OptCDR: a general computational method for the design of antibody complementarity determining regions for targeted epitope binding

Prediction of Paratope-Epitope Pairs Using Convolutional Neural Networks

Antibodies play a central role in the adaptive immune response of vertebrates through the specific recognition of exogenous or endogenous antigens. The rational design of antibodies has a wide range of biotechnological and medical applications, such as in disease diagnosis and treatment. However, there are currently no reliable methods for predicting the antibodies that recognize a specific antigen region (or epitope) and, conversely, epitopes that recognize the binding region of a given antibody (or paratope). To fill this gap, we developed ImaPEp, a machine learning-based tool for predicting the binding probability of paratope-epitope pairs, where the epitope and paratope patches were simplified into interacting two-dimensional patches, which were colored according to the values of selected features, and pixelated. The specific recognition of an epitope image by a paratope image was achieved by using a convolutional neural network-based model, which was trained on a set of two-dimensional paratope-epitope images derived from experimental structures of antibody-antigen complexes. Our method achieves good performances in terms of cross-validation with a balanced accuracy of 0.8. Finally, we showcase examples of application of ImaPep, including extensive screening of large libraries to identify paratope candidates that bind to a selected epitope, and rescoring and refining antibody-antigen docking poses.

Revolutionizing Synthetic Antibody Design: Harnessing Artificial Intelligence and Deep Sequencing Big Data for Unprecedented Advances

Synthetic antibodies (Abs) represent a category of engineered proteins meticulously crafted to replicate the functions of their natural counterparts. Such Abs are generated in vitro, enabling advanced molecular alterations associated with antigen recognition, paratope site engineering, and biochemical refinements. In a parallel realm, deep sequencing has brought about a paradigm shift in molecular biology. It facilitates the prompt and cost-effective high-throughput sequencing of DNA and RNA molecules, enabling the comprehensive big data analysis of Ab transcriptomes, including specific regions of interest. Significantly, the integration of artificial intelligence (AI), based on machine- and deep- learning approaches, has fundamentally transformed our capacity to discern patterns hidden within deep sequencing big data, including distinctive Ab features and protein folding free energy landscapes. Ultimately, current AI advances can generate approximations of the most stable Ab structural configurations, enabling the prediction of de novo synthetic Abs. As a result, this manuscript comprehensively examines the latest and relevant literature concerning the intersection of deep sequencing big data and AI methodologies for the design and development of synthetic Abs. Together, these advancements have accelerated the exploration of antibody repertoires, contributing to the refinement of synthetic Ab engineering and optimizations, and facilitating advancements in the lead identification process.

Evaluation of the Ability of AlphaFold to Predict the Three-Dimensional Structures of Antibodies and Epitopes

Being able to accurately predict the three-dimensional structure of an Ab can facilitate Ab characterization and epitope prediction, with important diagnostic and clinical implications. In this study, we evaluated the ability of AlphaFold to predict the structures of 222 recently published, high-resolution Fab H and L chain structures of Abs from different species directed against different Ags. We show that although the overall Ab prediction quality is in line with the results of CASP14, regions such as the complementarity-determining regions (CDRs) of the H chain, which are prone to higher variation, are predicted less accurately. Moreover, we discovered that AlphaFold mispredicts the bending angles between the variable and constant domains. To evaluate the ability of AlphaFold to model Ab-Ag interactions based only on sequence, we used AlphaFold-Multimer in combination with ZDOCK to predict the structures of 26 known Ab-Ag complexes. ZDOCK, which was applied on bound components of both the Ab and the Ag, succeeded in assembling 11 complexes, whereas AlphaFold succeeded in predicting only 2 of 26 models, with significant deviations in the docking contacts predicted in the rest of the molecules. Within the 11 complexes that were successfully predicted by ZDOCK, 9 involved short-peptide Ags (18-mer or less), whereas only 2 were complexes of Ab with a full-length protein. Docking of modeled unbound Ab and Ag was unsuccessful. In summary, our study provides important information about the abilities and limitations of using AlphaFold to predict Ab-Ag interactions and suggests areas for possible improvement.

Structure-based design of nanobodies that inhibit seeding of Alzheimer's patient-extracted tau fibrils

Despite much effort, antibody therapies for Alzheimer's disease (AD) have shown limited efficacy. Challenges to the rational design of effective antibodies include the difficulty of achieving specific affinity to critical targets, poor expression, and antibody aggregation caused by buried charges and unstructured loops. To overcome these challenges, we grafted previously determined sequences of fibril-capping amyloid inhibitors onto a camel heavy chain antibody scaffold. These sequences were designed to cap fibrils of tau, known to form the neurofibrillary tangles of AD, thereby preventing fibril elongation. The nanobodies grafted with capping inhibitors blocked tau aggregation in biosensor cells seeded with postmortem brain extracts from AD and progressive supranuclear palsy (PSP) patients. The tau capping nanobody inhibitors also blocked seeding by recombinant tau oligomers. Another challenge to the design of effective antibodies is their poor blood-brain barrier (BBB) penetration. In this study, we also designed a bispecific nanobody composed of a nanobody that targets a receptor on the BBB and a tau capping nanobody inhibitor, conjoined by a flexible linker. We provide evidence that the bispecific nanobody improved BBB penetration over the tau capping inhibitor alone after intravenous administration in mice. Our results suggest that the design of synthetic antibodies that target sequences that drive protein aggregation may be a promising approach to inhibit the prion-like seeding of tau and other proteins involved in AD and related proteinopathies.

Accelerating antibody discovery and design with artificial intelligence: Recent advances and prospects

Therapeutic antibodies are the largest class of biotherapeutics and have been successful in treating human diseases. However, the design and discovery of antibody drugs remains challenging and time-consuming. Recently, artificial intelligence technology has had an incredible impact on antibody design and discovery, resulting in significant advances in antibody discovery, optimization, and developability. This review summarizes major machine learning (ML) methods and their applications for computational predictors of antibody structure and antigen interface/interaction, as well as the evaluation of antibody developability. Additionally, this review addresses the current status of ML-based therapeutic antibodies under preclinical and clinical phases. While many challenges remain, ML may offer a new therapeutic option for the future direction of fully computational antibody design.

How can we discover developable antibody-based biotherapeutics?

Antibody-based biotherapeutics have emerged as a successful class of pharmaceuticals despite significant challenges and risks to their discovery and development. This review discusses the most frequently encountered hurdles in the research and development (R&D) of antibody-based biotherapeutics and proposes a conceptual framework called biopharmaceutical informatics. Our vision advocates for the syncretic use of computation and experimentation at every stage of biologic drug discovery, considering developability (manufacturability, safety, efficacy, and pharmacology) of potential drug candidates from the earliest stages of the drug discovery phase. The computational advances in recent years allow for more precise formulation of disease concepts, rapid identification, and validation of targets suitable for therapeutic intervention and discovery of potential biotherapeutics that can agonize or antagonize them. Furthermore, computational methods for de novo and epitope-specific antibody design are increasingly being developed, opening novel computationally driven opportunities for biologic drug discovery. Here, we review the opportunities and limitations of emerging computational approaches for optimizing antigens to generate robust immune responses, in silico generation of antibody sequences, discovery of potential antibody binders through virtual screening, assessment of hits, identification of lead drug candidates and their affinity maturation, and optimization for developability. The adoption of biopharmaceutical informatics across all aspects of drug discovery and development cycles should help bring affordable and effective biotherapeutics to patients more quickly.

