Shape complementarity and hydrogen bond preferences in protein-protein interfaces: implications for antibody modeling and protein-protein docking

Leveraging Artificial Intelligence to Expedite Antibody Design and Enhance Antibody-Antigen Interactions

This perspective sheds light on the transformative impact of recent computational advancements in the field of protein therapeutics, with a particular focus on the design and development of antibodies. Cutting-edge computational methods have revolutionized our understanding of protein-protein interactions (PPIs), enhancing the efficacy of protein therapeutics in preclinical and clinical settings. Central to these advancements is the application of machine learning and deep learning, which offers unprecedented insights into the intricate mechanisms of PPIs and facilitates precise control over protein functions. Despite these advancements, the complex structural nuances of antibodies pose ongoing challenges in their design and optimization. Our review provides a comprehensive exploration of the latest deep learning approaches, including language models and diffusion techniques, and their role in surmounting these challenges. We also present a critical analysis of these methods, offering insights to drive further progress in this rapidly evolving field. The paper includes practical recommendations for the application of these computational techniques, supplemented with independent benchmark studies. These studies focus on key performance metrics such as accuracy and the ease of program execution, providing a valuable resource for researchers engaged in antibody design and development. Through this detailed perspective, we aim to contribute to the advancement of antibody design, equipping researchers with the tools and knowledge to navigate the complexities of this field.

T-cell virtuosity in ''knowing thyself"

Major Histocompatibility Complex (MHC) I and II and the αβ T-cell antigen receptor (TCRαβ) govern fundamental traits of adaptive immunity. They form a membrane-borne ligand-receptor system weighing host proteome integrity to detect contamination by nonself proteins. MHC-I and -II exhibit the "MHC-fold", which is able to bind a large assortment of short peptides as proxies for self and nonself proteins. The ensuing varying surfaces are mandatory ligands for Ig-like TCRαβ highly mutable binding sites. Conserved molecular signatures guide TCRαβ ligand binding sites to focus on the MHC-fold (MHC-restriction) while leaving many opportunities for its most hypervariable determinants to contact the peptide. This riveting molecular strategy affords many options for binding energy compatible with specific recognition and signalling aimed to eradicated microbial pathogens and cancer cells. While the molecular foundations of αβ T-cell adaptive immunity are largely understood, uncertainty persists on how peptide-MHC binding induces the TCRαβ signals that instruct cell-fate decisions. Solving this mystery is another milestone for understanding αβ T-cells' self/nonself discrimination. Recent developments revealing the innermost links between TCRαβ structural dynamics and signalling modality should help dissipate this long-sought-after enigma.

Multidisciplinary Methods for Screening Toxic Proteins from Phages and Their Potential Molecular Targets

This chapter presents a comprehensive methodology for the identification, characterization, and functional analyses of potentially toxic hypothetical proteins of unknown function (toxHPUFs) in phages. The methods begin with in vivo toxicity verification of toxHPUFs in bacterial hosts, utilizing conventional drop tests and following growth curves. Computational methods for structural and functional predictions of toxHPUFs are outlined, incorporating the use of tools such as Phyre2, HHpred, and AlphaFold2. To ascertain potential targets, a comparative genomic approach is described using bioinformatics toolkits for sequence alignment and functional annotation. Moreover, steps are provided to predict protein-protein interactions and visualizing these using PyMOL. The culmination of these methods equips researchers with an effective pipeline to identify and analyze toxHPUFs and their potential targets, laying the groundwork for future experimental confirmations.

Ab Initio Prediction of 3-D Conformations for Protein Long Loops with High Accuracy and Applications to Antibody CDRH3 Modeling

Residue-level potentials of mean force were widely used for protein backbone refinements to avoid simultaneous sampling of side-chain conformations. The interaction energy between the reduced side chains and backbone atoms was not considered explicitly. In this study, we developed novel methods to calculate the residue-atom interaction energy in combination with atomic and residue-level terms. The parameters were optimized step by step to remove the overcounting or overlap problem between different energy terms. The mixing energy functions were then used to evaluate the generated backbone conformations at the initial sampling stage of protein loop modeling (OSCAR-loop), including the interaction energy between the reduced loop residues and full atoms of the protein framework. The accuracies of top-ranked decoys were 1.18 and 2.81 Å for 8-residue and 12-residue loops, respectively. We then selected diverse decoys for side-chain modeling, backbone refinement, and energy minimization. The procedure was repeated multiple times to select one prediction with the lowest energy. Consequently, we obtained an accuracy of 0.74 Å for a prevailing test set of 12-residue loops, compared with >1.4 Å reported by other researchers. The OSCAR-loop was also effective for modeling the H3 loops of antibody complementary determining regions (CDRs) in the crystal environment. The prediction accuracy of OSCAR-loop (1.74 Å) was better than the accuracy of the Rosetta NGK method (3.11 Å) or those achieved by deep learning methods (>2.2 Å) for the CDRH3 loops of 49 targets in the Rosetta antibody benchmark. The performance of OSCAR-loop in a model environment was also discussed.

Developing similarity matrices for antibody-protein binding interactions

The inventions of AlphaFold and RoseTTAFold are revolutionizing computational protein science due to their abilities to reliably predict protein structures. Their unprecedented successes are due to the parallel consideration of several types of information, one of which is protein sequence similarity information. Sequence homology has been studied for many decades and depends on similarity matrices to define how similar or different protein sequences are to one another. A natural extension of predicting protein structures is predicting the interactions between proteins, but similarity matrices for protein-protein interactions do not exist. This study conducted a mutational analysis of 384 non-redundant antibody-protein antigen complexes to calculate antibody-protein interaction similarity matrices. Every important residue in each antibody and each antigen was mutated to each of the other 19 commonly occurring amino acids and the percentage changes in interaction energies were calculated using three force fields: CHARMM, Amber, and Rosetta. The data were used to construct six interaction similarity matrices, one for antibodies and another for antigens using each force field. The matrices exhibited both commonalities, such as mutations of aromatic and charged residues being the most detrimental, and differences, such as Rosetta predicting mutations of serines to be better tolerated than either Amber or CHARMM. A comparison to nine previously published similarity matrices for protein sequences revealed that the new interaction matrices are more similar to one another than they are to any of the previous matrices. The created similarity matrices can be used in force field specific applications to help guide decisions regarding mutations in protein-protein binding interfaces.

