Interactions of two monoclonal antibodies with BNP: high resolution epitope mapping using fluorescence correlation spectroscopy

Structure-function studies of antibody-antigen systems include the identification of amino acid residues in the antigen that interact with an antibody and elucidation of their individual contributions to binding affinity. We used fluorescence correlation spectroscopy (FCS) and alanine-scanning mutagenesis to characterize the interactions of brain natriuretic peptide (BNP) with two monoclonal antibodies. Human BNP is a 32 amino acid residue long cyclic polypeptide with the ring structure confined between cysteines in positions 10 and 26. It is an important cardiovascular hormone and a valuable diagnostic cardiac marker. We compare the binding strength of the N-terminus Alexa488-labeled BNP, native cyclic BNP, BNP alanine-substituted mutants, linear BNP, and its short fragments to determine the individual contributions of amino acid residues included in the continuous antigenic epitopes that are recognized by two different monoclonal antibodies raised toward BNP. Implementation of FCS for these studies offers all of the advantages of solution phase measurements, including high sensitivity, simplicity of manipulation with reagents, and elimination of solid phase interferences or separation steps. Significant differences in the molecular masses of the free and antibody bound BNP results in a substantial ( approximately 2.5-times) increase in the diffusion rates. Determination of the binding constants and inhibition effects by measuring the diffusion rates of the ligand at the single molecule level introduces the ultimate opportunity for researching systems where the fluorescence intensity and/or fluorescence anisotropy do not change upon interaction of the ligand with the protein. Monoclonal antibodies 106.3 and BC203 demonstrate high affinities to BNP and bind two distant epitopes forming robust antibody sandwiches. Both antibodies are used in Abbott diagnostic assays on AxSYM, IMx, and Architect platforms.     

Effective binding to protein antigens by antibodies from antibody libraries designed with enhanced protein recognition propensities

Antibodies provide immune protection by recognizing antigens of diverse chemical properties, but elucidating the amino acid sequence-function relationships underlying the specificity and affinity of antibody-antigen interactions remains challenging. We designed and constructed phage-displayed synthetic antibody libraries with enriched protein antigen-recognition propensities calculated with machine learning predictors, which indicated that the designed single-chain variable fragment variants were encoded with enhanced distributions of complementarity-determining region (CDR) hot spot residues with high protein antigen recognition propensities in comparison with those in the human antibody germline sequences. Antibodies derived directly from the synthetic antibody libraries, without affinity maturation cycles comparable to those in in vivo immune systems, bound to the corresponding protein antigen through diverse conformational or linear epitopes with specificity and affinity comparable to those of the affinity-matured antibodies from in vivo immune systems. The results indicated that more densely populated CDR hot spot residues were sustainable by the antibody structural frameworks and could be accompanied by enhanced functionalities in recognizing protein antigens. Our study results suggest that synthetic antibody libraries, which are not limited by the sequences found in antibodies in nature, could be designed with the guidance of the computational machine learning algorithms that are programmed to predict interaction propensities to molecules of diverse chemical properties, leading to antibodies with optimal characteristics pertinent to their medical applications.     

Humanization of a murine monoclonal antibody by simultaneous optimization of framework and CDR residues.

Optimal protein function often depends on co-operative interactions between amino acid residues distant in the protein primary sequence yet spatially near one another following protein folding. For example, antibody affinity is influenced by interactions of framework residues with complementarity-determining region (CDR) residues. However, despite the abundance of antibody structural information and computational tools the humanization of rodent antibodies for clinical use often results in a significant loss of affinity. To date, antibody engineering efforts have focused either on optimizing CDR residues involved in antigen binding or on optimizing antibody framework residues that serve critical roles in preserving the conformation of CDRs. In the present study a new approach which permits the rapid identification of co-operatively interacting framework and CDR residues was used to simultaneously humanize and optimize a murine antibody directed against CD40. Specifically, a combinatorial library that examined eight potentially important framework positions concomitantly with focused CDR libraries consisting of variants containing random single amino acid mutations in the third CDR of the heavy and light chains was expressed. Multiple anti-CD40 Fab variants containing as few as one murine framework residue and displaying up to approximately 500-fold higher affinity than the initial chimeric Fab were identified. The higher affinity humanized variants demonstrated a co-operative interaction between light chain framework residue Y49 and heavy chain CDR3 residue R/K101 (coupling energy, DeltaGI=0.9 kcal/mol). Screening of combinatorial framework-CDR libraries permits identification of monoclonal antibodies (mAb) with structures optimized for function, including instances in which the antigen induces conformational changes in the mAb. Moreover, the enhanced humanized variants contain fewer murine framework residues and could not be identified by sequential in vitro humanization and affinity muturation strategies. This approach to identifying co-operatively interacting residues is not restricted to antibody-antigen interactions and consequently, may be used broadly to gain insight into protein structure-function relationships, including proteins that serve as catalysts.     

