While the release of ChatGPT and enterprise-level deployment of Large Language Models have been impactful, all organizations should also be digging deeper into recent AI and deep learning history and ask the question:  “How is Deepmind’s Alphafold representative of the future architectures, ecosystems, platforms and value creation opportunities in our industry sector and subsectors?”  

Breakthroughs in deep learning-based systems developed by DeepMind (now a subsidiary of Alphabet Inc. – Google’s parent company) for predicting the three-dimensional (3D) structures of proteins are, arguably, the tangible AI-based innovations which made the remakable “consumer-facing” introduction of OpenAI’s ChatGPT in October 2022 even possible.

Following is an exhaustive Deepmind reference libary which can be leveraged into your business model generation and systems thinking. A related post – The Future of AI-based Gene Sequencing – features an analysis of the latest Alphafold breakthrough and the What’s Next? of the future of AI and Genomics.

Sections of this post include:

• What is CRISPR-Cas9?
  • The Key Components of CRISPR-Cas9
  • The CRISPR-Cas9 Process
• The History of the Development of CRISPR-Cas9
• History of AlphaFold
• The Development Timeline of DeepMind’s AlphaFold
• What Next?
  • Implications of AlphaFold
  • The Future of DeepMind’s AlphaFold

What is CRISPR-Cas9?

CRISPR-Cas9 is a revolutionary gene-editing technology that allows scientists to precisely modify DNA within living organisms. The acronym “CRISPR” stands for “Clustered Regularly Interspaced Short Palindromic Repeats,” and “Cas9” refers to the protein associated with the system. CRISPR-Cas9 was originally discovered as a bacterial defense mechanism against viral infections.

The Key Components of CRISPR-Cas9

1. CRISPR Array: This consists of short, repeated DNA sequences interspaced with unique spacer sequences derived from viruses or other invaders that the bacteria encountered. These spacers serve as a molecular memory of past infections.
2. crRNA: CRISPR RNAs (crRNAs) are transcribed from the CRISPR array. Each crRNA is complementary to one of the spacer sequences and guides the Cas9 protein to the target DNA sequence.
3. Cas9 Protein: Cas9 is an enzyme that acts as molecular scissors. It can cleave DNA at a specific location guided by the crRNA. When the crRNA binds to its complementary DNA sequence, the Cas9 protein cuts both strands of the DNA, creating a double-stranded break.

The CRISPR-Cas9 Process

1. Selection of Target Sequence: Scientists design a synthetic guide RNA (sgRNA), which is a combination of the crRNA and a trans-activating CRISPR RNA (tracrRNA) that stabilizes the complex. The sgRNA is engineered to bind to a specific DNA sequence of interest.
2. Binding to Target DNA: The sgRNA guides the Cas9 protein to the target DNA sequence, where it forms a complex.
3. DNA Cleavage: Once the Cas9-sgRNA complex binds to the target DNA, the Cas9 protein cuts both strands of the DNA at the desired location, creating a double-stranded break.
4. Cellular Repair: When the DNA is broken, the cell’s natural repair mechanisms are triggered. There are two main repair pathways: non-homologous end joining (NHEJ) and homology-directed repair (HDR).

NHEJ: This pathway often results in small insertions or deletions (indels) at the break site, which can disrupt the gene’s function. NHEJ repair can be exploited to disrupt a specific gene’s activity.

HDR: In this pathway, a DNA template is provided to guide the repair process. This template can introduce desired changes at the target site, allowing for precise modifications. HDR is commonly used for introducing specific changes or correcting mutations.

CRISPR-Cas9 technology has revolutionized genetic research and biotechnology due to its efficiency, specificity, and relative ease of use.

It has numerous applications, including gene editing in various organisms, disease modeling, functional genomics studies, and potential therapeutic interventions. However, ethical considerations, regulatory oversight, and potential off-target effects remain important considerations as the technology continues to advance.

