USP28

Some cancer drugs cause severe side effects because they are not working accurately enough. Biochemists have now discovered why. The small protein ubiquitin is involved in almost every cellular process in our body: it orchestrates the stability and function of the vast majority of proteins. When it binds to other proteins, they are often released for degradation. However, this labelling can also be reversed by special enzymes. The enzyme USP28, for example, is known to stabilise proteins that are important for cell growth and division -- these can also play an important role in cancer growth. In order to reduce the stability of these proteins and thus inhibit cancer growth, inhibitors of USP28 have been developed. These inhibitors, which form the basis of many anti-cancer drugs currently in development, disrupt cell division by blocking the USP28 enzyme. The problem is that they often act not only against USP28 but also against USP25, a closely related enzyme that separates ubiquitin from other proteins and is considered a key protein of the immune system. Further development of USP28 inhibitors into therapeutics that can be used in the clinic is therefore very difficult because of the foreseeable side effects -- ranging from gastrointestinal problems to nerve damage and even autoimmune diseases. Risk of Confusion Between the Two Enzymes Researchers at the University of Würzburg (JMU) have now discovered why inhibitors not only target USP28, but also USP25: "Apparently, there is a high risk of confusion between USP28 and USP25," explains Caroline Kisker, Chair of Structural Biology at the Rudolf Virchow Centre in Würzburg and Vice President for Research and Young Scientists. "We have been able to show that the two enzymes are very similar or even identical in many areas, including precisely where the inhibitors act." As part of her research, the biochemist's team used X-ray crystallography to analyse the structure of USP28 in combination with the three inhibitors "AZ1," "Vismodegib" and "FT206" -- and thus determine the spatial binding site. Further biochemical experiments on USP25 showed that the sites where the inhibitors bind to USP28 and USP25 are identical. "The inhibitors are therefore unable to distinguish where they bind," says Kisker. "This explains the non-specific effect." Discovery Paves the Way for the Development of Precise Inhibitors The new scientific findings provide an important basis for the search for more specific drugs with fewer side effects. Their development is the next major goal of the Würzburg researchers. "Our structural biology data allows us to modify existing inhibitors so that they only work against either USP25 or USP28," says Kisker. "We also want to look for inhibitors that bind to less similar enzyme sites. This will give these molecules greater targeting precision." The research was funded by the German Research Foundation (DFG). The Rudolf-Virchow-Centre in Würzburg The Rudolf Virchow Centre (RVZ) for Integrative and Translational Imaging is an interdisciplinary research centre that focuses on the visualisation of elementary life processes -- from the sub-nano to the macro scale. As a central institution of the University of Würzburg, the centre is currently home to 13 translational research groups and around 100 researchers investigating the molecular causes of health and disease.

David Berry joined Flagship as a Harvard and MIT wunderkind in 2005 and by now he’s been at the firm almost as long as anyone not named Noubar. He’s played a hand in some of their biggest startups, including Seres, Omega, KSQ, Evelo and, of course, Moderna. Flagship is known for its flash, but over the last couple years, Berry has been quietly building a computational startup in Boston that he thinks can remake drug development in a way that, for all the buzz, machine learning and artificial intelligence have yet to do. He’s managed to convince at least a few investors of that vision, raising $100 million in a Series A, $50 million in an undisclosed financing and a $190 million in a Series B despite remaining far from the clinic. On Tuesday, Valo revealed another investor who bought in, adding $110 million from Koch Disruptive Technology, the venture capital arm of the famed and infamous holding company Koch Industries. Koch Disruptive Technologies has invested in a broad swatch of tech companies but not yet waded deeply into biotech. In backing Valo, they are buying into what Berry says is a new way of thinking about drugs and tech and data. “As we look at the process that exists in drug development or drug discovery today, it’s effectively a point-to-point process and each of the steps has its own data, its own way of making decisions,” Berry told Endpoints News. “As the drug moves through its various steps, it’s as if it’s been thrown over a wall.” AI and machine learning have largely been deployed at individual steps of the drug development process: used by Atomwise, for example, to screen massive libraries for the best molecule, or by Insitro to find the difference between diseased and healthy cells, and the places where developers might be able to intervene and turn one to the other. Separately, large pharmas and small biotechs like BlackThorn have used machine learning to design trials and find the best patients. Berry argues that we need a better system — a better platform — that can integrate all those various sources and applications of data from the outset, allowing companies to overcome the hurdles that tend to appear as a drug goes through development. And that data should be grounded in humans (as opposed to mice or monkey experiments) to increase the odds that basic lab insights actually translate into the clinic. With about 110 employees, large amounts of human data, including what they claim is the “largest and most-detailed cardio-metabolic dataset in the world,” and reams and reams of cloud space, they’ve built a platform they call Opal to do that. For example, Berry claimed, they’ve developed an algorithm that allows them to predict the toxicology of any given compound with 86% accuracy. They also use 3D physics software to simulate and design molecules, adopting a similar approach to the well-partnered computational biotech Schrödinger, which has long argued that the AI algorithms many startups use to screen for molecules struggle because they rely on 2D images of 3D molecules. “When you ask the computer to try learn that a 2d image represents a 3D structure — that’s a very hard thing for a computer to learn,” Berry said. To find new targets, the platform and Valo’s scientists combine “omics” data – genomics, proteomics, even the much less talked about metabolomics — with data that track how patients change as they age and their diseases progress or wane. “Diseases are dynamic,” he said. Valo will use the new proceeds to continue hiring a few dozen more employees and progress the platform. With two years of runway, it will also give them the cash to get into the clinic, Berry said. Although they haven’t gone deep into details, Valo did release their first batch of cancer targets earlier this year: NAMPT, PARP1, USP28 and HDAC3. Their targets for neurodegeneration and cardiovascular disease remain undisclosed.