#alphafold3

[cytgen]

Noncanonical Structures in the Genome of Bovine Foamy Virus - Cytology and Genetics

Abstract— Bioinformatic methods have been used to identify putative perfect G-quadruplexes (G4s) and three-way junctions (3WJs) in the bovin

Abstract— Bioinformatic methods have been used to identify putative perfect G-quadruplexes (G4s) and three-way junctions (3WJs) in the bovine foamy virus (BFV) genome. Artificial intelligence (AI) AlphaFold 3 was used to confirm putative G4s and 3WJs by building 3D models of these noncanonical structures. G4s are secondary structures formed by G-rich sequences. Multihelical 3WJs formed by three duplexes connected at the binding point and G4s are considered as alternative structures in DNA and RNA that differ from the classical double-stranded B-DNA. In the present paper, the localization map of putative conservative intramolecular G4s formed by two G-tetrads on the BFV genome was created. Seven putative conservative G-quadruplexes in the sense strand of BFV proviral DNA and 22 G4s in the antisense strand formed by two G-tetrads with G-score from 32 to 36 were found by the multiple alignment of 37 BFV isolates with complete genome. The density of G4s was 0.6 G4/kb for the sense strand of the BFV proviral DNA, while it was 1.8 G4/kb for the antisense strand. One conservative 3WJ motif with a length of 73 bp with 100% homology localized in the 5'-untranslated region and partially on the 5'-end of the gag gene was found for a set of 37 BFV isolates. The 3WJ structure in BFV RNA is stabilized by 20 complementary bp with a free energy ΔG of 19.8 kcal/mol. The significance of this structure for BFV functioning has been proven. The use of AI AlphaFold 3 to build 3D models of theoretically determined perfect G4s and 3WJs in the BFV genome allowed for reliably determining putative alternative structures in nucleic acids.

[drmichaelbroadly]

My First Blog

Today I briefly wrote about Alphafold 3 in my blogs. I plan to share more here. Here is the link to my blog post.

Why I Believe AlphaFold 3 is a Powerful Tool for the Future of Healthcare - Dr Mehmet Yildiz

Science readers can access the AlphaFold Protein Structure Database & the AlphaFold Server in predicting protein structures.

[govindhtech]

DeepMind’s AlphaFold 3 Server For Molecular Life Blueprint

How AlphaFold 3 Server was constructed to predict the composition and interactions of every molecule in life

Launched in 2020, Google DeepMind’s AlphaFold 2 protein prediction model has been applied by over 2 million researchers working on cancer treatments, vaccination development, and other fields. This has allowed scholars to solve a challenge they have been working on for more than fifty years. It would have been simple for the group to sit back and relax after assisting scientists in the prediction of hundreds of millions of structures.

Google DeepMind’s AlphaFold 3 Server

Rather, they began to work on AlphaFold 3 Server. The Google DeepMind and Isomorphic Labs teams released a newer model in May that improves on their earlier models by predicting not only the structure of proteins but also the interactions and structures of all other molecules in life, such as DNA, RNA, and ligands (small molecules that bind to proteins).

Research scientist Jonas Adler of Google DeepMind claims that “looking at recent high-impact research, we made enormous progress on this decades-old open problem of protein folding with AlphaFold 2.”, researchers are moving beyond that.” “Their findings frequently dealt with more intricate topics, such as the binding of RNA or tiny molecules, which AlphaFold 2 was unable to accomplish. In order to get to the current state of biology and chemistry, we needed to be able to cover every type of biomolecule because experimental research has advanced the field.

“Everything” includes ligands, which comprise roughly 50% of all pharmaceuticals. Adrian Stecula, the research head at Isomorphic Labs, states, “We see the tremendous potential of AlphaFold 3 for rational drug design, and we’re already using it in our day-to-day work.” “All of those capabilities are unlocked by the new model, including investigating the binding of novel small molecules to novel drug targets, responding to queries like ‘How do proteins interact with DNA and RNA?,’ and examining the impact of chemical modifications on protein structure.”

An order of magnitude more potential combinations were introduced with the advent of these other molecule kinds. “There is a lot of order in proteins. There are just 20 typical amino acids, for instance,” explains Jonas. Small molecules, on the other hand, have an endless amount of space and are capable of doing almost anything. They are really varied.”

This implied that it would have been impossible to create a database with all the features. Rather, Google DeepMind have made available AlphaFold Server, a free utility that allows scientists to enter their own sequences for which AlphaFold can produce molecular complexes. It has been used by researchers to create over a million structures since its May introduction.

