The future of protein design: How deep learning will change the world
Hello everyone, my name is Akiko.
Today, I picked up one technology from the “Seven Technologies to Watch This Year” that Nature is doing and blogged about the contents of that technology. This time, I’m going to focus on one of the trends from this year
I prepared a story about the use of deep learning for protein design.
To be honest, the content is very difficult and I’m not sure if I’m presenting the right information! Let’s deepen our understanding together.
Overview of Deep Learning and Its Evolution
Deep learning is an artificial intelligence technology that, as part of machine learning, significantly improves the ability of computers in solving complex problems. It uses neural networks that mimic the human brain to learn patterns from large amounts of data. This technology has applications in a wide range of fields, from automated driving to medical research, speech recognition, and image analysis.
Fields of application of deep learning
Automatic driving:
Analyzing a vehicle’s sensor data and understanding the surrounding environment to ensure safe driving.
Aerospace and Defense:
Used to analyze satellite imagery and data for ground object recognition.
Medical Research:
Used for diagnostic imaging, disease prediction, and new drug discovery to improve the quality of medical care.
Factory Automation:
Used to improve efficiency, quality control, and predictive maintenance of manufacturing processes.
Electronics:
We are helping to improve the quality of life in the electronics industry through the use of electronics technology.
Used in the development of voice recognition, facial recognition, personal assistant devices, and more.
Impact on Protein Design
Deep learning is revolutionizing the field of protein design. It is now possible to tackle challenges in protein structure prediction, functional analysis, and the design of new proteins that would be difficult to tackle using traditional methods. This will lead to significant advances in the development of customized therapeutics, the design of biomaterials, and the development of enzymes for environmental protection and industrial applications.
Proteins and Their Role
Proteins are the basic building blocks of life and are responsible for many functions in the body, including the construction of muscles, organs, skin, hair, and nails, metabolism, and regulation of the immune system. These proteins are formed by specific sequences of amino acids, and their structure and function are highly complex.
The Role of Deep Learning and Examples
About 20 years ago, David Baker and his team used a computer to design a new protein they named “Top7. This initially served no function. But now they can create a protein that actually works and is useful. This progress is due to a combination of huge data sets and deep learning technology.
Deep learning treats protein sequences like documents composed of polypeptides, and understanding the patterns in these “protein sentences” helps create new proteins that work. This approach, known as a sequence-based strategy, uses large-scale language models such as ChatGPT.
However, when creating a custom-made protein that sticks to a specific target, a “structure-based” approach is more appropriate. 2023 has seen significant progress in this area. Baker’s team is using RFdiffusion software to design proteins that can interact with DNA and small molecules, opening up new possibilities in medicine and biology.
In short, scientists are using very clever computer programs to design new proteins that do a variety of useful jobs. This is like becoming a designer of very important but very small proteins in real life. These scientists are using computers to create proteins with new forms and functions, opening up new possibilities in medicine and biology.
Advances in Modern Protein Design
Recent advances in protein design have been driven by the convergence of computer science and biology. Deep learning has enabled scientists to design new proteins with unprecedented speed and precision. This enables them to approach complex biological challenges that were previously impossible.
Real World Applications
Protein design using deep learning has a wide range of potential applications, from drug development and the design of biological materials to the creation of sustainable production processes. The technology is particularly promising in personalized medicine and in the development of treatments for specific diseases.
Future Prospects
Protein design using deep learning is still a developing field. Further research and refinement is needed before it can be applied to actual biological systems. However, progress in this field is accelerating, and in the future, it has the potential to transform our lives by treating diseases, developing new materials, and even addressing environmental issues.
As a Recherchiren since 2023, I have been a consultant and researcher in the new business sector of companies. I would like to be actively involved in new business business business in fusion and transversal areas. If you would like to discuss any work or have a casual chat, please feel free to contact me.
Contact: Akiko-Linkdein(Recherchieren/Medium blog-EN/日本語ブログはてな)