Structural delineation and computational design of SARS-CoV-2-neutralizing antibodies against Omicron subvariants

SARS-CoV-2 Omicron subvariants have evolved to evade receptor-binding site (RBS) antibodies that exist in diverse individuals as public antibody clones. We rationally selected RBS antibodies resilient to mutations in emerging Omicron subvariants. Y489 was identified as a site of virus vulnerability and a common footprint of broadly neutralizing antibodies against the subvariants. Multiple Y489-binding antibodies were encoded by public clonotypes and additionally recognized F486, potentially accounting for the emergence of Omicron subvariants harboring the F486V mutation. However, a subclass of antibodies broadly neutralized BA.4/BA.5 variants via hydrophobic binding sites of rare clonotypes along with high mutation-resilience under escape mutation screening. A computationally designed antibody based on one of the Y489-binding antibodies, NIV-10/FD03, was able to bind XBB with any 486 mutation and neutralized XBB.1.5. The structural basis for the mutation-resilience of this Y489-binding antibody group may provide important insights into the design of therapeutics resistant to viral escape.

Modeling of ACE2 and antibodies bound to SARS-CoV-2 provides insights into infectivity and immune evasion

Given the COVID-19 pandemic, there is interest in understanding ligand-receptor features and targeted antibody-binding attributes against emerging SARS-CoV-2 variants. Here, we developed a large-scale structure-based pipeline for analysis of protein-protein interactions regulating SARS-CoV-2 immune evasion. First, we generated computed structural models of the Spike protein of 3 SARS-CoV-2 variants (B.1.1.529, BA.2.12.1, and BA.5) bound either to a native receptor (ACE2) or to a large panel of targeted ligands (n = 282), which included neutralizing or therapeutic monoclonal antibodies. Moreover, by using the Barnes classification, we noted an overall loss of interfacial interactions (with gain of new interactions in certain cases) at the receptor-binding domain (RBD) mediated by substituted residues for neutralizing complexes in classes 1 and 2, whereas less destabilization was observed for classes 3 and 4. Finally, an experimental validation of predicted weakened therapeutic antibody binding was performed in a cell-based assay. Compared with the original Omicron variant (B.1.1.529), derivative variants featured progressive destabilization of antibody-RBD interfaces mediated by a larger set of substituted residues, thereby providing a molecular basis for immune evasion. This approach and findings provide a framework for rapidly and efficiently generating structural models for SARS-CoV-2 variants bound to ligands of mechanistic and therapeutic value.

Narrow funnel-like interaction energy distribution is an indicator of specific protein interaction partner

Protein interaction networks underlie countless biological mechanisms. However, most protein interaction predictions are based on biological evidence that are biased to well-known protein interaction or physical evidence that exhibits low accuracy for weak interactions and requires high computational power. In this study, a novel method has been suggested to predict protein interaction partners by investigating narrow funnel-like interaction energy distribution. In this study, it was demonstrated that various protein interactions including kinases and E3 ubiquitin ligases have narrow funnel-like interaction energy distribution. To analyze protein interaction distribution, modified scores of iRMS and TM-score are introduced. Then, using these scores, algorithm and deep learning model for prediction of protein interaction partner and substrate of kinase and E3 ubiquitin ligase were developed. The prediction accuracy was similar to or even better than that of yeast two-hybrid screening. Ultimately, this knowledge-free protein interaction prediction method will broaden our understanding of protein interaction networks.

De novo design of modular peptide-binding proteins by superhelical matching

General approaches for designing sequence-specific peptide-binding proteins would have wide utility in proteomics and synthetic biology. However, designing peptide-binding proteins is challenging, as most peptides do not have defined structures in isolation, and hydrogen bonds must be made to the buried polar groups in the peptide backbone1-3. Here, inspired by natural and re-engineered protein-peptide systems4-11, we set out to design proteins made out of repeating units that bind peptides with repeating sequences, with a one-to-one correspondence between the repeat units of the protein and those of the peptide. We use geometric hashing to identify protein backbones and peptide-docking arrangements that are compatible with bidentate hydrogen bonds between the side chains of the protein and the peptide backbone12. The remainder of the protein sequence is then optimized for folding and peptide binding. We design repeat proteins to bind to six different tripeptide-repeat sequences in polyproline II conformations. The proteins are hyperstable and bind to four to six tandem repeats of their tripeptide targets with nanomolar to picomolar affinities in vitro and in living cells. Crystal structures reveal repeating interactions between protein and peptide interactions as designed, including ladders of hydrogen bonds from protein side chains to peptide backbones. By redesigning the binding interfaces of individual repeat units, specificity can be achieved for non-repeating peptide sequences and for disordered regions of native proteins.

De novo design and Rosetta-based assessment of high-affinity antibody variable regions (Fv) against the SARS-CoV-2 spike receptor binding domain (RBD)

The continued emergence of new SARS-CoV-2 variants has accentuated the growing need for fast and reliable methods for the design of potentially neutralizing antibodies (Abs) to counter immune evasion by the virus. Here, we report on the de novo computational design of high-affinity Ab variable regions (Fv) through the recombination of VDJ genes targeting the most solvent-exposed hACE2-binding residues of the SARS-CoV-2 spike receptor binding domain (RBD) protein using the software tool OptMAVEn-2.0. Subsequently, we carried out computational affinity maturation of the designed variable regions through amino acid substitutions for improved binding with the target epitope. Immunogenicity of designs was restricted by preferring designs that match sequences from a 9-mer library of "human Abs" based on a human string content score. We generated 106 different antibody designs and reported in detail on the top five that trade-off the greatest computational binding affinity for the RBD with human string content scores. We further describe computational evaluation of the top five designs produced by OptMAVEn-2.0 using a Rosetta-based approach. We used Rosetta SnugDock for local docking of the designs to evaluate their potential to bind the spike RBD and performed "forward folding" with DeepAb to assess their potential to fold into the designed structures. Ultimately, our results identified one designed Ab variable region, P1.D1, as a particularly promising candidate for experimental testing. This effort puts forth a computational workflow for the de novo design and evaluation of Abs that can quickly be adapted to target spike epitopes of emerging SARS-CoV-2 variants or other antigenic targets.

Analysis of conformational stability of interacting residues in protein binding interfaces

After approximately 60 years of work, the protein folding problem has recently seen rapid advancement thanks to the inventions of AlphaFold and RoseTTAFold, which are machine-learning algorithms capable of reliably predicting protein structures from their sequences. A key component in their success was the inclusion of pairwise interaction information between residues. As research focus shifts towards developing algorithms to design and engineer binding proteins, it is likely that knowledge of interaction features at protein interfaces can improve predictions. Here, 574 protein complexes were analyzed to identify the stability features of their pairwise interactions, revealing that interactions between pre-stabilized residues are a selected feature in protein binding interfaces. In a retrospective analysis of 475 de novo designed binding proteins with an experimental success rate of 19%, inclusion of pairwise interaction pre-stabilization parameters increased the frequency of identifying experimentally successful binders to 40%.

