Biophysical Principles Emerging from Experiments on Protein-Protein Association and Aggregation

Protein-protein association and aggregation are fundamental processes that play critical roles in various biological phenomena, from cellular signaling to disease progression. Understanding the underlying biophysical principles governing these processes is crucial for elucidating their mechanisms and developing strategies for therapeutic intervention. In this review, we provide an overview of recent experimental studies focused on protein-protein association and aggregation. We explore the key biophysical factors that influence these processes, including protein structure, conformational dynamics, and intermolecular interactions. We discuss the effects of environmental conditions such as temperature, pH and related buffer-specific effects, and ionic strength and related ion-specific effects on protein aggregation. The effects of polymer crowders and sugars are also addressed. We list the techniques used to study aggregation. We analyze emerging trends and challenges in the field, including the development of computational models and the integration of multidisciplinary approaches for a comprehensive understanding of protein-protein association and aggregation.

Protonation-State Dependent Modulation of Hen Egg-White Lysozyme Fibrillation under the Influence of a Short Synthetic Peptide

Under the influence of various conditions, misfolding of soluble proteins occurs, leading to the formation of toxic insoluble amyloids. The formation and deposition of such amyloids within the body are associated with detrimental biological consequences such as the onset of several amyloid-related diseases. Previously, we established a strategy for the rational design of peptide inhibitors against amyloid formation based on the amyloidogenic-prone region of the protein. In the current study, we have designed and identified an Asp-containing rationally designed hexapeptide (SqP4) as an excellent inhibitor of hen egg-white lysozyme (HEWL) amyloid progression in vitro. First, SqP4 showed strong affinity toward the native monomeric HEWL leading to the stabilization of the native form and restriction in the unfolding process of monomeric HEWL. Second, SqP4 was found to arrest the amyloidogenic misfolded structure of HEWL in a nonfibrillar monomer-like stage. We also observed the differential effect of the protonation state of the charged amino acid (Asp) within the peptide inhibitor on the amyloid formation of HEWL and explored the reason behind the observations. The findings of this study can be implemented in future strategies for the development of potent therapeutics against other amyloid-related diseases.

Comparison of the Protective Effect of Polysorbates, Poloxamer and Brij on Antibody Stability Against Different Interfaces

Therapeutic proteins and antibodies are exposed to a variety of interfaces during their lifecycle, which can compromise their stability. Formulations, including surfactants, must be carefully optimized to improve interfacial stability against all types of surfaces. Here we apply a nanoparticle-based approach to evaluate the instability of four antibody drugs against different solid-liquid interfaces characterized by different degrees of hydrophobicity. We considered a model hydrophobic material as well as cycloolefin-copolymer (COC) and cellulose, which represent some of the common solid-liquid interfaces encountered during drug production, storage, and delivery. We assess the protective effect of polysorbate 20, polysorbate 80, Poloxamer 188 and Brij 35 in our assay and in a traditional agitation study. While all nonionic surfactants stabilize antibodies against the air-water interface, none of them can protect against hydrophilic charged cellulose. Polysorbates and Brij increase antibody stability in the presence of COC and the model hydrophobic interface, although to a lesser extent compared to the air-water interface, while Poloxamer 188 has a negligible stabilizing effect against these interfaces. These results highlight the challenge of fully protecting antibodies against all types of solid-liquid interfaces with traditional surfactants. In this context, our high-throughput nanoparticle-based approach can complement traditional shaking assays and assist in formulation design to ensure protein stability not only at air-water interfaces, but also at relevant solid-liquid interfaces encountered during the product lifecycle.

Understanding and controlling the molecular mechanisms of protein aggregation in mAb therapeutics

In antibody development and manufacturing, protein aggregation is a common challenge that can lead to serious efficacy and safety issues. To mitigate this problem, it is important to investigate its molecular origins. This review discusses (1) our current molecular understanding and theoretical models of antibody aggregation, (2) how various stress conditions related to antibody upstream and downstream bioprocesses can trigger aggregation, and (3) current mitigation strategies employed towards inhibiting aggregation. We discuss the relevance of the aggregation phenomenon in the context of novel antibody modalities and highlight how in silico approaches can be exploited to mitigate it.

