AI-Powered Breakthroughs in Antibody Optimization

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AI can analyze and improve antibodies, replacing the traditional time-consuming and expensive methods and making lead optimization faster and more effective. In this blog, we highlight three main ways AI can be used to enhance the lead optimization efforts, depending on the type of data available: the antibody sequence, the sequence and structure of antibody, or comprehensive data including the sequence and structure of both antibody and its’ target. Through real-world examples and specific AI tools, we demonstrate how AI can not only suggest improvements to existing antibodies but also generate new ones that could work even better.</p> <p><strong>Table of Contents:</strong></p> <ol> <li><a href="#AI-Powered Antibody Optimization: The Art of Precision in Lead Optimization">AI-Powered Antibody Optimization: The Art of Precision in Lead Optimization</a></li> <li><a href="#Tailored Computational Strategies for Unmatched Potential">Tailored Computational Strategies for Unmatched Potential</a></li> <li><a href="#AI-driven Antibody Optimization Based On Sequence">AI-driven Antibody Optimization Based On Sequence </a></li> <li><a href="#AI-Driven Antibody Optimization Based on Sequence and Structure">AI-Driven Antibody Optimization Based on Sequence and Structure</a></li> <li><a href="#AI-Driven Antibody Optimization Based on Binder-Target Structural Context">AI-Driven Antibody Optimization Based on Binder-Target Structural Context </a></li> <li><a href="#The Intersection of AI and Human Expertise in Antibody Optimization">The Intersection of AI and Human Expertise in Antibody Optimization</a></li> </ol> <h2><a id="image-segmentation-feature-extraction-challenges-solutions" name="image-segmentation-feature-extraction-challenges-solutions"></a>AI-Powered Antibody Optimization: The Art of Precision in Lead Optimization</h2> <p><span style="font-weight: 400;">In drug discovery, lead optimization is a crucial phase where potential therapies, especially monoclonal antibodies (mAbs), undergo meticulous refinement. The difference between a good and an exceptional therapeutic often relies on incredibly subtle molecular modifications. </span><span style="font-weight: 400;"><br /> </span><span style="font-weight: 400;">Traditional lead optimization practices, especially the ones focusing on affinity maturation, involve subjecting candidate antibodies to a laborious and costly </span><i><span style="font-weight: 400;">in vitro</span></i><span style="font-weight: 400;"> affinity maturation process. AI-based solutions streamline and enhance the antibody discovery process, reducing the time and increasing the success rate of lead optimization efforts.</span></p> </div> </div> <div class="container"> <div class="tc-simple-chapter" style="overflow: hidden"> <h2><a id="Tailored Computational Strategies for Unmatched Potential" name="exploring-few-shot-segmentation-approach-with-seggpt"></a>Tailored Computational Strategies for Unmatched Potential</h2> <p><span style="font-weight: 400;">The power of AI-based methods lies in their ability to utilize any type of available data. Depending on the input type, three distinct computational strategies can be applied:</span></p> <ol> <li style="font-weight: 400;" aria-level="1"><span style="font-weight: 400;">Utilizing the antibody sequence alone.</span></li> <li style="font-weight: 400;" aria-level="1"><span style="font-weight: 400;">Harnessing both the antibody sequence and its structure.</span></li> <li style="font-weight: 400;" aria-level="1"><span style="font-weight: 400;">Leveraging the sequence and structural data of the antibody and its target.</span></li> </ol> <p><span style="font-weight: 400;">Each data type unlocks specific AI capabilities, providing unique insights and paving the way toward novel therapeutic discoveries. At Ardigen, we develop cutting-edge AI-driven </span><i><span style="font-weight: 400;">in-silico</span></i><span style="font-weight: 400;"> methodologies to empower data-driven drug discovery. Follow along to discover how we redefine the boundaries of therapeutic innovation.