Improved HIV-1 neutralization breadth and potency of V2-apex antibodies by in silico design

Broadly neutralizing antibodies (bNAbs) against HIV can reduce viral transmission in humans, but an effective therapeutic will require unusually high breadth and potency of neutralization. We employ the OSPREY computational protein design software to engineer variants of two apex-directed bNAbs, PGT145 and PG9RSH, resulting in increases in potency of over 100-fold against some viruses. The top designed variants improve neutralization breadth from 39% to 54% at clinically relevant concentrations (IC80 < 1 μg/mL) and improve median potency (IC80) by up to 4-fold over a cross-clade panel of 208 strains. To investigate the mechanisms of improvement, we determine cryoelectron microscopy structures of each variant in complex with the HIV envelope trimer. Surprisingly, we find the largest increases in breadth to be a result of optimizing side-chain interactions with highly variable epitope residues. These results provide insight into mechanisms of neutralization breadth and inform strategies for antibody design and improvement.

Machine learning optimization of candidate antibody yields highly diverse sub-nanomolar affinity antibody libraries

Therapeutic antibodies are an important and rapidly growing drug modality. However, the design and discovery of early-stage antibody therapeutics remain a time and cost-intensive endeavor. Here we present an end-to-end Bayesian, language model-based method for designing large and diverse libraries of high-affinity single-chain variable fragments (scFvs) that are then empirically measured. In a head-to-head comparison with a directed evolution approach, we show that the best scFv generated from our method represents a 28.7-fold improvement in binding over the best scFv from the directed evolution. Additionally, 99% of designed scFvs in our most successful library are improvements over the initial candidate scFv. By comparing a library's predicted success to actual measurements, we demonstrate our method's ability to explore tradeoffs between library success and diversity. Results of our work highlight the significant impact machine learning models can have on scFv development. We expect our method to be broadly applicable and provide value to other protein engineering tasks.

T-Cell Engagers in Solid Cancers-Current Landscape and Future Directions

Monoclonal antibody treatment initially heralded an era of molecularly targeted therapy in oncology and is now widely applied in modulating anti-cancer immunity by targeting programmed cell receptors (PD-1, PD-L1), cytotoxic T-lymphocyte-associated protein 4 (CTLA-4) and, more recently, lymphocyte-activation gene 3 (LAG3). Chimeric antigen receptor T-cell therapy (CAR-T) recently proved to be a valid approach to inducing anti-cancer immunity by directly modifying the host's immune cells. However, such cell-based therapy requires extensive resources such as leukapheresis, ex vivo modification and expansion of cytotoxic T-cells and current Good Manufacturing Practice (cGMP) laboratories and presents significant logistical challenges. Bi-/trispecific antibody technology is a novel pharmaceutical approach to facilitate the engagement of effector immune cells to potentially multiple cancer epitopes, e.g., the recently approved blinatumomab. This opens the opportunity to develop 'off-the-shelf' anti-cancer agents that achieve similar and/or complementary anti-cancer effects as those of modified immune cell therapy. The majority of bi-/trispecific antibodies target the tumor-associated antigens (TAA) located on the extracellular surface of cancer cells. The extracellular antigens represent just a small percentage of known TAAs and are often associated with higher toxicities because some of them are expressed on normal cells (off-target toxicity). In contrast, the targeting of intracellular TAAs such as mutant RAS and TP53 may lead to fewer off-target toxicities while still achieving the desired antitumor efficacy (on-target toxicity). Here, we provide a comprehensive review on the emerging field of bi-/tri-specific T-cell engagers and potential therapeutic opportunities.

Binding affinity prediction for antibody-protein antigen complexes: A machine learning analysis based on interface and surface areas

Specific antibodies can bind to protein antigens with high affinity and specificity, and this property makes them one of the best protein-based therapeutics. Accurate prediction of antibody‒protein antigen binding affinity is crucial for designing effective antibodies. The current predictive methods for protein‒protein binding affinity usually fail to predict the binding affinity of an antibody‒protein antigen complex with a comparable level of accuracy. Here, new models specific for antibody‒antigen binding affinity prediction are developed according to the different types of interface and surface areas present in antibody‒antigen complex. The contacts-based descriptors are also employed to construct or train different models specific for antibody‒protein antigen binding affinity prediction. The results of this study show that (i) the area-based descriptors are slightly better than the contacts-based descriptors in terms of the predictive power; (ii) the new models specific for antibody‒protein antigen binding affinity prediction are superior to the previously-used general models for predicting the protein‒protein binding affinities; (iii) the performances of the best area-based and contacts-based models developed in this work are better than the performances of a recently-developed graph-based model (i.e., CSM-AB) specific for antibody‒protein antigen binding affinity prediction. The new models developed in this work would not only help understand the mechanisms underlying antibody‒protein antigen interactions, but would also be of some applicable utility in the design and virtual screening of antibody-based therapeutics.

Towards an optimal monoclonal antibody with higher binding affinity to the receptor-binding domain of SARS-CoV-2 spike proteins from different variants

A highly efficient and robust multiple scales in silico protocol, consisting of atomistic Molecular Dynamics (MD), coarse-grain (CG) MD, and constant-pH CG Monte Carlo (MC), has been developed and used to study the binding affinities of selected antigen-binding fragments of the monoclonal antibody (mAbs) CR3022 and several of its here optimized versions against 11 SARS-CoV-2 variants including the wild type. Totally 235,000 mAbs structures were initially generated using the RosettaAntibodyDesign software, resulting in top 10 scored CR3022-like-RBD complexes with critical mutations and compared to the native one, all having the potential to block virus-host cell interaction. Of these 10 finalists, two candidates were further identified in the CG simulations to be the best against all SARS-CoV-2 variants. Surprisingly, all 10 candidates and the native CR3022 exhibited a higher affinity for the Omicron variant despite its highest number of mutations. The multiscale protocol gives us a powerful rational tool to design efficient mAbs. The electrostatic interactions play a crucial role and appear to be controlling the affinity and complex building. Studied mAbs carrying a more negative total net charge show a higher affinity. Structural determinants could be identified in atomistic simulations and their roles are discussed in detail to further hint at a strategy for designing the best RBD binder. Although the SARS-CoV-2 was specifically targeted in this work, our approach is generally suitable for many diseases and viral and bacterial pathogens, leukemia, cancer, multiple sclerosis, rheumatoid, arthritis, lupus, and more.