Structure, function and properties of antibody binding sites

Do antibody combining sites possess general properties that enable them to bind different antigens with varying affinities and to bind novel antigens? Here, we address this question by examining the physical and chemical characteristics most favourable for residues involved in antigen accommodation and binding. Amphipathic amino acids could readily tolerate the change of environment from hydrophilic to hydrophobic that occurs upon antibody-antigen complex formation. Residues that are large and can participate in a wide variety of van der Waals' and electrostatic interactions would permit binding to a range of antigens. Amino acids with flexible side-chains could generate a structurally plastic region, i.e. a binding site possessing the ability to mould itself around the antigen to improve complementarity of the interacting surfaces. Hence, antibodies could bind to an array of novel antigens using a limited set of residues interspersed with more unique residues to which greater binding specificity can be attributed. An individual antibody molecule could thus be cross-reactive and have the capacity to bind structurally similar ligands. The accommodation of variations in antigenic structure by modest combining site flexibility could make an important contribution to immune defence by allowing antibody binding to distinct but closely related pathogens. Tyr and Trp most readily fulfil these catholic physicochemical requirements and thus would be expected to be common in combining sites on theoretical grounds. Experimental support for this comes from three sources, (1) the high frequency of participation by these amino acids in the antigen binding observed in six crystallographically determined antibody-antigen complexes, (2) their frequent occurrence in the putative binding regions of antibodies as determined from structural and sequence data and (3) the potential for movement of their side-chains in known antibody binding sites and model systems. The six bound antigens comprise two small different haptens, non-overlapping regions of the same large protein and a 19 amino acid residue peptide. Out of a total of 85 complementarity determining region positions, only 37 locations (plus 3 framework) are directly involved in antigen interaction. Of these, light chain residue 91 is utilized by all the complexes examined, whilst light chain 32, light chain 96 and heavy chain 33 are employed by five out of the six. The binding sites in known antibody-antigen complexes as well as the postulated combining sites in free Fab fragments show similar characteristics with regard to the types of amino acids present. The possible role of other amino acids is also assessed.(ABSTRACT TRUNCATED AT 400 WORDS)     

Local and global anatomy of antibody‐protein antigen recognition

Deciphering antibody‐protein antigen recognition is of fundamental and practical significance. We constructed an antibody structural dataset, partitioned it into human and murine subgroups, and compared it with nonantibody protein‐protein complexes. We investigated the physicochemical properties of regions on and away from the antibody‐antigen interfaces, including net charge, overall antibody charge distributions, and their potential role in antigen interaction. We observed that amino acid preference in antibody‐protein antigen recognition is entropy driven, with residues having low side‐chain entropy appearing to compensate for the high backbone entropy in interaction with protein antigens. Antibodies prefer charged and polar antigen residues and bridging water molecules. They also prefer positive net charge, presumably to promote interaction with negatively charged protein antigens, which are common in proteomes. Antibody‐antigen interfaces have large percentages of Tyr, Ser, and Asp, but little Lys. Electrostatic and hydrophobic interactions in the Ag binding sites might be coupled with Fab domains through organized charge and residue distributions away from the binding interfaces. Here we describe some features of antibody‐antigen interfaces and of Fab domains as compared with nonantibody protein‐protein interactions. The distributions of interface residues in human and murine antibodies do not differ significantly. Overall, our results provide not only a local but also a global anatomy of antibody structures.     

