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Excited to share a new preprint! We've discovered repeated evolution of protein 3D structure in recurrently emerged bacterial ALDH-ADH fusion enzymes. Repeatability of protein structural evolution following convergent gene fusions

Naoki Konno

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30,278 次观看 • 1 年前 •via X (Twitter)

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We're thrilled to present ESM3 in Science Magazine. ESM3 is a generative language model that reasons over the three fundamental properties of proteins: sequence, structure, and function. Today we're making ESM3 available free to researchers worldwide via the public beta of an API for biological intelligence. Trained with over a trillion teraflops of compute, this is the first time a model of this scale has been trained for biology, pushing the frontier of AI for biological discovery and engineering. ESM3 learns to represent the immense complexity of protein biology, learning from billions of natural proteins. From this training it developed the capability to design proteins, responding to complex prompts combining atomic level details and high level instructions to generate new proteins. ESM3 can explore protein space far beyond natural evolution. We prompted ESM3 to generate a fluorescent protein at a far distance from any known fluorescent proteins, searching an unknown region of protein space, to discover a new fluorescent protein. We estimate this is equivalent to simulating five hundred million years of evolution.

We're thrilled to present ESM3 in Science Magazine. ESM3 is a generative language model that reasons over the three fundamental properties of proteins: sequence, structure, and function. Today we're making ESM3 available free to researchers worldwide via the public beta of an API for biological intelligence. Trained with over a trillion teraflops of compute, this is the first time a model of this scale has been trained for biology, pushing the frontier of AI for biological discovery and engineering. ESM3 learns to represent the immense complexity of protein biology, learning from billions of natural proteins. From this training it developed the capability to design proteins, responding to complex prompts combining atomic level details and high level instructions to generate new proteins. ESM3 can explore protein space far beyond natural evolution. We prompted ESM3 to generate a fluorescent protein at a far distance from any known fluorescent proteins, searching an unknown region of protein space, to discover a new fluorescent protein. We estimate this is equivalent to simulating five hundred million years of evolution.

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We’re excited to unveil the evolution of 0N1 Force into ONI Force along with our new, updated brand guidelines! This marks a bold step forward in our journey, and we can’t wait to share every detail of our new look!

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Excited today to share with you the next step in the evolution of GM World Our new website features exceptional up-and-coming artists and offers more context on our curation, including the first episode of our new video series: From The Studio

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Proof that Life is Intelligently Designed: Proteins. They do everything in your cells that keeps you alive. These microscopic molecules must have been created. Here is why: Proteins are the building blocks of all the little nano-machines that make up your cells. They do all the major work in your body - from creating energy to recycling waste. Proteins are made from amino acids connected into a specific sequence and then folded into a functional shape. You can think of a protein like a paragraph, and amino acids as the individual letters that spell out the words. Here is where it gets interesting... There are just 20 amino acids used in all of Life. Just 20 amino acids. Responsible for ~200 million unique proteins making up tens of thousands of cell types across Life. And scientists are constantly finding new ones. Scientists categorize proteins into Families. Protein Families are groups of proteins that share amino acid sequence similarities. There are ~22,000 Protein Families. Here's the part that screams they are designed... Protein Families have no evolutionary history. Even evolutionist scientists agree - they are absolutely unique. It's been said that proteins are like stars in a galaxy, and families are like galaxies, with vast empty space between them. Evolution is supposed to build by tweaking things that already exist. But no evolutionary history connects fundamentally distinct protein families. Experiments have been done to intentionally evolve one protein family into another. They fail every time. One protein family cannot be evolved into another. They cannot arise through evolution. But the math really makes it impossible... The odds of evolution finding even a single functional protein family is 1 chance in 10^77 possible amino acid sequences. That's a 1 with 77 zeroes. The odds of evolution finding 22,000 distinct protein families? Roughly 1 chance in 10^1,694,000. You don't need to be a mathematician to understand that this is impossible. Evolution works by random mutations tweaking what's already there. But protein families can't evolve from one another. And the math makes it absolutely absurd to believe. Monkeys banging on keyboards will never type out Shakespeare. And random mutations in sequences of amino acids could never create a single functional protein family. There is only one thing we know of that creates functionally specified sequences: Intelligence. Now here is the final cherry on top: Proteins are built by other proteins, assembled into a complex machine to do a job in cells. The assembly instructions for those proteins and the machines are found in DNA. But DNA requires protein machines for replication & repair. So DNA is required to make Proteins... ...but Proteins are required to make DNA *and* more Proteins. You can't have one without the other. They rely on each other. Which means one couldn't have evolved and then waited for the other. They couldn't do anything without each other. They had to be created at the same time to function together. Life was Divinely Designed. Proteins prove it.