Predicting phenotype transition probabilities via conditional algorithmic probability approximations

Unravelling the structure of genotype-phenotype (GP) maps is an important problem in biology. Recently, arguments inspired by algorithmic information theory (AIT) and Kolmogorov complexity have been invoked to uncover simplicity bias in GP maps, an exponentially decaying upper bound in phenotype probability with the increasing phenotype descriptional complexity. This means that phenotypes with many genotypes assigned via the GP map must be simple, while complex phenotypes must have few genotypes assigned. Here, we use similar arguments to bound the probability P(x → y) that phenotype x, upon random genetic mutation, transitions to phenotype y. The bound is [Formula: see text], where [Formula: see text] is the estimated conditional complexity of y given x, quantifying how much extra information is required to make y given access to x. This upper bound is related to the conditional form of algorithmic probability from AIT. We demonstrate the practical applicability of our derived bound by predicting phenotype transition probabilities (and other related quantities) in simulations of RNA and protein secondary structures. Our work contributes to a general mathematical understanding of GP maps and may facilitate the prediction of transition probabilities directly from examining phenotype themselves, without utilizing detailed knowledge of the GP map.

MutDock: A computational docking approach for fixed-backbone protein scaffold design

Despite the successes of antibodies as therapeutic binding proteins, they still face production and design challenges. Alternative binding scaffolds of smaller size have been developed to overcome these issues. A subset of these alternative scaffolds recognizes target molecules through mutations to a set of surface resides, which does not alter their backbone structures. While the computational design of antibodies for target epitopes has been explored in depth, the same has not been done for alternative scaffolds. The commonly used dock-and-mutate approach for binding proteins, including antibodies, is limited because it uses a constant sequence and structure representation of the scaffold. Docking fixed-backbone scaffolds with a varied group of surface amino acids increases the chances of identifying superior starting poses that can be improved with subsequent mutations. In this work, we have developed MutDock, a novel computational approach that simultaneously docks and mutates fixed backbone scaffolds for binding a target epitope by identifying a minimum number of hydrogen bonds. The approach is broadly divided into two steps. The first step uses pairwise distance alignment of hydrogen bond-forming areas of scaffold residues and compatible epitope atoms. This step considers both native and mutated rotamers of scaffold residues. The second step mutates clashing variable interface residues and thermodynamically unfavorable residues to create additional strong interactions. MutDock was used to dock two scaffolds, namely, Affibodies and DARPins, with ten randomly selected antigens. The energies of the docked poses were minimized and binding energies were compared with docked poses from ZDOCK and HADDOCK. The top MutDock poses consisted of higher and comparable binding energies than the top ZDOCK and HADDOCK poses, respectively. This work contributes to the discovery of novel binders based on smaller-sized, fixed-backbone protein scaffolds.

Understanding and Modulating Antibody Fine Specificity: Lessons from Combinatorial Biology

Combinatorial biology methods such as phage and yeast display, suitable for the generation and screening of huge numbers of protein fragments and mutated variants, have been useful when dissecting the molecular details of the interactions between antibodies and their target antigens (mainly those of protein nature). The relevance of these studies goes far beyond the mere description of binding interfaces, as the information obtained has implications for the understanding of the chemistry of antibody–antigen binding reactions and the biological effects of antibodies. Further modification of the interactions through combinatorial methods to manipulate the key properties of antibodies (affinity and fine specificity) can result in the emergence of novel research tools and optimized therapeutics.

Shape Complementarity Optimization of Antibody–Antigen Interfaces: The Application to SARS-CoV-2 Spike Protein

Many factors influence biomolecule binding, and its assessment constitutes an elusive challenge in computational structural biology. In this aspect, 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 the investigated regions with an ordered set of numbers summarizing the local shape properties. Collecting a structural dataset of antibody–antigen complexes, we applied this method and we statistically distinguished, in terms of shape complementarity, pairs of the interacting regions from the non-interacting ones. Thus, we set up a novel computational strategy based on in silico mutagenesis of antibody-binding site residues. We developed a Monte Carlo procedure to increase the shape complementarity between the antibody paratope and a given epitope on a target protein surface. We applied our protocol against several molecular targets in SARS-CoV-2 spike protein, known to be indispensable for viral cell invasion. We, therefore, optimized the shape of template antibodies for the interaction with such regions. As the last step of our procedure, we performed an independent molecular docking validation of the results of our Monte Carlo simulations.

Redesigning an antibody H3 loop by virtual screening of a small library of human germline-derived sequences

The design of superior biologic therapeutics, including antibodies and engineered proteins, involves optimizing their specific ability to bind to disease-related molecular targets. Previously, we developed and applied the Assisted Design of Antibody and Protein Therapeutics (ADAPT) platform for virtual affinity maturation of antibodies (Vivcharuk et al. in PLoS One 12(7):e0181490, 10.1371/journal.pone.0181490, 2017). However, ADAPT is limited to point mutations of hot-spot residues in existing CDR loops. In this study, we explore the possibility of wholesale replacement of the entire H3 loop with no restriction to maintain the parental loop length. This complements other currently published studies that sample replacements for the CDR loops L1, L2, L3, H1 and H2. Given the immense sequence space theoretically available to H3, we focused on the virtual grafting of over 5000 human germline-derived H3 sequences from the IGMT/LIGM database increasing the diversity of the sequence space when compared to using crystalized H3 loop sequences. H3 loop conformations are generated and scored to identify optimized H3 sequences. Experimental testing of high-ranking H3 sequences grafted into the framework of the bH1 antibody against human VEGF-A led to the discovery of multiple hits, some of which had similar or better affinities relative to the parental antibody. In over 75% of the tested designs, the re-designed H3 loop contributed favorably to overall binding affinity. The hits also demonstrated good developability attributes such as high thermal stability and no aggregation. Crystal structures of select re-designed H3 variants were solved and indicated that although some deviations from predicted structures were seen in the more solvent accessible regions of the H3 loop, they did not significantly affect predicted affinity scores.

Challenges in Detection of Serum Oncoprotein: Relevance to Breast Cancer Diagnostics

Breast cancer is a highly prevalent malignancy that shows improved outcomes with earlier diagnosis. Current screening and monitoring methods have improved survival rates, but the limitations of these approaches have led to the investigation of biomarker evaluation to improve early diagnosis and treatment monitoring. The enzyme-linked immunosorbent assay (ELISA) is a specific and robust technique ideally suited for the quantification of protein biomarkers from blood or its constituents. The continued clinical relevancy of this assay format will require overcoming specific technical challenges, including the ultra-sensitive detection of trace biomarkers and the circumventing of potential assay interference due to the expanding use of monoclonal antibody (mAb) therapeutics. Approaches to increasing the sensitivity of ELISA have been numerous and include employing more sensitive substrates, combining ELISA with the polymerase chain reaction (PCR), and incorporating nanoparticles as shuttles for detection antibodies and enzymes. These modifications have resulted in substantial boosts in the ability to detect extremely low levels of protein biomarkers, with some systems reliably detecting antigen at sub-femtomolar concentrations. Extensive utilization of mAb therapies in oncology has presented an additional contemporary challenge for ELISA, particularly when both therapeutic and assay antibodies target the same protein antigen. Resolution of issues such as epitope overlap and steric hindrance requires a rational approach to the design of diagnostic antibodies that takes advantage of modern antibody generation pipelines, epitope binning techniques and computational methods to strategically target biomarker epitopes. This review discusses technical strategies in ELISA implemented to date and their feasibility to address current constraints on sensitivity and problems with interference in the clinical setting. The impact of these recent advancements will depend upon their transformation from research laboratory protocols into facile, reliable detection systems that can ideally be replicated in point-of-care devices to maximize utilization and transform both the diagnostic and therapeutic monitoring landscape.