Comparison of protein-protein interactions in serine protease-inhibitor and antibody-antigen complexes: implications for the protein docking problem

The protein-protein interaction energy of 12 nonhomologous serine protease-inhibitor and 15 antibody-antigen complexes is calculated using a molecular mechanics formalism and dissected in terms of the main-chain vs. side-chain contribution, nonrotameric side-chain contributions, and amino acid residue type involvement in the interface interaction. There are major differences in the interactions of the two types of protein-protein complex. Protease-inhibitor complexes interact predominantly through a main-chain-main-chain mechanism while antibody-antigen complexes interact predominantly through a side-chain-side-chain or a side-chain-main-chain mechanism. However, there is no simple correlation between the main-chain-main-chain interaction energy and the percentage of main-chain surface area buried on binding. The interaction energy is equally effected by the presence of nonrotameric side-chain conformations, which constitute approximately 20% of the interaction energy. The ability to reproduce the interface interaction energy of the crystal structure if original side-chain conformations are removed from the calculation is much greater in the protease-inhibitor complexes than the antibody-antigen complexes. The success of a rotameric model for protein-protein docking appears dependent on the extent of the main-chain-main-chain contribution to binding. Analysis of (1) residue type and (2) residue pair interactions at the interface show that antibody-antigen interactions are very restricted with over 70% of the antibody energy attributable to just six residue types (Tyr > Asp > Asn > Ser > Glu > Trp) in agreement with previous studies on residue propensity. However, it is found here that 50% of the antigen energy is attributable to just four residue types (Arg = Lys > Asn > Asp). On average just 12 residue pair interactions (6%) contribute over 40% of the favorable interaction energy in the antibody-antigen complexes, with charge-charge and charge/polar-tyrosine interactions being prominent. In contrast protease inhibitors use a diverse set of residue types and residue pair interactions.     

The effect of pH dependence of antibody-antigen interactions on subcellular trafficking dynamics

A drawback of targeting soluble antigens such as cytokines or toxins with long-lived antibodies is that such antibodies can prolong the half-life of the target antigen by a “buffering” effect. This has motivated the design of antibodies that bind to target with higher affinity at near neutral pH relative to acidic endosomal pH (~pH 6.0). Such antibodies are expected to release antigen within endosomes following uptake into cells, whereas antibody will be recycled and exocytosed in FcRn-expressing cells. To understand how the pH dependence of antibody-antigen interactions affects intracellular trafficking, we generated three antibodies that bind IL-6 with different pH dependencies in the range pH 6.0–7.4. The behavior of antigen in the presence of these antibodies has been characterized using a combination of fixed and live cell fluorescence microscopy. As the affinity of the antibody:IL-6 interaction at pH 6.0 decreases, an increasing amount of antigen dissociates from FcRn-bound antibody in early and late endosomes, and then enters lysosomes. Segregation of antibody and FcRn from endosomes in tubulovesicular transport carriers (TCs) into the recycling pathway can also be observed in live cells, and the extent of IL-6 association with TCs correlates with increasing affinity of the antibody:IL-6 interaction at acidic pH. These analyses result in an understanding, in spatiotemporal terms, of the effect of pH dependence of antibody-antigen interactions on subcellular trafficking and inform the design of antibodies with optimized binding properties for antigen elimination.     

Quantitative specificity‐based display library screening identifies determinants of antibody‐epitope binding specificity

Despite the critical importance of molecular specificity in bimolecular systems, in vitro display technologies have been applied extensively for affinity maturation of peptides and antibodies without explicitly measuring the specificity of the desired interaction. We devised a general strategy to measure, screen, and evolve specificity of protein ligand interactions analogous to widely used affinity maturation strategies. The specificity of binding to target and nontarget antibodies labeled with spectrally distinct fluorophores was measured simultaneously in protein mixtures via multiparameter flow cytometry, thereby enabling screening for high target antibody specificity. Isolated antibody specific ligands exhibited varying specificity, revealing critical amino acid determinants for target recognition and nontarget avoidance in complex mixtures. Molecular specificity in the mixture was further enhanced by quantitative directed evolution, yielding a family of epitopes exhibiting improved specificities equivalent, or superior to, the native peptide antigen to which the antibody was raised. Specificity screening simultaneously favored affinity, yielding ligands with three‐fold improved affinity relative to the parent epitope. Quantitative specificity screening will be useful to screen, evolve, and characterize the specificity of protein and peptide interactions for molecular recognition applications.     

