proteins

Controlling and mitigating the effects of ice growth is crucial to protect infrastructure, help preserve frozen cells, and enhance frozen foods' texture. An international collaboration of Warwick Scientists working with researchers from Switzerland has used a phage display platform to discover new, small, peptides which function like larger antifreeze proteins. This presents a route to new, easier to synthesize, cryoprotectants.

Ice binding proteins, which include antifreeze proteins, are produced by a large range of species from fish, to insects to plants, to prevent the damage caused by ice. The proteins achieve the remarkable function of recognizing and binding to ice, even in the huge excess of water (which ice is the solid form of). New antifreeze proteins have typically been discovered by isolation from the organisms.

In this work, the team took a very different approach by screening billions of possible peptides to find those which could bind to ice. This was achieved by Phage Display – a technology whereby a virus is used to generate vast numbers of peptides, and those which ‘bind’ to the ice can be isolated.

Using this a cyclic peptide of just 14 amino acids (which is very short compared to a typical protein) was discovered which could bind to ice. The team used supercomputer simulations to understand how the peptide binds to the ice, which is not possible by ‘wet’ experimental techniques alone. The team also showed how this short peptide can help purify other proteins by ace affinity purification. gibson sosso crop 5df26

By identifying these short peptides, it means researchers can now simply make (or buy) modified peptides to understand and probe how these interact with ice and help design new cryoprotectants with simplified structures, and hence lower cost.

In the paper A Minimalistic Cyclic Ice-Binding Peptide from Phage Display, the international team including the University of Warwick and led by EPFL, Switzerland, have demonstrated the use of phage display to discover new minimalistic antifreeze peptides, which could not be achieved by conventional tools, which would not allow the billions of potential structures to be screened.

Dr. Gabriele Sosso, Assistant Professor at the University of Warwick, in the Department of Chemistry commented, “This work highlights that even very small changes within the structure of these peptides can make a huge difference in their ability to control the formation of ice. Our computer simulations allowed us to identify and understand the importance of these structural changes – which is a key step toward the rational design of synthetic cryoprotectants.

“It is such a privilege to be able to leverage both the experimental work of Gibson’s group and the computational resources of the SCRTP. Truly, Warwick is a great place to be if you want to understand how ice forms and what can we do to have a say in this process.”

Professor Matthew Gibson, Professor at the University of Warwick in the Department of Chemistry and Warwick Medical School adds:

“We have been working on developing synthetic tools to understand, and interfere with, ice growth processes to help develop new cryoprotectants. This work was really exciting, as we used biotechnology tools (phage) to discover small, cyclic, peptides that are remarkably potent.

“These peptides are easy to synthesis and modify and will accelerate our research in this field. It also highlights the growing ‘team ice’ collaborative network at Warwick, combining experimental and computation studies. We are also grateful for the support from the IAS at Warwick, which allowed Dr. Stevens to visit us to complete this work, showing the need to support international scientific collaborations.”

Yale-Boehringer Ingelheim announce Biomedical Data Science Fellowship Program

Yale University, in partnership with Boehringer Ingelheim, today announced the launch of a Biomedical Data Science Fellowship program designed to attract and support some of the brightest and most innovative minds in data science from around the world.

Post-doctoral researchers awarded a three-year fellowship will have access to Yale's robust computational resources, biomedical data repositories, and faculty expertise. In addition, they will benefit from access to Boehringer Ingelheim's corporate labs, scientists and executives. Applicants are invited to submit research proposals for consideration by June 15. If approved for a fellowship, they will be jointly mentored throughout the research process by industry experts and scientists from Boehringer Ingelheim -- one of the world's leading pharmaceutical companies -- as well as Yale's world-class researchers and scholars. Fellowship training begins Sept. 1.

"This collaboration with Boehringer Ingelheim creates a world-class data science fellowship program that will drive the development of novel methods and tools to analyze and interpret the many large and complex biomedical datasets that have been created in recent years," said Yale School of Public Health Professor of Biostatistics, Genetics, Statistics, and Data Science Hongyu Zhao, Ph.D., principal investigator for the project.

The program will be based at the Yale Center for Biomedical Data Science (CBDS) in New Haven, Connecticut. The center is an essential part of a growing data science hub at Yale University, which has identified integrated data science as one of its primary investment areas over the next decade. CBDS is located within Yale School of Medicine and currently supports more than 100 faculty members and researchers representing such disciplines as bioinformatics, modeling, statistics, computer science, artificial intelligence, mathematics, biology, precision medicine, and public health.

"In partnering with a top-tier academic and research institution like Yale, we aim to recruit and train a new generation of highly skilled data scientists to help us accelerate the development of novel treatments and therapies for human disease and improve health outcomes for our patients," said Jan Nygaard Jensen, Ph.D., Global Head of Computational Biology and Digital Sciences at Boehringer Ingelheim.

The partnership reflects a mutual vision between Boehringer Ingelheim and Yale University. It is part of a comprehensive strategic program at Boehringer Ingelheim which will harness the power of data science to transform drug discovery and development. The aim is to catalyze the next breakthrough therapies that change lives by accelerating timelines, improving scientific and clinical success, and further elevating patient centricity.

"Boehringer Ingelheim is pleased to build upon our successful relationships with Yale to foster the next generation of scientists and harness the power of data science to bring our vision of making new and better medicines for patients in need," said Clive R. Wood, Ph.D., Senior Corporate Senior Vice President, Global Head, Discovery Research, Boehringer Ingelheim. "We believe our shared ambition and outlook will build a world-class data science community to attract outstanding researchers and work to achieve breakthroughs that patients need."

