Analysis of antibody A6 binding to the extracellular interferon gamma receptor alpha-chain by alanine-scanning mutagenesis and random mutagenesis with phage display

Computational identification of antibody-binding epitopes from mimotope datasets

Introduction: A fundamental challenge in computational vaccinology is that most B-cell epitopes are conformational and therefore hard to predict from sequence alone. Another significant challenge is that a great deal of the amino acid sequence of a viral surface protein might not in fact be antigenic. Thus, identifying the regions of a protein that are most promising for vaccine design based on the degree of surface exposure may not lead to a clinically relevant immune response. Methods: Linear peptides selected by phage display experiments that have high affinity to the monoclonal antibody of interest ("mimotopes") usually have similar physicochemical properties to the antigen epitope corresponding to that antibody. The sequences of these linear peptides can be used to find possible epitopes on the surface of the antigen structure or a homology model of the antigen in the absence of an antigen-antibody complex structure. Results and Discussion: Herein we describe two novel methods for mapping mimotopes to epitopes. The first is a novel algorithm named MimoTree that allows for gaps in the mimotopes and epitopes on the antigen. More specifically, a mimotope may have a gap that does not match to the epitope to allow it to adopt a conformation relevant for binding to an antibody, and residues may similarly be discontinuous in conformational epitopes. MimoTree is a fully automated epitope detection algorithm suitable for the identification of conformational as well as linear epitopes. The second is an ensemble approach, which combines the prediction results from MimoTree and two existing methods.

Assessment of software methods for estimating protein-protein relative binding affinities

A growing number of computational tools have been developed to accurately and rapidly predict the impact of amino acid mutations on protein-protein relative binding affinities. Such tools have many applications, for example, designing new drugs and studying evolutionary mechanisms. In the search for accuracy, many of these methods employ expensive yet rigorous molecular dynamics simulations. By contrast, non-rigorous methods use less exhaustive statistical mechanics, allowing for more efficient calculations. However, it is unclear if such methods retain enough accuracy to replace rigorous methods in binding affinity calculations. This trade-off between accuracy and computational expense makes it difficult to determine the best method for a particular system or study. Here, eight non-rigorous computational methods were assessed using eight antibody-antigen and eight non-antibody-antigen complexes for their ability to accurately predict relative binding affinities (ΔΔG) for 654 single mutations. In addition to assessing accuracy, we analyzed the CPU cost and performance for each method using a variety of physico-chemical structural features. This allowed us to posit scenarios in which each method may be best utilized. Most methods performed worse when applied to antibody-antigen complexes compared to non-antibody-antigen complexes. Rosetta-based JayZ and EasyE methods classified mutations as destabilizing (ΔΔG < -0.5 kcal/mol) with high (83-98%) accuracy and a relatively low computational cost for non-antibody-antigen complexes. Some of the most accurate results for antibody-antigen systems came from combining molecular dynamics with FoldX with a correlation coefficient (r) of 0.46, but this was also the most computationally expensive method. Overall, our results suggest these methods can be used to quickly and accurately predict stabilizing versus destabilizing mutations but are less accurate at predicting actual binding affinities. This study highlights the need for continued development of reliable, accessible, and reproducible methods for predicting binding affinities in antibody-antigen proteins and provides a recipe for using current methods.

PhoU2 but Not PhoU1 as an Important Regulator of Biofilm Formation and Tolerance to Multiple Stresses by Participating in Various Fundamental Metabolic Processes in Staphylococcus epidermidis