</span></p> <p><img loading="lazy" class="alignnone size-large wp-image-16535" src="https://ardigen.com/wp-content/uploads/2024/04/Tailored-Computational-Strategies-for-Unmatched-Potential-1024x572.png" alt="" width="1024" height="572" srcset="https://ardigen.com/wp-content/uploads/2024/04/Tailored-Computational-Strategies-for-Unmatched-Potential-1024x572.png 1024w, https://ardigen.com/wp-content/uploads/2024/04/Tailored-Computational-Strategies-for-Unmatched-Potential-300x168.png 300w, https://ardigen.com/wp-content/uploads/2024/04/Tailored-Computational-Strategies-for-Unmatched-Potential-768x429.png 768w, https://ardigen.com/wp-content/uploads/2024/04/Tailored-Computational-Strategies-for-Unmatched-Potential-1536x858.png 1536w, https://ardigen.com/wp-content/uploads/2024/04/Tailored-Computational-Strategies-for-Unmatched-Potential.png 1768w" sizes="(max-width: 1024px) 100vw, 1024px" /></p> </div> </div> <div class="container"> <div class="tc-simple-chapter" style="overflow: hidden"> <h2><a id="AI-driven Antibody Optimization Based On Sequence" name="feature-extraction-visualisation-adjustable-data-for-tumor-id"></a>AI-driven Antibody Optimization Based On Sequence</h2> <p><span style="font-weight: 400;">In cases when only the antibody sequence is available, AI algorithms based on large language models (LLMs) adapted for analyzing protein sequences present a novel avenue for antibody design. A recent study by </span><a href="https://www.nature.com/articles/s41587-023-01763-2" target="_blank" rel="noopener"><span style="font-weight: 400;">Hie, B.L. et al., published in Nature Biotechnology in 2023</span></a><span style="font-weight: 400;"> demonstrated that such models can suggest beneficial mutations for improving antibodies in the absence of detailed structural information or knowledge of the target antigen.</span></p> <p><span style="font-weight: 400;">The authors applied a language-model-driven process to the affinity maturation of seven antibodies targeting, among others, SARS-CoV-1, SARS-CoV-2, Ebola, and influenza viruses. By examining no more than 20 variants per antibody over just two iterations of AI-aided computational design, they were able to enhance the binding affinity of four clinically significant, well-developed antibodies by as much as seven-fold. They also achieved up to a 160-fold increase in affinity for three antibodies that were less mature with the best results for the one targeting the Ebola virus. Notably, several of the AI-optimized antibodies exhibited improved thermostability and showed promising activity in neutralizing viruses, specifically the Ebola and SARS-CoV-2 pseudoviruses. These remarkable results illustrate the potential of AI to streamline the antibody development process using protein sequence information alone. </span></p> <p><span style="font-weight: 400;">The study also suggests that the underlying models that enhance antibody affinity could be broadly applied to other protein engineering efforts. This general approach, from sequence analysis to optimized leads, is outlined below.</span></p> <p><img loading="lazy" class="alignnone size-large wp-image-16533" src="https://ardigen.com/wp-content/uploads/2024/04/AI-driven-Antibody-Optimization-Based-On-Sequence-1024x303.png" alt="" width="1024" height="303" srcset="https://ardigen.com/wp-content/uploads/2024/04/AI-driven-Antibody-Optimization-Based-On-Sequence-1024x303.png 1024w, https://ardigen.com/wp-content/uploads/2024/04/AI-driven-Antibody-Optimization-Based-On-Sequence-300x89.png 300w, https://ardigen.com/wp-content/uploads/2024/04/AI-driven-Antibody-Optimization-Based-On-Sequence-768x227.png 768w, https://ardigen.com/wp-content/uploads/2024/04/AI-driven-Antibody-Optimization-Based-On-Sequence-1536x454.png 1536w, https://ardigen.com/wp-content/uploads/2024/04/AI-driven-Antibody-Optimization-Based-On-Sequence.png 1761w" sizes="(max-width: 1024px) 100vw, 1024px" /></p> <p> </p> <p><span style="font-weight: 400;">Several sequence-based AI models have been developed specifically for antibody optimization, each featuring unique strengths. For example, the </span><a href="https://www.biorxiv.org/content/10.1101/2022.07.20.500902v1.full.pdf" target="_blank" rel="noopener"><span style="font-weight: 400;">ESM2</span></a><span style="font-weight: 400;"> and </span><a href="https://www.biorxiv.org/content/10.1101/2023.07.23.550085v2" target="_blank" rel="noopener"><span