DeepSP: Deep learning-based spatial properties to predict monoclonal antibody stability.

Therapeutic antibody development faces challenges due to high viscosities and aggregation tendencies. The spatial charge map (SCM) and spatial aggregation propensity (SAP) are computational techniques that aid in predicting viscosity and aggregation, respectively. These methods rely on structural data derived from molecular dynamics (MD) simulations, which are computationally demanding. DeepSCM, a deep learning surrogate model based on sequence information to predict SCM, was recently developed to screen high-concentration antibody viscosity. This study further utilized a dataset of 20,530 antibody sequences to train a convolutional neural network deep learning surrogate model called Deep Spatial Properties (DeepSP). DeepSP directly predicts SAP and SCM scores in different domains of antibody variable regions based solely on their sequences without performing MD simulations. The linear correlation coefficient between DeepSP scores and MD-derived scores for 30 properties achieved values between 0.76 and 0.96 with an average of 0.87. DeepSP descriptors were employed as features to build machine learning models to predict the aggregation rate of 21 antibodies, and the performance is similar to the results obtained from the previous study using MD simulations. This result demonstrates that the DeepSP approach significantly reduces the computational time required compared to MD simulations. The DeepSP model enables the rapid generation of 30 structural properties that can also be used as features in other research to train machine learning models for predicting various antibody stability using sequences only. DeepSP is freely available as an online tool via https://deepspwebapp.onrender.com and the codes and parameters are freely available at https://github.com/Lailabcode/DeepSP.

Prediction of protein aggregation.

The scientific community is very interested in protein aggregation because of its involvement in several neurodegenerative diseases and its significance in industry. Remarkably, fibrillar aggregates are utilized naturally for constructing structural scaffolds or creating biological switches and may be intentionally designed to construct versatile nanomaterials. Consequently, there is a significant need to rationalize and predict protein aggregation. Researchers have developed various computational methodologies and algorithms to predict protein aggregation and understand its underlying mechanics. This chapter aims to summarize the significant advancements in computational methods, accessible resources, and prospective developments in the field of in silico research. We assess the existing computational tools for predicting protein aggregation propensities, detecting areas that are prone to sequential and structural aggregation, analyzing the effects of mutations on protein aggregation, or identifying prion-like domains.

Efficient prediction of peptide self-assembly through sequential and graphical encoding.

In recent years, there has been an explosion of research on the application of deep learning to the prediction of various peptide properties, due to the significant development and market potential of peptides. Molecular dynamics has enabled the efficient collection of large peptide datasets, providing reliable training data for deep learning. However, the lack of systematic analysis of the peptide encoding, which is essential for artificial intelligence-assisted peptide-related tasks, makes it an urgent problem to be solved for the improvement of prediction accuracy. To address this issue, we first collect a high-quality, colossal simulation dataset of peptide self-assembly containing over 62 000 samples generated by coarse-grained molecular dynamics. Then, we systematically investigate the effect of peptide encoding of amino acids into sequences and molecular graphs using state-of-the-art sequential (i.e. recurrent neural network, long short-term memory and Transformer) and structural deep learning models (i.e. graph convolutional network, graph attention network and GraphSAGE), on the accuracy of peptide self-assembly prediction, an essential physiochemical process prior to any peptide-related applications. Extensive benchmarking studies have proven Transformer to be the most powerful sequence-encoding-based deep learning model, pushing the limit of peptide self-assembly prediction to decapeptides. In summary, this work provides a comprehensive benchmark analysis of peptide encoding with advanced deep learning models, serving as a guide for a wide range of peptide-related predictions such as isoelectric points, hydration free energy, etc.

Prediction of Protein Aggregation Propensity via Data-Driven Approaches.

Protein aggregation occurs when misfolded or unfolded proteins physically bind together and can promote the development of various amyloid diseases. This study aimed to construct surrogate models for predicting protein aggregation via data-driven methods using two types of databases. First, an aggregation propensity score database was constructed by calculating the scores for protein structures in the Protein Data Bank using Aggrescan3D 2.0. Moreover, feature- and graph-based models for predicting protein aggregation have been developed by using this database. The graph-based model outperformed the feature-based model, resulting in an R2 of 0.95, although it intrinsically required protein structures. Second, for the experimental data, a feature-based model was built using the Curated Protein Aggregation Database 2.0 to predict the aggregated intensity curves. In summary, this study suggests approaches that are more effective in predicting protein aggregation, depending on the type of descriptor and the database.