An antibody engineering platform using amino acid networks: A case study in development of antiviral therapeutics

We present here a case study of an antibody-engineering platform that selects, modifies, and assembles antibody parts to construct novel antibodies. A salient feature of this platform includes the role of amino acid networks in optimizing framework regions (FRs) and complementarity determining regions (CDRs) to engineer new antibodies with desired structure-function relationships. The details of this approach are described in the context of its utility in engineering ZAb_FLEP, a potent anti-Zika virus antibody. ZAb_FLEP comprises of distinct parts, including heavy chain and light chain FRs and CDRs, with engineered features such as loop lengths and optimal epitope-paratope contacts. We demonstrate, with different test antibodies derived from different FR-CDR combinations, that despite these test antibodies sharing high overall sequence similarity, they yield diverse functional readouts. Furthermore, we show that strategies relying on one dimensional sequence similarity-based analyses of antibodies miss important structural nuances of the FR-CDR relationship, which is effectively addressed by the amino acid networks approach of this platform.

Applications of Machine and Deep Learning in Adaptive Immunity

Adaptive immunity is mediated by lymphocyte B and T cells, which respectively express a vast and diverse repertoire of B cell and T cell receptors and, in conjunction with peptide antigen presentation through major histocompatibility complexes (MHCs), can recognize and respond to pathogens and diseased cells. In recent years, advances in deep sequencing have led to a massive increase in the amount of adaptive immune receptor repertoire data; additionally, proteomics techniques have led to a wealth of data on peptide-MHC presentation. These large-scale data sets are now making it possible to train machine and deep learning models, which can be used to identify complex and high-dimensional patterns in immune repertoires. This article introduces adaptive immune repertoires and machine and deep learning related to biological sequence data and then summarizes the many applications in this field, which span from predicting the immunological status of a host to the antigen specificity of individual receptors and the engineering of immunotherapeutics.

Computational approaches to therapeutic antibody design: established methods and emerging trends

Antibodies are proteins that recognize the molecular surfaces of potentially noxious molecules to mount an adaptive immune response or, in the case of autoimmune diseases, molecules that are part of healthy cells and tissues. Due to their binding versatility, antibodies are currently the largest class of biotherapeutics, with five monoclonal antibodies ranked in the top 10 blockbuster drugs. Computational advances in protein modelling and design can have a tangible impact on antibody-based therapeutic development. Antibody-specific computational protocols currently benefit from an increasing volume of data provided by next generation sequencing and application to related drug modalities based on traditional antibodies, such as nanobodies. Here we present a structured overview of available databases, methods and emerging trends in computational antibody analysis and contextualize them towards the engineering of candidate antibody therapeutics.

mmCSM-AB: guiding rational antibody engineering through multiple point mutations

While antibodies are becoming an increasingly important therapeutic class, especially in personalized medicine, their development and optimization has been largely through experimental exploration. While there have been many efforts to develop computational tools to guide rational antibody engineering, most approaches are of limited accuracy when applied to antibody design, and have largely been limited to analysing a single point mutation at a time. To overcome this gap, we have curated a dataset of 242 experimentally determined changes in binding affinity upon multiple point mutations in antibody-target complexes (89 increasing and 153 decreasing binding affinity). Here, we have shown that by using our graph-based signatures and atomic interaction information, we can accurately analyse the consequence of multi-point mutations on antigen binding affinity. Our approach outperformed other available tools across cross-validation and two independent blind tests, achieving Pearson's correlations of up to 0.95. We have implemented our new approach, mmCSM-AB, as a web-server that can help guide the process of affinity maturation in antibody design. mmCSM-AB is freely available at http://biosig.unimelb.edu.au/mmcsm_ab/.

Comparative Analysis of the CDR Loops of Antigen Receptors

The adaptive immune system uses two main types of antigen receptors: T-cell receptors (TCRs) and antibodies. While both proteins share a globally similar β-sandwich architecture, TCRs are specialized to recognize peptide antigens in the binding groove of the major histocompatibility complex, while antibodies can bind an almost infinite range of molecules. For both proteins, the main determinants of target recognition are the complementarity-determining region (CDR) loops. Five of the six CDRs adopt a limited number of backbone conformations, known as the "canonical classes"; the remaining CDR (β3in TCRs and H3 in antibodies) is more structurally diverse. In this paper, we first update the definition of canonical forms in TCRs, build an auto-updating sequence-based prediction tool (available at http://opig.stats.ox.ac.uk/resources) and demonstrate its application on large scale sequencing studies. Given the global similarity of TCRs and antibodies, we then examine the structural similarity of their CDRs. We find that TCR and antibody CDRs tend to have different length distributions, and where they have similar lengths, they mostly occupy distinct structural spaces. In the rare cases where we found structural similarity, the underlying sequence patterns for the TCR and antibody version are different. Finally, where multiple structures have been solved for the same CDR sequence, the structural variability in TCR loops is higher than that in antibodies, suggesting TCR CDRs are more flexible. These structural differences between TCR and antibody CDRs may be important to their different biological functions.

Third generation antibody discovery methods: in silico rational design

Owing to their outstanding performances in molecular recognition, antibodies are extensively used in research and applications in molecular biology, biotechnology and medicine. Recent advances in experimental and computational methods are making it possible to complement well-established in vivo (first generation) and in vitro (second generation) methods of antibody discovery with novel in silico (third generation) approaches. Here we describe the principles of computational antibody design and review the state of the art in this field. We then present Modular, a method that implements the rational design of antibodies in a modular manner, and describe the opportunities offered by this approach.

sdAb-DB: The Single Domain Antibody Database

The Single Domain Antibody Database, or sdAb-DB, ( www.sdab-db.ca ) is the first freely available repository for single domain antibodies and related classes of proteins. Due to their small size, modular structure, and ease of expression, single domain antibodies (sdAb) have a wide range of applications, including as a rational design tool, and are therefore of great interest for synthetic biologists and bioengineers. However, to enable effective use and sharing of existing sdAbs, including those with engineered functions ( e.g., fusions with fluorescent proteins), as well as the rational design and engineering of new sdAbs, it is necessary to have access to sequences and experimental data. We have therefore developed a publicly available, sdAb-focused database, providing access to manually curated sdAb data from protein databases, published scientific articles, and user submissions. The sdAb-DB is an open-source repository and sharing platform for the sdAb community, providing access to performance data and basic bioinformatic tools for use with previously described and validated sdAbs, as well as for the engineering of new sdAb-based designs and proteins.