Elucidating the inhibitory effects of rationally designed novel hexapeptide against hen egg white lysozyme fibrillation at acidic and physiological pH

Inhibition of highly ordered cross-β-sheet-rich aggregates of misfolded amyloid proteins using rationally designed sequence-based short peptides is a promising therapeutic strategy for the treatment of neurodegenerative diseases. Here, we have explored the anti-amyloidogenic potency of a rationally designed hexapeptide (Tyr-Pro-Gln-Ile-Pro-Asn) on in vitro hen egg white lysozyme (HEWL) amyloid fibril formation at acidic pH and physiological pH using computational docking as well as various biophysical techniques such as fluorescence spectroscopy, UV-vis spectroscopy, FTIR spectroscopy, confocal microscopy and TEM. The peptide was designed based on the aggregation-prone region (APR) of HEWL and thus referred to as SqP1 (Sequence-based Peptide 1). SqP1 showed over 70% inhibition of HEWL amyloid formation at pH 2.2 and approximately 50% inhibition at pH 7.5. We propose that SqP1 binds to the APR of HEWL and interacts strongly with the Trp62/Trp63, ultimately stabilizing monomeric HEWL at both the pH conditions and preventing conformation changes in the structure of HEWL, leading to the formation of amyloidogenic fibrillar structures. A sequence-based peptide inhibitor of HEWL amyloid formation was not reported previously, making this a critical study that will further emphasize the importance of short synthetic peptides as amyloid inhibitors.

Accelerating therapeutic protein design with computational approaches toward the clinical stage

Therapeutic protein, represented by antibodies, is of increasing interest in human medicine. However, clinical translation of therapeutic protein is still largely hindered by different aspects of developability, including affinity and selectivity, stability and aggregation prevention, solubility and viscosity reduction, and deimmunization. Conventional optimization of the developability with widely used methods, like display technologies and library screening approaches, is a time and cost-intensive endeavor, and the efficiency in finding suitable solutions is still not enough to meet clinical needs. In recent years, the accelerated advancement of computational methodologies has ushered in a transformative era in the field of therapeutic protein design. Owing to their remarkable capabilities in feature extraction and modeling, the integration of cutting-edge computational strategies with conventional techniques presents a promising avenue to accelerate the progression of therapeutic protein design and optimization toward clinical implementation. Here, we compared the differences between therapeutic protein and small molecules in developability and provided an overview of the computational approaches applicable to the design or optimization of therapeutic protein in several developability issues.

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).

Surface-Induced Protein Aggregation and Particle Formation in Biologics: Current Understanding of Mechanisms, Detection and Mitigation Strategies

Protein stability against aggregation is a major quality concern for the production of safe and effective biopharmaceuticals. Amongst the different drivers of protein aggregation, increasing evidence indicates that interactions between proteins and interfaces represent a major risk factor for the formation of protein aggregates in aqueous solutions. Potentially harmful surfaces relevant to biologics manufacturing and storage include air-water and silicone oil-water interfaces as well as materials from different processing units, storage containers, and delivery devices. The impact of some of these surfaces, for instance originating from impurities, can be difficult to predict and control. Moreover, aggregate formation may additionally be complicated by the simultaneous presence of interfacial, hydrodynamic and mechanical stresses, whose contributions may be difficult to deconvolute. As a consequence, it remains difficult to identify the key chemical and physical determinants and define appropriate analytical methods to monitor and predict protein instability at these interfaces. In this review, we first discuss the main mechanisms of surface-induced protein aggregation. We then review the types of contact materials identified as potentially harmful or detected as potential triggers of proteinaceous particle formation in formulations and discuss proposed mitigation strategies. Finally, we present current methods to probe surface-induced instabilities, which represent a starting point towards assays that can be implemented in early-stage screening and formulation development of biologics.

Assessing and Engineering Antibody Stability Using Experimental and Computational Methods

Engineering increased stability into antibodies can improve their developability. While a range of properties need to be optimized, thermal stability and aggregation are two key factors that affect the antibody yield, purity, and specificity throughout the development and manufacturing pipeline. Therefore, an ideal goal would be to apply protein engineering methods early-on, such as in parallel to affinity maturation, to screen out potential drug molecules with the desired conformational and colloidal stability. This chapter introduces our methods to computationally characterize an antibody Fab fragment, propose stabilizing variants, and then experimentally verify these predictions.

Protein Design: From the Aspect of Water Solubility and Stability

Water solubility and structural stability are key merits for proteins defined by the primary sequence and 3D-conformation. Their manipulation represents important aspects of the protein design field that relies on the accurate placement of amino acids and molecular interactions, guided by underlying physiochemical principles. Emulated designer proteins with well-defined properties both fuel the knowledge-base for more precise computational design models and are used in various biomedical and nanotechnological applications. The continuous developments in protein science, increasing computing power, new algorithms, and characterization techniques provide sophisticated toolkits for solubility design beyond guess work. In this review, we summarize recent advances in the protein design field with respect to water solubility and structural stability. After introducing fundamental design rules, we discuss the transmembrane protein solubilization and de novo transmembrane protein design. Traditional strategies to enhance protein solubility and structural stability are introduced. The designs of stable protein complexes and high-order assemblies are covered. Computational methodologies behind these endeavors, including structure prediction programs, machine learning algorithms, and specialty software dedicated to the evaluation of protein solubility and aggregation, are discussed. The findings and opportunities for Cryo-EM are presented. This review provides an overview of significant progress and prospects in accurate protein design for solubility and stability.

