Accurate profiling of full-length Fv in highly homologous antibody libraries using UMI tagged short reads.

Deep parallel sequencing (NGS) is a viable tool for monitoring scFv and Fab library dynamics in many antibody engineering high-throughput screening efforts. Although very useful, the commonly used Illumina NGS platform cannot handle the entire sequence of scFv or Fab in a single read, usually focusing on specific CDRs or resorting to sequencing VH and VL variable domains separately, thus limiting its utility in comprehensive monitoring of selection dynamics. Here we present a simple and robust method for deep sequencing repertoires of full length scFv, Fab and Fv antibody sequences. This process utilizes standard molecular procedures and unique molecular identifiers (UMI) to pair separately sequenced VH and VL. We show that UMI assisted VH-VL matching allows for a comprehensive and highly accurate mapping of full length Fv clonal dynamics in large highly homologous antibody libraries, as well as identification of rare variants. In addition to its utility in synthetic antibody discovery processes, our method can be instrumental in generating large datasets for machine learning (ML) applications, which in the field of antibody engineering has been hampered by conspicuous paucity of large scale full length Fv data.

DistilProtBert: a distilled protein language model used to distinguish between real proteins and their randomly shuffled counterparts.

SUMMARY: Recently, deep learning models, initially developed in the field of natural language processing (NLP), were applied successfully to analyze protein sequences. A major drawback of these models is their size in terms of the number of parameters needed to be fitted and the amount of computational resources they require. Recently, 'distilled' models using the concept of student and teacher networks have been widely used in NLP. Here, we adapted this concept to the problem of protein sequence analysis, by developing DistilProtBert, a distilled version of the successful ProtBert model. Implementing this approach, we reduced the size of the network and the running time by 50%, and the computational resources needed for pretraining by 98% relative to ProtBert model. Using two published tasks, we showed that the performance of the distilled model approaches that of the full model. We next tested the ability of DistilProtBert to distinguish between real and random protein sequences. The task is highly challenging if the composition is maintained on the level of singlet, doublet and triplet amino acids. Indeed, traditional machine-learning algorithms have difficulties with this task. Here, we show that DistilProtBert preforms very well on singlet, doublet and even triplet-shuffled versions of the human proteome, with AUC of 0.92, 0.91 and 0.87, respectively. Finally, we suggest that by examining the small number of false-positive classifications (i.e. shuffled sequences classified as proteins by DistilProtBert), we may be able to identify de novo potential natural-like proteins based on random shuffling of amino acid sequences. AVAILABILITY AND IMPLEMENTATION: https://github.com/yarongef/DistilProtBert.

How far from the SNP may the causative genes be?

While GWAS identify many disease-associated SNPs, using them to decipher disease mechanisms is hindered by the difficulty in mapping SNPs to genes. Most SNPs are in non-coding regions and it is often hard to identify the genes they implicate. To explore how far the SNP may be from the affected genes we used a pathway-based approach. We found that affected genes are often up to 2 Mbps away from the associated SNP, and are not necessarily the closest genes to the SNP. Existing approaches for mapping SNPs to genes leave many SNPs unmapped to genes and reveal only 86 significant phenotype-pathway associations for all known GWAS hits combined. Using the pathway-based approach we propose here allows mapping of virtually all SNPs to genes and reveals 435 statistically significant phenotype-pathway associations. In search for mechanisms that may explain the relationships between SNPs and distant genes, we found that SNPs that are mapped to distant genes have significantly more large insertions/deletions around them than other SNPs, suggesting that these SNPs may sometimes be markers for large insertions/deletions that may affect large genomic regions.

Integrating genomic data from high-throughput studies with computational modeling reveals differences in the molecular basis of hyposalivation between type 1 and type 2 diabetes.

OBJECTIVES: Both type 1 and type 2 diabetes are accompanied by a high prevalence of hyposalivation (decreased salivary secretion), resulting in oral tissue damage. However, the molecular basis for the hyposalivation is yet unknown. Identifying genes and proteins that account for diabetes-related hyposalivation will help understanding the basis for this condition and identifying disease biomarkers in saliva. MATERIALS AND METHODS: We integrated genomic data from 110 high-throughput studies with computational modeling, to explore the relationship between diabetes and salivary glands on a genomic scale. RESULTS: A significant overlap exists between genes that are altered in both types of diabetes and genes that are expressed in salivary glands; 87 type 1 diabetes and 34 type 2 diabetes associated genes are also common to salivary glands. However, the overlap between these genes is not significant. CONCLUSIONS: Type 1 and type 2 diabetes associated genes are involved in the salivary secretion process, but mostly at different parts of it. This suggests that type 1 and type 2 diabetes impair salivary secretion by affecting different processes in the salivary tissue. CLINICAL RELEVANCE: The genomic characteristics of Type 1 and type 2 diabetes may explain differences in salivary gland tissues morphology and saliva composition in people with diabetes, and suggest candidate proteins for diabetes salivary biomarkers.