style="font-weight: 400;">ProtT5</span></a><span style="font-weight: 400;"> models offer a broad-spectrum analysis based on general protein evolution, while </span><a href="https://github.com/oxpig/AbLang2"><span style="font-weight: 400;">AbLang</span></a><span style="font-weight: 400;"> specializes in more specific details of antibody sequences. Complementing these is </span><a href="https://ardigen.com/prism-a-writing-assistant-for-the-language-of-proteins-2/" target="_blank" rel="noopener"><span style="font-weight: 400;">Ardigen’s Prism</span></a><span style="font-weight: 400;">, which is a curated collection of protein LLMs that brings together the collective power of multiple models to support and enhance precision protein engineering. </span></p> </div> </div> <div class="container"> <div class="tc-simple-chapter" style="overflow: hidden"> <h2><a id="AI-Driven Antibody Optimization Based on Sequence and Structure" name="feature-extraction-visualisation-adjustable-data-for-tumor-id"></a>AI-Driven Antibody Optimization Based on Sequence and Structure</h2> <p><span style="font-weight: 400;">When both the antibody sequence and structure are available, scientists can capitalize on the value of structural insight. This approach employs various AI models that take the 3D protein structure as input and predict sequences with a high probability of folding into the given shape. Given the initial 3D structure of an antibody, this method focuses on the regions known as the Complementarity-Determining Regions (CDRs), which are essential for antigen binding. The original CDR sequences are then ‘masked’, allowing the models to generate a range of novel sequences anticipated to fold in a manner similar to the original antibody. Below is a schematic of this approach:</span></p> <p><img loading="lazy" class="alignnone size-large wp-image-16531" src="https://ardigen.com/wp-content/uploads/2024/04/AI-Driven-Antibody-Optimization-Based-on-Sequence-and-Structure-1024x353.png" alt="" width="1024" height="353" srcset="https://ardigen.com/wp-content/uploads/2024/04/AI-Driven-Antibody-Optimization-Based-on-Sequence-and-Structure-1024x353.png 1024w, https://ardigen.com/wp-content/uploads/2024/04/AI-Driven-Antibody-Optimization-Based-on-Sequence-and-Structure-300x103.png 300w, https://ardigen.com/wp-content/uploads/2024/04/AI-Driven-Antibody-Optimization-Based-on-Sequence-and-Structure-768x265.png 768w, https://ardigen.com/wp-content/uploads/2024/04/AI-Driven-Antibody-Optimization-Based-on-Sequence-and-Structure-1536x530.png 1536w, https://ardigen.com/wp-content/uploads/2024/04/AI-Driven-Antibody-Optimization-Based-on-Sequence-and-Structure.png 1777w" sizes="(max-width: 1024px) 100vw, 1024px" /></p> <p><span style="font-weight: 400;">This process often enables the regeneration of the original sequence and creation of diverse novel variants. The novel variants can display similar or even improved affinity to the original target and potential cross-reactivity with similar targets, thereby expanding the therapeutic potential of the antibody. </span></p> <p><span style="font-weight: 400;">The efficacy of the inverse folding approach has been demonstrated by</span><a href="https://arxiv.org/abs/2310.19513" target="_blank" rel="noopener"><span style="font-weight: 400;"> Frederic A. Dreyer et al., in the manuscript published in 2023 on arXiv</span></a><span style="font-weight: 400;">. The study develops a deep learning inverse folding model specifically adapted for antibody sequence design. The </span><a href="https://zenodo.org/records/8164693" target="_blank" rel="noopener"><span style="font-weight: 400;">AbMPNN</span></a><span style="font-weight: 400;"> model specifically trained to take into account the target structure as context has set new benchmarks for antibody designability, particularly for the hypervariable CDR-H3 loop. This model belongs to a suite of computational tools designed for sequence regeneration from a given structure, including others like </span><a href="https://www.biorxiv.org/content/10.1101/2022.06.03.494563v1" target="_blank" rel="noopener"><span style="font-weight: 400;">ProteinMPNN</span></a><span style="font-weight: 400;">. </span></p> <p><span style="font-weight: 400;">The results of the study underscore the high impact of integrating sequence and structural data for lead optimization. Intriguingly, the inverse folding method can be utilized even when an antibody’s experimental structure remains unknown. Thanks to recent advances in antibody structure prediction, a modeled structure derived from its sequence can serve as a starting point for optimization. Moreover, sequences developed in this way can undergo the sequence diversification process described above, potentially expanding the arsenal of antibodies explored by AI to refine the initial leads.