Breakout Ventures’ Post

Accurately predicting antibody structures is essential for developing monoclonal antibodies, pivotal in immune responses and therapeutic applications. Antibodies have two heavy and two light chains, with the variable regions featuring six CDR loops crucial for binding to antigens. The CDRH3 loop presents the greatest challenge due to its diversity. Traditional experimental methods for determining antibody structures are often slow and costly. Consequently, computational techniques such as IgFold, DeepAb, ABlooper, ABodyBuilder, and newer models like xTrimoPGLMAb are emerging as effective tools for precise antibody structure prediction.

Efficient Antibody Structure Refinement Using Energy-Guided SE(3) Flow Matching ABSTRACT Antibodies are proteins produced by the immune system that recognize and bind to specific antigens, and their 3D structures are crucial for understanding their binding mechanism and designing therapeutic interventions. The specificity of antibody-antigen binding predominantly depends on the complementarity-determining regions (CDR) within antibodies. Despite recent advancements in antibody structure prediction, the quality of predicted CDRs remains suboptimal. In this paper, we develop a novel antibody structure refinement method termed FlowAB based on energy-guided flow matching. FlowAB adopts the powerful deep generative method SE3 flow matching and simultaneously incorporates important physical prior knowledge into the flow model to guide the generation process. The extensive experiments demonstrate that FlowAB can significantly improve the antibody CDR structures. It achieves new state-of-the-art performance on the antibody structure prediction task when used in conjunction with an appropriate prior model while incurring only marginal computational overhead. This advantage makes FlowAB a practical tool in antibody engineering. PAPER: https://lnkd.in/gCSvnQ6W

In a significant advancement, scientists have successfully utilized artificial intelligence to design antibodies from scratch, opening new possibilities in the field of protein design and drug development. Through the modified protein design tool, RFdiffusion, this proof-of-concept work not only promises to streamline the creation of antibodies against complex pharmacological targets but also democratize this process. This achievement marks a step forward in the development of effective and personalized antibody drugs, anticipating a future where perhaps we can design treatments with just the push of a button. Learn more about this groundbreaking advancement here 👉🏼 https://lnkd.in/e4qW5_Uf #techtitute #AI #biomedicine

𝐓𝐡𝐞 𝐏𝐫𝐞𝐝𝐢𝐜𝐭𝐢𝐯𝐞 𝐌𝐨𝐝𝐞𝐥𝐢𝐧𝐠 𝐑𝐞𝐯𝐨𝐥𝐮𝐭𝐢𝐨𝐧!🧬⚙️ A recent lecture on Model systems by one of our professors got me thinking about how much research tools have advanced. In the rapidly evolving world of biotechnology, 𝗽𝗿𝗲𝗱𝗶𝗰𝘁𝗶𝘃𝗲 𝗺𝗼𝗱𝗲𝗹𝗶𝗻𝗴 is transforming the way we conduct research and development. But what exactly does this mean, and why should you care? Imagine this: Instead of lengthy, costly lab experiments, scientists can now use advanced algorithms to simulate biological processes and predict outcomes before even setting foot in the lab. Welcome to the world of 𝐢𝐧 𝐬𝐢𝐥𝐢𝐜𝐨 𝐚𝐧𝐚𝐥𝐲𝐬𝐢𝐬!📈📊 Predictive modeling leverages complex statistical techniques and ML to analyze vast datasets. By utilizing existing data on biological systems, researchers can create models that forecast how different variables will interact. 𝐃𝐫𝐮𝐠 𝐃𝐢𝐬𝐜𝐨𝐯𝐞𝐫𝐲: Predictive models can identify potential drug candidates by simulating their interactions with target proteins, significantly speeding up the process. For instance, a study published in Nature Biotechnology demonstrated that machine learning algorithms could predict the efficacy of drug compounds with remarkable accuracy (https://lnkd.in/dxEnnw9E) 𝐆𝐞𝐧𝐞𝐭𝐢𝐜 𝐑𝐞𝐬𝐞𝐚𝐫𝐜𝐡: In silico analysis allows scientists to predict the behavior of genes and their interactions within cellular environments. This has enormous implications for understanding diseases and developing gene therapies. A paper in Bioinformatics highlighted how predictive models successfully identified novel genetic variants associated with specific conditions. (Larrea-Sebal, Asier et al. “Predictive Modeling and Structure Analysis of Genetic Variants in Familial Hypercholesterolemia: Implications for Diagnosis and Protein Interaction Studies.” Current atherosclerosis reports vol. 25,11 (2023): 839-859. doi:10.1007/s11883-023-01154-7) 𝐄𝐧𝐯𝐢𝐫𝐨𝐧𝐦𝐞𝐧𝐭𝐚𝐥 𝐈𝐦𝐩𝐚𝐜𝐭 𝐀𝐬𝐬𝐞𝐬𝐬𝐦𝐞𝐧𝐭𝐬: Predictive modeling can assess the impact of biotechnological interventions on ecosystems, helping to ensure that innovations are sustainable and eco-friendly.🍃🌍 By reducing the time and cost associated with traditional methods, we can bring new therapies and solutions to market faster than ever. #Predictivemodelling #Bioinformatics #Modelsystems #Biotechnology #Drugdiscovery #AI #Machinelearning