A likelihood-based index of protein protein binding affinities with application to influenza HA escape from antibodies

In many biological systems, proteins interact with other organic molecules to produce indispensable functions, in which molecular recognition phenomena are essential. Proteins have kept or gained their functions during molecular evolution. Their functions seem to be flexible, and a few amino acid substitutions sometimes cause drastic changes in function. In order to monitor and predict such drastic changes in the early stages in target populations, we need to identify patterns of structural changes during molecular evolution causing decreases or increases in the binding affinity of protein complexes. In previous work, we developed a likelihood-based index to quantify the degree to which a sequence fits a given structure. This index was named the sequence-structure fitness (SSF) and is calculated empirically based on amino acid preferences and pairwise interactions in the structural environment present in template structures. In the present work, we used the SSF to develop an index to measure the binding affinity of protein-protein complexes defined as the log likelihood ratio, contrasting the fitness of the sequences to the structure of the complex and that of the uncomplexed proteins. We applied the developed index to the complexes formed between influenza A hemagglutinin (HA) and four antibodies. The antibody-antigen binding region of HA is under strong selection pressure by the host immune system. Hence, examination of the long-term adaptation of HA to the four antibodies could reveal the strategy of the molecular evolution of HA. Two antibodies cover the HA receptor-binding region, while the other two bind away from the receptor-binding region. By focusing on branches with a significant decline in binding ability, we could detect key amino acid replacements and investigate the mechanism via conditional probabilities. The contrast between the adaptations to the two types of antibodies suggests that the virus adapts to the immune system at the cost of structural change.     

Design of multi-specificity in protein interfaces

Interactions in protein networks may place constraints on protein interface sequences to maintain correct and avoid unwanted interactions. Here we describe a “multi-constraint” protein design protocol to predict sequences optimized for multiple criteria, such as maintaining sets of interactions, and apply it to characterize the mechanism and extent to which 20 multi-specific proteins are constrained by binding to multiple partners. We find that multi-specific binding is accommodated by at least two distinct patterns. In the simplest case, all partners share key interactions, and sequences optimized for binding to either single or multiple partners recover only a subset of native amino acid residues as optimal. More interestingly, for signaling interfaces functioning as network “hubs,” we identify a different, “multi-faceted” mode, where each binding partner prefers its own subset of wild-type residues within the promiscuous binding site. Here, integration of preferences across all partners results in sequences much more “native-like” than seen in optimization for any single binding partner alone, suggesting these interfaces are substantially optimized for multi-specificity. The two strategies make distinct predictions for interface evolution and design. Shared interfaces may be better small molecule targets, whereas multi-faceted interactions may be more “designable” for altered specificity patterns. The computational methodology presented here is generalizable for examining how naturally occurring protein sequences have been selected to satisfy a variety of positive and negative constraints, as well as for rationally designing proteins to have desired patterns of altered specificity.     

Applications of dual-color fluorescence cross-correlation spectroscopy in antibody binding studies

Two-photon dual-color fluorescence cross-correlation spectroscopy (DC-FCCS) was applied to study the binding interactions of monoclonal antibodies (mAbs) and protein antigens. We measured the binding constant of the interaction of a 32-amino acid brain natriuretic peptide (BNP) with a mAbs and demonstrated the utility of DC-FCCS in studies of antibody sandwiches, trimolecular formations, where two different antibodies bind the same antigen simultaneously. We also show the use of DC-FCCS for monitoring competitive displacement of the labeled antibody in antibody-antigen complexes and subsequent determination of the pertinent dissociation rate (off-rate). The off-rate measurements were performed for two mAbs toward tissue inhibitor 1 of metalloproteinases (TIMP-1). From a methodological perspective, selection of the best labeling protocols and careful optimization of the FCCS instrumentation are essential to achieve the highest cross-correlation signal. When working in vitro, it is practical to generate a complete binding curve using the normalized cross-correlation signal and then fit the experimental points to a binding model. DC-FCCS offers the sensitivity and all other advantages of a solution phase fluorescence-based technique. For systems containing proteins of a similar size that interact without substantial changes in the fluorescence intensity, DC-FCCS serves as a preferred means of measuring solution phase binding constants.     

