PhosIDP: a web tool to visualize the location of phosphorylation sites in disordered regions.

Charge is a key determinant of intrinsically disordered protein (IDP) and intrinsically disordered region (IDR) properties. IDPs and IDRs are enriched in sites of phosphorylation, which alters charge. Visualizing the degree to which phosphorylation modulates the charge profile of a sequence would assist in the functional interpretation of IDPs and IDRs. PhosIDP is a web tool that shows variation of charge and fold propensity upon phosphorylation. In combination with the displayed location of protein domains, the information provided by the web tool can lead to functional inferences for the consequences of phosphorylation. IDRs are components of many proteins that form biological condensates. It is shown that IDR charge, and its modulation by phosphorylation, is more tightly controlled for proteins that are essential for condensate formation than for those present in condensates but inessential.

Protein-sol pKa: prediction of electrostatic frustration, with application to coronaviruses.

MOTIVATION: Evolution couples differences in ambient pH to biological function through protonatable groups, in particular, those that switch from buried to exposed and alter protonation state in doing so. We present a tool focusing on structure-based discovery and display of these groups. RESULTS: Since prediction of buried group pKas is computationally intensive, solvent accessibility of ionizable groups is displayed, from which the user can iteratively select pKa calculation centers. Results are color-coded, with emphasis on buried groups. Utility is demonstrated with benchmarking against known pH sensing sites in influenza virus hemagglutinin and in variants of murine hepatitis virus, a coronavirus. A pair of histidine residues, which are conserved in coronavirus spike proteins, are predicted to be electrostatically frustrated at acidic pH in both pre- and post-fusion conformations. We suggest that an intermediate expanded conformation at endosomal pH could relax the frustration, allowing histidine protonation and facilitating conformational conversion of coronavirus spike protein. AVAILABILITY AND IMPLEMENTATION: This tool is available at http://www.protein-sol.manchester.ac.uk/pka/.

Modelling of pH-dependence to develop a strategy for stabilising mAbs at acidic steps in production.

Engineered proteins are increasingly being required to function or pass through environmental stresses for which the underlying protein has not evolved. A major example in health are antibody therapeutics, where a low pH step is used for purification and viral inactivation. In order to develop a computational model for analysis of pH-stability, predictions are compared with experimental data for the relative pH-sensitivities of antibody domains. The model is then applied to proteases that have evolved to be functional in an acid environment, showing a clear signature for low pH-dependence of stability in the neutral to acidic pH region, largely through reduction of salt-bridges. Interestingly, an extensively acidic protein surface can maintain contribution to structural stabilisation at acidic pH through replacement of basic sidechains with polar, hydrogen-bonding groups. These observations form a design principle for engineering acid-stable proteins.

Web-based display of protein surface and pH-dependent properties for assessing the developability of biotherapeutics.

Protein instability leads to reversible self-association and irreversible aggregation which is a major concern for developing new biopharmaceutical leads. Protein solution behaviour is dictated by the physicochemical properties of the protein and the solution. Optimising protein solutions through experimental screens and targeted protein engineering can be a difficult and time consuming process. Here, we describe development of the protein-sol web server, which was previously restricted to protein solubility prediction from amino acid sequence. Tools are presented for calculating and mapping patches of hydrophobicity and charge on the protein surface. In addition, predictions of folded state stability and net charge are displayed as a heatmap for a range of pH and ionic strength conditions. Tools are evaluated in the context of antibodies, their fragments and interactions. Surprisingly, antibody-antigen interfaces are, on average, at least as polar as Fab surfaces. This benchmarking process provides the user with thresholds with which to assess non-polar surface patches, and possible solubility implications, in proteins of interest. Stability heatmaps compare favourably with experimental data for CH2 and CH3 domains. Display and quantification of surface polarity and pH/ionic strength dependence will be useful generally for investigation of protein biophysics.

Charge and hydrophobicity are key features in sequence-trained machine learning models for predicting the biophysical properties of clinical-stage antibodies.