Structural Classification of CDR-H3 in Single-Domain VHH Antibodies

The immune systems protect vertebrates from foreign molecules or antigens, and antibodies are important mediators of this system. The sequences and structural features of antibodies vary depending on species. Many of antibodies from vertebrates, including camelids, have both heavy and light chain variable domains, but camelids also have antibodies that lack the light chains. In antibodies that lack light chains, the C-terminal variable region is called the V_HH domain. Antibodies recognize antigens through six complementarity-determining regions (CDRs). The third CDR of the heavy chain (CDR-H3) is at the center of the antigen-binding site and is diverse in terms of sequence and structure. Due to the importance of antibodies in basic science as well as in medical applications, there have been many studies of CDR-H3s of antibodies that possess both light and heavy chains. However, nature of CDR-H3s of single-domain V_HH antibodies is less well studied. In this chapter, we describe current knowledge of sequence-structure-function correlations of single-domain V_HH antibodies with emphasis on CDR-H3. Based on the 370 crystal structures in the Protein Data Bank, we also attempt structural classification of CDR-H3 in single-domain V_HH antibodies and discuss lessons learned from the ever-increasing number of the structures.

Protein Design: From the Aspect of Water Solubility and Stability

Water solubility and structural stability are key merits for proteins defined by the primary sequence and 3D-conformation. Their manipulation represents important aspects of the protein design field that relies on the accurate placement of amino acids and molecular interactions, guided by underlying physiochemical principles. Emulated designer proteins with well-defined properties both fuel the knowledge-base for more precise computational design models and are used in various biomedical and nanotechnological applications. The continuous developments in protein science, increasing computing power, new algorithms, and characterization techniques provide sophisticated toolkits for solubility design beyond guess work. In this review, we summarize recent advances in the protein design field with respect to water solubility and structural stability. After introducing fundamental design rules, we discuss the transmembrane protein solubilization and de novo transmembrane protein design. Traditional strategies to enhance protein solubility and structural stability are introduced. The designs of stable protein complexes and high-order assemblies are covered. Computational methodologies behind these endeavors, including structure prediction programs, machine learning algorithms, and specialty software dedicated to the evaluation of protein solubility and aggregation, are discussed. The findings and opportunities for Cryo-EM are presented. This review provides an overview of significant progress and prospects in accurate protein design for solubility and stability.

Antibody-Antigen Binding Interface Analysis in the Big Data Era

Antibodies have become the Swiss Army tool for molecular biology and nanotechnology. Their outstanding ability to specifically recognise molecular antigens allows their use in many different applications from medicine to the industry. Moreover, the improvement of conventional structural biology techniques (e.g., X-ray, NMR) as well as the emergence of new ones (e.g., Cryo-EM), have permitted in the last years a notable increase of resolved antibody-antigen structures. This offers a unique opportunity to perform an exhaustive structural analysis of antibody-antigen interfaces by employing the large amount of data available nowadays. To leverage this factor, different geometric as well as chemical descriptors were evaluated to perform a comprehensive characterization.

Structural Features of Antibody-Peptide Recognition

Antibody recognition of antigens is a critical element of adaptive immunity. One key class of antibody-antigen complexes is comprised of antibodies targeting linear epitopes of proteins, which in some cases are conserved elements of viruses and pathogens of relevance for vaccine design and immunotherapy. Here we report a detailed analysis of the structural and interface features of this class of complexes, based on a set of nearly 200 nonredundant high resolution antibody-peptide complex structures that were assembled from the Protein Data Bank. We found that antibody-bound peptides adopt a broad range of conformations, often displaying limited secondary structure, and that the same peptide sequence bound by different antibodies can in many cases exhibit varying conformations. Propensities of contacts with antibody loops and extent of antibody binding conformational changes were found to be broadly similar to those for antibodies in complex with larger protein antigens. However, antibody-peptide interfaces showed lower buried surface areas and fewer hydrogen bonds than antibody-protein antigen complexes, while calculated binding energy per buried interface area was found to be higher on average for antibody-peptide interfaces, likely due in part to a greater proportion of buried hydrophobic residues and higher shape complementarity. This dataset and these observations can be of use for future studies focused on this class of interactions, including predictive computational modeling efforts and the design of antibodies or epitope-based vaccine immunogens.

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.

Probing the structure and function of the protease domain of botulinum neurotoxins using single-domain antibodies

Botulinum neurotoxins (BoNTs) are among the deadliest of bacterial toxins. BoNT serotype A and B in particular pose the most serious threat to humans because of their high potency and persistence. To date, there is no effective treatment for late post-exposure therapy of botulism patients. Here, we aim to develop single-domain variable heavy-chain (VHH) antibodies targeting the protease domains (also known as the light chain, LC) of BoNT/A and BoNT/B as antidotes for post-intoxication treatments. Using a combination of X-ray crystallography and biochemical assays, we investigated the structures and inhibition mechanisms of a dozen unique VHHs that recognize four and three non-overlapping epitopes on the LC of BoNT/A and BoNT/B, respectively. We show that the VHHs that inhibit the LC activity occupy the extended substrate-recognition exosites or the cleavage pocket of LC/A or LC/B and thus block substrate binding. Notably, we identified several VHHs that recognize highly conserved epitopes across BoNT/A or BoNT/B subtypes, suggesting that these VHHs exhibit broad subtype efficacy. Further, we identify two novel conformations of the full-length LC/A, that could aid future development of inhibitors against BoNT/A. Our studies lay the foundation for structure-based engineering of protein- or peptide-based BoNT inhibitors with enhanced potencies and cross-subtypes properties.

Structural models of SARS-CoV-2 Omicron variant in complex with ACE2 receptor or antibodies suggest altered binding interfaces