PhoU, a conserved protein that has been proposed to coordinate phosphate import, is a negative regulator of drug tolerance in most bacteria. In Staphylococcus epidermidis, the role of PhoU in biofilm formation and drug tolerance has not yet been investigated. Two PhoU homologs in the genome of S. epidermidis have been identified by the presence of the conserved motif E(D)XXXD of PhoU. We separately constructed ΔphoU1 and ΔphoU2 mutants of S. epidermidis strain 1457. The ΔphoU2 mutant displayed growth retardation, a weakened biofilm formation capacity, a higher sensitivity to H₂O₂, and reduced tolerance to multiple antibiotics. However, deletion of phoU1 had no effect on those. We compared the transcriptome profiles of the ΔphoU2 and ΔphoU1 mutants with that of the parent strain. In the ΔphoU2 mutant, expression of genes related to inorganic phosphate uptake was significantly upregulated (pst operon) and the levels of intracellular inorganic polyphosphate (polyP) were increased. In the ΔphoU2 mutant, expression of enzymes in the pentose phosphate pathway (PPP) was downregulated and less NADP (NADPH) was detected, consistent with the high sensitivity to H₂O₂ and the growth retardation of the ΔphoU2 mutant. The upregulated expression of ATP synthase was consistent with the high intracellular ATP content in the ΔphoU2 mutant, which may have been related to the lower drug tolerance of the ΔphoU2 mutant. This study demonstrates that PhoU2, but not PhoU1, in S. epidermidis regulates bacterial growth, biofilm formation, oxidative stress, and drug tolerance in association with alterations to inorganic phosphate metabolism, the pentose phosphate pathway, galactose metabolism, the tricarboxylic acid (TCA) or citric cycle, glycolysis and gluconeogenesis, and respiratory reactions.

IMPORTANCE PhoU is widely conserved throughout the bacterial kingdom and plays an important role in response to stress and metabolic maintenance. In our study, two PhoU homologs were found in S. epidermidis. The function of phoU2, but not phoU1, in S. epidermidis is related to growth, drug tolerance, the oxidative stress response, polyP levels, and ATP accumulation. In addition, phoU2 regulates biofilm formation. Hence, phoU2 is a regulator of both drug tolerance and biofilm formation, which are two bacterial properties that present major challenges to the clinical treatment of infections. Analysis of differential gene expression revealed that phoU2 is involved in fundamental metabolic processes, such as the PPP pathway. These findings indicate that phoU2 is a crucial regulator in S. epidermidis.

Assessment of Solvated Interaction Energy Function for Ranking Antibody-Antigen Binding Affinities

Affinity modulation of antibodies and antibody fragments of therapeutic value is often required in order to improve their clinical efficacies. Virtual affinity maturation has the potential to quickly focus on the critical hotspot residues without the combinatorial explosion problem of conventional display and library approaches. However, this requires a binding affinity scoring function that is capable of ranking single-point mutations of a starting antibody. We focus here on assessing the solvated interaction energy (SIE) function that was originally developed for and is widely applied to scoring of protein-ligand binding affinities. To this end, we assembled a structure-function data set called Single-Point Mutant Antibody Binding (SiPMAB) comprising several antibody-antigen systems suitable for this assessment, i.e., based on high-resolution crystal structures for the parent antibodies and coupled with high-quality binding affinity measurements for sets of single-point antibody mutants in each system. Using this data set, we tested the SIE function with several mutation protocols based on the popular methods SCWRL, Rosetta, and FoldX. We found that the SIE function coupled with a protocol limited to sampling only the mutated side chain can reasonably predict relative binding affinities with a Spearman rank-order correlation coefficient of about 0.6, outperforming more aggressive sampling protocols. Importantly, this performance is maintained for each of the seven system-specific component subsets as well as for other relevant subsets including non-alanine and charge-altering mutations. The transferability and enrichment in affinity-improving mutants can be further enhanced using consensus ranking over multiple methods, including the SIE, Talaris, and FOLDEF energy functions. The knowledge gained from this study can lead to successful prospective applications of virtual affinity maturation.

Applying physics-based scoring to calculate free energies of binding for single amino acid mutations in protein-protein complexes

Predicting changes in protein binding affinity due to single amino acid mutations helps us better understand the driving forces underlying protein-protein interactions and design improved biotherapeutics. Here, we use the MM-GBSA approach with the OPLS2005 force field and the VSGB2.0 solvent model to calculate differences in binding free energy between wild type and mutant proteins. Crucially, we made no changes to the scoring model as part of this work on protein-protein binding affinity--the energy model has been developed for structure prediction and has previously been validated only for calculating the energetics of small molecule binding. Here, we compare predictions to experimental data for a set of 418 single residue mutations in 21 targets and find that the MM-GBSA model, on average, performs well at scoring these single protein residue mutations. Correlation between the predicted and experimental change in binding affinity is statistically significant and the model performs well at picking "hotspots," or mutations that change binding affinity by more than 1 kcal/mol. The promising performance of this physics-based method with no tuned parameters for predicting binding energies suggests that it can be transferred to other protein engineering problems.