Working toward precision medicine: Predicting phenotypes from exomes in the Critical Assessment of Genome Interpretation (CAGI) challenges.

Precision medicine aims to predict a patient's disease risk and best therapeutic options by using that individual's genetic sequencing data. The Critical Assessment of Genome Interpretation (CAGI) is a community experiment consisting of genotype-phenotype prediction challenges; participants build models, undergo assessment, and share key findings. For CAGI 4, three challenges involved using exome-sequencing data: Crohn's disease, bipolar disorder, and warfarin dosing. Previous CAGI challenges included prior versions of the Crohn's disease challenge. Here, we discuss the range of techniques used for phenotype prediction as well as the methods used for assessing predictive models. Additionally, we outline some of the difficulties associated with making predictions and evaluating them. The lessons learned from the exome challenges can be applied to both research and clinical efforts to improve phenotype prediction from genotype. In addition, these challenges serve as a vehicle for sharing clinical and research exome data in a secure manner with scientists who have a broad range of expertise, contributing to a collaborative effort to advance our understanding of genotype-phenotype relationships.

PEASE: predicting B-cell epitopes utilizing antibody sequence.

UNLABELLED: Antibody epitope mapping is a key step in understanding antibody-antigen recognition and is of particular interest for drug development, diagnostics and vaccine design. Most computational methods for epitope prediction are based on properties of the antigen sequence and/or structure, not taking into account the antibody for which the epitope is predicted. Here, we introduce PEASE, a web server predicting antibody-specific epitopes, utilizing the sequence of the antibody. The predictions are provided both at the residue level and as patches on the antigen structure. The tradeoff between recall and precision can be tuned by the user, by changing the default parameters. The results are provided as text and HTML files as well as a graph, and can be viewed on the antigen 3D structure. AVAILABILITY AND IMPLEMENTATION: PEASE is freely available on the web at www.ofranlab.org/PEASE. CONTACT: yanay@ofranlab.org.

Potent neutralization of vaccinia virus by divergent murine antibodies targeting a common site of vulnerability in L1 protein.

UNLABELLED: Vaccinia virus (VACV) L1 is an important target for viral neutralization and has been included in multicomponent DNA or protein vaccines against orthopoxviruses. To further understand the protective mechanism of the anti-L1 antibodies, we generated five murine anti-L1 monoclonal antibodies (MAbs), which clustered into 3 distinct epitope groups. While two groups of anti-L1 failed to neutralize, one group of 3 MAbs potently neutralized VACV in an isotype- and complement-independent manner. This is in contrast to neutralizing antibodies against major VACV envelope proteins, such as H3, D8, or A27, which failed to completely neutralize VACV unless the antibodies are of complement-fixing isotypes and complement is present. Compared to nonneutralizing anti-L1 MAbs, the neutralization antibodies bound to the recombinant L1 protein with a significantly higher affinity and also could bind to virions. By using a variety of techniques, including the isolation of neutralization escape mutants, hydrogen/deuterium exchange mass spectrometry, and X-ray crystallography, the epitope of the neutralizing antibodies was mapped to a conformational epitope with Asp35 as the key residue. This epitope is similar to the epitope of 7D11, a previously described potent VACV neutralizing antibody. The epitope was recognized mainly by CDR1 and CDR2 of the heavy chain, which are highly conserved among antibodies recognizing the epitope. These antibodies, however, had divergent light-chain and heavy-chain CDR3 sequences. Our study demonstrates that the conformational L1 epitope with Asp35 is a common site of vulnerability for potent neutralization by a divergent group of antibodies. IMPORTANCE: Vaccinia virus, the live vaccine for smallpox, is one of the most successful vaccines in human history, but it presents a level of risk that has become unacceptable for the current population. Studying the immune protection mechanism of smallpox vaccine is important for understanding the basic principle of successful vaccines and the development of next-generation, safer vaccines for highly pathogenic orthopoxviruses. We studied antibody targets in smallpox vaccine by developing potent neutralizing antibodies against vaccinia virus and comprehensively characterizing their epitopes. We found a site in vaccinia virus L1 protein as the target of a group of highly potent murine neutralizing antibodies. The analysis of antibody-antigen complex structure and the sequences of the antibody genes shed light on how these potent neutralizing antibodies are elicited from immunized mice.

