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The specificity of cross-reactivity: promiscuous antibody binding involves specific hydrogen bonds rather than nonspecific hydrophobic stickiness.

ABSTRACT: Proteins are renowned for their specificity of function. There is, however, accumulating evidence that many proteins, from enzymes to antibodies, are functionally promiscuous. Promiscuity is of considerable physiological importance. In the immune system, cross-reactive or multispecific antibodies are implicated in autoimmune and allergy conditions. In most cases, however, the mechanism behind promiscuity and the relationship between specific and promiscuous activities are unknown. Are the two contradictory? Or can a protein exhibit several unrelated activities each of which is highly specific? To address these questions, we studied a multispecific IgE antibody (SPE7) elicited against a 2,4-dinitrophenyl hapten (DNP). SPE7 is able to distinguish between closely related derivatives such as NP (nitrophenol) and DNP, yet it can also bind a number of unrelated ligands. We find that, like DNP, the cross-reactants are themselves bound specifically-close derivatives of these cross-reactants show very low or no binding to SPE7. It has been suggested that cross-reactivity is simply due to "hydrophobic stickiness", nonspecific interactions between hydrophobic ligands and binding sites. However, partitioning experiments reveal that affinity for SPE7 is unrelated to ligand hydrophobicity. These data, combined with crystal structures of SPE7 in complex with four different ligands, demonstrate that each cross-reactant is bound specifically, forming different hydrogen bonds dependant upon its particular chemistry and the availability of complementary antibody residues. SPE7 is highly homologous to the germline antinitrophenol (NP) antibody B1-8. By comparing the sequences and binding patterns of SPE7 and B1-8, we address the relationship between affinity maturation, specificity, and cross-reactivity.

SUBMITTER: James LC

PROVIDER: S-EPMC2366915 | biostudies-literature | 2003 Oct

REPOSITORIES: biostudies-literature

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The specificity of cross-reactivity: promiscuous antibody binding involves specific hydrogen bonds rather than nonspecific hydrophobic stickiness.

James Leo C LC Tawfik Dan S DS

Protein science : a publication of the Protein Society 20031001 10

Proteins are renowned for their specificity of function. There is, however, accumulating evidence that many proteins, from enzymes to antibodies, are functionally promiscuous. Promiscuity is of considerable physiological importance. In the immune system, cross-reactive or multispecific antibodies are implicated in autoimmune and allergy conditions. In most cases, however, the mechanism behind promiscuity and the relationship between specific and promiscuous activities are unknown. Are the two co ...[more]

PMID: 14500876

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Project description:Membraneless organelles are cellular compartments that form by liquid-liquid phase separation of one or more components. Other molecules, such as proteins and nucleic acids, will distribute between the cytoplasm and the liquid compartment in accordance with the thermodynamic drive to lower the free energy of the system. The resulting distribution colocalizes molecular species to carry out a diversity of functions. Two factors could drive this partitioning: the difference in solvation between the dilute versus dense phase and intermolecular interactions between the client and scaffold proteins. Here, we develop a set of knowledge-based potentials that allow for the direct comparison between stickiness, which is dominated by desolvation energy, and pairwise residue contact propensity terms. We use these scales to examine experimental data from two systems: protein cargo dissolving within phase-separated droplets made from FG repeat proteins of the nuclear pore complex and client proteins dissolving within phase-separated FUS droplets. These analyses reveal a close agreement between the stickiness of the client proteins and the experimentally determined values of the partition coefficients (R > 0.9), while pairwise residue contact propensities between client and scaffold show weaker correlations. Hence, the stickiness of client proteins is sufficient to explain their differential partitioning within these two phase-separated systems without taking into account the composition of the condensate. This result implies that selective trafficking of client proteins to distinct membraneless organelles requires recognition elements beyond the client sequence composition. STATEMENT: Empirical potentials for amino acid stickiness and pairwise residue contact propensities are derived. These scales are unique in that they enable direct comparison of desolvation versus contact terms. We find that partitioning of a client protein to a condensate is best explained by amino acid stickiness.

Project description:Scientists rely on high-throughput screening tools to identify promising small-molecule compounds for the development of biochemical probes and drugs. This study focuses on the identification of promiscuous bioactive compounds, which are compounds that appear active in many high-throughput screening experiments against diverse targets but are often false-positives which may not be easily developed into successful probes. These compounds can exhibit bioactivity due to nonspecific, intractable mechanisms of action and/or by interference with specific assay technology readouts. Such "frequent hitters" are now commonly identified using substructure filters, including pan assay interference compounds (PAINS). Herein, we show that mechanistic modeling of small-molecule reactivity using deep learning can improve upon PAINS filters when modeling promiscuous bioactivity in PubChem assays. Without training on high-throughput screening data, a deep learning model of small-molecule reactivity achieves a sensitivity and specificity of 18.5% and 95.5%, respectively, in identifying promiscuous bioactive compounds. This performance is similar to PAINS filters, which achieve a sensitivity of 20.3% at the same specificity. Importantly, such reactivity modeling is complementary to PAINS filters. When PAINS filters and reactivity models are combined, the resulting model outperforms either method alone, achieving a sensitivity of 24% at the same specificity. However, as a probabilistic model, the sensitivity and specificity of the deep learning model can be tuned by adjusting the threshold. Moreover, for a subset of PAINS filters, this reactivity model can help discriminate between promiscuous and nonpromiscuous bioactive compounds even among compounds matching those filters. Critically, the reactivity model provides mechanistic hypotheses for assay interference by predicting the precise atoms involved in compound reactivity. Overall, our analysis suggests that deep learning approaches to modeling promiscuous compound bioactivity may provide a complementary approach to current methods for identifying promiscuous compounds.

Dataset Information

The specificity of cross-reactivity: promiscuous antibody binding involves specific hydrogen bonds rather than nonspecific hydrophobic stickiness.

Publications

The specificity of cross-reactivity: promiscuous antibody binding involves specific hydrogen bonds rather than nonspecific hydrophobic stickiness.

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