The History of the Development of CRISPR-Cas9

The development of CRISPR-Cas9 is a fascinating story that involves several key discoveries and innovations in the field of molecular biology. Here’s a timeline of the major events in the history of CRISPR-Cas9:

1987:  Japanese researcher Yoshizumi Ishino publishes a paper describing unusual repeating sequences in the genome of Escherichia coli (E. coli) bacteria.

1993:  Francisco Mojica, a Spanish microbiologist, independently discovers similar repetitive sequences in a different type of bacteria, Haloferax mediterranei, and coins the term “CRISPR.”

2005:  Researchers show that CRISPR sequences are transcribed into RNA molecules.

2007:  Researchers independently discover that CRISPR RNAs (crRNAs) are involved in defense mechanisms against viruses in bacteria.

2011:  Scientists demonstrate that CRISPR-associated (Cas) proteins are essential for the bacterial immune response and can target and cleave specific DNA sequences.

2012:  Jennifer Doudna and Emmanuelle Charpentier publish a landmark paper showing that the Cas9 protein from the bacterium Streptococcus pyogenes can be programmed with a small RNA molecule to cut DNA at specific locations. This discovery lays the foundation for the CRISPR-Cas9 gene editing technology.

2013:

– Feng Zhang’s lab at the Broad Institute demonstrates that the CRISPR-Cas9 system can be used for gene editing in human cells.

– Numerous research groups around the world begin adopting and adapting the CRISPR-Cas9 system for a wide range of applications.

2014:

– Researchers develop variations of CRISPR-Cas9, such as using different types of Cas proteins, for various genome editing purposes.

– Patent disputes arise over the intellectual property rights for CRISPR-Cas9 technology between the Broad Institute and the University of California, Berkeley.

2015:

– The first clinical trial using CRISPR-Cas9 for human genome editing is approved in China.

– The patent dispute between the Broad Institute and the University of California, Berkeley is resolved, with the Broad Institute maintaining its patents for using CRISPR-Cas9 in eukaryotic cells.

2016:

– CRISPR-Cas9 is used in more diverse applications, including agriculture, biotechnology, and therapeutic research.

– Researchers continue to refine the technology, developing more precise and efficient gene editing techniques.

2017:

– Researchers announce the development of base editing techniques that allow for more controlled and precise changes to DNA without causing double-strand breaks.

2020s and Beyond:

– Research and applications of CRISPR-Cas9 continue to advance, including improvements in delivery methods, off-target effects reduction, and expansion to epigenome editing.

The history of CRISPR-Cas9 underscores the power of basic research, collaboration, and interdisciplinary efforts in scientific discovery. The technology has revolutionized the field of genetic engineering and opened up new possibilities for precision genome editing, disease research, biotechnology, and potential therapeutic interventions.

History of AlphaFold

AlphaFold is a deep learning-based system developed by DeepMind, a subsidiary of Alphabet Inc. (Google’s parent company), for predicting the three-dimensional (3D) structures of proteins. The project was first announced in 2018, but it gained widespread attention and acclaim in 2020 when DeepMind’s AlphaFold2 achieved remarkable success in the Critical Assessment of Structure Prediction (CASP) competition.

CASP is a biennial competition where research groups from around the world compete to predict the 3D structures of proteins. Accurate prediction of protein structures is a significant challenge in biology, as the 3D structure of a protein is crucial for understanding its function and interactions with other molecules. AlphaFold’s breakthrough in CASP14, held in 2020, demonstrated its ability to predict protein structures with remarkable accuracy.

The Development Timeline of DeepMind’s AlphaFold

DeepMind’s AlphaFold is a breakthrough deep learning system for predicting protein structures. The development of AlphaFold can be summarized in the following timeline:

2018: DeepMind announced its intention to participate in the Critical Assessment of Structure Prediction (CASP) competition, a biennial event where research groups worldwide compete to predict protein structures accurately. DeepMind’s involvement marked the beginning of AlphaFold’s development.

2018-2019: DeepMind worked on developing and refining the AlphaFold model, which combines deep learning techniques, neural networks, and vast protein structural databases to predict the 3D structure of proteins.