OptMAVEn-2.0: De novo Design of Variable Antibody Regions against Targeted Antigen Epitopes

Monoclonal antibodies are becoming increasingly important therapeutic agents for the treatment of cancers, infectious diseases, and autoimmune disorders. However, laboratory-based methods of developing therapeutic monoclonal antibodies (e.g., immunized mice, hybridomas, and phage display) are time-consuming and are often unable to target a specific antigen epitope or reach (sub)nanomolar levels of affinity. To this end, we developed Optimal Method for Antibody Variable region Engineering (OptMAVEn) for de novo design of humanized monoclonal antibody variable regions targeting a specific antigen epitope. In this work, we introduce OptMAVEn-2.0, which improves upon OptMAVEn by (1) reducing computational resource requirements without compromising design quality; (2) clustering the designs to better identify high-affinity antibodies; and (3) eliminating intra-antibody steric clashes using an updated set of clashing parts from the Modular Antibody Parts (MAPs) database. Benchmarking on a set of 10 antigens revealed that OptMAVEn-2.0 uses an average of 74% less CPU time and 84% less disk storage relative to OptMAVEn. Testing on 54 additional antigens revealed that computational resource requirements of OptMAVEn-2.0 scale only sub-linearly with respect to antigen size. OptMAVEn-2.0 was used to design and rank variable antibody fragments targeting five epitopes of Zika envelope protein and three of hen egg white lysozyme. Among the top five ranked designs for each epitope, recovery of native residue identities is typically 45–65%. MD simulations of two designs targeting Zika suggest that at least one would bind with high affinity. OptMAVEn-2.0 can be downloaded from our GitHub repository and webpage as (links in Summary and Discussion section).

RosettaAntibodyDesign (RAbD): A general framework for computational antibody design

A structural-bioinformatics-based computational methodology and framework have been developed for the design of antibodies to targets of interest. RosettaAntibodyDesign (RAbD) samples the diverse sequence, structure, and binding space of an antibody to an antigen in highly customizable protocols for the design of antibodies in a broad range of applications. The program samples antibody sequences and structures by grafting structures from a widely accepted set of the canonical clusters of CDRs (North et al., J. Mol. Biol., 406:228-256, 2011). It then performs sequence design according to amino acid sequence profiles of each cluster, and samples CDR backbones using a flexible-backbone design protocol incorporating cluster-based CDR constraints. Starting from an existing experimental or computationally modeled antigen-antibody structure, RAbD can be used to redesign a single CDR or multiple CDRs with loops of different length, conformation, and sequence. We rigorously benchmarked RAbD on a set of 60 diverse antibody-antigen complexes, using two design strategies-optimizing total Rosetta energy and optimizing interface energy alone. We utilized two novel metrics for measuring success in computational protein design. The design risk ratio (DRR) is equal to the frequency of recovery of native CDR lengths and clusters divided by the frequency of sampling of those features during the Monte Carlo design procedure. Ratios greater than 1.0 indicate that the design process is picking out the native more frequently than expected from their sampled rate. We achieved DRRs for the non-H3 CDRs of between 2.4 and 4.0. The antigen risk ratio (ARR) is the ratio of frequencies of the native amino acid types, CDR lengths, and clusters in the output decoys for simulations performed in the presence and absence of the antigen. For CDRs, we achieved cluster ARRs as high as 2.5 for L1 and 1.5 for H2. For sequence design simulations without CDR grafting, the overall recovery for the native amino acid types for residues that contact the antigen in the native structures was 72% in simulations performed in the presence of the antigen and 48% in simulations performed without the antigen, for an ARR of 1.5. For the non-contacting residues, the ARR was 1.08. This shows that the sequence profiles are able to maintain the amino acid types of these conserved, buried sites, while recovery of the exposed, contacting residues requires the presence of the antigen-antibody interface. We tested RAbD experimentally on both a lambda and kappa antibody-antigen complex, successfully improving their affinities 10 to 50 fold by replacing individual CDRs of the native antibody with new CDR lengths and clusters.

Substructure-activity relationship studies on antibody recognition for phenylurea compounds using competitive immunoassay and computational chemistry

Based on the structural features of fluometuron, an immunizing hapten was synthesized and conjugated to bovine serum albumin as an immunogen to prepare a polyclonal antibody. However, the resultant antibody indicated cross-reactivity with 6 structurally similar phenylurea herbicides, with binding activities (expressed by IC₅₀ values) ranging from 1.67 µg/L to 42.71 µg/L. All 6 phenylurea herbicides contain a common moiety and three different substitutes. To understand how these three different chemical groups affect the antibody-phenylurea recognition activity, quantum chemistry, using density function theory (DFT) at the B3LYP/6-311++ G(d,p) level of theory, was employed to optimize all phenylurea structures, followed by determination of the 3D conformations of these molecules, pharmacophore analysis, and molecular electrostatic potential (ESP) analysis. The molecular modeling results confirmed that the geometry configuration, pharmacophore features and electron distribution in the substituents were related to the antibody binding activity. Spearman correlation analysis further elucidated that the geometrical and electrostatic properties on the van der Waals (vdW) surface of the substituents played a critical role in the antibody-phenylurea recognition process.

Antibody side chain conformations are position-dependent

Side chain prediction is an integral component of computational antibody design and structure prediction. Current antibody modelling tools use backbone-dependent rotamer libraries with conformations taken from general proteins. Here we present our antibody-specific rotamer library, where rotamers are binned according to their immunogenetics (IMGT) position, rather than their local backbone geometry. We find that for some amino acid types at certain positions, only a restricted number of side chain conformations are ever observed. Using this information, we are able to reduce the breadth of the rotamer sampling space. Based on our rotamer library, we built a side chain predictor, position-dependent antibody rotamer swapper (PEARS). On a blind test set of 95 antibody model structures, PEARS had the highest average χ₁ and χ1+2 accuracy (78.7% and 64.8%) compared to three leading backbone-dependent side chain predictors. Our use of IMGT position, rather than backbone ϕ/ψ, meant that PEARS was more robust to errors in the backbone of the model structure. PEARS also achieved the lowest number of side chain-side chain clashes. PEARS is freely available as a web application at http://opig.stats.ox.ac.uk/webapps/pears.

Integration of molecular dynamics simulation and hotspot residues grafting for de novo scFv design against Salmonella Typhi TolC protein

With the development of de novo binders for protein targets from non-related scaffolds, many possibilities for therapeutics and diagnostics have been created. In this study, we described the use of de novo design approach to create single-chain fragment variable (scFv) for Salmonella enterica subspecies enterica serovar Typhi TolC protein. Typhoid fever is a global health concern in developing and underdeveloped countries. Rapid typhoid diagnostics will improve disease management and therapy. In this work, molecular dynamics simulation was first performed on a homology model of TolC protein in POPE membrane bilayer to obtain the central structure that was subsequently used as the target for scFv design. Potential hotspot residues capable of anchoring the binders to the target were identified by docking "disembodied" amino acid residues against TolC surface. Next, scFv scaffolds were selected from Protein Data Bank to harbor the computed hotspot residues. The hotspot residues were then incorporated into the scFv scaffold complementarity determining regions. The designs recapitulated binding energy, shape complementarity, and interface surface area of natural protein-antibody interfaces. This approach has yielded 5 designs with high binding affinity against TolC that may be beneficial for the future development of antigen-based detection agents for typhoid diagnostics.