Initially, the program will award as many as three competitive fellowships per year, up to a total of nine over the first five years. In addition to receiving research funding and mentorship, program fellows will be invited to participate in campus and corporate visits, networking events, and annual symposia.

A joint selection committee comprising representatives of Yale and Boehringer Ingelheim will set annual data-driven research themes for the program. These themes may include such topics as genomic analysis, biomarkers, data-driven therapeutic research, medical image informatics, precision medicine, and translational medicine. The selection committee will consider proposed research projects' alignment with prioritized themes in judging submissions and post-doctoral applicants.

Yale's data science ecosystem is supported by a host of cutting-edge research institutions working collaboratively. In addition to CBDS, they include Yale's Systems Biology Institute, Center for Mendelian Genomics, Center for RNA Science and Medicine, and Center for Medical Informatics. Yale's biobanks and technology core include the Yale BioBank GENERATIONS, VA Million Veteran Program, Center for Research Computing, Center for Genomic Health, and Center for Genomic Analysis, which houses the ninth-largest genomic library in the world.

Xinxin (Katie) Zhu, MD, Ph.D., executive director of the Yale Center for Biomedical Data Science, said the fellowship program offers an exciting opportunity for the development of innovative data-driven approaches for different medical conditions that can be translated from the lab to the patient's bedside. It is an especially opportune time, she said.

"The vast amount of biomedical data being generated today has created a tremendous need for highly skilled data scientists who can use this information to advance care," said Zhu.

Specialists in biomedical data science and health informatics can identify statistical associations and patterns of disease. They also can develop complex machine learning models and simulations of molecular, cellular, and organismic systems to increase the probability of clinical success through precision medicine and other methods.

"This helps clinicians and pharmaceutical companies such as Boehringer Ingelheim identify potential new pathways for treatment and eradication of disease," Zhu said.

To apply for a Yale-Boehringer Ingelheim Biomedical Data Science Fellowship, please go to https://medicine.yale.edu/cbds/bdsfellowship.

Harvard Med develops AI that reveals current drugs help to combat Alzheimer's disease

The analysis points to new treatment targets for the disease.

New treatments for Alzheimer's disease are desperately needed, but numerous clinical trials of investigational drugs have failed to generate promising options. Now a team at Massachusetts General Hospital (MGH) and Harvard Medical School (HMS) has developed an artificial intelligence-based method to screen currently available medications as possible treatments for Alzheimer's disease. The method could represent a rapid and inexpensive way to repurpose existing therapies into new treatments for this progressive, debilitating neurodegenerative condition. Importantly, it could also help reveal new, unexplored targets for therapy by pointing to mechanisms of drug action.

"Repurposing FDA-approved drugs for Alzheimer's disease is an attractive idea that can help accelerate the arrival of effective treatment--but unfortunately, even for previously approved drugs, clinical trials require substantial resources, making it impossible to evaluate every drug in patients with Alzheimer's disease," explains Artem Sokolov, Ph.D., director of Informatics and Modeling at the Laboratory of Systems Pharmacology at HMS. "We, therefore, built a framework for prioritizing drugs, helping clinical studies to focus on the most promising ones."

In an article published in an academic journal, Sokolov and his colleagues describe their framework, called DRIAD (Drug Repurposing In Alzheimer's Disease), which relies on machine learning--a branch of artificial intelligence in which systems are "trained" on vast amounts of data, "learn" to identify telltale patterns and augment researchers' and clinicians' decision-making.

DRIAD works by measuring what happens to human brain neural cells when treated with a drug. The method then determines whether the changes induced by a drug correlate with molecular markers of disease severity.

The approach also allowed the researchers to identify drugs that had protective as well as damaging effects on brain cells.

"We also approximate the directionality of such correlations, helping to identify and filter out neurotoxic drugs that accelerate neuronal death instead of preventing it," says co-first author Steve Rodriguez, Ph.D., an investigator in the Department of Neurology at MGH and an instructor at HMS.

DRIAD also allows researchers to examine which proteins are targeted by the most promising drugs and if there are common trends among the targets, an approach designed by Clemens Hug, Ph.D., a research associate in the Laboratory of Systems Pharmacology and a co-first author.

The team applied the screening method to 80 FDA-approved and clinically tested drugs for a wide range of conditions. The analysis yielded a ranked list of candidates, with several anti-inflammatory drugs used to treat rheumatoid arthritis and blood cancers emerging as top contenders. These drugs belong to a class of medications known as Janus kinase inhibitors. The drugs work by blocking the action of inflammation-fueling Janus kinase proteins, suspected to play a role in Alzheimer's disease and known for their role in autoimmune conditions. The team's analyses also pointed to other potential treatment targets for further investigation.

"We are excited to share these results with the academic and pharmaceutical research communities. Our hope is that further validation by other researchers will refine the prioritization of these drugs for clinical investigation," says Mark Albers, MD, Ph.D., the Frank Wilkins Jr. and Family Endowed Scholar and associate director of the Massachusetts Center for Alzheimer Therapeutic Science at MGH and a faculty member of the Laboratory of Systems Pharmacology at HMS. One of these drugs, baricitinib, will be investigated by Albers in a clinical trial for patients with subjective cognitive complaints, mild cognitive impairment, and Alzheimer's disease that will be launching soon at MGH in Boston and at Holy Cross Health in Fort Lauderdale, Florida. "In addition, independent validation of the nominated drug targets could provide new insights into the mechanisms behind Alzheimer's disease and lead to novel therapies," says Albers.