Scan-Find-Scan-Model: Discrete Site-Targeted Suppressor Design Strategy for Amyloid-β

Alzheimer's disease is undoubtedly the most well-studied neurodegenerative disease. Consequently, the amyloid-β (Aβ) protein ranks at the top in terms of getting attention from the scientific community for structural property-based characterization. Even after decades of extensive research, there is existing volatility in terms of understanding and hence the effective tackling procedures against the disease that arises due to the lack of knowledge of both specific target- and site-specific drugs. Here, we develop a multidimensional approach based on the characterization of the common static-dynamic-thermodynamic trait of the monomeric protein, which efficiently identifies a small target sequence that contains an inherent tendency to misfold and consequently aggregate. The robustness of the identification of the target sequence comes with an abundance of a priori knowledge about the length and sequence of the target and hence guides toward effective designing of the target-specific drug with a very low probability of bottleneck and failure. Based on the target sequence information, we further identified a specific mutant that showed the maximum potential to act as a destabilizer of the monomeric protein as well as enormous success as an aggregation suppressor. We eventually tested the drug efficacy by estimating the extent of modulation of binding affinity existing within the fibrillar form of the Aβ protein due to a single-point mutation and hence provided a proof of concept of the entire protocol.

Assessment of Therapeutic Antibody Developability by Combinations of In Vitro and In Silico Methods

Although antibodies have become the fastest-growing class of therapeutics on the market, it is still challenging to develop them for therapeutic applications, which often require these molecules to withstand stresses that are not present in vivo. We define developability as the likelihood of an antibody candidate with suitable functionality to be developed into a manufacturable, stable, safe, and effective drug that can be formulated to high concentrations while retaining a long shelf life. The implementation of reliable developability assessments from the early stages of antibody discovery enables flagging and deselection of potentially problematic candidates, while focussing available resources on the development of the most promising ones. Currently, however, thorough developability assessment requires multiple in vitro assays, which makes it labor intensive and time consuming to implement at early stages. Furthermore, accurate in vitro analysis at the early stage is compromised by the high number of potential candidates that are often prepared at low quantities and purity. Recent improvements in the performance of computational predictors of developability potential are beginning to change this scenario. Many computational methods only require the knowledge of the amino acid sequences and can be used to identify possible developability issues or to rank available candidates according to a range of biophysical properties. Here, we describe how the implementation of in silico tools into antibody discovery pipelines is increasingly offering time- and cost-effective alternatives to in vitro experimental screening, thus streamlining the drug development process. We discuss in particular the biophysical and biochemical properties that underpin developability potential and their trade-offs, review various in vitro assays to measure such properties or parameters that are predictive of developability, and give an overview of the growing number of in silico tools available to predict properties important for antibody development, including the CamSol method developed in our laboratory.

A computational method for predicting the aggregation propensity of IgG1 and IgG4(P) mAbs in common storage buffers

The propensity for some monoclonal antibodies (mAbs) to aggregate at physiological and manufacturing pH values can prevent their use as therapeutic molecules or delay time to market. Consequently, developability assessments are essential to select optimum candidates, or inform on mitigation strategies to avoid potential late-stage failures. These studies are typically performed in a range of buffer solutions because factors such as pH can dramatically alter the aggregation propensity of the test mAbs (up to 100-fold in extreme cases). A computational method capable of robustly predicting the aggregation propensity at the pH values of common storage buffers would have substantial value. Here, we describe a mAb aggregation prediction tool (MAPT) that builds on our previously published isotype-dependent, charge-based model of aggregation. We show that the addition of a homology model-derived hydrophobicity descriptor to our electrostatic aggregation model enabled the generation of a robust mAb developability indicator. To contextualize our aggregation scoring system, we analyzed 97 clinical-stage therapeutic mAbs. To further validate our approach, we focused on six mAbs (infliximab, tocilizumab, rituximab, CNTO607, MEDI1912 and MEDI1912_STT) which have been reported to cover a large range of aggregation propensities. The different aggregation propensities of the case study molecules at neutral and slightly acidic pH were correctly predicted, verifying the utility of our computational method.

Determination of amyloid core regions of insulin analogues fibrils

A rapid-acting insulin lispro and long-acting insulin glargine are commonly used for the treatment of diabetes. Clinical cases have described the formation of injectable amyloidosis with these insulin analogues, but their amyloid core regions of fibrils were unknown. To reveal these regions, we have analysed the hydrolyzates of insulin fibrils and its analogues using high-performance liquid chromatography and mass spectrometry methods and found that insulin and its analogues have almost identical amyloid core regions that intersect with the predicted amyloidogenic regions. The obtained results can be used to create new insulin analogues with a low ability to form fibrils.

Abbreviations

a.a., amino acid residues; HPLC-MS, high-performance liquid chromatography/mass spectrometry; m/z, mass-to-charge ratio; TEM, transmission electron microscopy.