Improved understanding of properties that mediate protein solubility and resistance to aggregation are important for developing biopharmaceuticals, and more generally in biotechnology and synthetic biology. Recent acquisition of large datasets for antibody biophysical properties enables the search for predictive models. In this report, machine learning methods are used to derive models for 12 biophysical properties. A physicochemical perspective is maintained in analysing the models, leading to the observation that models cluster largely according to charge (cross-interaction measurements) and hydrophobicity (self-interaction methods). These two properties also overlap in some cases, for example in a new interpretation of variation in hydrophobic interaction chromatography. Since the models are developed from differences of antibody variable loops, the next stage is to extend models to more diverse protein sets. AVAILABILITY: The web application for the sequence-based algorithms are available on the protein-sol webserver, at https://protein-sol.manchester.ac.uk/abpred, with models and virtualisation software available at https://protein-sol.manchester.ac.uk/software.

Models for Antibody Behavior in Hydrophobic Interaction Chromatography and in Self-Association.

Monoclonal antibodies (mAbs) form an increasingly important sector of the pharmaceutical market, and their behavior in production, processing, and formulation is a key factor in development. With data sets of solution properties for mAbs becoming available, and with amino acid sequences, and structures for many Fabs, it is timely to examine what features correlate with measured data. Here, previously published data for hydrophobic interaction chromatography and the formation of high molecular weight species are studied. Unsurprisingly, aromatic sidechain content of complementarity-determining regions (CDRs), underpins much of the variability in hydrophobic interaction chromatography data. However, this is not reflected in nonpolar solvent accessible surface enrichment at the antigen-combining site, consistent with a view in which hydrophobic interaction strength is dependent on curvature as well as on the extent of an interface. Sequence properties are also superior to surface-based structural properties in correlations with the high molecular weight species data. Combined length of CDRs is the most important factor, which could be an indication of flexibility that facilitates CDR-CDR interactions in mAb self-association. These observations couple to our understanding of protein physicochemical properties, laying the groundwork for improved developability models.

Self-Interaction of Human Serum Albumin: A Formulation Perspective.

In the present study, small-angle X-ray scattering (SAXS) and static light scattering (SLS) have been used to study the solution properties and self-interaction of recombinant human serum albumin (rHSA) molecules in three pharmaceutically relevant buffer systems. Measurements are carried out up to high protein concentrations and as a function of ionic strength by adding sodium chloride to probe the role of electrostatic interactions. The effective structure factors (S eff) as a function of the scattering vector magnitude q have been extracted from the scattering profiles and fit to the solution of the Ornstein-Zernike equation using a screened Yukawa potential to describe the double-layer force. Although only a limited q range is used, accurate fits required including an electrostatic repulsion element in the model at low ionic strength, while only a hard sphere model with a tunable diameter is necessary for fitting to high-ionic-strength data. The fit values of net charge agree with available data from potentiometric titrations. Osmotic compressibility data obtained by extrapolating the SAXS profiles or directly from SLS measurements has been fit to a 10-term virial expansion for hard spheres and an equation of state for hard biaxial ellipsoids. We show that modeling rHSA as an ellipsoid, rather than a sphere, provides a much more accurate fit for the thermodynamic data over the entire concentration range. Osmotic virial coefficient data, derived at low protein concentration, can be used to parameterize the model for predicting the behavior up to concentrations as high as 450 g/L. The findings are especially important for the biopharmaceutical sector, which require approaches for predicting concentrated protein solution behavior using minimal sample consumption.

Sequence composition predicts immunoglobulin superfamily members that could share the intrinsically disordered properties of antibody CH1 domains.