Computational identification of antigen-binding antibody fragments.

Determining which parts of the Ab are essential for Ag recognition and binding is crucial for understanding B cell-mediated immunity. Identification of fragments of Abs that maintain specificity to the Ag will also allow for the development of improved Ab-based therapy and diagnostics. In this article, we show that structural analysis of Ab-Ag complexes reveals which fragments of the Ab may bind the Ag on their own. In particular, it is possible to predict whether a given CDR is likely to bind the Ag as a peptide by analyzing the energetic contribution of each CDR to Ag binding and by assessing to what extent the interaction between that CDR and the Ag depends on other CDRs. To demonstrate this, we analyzed five Ab-Ag complexes and predicted for each of them which of the CDRs may bind the Ag on its own as a peptide. We then show that these predictions are in agreement with our experimental analysis and with previously published experimental results. These findings promote our understanding of the modular nature of Ab-Ag interactions and lay the foundation for the rational design of active CDR-derived peptides.

Non-local residue-residue contacts in proteins are more conserved than local ones.

Non-covalent residue-residue contacts drive the folding of proteins and stabilize them. They may be local-i.e. involve residues that are close in sequence, or non-local. It has been suggested that, in most proteins, local contacts drive protein folding by providing crucial constraints of the conformational space, thus allowing proteins to fold. We compared residues that are involved in local contacts to residues that are involved in non-local contacts and found that, in most proteins, residues in non-local contacts are significantly more conserved evolutionarily than residues in local contacts. Moreover, non-local contacts are more structurally conserved: a contact between positions that are distant in sequence is more likely to exist in many structural homologues compared with a contact between positions that are close in sequence. These results provide new insights into the mechanisms of protein folding and may allow for better prediction of critical intra-chain contacts.

A systematic comparison of free and bound antibodies reveals binding-related conformational changes.

To study structural changes that occur in Abs upon Ag binding, we systematically compared free and bound structures of all 141 crystal structures of the 49 Abs that were solved in these two forms. We found that many structural changes occur far from the Ag binding site. Some of them may constitute a mechanism for the recently suggested allosteric effects in Abs. Within the binding site itself, CDR-H3 is the only element that shows significant binding-related conformational changes; however, this occurs in only one third of the Abs. Beyond the binding site, Ag binding is associated with changes in the relative orientation of the H and L chains in both the variable and constant domains. An even larger change occurs in the elbow angle between the variable and the constant domains, and it is significantly larger for binding of big Ags than for binding of small ones. The most consistent and substantial conformational changes occur in a loop in the H chain constant domain. This loop is implicated in the interaction between the H and L chains, is often intrinsically disordered, and is involved in complement binding. Hence, we suggest that it may have a role in Ab function. These findings provide structural insight into the recently proposed allosteric effects in Abs.

Paratome: an online tool for systematic identification of antigen-binding regions in antibodies based on sequence or structure.

Antibodies are capable of specifically recognizing and binding antigens. Identification of the antigen-binding site, commonly dubbed paratope, is of high importance both for medical and biological applications. To date, the identification of antigen-binding regions (ABRs) relies on tools for the identification of complementarity-determining regions (CDRs). However, we have shown that up to 22% of the residues that actually bind the antigen fall outside the traditionally defined CDRs. The Paratome web server predicts the ABRs of an antibody, given its amino acid sequence or 3D structure. It is based on a set of consensus regions derived from a structural alignment of a non-redundant set of all known antibody-antigen complexes. Given a query sequence or structure, the server identifies the regions in the query antibody that correspond to the consensus ABRs. An independent set of antibody-antigen complexes was used to test the server and it was shown to correctly identify at least 94% of the antigen-binding residues. The Paratome web server is freely available at http://www.ofranlab.org/paratome/.

Lateral acquisition of genes is affected by the friendliness of their products.