An accelerated surface-mediated stress assay of antibody instability for developability studies

High physical stability is required for the development of monoclonal antibodies (mAbs) into successful therapeutic products. Developability assays are used to predict physical stability issues such as high viscosity and poor conformational stability, but protein aggregation remains a challenging property to predict. Among different types of stresses, air–water and solid–liquid interfaces are well known to potentially trigger protein instability and induce aggregation. Yet, in contrast to the increasing number of developability assays to evaluate bulk properties, there is still a lack of experimental methods to evaluate antibody stability against interfaces. Here, we investigate the potential of a hydrophobic nanoparticle surface-mediated stress assay to assess the stability of mAbs during the early stages of development. We evaluate this surface-mediated accelerated stability assay on a rationally designed library of 14 variants of a humanized IgG4, featuring a broad span of solubility values and other developability properties. The assay could identify variants characterized by high instability against agitation in the presence of air–water interfaces. Remarkably, for the set of investigated molecules, we observe strong correlations between the extent of aggregation induced by the surface-mediated stress assay and other developability properties of the molecules, such as aggregation upon storage at 45°C, self-association (evaluated by affinity-capture self-interaction nanoparticle spectroscopy) and nonspecific interactions (estimated by cross-interaction chromatography, stand-up monolayer chromatography (SMAC), SMAC*). This highly controlled surface-mediated stress assay has the potential to complement and increase the ability of the current set of screening techniques to assess protein aggregation and developability potential of mAbs during the early stages of drug development.

Abbreviations:AC-SINS: Affinity-Capture Self-Interaction Nanoparticle Spectroscopy; AMS: Ammonium sulfate precipitation; ANS: 1-anilinonaphtalene-8-sulfonate; CIC: Cross-interaction chromatography; DLS: Dynamic light scattering; HIC: Hydrophobic interaction chromatography; HNSSA: Hydrophobic nanoparticles surface-stress assay; mAb: Monoclonal antibody; NP: Nanoparticle; SEC: Size exclusion chromatography; SMAC: Stand-up monolayer chromatography; WT: Wild type

Exploring amyloid oligomers with peptide model systems

The assembly of amyloidogenic peptides and proteins, such as the β-amyloid peptide, α-synuclein, huntingtin, tau, and islet amyloid polypeptide, into amyloid fibrils and oligomers is directly linked to amyloid diseases, such as Alzheimer's, Parkinson's, and Huntington's diseases, frontotemporal dementias, and type II diabetes. Although amyloid oligomers have emerged as especially important in amyloid diseases, high-resolution structures of the oligomers formed by full-length amyloidogenic peptides and proteins have remained elusive. Investigations of oligomers assembled from fragments or stabilized β-hairpin segments of amyloidogenic peptides and proteins have allowed investigators to illuminate some of the structural, biophysical, and biological properties of amyloid oligomers. Here, we summarize recent advances in the application of these peptide model systems to investigate and understand the structures, biological properties, and biophysical properties of amyloid oligomers.

Evaluation of in silico tools for the prediction of protein and peptide aggregation on diverse datasets

Several prediction algorithms and tools have been developed in the last two decades to predict protein and peptide aggregation. These in silico tools aid to predict the aggregation propensity and amyloidogenicity as well as the identification of aggregation-prone regions. Despite the immense interest in the field, it is of prime importance to systematically compare these algorithms for their performance. In this review, we have provided a rigorous performance analysis of nine prediction tools using a variety of assessments. The assessments were carried out on several non-redundant datasets ranging from hexapeptides to protein sequences as well as amyloidogenic antibody light chains to soluble protein sequences. Our analysis reveals the robustness of the current prediction tools and the scope for improvement in their predictive performances. Insights gained from this work provide critical guidance to the scientific community on advantages and limitations of different aggregation prediction methods and make informed decisions about their research needs.

Size-based Degradation of Therapeutic Proteins - Mechanisms, Modelling and Control

Protein therapeutics are in great demand due to their effectiveness towards hard-to-treat diseases. Despite their high demand, these bio-therapeutics are very susceptible to degradation via aggregation, fragmentation, oxidation, and reduction, all of which are very likely to affect the quality and efficacy of the product. Mechanisms and modelling of these degradation (aggregation and fragmentation) pathways is critical for gaining a deeper understanding of stability of these products. This review aims to provide a summary of major developments that have occurred towards unravelling the mechanisms of size-based protein degradation (particularly aggregation and fragmentation), modelling of these size-based degradation pathways, and their control. Major caveats that remain in our understanding and control of size-based protein degradation have also been presented and discussed.