SKEMPI: a Structural Kinetic and Energetic database of Mutant Protein Interactions and its use in empirical models

MOTIVATION

Empirical models for the prediction of how changes in sequence alter protein-protein binding kinetics and thermodynamics can garner insights into many aspects of molecular biology. However, such models require empirical training data and proper validation before they can be widely applied. Previous databases contained few stabilizing mutations and no discussion of their inherent biases or how this impacts model construction or validation.

RESULTS

We present SKEMPI, a database of 3047 binding free energy changes upon mutation assembled from the scientific literature, for protein-protein heterodimeric complexes with experimentally determined structures. This represents over four times more data than previously collected. Changes in 713 association and dissociation rates and 127 enthalpies and entropies were also recorded. The existence of biases towards specific mutations, residues, interfaces, proteins and protein families is discussed in the context of how the data can be used to construct predictive models. Finally, a cross-validation scheme is presented which is capable of estimating the efficacy of derived models on future data in which these biases are not present.

AVAILABILITY

The database is available online at http://life.bsc.es/pid/mutation_database/.

MimoPro: a more efficient Web-based tool for epitope prediction using phage display libraries

Background

A B-cell epitope is a group of residues on the surface of an antigen which stimulates humoral responses. Locating these epitopes on antigens is important for the purpose of effective vaccine design. In recent years, mapping affinity-selected peptides screened from a random phage display library to the native epitope has become popular in epitope prediction. These peptides, also known as mimotopes, share the similar structure and function with the corresponding native epitopes. Great effort has been made in using this similarity between such mimotopes and native epitopes in prediction, which has resulted in better outcomes than statistics-based methods can. However, it cannot maintain a high degree of satisfaction in various circumstances.

Results

In this study, we propose a new method that maps a group of mimotopes back to a source antigen so as to locate the interacting epitope on the antigen. The core of this method is a searching algorithm that is incorporated with both dynamic programming (DP) and branch and bound (BB) optimization and operated on a series of overlapping patches on the surface of a protein. These patches are then transformed to a number of graphs using an adaptable distance threshold (ADT) regulated by an appropriate compactness factor (CF), a novel parameter proposed in this study. Compared with both Pep-3D-Search and PepSurf, two leading graph-based search tools, on average from the results of 18 test cases, MimoPro, the Web-based implementation of our proposed method, performed better in sensitivity, precision, and Matthews correlation coefficient (MCC) than both did in epitope prediction. In addition, MimoPro is significantly faster than both Pep-3D-Search and PepSurf in processing.

Conclusions

Our search algorithm designed for processing well constructed graphs using an ADT regulated by CF is more sensitive and significantly faster than other graph-based approaches in epitope prediction. MimoPro is a viable alternative to both PepSurf and Pep-3D-Search for epitope prediction in the same kind, and freely accessible through the MimoPro server located at http://informatics.nenu.edu.cn/MimoPro.

Prediction of protein interaction hot spots using rough set-based multiple criteria linear programming

Protein-protein interactions are fundamentally important in many biological processes and it is in pressing need to understand the principles of protein-protein interactions. Mutagenesis studies have found that only a small fraction of surface residues, known as hot spots, are responsible for the physical binding in protein complexes. However, revealing hot spots by mutagenesis experiments are usually time consuming and expensive. In order to complement the experimental efforts, we propose a new computational approach in this paper to predict hot spots. Our method, Rough Set-based Multiple Criteria Linear Programming (RS-MCLP), integrates rough sets theory and multiple criteria linear programming to choose dominant features and computationally predict hot spots. Our approach is benchmarked by a dataset of 904 alanine-mutated residues and the results show that our RS-MCLP method performs better than other methods, e.g., MCLP, Decision Tree, Bayes Net, and the existing HotSprint database. In addition, we reveal several biological insights based on our analysis. We find that four features (the change of accessible surface area, percentage of the change of accessible surface area, size of a residue, and atomic contacts) are critical in predicting hot spots. Furthermore, we find that three residues (Tyr, Trp, and Phe) are abundant in hot spots through analyzing the distribution of amino acids.

