Revolutionizing Biological Research: The Role of AI in Understanding Protein Dynamics

Artificial Intelligence is one of the powerful drivers of many scientific disciplines today and is really changing the way science is done. Biological research has been characterized by high complexity and large volumes of data. In the case of conventional methods, the discovery and understanding of complicated biological structures are slow and full of errors. For example, the problem of determining the structure of proteins—something very basic to an understanding of biological processes—has traditionally been a time-consuming operation, requiring vast resources. With AI, such complications will be done away with when researchers process huge amounts of information at an unprecedentedly fast speed and high accuracy. The impact of AI in biological research is revolutionary, notably on the key breakthroughs that have been made in protein folding. 
​

AI and Biological Research ​

AI is capable of doing much more than a simple processing of information. It can uncover really complex patterns, make outcome predictions, and model a number of biological processes. One of the most important applications of AI in biology is related to protein folding. Proteins are key molecules that execute a broad variety of functions within organisms whose roles are often linked to their three-dimensional structure.  

Knowing the mechanism of folding of a protein into its functional form has been one of the prime focuses of researchers for many decades. The role of knowing protein folding is very important because proteins are considered to be the building blocks of life. They build enzymes, cells, tissues, and in fact, almost every biological function is linked with a protein or another.  

Traditional techniques of protein structure determination like X-ray crystallography are very time-consuming and expensive since crystallization of proteins is necessary, followed by the requirement of huge equipment. While these methods have revealed only a few structures, AI is moving into this space, promising faster and more accurate predictions of protein structures, which otherwise can hugely progress biology and medicine. 

DeepMind's AlphaFold ​

In November 2020, DeepMind, a London-based AI firm, published a breakthrough in protein folding research. Their AI system, AlphaFold, had the chance to identify a protein's complex shape with close-to-experimental accuracy, all by prediction. Years of research and development have borne fruit. The success of AlphaFold lies in techniques dealing with deep learning to analyze extensive data on proteins. It was trained on a very wide and deep set of protein structures, thus allowing extremely good predictions. ​

The AlphaFold breakthrough answered a scientific challenge that had not been met in nearly half a century. Since the early '70s, it was known that the sequence of amino acids in a protein should uniquely determine its final structure, yet predicting this structure from the sequence alone remained elusive. Until now, laborious and costly experimental methods were the only ones researchers could resort to. AlphaFold was something that broke ground: it surpassed other methods in CASP14—that is, the 14th Critical Assessment of Protein Structure Prediction—reaching effectiveness comparable to experimental information and considerably bending the arc of protein folding studies. 

Q8I3H7: May protect the malaria parasite against attack by the immune system. Mean pLDDT 85.57. — IMAGE COURTESY OF DEEPMIND 
​

​Recent Developments 

Building on the success of AlphaFold, DeepMind published a statement in July 2022 announcing that AlphaFold had predicted nearly all known proteins, surpassing 200 million. This step will change the face of biological research and drug discovery forever. Proteins are at the core of all biological processes; to further advance our knowledge regarding diseases and set up treatments against them, knowledge of protein structures is very important. 

For example, AlphaFold's predictions have already contributed to research into diseases such as Alzheimer's and cancer, providing new insights into their mechanisms and possible avenues for treatment. Besides, AlphaFold can also provide fast and accurate protein structure prediction, which may assist in accelerating drug discovery. The traditional process of drug development is time-consuming and financially exhaustive; it takes years or even billions of dollars. With AlphaFold's detailed information on protein structure, designing drugs would be more effective, hence reducing the time and cost associated with drug development. 
​
It is important because it is not only going to improve our understanding of protein folding but will also give rise to future technologies and applications that can be applied in biological research, to which we turn now.
​

Future Prospects 

The future of AI in biological research is incredibly promising. As AI technology continues to advance, its applications in this field are expected to grow exponentially. Here are some key areas where AI is poised to make a significant impact: 

1. Personalized Medicine 

AI has the potential to revolutionize personalized medicine by analyzing individual genetic data to create customized treatment plans. This approach considers a person's unique genetic makeup, allowing AI to predict their response to specific treatments. For instance, AI algorithms can analyze genetic mutations and biomarkers to identify the most effective therapies for cancer patients. This can lead to more effective and tailored healthcare, reducing adverse reactions and increasing treatment success rates. Personalized medicine can also extend to managing chronic diseases, where AI can help optimize medication dosages and lifestyle recommendations, improving the overall quality of life for patients. ​

Arianna Huffington, CEO and founder of Thrive Global, a company which develop an AI health coach to give personalized medicine and treatments. 