Challenges for design of aggregation-resistant variants of granulocyte colony-stimulating factor

Non-native protein aggregation is a long-standing issue in pharmaceutical biotechnology. A rational design approach was used in order to identify variants of recombinant human granulocyte colony-stimulating factor (rhG-CSF) with lower aggregation propensity at solution conditions that are typical of commercial formulation. The approach used aggregation-prone-region (APR) predictors to select single amino acid substitutions that were predicted to decrease intrinsic aggregation propensity (IAP). The results of static light scattering temperature-ramps and chemical unfolding experiments demonstrated that none of the selected variants exhibited improved aggregation resistance, and the apparent conformational stability of each variant was lower than that of WT. Aggregation studies under partly denaturing conditions suggested that the IAP of at least one variant remained unaltered. Overall, this study highlights a general challenge in designing aggregation resistance for proteins, due to the need to accurately predict both APRs and conformational stability.

Potential aggregation hot spots in recombinant human keratinocyte growth factor: a computational study

The recombinant human keratinocyte growth factor (rhKGF) is a highly aggregation-prone therapeutic protein. The high aggregation liability of rhKGF is manifested by loss of the monomeric state, and accumulation of the aggregated species even at moderate temperatures. Here, we analyzed the rhKGF for its vulnerability toward aggregation by detection of aggregation-prone regions (APRs) using several sequence-based computational tools including TANGO, ZipperDB, AGGRESCAN, Zyggregator, Camsol, PASTA, SALSA, WALTZ, SODA, Amylpred, AMYPDB, and structure-based tools including SolubiS, CamSol structurally corrected, Aggrescan3D and spatial aggregation propensity (SAP) algorithm. The sequence-based prediction of APRs in rhKGF indicated that they are mainly located at positions 10-30, 40-60, 61-66, 88-120, and 130-140. Mapping on the rhKGF structure revealed that most of these residues including F16-R25, I43, E45, R47-I56, F61, Y62, N66, L88-E91, E108-F110, A112, N114, T131, and H133-T140 are surface-exposed in the native state which can promote aggregation without major unfolding event, or the conformational change may occur in the oligomers. The other regions are buried in the native state and their contribution to non-native aggregation is mediated by a preceding unfolding event. The structure-based prediction of APRs using the SAP tool limited the number of identified APRs to the dynamically-exposed hydrophobic residues including V12, A50, V51, L88, I89, L90, I118, L135, and I139 mediating the native-state aggregation. Our analysis of APRs in rhKGF identified the regions determining the intrinsic aggregation propensity of the rhKGF which are the candidate positions for engineering the rhKGF to reduce its aggregation tendency.Communicated by Ramaswamy H. Sarma.

Protein aggregation: in silico algorithms and applications

Protein aggregation is a topic of immense interest to the scientific community due to its role in several neurodegenerative diseases/disorders and industrial importance. Several in silico techniques, tools, and algorithms have been developed to predict aggregation in proteins and understand the aggregation mechanisms. This review attempts to provide an essence of the vast developments in in silico approaches, resources available, and future perspectives. It reviews aggregation-related databases, mechanistic models (aggregation-prone region and aggregation propensity prediction), kinetic models (aggregation rate prediction), and molecular dynamics studies related to aggregation. With a multitude of prediction models related to aggregation already available to the scientific community, the field of protein aggregation is rapidly maturing to tackle new applications.

Modeling and simulation in medical sciences: an overview of specific applications based on research experience in EMRI (Endocrinology and Metabolism Research Institute of Tehran University of Medical Sciences)

The concomitant use of various types of models (in silico, in vitro, and in vivo) has been exemplified here within the context of biomedical researches performed in the Endocrinology and Metabolism Research Institute (EMRI) of Tehran University of Medical Sciences. Two main research aeras have been discussed: the search for new small molecules as therapeutics for diabetes and related metabolic conditions, and diseases related to protein aggregation. Due to their multidisciplinary nature, the majority of these studies have needed the collaboration of different specialties. In both cases, a brief overview of the subject is provided through literature examples, and sequential use of these methods is described.

Williams G, Brocchini S, Awwad S. Injectables and Depots to Prolong Drug Action of Proteins and Peptides

Proteins and peptides have emerged in recent years to treat a wide range of multifaceted diseases such as cancer, diabetes and inflammation. The emergence of polypeptides has yielded advancements in the fields of biopharmaceutical production and formulation. Polypeptides often display poor pharmacokinetics, limited permeability across biological barriers, suboptimal biodistribution, and some proclivity for immunogenicity. Frequent administration of polypeptides is generally required to maintain adequate therapeutic levels, which can limit efficacy and compliance while increasing adverse reactions. Many strategies to increase the duration of action of therapeutic polypeptides have been described with many clinical products having been developed. This review describes approaches to optimise polypeptide delivery organised by the commonly used routes of administration. Future innovations in formulation may hold the key to the continued successful development of proteins and peptides with optimal clinical properties.

