In the recent years, therapeutic use of antibodies has seen a huge growth, due to their inherent proprieties and technological advances in the methods used to study and characterize them. Effective design and engineering of antibodies for therapeutic purposes are heavily dependent on knowledge of the structural principles that regulate antibody-antigen interactions. Several experimental techniques such as X-ray crystallography, cryo-electron microscopy, NMR or mutagenesis analysis can be applied, but these are usually expensive and time consuming. Therefore computational approaches like molecular docking may offer a valuable alternative for the characterisation of antibody-antigen complexes.
Here we describe a protocol for the prediction of the 3D structure of antibody-antigen complexes using the integrative modelling platform HADDOCK. The protocol consists of: 1) The identification of the antibody residues belonging to the hyper variable loops which are known to be crucial for the binding and can be used to guide the docking; 2) The detailed steps to perform docking with the HADDOCK 2.4 webserver following different strategies depending on the availability of information about epitope residues.
Many factors influence biomolecules binding, and its assessment constitutes an elusive challenge in computational structural biology. In this respect, the evaluation of shape complementarity at molecular interfaces is one of the main factors to be considered. We focus on the particular case of antibody-antigen complexes to quantify the complementarities occurring at molecular interfaces. We relied on a method we recently developed, which employs the 2D Zernike descriptors, to characterize investigated regions with an ordered set of numbers summarizing the local shape properties. Collected a st…
Broad Institute of MIT and Harvard（麻省理工学院和哈佛大学博德研究所）；Massachusetts Institute of Technology（麻省理工学院）
Computational antibody design seeks to automatically create an antibody that binds to an antigen. The binding affinity is governed by the 3D binding interface where antibody residues (paratope) closely interact with antigen residues (epitope). Thus, predicting 3D paratope-epitope complex (docking) is the key to finding the best paratope. In this paper, we propose a new model called Hierarchical Equivariant Refinement Network (HERN) for paratope docking and design. During docking, HERN employs a hierarchical message passing network to predict atomic forces and use them to refine a binding compl…
We describe the representation of the chemical affinity between the antigen-combining site of the immunoglobulin molecule and the antigen molecule as the probability of the two molecules existing in a bound state. Our model is based on the identification of shape attractors in the configuration space for the joint antibody / antigen combining site sequence. We parameterize configuration space in terms of Ramachandran angles. The shape attractors allow us to construct a Markov chain whose steady state distribution gives rise to the desired attachment probability. As a result we are able to deli…
ABConformer: Physics-inspired Sliding Attention for Antibody-Antigen Interface Prediction
Material Innovation Institute for Life Sciences and Energy（生命科学与能源材料创新研究院）；The University of Hong Kong（香港大学）；Generative AI Research and Development Center（生成式人工智能研究与开发中心）；The Hong Kong University of Science and Technology（香港科学与技术大学）；Computational Immunology Centre（计算免疫学中心）；BayVax Biotech Limited（湾生生物科技有限公司）；School of Biomedical Sciences（生物医学科学学院）
AbRank: A Benchmark Dataset and Metric-Learning Framework for Antibody-Antigen Affinity Ranking
Structural Molecular Biology, Division of Biosciences, University College London（伦敦大学学院生物结构分子生物学系）；Institute of Computational Life Sciences, Zürich University of Applied Sciences (ZHAW)（苏黎世应用科学大学计算生命科学研究所）；Biomedical Informatics and Data Science, Yale School of Medicine（耶鲁医学院生物医学信息学与数据科学）；Centre for Artificial Intelligence, University College London（伦敦大学学院人工智能中心）
Affinity maturation produces antibodies that bind antigens with high specificity by accumulating mutations in the antibody sequence. Mapping out the antibody-antigen affinity landscape can give us insight into the accessible paths during this rapid evolutionary process. By developing a carefully controlled null model for noninteracting mutations, we characterized epistasis in affinity measurements of a large library of antibody variants obtained by Tite-Seq, a recently introduced Deep Mutational Scan method yielding physical values of the binding constant. We show that representing affinity as…
Antigen-Specific Antibody Design via Direct Energy-based Preference Optimization
School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；Institute of Automation, Chinese Academy of Sciences (CASIA)（中国科学院自动化研究所）；ByteDance Research（字节跳动研究院）；College of Computer Science and Electronic Engineering, Hunan University（湖南大学计算机科学与电子工程学院）