Antibodies are central to the growing sector of protein therapeutics, and increasingly they are being manipulated as fragments and combinations. An improved understanding of the properties of antibody domains in isolation would aid in their engineering. We have conducted an analysis of sequence and domain interactions for IgG antibodies and Fab fragments in the structural database. Of sequence-related properties studied, relative lysine to arginine content was found to be higher in CH1 and CL than in variable domains. As earlier work shows that lysine is favoured over arginine in more soluble proteins, this suggests that individual domains may not be optimised for greater solubility, giving scope for fragment engineering. Across other sequence-based features, CH1 is anomalous. A sequence-based scheme predicts CH1 to be folded, although it is known that CH1 folding is linked to IgG assembly and secretion. Calculations indicate that charge interactions in CH1 domains contribute less to folded state stability than in other Fab domains. Expanding to the immunoglobulin superfamily reveals that a subset of non-antibody domains shares sequence composition properties with CH1, leading us to suggest that some of these may also couple folding, assembly and secretion.

Coarse-Grained Modeling of Antibodies from Small-Angle Scattering Profiles.

Predicting the concentrated solution behavior for monoclonal antibodies requires developing and using minimal models to describe their shape and interaction potential. Toward this end, the small-angle X-ray scattering (SAXS) profiles for a monoclonal antibody (COE-03) have been measured under solution conditions chosen to produce weak self-association. The experiments are complemented with molecular simulations of a three-bead antibody model with and without interbead attraction. The scattering profile is extracted directly from the molecular simulation to avoid using the decoupling approximation. We examine the ability of the three-bead model to capture features of the scattering profile and the dependence of compressibilty on protein concentration. The three-bead model is able to reproduce generic features of the experimental structure factor as a function of wave vector S(k) including a well-defined shoulder, which is a consequence of the planar structure of the antibody, and a well-defined minimum in S(k) at k ∼ 0.025 Å-1. We also show the decoupling approximation is incapable of accounting for highly anisotropic shapes. The best-fit parameters obtained from matching spherical models to simulated scattering profiles are protein concentration dependent, which limits their applicability for predicting thermodynamic properties. Nevertheless, the experimental compressibility curves can be accurately reproduced by an appropriate parametrization of the Baxter adhesive model, indicating the model provides a semiempirical equation of state for the antibody. The results provide insights into how equations of state can be improved for antibodies by accounting for their anisotropic shapes.

Protein-Sol: a web tool for predicting protein solubility from sequence.

MOTIVATION: Protein solubility is an important property in industrial and therapeutic applications. Prediction is a challenge, despite a growing understanding of the relevant physicochemical properties. RESULTS: Protein-Sol is a web server for predicting protein solubility. Using available data for Escherichia coli protein solubility in a cell-free expression system, 35 sequence-based properties are calculated. Feature weights are determined from separation of low and high solubility subsets. The model returns a predicted solubility and an indication of the features which deviate most from average values. Two other properties are profiled in windowed calculation along the sequence: fold propensity, and net segment charge. The utility of these additional features is demonstrated with the example of thioredoxin. AVAILABILITY AND IMPLEMENTATION: The Protein-Sol webserver is available at http://protein-sol.manchester.ac.uk. CONTACT: jim.warwicker@manchester.ac.uk.

The Use of Antibodies in Small-Molecule Drug Discovery.

Antibodies are powerful research tools that can be used in many areas of biology to probe, measure, and perturb various biological structures. Successful drug discovery is dependent on the correct identification of a target implicated in disease, coupled with the successful selection, optimization, and development of a candidate drug. Because of their specific binding characteristics, with regard to specificity, affinity, and avidity, coupled with their amenability to protein engineering, antibodies have become a key tool in drug discovery, enabling the quantification, localization, and modulation of proteins of interest. This review summarizes the application of antibodies and other protein affinity reagents as specific research tools within the drug discovery process.

Detection of salt bridges to lysines in solution in barnase.

We show that salt bridges involving lysines can be detected by deuterium isotope effects on NMR chemical shifts of the sidechain amine. Lys27 in the ribonuclease barnase is salt bridged, and mutation of Arg69 to Lys retains a partially buried salt bridge. The salt bridges are functionally important.