There is enormous ongoing interest in characterizing the binding properties of the SARS-CoV-2 Omicron Variant of Concern (VOC) (B.1.1.529), which continues to spread towards potential dominance worldwide. To aid these studies, based on the wealth of available structural information about several SARS-CoV-2 variants in the Protein Data Bank (PDB) and a modeling pipeline we have previously developed for tracking the ongoing global evolution of SARS-CoV-2 proteins, we provide a set of computed structural models (henceforth models) of the Omicron VOC receptor-binding domain (omRBD) bound to its corresponding receptor Angiotensin-Converting Enzyme (ACE2) and a variety of therapeutic entities, including neutralizing and therapeutic antibodies targeting previously-detected viral strains. We generated bound omRBD models using both experimentally-determined structures in the PDB as well as machine learningbased structure predictions as starting points. Examination of ACE2-bound omRBD models reveals an interdigitated multi-residue interaction network formed by omRBD-specific substituted residues (R493, S496, Y501, R498) and ACE2 residues at the interface, which was not present in the original Wuhan-Hu-1 RBD-ACE2 complex. Emergence of this interaction network suggests optimization of a key region of the binding interface, and positive cooperativity among various sites of residue substitutions in omRBD mediating ACE2 binding. Examination of neutralizing antibody complexes for Barnes Class 1 and Class 2 antibodies modeled with omRBD highlights an overall loss of interfacial interactions (with gain of new interactions in rare cases) mediated by substituted residues. Many of these substitutions have previously been found to independently dampen or even ablate antibody binding, and perhaps mediate antibody-mediated neutralization escape ( e.g ., K417N). We observe little compensation of corresponding interaction loss at interfaces when potential escape substitutions occur in combination. A few selected antibodies ( e.g ., Barnes Class 3 S309), however, feature largely unaltered or modestly affected protein-protein interfaces. While we stress that only qualitative insights can be obtained directly from our models at this time, we anticipate that they can provide starting points for more detailed and quantitative computational characterization, and, if needed, redesign of monoclonal antibodies for targeting the Omicron VOC Spike protein. In the broader context, the computational pipeline we developed provides a framework for rapidly and efficiently generating retrospective and prospective models for other novel variants of SARS-CoV-2 bound to entities of virological and therapeutic interest, in the setting of a global pandemic.

Paratope states in solution improve structure prediction and docking

Structure-based antibody design and accurate predictions of antibody-antigen interactions remain major challenges in computational biology. By using molecular dynamics simulations, we show that a single static X-ray structure is not sufficient to identify determinants of antibody-antigen recognition. Here, we investigate antibodies that undergo substantial conformational changes upon antigen binding and have been classified as difficult cases in an extensive benchmark for antibody-antigen docking. We present thermodynamics and transition kinetics of these conformational rearrangements and show that paratope states can be used to improve antibody-antigen docking. By using the unbound antibody X-ray structure as starting structure for molecular dynamics simulations, we retain a binding competent conformation substantially different to the unbound antibody X-ray structure. We also observe that the kinetically dominant antibody paratope conformations are chosen by the bound antigen conformation with the highest probability. Thus, we show that paratope states in solution can improve antibody-antigen docking and structure prediction.

Structure-based engineering of an antiangiogenic scFv antibody for soluble production in E. coli without loss of activity

Development of monoclonal antibody therapeutics against vascular endothelial growth factor receptor 2 (VEGFR-2) protein, which is the main regulator in angiogenesis, has been a major challenge for years. In the current study, we engineer an inclusion body forming single-chain variable fragment (scFv) against VEGFR-2 by using complementarity determining regions (CDR) grafting technique to improve its solubility and investigate the activity of the engineered molecule. CDR sequences of the target scFv were grafted into the framework of another intrinsically soluble scFv molecule. Based on the computational results, CDR grafting has increased the solubility of the grafted scFv molecule. Results confirmed that the grafting approach increased in vivo folding properties of the target scFv molecule compared with the original scFv molecule. Similar binding affinities to the VEGFR-2 were observed for the original and the grafted scFv by surface plasmon resonance assays. Biological activity assays, including human umbilical vein endothelial cells proliferation and wound healing assays, showed that grafted scFv molecule has an antiangiogenic property. This study suggests that an antiangiogenic scFv fully expressed as an inclusion body can be rescued by grafting its CDR regions to a scFv expressed in a soluble form without any loss in its binding property and its activity.

Quantitative Description of Surface Complementarity of Antibody-Antigen Interfaces

Antibodies have the remarkable ability to recognise their cognate antigens with extraordinary affinity and specificity. Discerning the rules that define antibody-antigen recognition is a fundamental step in the rational design and engineering of functional antibodies with desired properties. In this study we apply the 3D Zernike formalism to the analysis of the surface properties of the antibody complementary determining regions (CDRs). Our results show that shape and electrostatic 3DZD descriptors of the surface of the CDRs are predictive of antigen specificity, with classification accuracy of 81% and area under the receiver operating characteristic curve (AUC) of 0.85. Additionally, while in terms of surface size, solvent accessibility and amino acid composition, antibody epitopes are typically not distinguishable from non-epitope, solvent-exposed regions of the antigen, the 3DZD descriptors detect significantly higher surface complementarity to the paratope, and are able to predict correct paratope-epitope interaction with an AUC = 0.75.

The role of complementary shape in protein dimerization

Shape guides colloidal nanoparticles to form complex assemblies, but its role in defining interfaces in biomolecular complexes is less clear. In this work, we isolate the role of shape in protein complexes by studying the reversible binding processes of 46 protein dimer pairs, and investigate when entropic effects from shape complementarity alone are sufficient to predict the native protein binding interface. We employ depletants using a generic, implicit depletion model to amplify the magnitude of the entropic forces arising from lock-and-key binding and isolate the effect of shape complementarity in protein dimerization. For 13% of the complexes studied here, protein shape is sufficient to predict native complexes as equilibrium assemblies. We elucidate the results by analyzing the importance of competing binding configurations and how it affects the assembly. A machine learning classifier, with a precision of 89.14% and a recall of 77.11%, is able to identify the cases where shape alone predicts the native protein interface.

A combination of cross-neutralizing antibodies synergizes to prevent SARS-CoV-2 and SARS-CoV pseudovirus infection

Coronaviruses have caused several human epidemics and pandemics including the ongoing coronavirus disease 2019 (COVID-19). Prophylactic vaccines and therapeutic antibodies have already shown striking effectiveness against COVID-19. Nevertheless, concerns remain about antigenic drift in SARS-CoV-2 as well as threats from other sarbecoviruses. Cross-neutralizing antibodies to SARS-related viruses provide opportunities to address such concerns. Here, we report on crystal structures of a cross-neutralizing antibody, CV38-142, in complex with the receptor-binding domains from SARS-CoV-2 and SARS-CoV. Recognition of the N343 glycosylation site and water-mediated interactions facilitate cross-reactivity of CV38-142 to SARS-related viruses, allowing the antibody to accommodate antigenic variation in these viruses. CV38-142 synergizes with other cross-neutralizing antibodies, notably COVA1-16, to enhance neutralization of SARS-CoV and SARS-CoV-2, including circulating variants of concern B.1.1.7 and B.1.351. Overall, this study provides valuable information for vaccine and therapeutic design to address current and future antigenic drift in SARS-CoV-2 and to protect against zoonotic SARS-related coronaviruses.