Pep-3D-Search: a method for B-cell epitope prediction based on mimotope analysis

Background

The prediction of conformational B-cell epitopes is one of the most important goals in immunoinformatics. The solution to this problem, even if approximate, would help in designing experiments to precisely map the residues of interaction between an antigen and an antibody. Consequently, this area of research has received considerable attention from immunologists, structural biologists and computational biologists. Phage-displayed random peptide libraries are powerful tools used to obtain mimotopes that are selected by binding to a given monoclonal antibody (mAb) in a similar way to the native epitope. These mimotopes can be considered as functional epitope mimics. Mimotope analysis based methods can predict not only linear but also conformational epitopes and this has been the focus of much research in recent years. Though some algorithms based on mimotope analysis have been proposed, the precise localization of the interaction site mimicked by the mimotopes is still a challenging task.

Results

In this study, we propose a method for B-cell epitope prediction based on mimotope analysis called Pep-3D-Search. Given the 3D structure of an antigen and a set of mimotopes (or a motif sequence derived from the set of mimotopes), Pep-3D-Search can be used in two modes: mimotope or motif. To evaluate the performance of Pep-3D-Search to predict epitopes from a set of mimotopes, 10 epitopes defined by crystallography were compared with the predicted results from a Pep-3D-Search: the average Matthews correlation oefficient (MCC), sensitivity and precision were 0.1758, 0.3642 and 0.6948. Compared with other available prediction algorithms, Pep-3D-Search showed comparable MCC, specificity and precision, and could provide novel, rational results. To verify the capability of Pep-3D-Search to align a motif sequence to a 3D structure for predicting epitopes, 6 test cases were used. The predictive performance of Pep-3D-Search was demonstrated to be superior to that of other similar programs. Furthermore, a set of test cases with different lengths of sequences was constructed to examine Pep-3D-Search's capability in searching sequences on a 3D structure. The experimental results demonstrated the excellent search capability of Pep-3D-Search, especially when the length of the query sequence becomes longer; the iteration numbers of Pep-3D-Search to precisely localize the target paths did not obviously increase. This means that Pep-3D-Search has the potential to quickly localize the epitope regions mimicked by longer mimotopes.

Conclusion

Our Pep-3D-Search provides a powerful approach for localizing the surface region mimicked by the mimotopes. As a publicly available tool, Pep-3D-Search can be utilized and conveniently evaluated, and it can also be used to complement other existing tools. The data sets and open source code used to obtain the results in this paper are available on-line and as supplementary material. More detailed materials may be accessed at .

Critical contribution of aromatic rings to specific recognition of polyether rings. The case of ciguatoxin CTX3C-ABC and its specific antibody 1C49

To address how proteins recognize polyether toxin compounds, we focused on the interaction between the ABC ring compound of ciguatoxin 3C and its specific antibody, 1C49. Surface plasmon resonance analyses indicated that Escherichia coli-expressed variable domain fragments (Fv) of 1C49 had the high affinity constants and slow dissociation constants typical of antigen-antibody interactions. Linear van't Hoff analyses suggested that the interaction is enthalpy-driven. We resolved the crystal structure of 1C49 Fv bound to ABC ring compound of ciguatoxin 3C at a resolution of 1.7A. The binding pocket of the antibody had many aromatic rings and bound the antigen by shape complementarity typical of hapten-antibody interactions. Three hydrogen bonds and many van der Waals interactions were present. We mutated several residues of the antibody to Ala, and we used surface plasmon resonance to analyze the interactions between the mutated antibodies and the antigen. This analysis identified Tyr-91 and Trp-96 in the light chain as hot spots for the interaction, and other residues made incremental contributions by conferring enthalpic advantages and reducing the dissociation rate constant. Systematic mutation of Tyr-91 indicated that CH-pi and pi-pi interactions between the aromatic ring at this site and the antigen made substantial contributions to the association, and van der Waals interactions inhibited dissociation, suggesting that aromaticity and bulkiness are critical for the specific recognition of polyether compounds by proteins.

