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Many protein complexes that drive key processes in cells are “molecular motors”—assemblies that consume (electro)chemical energy to produce mechanical work. A 2023 #SciencePerspective discusses how insights from biophysical, biochemical, and structural studies are starting to yield an understanding of the mechanism by which these motors extrude loops of DNA...

43,702 次观看 • 10 个月前 •via X (Twitter)

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This is how DNA turns coded information into functional proteins - the building blocks of the nanomachines that keep the cells in your body alive. This complex process highlights the sophisticated interconnected systems of Life which must all exist together from the beginning, or Life doesn't happen. First, an RNA molecule is copied from a short segment of DNA. Without the specifically ordered DNA information, RNA cannot form, proteins cannot be built, cells stop working, and life ceases to exist. Life is information first. Once the RNA Molecule is created, it gets ejected from the Polymerase where it was built, and it travels through a complex molecular machine called a Nuclear Pore Complex (NPC), which is an information recognition device that controls the flow of information in and out of a cell's nucleus. The NPC is highly complex - composed of about 500-1,000 protein subunits, derived from a set of about 35 distinct proteins. Without this molecular machine, there is no regulation for what goes in and out of the cell's nucleus, which would lead to catastrophic death for the cell. It must exist for cells to exist. Once the RNA Molecule passes through the NPC, it travels to the Ribosome, a 2-part chemical factory which reads the information on RNA and uses it to construct functional proteins using a specifically sequenced chain of amino acids. Once complete, this protein will then be sent to the section of the cell it belongs to integrate into another molecular machine and do its job. The Ribosome is another highly complex molecular machine - consisting of between 56-80 proteins. Without this molecular machines, proteins cannot be built. Proteins are the building blocks of every cell in every organism on Earth. Without Ribosomes, Life doesn't exist. If you're paying attention, you'll start to realize that Life relies on a highly sophisticated interdependent network of complex machines, which all rely on each other for the function of the system. DNA requires the cell for stability, but the cell requires the proteins for its structure and function, but those proteins require DNA and RNA to be built - it's a circle of necessary interdependence. Systems like this cannot be built by evolutionary processes, which requires that each piece of the process is built by gradual incremental means over lots of time. Without all the pieces there, from the beginning, none of it works. There is only one known source of complex & interdependent informational systems like those we find in life: and that is from Intelligence. Molecular Biology is the best and most obvious evidence of the Intelligent Design in Life.

Divinely Designed

62,517 次观看 • 5 个月前

What seemed like an intractable problem is now possible: To design proteins with a specified nonlinear mechanical response, capturing complex folding and unfolding mechanisms in singe and few-shot computations. We present ForceGen, an end-to-end algorithm for de novo protein generation based on nonlinear mechanical unfolding responses. Rooted in the physics of protein mechanics, this generative strategy provides a powerful way to design new proteins rapidly, including exquisite and rapid predictions about their dynamical behavior. Proteins, like any other mechanical object, respond to forces in peculiar ways. Think of the different response you'd get from pulling on a steel cable versus pulling on a rubber band, or the difference between honey and glass. Now, we can design proteins with a set of desirable mechanical characteristics, with applications from health to sustainable plastics. The key to solving this problem was to integrate a protein language model with denoising diffusion methods, and using accurate atomistic-level physical simulation data to endow the model a first-principles understanding. ForceGen can solve both forward and inverse tasks: In the forward task, we can predict how stable a protein is, how it will unfold and what the forces involved are, all given just the sequence of amino acids. In the inverse task, we can design new proteins that meet complex nonlinear mechanical signature targets. Read the paper, led by LAMM@MIT postdoc Bo Ni, published in Science Advances: Why do we care about the mechanics of proteins? The mechanics of proteins are critical elements of many living systems - as evidenced in many studies of mechanobiology. Through evolution, nature has presented a set of remarkable protein materials with unique mechanical functions like elastins, silks, keratins or collagens that play crucial roles in biology. However, going beyond natural designs to discover proteins that meet specified mechanical properties remains challenging. So far, the only way to do this was to use existing evolutionary concepts or to manually alter proteins. With our new generative model we can directly design proteins to meet complex nonlinear mechanical property-design objectives. ForceGen leverages deep knowledge on protein sequences from a pretrained protein language model and maps mechanical unfolding responses to create proteins. Via full-atom molecular simulations for direct validation from physical and chemical principles, we demonstrate that the designed proteins are de novo, and fulfill the targeted mechanical properties, including unfolding energy and mechanical strength, and a detailed unfolding force-separation curves. ForceGen offers rapid pathways to explore the enormous mechanobiological protein sequence space unconstrained by biological synthesis, to enable the discovery of new protein materials with superior mechanical properties. B. Ni, D.L. Kaplan, M.J. Buehler, ForceGen: End-to-end de novo protein generation based on nonlinear mechanical unfolding responses using a language diffusion model. Sci. Adv. 10, eadl4000 (2024). DOI: 10.1126/sciadv.adl4000 Codes and model weights available Hugging Face: David Kaplan

Markus J. Buehler

47,242 次观看 • 2 年前