{"id":6066,"date":"2022-01-04T18:57:29","date_gmt":"2022-01-04T23:57:29","guid":{"rendered":"https:\/\/sites.une.edu\/research\/?p=6066"},"modified":"2022-01-04T18:57:31","modified_gmt":"2022-01-04T23:57:31","slug":"biomedical-research-leads-sciences-2021-breakthroughs","status":"publish","type":"post","link":"https:\/\/sites.une.edu\/research\/biomedical-research-leads-sciences-2021-breakthroughs\/","title":{"rendered":"Biomedical Research Leads Science\u2019s 2021 Breakthroughs"},"content":{"rendered":"\n

Posted on\u00a0January 4th, 2022\u00a0by\u00a0Lawrence Tabak, D.D.S., Ph.D.<\/a>, NIH’s Directors Blog<\/p>\n\n\n\n

Breakthrough of the Year: AI-Powered Predictions of Protein Structure<\/strong><\/p>\n\n\n\n

The biochemist Christian Anfinsen, who had a distinguished career at NIH, shared the 1972 Nobel Prize in Chemistry, for work suggesting that the biochemical interactions among the amino acid building blocks of proteins were responsible for pulling them into the final shapes that are essential to their functions. In his Nobel acceptance speech, Anfinsen also made a bold prediction: one day it would be possible to determine the three-dimensional structure of any protein based on its amino acid sequence alone. Now, with advances in applying artificial intelligence to solve biological problems\u2014Anfinsen\u2019s bold prediction has been realized.<\/p>\n\n\n\n

But getting there wasn\u2019t easy. Every two years since 1994, research teams from around the world have gathered to compete against each other in developing computational methods for predicting protein structures from sequences alone. A score of 90 or above means that a predicted structure is extremely close to what\u2019s known from more time-consuming work in the lab. In the early days, teams more often finished under 60.<\/p>\n\n\n\n

In 2020, a London-based company called DeepMind made a leap with their entry called AlphaFold. Their deep learning approach\u2014which took advantage of 170,000 proteins with known structures\u2014most often scored above 90, meaning it could solve most protein structures about as well as more time-consuming and costly experimental protein-mapping techniques. (AlphaFold was one of Science<\/em>\u2019s runner-up breakthroughs<\/a> last year.)<\/p>\n\n\n\n

This year, the NIH-funded lab of David Baker and Minkyung Baek, University of Washington, Seattle, Institute for Protein Design, published that their artificial intelligence approach<\/a>, dubbed RoseTTAFold, could accurately predict 3D protein structures from amino acid sequences with only a fraction of the computational processing power and time that AlphaFold required [1]. They immediately applied it to solve hundreds of new protein structures, including many poorly known human proteins with important implications for human health.<\/p>\n\n\n\n

The DeepMind and RoseTTAFold scientists continue to solve more and more proteins [1,2], both alone and in complex with other proteins. The code is now freely available for use by researchers anywhere in the world. In one timely example, AlphaFold helped to predict the structural changes in spike proteins of SARS-CoV-2 variants Delta and Omicron [3]. This ability to predict protein structures, first envisioned all those years ago, now promises to speed fundamental new discoveries and the development of new ways to treat and prevent any number of diseases, making it this year\u2019s Breakthrough of the Year.<\/p>\n\n\n\n

Anti-Viral Pills for COVID-19<\/strong><\/p>\n\n\n\n

The development of the first vaccines to protect against COVID-19 topped Science<\/em>\u2019s 2020 breakthroughs. This year, we\u2019ve also seen important progress in treating COVID-19, including the development of anti-viral pills<\/a>.<\/p>\n\n\n\n

First, there was the announcement in October of interim data from Merck, Kenilworth, NJ, and Ridgeback Biotherapeutics, Miami, FL, of a significant reduction in hospitalizations for those taking the anti-viral drug molnupiravir [4] (originally developed with an NIH grant to Emory University, Atlanta). Soon after came reports of a Pfizer anti-viral pill that might target SARS-CoV-2, the novel coronavirus that causes COVID-19, even more effectively. Trial results show that, when taken within three days of developing COVID-19 symptoms, the pill reduced the risk of hospitalization or death in adults at high risk of progressing to severe illness by 89 percent [5].<\/p>\n\n\n\n

On December 22, the Food and Drug Administration (FDA) granted Emergency Use Authorization (EUA) for Pfizer\u2019s Paxlovid to treat mild-to-moderate COVID-19 in people age 12 and up at high risk for progressing to severe illness, making it the first available pill to treat COVID-19 [6]. The following day, the FDA granted an EUA for Merck\u2019s molnupiravir to treat mild-to-moderate COVID-19 in unvaccinated, high-risk adults for whom other treatment options aren\u2019t accessible or recommended, based on a final analysis showing a 30 percent reduction in hospitalization or death [7].<\/p>\n\n\n\n

Additional promising anti-viral pills for COVID-19 are currently in development. For example, a recent NIH-funded preclinical study suggests that a drug related to molnupiravir, known as 4\u2019-fluorouridine, might serve as a broad spectrum anti-viral with potential to treat infections with SARS-CoV-2 as well as respiratory syncytial virus (RSV) [8].<\/p>\n\n\n\n

Artificial Antibody Therapies<\/strong><\/p>\n\n\n\n

Before anti-viral pills came on the scene, there\u2019d been progress in treating COVID-19, including the development of monoclonal antibody infusions<\/a>. Three monoclonal antibodies now have received an EUA for treating mild-to-moderate COVID-19, though not all are effective against the Omicron variant [9]. This is also an area in which NIH\u2019s Accelerating COVID-19 Therapeutic Interventions and Vaccines (ACTIV<\/a>) public-private partnership has made big contributions.<\/p>\n\n\n\n