2. Synthetic Biology 

AI is set to play a pivotal role in synthetic biology, enabling the design of synthetic organisms and biomolecules with desired functions. This technology has far-reaching applications in agriculture, energy production, and environmental protection. For example, AI-designed enzymes could break down plastic waste, offering a sustainable solution to the global plastic pollution crisis. In agriculture, AI can help create genetically modified crops that are more resistant to pests and diseases, increasing food security. Additionally, AI-driven synthetic biology can lead to the production of biofuels, reducing reliance on fossil fuels and mitigating climate change. The ability to engineer biological systems with precision opens up new possibilities for addressing some of the world's most pressing challenges. 
​​

3. Disease Prediction and Prevention

AI's capability to analyze vast datasets for patterns and predict disease outbreaks is transformative for public health. By processing data from sources such as social media, healthcare records, and climate information, AI can provide early warnings of potential outbreaks. This enables timely intervention and better control of infectious diseases. For instance, during the COVID-19 pandemic, AI models were used to track the spread of the virus and predict hotspots, aiding in resource allocation and containment strategies. In the future, AI could help monitor emerging diseases and provide real-time surveillance, ultimately saving lives and reducing the economic impact of pandemics. 

Another example is an artificial intelligence tool named Sybil to revolutionize cancer diagnosis. Sybil, a deep learning model that is leveraged by Medical professionals and technologists from Massachusetts General Hospital and MIT, can predict lung cancer risk using data from a single low-dose chest CT scan. According to their research, Sybil can accurately predict whether an individual will develop lung cancer within the next one to six years, with an accuracy rate of up to 94% for one-year predictions. This tool does not rely on clinical data or radiologist annotations, making it a powerful aid in early cancer detection and potentially improving patient outcomes significantly.​​

Sybil, The AI lung cancer system. Ultromics, can give very early warning of the disease. 

4. Understanding Complex Biological Systems 

AI's ability to simulate and model intricate biological systems offers insights that traditional methods cannot achieve. This helps researchers understand complex processes like cellular signaling pathways and gene regulation networks, leading to new discoveries in biology. For instance, AI can model how cells communicate and respond to external stimuli, providing a deeper understanding of immune responses and disease mechanisms. These insights can drive the development of novel therapies and interventions. Furthermore, AI can assist in deciphering the human microbiome's role in health and disease, opening up new avenues for probiotic treatments and personalized nutrition. 
​

5. Research Acceleration

AI can significantly accelerate scientific research by automating repetitive tasks and analyzing large datasets. This allows scientists to focus on more creative and complex aspects of their work, fostering innovation across various biological fields. For example, AI can streamline the process of drug discovery by identifying potential drug candidates and predicting their efficacy and safety. This reduces the time and cost associated with bringing new drugs to market. Additionally, AI can assist in literature reviews, data mining, and experimental design, making research more efficient and productive. By handling data-intensive tasks, AI empowers researchers to explore new hypotheses and push the boundaries of scientific knowledge. 
 ​

Conclusion

AI is rapidly revolutionizing biological research by offering solutions to the most intractable problems in this field. DeepMind's AlphaFold has gone ahead to show this by hitting almost unimaginable success in the prediction of protein structures with a high degree of accuracy. Now, it can open totally new avenues into mechanisms of disease and treatments by letting researchers understand the tortuous shape of proteins. With the advancing technology in AI, it is expected to have more extended applications in biological studies that will bring with it personalized medicine, synthetic biology, diseases prediction, and an understanding of various complicated biological systems. The future of biological research goes without saying; it's anything but completely intertwined with developments in AI, promising a whole new frontier of scientific exploration and innovation. 
​​

By Hon Ming To James
​
James is a passionate biotechnology student who is captivated by the boundless possibilities of Artificial Intelligence in the realm of scientific research. He is intrigued by how AI can revolutionize understanding and innovation, particularly in areas such as protein dynamics, personalized medicine, and synthetic biology. 

LinkedIn: https://www.linkedin.com/in/hon-ming-to-9402172b4/  ​