In this new post (4-min read), John S. Kenney, Ph.D. (Antibody Solutions) offers a brief recap of content presented at Antibody Engineering & Therapeutics 2023, sharing his thoughts on Will AI Kill the Lab?, Accessing New Technologies, and New Horizons. Thanks, John! #antibodyengineering #ai #antibodies #antibodytherapeutics https://lnkd.in/d_gUGYcN

Google DeepMind has launched AlphaProteo, an AI system that designs novel proteins, potentially revolutionizing drug development and disease research. 🔬 Breakthrough in protein design: AlphaProteo successfully creates protein binders for diseases like cancer, achieving up to 300x better binding than existing methods. 🧬 Impressive experimental success: With a success rate of 88% in binding viral proteins, AlphaProteo significantly improves efficiency in research. 🚀 Vast training data: The system leverages data from the Protein Data Bank and 100M+ predicted structures, enhancing its molecular binding knowledge. 🤝 Collaborative approach: DeepMind works with external experts to ensure responsible development and address AlphaProteo’s limitations. #AI #DrugDiscovery #DeepMind 🧑⚕️ Potential impact: Faster protein design could accelerate drug development, disease diagnosis, and even agriculture. 🔧 Limitations remain: While effective, AlphaProteo couldn't design binders for all target proteins, leaving room for improvement. https://lnkd.in/gY-VUCmt

From the article: „High-quality structural information remains critical for the antibody optimization process, but antibody-antigen complex structures are often unavailable and in silico antibody docking methods are still unreliable. In this study, DeepAb, a deep learning model for predicting antibody Fv structure directly from sequence, was used in conjunction with single-point experimental deep mutational scanning (DMS) enrichment data to design 200 potentially optimized variants“ #mabs #thermostability #developability

Revolutionizing Medicine, One Antibody at a Time. Imagine a future where diseases are not just managed, but cured, thanks to precision-engineered antibody drugs. It's not just a dream. It's becoming a reality, and AI is at the heart of this transformation. Scientists are leveraging AI to optimize the development of antibody drugs, pushing the boundaries of what's possible in medicine. By iteratively mutating amino acids, AI algorithms are pinpointing the most effective sequences for targeting diseases. It's a game-changer. This approach isn't just about speeding up drug development. It's about precision, efficiency, and the potential to save millions of lives by creating more effective therapies. As a CEO deeply entrenched in the AI space, I see this as a monumental step forward. It validates the power of generative AI in solving complex biological challenges. The implications for healthcare are profound, offering a glimpse into a future where our fight against diseases is bolstered by AI's unparalleled capabilities. For those of us at the intersection of AI and healthcare, this is a moment of validation and anticipation. We're not just watching the future unfold; we're helping to shape it. Read more about this groundbreaking work and its implications for the future of medicine: [Link to the article] The fusion of AI and biology is not just the next chapter in healthcare; it's a whole new book. Let's turn the page together. Check this out: https://lnkd.in/gUKdpkBP

ABodyBuilder3: Improved and scalable antibody structure predictions Abstract: Accurate prediction of antibody structure is a central task in the design and development of monoclonal antibodies, notably to understand both their developability and their binding properties. In this article, we introduce ABodyBuilder3, an improved and scalable antibody structure prediction model based on ImmuneBuilder. We achieve a new state-of-the-art accuracy in the modelling of CDR loops by leveraging language model embeddings, and show how predicted structures can be further improved through careful relaxation strategies. Finally, we incorporate a predicted Local Distance Difference Test into the model output to allow for a more accurate estimation of Paper: https://lnkd.in/dctzgGVS Code: https://lnkd.in/dg5awjD6