A compact vocabulary of paratope-epitope interactions enables predictability of antibody-antigen binding

Antibody-antigen binding relies on the specific interaction of amino acids at the paratope-epitope interface. The predictability of antibody-antigen binding is a prerequisite for de novo antibody and (neo-)epitope design. A fundamental premise for the predictability of antibody-antigen binding is the existence of paratope-epitope interaction motifs that are universally shared among antibody-antigen structures. In a dataset of non-redundant antibody-antigen structures, we identify structural interaction motifs, which together compose a commonly shared structure-based vocabulary of paratope-epitope interactions. We show that this vocabulary enables the machine learnability of antibody-antigen binding on the paratope-epitope level using generative machine learning. The vocabulary (1) is compact, less than 10⁴ motifs; (2) distinct from non-immune protein-protein interactions; and (3) mediates specific oligo- and polyreactive interactions between paratope-epitope pairs. Our work leverages combined structure- and sequence-based learning to demonstrate that machine-learning-driven predictive paratope and epitope engineering is feasible.

Simplified geometric representations of protein structures identify complementary interaction interfaces

Protein-protein interactions are critical to protein function, but three-dimensional (3D) arrangements of interacting proteins have proven hard to predict, even given the identities and 3D structures of the interacting partners. Specifically, identifying the relevant pairwise interaction surfaces remains difficult, often relying on shape complementarity with molecular docking while accounting for molecular motions to optimize rigid 3D translations and rotations. However, such approaches can be computationally expensive, and faster, less accurate approximations may prove useful for large-scale prediction and assembly of 3D structures of multi-protein complexes. We asked if a reduced representation of protein geometry retains enough information about molecular properties to predict pairwise protein interaction interfaces that are tolerant of limited structural rearrangements. Here, we describe a reduced representation of 3D protein accessible surfaces on which molecular properties such as charge, hydrophobicity, and evolutionary rate can be easily mapped, implemented in the MorphProt package. Pairs of surfaces are compared to rapidly assess partner-specific potential surface complementarity. On two available benchmarks of 185 overall known protein complexes, we observe predictions comparable to other structure-based tools at correctly identifying protein interaction surfaces. Furthermore, we examined the effect of molecular motion through normal mode simulation on a benchmark receptor-ligand pair and observed no marked loss of predictive accuracy for distortions of up to 6 Å Cα-RMSD. Thus, a shape reduction of protein surfaces retains considerable information about surface complementarity, offers enhanced speed of comparison relative to more complex geometric representations, and exhibits tolerance to conformational changes.

A combination of cross-neutralizing antibodies synergizes to prevent SARS-CoV-2 and SARS-CoV pseudovirus infection

Coronaviruses have caused several epidemics and pandemics including the ongoing coronavirus disease 2019 (COVID-19). Some prophylactic vaccines and therapeutic antibodies have already showed striking effectiveness against COVID-19. Nevertheless, concerns remain about antigenic drift in SARS-CoV-2 as well as threats from other sarbecoviruses. Cross-neutralizing antibodies to SARS-related viruses provide opportunities to address such concerns. Here, we report on crystal structures of a cross-neutralizing antibody CV38-142 in complex with the receptor binding domains from SARS-CoV-2 and SARS-CoV. Our structural findings provide mechanistic insights into how this antibody can accommodate antigenic variation in these viruses. CV38-142 synergizes with other cross-neutralizing antibodies, in particular COVA1-16, to enhance neutralization of SARS-CoV-2 and SARS-CoV. Overall, this study provides valuable information for vaccine and therapeutic design to address current and future antigenic drift in SARS-CoV-2 and to protect against zoonotic coronaviruses.

A toxin complex protein from Photorhabdus akhurstii conferred oral insecticidal activity against Galleria mellonella by targeting the midgut epithelium

The nematode-bacterium pair Heterorhabditis indica-Photorhabdus akhurstii is a malleable model system to investigate mutualistic relations. A number of toxins produced by P. akhurstii allow the bacterium to kill the insect host. However, a few of these heterologously expressed toxins are orally active against different insects which possibly caused neglected attention to Photorhabdus toxins compared to Bt (Bacillus thuringiensis). In the current study, a functional subunit of orally active toxin complex (Tc) protein, TcaB (63 kDa), isolated from two strains of P. akhurstii namely IARI-SGHR2 and IARI-SGMS1, was tested for biological activity against Galleria mellonella. A force feeding-based administration of the toxin translated into LD₅₀ values of 45.63-58.90 ng/g which was even lower compared to injection LD₅₀ values (51.48-64.30 ng/g) at 48 h after inoculation. An oral uptake of 500 ng toxin caused extensive gut damage in G. mellonella during 6-24 h incubation period coupled with a gradual disruption of gut integrity leading to escape of TcaB into the hemocoel. This finding was supported by the cytotoxic and immune-stimulatory effect of TcaB in the insect hemocoel at 6-24 h after force feeding. The circulatory hemocyte numbers and cell viability was markedly reduced to 0.66-0.68 × 10⁶ ml^-1 and 49-52 %, respectively, in TcaB force fed insect at 24 h, compared to control (2.55 × 10⁶ ml^-1; 100 %). The hemolymph phenoloxidase (PO) activity was elevated by 10.2-fold in force fed larvae than control at 24 h. An in silico docking study revealed that TcaB putatively interacts with a number of G. mellonella receptor proteins in order to become a gut-active toxin. Present research reinforces the potential of gut-active Photorhabdus toxins for their inclusion in sustainable insect management tactics and strengthens the existing Bt-dominated management repository.

Identification of Structurally Related Antibodies in Antibody Sequence Databases Using Rosetta-Derived Position-Specific Scoring

The amount of antibody (Ab) variable gene sequence information is expanding rapidly, but our ability to predict the function of Abs from sequence alone is limited. Here, we describe a sequence-to-function prediction method that couples structural data for a single Ab/antigen (Ag) complex with repertoire data. We used a position-specific structure-scoring matrix (P3SM) incorporating structure-prediction scores from Rosetta to identify Ab variable loops that have predicted structural similarity to the influenza virus-specific human Ab CH65. The P3SM approach identified new members of this Ab class. Recombinant Ab expression, crystallography, and virus inhibition assays showed that the HCDR3 loops of the newly identified Abs possessed similar structure and antiviral activity as the comparator CH65. This approach enables discovery of new human Abs with desired structure and function using cDNA repertoires that are obtained readily with current amplicon sequencing techniques.

Functional characterization of the chlorzoxazone 6-hydroxylation activity of human cytochrome P450 2E1 allelic variants in Han Chinese

BACKGROUNDS

Cytochrome P450 (P450) 2E1 is one of the primary enzymes responsible for the metabolism of xenobiotics, such as drugs and environmental carcinogens. The genetic polymorphisms of the CYP2E1 gene in promoter and coding regions have been identified previously in the Han Chinese population from four different geographic areas of Mainland China.