Lindsay Willmore, a Google DeepMind research engineer, compares it to “Google Maps for molecular complexes.” “Any user who is completely non-technical can simply copy and paste the names of their small molecules, DNA, RNA, and protein sequences, hit a button, and wait a short while.” They will be able to view and assess their forecast thanks to the release of their structure and confidence metrics.

The team greatly increased the amount of data that the newer model was trained on to include DNA, RNA, tiny molecules, and more in order to enable AlphaFold 3 to function with this far wider spectrum of biomolecules. Google is able to decide, “Let’s just train on everything that exists in this dataset that has really helped us with proteins, and let’s see how far we can get,” according to Lindsay. “And it looks like we can go a fair distance.” A change in the design for the last portion of the model that creates the structure is another significant alteration to AlphaFold 3.

AlphaFold 3 Server employs a generative model that is based on diffusion, similar to their other state-of-the-art image generation models, like Imagen. This considerably simplifies the way the model handles all the new molecule kinds, whereas AlphaFold 2 employed a complicated bespoke geometry-based module.

However, that change brought up a fresh problem: Instead of anticipating disordered sections, the diffusion model would attempt to construct an erroneous “ordered” structure with a distinct spiral form because the so-called “disordered regions” of proteins weren’t included in the training data.

The group decided to use AlphaFold 2, which is already very adept at identifying which interactions which resemble a pile of disorganised spaghetti would be disordered and which ones wouldn’t. According to Lindsay, “We were able to use those predicted structures from AlphaFold 2 as distillation training for AlphaFold 3 Server, so that AlphaFold 3 could learn to predict disorder.”

The group is excited to watch how scientists will employ AlphaFold 3 Server to progress a variety of areas, including medication development and genomics research.

“The amount of progress Google DeepMind made is amazing,” remarks Jonas. “What was extremely difficult before has now become really simple. Even though there are still many challenging issues to resolve, they are enthusiastic about AlphaFold 3 Server‘s potential to contribute to their resolution. What was once unthinkable is now achievable.

Read more on Govindhtech.com

[sifytech]

AlphaFold 3 by DeepMind: The Future of Protein Folding and Drug Discovery

A super artificial intelligence that is as smart as an architect and may predict the molecular configuration of proteins to a great extent. Read More. https://www.sify.com/ai-analytics/alphafold-3-by-deepmind-the-future-of-protein-folding-and-drug-discovery/

[govindhtech]

AlphaFold 3 Predicts All Life’s Molecules And Relations

Billions of molecular machinery are found inside the cells of every plant, animal, and human. Although proteins, DNA, and other molecules make up their composition, no one component functions by itself. Google DeepMind can only begin to genuinely comprehend the workings of life by observing how these components interact among millions of possible configurations.

Google DeepMind presents AlphaFold 3, a ground-breaking model that can predict the composition and interactions of every molecule found in life with previously unheard-of precision, in a research that was published in Nature. In comparison to current prediction techniques, Google DeepMind observes at least a 50% improvement in protein interactions with other molecule types. In certain significant interaction categories, Google DeepMind has doubled prediction accuracy.

AlphaFold 3 is expected to revolutionise Google DeepMind’s comprehension of biology and drug discovery. The majority of its features are available to scientists without charge thanks to Google DeepMind’s recently released AlphaFold Server, an intuitive research tool. Isomorphic Labs is already working with pharmaceutical companies to apply AlphaFold 3 to real-world drug design difficulties in order to improve on its potential for drug design and, ultimately, discover breakthrough medicines that could change the lives of patients.

The new model from Google DeepMind expands upon the work of AlphaFold 2, which achieved a significant breakthrough in the prediction of protein structures in 2020. AlphaFold 2 has been utilised by millions of researchers worldwide to date to create breakthroughs in the creation of enzymes, cancer therapies, and vaccinations against malaria.

More than 20,000 citations have been made to AlphaFold, and its contributions to science have been acknowledged with numerous awards most recently, the Breakthrough Prize in Life Sciences. With AlphaFold 3, Google DeepMind may explore a wide range of biomolecules in addition to proteins. This breakthrough could lead to the development of more revolutionary technology, such as faster drug design and genomics research, as well as the creation of biorenewable materials and more resilient crops.

How AlphaFold 3 shows life molecules

AlphaFold 3 creates a 3D structure of molecules from a list, showing how they fit together. It simulates both small compounds, or ligands, which include a wide range of pharmaceuticals, and major macromolecules like proteins, DNA, and RNA. Moreover, AlphaFold 3 is able to simulate chemical changes to these molecules that regulate the proper operation of cells and, if disturbed, can result in illness.