Length-independent structural similarities enrich the antibody CDR canonical class model

Complementarity-determining regions (CDRs) are antibody loops that make up the antigen binding site. Here, we show that all CDR types have structurally similar loops of different lengths. Based on these findings, we created length-independent canonical classes for the non-H3 CDRs. Our length variable structural clusters show strong sequence patterns suggesting either that they evolved from the same original structure or result from some form of convergence. We find that our length-independent method not only clusters a larger number of CDRs, but also predicts canonical class from sequence better than the standard length-dependent approach.

To demonstrate the usefulness of our findings, we predicted cluster membership of CDR-L3 sequences from 3 next-generation sequencing datasets of the antibody repertoire (over 1,000,000 sequences). Using the length-independent clusters, we can structurally classify an additional 135,000 sequences, which represents a ∼20% improvement over the standard approach. This suggests that our length-independent canonical classes might be a highly prevalent feature of antibody space, and could substantially improve our ability to accurately predict the structure of novel CDRs identified by next-generation sequencing.

A comprehensive structural model for the human KCNQ1/KCNE1 ion channel

The voltage-gated KCNQ1/KCNE1 potassium ion channel complex, forms the slow delayed rectifier (I_Ks) current in the heart, which plays an important role in heart signaling. The importance of KCNQ1/KCNE1 channel's function is further implicated by the linkage between loss-of-function and gain-of-function mutations in KCNQ1 or KCNE1, and long QT syndromes, congenital atrial fibrillation, and short QT syndrome. Also, KCNQ1/KCNE1 channels are an off-target for many non-cardiovascular drugs, leading to fatal cardiac irregularities. One solution to address and study the mentioned aspects of KCNQ1/KNCE1 channel would be the structural studies using a validated and accurate model. Along the same line in this study, we have used several top-notch modeling approaches to build a structural model for the open state of KCNQ1 protein, which is both accurate and compatible with available experimental data. Next, we included the KCNE1 protein components using data-driven protein-protein docking simulations, encompassing a 4:2 stoichiometry to complete the picture of the channel complex formed by these two proteins. All the protein systems generated through these processes were refined by long Molecular Dynamics simulations. The refined models were analyzed extensively to infer data about the interaction of KCNQ1 channel with its accessory KCNE1 beta subunits.

De novo design of antibody complementarity determining regions binding a FLAG tetra-peptide

Computational antibody engineering efforts to date have focused on improving binding affinities or biophysical characteristics. De novo design of antibodies binding specific epitopes could greatly accelerate discovery of therapeutics as compared to conventional immunization or synthetic library selection strategies. Here, we employed de novo complementarity determining region (CDR) design to engineer targeted antibody-antigen interactions using previously described in silico methods. CDRs predicted to bind the minimal FLAG peptide (Asp-Tyr-Lys-Asp) were grafted onto a single-chain variable fragment (scFv) acceptor framework. Fifty scFvs comprised of designed heavy and light or just heavy chain CDRs were synthesized and screened for peptide binding by phage ELISA. Roughly half of the designs resulted in detectable scFv expression. Four antibodies, designed entirely in silico, bound the minimal FLAG sequence with high specificity and sensitivity. When reformatted as soluble antigen-binding fragments (Fab), these clones expressed well, were predominantly monomeric and retained peptide specificity. In both formats, the antibodies bind the peptide only when present at the amino-terminus of a carrier protein and even conservative peptide amino acid substitutions resulted in a complete loss of binding. These results support in silico CDR design of antibody specificity as an emerging antibody engineering strategy.

Computational de novo design of antibodies binding to a peptide with high affinity

Antibody drugs play a critical role in infectious diseases, cancer, autoimmune diseases, and inflammation. However, experimental methods for the generation of therapeutic antibodies such as using immunized mice or directed evolution remain time consuming and cannot target a specific antigen epitope. Here, we describe the application of a computational framework called OptMAVEn combined with molecular dynamics to de novo design antibodies. Our reference system is antibody 2D10, a single-chain antibody (scFv) that recognizes the dodecapeptide DVFYPYPYASGS, a peptide mimic of mannose-containing carbohydrates. Five de novo designed scFvs sharing less than 75% sequence similarity to all existing natural antibody sequences were generated using OptMAVEn and their binding to the dodecapeptide was experimentally characterized by biolayer interferometry and isothermal titration calorimetry. Among them, three scFvs show binding affinity to the dodecapeptide at the nM level. Critically, these de novo designed scFvs exhibit considerably diverse modeled binding modes with the dodecapeptide. The results demonstrate the potential of OptMAVEn for the de novo design of thermally and conformationally stable antibodies with high binding affinity to antigens and encourage the targeting of other antigen targets in the future. Biotechnol. Bioeng. 2017;114: 1331-1342. © 2017 Wiley Periodicals, Inc.

Computational Tools for Aiding Rational Antibody Design

Antibodies are a group of proteins responsible for mediating immune reactions in vertebrates. They are able to bind a variety of structural motifs on noxious molecules tagging them for elimination from the organism. As a result of their versatile binding properties, antibodies are currently one of the most important classes of biopharmaceuticals. In this chapter, we discuss how knowledge-based computational methods can aid experimentalists in the development of potent antibodies. When using common experimental methods for antibody development, we often know the sequence of an antibody that binds to our molecule, antigen, of interest. We may also have a structure or model of the antigen. In these cases, computational methods can help by both modeling the antibody and identifying the antibody-antigen contact residues. This information can then play a key role in the rational design of more potent antibodies.

Advances in Antibody Design

The use of monoclonal antibodies as therapeutics requires optimizing several of their key attributes. These include binding affinity and specificity, folding stability, solubility, pharmacokinetics, effector functions, and compatibility with the attachment of additional antibody domains (bispecific antibodies) and cytotoxic drugs (antibody-drug conjugates). Addressing these and other challenges requires the use of systematic design methods that complement powerful immunization and in vitro screening methods. We review advances in designing the binding loops, scaffolds, domain interfaces, constant regions, post-translational and chemical modifications, and bispecific architectures of antibodies and fragments thereof to improve their bioactivity. We also highlight unmet challenges in antibody design that must be overcome to generate potent antibody therapeutics.

Rational design of antibodies targeting specific epitopes within intrinsically disordered proteins

Antibodies are powerful tools in life sciences research, as well as in diagnostic and therapeutic applications, because of their ability to bind given molecules with high affinity and specificity. Using current methods, however, it is laborious and sometimes difficult to generate antibodies to target specific epitopes within a protein, in particular if these epitopes are not effective antigens. Here we present a method to rationally design antibodies to enable them to bind virtually any chosen disordered epitope in a protein. The procedure consists in the sequence-based design of one or more complementary peptides targeting a selected disordered epitope and the subsequent grafting of such peptides on an antibody scaffold. We illustrate the method by designing six single-domain antibodies to bind different epitopes within three disease-related intrinsically disordered proteins and peptides (α-synuclein, Aβ42, and IAPP). Our results show that all these designed antibodies bind their targets with good affinity and specificity. As an example of an application, we show that one of these antibodies inhibits the aggregation of α-synuclein at substoichiometric concentrations and that binding occurs at the selected epitope. Taken together, these results indicate that the design strategy that we propose makes it possible to obtain antibodies targeting given epitopes in disordered proteins or protein regions.