Proof that Life is Intelligently Designed: Proteins. They do everything in your cells that keeps you alive. These microscopic molecules must have been created. Here is why: Proteins are the building blocks of all the little nano-machines that make up your cells. They do all the major work in your body - from creating energy to recycling waste. Proteins are made from amino acids connected into a specific sequence and then folded into a functional shape. You can think of a protein like a paragraph, and amino acids as the individual letters that spell out the words. Here is where it gets interesting... There are just 20 amino acids used in all of Life. Just 20 amino acids. Responsible for ~200 million unique proteins making up tens of thousands of cell types across Life. And scientists are constantly finding new ones. Scientists categorize proteins into Families. Protein Families are groups of proteins that share amino acid sequence similarities. There are ~22,000 Protein Families. Here's the part that screams they are designed... Protein Families have no evolutionary history. Even evolutionist scientists agree - they are absolutely unique. It's been said that proteins are like stars in a galaxy, and families are like galaxies, with vast empty space between them. Evolution is supposed to build by tweaking things that already exist. But no evolutionary history connects fundamentally distinct protein families. Experiments have been done to intentionally evolve one protein family into another. They fail every time. One protein family cannot be evolved into another. They cannot arise through evolution. But the math really makes it impossible... The odds of evolution finding even a single functional protein family is 1 chance in 10^77 possible amino acid sequences. That's a 1 with 77 zeroes. The odds of evolution finding 22,000 distinct protein families? Roughly 1 chance in 10^1,694,000. You don't need to be a mathematician to understand that this is impossible. Evolution works by random mutations tweaking what's already there. But protein families can't evolve from one another. And the math makes it absolutely absurd to believe. Monkeys banging on keyboards will never type out Shakespeare. And random mutations in sequences of amino acids could never create a single functional protein family. There is only one thing we know of that creates functionally specified sequences: Intelligence. Now here is the final cherry on top: Proteins are built by other proteins, assembled into a complex machine to do a job in cells. The assembly instructions for those proteins and the machines are found in DNA. But DNA requires protein machines for replication & repair. So DNA is required to make Proteins... ...but Proteins are required to make DNA and more Proteins. You can't have one without the other. They rely on each other. Which means one couldn't have evolved and then waited for the other. They couldn't do anything without each other. They had to be created at the same time to function together. Life was Divinely Designed. Proteins prove it.

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The Oja is making a comeback in Nigerian music scene, adding a delightful blend of traditional flavor to our contemporary tunes. A new genre, Ojapiano, has emerged, fusing the captivating rhythms of Oja with the South African Amapiano genre. Let's explore this musical evolution.

The Oja is making a comeback in Nigerian music scene, adding a delightful blend of traditional flavor to our contemporary tunes. A new genre, Ojapiano, has emerged, fusing the captivating rhythms of Oja with the South African Amapiano genre. Let's explore this musical evolution.