A major factor in the evolution of microbial genomes is the lateral acquisition of genes that evolved under the functional constraints of other species. Integration of foreign genes into a genome that has different components and circuits poses an evolutionary challenge. Moreover, genes belonging to complex modules in the pretransfer species are unlikely to maintain their functionality when transferred alone to new species. Thus, it is widely accepted that lateral gene transfer favors proteins with only a few protein-protein interactions. The propensity of proteins to participate in protein-protein interactions can be assessed using computational methods that identify putative interaction sites on the protein. Here we report that laterally acquired proteins contain significantly more putative interaction sites than native proteins. Thus, genes encoding proteins with multiple protein-protein interactions may in fact be more prone to transfer than genes with fewer interactions. We suggest that these proteins have a greater chance of forming new interactions in new species, thus integrating into existing modules. These results reveal basic principles for the incorporation of novel genes into existing systems.

Assessing the relationship between conservation of function and conservation of sequence using photosynthetic proteins.

MOTIVATION: Assessing the false positive rate of function prediction methods is difficult, as it is hard to establish that a protein does not have a certain function. To determine to what extent proteins with similar sequences have a common function, we focused on photosynthesis-related proteins. A protein that comes from a non-photosynthetic organism is, undoubtedly, not involved in photosynthesis. RESULTS: We show that function diverges very rapidly: 70% of the close homologs of photosynthetic proteins come from non-photosynthetic organisms. Therefore, high sequence similarity, in most cases, is not tantamount to similar function. However, we found that many functionally similar proteins often share short sequence elements, which may correspond to a functional site and could reveal functional similarities more accurately than sequence similarity. CONCLUSIONS: These results shed light on the way biological function is conserved in evolution and may help improve large-scale analysis of protein function.

Static network structure can be used to model the phenotypic effects of perturbations in regulatory networks.

MOTIVATION: Biological processes are dynamic, whereas the networks that depict them are typically static. Quantitative modeling using differential equations or logic-based functions can offer quantitative predictions of the behavior of biological systems, but they require detailed experimental characterization of interaction kinetics, which is typically unavailable. To determine to what extent complex biological processes can be modeled and analyzed using only the static structure of the network (i.e. the direction and sign of the edges), we attempt to predict the phenotypic effect of perturbations in biological networks from the static network structure. RESULTS: We analyzed three networks from different sources: The EGFR/MAPK and PI3K/AKT network from a detailed experimental study, the TNF regulatory network from the STRING database and a large network of all NCI-curated pathways from the Protein Interaction Database. Altogether, we predicted the effect of 39 perturbations (e.g. by one or two drugs) on 433 target proteins/genes. In up to 82% of the cases, an algorithm that used only the static structure of the network correctly predicted whether any given protein/gene is upregulated or downregulated as a result of perturbations of other proteins/genes. CONCLUSION: While quantitative modeling requires detailed experimental data and heavy computations, which limit its scalability for large networks, a wiring-based approach can use available data from pathway and interaction databases and may be scalable. These results lay the foundations for a large-scale approach of predicting phenotypes based on the schematic structure of networks.

Structural consensus among antibodies defines the antigen binding site.

The Complementarity Determining Regions (CDRs) of antibodies are assumed to account for the antigen recognition and binding and thus to contain also the antigen binding site. CDRs are typically discerned by searching for regions that are most different, in sequence or in structure, between different antibodies. Here, we show that ~20% of the antibody residues that actually bind the antigen fall outside the CDRs. However, virtually all antigen binding residues lie in regions of structural consensus across antibodies. Furthermore, we show that these regions of structural consensus which cover the antigen binding site are identifiable from the sequence of the antibody. Analyzing the predicted contribution of antigen binding residues to the stability of the antibody-antigen complex, we show that residues that fall outside of the traditionally defined CDRs are at least as important to antigen binding as residues within the CDRs, and in some cases, they are even more important energetically. Furthermore, antigen binding residues that fall outside of the structural consensus regions but within traditionally defined CDRs show a marginal energetic contribution to antigen binding. These findings allow for systematic and comprehensive identification of antigen binding sites, which can improve the understanding of antigenic interactions and may be useful in antibody engineering and B-cell epitope identification.

"Redirecting an anti-IL-1ß antibody to bind a new, unrelated and computationally predicted epitope on hIL-17A".