AbDesign: An algorithm for combinatorial backbone design guided by natural conformations and sequences

Computational design of protein function has made substantial progress, generating new enzymes, binders, inhibitors, and nanomaterials not previously seen in nature. However, the ability to design new protein backbones for function--essential to exert control over all polypeptide degrees of freedom--remains a critical challenge. Most previous attempts to design new backbones computed the mainchain from scratch. Here, instead, we describe a combinatorial backbone and sequence optimization algorithm called AbDesign, which leverages the large number of sequences and experimentally determined molecular structures of antibodies to construct new antibody models, dock them against target surfaces and optimize their sequence and backbone conformation for high stability and binding affinity. We used the algorithm to produce antibody designs that target the same molecular surfaces as nine natural, high-affinity antibodies; in five cases interface sequence identity is above 30%, and in four of those the backbone conformation at the core of the antibody binding surface is within 1 Å root-mean square deviation from the natural antibodies. Designs recapitulate polar interaction networks observed in natural complexes, and amino acid sidechain rigidity at the designed binding surface, which is likely important for affinity and specificity, is high compared to previous design studies. In designed anti-lysozyme antibodies, complementarity-determining regions (CDRs) at the periphery of the interface, such as L1 and H2, show greater backbone conformation diversity than the CDRs at the core of the interface, and increase the binding surface area compared to the natural antibody, potentially enhancing affinity and specificity.

The Iterative Protein Redesign and Optimization (IPRO) suite of programs

Proteins are an important class of biomolecules with applications spanning across biotechnology and medicine. In many cases, native proteins must be redesigned to improve various performance metrics by changing their amino acid sequences. Algorithms can help sharpen protein library design by focusing the library on sequences that optimize computationally accessible proxies. The Iterative Protein Redesign and Optimization (IPRO) suite of programs offers an integrated environment for (1) altering protein binding affinity and specificity, (2) grafting a binding pocket into an existing protein scaffold, (3) predicting an antibody's tertiary structure based on its sequence, (4) enhancing enzymatic activity, and (5) assessing the structure and binding energetics for a specific mutant. This manuscript provides an overview of the methods involved in IPRO, input language terminology, algorithmic details, software implementation specifics and application highlights. IPRO can be downloaded at http://maranas.che.psu.edu.

Antibodies: Computer-Aided Prediction of Structure and Design of Function

With the advent of high-throughput sequencing, and the increased availability of experimental structures of antibodies and antibody-antigen complexes, comes the improvement of computational approaches to predict the structure and design the function of antibodies and antibody-antigen complexes. While antibodies pose formidable challenges for protein structure prediction and design due to their large size and highly flexible loops in the complementarity-determining regions, they also offer exciting opportunities: the central importance of antibodies for human health results in a wealth of structural and sequence information that—as a knowledge base—can drive the modeling algorithms by limiting the conformational and sequence search space to likely regions of success. Further, efficient experimental platforms exist to test predicted antibody structure or designed antibody function, thereby leading to an iterative feedback loop between computation and experiment. We briefly review the history of computer-aided prediction of structure and design of function in the antibody field before we focus on recent methodological developments and the most exciting application examples.

OptMAVEn--a new framework for the de novo design of antibody variable region models targeting specific antigen epitopes

Antibody-based therapeutics provides novel and efficacious treatments for a number of diseases. Traditional experimental approaches for designing therapeutic antibodies rely on raising antibodies against a target antigen in an immunized animal or directed evolution of antibodies with low affinity for the desired antigen. However, these methods remain time consuming, cannot target a specific epitope and do not lead to broad design principles informing other studies. Computational design methods can overcome some of these limitations by using biophysics models to rationally select antibody parts that maximize affinity for a target antigen epitope. This has been addressed to some extend by OptCDR for the design of complementary determining regions. Here, we extend this earlier contribution by addressing the de novo design of a model of the entire antibody variable region against a given antigen epitope while safeguarding for immunogenicity (Optimal Method for Antibody Variable region Engineering, OptMAVEn). OptMAVEn simulates in silico the in vivo steps of antibody generation and evolution, and is capable of capturing the critical structural features responsible for affinity maturation of antibodies. In addition, a humanization procedure was developed and incorporated into OptMAVEn to minimize the potential immunogenicity of the designed antibody models. As case studies, OptMAVEn was applied to design models of neutralizing antibodies targeting influenza hemagglutinin and HIV gp120. For both HA and gp120, novel computational antibody models with numerous interactions with their target epitopes were generated. The observed rates of mutations and types of amino acid changes during in silico affinity maturation are consistent with what has been observed during in vivo affinity maturation. The results demonstrate that OptMAVEn can efficiently generate diverse computational antibody models with both optimized binding affinity to antigens and reduced immunogenicity.

De novo design and experimental characterization of ultrashort self-associating peptides

Self-association is a common phenomenon in biology and one that can have positive and negative impacts, from the construction of the architectural cytoskeleton of cells to the formation of fibrils in amyloid diseases. Understanding the nature and mechanisms of self-association is important for modulating these systems and in creating biologically-inspired materials. Here, we present a two-stage de novo peptide design framework that can generate novel self-associating peptide systems. The first stage uses a simulated multimeric template structure as input into the optimization-based Sequence Selection to generate low potential energy sequences. The second stage is a computational validation procedure that calculates Fold Specificity and/or Approximate Association Affinity (K*association) based on metrics that we have devised for multimeric systems. This framework was applied to the design of self-associating tripeptides using the known self-associating tripeptide, Ac-IVD, as a structural template. Six computationally predicted tripeptides (Ac-LVE, Ac-YYD, Ac-LLE, Ac-YLD, Ac-MYD, Ac-VIE) were chosen for experimental validation in order to illustrate the self-association outcomes predicted by the three metrics. Self-association and electron microscopy studies revealed that Ac-LLE formed bead-like microstructures, Ac-LVE and Ac-YYD formed fibrillar aggregates, Ac-VIE and Ac-MYD formed hydrogels, and Ac-YLD crystallized under ambient conditions. An X-ray crystallographic study was carried out on a single crystal of Ac-YLD, which revealed that each molecule adopts a β-strand conformation that stack together to form parallel β-sheets. As an additional validation of the approach, the hydrogel-forming sequences of Ac-MYD and Ac-VIE were shuffled. The shuffled sequences were computationally predicted to have lower K*association values and were experimentally verified to not form hydrogels. This illustrates the robustness of the framework in predicting self-associating tripeptides. We expect that this enhanced multimeric de novo peptide design framework will find future application in creating novel self-associating peptides based on unnatural amino acids, and inhibitor peptides of detrimental self-aggregating biological proteins.