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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 to structure genomes. Learn more:

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 to structure genomes. Learn more:

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We have trained ESM3 and we're excited to introduce EvolutionaryScale. ESM3 is a generative language model for programming biology. In experiments, we found ESM3 can simulate 500M years of evolution to generate new fluorescent proteins. Read more:

We have trained ESM3 and we're excited to introduce EvolutionaryScale. ESM3 is a generative language model for programming biology. In experiments, we found ESM3 can simulate 500M years of evolution to generate new fluorescent proteins. Read more:

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REPORT: A new study confirms that COVID-19 mRNA injections spread to vital organs and trigger systemic, toxic spike protein production. This “conspiracy theory” just became a reality. The new study, published in Nature Biotechnology, reveals alarming details about the biodistribution of lipid nanoparticles (LNPs) carrying SARS-CoV-2 spike mRNA. The study demonstrated that even at extremely low doses (0.0005 mg/kg), these LNPs deliver mRNA to cells in vital organs, leading to the production of toxic spike protein. According to the researchers, this protein triggers immune activation and causes damage to blood vessels. Meanwhile, autopsy findings now confirm the systemic presence of spike protein in tissues and organs, raising serious questions about the safety of these so-called vaccines. Epidemiologist Nicolas Hulscher, MPH didn’t hold back in his response to the news of this study, stating: “Biodistribution studies should have been performed BEFORE mass ‘vaccination’ of the entire world’s population. These invasive gene therapy injections must be pulled from global markets immediately.”

REPORT: A new study confirms that COVID-19 mRNA injections spread to vital organs and trigger systemic, toxic spike protein production. This “conspiracy theory” just became a reality. The new study, published in Nature Biotechnology, reveals alarming details about the biodistribution of lipid nanoparticles (LNPs) carrying SARS-CoV-2 spike mRNA. The study demonstrated that even at extremely low doses (0.0005 mg/kg), these LNPs deliver mRNA to cells in vital organs, leading to the production of toxic spike protein. According to the researchers, this protein triggers immune activation and causes damage to blood vessels. Meanwhile, autopsy findings now confirm the systemic presence of spike protein in tissues and organs, raising serious questions about the safety of these so-called vaccines. Epidemiologist Nicolas Hulscher, MPH didn’t hold back in his response to the news of this study, stating: “Biodistribution studies should have been performed BEFORE mass ‘vaccination’ of the entire world’s population. These invasive gene therapy injections must be pulled from global markets immediately.”

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A transformative new chapter for #TeamWass. We’re excited to announce that CSM Sport & Entertainment - acquired in October 2023 - has been rebranded as Wasserman! This is more than a name change. Learn more about the next evolution of our business at

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Wasserman

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New findings from Dr. Peter McCullough and a German research lab confirm that Pfizer and Moderna mRNA vaccines leave behind synthetic spike protein in the body for at least 3.2 years post-injection. These proteins circulate in the bloodstream and embed in tissues, with no known human enzymes capable of breaking them down. Key Revelations: - Pseudouridinated mRNA does not degrade naturally, leading to prolonged spike protein production. - Cancer trends shifting—2023 data shows rising breast and prostate cancers, while overall rates remain stable. - 2021 global mortality was among the worst in history, raising urgent questions about long-term vaccine effects. Potential Solutions? Natural enzymes like nattokinase, bromelain, serrapeptase, and lumbrokinase show promise in breaking down spike protein—but public awareness is critical. Why This Matters: If mRNA and spike protein persist for years, the long-term health consequences could be devastating. The medical establishment must address this—before it’s too late.

New findings from Dr. Peter McCullough and a German research lab confirm that Pfizer and Moderna mRNA vaccines leave behind synthetic spike protein in the body for at least 3.2 years post-injection. These proteins circulate in the bloodstream and embed in tissues, with no known human enzymes capable of breaking them down. Key Revelations: - Pseudouridinated mRNA does not degrade naturally, leading to prolonged spike protein production. - Cancer trends shifting—2023 data shows rising breast and prostate cancers, while overall rates remain stable. - 2021 global mortality was among the worst in history, raising urgent questions about long-term vaccine effects. Potential Solutions? Natural enzymes like nattokinase, bromelain, serrapeptase, and lumbrokinase show promise in breaking down spike protein—but public awareness is critical. Why This Matters: If mRNA and spike protein persist for years, the long-term health consequences could be devastating. The medical establishment must address this—before it’s too late.