Isolation of Amyloid-like Protein Aggregates (APA) from white bread and their characterisation.

High temperature, acidic pH, and physical agitation are commonly observed during cooking or industrial food processing, which are often considered as favorable conditions, at least for some proteins, to misfold and form amyloid-like protein aggregates (APA). The proteins in various bakery products generally experience high temperatures that might lead to the formation of APA. To test this hypothesis, the presence of APA in white bread was examined in this study. The APA isolated from white bread displayed typical characteristics of amyloids, like bathochromic shift in Congo red (CR) absorbance maxima, increased fluorescence of Thioflavin T (ThT) & 8-anilino-1-naphthalene sulfonic acid (ANS), fibrillar morphology of >200 nm long with average diameter of 10-12 nm and negative minima at 223 nm in Circular Dichroism (CD) spectrum. The SDS- and native PAGE revealed the presence of gliadin and glutenin as the constituent proteins in the isolated protein aggregates. Although, the presence of amyloid-like structures in white bread is evident, further studies would be essential to establish their functional role and health implications.

Spreading of Tau Protein Does Not Depend on Aggregation Propensity.

The stereotypical progression of Tau pathology during Alzheimer disease has been attributed to trans-neuronal spreading of misfolded Tau proteins, followed by prion-like templated aggregation of Tau. The nature of Tau and the cellular mechanisms of Tau spreading are still under debate. We hypothesized that Tau's propensity for aggregation would correlate with its ability to spread across synapses and propagate pathology. To study the progressive propagation of Tau proteins in brain regions relevant for Alzheimer disease, we used mice expressing near-physiological levels of full-length human Tau protein carrying pro-aggregant (TauΔK280, TauΔK) or anti-aggregant (TauΔK280-PP, TauΔK-PP) mutations in the entorhinal cortex (EC). To enhance Tau expression in the EC, we performed EC injections of adeno-associated virus (AAV) particles encoding TauΔK or TauΔK-PP. The brains of injected and non-injected EC/TauΔK and EC/TauΔK-PP mice were studied by immunohistological and biochemical techniques to detect Tau propagation to dentate gyrus (DG) neurons and Tau-induced pathological changes. Pro- and anti-aggregant mice had comparable low transgene expression (~0.2 times endogenous mouse Tau). They accumulated human Tau at similar rates and only in expressing EC neurons, including their axonal projections of the perforant path and presynaptic terminals in the molecular layer of the DG. Pro-aggregant EC/TauΔK mice showed misfolded Tau and synaptic protein alterations in EC neurons, not observed in anti-aggregant EC/TauΔK-PP mice. Additional AAV-mediated expression of TauΔK or TauΔK-PP in EC/TauΔK or EC/TauΔK-PP mice, respectively, increased the human Tau expression to ~0.65 times endogenous mouse Tau, with comparable spreading of TauΔK and TauΔK-PP throughout the EC. There was a low level of transcellular propagation of Tau protein, without pathological phosphorylation or misfolding, as judged by diagnostic antibodies. Additionally, TauΔK but not TauΔK-PP expression induced hippocampal astrogliosis. Low levels of pro- or anti-aggregant full-length Tau show equivalent distributions in EC neurons, independent of their aggregation propensity. Increasing the expression via AAV induce local Tau misfolding in the EC neurons, synaptotoxicity, and astrogliosis and lead to a low level of detectable trans-neuronal spreading of Tau. This depends on its concentration in the EC, but, contrary to expectations, does not depend on Tau's aggregation propensity/misfolding and does not lead to templated misfolding in recipient neurons.

Exploring the aggregation of amyloid-ß 42 through Monte Carlo simulations.