The next-generation architecture and training that encompasses all molecules in life are what give AlphaFold 3 its powers. The Google DeepMind Evoformer module, a deep learning architecture that enabled AlphaFold 2’s remarkable performance, has been refined and is at the heart of the model. Following the input processing, AlphaFold 3 uses a diffusion network, similar to those in AI picture generators, to put together its predictions. After taking numerous steps, the diffusion process eventually converges on the most precise molecular structure from a starting cloud of atoms.

The molecular interactions predicted by AlphaFold 3 are more accurate than those by any other technology now in use. It is the only model that can comprehensively compute complete chemical complexes, making it unique in its ability to bring scientific discoveries together.

Lead drug discovery at Isomorphic Labs

With predictions for frequently used compounds in medications, like ligands and antibodies, which bind to proteins to alter how they interact in human health and disease, AlphaFold 3 develops capabilities for drug design.

When it comes to anticipating drug-like interactions, such as the binding of ligands and antibodies to their target proteins, AlphaFold 3 achieves previously unheard-of levels of accuracy. Without requiring structural knowledge to be input, AlphaFold 3 outperforms the best traditional approaches by 50% on the PoseBusters benchmark, becoming the first artificial intelligence system to outperform physics-based tools for biomolecular structure prediction. Antibody-protein binding prediction is essential for comprehending the human immune response and for developing novel antibodies, which are an expanding class of medicines.

Drug design for both internal projects and pharmaceutical partners is being worked on by Isomorphic Labs using AlphaFold 3 in conjunction with a corresponding suite of in-house AI models. By utilising AlphaFold 3 to aid comprehend how to approach new disease targets and provide creative approaches to pursue existing ones that were previously unattainable, Isomorphic Labs is able to expedite and enhance the success of medication design.

AlphaFold Server: Free, simple research tool

The most precise tool in the world for predicting how proteins interact with other molecules throughout the cell is Google DeepMind’s recently released AlphaFold Server. Scientists from all over the world can use it for free for non-commercial study. Biologists can use AlphaFold 3’s capabilities to simulate structures made of proteins, DNA, RNA, and a variety of ligands, ions, and chemical changes with only a few clicks.

AlphaFold Server expedites workflows and fosters additional creativity by assisting scientists in developing new ideas to test in the lab. Regardless of their level of machine learning experience or access to computational resources, researchers may easily make predictions using the Google DeepMind platform.

Approximately as long as a PhD programme, experimental protein-structure prediction can go into the hundreds of thousands of dollars. At the current rate of experimental structural biology, it would have required hundreds of millions of researcher-years to forecast hundreds of millions of structures using Google DeepMind’s previous model, AlphaFold 2.

Responsible AlphaFold 3 power sharing

Google DeepMind has collaborated with the safety and scientific communities to comprehend the wider implications of each AlphaFold release. Google DeepMind has a science-led strategy and has carried out in-depth analyses to convey the broad advantages to biology and humanity while minimising possible hazards.

Expanding upon the external consultations Google DeepMind conducted for AlphaFold 2, Google DeepMind is currently interacting with over fifty domain experts and specialised third parties from the fields of biosecurity, research, and industry to comprehend the potential hazards and capabilities of the upcoming AlphaFold models. Prior to AlphaFold 3’s release, Google DeepMind also took part in community-wide forums and conversations.

AlphaFold Server is a reflection of Google DeepMind’s continued dedication to sharing AlphaFold’s advantages, such as the 200 million protein structures in Google DeepMind’s free database. Together with EMBL-EBI and partnerships with organizations in the Global South, Google DeepMind will also be expanding its free online course AlphaFold education in order to give scientists the resources they need to speed up adoption and research, particularly in underfunded fields like food security and neglected diseases. Google DeepMind will keep collaborating with academics, policymakers, and industry leaders to responsibly develop and implement AI technology.

New AI-powered cell biology frontier

The biological world is brought to life in high resolution in AlphaFold 3. It enables researchers to view biological systems in all of their complexity, including their connections, structures, and alterations. This new perspective on life’s molecules shows how they are interconnected and aids in understanding how those connections impact biological processes including the action of medications, hormone synthesis, and DNA repair, which maintains health.

The benefits of Google DeepMind’s free AlphaFold Server and AlphaFold 3 will be felt in the way they enable researchers to explore new areas of study and answer unanswered biological issues more quickly. AlphaFold 3’s potential is only now being realised by Google DeepMind, and they are eager to see what the future brings.

Read more on govindhtech.com