Prediction of RNA binding sites in proteins from amino acid sequence

RNA-protein interactions are vitally important in a wide range of biological processes, including regulation of gene expression, protein synthesis, and replication and assembly of many viruses. We have developed a computational tool for predicting which amino acids of an RNA binding protein participate in RNA-protein interactions, using only the protein sequence as input. RNABindR was developed using machine learning on a validated nonredundant data set of interfaces from known RNA-protein complexes in the Protein Data Bank. It generates a classifier that captures primary sequence signals sufficient for predicting which amino acids in a given protein are located in the RNA-protein interface. In leave-one-out cross-validation experiments, RNABindR identifies interface residues with >85% overall accuracy. It can be calibrated by the user to obtain either high specificity or high sensitivity for interface residues. RNABindR, implementing a Naive Bayes classifier, performs as well as a more complex neural network classifier (to our knowledge, the only previously published sequence-based method for RNA binding site prediction) and offers the advantages of speed, simplicity and interpretability of results. RNABindR predictions on the human telomerase protein hTERT are in good agreement with experimental data. The availability of computational tools for predicting which residues in an RNA binding protein are likely to contact RNA should facilitate design of experiments to directly test RNA binding function and contribute to our understanding of the diversity, mechanisms, and regulation of RNA-protein complexes in biological systems. (RNABindR is available as a Web tool from http://bindr.gdcb.iastate.edu.).     

Antigen-antibody interaction. Synthetic peptides define linear antigenic determinants recognized by monoclonal antibodies directed to the cytoplasmic carboxyl terminus of rhodopsin

The specificities of four monoclonal antibodies rho 1D4, 1C5, 3A6, and 3D6 prepared by immunization of rod outer segments containing rhodopsin have been defined using synthetic peptides. All of these antibodies interact within the 18 residues at the COOH terminus of rhodopsin and recognize linear antigenic determinants of 4-11 residues. Twenty-seven synthetic peptide analogs of varying lengths of native sequence or containing single amino acid substitutions at each position of the COOH-terminal 18 residues have provided some insight into the mechanism of antigen-antibody binding. Our results clearly demonstrate that antibodies can be highly specific at key positions as shown by the loss of binding on single amino acid substitutions in the binding site. In contrast single amino acid substitutions at other positions in the binding site only affect affinity for some antibodies. Ionic interactions can dominate immunogenic determinants. Immunogenic determinants are not restricted to highly charged hydrophilic regions on the surface of a protein and may be dominated by hydrophobic interactions. Although certain side chains can dominate the interaction of the antigen with antibody, our results are in agreement with the interpretation that the free energies of all the contact points are additive and a certain free energy must be present to achieve binding. Antibodies with different specificities directed to the same region of the protein antigen can be produced in an immune response. Peptide antigens representing regions of a protein antigen bind best to the anti-protein antibody when the sequence is shortened to contain only those residues binding to the specificity site in the antibody. Cross-reactivity between protein antigens can be explained by conservation of the critical residues in the combining site.     

The use of recombinant antibodies in proteomics

Recombinant antibodies are becoming increasingly important in the field of proteomics. Recent advances include the development of large phage-antibody libraries that contain high-affinity binders to almost any target protein, and new methods for high-throughput selection of antibody-antigen interactions. Coupled with a range of new screening technologies that use high-density antibody arrays to identify differentially expressed proteins, these antibody libraries can be applied to whole proteome analysis.     

Microarray of recombinant antibodies using a streptavidin sensor surface self-assembled onto a gold layer

We have developed a sensitive method for the detection of recombinant antibody-antigen interactions in a microarray format. The biochip sensor platform used in this study is based on an oriented streptavidin monolayer that provides a biological interface with well-defined surface architecture that dramatically reduces nonspecific binding interactions. All the antibody or antigen probes were biotinylated and coupled onto streptavidin-coated biochip surfaces (1 microL total volume). The detection limits for the immobilized probes on the microarray surface were 0.5 microgram/mL (200 fmol/spot) for the peptide antigen and 0.1 microgram/mL (3 fmol/spot) for the recombinant antibodies. Optimal concentrations for the detection of the Cy5-labeled protein target were in the range of 20 micrograms/mL. Protein microchips were used to measure antibody-antigen kinetics, to find optimal temperature conditions, and to establish the shelf life of recombinant antibodies immobilized on the streptavidin surface. For recombinant antibody fragments with a kDa of 10-100 nM, we have established an easy and direct immunoassay. In addition, we developed an indirect method for antibody detection with no need for expensive and time-consuming antibody purifications and modifications. Such a method was shown to be useful for large-scale screening of recombinant antibody fragments directly after their functional expression in bacteria. Our data demonstrate that recombinant antibody fragments are suitable components in the construction of antibody chips.     