Coarse-grained Monte Carlo simulations are performed for a disordered protein, amyloid-ß 42 to identify the interactions and understand the mechanism of its aggregation. A statistical potential is developed from a selected dataset of intrinsically disordered proteins, which accounts for the respective contributions of the bonded and non-bonded potentials. While, the bonded potential comprises the bond, bend, and dihedral constraints, the nonbonded interactions include van der Waals interactions, hydrogen bonds, and the two-body potential. The two-body potential captures the features of both hydrophobic and electrostatic interactions that brings the chains at a contact distance, while the repulsive van der Waals interactions prevent them from a collapse. Increased two-body hydrophobic interactions facilitate the formation of amorphous aggregates rather than the fibrillar ones. The formation of aggregates is validated from the interchain distances, and the total energy of the system. The aggregate is structurally characterized by the root-mean-square deviation, root-mean-square fluctuation and the radius of gyration. The aggregates are characterized by a decrease in SASA, an increase in the non-local interactions and a distinct free energy minimum relative to that of the monomeric state of amyloid-ß 42. The hydrophobic residues help in nucleation, while the charged residues help in oligomerization and aggregation.

Connection between MHC class II binding and aggregation propensity: The antigenic peptide 10 of Paracoccidioides brasiliensis as a benchmark study.

The aggregation of epitopes that are also able to bind major histocompatibility complex (MHC) alleles raises questions around the potential connection between the formation of epitope aggregates and their affinities to MHC receptors. We first performed a general bioinformatic assessment over a public dataset of MHC class II epitopes, finding that higher experimental binding correlates with higher aggregation-propensity predictors. We then focused on the case of P10, an epitope used as a vaccine candidate against Paracoccidioides brasiliensis that aggregates into amyloid fibrils. We used a computational protocol to design variants of the P10 epitope to study the connection between the binding stabilities towards human MHC class II alleles and their aggregation propensities. The binding of the designed variants was tested experimentally, as well as their aggregation capacity. High-affinity MHC class II binders in vitro were more disposed to aggregate forming amyloid fibrils capable of binding Thioflavin T and congo red, while low affinity MHC class II binders remained soluble or formed rare amorphous aggregates. This study shows a possible connection between the aggregation propensity of an epitope and its affinity for the MHC class II cleft.

Rational Design and Production of Bioactive Analogs of Recombinant Human Keratinocyte Growth Factor (rhKGF) with Reduced Aggregation Propensity.

Recombinant human keratinocyte growth factor (rhKGF) is a highly aggregation-prone therapeutic protein. The present study aimed to reduce aggregation propensity of rhKGF by engineering the aggregation hotspots. Initially, 21 mutants were designed based on the previously-identified aggregation-prone regions (APRs) and then four of them including mutants No. 4 (L91K, I119K), 7 (V13S, L91K), 14 (L91D, I119D), and 21 (A51E) were selected based on molecular dynamics (MD) simulations for further experimental studies. The recombinantly produced rhKGF and mutants were analyzed regarding secondary structure, thermal stability, aggregation propensity, and biological activity. Far-UV CD spectroscopy showed that the mutants have similar secondary structure with rhKGF. A51E mutant showed enhanced stability and decreased monomer loss under heat stress suggesting its reduced aggregation propensity compared to rhKGF. Mutant No. 14 showed higher stability and less aggregation tendency than mutant No. 4 indicating that only mutations decreasing pI of rhKGF are effective in reducing its aggregation tendency. All of the mutants were at least as potent as rhKGF in stimulating proliferation of MCF-7 epithelial cells. Our results identified A51E as an equally potent, more stable, and less aggregation-prone analog of rhKGF which could be a promising alternative drug candidate for the commercially available rhKGF (Palifermin).

Pathogenicity classification of SOD1 variants of uncertain significance by in vitro aggregation propensity.

Deposition of insoluble SOD1 aggregates in motor neurons is the hallmark of SOD1-associated ALS. Mutant SOD1 protein promotes structural instability that leads to misfolded SOD1 protein aggregates, which can be recapitulated in vitro. Therefore, aggregation propensity in cell lines can be a reliable indicator for the pathogenicity classification of SOD1 variants. Herein, we performed in vitro experiment to classify the pathogenicity of 34 SOD1 variants of uncertain significance (VUS) from 215 variants reported previously. The clinical features of 234 ALS patients with 31 SOD1 likely pathogenic (LP) variants were summarized. 31 VUS variants formed aggregates spontaneously, indicating LP variants. Missense variants were mainly located in the C-terminal of SOD1. Among patients with 31 SOD1 LP variants, 75% of patients had lower limb onset. The onset of familial ALS patients (45.7±14.0 years) is earlier than sporadic ALS patients (50.6±13.1 years). Our results expand the spectrum of SOD1 mutations and highlight the natural history of SOD1-positive ALS patients for further clinical trials in SOD1-related ALS.