Global Health Drug Discovery Institute（全球健康药物研发中心）
Antibody therapeutics has been extensively studied in drug discovery and development within the past decades. One increasingly popular focus in the antibody discovery pipeline is the optimization step for therapeutic leads. Both traditional methods and in silico approaches aim to generate candidates with high binding affinity against specific target antigens. Traditional in vitro approaches use hybridoma or phage display for candidate selection, and surface plasmon resonance (SPR) for evaluation, while in silico computational approaches aim to reduce the high cost and improve efficiency by inc…
We examined the potential of antibody-functionalized single-walled carbon nanotube (SWNT) field-effect transistors (FETs) for use as a fast and accurate sensor for a Lyme disease antigen. Biosensors were fabricated on oxidized silicon wafers using chemical vapor deposition grown carbon nanotubes that were functionalized using diazonium salts. Attachment of Borrelia burgdorferi (Lyme) flagellar antibodies to the nanotubes was verified by Atomic Force Microscopy and electronic measurements. A reproducible shift in the turn-off voltage of the semiconducting SWNT FETs was seen upon incubation with…
In response to pathogens, the adaptive immune system generates specific antibodies that bind and neutralize foreign antigens. Understanding the composition of an individual's immune repertoire can provide insights into this process and reveal potential therapeutic antibodies. In this work, we explore the application of antibody-specific language models to aid understanding of immune repertoires. We introduce AntiBERTy, a language model trained on 558M natural antibody sequences. We find that within repertoires, our model clusters antibodies into trajectories resembling affinity maturation. Imp…
Tsinghua University（清华大学）；Institute for AI Industry Research (AIR), Tsinghua University（清华大学人工智能产业研究院（AIR））；Beijing Academy of Artificial Intelligence（北京人工智能研究院）；Renmin University of China（中国人民大学）
Antibody design is valuable for therapeutic usage and biological research. Existing deep-learning-based methods encounter several key issues: 1) incomplete context for Complementarity-Determining Regions (CDRs) generation; 2) incapability of capturing the entire 3D geometry of the input structure; 3) inefficient prediction of the CDR sequences in an autoregressive manner. In this paper, we propose Multi-channel Equivariant Attention Network (MEAN) to co-design 1D sequences and 3D structures of CDRs. To be specific, MEAN formulates antibody design as a conditional graph translation problem by i…
Huazhong University of Science and Technology（华中科技大学）；Microsoft Research AI4Science（微软研究院AI4Science）；University of Science and Technology of China（中国科学技术大学）；Peking University（北京大学）
Antibodies are versatile proteins that can bind to pathogens and provide effective protection for human body. Recently, deep learning-based computational antibody design has attracted popular attention since it automatically mines the antibody patterns from data that could be complementary to human experiences. However, the computational methods heavily rely on high-quality antibody structure data, which is quite limited. Besides, the complementarity-determining region (CDR), which is the key component of an antibody that determines the specificity and binding affinity, is highly variable and …
Accurate in-silico prediction of conformational B-cell epitopes would lead to major improvements in disease diagnostics, drug design and vaccine development. A variety of computational methods, mainly based on machine learning approaches, have been developed in the last decades to tackle this challenging problem. Here, we rigorously benchmarked nine state-of-the-art conformational B-cell epitope prediction webservers, including generic and antibody-specific methods, on a dataset of over 250 antibody-antigen structures. The results of our assessment and statistical analyses show that all the me…
Antibodies are a critical part of the immune system, having the function of directly neutralising or tagging undesirable objects (the antigens) for future destruction. Being able to predict which amino acids belong to the paratope, the region on the antibody which binds to the antigen, can facilitate antibody design and contribute to the development of personalised medicine. The suitability of deep neural networks has recently been confirmed for this task, with Parapred outperforming all prior physical models. Our contribution is twofold: first, we significantly outperform the computational ef…