Efficient enzyme formulation promotes Leloir glycosyltransferases for glycoside synthesis

Sugar nucleotide-dependent (Leloir) glycosyltransferases are powerful catalysts for glycoside synthesis. Their applicability can be limited due to elaborate production of enzyme preparations deployable in biocatalytic processes. Here, we show that efficient enzyme formulation promotes glycosyltransferases for the synthesis of the natural C-glycoside nothofagin. Adding Brij-35 detergent (1 %, w/v) during sonication of the E. coli BL21-Gold (DE3) expression strain, recovery of Oryza sativa C-glycosyltransferase was enhanced by ∼3-fold, partly due to the release of enzyme activity trapped in insoluble pellet. Freeze drying of the resulting cell-free extract (∼17 U ml-1) reduced the volume ∼20-fold and gave ∼55 mg solids ml-1 liquid processed, with 83 % retention of the original activity and a specific activity of 0.20 U mg-1 solids. The Glycine max sucrose synthase was processed analogously, giving a solid enzyme preparation of 0.28 U mg-1 in 63 % yield. Both enzyme formulations were stable for several weeks. The glycosyltransferase cascade reaction for 3'-β-C-glucosylation of phloretin (60 mM; as inclusion complex with hydroxypropyl-β-cyclodextrin) from UDP-glucose (generated in situ by sucrose synthase from 500 mM sucrose and 0.5 mM UDP) showed excellent performance metrics (≥ 98 % yield; 3.2 g l-1 h-1 space-time yield; ∼90 regeneration cycles for UDP). Collectively, our study demonstrates a facile procedure for solid glycosyltransferase formulations practically usable in glycoside synthesis.

Aggregation and Amyloidogenicity of the Nuclear Coactivator Binding Domain of CREB-Binding Protein

The nuclear coactivator binding domain (NCBD) of transcriptional co-regulator CREB-binding protein (CBP) is an example of conformationally malleable proteins that can bind to structurally unrelated protein targets and adopt distinct folds in the respective protein complexes. Here, we show that the folding landscape of NCBD contains an alternative pathway that results in protein aggregation and self-assembly into amyloid fibers. The initial steps of such protein misfolding are driven by intermolecular interactions of its N-terminal α-helix bringing multiple NCBD molecules into contact. These oligomers then undergo slow but progressive interconversion into β-sheet-containing aggregates. To reveal the concealed aggregation potential of NCBD we used a chemically synthesized mirror-image d-NCBD form. The addition of d-NCBD promoted self-assembly into amyloid precipitates presumably due to formation of thermodynamically more stable racemic β-sheet structures. The unexpected aggregation of NCBD needs to be taken into consideration given the multitude of protein-protein interactions and resulting biological functions mediated by CBP.

WITHDRAWN: Efficient enzyme formulation promotes Leloir glycosyltransferases for glycoside synthesis

The Publisher regrets that this article is an accidental duplication of an article that has already been published in BIOTEC, 322C (2020) 74-78, https://doi.org/10.1016/j.jbiotec.2020.06.023. The duplicate article has therefore been withdrawn. The full Elsevier Policy on Article Withdrawal can be found at https://www.elsevier.com/about/our-business/policies/article-withdrawal.

Aggrescan3D (A3D) 2.0: prediction and engineering of protein solubility

Protein aggregation is a hallmark of a growing number of human disorders and constitutes a major bottleneck in the manufacturing of therapeutic proteins. Therefore, there is a strong need of in-silico methods that can anticipate the aggregative properties of protein variants linked to disease and assist the engineering of soluble protein-based drugs. A few years ago, we developed a method for structure-based prediction of aggregation properties that takes into account the dynamic fluctuations of proteins. The method has been made available as the Aggrescan3D (A3D) web server and applied in numerous studies of protein structure-aggregation relationship. Here, we present a major update of the A3D web server to version 2.0. The new features include: extension of dynamic calculations to significantly larger and multimeric proteins, simultaneous prediction of changes in protein solubility and stability upon mutation, rapid screening for functional protein variants with improved solubility, a REST-ful service to incorporate A3D calculations in automatic pipelines, and a new, enhanced web server interface. A3D 2.0 is freely available at: http://biocomp.chem.uw.edu.pl/A3D2/

The uniqueness of flow in probing the aggregation behavior of clinically relevant antibodies

The development of therapeutic monoclonal antibodies (mAbs) can be hindered by their tendency to aggregate throughout their lifetime, which can illicit immunogenic responses and render mAb manufacturing unfeasible. Consequently, there is a need to identify mAbs with desirable thermodynamic stability, solubility, and lack of self-association. These behaviors are assessed using an array of in silico and in vitro assays, as no single assay can predict aggregation and developability. We have developed an extensional and shear flow device (EFD), which subjects proteins to defined hydrodynamic forces which mimic those experienced in bioprocessing. Here, we utilize the EFD to explore the aggregation propensity of 33 IgG1 mAbs, whose variable domains are derived from clinical antibodies. Using submilligram quantities of material per replicate, wide-ranging EFD-induced aggregation (9-81% protein in pellet) was observed for these mAbs, highlighting the EFD as a sensitive method to assess aggregation propensity. By comparing the EFD-induced aggregation data to those obtained previously from 12 other biophysical assays, we show that the EFD provides distinct information compared with current measures of adverse biophysical behavior. Assessing a candidate's liability to hydrodynamic force thus adds novel insight into the rational selection of developable mAbs that complements other assays.