A guide to studying protein aggregation.

Disrupted protein folding or decreased protein stability can lead to the accumulation of (partially) un- or misfolded proteins, which ultimately cause the formation of protein aggregates. Much of the interest in protein aggregation is associated with its involvement in a wide range of human diseases and the challenges it poses for large-scale biopharmaceutical manufacturing and formulation of therapeutic proteins and peptides. On the other hand, protein aggregates can also be functional, as observed in nature, which triggered its use in the development of biomaterials or therapeutics as well as for the improvement of food characteristics. Thus, unmasking the various steps involved in protein aggregation is critical to obtain a better understanding of the underlying mechanism of amyloid formation. This knowledge will allow a more tailored development of diagnostic methods and treatments for amyloid-associated diseases, as well as applications in the fields of new (bio)materials, food technology and therapeutics. However, the complex and dynamic nature of the aggregation process makes the study of protein aggregation challenging. To provide guidance on how to analyse protein aggregation, in this review we summarize the most commonly investigated aspects of protein aggregation with some popular corresponding methods.

Orthogonal and complementary measurements of properties of drug products containing nanomaterials.

Quality control of pharmaceutical and biopharmaceutical products, and verification of their safety and efficacy, depends on reliable measurements of critical quality attributes (CQAs). The task becomes particularly challenging for drug products and vaccines containing nanomaterials, where multiple complex CQAs must be identified and monitored. To reduce (i) the risk of measurement bias and (ii) the uncertainty in decision-making during product development, the combination of orthogonal and complementary analytical techniques are generally recommended by regulators. However, despite frequent reference to "orthogonal" and "complementary" in guidance documents, neither term is clearly defined. How does one determine if two analytical methods are orthogonal or complementary to one another? Definitions are needed to design a robust characterization strategy aligned to regulatory needs. Definitions for "orthogonal" and "complementary" are proposed that are compatible with existing metrological terminology and are applicable to complex measurement problems. Orthogonal methods target the quantitative evaluation of the true value of a product attribute to address unknown bias or interference. Complementary measurements include a broader scope of methods that reinforce each other to support a common decision. Examples of the application of these terms are presented, with a focus on measurement of physical properties of nano-enabled drug products, including liposomes and polymeric nanoparticles for cancer treatment, lipid-based nanoparticles (LNPs) and virus-like particles for nucleic acid delivery. The proposed framework represents a first step in advancing the assessment of the orthogonality and complementarity of two measurements and it can potentially serve as the basis for a future international standard. This framework may help product developers to implement more efficient product characterization strategies, accelerate the introduction of novel medicines to the clinic and be applicable to other therapeutics beyond nanomaterial-containing pharmaceuticals.

Aggregation propensities of proteins with varying degrees of disorder.

The hydration thermodynamics of a globular protein (AcP), three intrinsically disordered protein regions (1CD3, 1MVF, 1F0R) and a fully disordered protein (α-synuclein) is studied by an approach that combines an all-atom explicit water molecular dynamics simulations and three-dimensional reference interaction site model (3D-RISM) theory. The variation in hydration free energy with percentage disorder of the selected proteins is investigated through its nonelectrostatic and electrostatic components. A decrease in hydration free energy is observed with an increase in percentage disorder, indicating favorable interactions of the disordered proteins with the solvent. This confirms the role of percentage disorder in determining the aggregation propensity of proteins which is measured in terms of the hydration free energy in addition to their respective mean net charge and mean hydrophobicity. The hydration free energy is decoupled into energetic and entropic terms. A residue-wise decomposition analysis of the hydration free energy for the selected proteins is evaluated. The decomposition shows that the disordered regions contribute more than the ordered ones for the intrinsically disordered protein regions. The dominant role of electrostatic interactions is confirmed from the residue-wise decomposition of the hydration free energy. The results depict that the negatively charged residues contribute more to the total hydration free energy for the proteins with negative mean net charge, while the positively charged residues contribute more for proteins with positive mean net charge.

Understanding the Stabilizing Effect of Histidine on mAb Aggregation: A Molecular Dynamics Study.