</span></p> <p> </p> </div> </div> <div class="container"> <div class="tc-simple-chapter" style="overflow: hidden"> <h2><span style="font-weight: 400;"><a id="AI-Driven Antibody Optimization Based on Binder-Target Structural Context" name=""conclusion-efficient-data-analysis-with-seggpt-feature-extraction"></a><strong>AI-Driven Antibody Optimization Based on Binder-Target Structural Context </strong></span></h2> <p><span style="font-weight: 400;">The third approach to lead optimization is at the forefront of innovation. It harnesses the latest AI techniques for </span><i><span style="font-weight: 400;">de novo</span></i><span style="font-weight: 400;"> generation of protein structures and sequences and is being actively pursued by the scientific community for its groundbreaking potential. This strategy is based on generating protein configurations in specific contexts. </span></p> <p><span style="font-weight: 400;">Starting with the structure of an antibody-target complex, this particular type of AI models focus on redesigning the CDRs. These are the regions of the antibody that directly engage with the target and determine the strength and specificity of the antigen binding and, consequently, modulate the immune response. Through the CDR redesigning process, AI is able to design a completely new interface, one that is more likely to exhibit a high specificity for the antigen.</span></p> <p><span style="font-weight: 400;">An array of sophisticated models has been developed to support this novel approach. One of them is RFdiffusion, which was showcased by Charlotte Deane at the PEGS 2023 conference in Lisbon. What is more, a recent </span><a href="https://doi.org/10.1101/2024.03.14.585103" target="_blank" rel="noopener"><span style="font-weight: 400;">study from Baker’s group</span></a><span style="font-weight: 400;"> has shown that the RFdiffusion model, fine-tuned for specificity, can create new antibody variable heavy chains that bind precisely to designated epitopes. Other models such as </span><a href="https://www.nature.com/articles/s41586-023-06728-8"><span style="font-weight: 400;">Chroma</span></a><span style="font-weight: 400;">, </span><a href="https://www.biorxiv.org/content/10.1101/2023.09.25.559190v1"><span style="font-weight: 400;">AbDesign & AbDock</span></a><span style="font-weight: 400;">, and </span><a href="https://openreview.net/forum?id=Enqxq6TWoZ" target="_blank" rel="noopener"><span style="font-weight: 400;">EAGLE</span></a><span style="font-weight: 400;"> also bring unique capabilities to the table. These models specialize in the de novo generation of protein sequences and structures, a task that is energizing the scientific community with its promise and complexity.</span></p> <p><span style="font-weight: 400;">The figure below captures the utility of this approach for lead optimization, from initial structure to optimized leads. As AI continues to evolve, such solutions will likely become central to the discovery and refinement of therapeutic antibodies. </span></p> <p><img loading="lazy" class="alignnone size-large wp-image-16529" src="https://ardigen.com/wp-content/uploads/2024/04/AI-Driven-Antibody-Optimization-Based-on-Binder-Target-Structural-Context-1024x418.png" alt="" width="1024" height="418" srcset="https://ardigen.com/wp-content/uploads/2024/04/AI-Driven-Antibody-Optimization-Based-on-Binder-Target-Structural-Context-1024x418.png 1024w, https://ardigen.com/wp-content/uploads/2024/04/AI-Driven-Antibody-Optimization-Based-on-Binder-Target-Structural-Context-300x122.png 300w, https://ardigen.com/wp-content/uploads/2024/04/AI-Driven-Antibody-Optimization-Based-on-Binder-Target-Structural-Context-768x314.png 