December 2020: AlphaFold gained significant attention when it participated in CASP14, the 14th round of the CASP competition. During the competition, AlphaFold demonstrated remarkable accuracy in predicting protein structures, outperforming other methods by a significant margin.

March 2021: DeepMind published detailed information about the AlphaFold system and its approach in the journal Nature. The paper explained the techniques used, such as attention-based neural networks and the incorporation of multiple sequence alignments.

July 2021: DeepMind released the open-source AlphaFold software and made the protein structure predictions for a significant portion of the human proteome publicly available. This marked a significant step toward advancing protein science and facilitating research in various domains.

Ongoing: Since the release, ongoing developments and improvements to AlphaFold’s model and capabilities are likely as researchers continue to refine the technology and integrate it into various scientific and medical applications.

AlphaFold’s development timeline showcases how DeepMind’s dedication to artificial intelligence and deep learning methodologies led to a breakthrough that could significantly impact various scientific fields, including bioinformatics, drug discovery, and molecular biology.

What Next?

Implications of AlphaFold

1. Advancing Structural Biology: AlphaFold’s accurate protein structure predictions hold immense potential for advancing our understanding of biology and medicine. Knowing the 3D structure of proteins can shed light on how they function, interact with other molecules, and contribute to diseases. This knowledge could accelerate drug discovery, enzyme engineering, and other applications in biotechnology.
2. Drug Discovery: Accurate protein structure prediction can significantly aid in drug discovery by enabling researchers to design drugs that specifically target certain protein structures implicated in diseases. This could lead to the development of more effective and targeted treatments.
3. Disease Understanding: AlphaFold’s predictions could help researchers better understand the molecular basis of various diseases. By understanding how misfolded proteins contribute to conditions like Alzheimer’s, Parkinson’s, and certain cancers, scientists could potentially develop interventions to prevent or treat these diseases.
4. Biotechnology and Enzyme Design: Protein engineering is essential in fields like biotechnology, where optimized enzymes can improve industrial processes. AlphaFold’s predictions could guide the design of novel enzymes with specific functions, leading to advancements in various industries.
5. Personalized Medicine: Personalized medicine aims to tailor medical treatments to individual patients based on their genetic makeup. Accurate protein structure predictions could provide insights into how genetic variations affect protein structures and functions, enabling more personalized treatment strategies.
6. Data Availability: While AlphaFold’s predictions are groundbreaking, they also raise concerns about data availability and ownership. The models require vast amounts of protein structural data for training, which can lead to discussions about data sharing, accessibility, and potential monopolization of valuable biological information.
7. Ethical and Societal Implications: The advent of highly accurate protein structure prediction also brings about ethical considerations. As with any technological advancement, there’s a need to ensure that the technology is used responsibly and that potential negative consequences, such as bioweapons development or unintended health risks, are addressed.

AlphaFold’sdevelopment has the potential to revolutionize our understanding of biology, medicine, and biotechnology, but it also raises important ethical and societal questions that must be carefully considered as the technology is integrated into research and industry.

The Future of DeepMind’s AlphaFold

The future of DeepMind’s AlphaFold is promising and holds significant potential across various scientific and medical domains. Here are some directions in which AlphaFold could continue to make an impact:

1. Advancing Structural Biology: AlphaFold’s accurate protein structure predictions have the potential to revolutionize structural biology. Researchers can use these predictions to understand the functions of proteins, their interactions with other molecules, and the underlying mechanisms of diseases.
2. Drug Discovery and Design: AlphaFold’s predictions could expedite drug discovery by identifying potential drug targets, predicting interactions between drugs and proteins, and assisting in rational drug design. This could lead to the development of more effective and targeted therapies.
3. Disease Understanding and Personalized Medicine: By understanding the structures of disease-related proteins, researchers can gain insights into the molecular basis of various illnesses. This knowledge can pave the way for personalized treatment strategies and innovative therapies.
4. Biotechnology and Enzyme Engineering: AlphaFold’s predictions could guide the design of enzymes with specific functions, benefiting industries such as biotechnology, agriculture, and biofuels production.
5. Protein Engineering: Scientists could use AlphaFold’s predictions to engineer proteins with desired properties for various applications, such as creating enzymes with enhanced catalytic activity or proteins with novel functions.
6. Synthetic Biology: Predicting protein structures accurately is crucial for synthetic biology, where researchers engineer organisms with specific functions. AlphaFold’s capabilities could aid in designing and optimizing biological systems.
7. Basic Research and Discovery: AlphaFold’s predictions can accelerate basic research by providing insights into protein structures that were previously challenging to determine experimentally.
8. Drug Safety and Toxicity Prediction: The accurate prediction of protein structures can contribute to predicting the interactions between drugs and proteins, helping to identify potential adverse effects and toxicities.
9. Structural Genomics: AlphaFold’s predictions could contribute to mapping the 3D structures of entire proteomes, enabling a deeper understanding of cellular functions and interactions.
10. Public Health and Pathogen Research: Understanding the structures of viral and bacterial proteins could aid in developing treatments and interventions against infectious diseases.
11. Continued Model Improvements: DeepMind is likely to continue refining the AlphaFold model and addressing limitations to make it even more accurate and versatile.

It’s important to note that the field of AI and protein structure prediction is rapidly evolving, and AlphaFold’s future impact will depend on continued research, collaborations, and its integration into various research and industry applications.

Additional OODA Loop Resources

The Future of AI-based Gene Sequencing:  An analysis of the latest Alphafold breakthrough  – Enformer, a neural network architecture that accurately predicts gene expression from DNA sequences – and the What’s Next? of the future of AI and Genomics.

The First FDA Approved CRISPR-based Medicine:  The next ten years will be marked by all the uncertainties and unintended consequences that underpin so many doom and gloom scenarios. It is time to start tracking the abundance and breakthroughs that will also come fast and furious in the next decade – equally as overwhelming, while also breathtaking, positive, highly technical and scientific – and transformative. Here are a couple of those recent “firsts.”

Recent Developments in and the Future of the Bioeconomy in 2024:   We have tracked vital recent developments in health security, bioengineering, synthetic biology, biotechnology, and medical technology – which are compiled here. The future of the U.S. Bioeconomy is crucial to the future of strategic competitive advantage globally – all of which was discussed in a future-forward fashion at OODAcon 2023.

Overall, the bioeconomy and medical technology platforms also show clear signs in 2024 as the innovation space best positioned to deploy best-in-class enterprise platforms and use cases of generative AI, artificial intelligence, and machine learning (which other emerging technology innovators, cybersecurity professionals, and industry sectors should have an instinct to track closely and to emulate).

The New Tech Trinity: Artificial Intelligence, BioTech, Quantum Tech: Will make monumental shifts in the world. This new Tech Trinity will redefine our economy, both threaten and fortify our national security, and revolutionize our intelligence community. None of us are ready for this. This convergence requires a deepened commitment to foresight and preparation and planning on a level that is not occurring anywhere. The New Tech Trinity.

The Revolution in Biology: This post provides an overview of key thrusts of the transformation underway in biology and offers seven topics business leaders should consider when updating business strategy to optimize opportunity because of these changes. For more see:  The Executive’s Guide To The Revolution in Biology

Quantum Computing and Quantum Sensemaking: Quantum Computing, Quantum Security and Quantum Sensing insights to drive your decision-making process. Quantum Computing and Quantum Security

Materials Science Revolution: Room-temperature ambient pressure superconductors represent a significant innovation. Sustainability gets a boost with reprocessable materials. Energy storage sees innovations in solid-state batteries and advanced supercapacitors. Smart textiles pave the way for health-monitoring and self-healing fabrics. 3D printing materials promise disruptions in various sectors. Perovskites offer versatile applications, from solar power to quantum computing. See: Materials Science