Histidine, a widely used buffer in monoclonal antibody (mAb) formulations, is known to reduce antibody aggregation. While experimental studies suggest a nonelectrostatic, nonstructural (relating to secondary structure preservation) origin of the phenomenon, the underlying microscopic mechanism behind the histidine action is still unknown. Understanding this mechanism will help evaluate and predict the stabilizing effect of this buffer under different experimental conditions and for different mAbs. We have used all-atom molecular dynamics simulations and contact-based free energy calculations to investigate molecular-level interactions between the histidine buffer and mAbs, which lead to the observed stability of therapeutic formulations in the presence of histidine. We reformulate the Spatial Aggregation Propensity index by including the buffer-protein interactions. The buffer adsorption on the protein surface leads to lower exposure of the hydrophobic regions to water. Our analysis indicates that the mechanism behind the stabilizing action of histidine is connected to the shielding of the solvent-exposed hydrophobic regions on the protein surface by the buffer molecules.

Isolation and characterization of amyloid-like protein aggregates from soya beans and the effect of low pH and heat treatment on their stability.

Purified soya bean proteins (glycinin and conglycinin) are known to form amyloid-like aggregates in vitro at a higher temperature. Soya beans (chunks) are textured proteinaceous vegetables made from defatted soya flour by heating it above 100°C and extruding under high pressure. Therefore, it was assumed that subjecting the soya bean proteins to high temperatures raises the possibility of forming amyloids or amyloid-like protein aggregates. Hence, the present study aimed to examine the presence of amyloid-like protein aggregates in soya beans. The isolated protein aggregates from hydrated soya beans displayed typical characteristics of amyloids, such as the red shift in the absorption maximum (λmax ) of Congo red (CR), high Thioflavin T (ThT), and 8-Anilinonapthalene-1-sulfonate (ANS) binding, and fibrilar morphology. Furthermore, these aggregates were found to be stable against proteolytic hydrolysis, confirming the specific property of amyloids. The presence of amyloid-like structures in soya beans raises concerns about their implications for human nutrition and health. PRACTICAL APPLICATIONS: Protein aggregation has usually been considered detrimental. The traditional food-processing conditions, such as thermal processing, are associated with protein denaturation and aggregation. The formation of ordered protein aggregates with extensive ß-sheet are progressively evident in various protein-rich foods known as amyloid, which expands food safety concerns. Instead, it is also associated with poor nutritional characteristics. The present study concerns the presence of amyloid-like protein aggregates in widely consumed native soya beans, which are manufactured by extensive heat treatment of defatted soy flour. Although there is no indication of their toxicity, these aggregates are found to be proteolytically resistant. The seminal findings in this manuscript suggest that it is time to adapt innovative food processing and supplementation of bioactive molecules that can prevent the formation of such protein aggregates and help maximize the utilization of protein-based nutritional values.

Orange G is a potential inhibitor of human insulin amyloid fibrillation and can be used as a probe to study mechanism of amyloid fibrillation and its inhibition.

The extracellular insoluble deposits of highly ordered cross-ß-structure-containing amyloid fibrils form the pathological basis for protein misfolding diseases. As amyloid fibrils are cytotoxic, inhibition of the process is a therapeutic strategy. Several small molecules have been identified and used as fibrillation inhibitors in the recent past. In this work, we investigate the effect of Orange G on insulin amyloid formation using fluorescence-based assays and negative-stain electron microscopy (EM). We show that Orange G effectively attenuates nucleation, thereby inhibiting amyloid fibrillation in a dose-dependent manner. Fluorescence quenching titrations of Orange G showed a reasonably strong binding affinity to native insulin. Binding isotherm measurements revealed the binding of Orange G to pre-formed insulin fibrils too, indicating that Orange G likely binds and stabilizes the mature fibrils and prevents the release of toxic oligomers which could be potential nuclei or templates for further fibrillation. Molecular docking of Orange G with native insulin and amyloid-like peptide structures were also carried out to analyse the contributing interactions and binding free energy. The findings of our study emphasize the use of Orange G as a molecular probe to identify and design inhibitors of amyloid fibrillation and to investigate the structural and toxic mechanisms underlying amyloid formation.

Two physics-based models for pH-dependent calculations of protein solubility.