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Introducing the next evolution of Peerlist: The All-New Peerlist Profile! 🎉 → Clean and minimalist design! → A brand new About tab on your Peerlist profile. → Collections: You can create and showcase the collections of your books you read, favorite podcasts, must-watch videos, and links. Don't forget to share your collections in comments 😃

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it's been a huge few weeks for ai in bio: a $2.25b Profluent x Eli Lilly and Company deal on ai-designed gene editors, verve's base-editing data, new scaling results on protein models from Chan Zuckerberg Biohub Network, Isomorphic Labs' haul. Ali Madani and i recorded a pod diving into all of it we get into taking biology from discovery to design, sequence-first vs structure-first, and why he calls this the "gpt-1.5 era" of biology... enjoy!

it's been a huge few weeks for ai in bio: a $2.25b Profluent x Eli Lilly and Company deal on ai-designed gene editors, verve's base-editing data, new scaling results on protein models from Chan Zuckerberg Biohub Network, Isomorphic Labs' haul. Ali Madani and i recorded a pod diving into all of it we get into taking biology from discovery to design, sequence-first vs structure-first, and why he calls this the "gpt-1.5 era" of biology... enjoy!

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Finally got a chance to sit down with Samuel Westrich and break down Dash Evolution It's what really got me excited about the prospect of digital cash for the whole world back in the day, and now it's actually out. Really good chat. Dash broke new ground with lots of things here! 💪

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It's very interesting that cryptographic protocols and neural networks have the same high-level architecture (where they jumble information as it moves sequentially across many layers). This is the result of a convergent evolution - cryptographic protocols need every output bit to depend on every input bit in complicated ways, and similarly, NNs need output to make connections between inputs. But they're in some sense doing opposite things. While cryptographic protocols take something which has a lot of structure and make it seem indistinguishable from random, NNs take something which may look random and extract structure from it. Much more on this idea in the full episode with Reiner Pope

It's very interesting that cryptographic protocols and neural networks have the same high-level architecture (where they jumble information as it moves sequentially across many layers). This is the result of a convergent evolution - cryptographic protocols need every output bit to depend on every input bit in complicated ways, and similarly, NNs need output to make connections between inputs. But they're in some sense doing opposite things. While cryptographic protocols take something which has a lot of structure and make it seem indistinguishable from random, NNs take something which may look random and extract structure from it. Much more on this idea in the full episode with Reiner Pope

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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

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

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Excited to announce our new preprint! We developed a method to automatically extract standard 2D views from 3D echocardiography volumes. This method allows for novel approach to echo acquisition: Instead of moving the probe across patients chest in the search of standard views, a sonographer would only have to place the probe in the apical position to capture the whole snapshot of the heart, which is then decomposed into all standard views with the similar quality to the views obtained in the traditional pipeline. That way we benefit from the speed of 3D acquisition while allowing clinicians to preserve their familiar 2D interpretation workflow.

Excited to announce our new preprint! We developed a method to automatically extract standard 2D views from 3D echocardiography volumes. This method allows for novel approach to echo acquisition: Instead of moving the probe across patients chest in the search of standard views, a sonographer would only have to place the probe in the apical position to capture the whole snapshot of the heart, which is then decomposed into all standard views with the similar quality to the views obtained in the traditional pipeline. That way we benefit from the speed of 3D acquisition while allowing clinicians to preserve their familiar 2D interpretation workflow.

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My family members☺️ Happy new month! Our star ingredient for this month of April is Ugba. It’s known for its strong flavor and is often used in soups, stews, and sauces. It's a nutrient rich food, high in protein, and vitamins. I cannot wait to share all the recipes that I’d make

My family members☺️ Happy new month! Our star ingredient for this month of April is Ugba. It’s known for its strong flavor and is often used in soups, stews, and sauces. It's a nutrient rich food, high in protein, and vitamins. I cannot wait to share all the recipes that I’d make

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11,337 次观看 • 1 年前