Forcefield_PTM: Ab Initio Charge and AMBER Forcefield Parameters for Frequently Occurring Post-Translational Modifications

In this work, we introduce Forcefield_PTM, a set of AMBER forcefield parameters consistent with ff03 for 32 common post-translational modifications. Partial charges were calculated through ab initio calculations and a two-stage RESP-fitting procedure in an ether-like implicit solvent environment. The charges were found to be generally consistent with others previously reported for phosphorylated amino acids, and trimethyllysine, using different parameterization methods. Pairs of modified and their corresponding unmodified structures were curated from the PDB for both single and multiple modifications. Background structural similarity was assessed in the context of secondary and tertiary structures from the global dataset. Next, the charges derived for Forcefield_PTM were tested on a macroscopic scale using unrestrained all-atom Langevin molecular dynamics simulations in AMBER for 34 (17 pairs of modified/unmodified) systems in implicit solvent. Assessment was performed in the context of secondary structure preservation, stability in energies, and correlations between the modified and unmodified structure trajectories on the aggregate. As an illustration of their utility, the parameters were used to compare the structural stability of the phosphorylated and dephosphorylated forms of OdhI. Microscopic comparisons between quantum and AMBER single point energies along key χ torsions on several PTMs were performed and corrections to improve their agreement in terms of mean squared errors and squared correlation coefficients were parameterized. This forcefield for post-translational modifications in condensed-phase simulations can be applied to a number of biologically relevant and timely applications including protein structure prediction, protein and peptide design, docking, and to study the effect of PTMs on folding and dynamics. We make the derived parameters and an associated interactive webtool capable of performing post-translational modifications on proteins using Forcefield_PTM available at http://selene.princeton.edu/FFPTM.

Protein folding and de novo protein design for biotechnological applications

In the postgenomic era, the medical/biological fields are advancing faster than ever. However, before the power of full-genome sequencing can be fully realized, the connection between amino acid sequence and protein structure, known as the protein folding problem, needs to be elucidated. The protein folding problem remains elusive, with significant difficulties still arising when modeling amino acid sequences lacking an identifiable template. Understanding protein folding will allow for unforeseen advances in protein design; often referred to as the inverse protein folding problem. Despite challenges in protein folding, de novo protein design has recently demonstrated significant success via computational techniques. We review advances and challenges in protein structure prediction and de novo protein design, and highlight their interplay in successful biotechnological applications.

Structure-Based, Rational Design of T Cell Receptors

Adoptive cell transfer using engineered T cells is emerging as a promising treatment for metastatic melanoma. Such an approach allows one to introduce T cell receptor (TCR) modifications that, while maintaining the specificity for the targeted antigen, can enhance the binding and kinetic parameters for the interaction with peptides (p) bound to major histocompatibility complexes (MHC). Using the well-characterized 2C TCR/SIYR/H-2K(b) structure as a model system, we demonstrated that a binding free energy decomposition based on the MM-GBSA approach provides a detailed and reliable description of the TCR/pMHC interactions at the structural and thermodynamic levels. Starting from this result, we developed a new structure-based approach, to rationally design new TCR sequences, and applied it to the BC1 TCR targeting the HLA-A2 restricted NY-ESO-1_157–165 cancer-testis epitope. Fifty-four percent of the designed sequence replacements exhibited improved pMHC binding as compared to the native TCR, with up to 150-fold increase in affinity, while preserving specificity. Genetically engineered CD8⁺ T cells expressing these modified TCRs showed an improved functional activity compared to those expressing BC1 TCR. We measured maximum levels of activities for TCRs within the upper limit of natural affinity, K_D = ∼1 − 5 μM. Beyond the affinity threshold at K_D < 1 μM we observed an attenuation in cellular function, in line with the “half-life” model of T cell activation. Our computer-aided protein-engineering approach requires the 3D-structure of the TCR-pMHC complex of interest, which can be obtained from X-ray crystallography. We have also developed a homology modeling-based approach, TCRep 3D, to obtain accurate structural models of any TCR-pMHC complexes when experimental data is not available. Since the accuracy of the models depends on the prediction of the TCR orientation over pMHC, we have complemented the approach with a simplified rigid method to predict this orientation and successfully assessed it using all non-redundant TCR-pMHC crystal structures available. These methods potentially extend the use of our TCR engineering method to entire TCR repertoires for which no X-ray structure is available. We have also performed a steered molecular dynamics study of the unbinding of the TCR-pMHC complex to get a better understanding of how TCRs interact with pMHCs. This entire rational TCR design pipeline is now being used to produce rationally optimized TCRs for adoptive cell therapies of stage IV melanoma.

MAPs: a database of modular antibody parts for predicting tertiary structures and designing affinity matured antibodies

BACKGROUND

The de novo design of a novel protein with a particular function remains a formidable challenge with only isolated and hard-to-repeat successes to date. Due to their many structurally conserved features, antibodies are a family of proteins amenable to predictable rational design. Design algorithms must consider the structural diversity of possible naturally occurring antibodies. The human immune system samples this design space (2 1012) by randomly combining variable, diversity, and joining genes in a process known as V-(D)-J recombination.

DESCRIPTION

By analyzing structural features found in affinity matured antibodies, a database of Modular Antibody Parts (MAPs) analogous to the variable, diversity, and joining genes has been constructed for the prediction of antibody tertiary structures. The database contains 929 parts constructed from an analysis of 1168 human, humanized, chimeric, and mouse antibody structures and encompasses all currently observed structural diversity of antibodies.

CONCLUSIONS

The generation of 260 antibody structures shows that the MAPs database can be used to reliably predict antibody tertiary structures with an average all-atom RMSD of 1.9 Å. Using the broadly neutralizing anti-influenza antibody CH65 and anti-HIV antibody 4E10 as examples, promising starting antibodies for affinity maturation are identified and amino acid changes are traced as antibody affinity maturation occurs.

Structure-based non-canonical amino acid design to covalently crosslink an antibody-antigen complex

Engineering antibodies to utilize non-canonical amino acids (NCAA) should greatly expand the utility of an already important biological reagent. In particular, introducing crosslinking reagents into antibody complementarity determining regions (CDRs) should provide a means to covalently crosslink residues at the antibody-antigen interface. Unfortunately, finding the optimum position for crosslinking two proteins is often a matter of iterative guessing, even when the interface is known in atomic detail. Computer-aided antibody design can potentially greatly restrict the number of variants that must be explored in order to identify successful crosslinking sites. We have therefore used Rosetta to guide the introduction of an oxidizable crosslinking NCAA, l-3,4-dihydroxyphenylalanine (l-DOPA), into the CDRs of the anti-protective antigen scFv antibody M18, and have measured crosslinking to its cognate antigen, domain 4 of the anthrax protective antigen. Computed crosslinking distance, solvent accessibility, and interface energetics were three factors considered that could impact the efficiency of l-DOPA-mediated crosslinking. In the end, 10 variants were synthesized, and crosslinking efficiencies were generally 10% or higher, with the best variant crosslinking to 52% of the available antigen. The results suggest that computational analysis can be used in a pipeline for engineering crosslinking antibodies. The rules learned from l-DOPA crosslinking of antibodies may also be generalizable to the formation of other crosslinked interfaces and complexes.