Exploring the capacity of minimalist protein interfaces: interface energetics and affinity maturation to picomolar KD of a single-domain antibody with a flat paratope

A major architectural class in engineered binding proteins ("antibody mimics") involves the presentation of recognition loops off a single-domain scaffold. This class of binding proteins, both natural and synthetic, has a strong tendency to bind a preformed cleft using a convex binding interface (paratope). To explore their capacity to produce high-affinity interfaces with diverse shape and topography, we examined the interface energetics and explored the affinity limit achievable with a flat paratope. We chose a minimalist paratope limited to two loops found in a natural camelid heavy-chain antibody (VHH) that binds to ribonuclease A. Ala scanning of the VHH revealed only three "hot spot" side chains and additional four residues important for supporting backbone-mediated interactions. The small number of critical residues suggested that this is not an optimized paratope. Using selection from synthetic combinatorial libraries, we enhanced its affinity by >100-fold, resulting in variants with Kd as low as 180 pM with no detectable loss of binding specificity. High-resolution crystal structures revealed that the mutations induced only subtle structural changes but extended the network of interactions. This resulted in an expanded hot spot region including four additional residues located at the periphery of the paratope with a concomitant loss of the so-called "O-ring" arrangement of energetically inert residues. These results suggest that this class of simple, single-domain scaffolds is capable of generating high-performance binding interfaces with diverse shape. More generally, they suggest that highly functional interfaces can be designed without closely mimicking natural interfaces.

Broadly neutralizing anti-hepatitis B virus antibody reveals a complementarity determining region H3 lid-opening mechanism

The humanized monoclonal antibody HzKR127 recognizes the preS1 domain of the human hepatitis B virus surface proteins with a broadly neutralizing activity in vivo. We present the crystal structures of HzKR127 Fab and its complex with a major epitope peptide. In the complex structure, the bound peptide forms a type IV beta-turn followed by 3(10) helical turn, the looped-out conformation of which provides a structural basis for broad neutralization. Upon peptide binding, the antibody undergoes a dramatic complementarity determining region H3 lid opening. To understand the structural implication of the virus neutralization, we carried out comprehensive alanine-scanning mutagenesis of all complementarity determining region residues in HzKR127 Fab. The functional mapping of the antigen-combining site demonstrates the specific roles of major binding determinants in antigen binding, contributing to the rational design for maximal humanization and affinity maturation of the antibody.

Investigation of de novo totally random biosequences, Part I: A general method for in vitro selection of folded domains from a random polypeptide library displayed on phage

This paper reports the initial phase of a research aimed at investigating the folding frequency within a large library of polypeptides generated with a totally random sequence by phage-display technique. Resistance to proteolytic digestion has been used as a first, rudimentary folding criterion. The present paper describes, in particular, the development of a phage-display vector which has a selectable N-terminal affinity tag so that, after controlled proteolysis, the tag is cleaved from the phage. This enables the positive selection of phages that carry proteolytically resistant proteins. To test this system, avian pancreatic polypeptide (APP), one of the smallest proteins with a known structure, was chosen as a model, and its gene was inserted in a plasmid that was then used for phage display. A sequence of three amino acids, corresponding to a substrate for thrombin, was introduced at different locations within the APP sequence without significantly modifying the tertiary structure, as determined by circular dichroism (CD) analysis. These sequences were then used to show that the target tripeptide sequence was protected against proteolysis by the overall folding of the chain. Thus, these results show that the method permits the discrimination between folded and unfolded protein domains displayed on phage. The application of this protocol to a large library of totally random polypeptide chains is discussed as a preliminary to successive work, dealing with the production of totally random polypeptide sequences.

Epitope mapping using combinatorial phage-display libraries: a graph-based algorithm

A phage-display library of random peptides is a combinatorial experimental technique that can be harnessed for studying antibody–antigen interactions. In this technique, a phage peptide library is scanned against an antibody molecule to obtain a set of peptides that are bound by the antibody with high affinity. This set of peptides is regarded as mimicking the genuine epitope of the antibody's interacting antigen and can be used to define it. Here we present PepSurf, an algorithm for mapping a set of affinity-selected peptides onto the solved structure of the antigen. The problem of epitope mapping is converted into the task of aligning a set of query peptides to a graph representing the surface of the antigen. The best match of each peptide is found by aligning it against virtually all possible paths in the graph. Following a clustering step, which combines the most significant matches, a predicted epitope is inferred. We show that PepSurf accurately predicts the epitope in four cases for which the epitope is known from a solved antibody–antigen co-crystal complex. We further examine the capabilities of PepSurf for predicting other types of protein–protein interfaces. The performance of PepSurf is compared to other available epitope mapping programs.