Quantitative protein profiling using antibody arrays

Traditional approaches to microarrays rely on direct binding assays where the extent of hybridisation and the signal detected are a measure of the analyte concentration in the experimental sample. This approach, directly imported from the nucleic acid field, may fail if applied to antibody-antigen interactions due to the shortage of characterised antibodies, the significant heterogeneity of antibody affinities, their dependence on the extent of protein modification during labelling and the inherent antibody cross-reactivity. These problems can potentially limit the multiplexing capabilities of protein affinity assays and in many cases rule out quantitative protein profiling using antibody microarrays. A number of approaches aimed at achieving quantitative protein profiling in a multiplex format have been reported recently. Of those reported, the three most promising routes include signal amplification, multicolour detection and competitive displacement approaches to multiplex affinity assays. One in particular, competitive displacement, also overcomes the problems associated with quantitation of affinity interactions and provides the most generic approach to highly parallel affinity assays, including antibody arrays.     

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. Proteins 2015; 83:1385–1406. © 2015 Wiley Periodicals, Inc.     

A combinatorial approach to engineering a dual-specific metal switch antibody

There is considerable interest in understanding how multiple binding events can be mediated through a single protein interface. Here, a synthetic library approach was developed to generate a novel dual-specific antibody. Using a combinatorial histidine-scanning phage display library, potential metal binding sites were introduced throughout an anti-RNase A antibody interface. Stepwise selection of RNase A and metal binding produced a dual-specific antibody that retained near wild-type affinity for its target antigen while acquiring a competitive metal binding site that is capable of controlling the antibody-antigen interaction. Structure analysis of the original antibody-RNase A complex suggested peripheral interface residues and loop flexibility are key contributors for obtaining the dual specificity.     

A computational approach for studying antibody–antigen interactions without prior structural information: the anti‐testosterone binding antibody as a case study

Given the increasing exploitation of antibodies in different contexts such as molecular diagnostics and therapeutics, it would be beneficial to unravel the atomistic level properties of antibody‐antigen complexes with the help of computational modeling. Thus, here we have studied the feasibility of computational tools to gather atomic scale information regarding the antibody‐antigen complexes solely starting from an amino acid sequence. First, we constructed a homology model for the anti‐testosterone binding antibody based on the knowledge based classification of complementary determining regions (CDRs) and implicit solvent molecular dynamics simulations. To further examine whether the generated homology model is suitable for studying antibody‐antigen interactions, docking calculations were carried out followed by binding free‐energy simulations. Our results indicate that with the antibody modeling approach presented here it is possible to construct accurate homology models for antibodies which correctly describes the antibody‐antigen interactions, and produces absolute binding free‐energies that are comparable with experimental values. In addition, our simulations suggest that the conformations of complementary determining regions (CDRs) may considerably change from the X‐ray configuration upon solvation. In conclusion, here we have introduced an antibody modeling workflow that can be used in studying the interactions between antibody and antigen solely based on an amino acid sequence, which in turn provides novel opportunities to tune the properties of antibodies in different applications. Proteins 2017; 85:322–331. © 2016 Wiley Periodicals, Inc.     

A Unified Conformational Selection and Induced Fit Approach to Protein-Peptide Docking

Protein-peptide interactions are vital for the cell. They mediate, inhibit or serve as structural components in nearly 40% of all macromolecular interactions, and are often associated with diseases, making them interesting leads for protein drug design. In recent years, large-scale technologies have enabled exhaustive studies on the peptide recognition preferences for a number of peptide-binding domain families. Yet, the paucity of data regarding their molecular binding mechanisms together with their inherent flexibility makes the structural prediction of protein-peptide interactions very challenging. This leaves flexible docking as one of the few amenable computational techniques to model these complexes. We present here an ensemble, flexible protein-peptide docking protocol that combines conformational selection and induced fit mechanisms. Starting from an ensemble of three peptide conformations (extended, a-helix, polyproline-II), flexible docking with HADDOCK generates 79.4% of high quality models for bound/unbound and 69.4% for unbound/unbound docking when tested against the largest protein-peptide complexes benchmark dataset available to date. Conformational selection at the rigid-body docking stage successfully recovers the most relevant conformation for a given protein-peptide complex and the subsequent flexible refinement further improves the interface by up to 4.5 Å interface RMSD. Cluster-based scoring of the models results in a selection of near-native solutions in the top three for ∼75% of the successfully predicted cases. This unified conformational selection and induced fit approach to protein-peptide docking should open the route to the modeling of challenging systems such as disorder-order transitions taking place upon binding, significantly expanding the applicability limit of biomolecular interaction modeling by docking.