Short disordered protein segment regulates cross-species transmission of a yeast prion

Soluble prion proteins contingently encounter foreign prion aggregates, leading to cross-species prion transmission. However, how its efficiency is regulated by structural fluctuation of the host soluble prion protein remains unsolved. In the present study, through the use of two distantly related yeast prion Sup35 proteins, we found that a specific conformation of a short disordered segment governs interspecies prion transmissibility. Using a multidisciplinary approach including high-resolution NMR and molecular dynamics simulation, we identified critical residues within this segment that allow interspecies prion transmission in vitro and in vivo, by locally altering dynamics and conformation of soluble prion proteins. Remarkably, subtle conformational differences caused by a methylene group between asparagine and glutamine sufficed to change the short segment structure and substantially modulate the cross-seeding activity. Thus, our findings uncover how conformational dynamics of the short segment in the host prion protein impacts cross-species prion transmission. More broadly, our study provides mechanistic insights into cross-seeding between heterologous proteins.

Using protein engineering to understand and modulate aggregation

Protein aggregation occurs through a variety of mechanisms, initiated by the unfolded, non-native, or even the native state itself. Understanding the molecular mechanisms of protein aggregation is challenging, given the array of competing interactions that control solubility, stability, cooperativity and aggregation propensity. An array of methods have been developed to interrogate protein aggregation, spanning computational algorithms able to identify aggregation-prone regions, to deep mutational scanning to define the entire mutational landscape of a protein's sequence. Here, we review recent advances in this exciting and emerging field, focussing on protein engineering approaches that, together with improved computational methods, hold promise to predict and control protein aggregation linked to human disease, as well as facilitating the manufacture of protein-based therapeutics.

Combining molecular dynamics simulations and experimental analyses in protein misfolding

The fold of a protein determines its function and its misfolding can result in loss-of-function defects. In addition, for certain proteins their misfolding can lead to gain-of-function toxicities resulting in protein misfolding diseases such as Alzheimer's, Parkinson's, or the prion diseases. In all of these diseases one or more proteins misfold and aggregate into disease-specific assemblies, often in the form of fibrillar amyloid deposits. Most, if not all, protein misfolding diseases share a fundamental molecular mechanism that governs the misfolding and subsequent aggregation. A wide variety of experimental methods have contributed to our knowledge about misfolded protein aggregates, some of which are briefly described in this review. The misfolding mechanism itself is difficult to investigate, as the necessary timescale and resolution of the misfolding events often lie outside of the observable parameter space. Molecular dynamics simulations fill this gap by virtue of their intrinsic, molecular perspective and the step-by-step iterative process that forms the basis of the simulations. This review focuses on molecular dynamics simulations and how they combine with experimental analyses to provide detailed insights into protein misfolding and the ensuing diseases.

Prediction of amyloid aggregation rates by machine learning and feature selection

A novel data-based machine learning algorithm for predicting amyloid aggregation rates is reported in this paper. Based on a highly nonlinear projection from 16 intrinsic features of a protein and 4 extrinsic features of the environment to the protein aggregation rate, a feedforward fully connected neural network (FCN) with one hidden layer is trained on a dataset composed of 21 different kinds of amyloid proteins and tested on 4 rest proteins. FCN shows a much better performance than traditional algorithms, such as multivariable linear regression and support vector regression, with an average accuracy higher than 90%. Furthermore, by the correlation analysis and the principal component analysis, seven key features, folding energy, HP patterns for helix, sheet and helices cross membrane, pH, ionic strength, and protein concentration, are shown to constitute a minimum feature set for characterizing the amyloid aggregation kinetics.

Binding symmetry and surface flexibility mediate antibody self-association

Solution stability is an important factor in the optimization of engineered biotherapeutic candidates such as monoclonal antibodies because of its possible effects on manufacturability, pharmacology, efficacy and safety. A detailed atomic understanding of the mechanisms governing self-association of natively folded protein monomers is required to devise predictive tools to guide screening and re-engineering along the drug development pipeline. We investigated pairs of affinity-matured full-size antibodies and observed drastically different propensities to aggregate from variants differing by a single amino-acid. Biophysical testing showed that antigen-binding fragments (Fabs) from the aggregating antibodies also reversibly associated with equilibrium dissociation constants in the low-micromolar range. Crystal structures (PDB accession codes 6MXR, 6MXS, 6MY4, 6MY5) and bottom-up hydrogen-exchange mass spectrometry revealed that Fab self-association occurs in a symmetric mode that involves the antigen complementarity-determining regions. Subtle local conformational changes incurred upon point mutation of monomeric variants foster formation of complementary polar interactions and hydrophobic contacts to generate a dimeric Fab interface. Testing of popular in silico tools generally indicated low reliabilities for predicting the aggregation propensities observed. A structure-aggregation data set is provided here in order to stimulate further improvements of in silico tools for prediction of native aggregation. Incorporation of intermolecular docking, conformational flexibility, and short-range packing interactions may all be necessary features of the ideal algorithm.