768w, https://ardigen.com/wp-content/uploads/2024/04/AI-Driven-Antibody-Optimization-Based-on-Binder-Target-Structural-Context-1536x627.png 1536w, https://ardigen.com/wp-content/uploads/2024/04/AI-Driven-Antibody-Optimization-Based-on-Binder-Target-Structural-Context.png 1778w" sizes="(max-width: 1024px) 100vw, 1024px" /></p> </div> </div> <div class="container"> <div class="tc-simple-chapter" style="overflow: hidden"> <h2><a id="The Intersection of AI and Human Expertise in Antibody Optimization" name="The Intersection of AI and Human Expertise in Antibody Optimization"></a>The Intersection of AI and Human Expertise in Antibody Optimization</h2> <p><span style="font-weight: 400;">The future of antibody development is defined by the dynamic intersection of computational power and biological innovation. AI methods hold remarkable potential to accelerate and refine the process of lead optimization. However, it’s essential to recognize that these advanced computational tools do not operate in isolation. The true power of AI is realized when it is employed by experts who understand both the science of antibody development and the nuances of machine learning models.</span></p> <p><span style="font-weight: 400;">While AI opens new possibilities in antibody lead optimization, it is the human expertise in using these tools that propels successful outcomes. At Ardigen, we leverage our deep understanding of both AI algorithms and biological systems to selectively advance the most promising antibodies. This strategic approach ensures efficient use of resources and timely execution to move us closer to breakthrough therapies. Our team embraces AI innovation in addition to our established scientific acumen to maintain our position as leaders in antibody development.</span></p> </div> </div> <div class="container"> <div class="tc-simple-chapter" style="overflow: hidden"> <h3>Works Cited:</h3> <ol> <li style="font-weight: 400;" aria-level="1"><span style="font-weight: 400;">B.L. Hie, et al. (2023). “Efficient evolution of human antibodies from general protein language models,” </span><i><span style="font-weight: 400;">Nature Biotechnology</span></i><span style="font-weight: 400;">. Available at:</span><a href="https://www.nature.com/articles/s41587-023-01763-2" target="_blank" rel="noopener"> <span style="font-weight: 400;">https://www.nature.com/articles/s41587-023-01763-2</span></a></li> <li style="font-weight: 400;" aria-level="1"><span style="font-weight: 400;">Zeming Lin, et al. (2022). “Language models of protein sequences at the scale of evolution enable accurate structure prediction” </span><i><span style="font-weight: 400;">BioRxiv</span></i><span style="font-weight: 400;">. Available at:</span><a href="https://www.biorxiv.org/content/10.1101/2022.07.20.500902v1.full.pdf" target="_blank" rel="noopener"> <span style="font-weight: 400;">https://www.biorxiv.org/content/10.1101/2022.07.20.500902v1.full.pdf</span></a></li> <li style="font-weight: 400;" aria-level="1"><span style="font-weight: 400;">M. Heinzinger, et al. (2024). “Bilingual Language Model for Protein Sequence and Structure” </span><i><span style="font-weight: 400;">BioRxiv. </span></i><span style="font-weight: 400;">Available at:</span> <a href="https://www.biorxiv.org/content/10.1101/2023.07.23.550085v2" target="_blank" rel="noopener">https://www.biorxiv.org/content/10.1101/2023.07.23.550085v2</a></li> <li style="font-weight: 400;" aria-level="1"><span style="font-weight: 400;">T.H. Olsen, et al. (2024). “Addressing the antibody germline bias and its effect on language models for improved antibody design” </span><i><span style="font-weight: 400;">BioRxiv</span></i><span style="font-weight: 400;">. Available at: </span><a href="https://www.biorxiv.org/content/10.1101/2024.02.02.578678v1" target="_blank" rel="noopener">https://www.biorxiv.org/content/10.1101/2024.02.02.578678v1</a></li> <li style="font-weight: 400;" aria-level="1"><span style="font-weight: 400;">Ardigen, (2020). “PRISM: A Writing Assistant for the Language of Proteins” Available at:</span><a href="https://ardigen.com/prism-a-writing-assistant-for-the-language-of-proteins-2/" target="_blank" rel="noopener"> <span style="font-weight: 400;">https://ardigen.com/prism-a-writing-assistant-for-the-language-of-proteins-2/</span></a></li> <li