METHODS

To investigate whether genetic variants identified in the CYP2E1 coding region affect enzyme function, the enzymes of four single nucleotide polymorphism (SNP) variants in the coding region (novel c.1009C>T, causing p.Arg337X, where X represents the translational stop codon; c.227G>A, causing p.Arg76His; c.517G>A, yielding p.Gly173Ser; and c.1263C>T, presenting the highest allele frequency), two novel alleles (c.[227G>A;1263C>T] and c.[517G>A;1263C>T]), and the wild-type CYP2E1 were heterologously expressed in COS-7 cells and functionally characterized in terms of expression level and chlorzoxazone 6-hydroxylation activity. The impact of the CYP2E1 variant sequence on enzyme activity was predicted with three programs: Polyphen 2, PROVEAN and SIFT.

RESULTS

The prematurely terminated p.Arg337X variant enzyme was undetectable by western blotting and inactive toward chlorzoxazone 6-hydroxylation. The c.1263C>T and c.[517G>A;1263C>T] variant enzymes exhibited properties similar to those of the wild-type CYP2E1. The CYP2E1 variants c.227G>A and c.[227G>A;1263C>T] displayed significantly reduced enzyme activity relative to that of the wild-type enzyme (decreased by 42.8% and 32.8%, respectively; P < 0.01). The chlorzoxazone 6-hydroxylation activity of the c.517G>A transfectant was increased by 31% compared with the wild-type CYP2E1 enzyme (P < 0.01). Positive correlations were observed between the protein content and enzyme activity for CYP2E1 (P = 0.0005, r 2 = 0.8833). The characterization of enzyme function allelic variants in vitro was consistent with the potentially deleterious effect of the amino acid changes as determined by prediction tools.

CONCLUSIONS

These findings indicate that the genetic polymorphisms of CYP2E1, i.e., c.1009C>T (p.Arg337X), c.227G>A (p.Arg76His), and c.517G>A (p.Gly173Ser), could influence the metabolism of CYP2E1 substrates, such as chlorzoxazone.

A Review of Deep Learning Methods for Antibodies

Driven by its successes across domains such as computer vision and natural language processing, deep learning has recently entered the field of biology by aiding in cellular image classification, finding genomic connections, and advancing drug discovery. In drug discovery and protein engineering, a major goal is to design a molecule that will perform a useful function as a therapeutic drug. Typically, the focus has been on small molecules, but new approaches have been developed to apply these same principles of deep learning to biologics, such as antibodies. Here we give a brief background of deep learning as it applies to antibody drug development, and an in-depth explanation of several deep learning algorithms that have been proposed to solve aspects of both protein design in general, and antibody design in particular.

Structure-Guided Design of a Peptide Lock for Modular Peptide Binders

Peptides play an important role in intermolecular interactions and are frequent analytes in diagnostic assays, also as unstructured, linear epitopes in whole proteins. Yet, due to the many different sequence possibilities even for short peptides, classical selection of binding proteins from a library, one at a time, is not scalable to proteomes. However, moving away from selection to a rational assembly of preselected modules binding to predefined linear epitopes would split the problem into smaller parts. These modules could then be reassembled in any desired order to bind to, in principle, arbitrary sequences, thereby circumventing any new rounds of selection. Designed Armadillo repeat proteins (dArmRPs) are modular, and they do bind elongated peptides in a modular way. Their consensus sequence carries pockets that prefer arginine and lysine. In our quest to select pockets for all amino acid side chains, we had discovered that repetitive sequences can lead to register shifts and peptide flipping during selections from libraries, hindering the selection of new binding specificities. To solve this problem, we now created an orthogonal binding specificity by a combination of grafting from β-catenin, computational design and mutual optimization of the pocket and the bound peptide. We have confirmed the design and the desired interactions by X-ray structure determination. Furthermore, we could confirm the absence of sliding in solution by a single-molecule Förster resonance energy transfer. The new pocket could be moved from the N-terminus of the protein to the middle, retaining its properties, further underlining the modularity of the system.

Pushing the accuracy limit of shape complementarity for protein-protein docking

Background

Protein-protein docking is a valuable computational approach for investigating protein-protein interactions. Shape complementarity is the most basic component of a scoring function and plays an important role in protein-protein docking. Despite significant progresses, shape representation remains an open question in the development of protein-protein docking algorithms, especially for grid-based docking approaches.

Results

We have proposed a new pairwise shape-based scoring function (LSC) for protein-protein docking which adopts an exponential form to take into account long-range interactions between protein atoms. The LSC scoring function was incorporated into our FFT-based docking program and evaluated for both bound and unbound docking on the protein docking benchmark 4.0. It was shown that our LSC achieved a significantly better performance than four other similar docking methods, ZDOCK 2.1, MolFit/G, GRAMM, and FTDock/G, in both success rate and number of hits. When considering the top 10 predictions, LSC obtained a success rate of 51.71% and 6.82% for bound and unbound docking, respectively, compared to 42.61% and 4.55% for the second-best program ZDOCK 2.1. LSC also yielded an average of 8.38 and 3.94 hits per complex in the top 1000 predictions for bound and unbound docking, respectively, followed by 6.38 and 2.96 hits for the second-best ZDOCK 2.1.

Conclusions

The present LSC method will not only provide an initial-stage docking approach for post-docking processes but also have a general implementation for accurate representation of other energy terms on grids in protein-protein docking. The software has been implemented in our HDOCK web server at http://hdock.phys.hust.edu.cn/.

Conformational selection of allergen-antibody complexes-surface plasticity of paratopes and epitopes

Antibodies have the ability to bind various types of antigens and to recognize different antibody-binding sites (epitopes) of the same antigen with different binding affinities. Due to the conserved structural framework of antibodies, their specificity to antigens is mainly determined by their antigen-binding site (paratope). Therefore, characterization of epitopes in combination with describing the involved conformational changes of the paratope upon binding is crucial in understanding and predicting antibody-antigen binding. Using molecular dynamics simulations complemented with strong experimental structural information, we investigated the underlying binding mechanism and the resulting local and global surface plasticity in the binding interfaces of distinct antibody-antigen complexes. In all studied allergen-antibody complexes, we clearly observe that experimentally suggested epitopes reveal less plasticity, while non-epitope regions show high surface plasticity. Surprisingly, the paratope shows higher conformational diversity reflected in substantially higher surface plasticity, compared to the epitope. This work allows a visualization and characterization of antibody-antigen interfaces and might have strong implications for antibody-antigen docking and in the area of epitope prediction.