Investigation ofde novo Totally Random Biosequences, Part III

Fold is essential to RNA properties, and, in particular, its thermodynamic stability can be used to monitor RNA-protein or RNA-ligand interactions, and to engineer RNA with novel or improved properties. While clearly valuable, experimental determination of RNA folding stability by traditional biophysical techniques requires substantial amounts of pure sample and rather expensive equipment. In this paper, we report a new, simple approach to the determination of RNA folding stability by coupling enzymatic digestion and temperature denaturation. The assay, named RNA folding stability Test (RNA Foster), is designed to probe the fraction of folded RNA (f(fold)) in an equilibrium mixture of folded and unfolded ones as a function of temperature. The simplicity of RNA Foster suggests that it can easily be scaled up for high-throughput studies of RNA folding stability both in basic and applied research.

Probing a protein-protein interaction by in vitro evolution

In this study, we used in vitro protein evolution with ribosome and phage display to optimize the affinity of a human IL-13-neutralizing antibody, a therapeutic candidate for the treatment of asthma, >150-fold to 81 pM by using affinity-driven stringency selections. Simultaneously, the antibody potency to inhibit IL-13-dependent proliferation in a cell-based functional assay increased 345-fold to an IC50 of 229 pM. The panoply of different optimized sequences resulting from complementarity-determining region-targeted mutagenesis and error-prone PCR using ribosome display was contrasted with that of complementarity-determining region-targeted mutagenesis alone using phage display. The data highlight the advantage of the ribosome-display approach in identifying beneficial mutations across the entire sequence space. A comparison of mutation hotspots from in vitro protein evolution to knockout mutations from alanine scanning demonstrated that in vitro evolution selects the most appropriate positions for improvements in potency without mutating any of the key residues within the functional paratope.

Alanine scanning of MS2 coat protein reveals protein-phosphate contacts involved in thermodynamic hot spots

The co-crystal structure of the MS2 coat protein dimer with its RNA operator reveals eight amino acid side-chains contacting seven of the RNA phosphates. These eight amino acids and five nearby control positions were individually changed to an alanine residue and the binding affinities of the mutant proteins to the RNA were determined. In general, the data agreed well with the crystal structure and previous RNA modification data. Interestingly, amino acid residues that are energetically most important for complex formation cluster in the middle of the RNA binding interface, forming thermodynamic hot spots, and are surrounded by energetically less relevant amino acids. In order to evaluate whether or not a given alanine mutation causes a global change in the RNA-protein interface, the affinities of the mutant proteins to RNAs containing one of 14 backbone modifications spanning the entire interface were determined. In three of six protein mutations tested, thermodynamic coupling between the site of the mutation and RNA groups that can be even more than 16 A away was detected. This suggests that, in some cases, the mutation may subtly alter the entire protein-RNA interface.

Epitope map for a growth hormone receptor agonist monoclonal antibody, MAb 263

Monoclonal antibody (MAb) 263 is a widely used monoclonal antibody that recognizes the extracellular domain (ECD) of the GH receptor. It has been shown to act as a GH agonist both in vitro and in vivo, and we report here that it must be divalent to exert its effect on the full-length receptor. To understand the mechanism of its agonist action, we have determined the precise epitope for this antibody using a novel random PCR mutagenesis approach together with expression screening in yeast. A library of 5200 clones of rabbit GH receptor ECD mutants were screened both with MAb 263 and with an anticarboxy-tag antibody to verify complete ECD expression. Sequencing for clones that expressed complete ECD but were not MAb 263 positive identified 20 epitope residues distributed in a discontinuous manner throughout the ECD. The major part of the epitope, as revealed after mapping onto the crystal structure model of the ECD molecule, was located on the side and upper portion of domain 1, particularly within the D-E strand disulfide loop 79-96. Molecular dynamics docking of an antibody of the same isotype as MAb 263 was used to dock the bivalent antibody to the 1528-A2 epitope and to visualize the likely consequences of MAb binding. The minimized model enables the antibody to grasp two receptors in a pincer-like movement from opposite sides, facilitating alignment of the receptor dimerization domains in a manner similar to, but not identical with, GH.

