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ASBMB launches Insights in Biochemistry and Molecular Biology

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  Molecular movement and interaction form an intricate choreography, where each insight enriches our understanding and downstream applications. The American Society for Biochemistry and Molecular Biology is expanding ways to share emerging research across the breadth of the molecular life sciences by adding  Insights in Biochemistry and Molecular Biology , or IBMB, to its family of journals. Joining the society’s flagship  Journal of Biological Chemistry , as well as  Molecular & Cellular Proteomics  and the  Journal of Lipid Research , IBMB will further strengthen ASBMB’s commitment to advancing discovery through biochemistry and molecular biology. Together, ASBMB’s family of journals will reinforce ASBMB’s leadership as a home for research that drives fundamental understanding across the molecular life sciences, which is essential for long-term progress for health and human benefit. Like all ASBMB journals, IBMB will be gold open access, a leadin...

Study across multiple brain regions discerns Alzheimer’s vulnerability and resilience factors

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  Genomics and lab studies reveal numerous findings, including a key role for Reelin amid neuronal vulnerability, and for choline and antioxidants in sustaining cognition. An open-access MIT study published today in  Nature  provides new evidence for how specific cells and circuits become vulnerable in Alzheimer’s disease, and hones in on other factors that may help some people show resilience to cognitive decline, even amid clear signs of disease pathology.  To highlight potential targets for interventions to sustain cognition and memory, the authors engaged in a novel comparison of gene expression across multiple brain regions in people with or without Alzheimer’s disease, and conducted lab experiments to test and validate their major findings. Brain cells all have the same DNA but what makes them differ, both in their identity and their activity, are their patterns of how they express those genes. The new analysis measured gene expression differences in more ...

A targeted approach to using AI in drug discovery

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  How are we improving the way the field of drug discovery creates machine learning algorithms to predict a protein’s interactions with a small molecule? The drug development pipeline is a costly and lengthy process. Identifying high-quality ‘hit’ compounds – those with high potency, selectivity and favorable metabolic properties – at the earliest stages is important for reducing cost and accelerating the path to clinical trials. For the last decade, scientists have looked to machine learning to make this initial screening process more efficient. Computer-aided drug design is used to computationally screen for compounds that interact with a target protein. However, the ability to accurately and rapidly estimate the strength of these interactions remains a challenge. “Machine learning (ML) promised to bridge the gap between the accuracy of gold-standard, physics-based computational methods and the speed of simpler empirical scoring functions,” commented Benjamin P. Brown, an assista...

AI in Biotechnology: Transforming Medicine, Food, and Animal Health

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  The Unresolved Challenges in Human Health Despite decades of progress in medical science, humanity continues to face significant health challenges. Diseases like cancer, diabetes, and Alzheimer’s remain unpredictable and difficult to cure. Millions of people worldwide suffer from these conditions, often with limited treatment options and poor outcomes. Traditional medicine, while effective in some cases, often falls short in addressing the complexity of these diseases. For instance,  cancer treatments like chemotherapy and radiation therapy  often come with severe side effects and are not effective for all patients. Similarly, diabetes management relies heavily on lifestyle changes and insulin therapy, but a definitive cure remains elusive. Alzheimer’s disease, which affects millions globally, still lacks effective treatments to halt or reverse its progression. This is where  AI in Biotechnology  steps in. By leveraging AI, scientists and researchers are tackl...

Revolutionizing Biology: Large Perturbation Models Unleashed

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  In the ever-evolving landscape of scientific research, the intersection of computational modeling and biological discovery has emerged as a cornerstone in the quest for innovative solutions to complex biological problems. The researchers Miladinovic, Höppe, Chevalley, and their colleagues have ventured into this fascinating domain, presenting a groundbreaking study that delves into the use of large perturbation models for in silico biological discovery. This research seeks to expand our understanding of biological systems and unlock the potential for significant advancements in medicine, genetics, and environmental science. The premise of their study revolves around the concept of perturbation models, powerful computational tools that simulate the effects of changes within biological systems. By manipulating various parameters and observing the resultant behaviors, these models provide insights that are often unattainable through traditional experimental methods. This innovative ...

Computational biology unlocks rules of tissue self-organization

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  Every day, your body replaces billions of cells-and yet, your tissues stay perfectly organized. How is that possible? A team of researchers at ChristianaCare's Helen F. Graham Cancer Center & Research Institute and the University of Delaware believe they've found an answer. In a new study published today in the scientific journal  Biology of the Cell , they show that just five basic rules may explain how the body maintains the complex structure of tissues like those in the colon, for example, even as its cells are constantly dying and being replaced. This research is the product of more than 15 years of collaboration between mathematicians and cancer biologists to unlock the rules that govern tissue structure and cellular behavior. "This may be the biological version of a blueprint," said Bruce Boman, M.D., Ph.D., senior research scientist at ChristianaCare's Cawley Center for Translational Cancer Research and faculty member in the departments of Biologic...

David Baker | American Biochemist & Computational Biologist

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  David Baker    is an American biochemist and   computational biologist   who developed computerized methods for the de novo (from scratch) design of   proteins   with entirely new functions. Baker’s work on protein structure   prediction   and design fueled advances in   synthetic biology   and in the development of novel   drugs   and proteins for industrial applications. He was awarded the 2024   Nobel Prize   in Chemistry (shared with English computer scientist   Demis Hassabis  and American researcher  John M. Jumper ) for his breakthroughs in computational protein design. Education and early career Baker grew up in Seattle. As an undergraduate, he attended Harvard University, where he studied philosophy and social science until his last year, when he transitioned to biology. After earning a bachelor’s degree from Harvard in 1984, he went to the  U...