Discovery of novel helix binding sites at protein-protein interfaces

Protein-protein interactions (PPIs) play a key role in numerous biological processes. Many efforts have been undertaken to develop PPI modulators for therapeutic applications; however, to date, most of the peptide binders designed to target PPIs are derived from native binding helices or using the native helix binding site, which has limited the applications of protein-protein interface binding peptide design. Here, we developed a general computational algorithm, HPer (Helix Positioner), that locates single-helix binding sites at protein-protein interfaces based on the structure of protein targets. HPer performed well on known single-helix-mediated PPIs and recaptured the key interactions and hot-spot residues of native helical binders. We also screened non-helical-mediated PPIs in the PDBbind database and identified 17 PPIs that were suitable for helical peptide binding, and the helical binding sites in these PPIs were also predicted for designing novel peptide ligands. The L2 domain of EGFR, which was the top ranked, was selected as an example to show the protocol and results of designing novel helical peptide ligands on the searched binding site. The binding stability of the designed sequences were further investigated using molecular dynamics simulations.

Underestimated Noncovalent Interactions in Protein Data Bank

Noncovalent interactions (NCIs) play essential roles in the structure and function of biomacromolecules. There are various NCIs, e.g., hydrogen bonds (HBs), cation-π and π-π interactions, and ionic bonds, among which HBs are the most widespread and well-studied. By utilizing the ratio of the observed HBs over pseudo HBs (1.0 Å longer than the HB distance criteria without angle constraints), we demonstrated that HBs in both protein-ligand and protein-protein interfaces are overlooked in structures deposited in PDB. After the QM/MM optimization of 12 protein-ligand complexes, we showed that the overlooked HBs could be recovered. With a systematic search in the PDB, we found that the HB number per residue (N_HB/R) in proteins decreases as structural resolution becomes lower, implying that HBs are overlooked even today, regardless of the type of refinement approach used. Similarly, cation-π, π-π, and ionic interactions were found to be significantly lost, manifesting the universal underestimation of various NCIs. Considering the vital role of NCIs, it is important to recover the NCIs to facilitate drug design, to explore protein-protein interaction, and to study protein structure and function.

Exploring designability of electrostatic complementarity at an antigen-antibody interface directed by mutagenesis, biophysical analysis, and molecular dynamics simulations

Antibodies protect organisms from a huge variety of foreign antigens. Antibody diversity originates from both genetic and structural levels. Antigen recognition relies on complementarity between antigen-antibody interfaces. Recent methodological advances in structural biology and the accompanying rapid increase of the number of crystal structures of proteins have enabled atomic-level manipulation of protein structures to effect alterations in function. In this study, we explored the designability of electrostatic complementarity at an antigen-antibody interface on the basis of a crystal structure of the complex. We designed several variants with altered charged residues at the interface and characterized the designed variants by surface plasmon resonance, circular dichroism, differential scanning calorimetry, and molecular dynamics simulations. Both successes and failures of the structure-based design are discussed. The variants that compensate electrostatic interactions can restore the interface complementarity, enabling the cognate antigen-antibody binding. Retrospectively, we also show that these mutational effects could be predicted by the simulations. Our study demonstrates the importance of charged residues on the physical properties of this antigen-antibody interaction and suggests that computational approaches can facilitate design of antibodies that recognize a weakly immunogenic antigen.

Computational design of structured loops for new protein functions

The ability to engineer the precise geometries, fine-tuned energetics and subtle dynamics that are characteristic of functional proteins is a major unsolved challenge in the field of computational protein design. In natural proteins, functional sites exhibiting these properties often feature structured loops. However, unlike the elements of secondary structures that comprise idealized protein folds, structured loops have been difficult to design computationally. Addressing this shortcoming in a general way is a necessary first step towards the routine design of protein function. In this perspective, we will describe the progress that has been made on this problem and discuss how recent advances in the field of loop structure prediction can be harnessed and applied to the inverse problem of computational loop design.

Antibody specificity and promiscuity

The immune system is capable of making antibodies against anything that is foreign, yet it does not react against components of self. In that sense, a fundamental requirement of the body's immune defense is specificity. Remarkably, this ability to specifically attack foreign antigens is directed even against antigens that have not been encountered a priori by the immune system. The specificity of an antibody for the foreign antigen evolves through an iterative process of somatic mutations followed by selection. There is, however, accumulating evidence that the antibodies are often functionally promiscuous or multi-specific which can lead to their binding to more than one antigen. An important cause of antibody cross-reactivity is molecular mimicry. Molecular mimicry has been implicated in the generation of autoimmune response. When foreign antigen shares similarity with the component of self, the antibodies generated could result in an autoimmune response. The focus of this review is to capture the contrast between specificity and promiscuity and the structural mechanisms employed by the antibodies to accomplish promiscuity, at the molecular level. The conundrum between the specificity of the immune system for foreign antigens on the one hand and the multi-reactivity of the antibody on the other has been addressed. Antibody specificity in the context of the rapid evolution of the antigenic determinants and molecular mimicry displayed by antigens are also discussed.

Characterizing the Diversity of the CDR-H3 Loop Conformational Ensembles in Relationship to Antibody Binding Properties

We present an approach to assess antibody CDR-H3 loops according to their dynamic properties using molecular dynamics simulations. We selected six antibodies in three pairs differing substantially in their individual promiscuity respectively specificity. For two pairs of antibodies crystal structures are available in different states of maturation and used as starting structures for the analyses. For a third pair we chose two antibody CDR sequences obtained from a synthetic library and predicted the respective structures. For all three pairs of antibodies we performed metadynamics simulations to overcome the limitations in conformational sampling imposed by high energy barriers. Additionally, we used classic molecular dynamics simulations to describe nano- to microsecond flexibility and to estimate up to millisecond kinetics of captured conformational transitions. The methodology represents the antibodies as conformational ensembles and allows comprehensive analysis of structural diversity, thermodynamics of conformations and kinetics of structural transitions. Referring to the concept of conformational selection we investigated the link between promiscuity and flexibility of the antibodies' binding interfaces. The obtained detailed characterization of the binding interface clearly indicates a link between structural flexibility and binding promiscuity for this set of antibodies.

Shape induced sorting via rim-to-rim complementarity in the formation of pillar[5, 6]arene-based supramolecular organogels

The first two-component rim-to-rim pillar[6]arene-based supramolecular organogels were prepared. Shape complementarity was found to be an important determinant in the formation of such gels which also show shape-induced sorting in their formation.