Despite the central role that antibodies play in the adaptive immune system and in biotechnology, much remains unknown about the quantitative relationship between an antibody's amino acid sequence and its antigen binding affinity. Here we describe a new experimental approach, called Tite-Seq, that is capable of measuring binding titration curves and corresponding affinities for thousands of variant antibodies in parallel. The measurement of titration curves eliminates the confounding effects of antibody expression and stability that arise in standard deep mutational scanning assays. We demonst…
The affinity of antibodies (Abs) produced in vivo for their target antigens (Ags) is typically well below the maximum affinity possible. Nearly 25 years ago, Foote and Eisen explained how an 'affinity ceiling' could arise from constraints associated with the acquisition of soluble antigen by B cells. However, recent studies have shown that B cells in germinal centers (where Ab affinity maturation occurs) acquire Ag not in soluble form but presented as receptor-bound immune complexes on follicular dendritic cells (FDCs). How the affinity ceiling arises in such a scenario is unclear. Here, we ar…
University of Cambridge（剑桥大学）；Mila（米拉研究所）；Université de Montréal（蒙特利尔大学）；DeepMind（深度思维）
Antibodies are proteins in the immune system which bind to antigens to detect and neutralise them. The binding sites in an antibody-antigen interaction are known as the paratope and epitope, respectively, and the prediction of these regions is key to vaccine and synthetic antibody development. Contrary to prior art, we argue that paratope and epitope predictors require asymmetric treatment, and propose distinct neural message passing architectures that are geared towards the specific aspects of paratope and epitope prediction, respectively. We obtain significant improvements on both tasks, set…
University of Edinburgh（爱丁堡大学）；Technische Universität Darmstadt（达姆施塔特工业大学）；Huawei Noah’s Ark Lab（华为诺亚方舟实验室）；University of Oslo（奥斯陆大学）；American University of Beirut Medical Centre（贝鲁特美国大学医学中心）；University College London（伦敦大学学院）
Antibodies are canonically Y-shaped multimeric proteins capable of highly specific molecular recognition. The CDRH3 region located at the tip of variable chains of an antibody dominates antigen-binding specificity. Therefore, it is a priority to design optimal antigen-specific CDRH3 regions to develop therapeutic antibodies. However, the combinatorial nature of CDRH3 sequence space makes it impossible to search for an optimal binding sequence exhaustively and efficiently using computational approaches. Here, we present \texttt{AntBO}: a combinatorial Bayesian optimisation framework enabling ef…
BConformeR: A Conformer Based on Mutual Sampling for Unified Prediction of Continuous and Discontinuous Antibody Binding Sites
Material Innovation Institute for Life Sciences and Energy The University of Hong Kong Hetao SZ-HK Cooperation Zone（生命科学与能源创新材料研究所 香港大学 河套 SZ-HK 合作区）；Hong Kong Generative AI Research and Development Center The Hong Kong University of Science and Technology（香港生成式人工智能研究与发展中心 香港科学大学）；Computational Immunology Centre BayVax Biotech Limited（计算免疫学中心 BayVax 生物技术有限公司）；School of Biomedical Sciences The University of Hong Kong（生物医学科学学院 香港大学）
Stable Online and Offline Reinforcement Learning for Antibody CDRH3 Design
University of Freiburg（弗赖堡大学）；Collaborative Research Institute Intelligent Oncology (CRIION)（智能肿瘤学合作研究所（CRIION））；University Medical Center Freiburg（弗赖堡大学医学中心）；BrainLinks-BrainTools（脑连接-脑工具研究中心）
University of California, San Diego（加州大学圣地亚哥分校）；National Central University（中央大学）；SciCrunch, Inc.（SciCrunch公司）
Antibodies are widely used reagents to test for expression of proteins and other antigens. However, they might not always reliably produce results when they do not specifically bind to the target proteins that their providers designed them for, leading to unreliable research results. While many proposals have been developed to deal with the problem of antibody specificity, it is still challenging to cover the millions of antibodies that are available to researchers. In this study, we investigate the feasibility of automatically generating alerts to users of problematic antibodies by extracting…
BetterBodies: Reinforcement Learning guided Diffusion for Antibody Sequence Design
University of Freiburg（弗莱堡大学）；Collaborative Research Institute Intelligent Oncology (CRIION)（智能肿瘤学合作研究所（CRIION））；University Medical Center Freiburg（弗莱堡大学医学中心）；BrainLinks-BrainTools
Proteins play crucial roles in every cellular process by interacting with each other, with nucleic acids, metabolites, and other molecules. The resulting assemblies can be very large and intricate and pose challenges to experimental methods. In the current era of integrative modeling, it is often only by a combination of various experimental techniques and computations that 3D models of those molecular machines can be obtained. Among the various computational approaches available, molecular docking is often the method of choice when it comes to predicting 3D structures of complexes. Docking ca…