When engineering a protein for its biological function, many physicochemical properties are also optimized throughout the engineering process, and the protein's solubility is among the most important properties to consider. Here, we report two novel computational methods to calculate the pH-dependent protein solubility, and to rank the solubility of mutants. The first is an empirical method developed for fast ranking of the solubility of a large number of mutants of a protein. It takes into account electrostatic solvation energy term calculated using Generalized Born approximation, hydrophobic patches, protein charge, and charge asymmetry, as well as the changes of protein stability upon mutation. This method has been tested on over 100 mutations for 17 globular proteins, as well as on 44 variants of five different antibodies. The prediction rate is over 80%. The antibody tests showed a Pearson correlation coefficient, R, with experimental data from .83 to .91. The second method is based on a novel, completely force-field-based approach using CHARMm program modules to calculate the binding energy of the protein to a part of the crystal lattice, generated from X-ray structure. The method predicted with very high accuracy the solubility of Ribonuclease SA and its 3K and 5K mutants as a function of pH without any parameter adjustments of the existing BIOVIA Discovery Studio binding affinity model. Our methods can be used for rapid screening of large numbers of design candidates based on solubility, and to guide the design of solution conditions for antibody formulation.

Comparative Study of Protein Aggregation Propensity and Mutation Tolerance Between Naked Mole-Rat and Mouse.

The molecular mechanisms of aging and life expectancy have been studied in model organisms with short lifespans. However, long-lived species may provide insights into successful strategies for healthy aging, potentially opening the door for novel therapeutic interventions in age-related diseases. Notably, naked mole-rats, the longest-lived rodent, present attenuated aging phenotypes compared with mice. Their resistance toward oxidative stress has been proposed as one hallmark of their healthy aging, suggesting their ability to maintain cell homeostasis, specifically their protein homeostasis. To identify the general principles behind their protein homeostasis robustness, we compared the aggregation propensity and mutation tolerance of naked mole-rat and mouse orthologous proteins. Our analysis showed no proteome-wide differential effects in aggregation propensity and mutation tolerance between these species, but several subsets of proteins with a significant difference in aggregation propensity. We found an enrichment of proteins with higher aggregation propensity in naked mole-rat, and these are functionally involved in the inflammasome complex and nucleic acid binding. On the other hand, proteins with lower aggregation propensity in naked mole-rat have a significantly higher mutation tolerance compared with the rest of the proteins. Among them, we identified proteins known to be associated with neurodegenerative and age-related diseases. These findings highlight the intriguing hypothesis about the capacity of the naked mole-rat proteome to delay aging through its proteomic intrinsic architecture.

Investigation of the pH-dependent aggregation mechanisms of GCSF using low resolution protein characterization techniques and advanced molecular dynamics simulations.

Granulocyte-colony stimulating factor (GCSF) is a widely used therapeutic protein to treat neutropenia. GCSF has an increased propensity to aggregate if the pH is increased above 5.0. Although GCSF is very well experimentally characterized, the exact pH-dependent aggregation mechanism of GCSF is still under debate. This study aimed to model the complex pH-dependent aggregation behavior of GCSF using state-of-the-art simulation techniques. The conformational stability of GCSF was investigated by performing metadynamics simulations, while the protein-protein interactions were investigated using coarse-grained (CG) simulations of multiple GCSF monomers. The CG simulations were directly compared with small-angle X-ray (SAXS) data. The metadynamics simulations demonstrated that the orientations of Trp residues in GCSF are dependent on pH. The conformational change of Trp residues is due to the loss of Trp-His interactions at the physiological pH, which in turn may increase protein flexibility. The helical structure of GCSF was not affected by the pH conditions of the simulations. Our CG simulations indicate that at pH 4.0, the colloidal stability may be more important than the conformational stability of GCSF. The electrostatic potential surface and CG simulations suggested that the basic residues are mainly responsible for colloidal stability as deprotonation of these residues causes a reduction of the highly positively charged electrostatic barrier close to the aggregation-prone long loop regions.

Assessing the Aggregation Propensity of Single-Domain Antibodies upon Heat-Denaturation Employing the ΔTm Shift.

Nano differential scanning fluorimetry is used to quantify protein thermostability and has substantially expanded the spectrum of convenient biophysical parameters used to characterize proteins. Here, this technique is used to measure the ΔTm shift for single-domain antibodies (sdAbs), which represents a comprehensive metric for the aggregation propensity of sdAbs upon heat-denaturation. By relating two melting curves at different protein concentrations, the ΔTm shift described in this protocol is ideally suited for high-throughput measurements to guide protein engineering, formulation development, and developability assessment of sdAbs.