Identification of amyloidogenic peptides via optimized integrated features space based on physicochemical properties and PSSM

At present, the identification of amyloid becomes more and more essential and meaningful. Because its mis-aggregation may cause some diseases such as Alzheimer's and Parkinson's diseases. This paper focus on the classification of amyloidogenic peptides and a novel feature representation called PhyAve_PSSMDwt is proposed. It includes two parts. One is based on physicochemical properties involving hydrophilicity, hydrophobicity, aggregation tendency, packing density and H-bonding which extracts 15-dimensional features in total. And the other is 60-dimensional features through recursive feature elimination from PSSM by discrete wavelet transform. In this period, sliding window is introduced to reconstruct PSSM so that the evolutionary information of short sequences can still be extracted. At last, the support vector machine is adopted as a classifier. The experimental result on Pep424 dataset shows that PSSM's information makes a great contribution on performance. And compared with other existing methods, our results after cross-validation increase by 3.1%, 3.3%, 0.136 and 0.007 in accuracy, specificity, Matthew's correlation coefficient and AUC value, respectively. It indicates that our method is effective and competitive.

The Relation between α-Helical Conformation and Amyloidogenicity

Amyloid fibrils are stable aggregates of misfolded proteins and polypeptides that are insoluble and resistant to protease activity. Abnormal formation of amyloid fibrils in vivo may lead to neurodegenerative disorders and other systemic amyloidosis, such as Alzheimer's, Parkinson's, and atherosclerosis. Because of their clinical importance, amyloids are under intense scientific research. It is believed that short polypeptide segments within proteins are responsible for the transformation of correctly folded proteins into parts of larger amyloid fibrils and that this transition is modulated by environmental factors, such as pH, salt concentration, interaction with the cell membrane, and interaction with metal ions. Most studies on amyloids focus on the amyloidogenic sequences. The focus of this study is on the structure of the amyloidogenic α-helical segments because the α-helical secondary structure has been recognized to be a key player in different stages of the amyloidogenesis process. We have previously shown that the α-helical conformation may be expressed by two parameters (θ and ρ) that form orthogonal coordinates based on the Ramachandran dihedrals (φ and ψ) and provide an illuminating interpretation of the α-helical conformation. By performing statistical analysis on α-helical conformations found in the Protein Data Bank, an apparent relation between α-helical conformation, as expressed by θ and ρ, and amyloidogenicity is revealed. Remarkably, random amino acid sequences, whose helical structures were obtained from the most probable dihedral angles, revealed the same dependency of amyloidogenicity, suggesting the importance of α-helical structure as opposed to sequence.

Chloroplast SRP43 acts as a chaperone for glutamyl-tRNA reductase, the rate-limiting enzyme in tetrapyrrole biosynthesis

Assembly of light-harvesting complexes requires synchronization of chlorophyll (Chl) biosynthesis with biogenesis of light-harvesting Chl a/b-binding proteins (LHCPs). The chloroplast signal recognition particle (cpSRP) pathway is responsible for transport of nucleus-encoded LHCPs in the stroma of the plastid and their integration into the thylakoid membranes. Correct folding and assembly of LHCPs require the incorporation of Chls, whose biosynthesis must therefore be precisely coordinated with membrane insertion of LHCPs. How the spatiotemporal coordination between the cpSRP machinery and Chl biosynthesis is achieved is poorly understood. In this work, we demonstrate a direct interaction between cpSRP43, the chaperone that mediates LHCP targeting and insertion, and glutamyl-tRNA reductase (GluTR), a rate-limiting enzyme in tetrapyrrole biosynthesis. Concurrent deficiency for cpSRP43 and the GluTR-binding protein (GBP) additively reduces GluTR levels, indicating that cpSRP43 and GBP act nonredundantly to stabilize GluTR. The substrate-binding domain of cpSRP43 binds to the N-terminal region of GluTR, which harbors aggregation-prone motifs, and the chaperone activity of cpSRP43 efficiently prevents aggregation of these regions. Our work thus reveals a function of cpSRP43 in Chl biosynthesis and suggests a striking mechanism for posttranslational coordination of LHCP insertion with Chl biosynthesis.