SiteLight: binding-site prediction using phage display libraries

Phage display enables the presentation of a large number of peptides on the surface of phage particles. Such libraries can be tested for binding to target molecules of interest by means of affinity selection. Here we present SiteLight, a novel computational tool for binding site prediction using phage display libraries. SiteLight is an algorithm that maps the 1D peptide library onto a three-dimensional (3D) protein surface. It is applicable to complexes made up of a protein Template and any type of molecule termed Target. Given the three-dimensional structure of a Template and a collection of sequences derived from biopanning against the Target, the Template interaction site with the Target is predicted. We have created a large diverse data set for assessing the ability of SiteLight to correctly predict binding sites. SiteLight predictive mapping enables discrimination between the binding and nonbinding parts of the surface. This prediction can be used to effectively reduce the surface by 75% without excluding the binding site. In 63% of the cases we have tested, there is at least one binding site prediction that overlaps the interface by at least 50%. These results suggest the applicability of phage display libraries for automated binding site prediction on three-dimensional structures. For most effective binding site prediction we propose using a random phage display library twice, to scan both binding partners of a given complex. The derived peptides are mapped to the other binding partner (now used as a Template). Here, the surface of each partner is reduced by 75%, focusing their relative positions with respect to each other significantly. Such information can be utilized to improve docking algorithms and scoring functions.

Localization of the discontinuous immunodominant region recognized by human anti-thyroperoxidase autoantibodies in autoimmune thyroid diseases

The discontinuous immunodominant region (IDR) recognized by autoantibodies directed against the thyroperoxidase (TPO) molecule, a major autoantigen in autoimmune thyroid diseases, has not yet been completely localized. By using peptide phage-displayed technology, we identified three critical motifs, LXPEXD, QSYP, and EX(E/D)PPV, within selected mimotopes which interacted with the human recombinant anti-TPO autoantibody (aAb) T13, derived from an antibody phage-displayed library obtained from thyroid-infiltrating TPO-selected B cells of Graves' disease patients. Mimotope sequence alignment on the TPO molecule, together with the binding analysis of the T13 aAb on TPO mutants expressed by Chinese hamster ovary cells, demonstrated that regions 353-363, 377-386, and 713-720 from the myeloperoxidase-like domain and region 766-775 from the complement control protein-like domain are a part of the IDR recognized by the recombinant aAb T13. Furthermore, we demonstrated that these regions were involved in the binding to TPO of sera containing TPO-specific autoantibodies from patients suffering from Hashimoto's and Graves' autoimmune diseases. Identification of the IDR could lead to improved diagnosis of thyroid autoimmune diseases by engineering "mini-TPO" as a target autoantigen or designing therapeutic peptides able to block undesired autoimmune responses.

Contacts between influenza virus N9 neuraminidase and monoclonal antibody NC10

We are interested in studying how influenza virus escapes antibody inhibition. Based on the structure of the complex between N9 NA and monoclonal antibody NC10 Fab (R. L. Malby, W. R. Tulip, V. R. Harley, J. L. McKimm-Breschkin, W. G. Laver, R. G. Webster, and P. M. Colman, 1994, Structure 2, 733-746), we investigated the contribution made by individual amino acids to the stability of the complex. We made conservative changes in residues that are centrally located in the epitope and more drastic changes in peripheral contacts. The mutations made were N200L (removing an N-linked oligosaccharide), N329Q, N345Q, S370T, S372A, N400L, and K432M. Binding of each mutant to NC10 was quantitated by NA inhibition assays and ELISA. Except for N200L and N329Q, the mutants were inhibited by NC10 to the same extent as wild-type NA although with less affinity. The enzyme activity (K(cat)) of N200L is 80% reduced, indicating a defect in folding or assembly; therefore, the loss in binding activity due to the missing sugar residue cannot be assessed. The K(d) for N329Q is sixfold higher than for wild-type NA in the inhibition test, but the same as wild-type in ELISA, indicating a change in disposition of the antibody but no loss of affinity. The results show that the NC10 epitope can accommodate a change at any site and is not dominated by a few high-energy interactions as was found in the NC41 epitope. We propose that the difference lies in the contribution of buried water molecules to the NA-NC10 complex.

Survey of the year 2000 commercial optical biosensor literature

We have compiled a comprehensive list of the articles published in the year 2000 that describe work employing commercial optical biosensors. Selected reviews of interest for the general biosensor user are highlighted. Emerging applications in areas of drug discovery, clinical support, food and environment monitoring, and cell membrane biology are emphasized. In addition, the experimental design and data processing steps necessary to achieve high-quality biosensor data are described and examples of well-performed kinetic analysis are provided.