Monoclonal antibodies are artificially produced versions of the most powerful antibodies found in animal or human immune systems, made in large quantities for therapeutic use in the lab. Until recently, this approach had primarily been put to work in the fight against conditions including cancer, asthma, and autoimmune diseases. That changed in 2021 with success using monoclonal antibodies against infections with SARS-CoV-2 as well as respiratory syncytial virus (RSV), human immunodeficiency virus (HIV), and other infectious diseases. This earned them a prominent spot among Science<\/em>\u2019s breakthroughs of 2021.<\/p>\n\n\n\n

Monoclonal antibodies delivered via intravenous infusions continue to play an important role in saving lives during the pandemic. But, there\u2019s still room for improvement, including new formulations highlighted on the blog last year<\/a> that might be much easier to deliver.<\/p>\n\n\n\n

CRISPR Fixes Genes Inside the Body<\/strong><\/p>\n\n\n\n

One of the most promising areas of research in recent years has been gene editing, including CRISPR\/Cas9, for fixing misspellings in genes to treat or even cure many conditions. This year has certainly been no exception.<\/p>\n\n\n\n

CRISPR is a highly precise gene-editing system that uses guide RNA molecules to direct a scissor-like Cas9 enzyme to just the right spot in the genome to cut out or correct disease-causing misspellings. Science<\/em> highlights a small study reported in The New England Journal of Medicine<\/em> by researchers at Intellia Therapeutics, Cambridge, MA, and Regeneron Pharmaceuticals, Tarrytown, NY, in which six people with hereditary transthyretin (TTR) amyloidosis<\/a>, a condition in which TTR proteins build up and damage the heart and nerves, received an infusion of guide RNA and CRISPR RNA encased in tiny balls of fat [10]. The goal was for the liver to take them up, allowing Cas9 to cut and disable the TTR gene. Four weeks later, blood levels of TTR had dropped by at least half.<\/p>\n\n\n\n

In another study not yet published, researchers at Editas Medicine, Cambridge, MA, injected a benign virus carrying a CRISPR gene-editing system into the eyes of six people with an inherited vision disorder called Leber congenital amaurosis 10. The goal was to remove extra DNA responsible for disrupting a critical gene expressed in the eye. A few months later, two of the six patients could sense more light, enabling one of them to navigate a dimly lit obstacle course [11]. This work builds on earlier gene transfer studies<\/a> begun more than a decade ago at NIH\u2019s National Eye Institute.<\/p>\n\n\n\n

Last year, in a research collaboration that included former NIH Director Francis Collins\u2019s lab at the National Human Genome Research Institute (NHGRI), we also saw encouraging early evidence in mice<\/a> that another type of gene editing, called DNA base editing, might one day correct Hutchinson-Gilford Progeria Syndrome, a rare genetic condition that causes rapid premature aging. Preclinical work has even suggested that gene-editing tools might help deliver long-lasting pain relief<\/a>. The technology keeps getting better<\/a>, too. This isn\u2019t the first time that gene-editing advances have landed on Science<\/em>\u2019s annual Breakthrough of the Year list, and it surely won\u2019t be the last.<\/p>\n\n\n\n

The year 2021 was a difficult one as the pandemic continued in the U.S. and across the globe, taking far too many lives far too soon. But through it all, science has been relentless in seeking and finding life-saving answers, from the rapid development of highly effective COVID-19 vaccines to the breakthroughs highlighted above.<\/p>\n\n\n\n

As this list also attests, the search for answers has progressed impressively in other research areas during these difficult times. These groundbreaking discoveries are something in which we can all take pride\u2014even as they encourage us to look forward to even bigger breakthroughs in 2022. Happy New Year!<\/p>\n\n\n\n

Read more <\/a><\/p>\n","protected":false},"excerpt":{"rendered":"

Posted on\u00a0January 4th, 2022\u00a0by\u00a0Lawrence Tabak, D.D.S., Ph.D., NIH’s Directors Blog Breakthrough of the Year: AI-Powered Predictions …<\/p>\n","protected":false},"author":49,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[36],"tags":[],"post_folder":[],"class_list":["post-6066","post","type-post","status-publish","format-standard","hentry","category-faculty-development"],"acf":[],"_links":{"self":[{"href":"https:\/\/sites.une.edu\/research\/wp-json\/wp\/v2\/posts\/6066","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/sites.une.edu\/research\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/sites.une.edu\/research\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/sites.une.edu\/research\/wp-json\/wp\/v2\/users\/49"}],"replies":[{"embeddable":true,"href":"https:\/\/sites.une.edu\/research\/wp-json\/wp\/v2\/comments?post=6066"}],"version-history":[{"count":3,"href":"https:\/\/sites.une.edu\/research\/wp-json\/wp\/v2\/posts\/6066\/revisions"}],"predecessor-version":[{"id":6069,"href":"https:\/\/sites.une.edu\/research\/wp-json\/wp\/v2\/posts\/6066\/revisions\/6069"}],"wp:attachment":[{"href":"https:\/\/sites.une.edu\/research\/wp-json\/wp\/v2\/media?parent=6066"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/sites.une.edu\/research\/wp-json\/wp\/v2\/categories?post=6066"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/sites.une.edu\/research\/wp-json\/wp\/v2\/tags?post=6066"},{"taxonomy":"post_folder","embeddable":true,"href":"https:\/\/sites.une.edu\/research\/wp-json\/wp\/v2\/post_folder?post=6066"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}