Thermodynamic and computational analyses reveal the functional roles of the galloyl group of tea catechins in molecular recognition

Catechins, biologically active polyphenols in green tea, exhibit various biological activities, such as anticancer and antiviral activities, arising from interactions with functional proteins. However, the molecular details of these interactions remain unclear. In this study, we investigated the interactions between human serum albumin (HSA) and various catechins, including some with a galloyl group, by means of isothermal titration calorimetry (ITC), differential scanning calorimetry (DSC), and docking simulations. Our results indicate that the galloyl group was important for recognition by HSA and was responsible for enthalpic gains derived from a larger buried surface area and more van der Waals contacts. Thus, our thermodynamic and computational analyses suggest that the galloyl group plays important functional roles in the specific binding of catechins to proteins, implying that the biological activities of these compounds may be due in part to the physicochemical characteristics of the galloyl group.

Structural Basis of Epitope Recognition by Heavy-Chain Camelid Antibodies

Truncated versions of heavy-chain antibodies (HCAbs) from camelids, also termed nanobodies, comprise only one-tenth the mass of conventional antibodies, yet retain similar, high binding affinities for the antigens. Here we analyze a large data set of nanobody-antigen crystal structures and investigate how nanobody-antigen recognition compares to the one by conventional antibodies. We find that nanobody paratopes are enriched in aromatic residues just like conventional antibodies, but additionally, they also bear a more hydrophobic character. Most striking differences were observed in the characteristics of the antigen's epitope. Unlike conventional antibodies, nanobodies bind to more rigid, concave, conserved and structured epitopes enriched with aromatic residues. Nanobodies establish fewer interactions with the antigens compared to conventional antibodies, and we speculate that high binding affinities are achieved due to less unfavorable conformational and more favorable solvation entropy contributions. We observed that interactions with antigen are mediated not only by three CDR loops but also by numerous residues from the nanobody framework. These residues are not distributed uniformly; rather, they are concentrated into four structurally distinct regions and mediate mostly charged interactions. Our findings suggest that in some respects nanobody-antigen interactions are more similar to the general protein-protein interactions rather than antibody-antigen interactions.

Antigen recognition by single-domain antibodies: structural latitudes and constraints

Single-domain antibodies (sdAbs), the autonomous variable domains of heavy chain-only antibodies produced naturally by camelid ungulates and cartilaginous fishes, have evolved to bind antigen using only three complementarity-determining region (CDR) loops rather than the six present in conventional V_H:V_L antibodies. It has been suggested, based on limited evidence, that sdAbs may adopt paratope structures that predispose them to preferential recognition of recessed protein epitopes, but poor or non-recognition of protuberant epitopes and small molecules. Here, we comprehensively surveyed the evidence in support of this hypothesis. We found some support for a global structural difference in the paratope shapes of sdAbs compared with those of conventional antibodies: sdAb paratopes have smaller molecular surface areas and diameters, more commonly have non-canonical CDR1 and CDR2 structures, and have elongated CDR3 length distributions, but have similar amino acid compositions and are no more extended (interatomic distance measured from CDR base to tip) than conventional antibody paratopes. Comparison of X-ray crystal structures of sdAbs and conventional antibodies in complex with cognate antigens showed that sdAbs and conventional antibodies bury similar solvent-exposed surface areas on proteins and form similar types of non-covalent interactions, although these are more concentrated in the compact sdAb paratope. Thus, sdAbs likely have privileged access to distinct antigenic regions on proteins, but only owing to their small molecular size and not to general differences in molecular recognition mechanism. The evidence surrounding the purported inability of sdAbs to bind small molecules was less clear. The available data provide a structural framework for understanding the evolutionary emergence and function of autonomous heavy chain-only antibodies.

Analysis of nanobody paratopes reveals greater diversity than classical antibodies

Nanobodies (Nbs) are a class of antigen-binding protein derived from camelid immune systems, which achieve equivalent binding affinities and specificities to classical antibodies (Abs) despite being comprised of only a single variable domain. Here, we use a data set of 156 unique Nb:antigen complex structures to characterize Nb-antigen binding and draw comparison to a set of 156 unique Ab:antigen structures. We analyse residue composition and interactions at the antigen interface, together with structural features of the paratopes of both data sets. Our analysis finds that the set of Nb structures displays much greater paratope diversity, in terms of the structural segments involved in the paratope, the residues used at these positions to contact the antigen and furthermore the type of contacts made with the antigen. Our findings suggest a different relationship between contact propensity and sequence variability from that observed for Ab VH domains. The distinction between sequence positions that control interaction specificity and those that form the domain scaffold is much less clear-cut for Nbs, and furthermore H3 loop positions play a much more dominant role in determining interaction specificity.

Rapid Sampling of Hydrogen Bond Networks for Computational Protein Design

Hydrogen bond networks play a critical role in determining the stability and specificity of biomolecular complexes, and the ability to design such networks is important for engineering novel structures, interactions, and enzymes. One key feature of hydrogen bond networks that makes them difficult to rationally engineer is that they are highly cooperative and are not energetically favorable until the hydrogen bonding potential has been satisfied for all buried polar groups in the network. Existing computational methods for protein design are ill-equipped for creating these highly cooperative networks because they rely on energy functions and sampling strategies that are focused on pairwise interactions. To enable the design of complex hydrogen bond networks, we have developed a new sampling protocol in the molecular modeling program Rosetta that explicitly searches for sets of amino acid mutations that can form self-contained hydrogen bond networks. For a given set of designable residues, the protocol often identifies many alternative sets of mutations/networks, and we show that it can readily be applied to large sets of residues at protein-protein interfaces or in the interior of proteins. The protocol builds on a recently developed method in Rosetta for designing hydrogen bond networks that has been experimentally validated for small symmetric systems but was not extensible to many larger protein structures and complexes. The sampling protocol we describe here not only recapitulates previously validated designs with performance improvements but also yields viable hydrogen bond networks for cases where the previous method fails, such as the design of large, asymmetric interfaces relevant to engineering protein-based therapeutics.

Comparative analysis of nanobody sequence and structure data

Nanobodies are a class of antigen‐binding protein derived from camelids that achieve comparable binding affinities and specificities to classical antibodies, despite comprising only a single 15 kDa variable domain. Their reduced size makes them an exciting target molecule with which we can explore the molecular code that underpins binding specificity—how is such high specificity achieved? Here, we use a novel dataset of 90 nonredundant, protein‐binding nanobodies with antigen‐bound crystal structures to address this question. To provide a baseline for comparison we construct an analogous set of classical antibodies, allowing us to probe how nanobodies achieve high specificity binding with a dramatically reduced sequence space. Our analysis reveals that nanobodies do not diversify their framework region to compensate for the loss of the VL domain. In addition to the previously reported increase in H3 loop length, we find that nanobodies create diversity by drawing their paratope regions from a significantly larger set of aligned sequence positions, and by exhibiting greater structural variation in their H1 and H2 loops.