style="font-weight: 400;" aria-level="1"><span style="font-weight: 400;">F.A. Dreyer, et al. (2023). “Inverse folding for antibody sequence design using deep learning”</span> <i><span style="font-weight: 400;">arXiv</span></i><span style="font-weight: 400;">. </span><span style="font-weight: 400;">Available at: </span><a href="https://arxiv.org/abs/2310.19513" target="_blank" rel="noopener"><span style="font-weight: 400;">https://arxiv.org/abs/2310.19513</span></a><span style="font-weight: 400;"> </span></li> <li style="font-weight: 400;" aria-level="1"><span style="font-weight: 400;">F.A. Dreyer, et al. (2023). “Inverse folding for antibody sequence design using deep learning” </span><i><span style="font-weight: 400;">Zenodo</span></i><span style="font-weight: 400;">.</span> <span style="font-weight: 400;">Available at:</span> <a href="https://zenodo.org/records/8164693" target="_blank" rel="noopener"><span style="font-weight: 400;">https://zenodo.org/records/8164693</span></a><span style="font-weight: 400;"> </span></li> <li style="font-weight: 400;" aria-level="1"><span style="font-weight: 400;">J. Dauparas, et al. (2022). “Robust deep learning-based protein sequence design using ProteinMPNN” J</span><span style="font-weight: 400;">.</span> <i><span style="font-weight: 400;">BioRxiv</span></i><span style="font-weight: 400;">. Available at:</span> <a href="https://www.biorxiv.org/content/10.1101/2022.06.03.494563v1" target="_blank" rel="noopener"><span style="font-weight: 400;">https://www.biorxiv.org/content/10.1101/2022.06.03.494563v1</span></a><span style="font-weight: 400;"> </span></li> <li style="font-weight: 400;" aria-level="1"><span style="font-weight: 400;">N. R. Bennett, et al. (2024). “Atomically accurate de novo design of single-domain antibodies” </span><i><span style="font-weight: 400;">BioRxiv</span></i><span style="font-weight: 400;">. Available at:</span> <a href="https://www.biorxiv.org/content/10.1101/2024.03.14.585103v1" target="_blank" rel="noopener"><span style="font-weight: 400;">https://www.biorxiv.org/content/10.1101/2024.03.14.585103v1</span></a></li> <li style="font-weight: 400;" aria-level="1"><span style="font-weight: 400;">J.B. Ingraham, et al. (2023). “Illuminating protein space with a programmable generative model” </span><i><span style="font-weight: 400;">Nature</span></i><span style="font-weight: 400;">. Available at: </span><a href="https://www.nature.com/articles/s41586-023-06728-8#citeas" target="_blank" rel="noopener"><span style="font-weight: 400;">https://www.nature.com/articles/s41586-023-06728-8#citeas</span></a><span style="font-weight: 400;"> </span></li> <li style="font-weight: 400;" aria-level="1"><span style="font-weight: 400;">Z. Peng, et al. (2023). “Generative Diffusion Models for Antibody Design, Docking, and Optimization” </span><i><span style="font-weight: 400;">BioRxiv</span></i><span style="font-weight: 400;">. Available at:</span> <a href="https://www.biorxiv.org/content/10.1101/2023.09.25.559190v1" target="_blank" rel="noopener">https://www.biorxiv.org/content/10.1101/2023.09.25.559190v1</a></li> <li style="font-weight: 400;" aria-level="1"><span style="font-weight: 400;">T. Cohen and D. Schneidman-Duhovny (2023). “Epitope-specific antibody design using diffusion models on the latent space of ESM embeddings” </span><i><span style="font-weight: 400;">OpenReview.net</span></i><span style="font-weight: 400;">. </span><span style="font-weight: 400;">Available at:</span><a href="https://openreview.net/forum?id=Enqxq6TWoZ" target="_blank" rel="noopener"> <span style="font-weight: 400;">https://openreview.net/forum?id=Enqxq6TWoZ</span></a><span style="font-weight: 400;"> </span></li> </ol> <p><span style="font-weight: 400;"> </span></p> </div> </div> </section> <section class="latest" style="background-image: url('https://ardigen.com/wp-content/uploads/2018/04/img_bg_call_to.png')"> <div class="container"> <div class="mirror-flex mirror"> <div class="mirror-flex-item"> <div class="mirror-item" uk-scrollspy="cls:uk-animation-slide-left-medium"> <div class="mirror-image" style="background-image: url('https://ardigen.com/wp-content/uploads/2023/11/tlo_1.jpg')"> <div 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