Antibody engineering technology is at the forefront of therapeutic antibody development. The primary goal for engineering a therapeutic antibody is the generation of an antibody with a desired specificity, affinity, function, and developability profile. Mature antibodies are considered antigen specific, which may preclude their use as a starting point for antibody engineering. Here, we explore the plasticity of mature antibodies by engineering novel specificity and function to a pre-selected antibody template. Using a small, focused library, we engineered AAL160, an anti-IL-1ß antibody, to bind the unrelated antigen IL-17A, with the introduction of seven mutations. The final redesigned antibody, 11.003, retains favorable biophysical properties, binds IL-17A with sub-nanomolar affinity, inhibits IL-17A binding to its cognate receptor and is functional in a cell-based assay. The epitope of the engineered antibody can be computationally predicted based on the sequence of the template antibody, as is confirmed by the crystal structure of the 11.003/IL-17A complex. The structures of the 11.003/IL-17A and the AAL160/IL-1ß complexes highlight the contribution of germline residues to the paratopes of both the template and re-designed antibody. This case study suggests that the inherent plasticity of antibodies allows for re-engineering of mature antibodies to new targets, while maintaining desirable developability profiles.

Targeting the actin nucleation promoting factor WASp provides a therapeutic approach for hematopoietic malignancies.

Cancer cells depend on actin cytoskeleton rearrangement to carry out hallmark malignant functions including activation, proliferation, migration and invasiveness. Wiskott-Aldrich Syndrome protein (WASp) is an actin nucleation-promoting factor and is a key regulator of actin polymerization in hematopoietic cells. The involvement of WASp in malignancies is incompletely understood. Since WASp is exclusively expressed in hematopoietic cells, we performed in silico screening to identify small molecule compounds (SMCs) that bind WASp and promote its degradation. We describe here one such identified molecule; this WASp-targeting SMC inhibits key WASp-dependent actin processes in several types of hematopoietic malignancies in vitro and in vivo without affecting naïve healthy cells. This small molecule demonstrates limited toxicity and immunogenic effects, and thus, might serve as an effective strategy to treat specific hematopoietic malignancies in a safe and precisely targeted manner.

Large-scale analysis of secondary structure changes in proteins suggests a role for disorder-to-order transitions in nucleotide binding proteins.

Conformational changes in proteins often involve secondary structure transitions. Such transitions can be divided into two types: disorder-to-order changes, in which a disordered segment acquires an ordered secondary structure (e.g., disorder to alpha-helix, disorder to beta-strand), and order-to-order changes, where a segment switches from one ordered secondary structure to another (e.g., alpha-helix to beta-strand, alpha-helix to turn). In this study, we explore the distribution of these transitions in the proteome. Using a comprehensive, yet highly conservative method, we compared solved three-dimensional structures of identical protein sequences, looking for differences in the secondary structures with which they were assigned. Protein chains in which such secondary structure transitions were detected, were classified into two sets according to the type of transition that is involved (disorder-to-order or order-to-order), allowing us to characterize each set by examining enrichment of gene ontology terms. The results reveal that the disorder-to-order set is significantly enriched with nucleotide binding proteins, whereas the order-to-order set is more diverse. Remarkably, further examination reveals that >22% of the purine nucleotide binding proteins include segments which undergo disorder-to-order transitions, suggesting that such transitions play an important role in this process.

Automated identification of complementarity determining regions (CDRs) reveals peculiar characteristics of CDRs and B cell epitopes.

Exact identification of complementarity determining regions (CDRs) is crucial for understanding and manipulating antigenic interactions. One way to do this is by marking residues on the antibody that interact with B cell epitopes on the antigen. This, of course, requires identification of B cell epitopes, which could be done by marking residues on the antigen that bind to CDRs, thus requiring identification of CDRs. To circumvent this vicious circle, existing tools for identifying CDRs are based on sequence analysis or general biophysical principles. Often, these tools, which are based on partial data, fail to agree on the boundaries of the CDRs. Herein we present an automated procedure for identifying CDRs and B cell epitopes using consensus structural regions that interact with the antigens in all known antibody-protein complexes. Consequently, we provide the first comprehensive analysis of all CDR-epitope complexes of known three-dimensional structure. The CDRs we identify only partially overlap with the regions suggested by existing methods. We found that the general physicochemical properties of both CDRs and B cell epitopes are rather peculiar. In particular, only four amino acids account for most of the sequence of CDRs, and several types of amino acids almost never appear in them. The secondary structure content and the conservation of B cell epitopes are found to be different than previously thought. These characteristics of CDRs and epitopes may be instrumental in choosing which residues to mutate in experimental search for epitopes. They may also assist in computational design of antibodies and in predicting B cell epitopes.

Erratum.

[This corrects the article on p. e1000160 in vol. 4, PMID: 18974821.].