Prediction of amyloid aggregation in vivo

Aggrescan4D: A comprehensive tool for pH-dependent analysis and engineering of protein aggregation propensity

Aggrescan4D (A4D) is an advanced computational tool designed for predicting protein aggregation, leveraging structural information and the influence of pH. Building upon its predecessor, Aggrescan3D (A3D), A4D has undergone numerous enhancements aimed at assisting the improvement of protein solubility. This manuscript reviews A4D's updated functionalities and explains the fundamental principles behind its pH-dependent calculations. Additionally, it presents an antibody case study to evaluate its performance in comparison with other structure-based predictors. Notably, A4D integrates advanced protein engineering protocols with pH-dependent calculations, enhancing its utility in advising solubility-enhancing mutations. A4D considers the impact of structural flexibility on aggregation propensities, and includes a large set of precalculated predictions. These capabilities should help to open new avenues for both understanding and managing protein aggregation. A4D is accessible through a dedicated web server at https://biocomp.chem.uw.edu.pl/a4d/.

Advanced computational approaches to understand protein aggregation

Protein aggregation is a widespread phenomenon implicated in debilitating diseases like Alzheimer's, Parkinson's, and cataracts, presenting complex hurdles for the field of molecular biology. In this review, we explore the evolving realm of computational methods and bioinformatics tools that have revolutionized our comprehension of protein aggregation. Beginning with a discussion of the multifaceted challenges associated with understanding this process and emphasizing the critical need for precise predictive tools, we highlight how computational techniques have become indispensable for understanding protein aggregation. We focus on molecular simulations, notably molecular dynamics (MD) simulations, spanning from atomistic to coarse-grained levels, which have emerged as pivotal tools in unraveling the complex dynamics governing protein aggregation in diseases such as cataracts, Alzheimer's, and Parkinson's. MD simulations provide microscopic insights into protein interactions and the subtleties of aggregation pathways, with advanced techniques like replica exchange molecular dynamics, Metadynamics (MetaD), and umbrella sampling enhancing our understanding by probing intricate energy landscapes and transition states. We delve into specific applications of MD simulations, elucidating the chaperone mechanism underlying cataract formation using Markov state modeling and the intricate pathways and interactions driving the toxic aggregate formation in Alzheimer's and Parkinson's disease. Transitioning we highlight how computational techniques, including bioinformatics, sequence analysis, structural data, machine learning algorithms, and artificial intelligence have become indispensable for predicting protein aggregation propensity and locating aggregation-prone regions within protein sequences. Throughout our exploration, we underscore the symbiotic relationship between computational approaches and empirical data, which has paved the way for potential therapeutic strategies against protein aggregation-related diseases. In conclusion, this review offers a comprehensive overview of advanced computational methodologies and bioinformatics tools that have catalyzed breakthroughs in unraveling the molecular basis of protein aggregation, with significant implications for clinical interventions, standing at the intersection of computational biology and experimental research.

A3D Model Organism Database (A3D-MODB): a database for proteome aggregation predictions in model organisms

Protein aggregation has been associated with aging and different pathologies and represents a bottleneck in the industrial production of biotherapeutics. Numerous past studies performed in Escherichia coli and other model organisms have allowed to dissect the biophysical principles underlying this process. This knowledge fuelled the development of computational tools, such as Aggrescan 3D (A3D) to forecast and re-design protein aggregation. Here, we present the A3D Model Organism Database (A3D-MODB) http://biocomp.chem.uw.edu.pl/A3D2/MODB, a comprehensive resource for the study of structural protein aggregation in the proteomes of 12 key model species spanning distant biological clades. In addition to A3D predictions, this resource incorporates information useful for contextualizing protein aggregation, including membrane protein topology and structural model confidence, as an indirect reporter of protein disorder. The database is openly accessible without any need for registration. We foresee A3D-MOBD evolving into a central hub for conducting comprehensive, multi-species analyses of protein aggregation, fostering the development of protein-based solutions for medical, biotechnological, agricultural and industrial applications.

Protein reservoirs of seeds are amyloid composites employed differentially for germination and seedling emergence

Seed protein localization in seed storage protein bodies (SSPB) and their significance in germination are well recognized. SSPB are spherical and contain an assembly of water-soluble and salt-soluble proteins. Although the native structures of some SSPB proteins are explored, their structural arrangement to the functional correlation in SSPB remains unknown. SSPB are morphologically analogous to electron-dense amyloid-containing structures reported in other organisms. Here, we show that wheat, mungbean, barley, and chickpea SSPB exhibit a speckled pattern of amyloids interspersed in an amyloid-like matrix along with native structures, suggesting the composite nature of SSPB. This is confirmed by multispectral imaging methods, electron microscopy, infrared, and X-ray diffraction analysis, using in situ tissue sections, ex vivo protoplasts, and in vitro SSPB. Laser capture microdissection coupled with peptide fingerprinting has shown that globulin 1 and 3 in wheat, and 8S globulin and conglycinin in mungbean are the major amyloidogenic proteins. The amyloid composites undergo a sustained degradation during germination and seedling growth, facilitated by an intricate interplay of plant hormones and proteases. These results would lay down the foundation for understanding the amyloid composite structure during SSPB biogenesis and its evolution across the plant kingdom and have implications in both basic and applied plant biology.

A3DyDB: exploring structural aggregation propensities in the yeast proteome

BACKGROUND

The budding yeast Saccharomyces cerevisiae (S. cerevisiae) is a well-established model system for studying protein aggregation due to the conservation of essential cellular structures and pathways found across eukaryotes. However, limited structural knowledge of its proteome has prevented a deeper understanding of yeast functionalities, interactions, and aggregation.

RESULTS

In this study, we introduce the A3D yeast database (A3DyDB), which offers an extensive catalog of aggregation propensity predictions for the S. cerevisiae proteome. We used Aggrescan 3D (A3D) and the newly released protein models from AlphaFold2 (AF2) to compute the structure-based aggregation predictions for 6039 yeast proteins. The A3D algorithm exploits the information from 3D protein structures to calculate their intrinsic aggregation propensities. To facilitate simple and intuitive data analysis, A3DyDB provides a user-friendly interface for querying, browsing, and visualizing information on aggregation predictions from yeast protein structures. The A3DyDB also allows for the evaluation of the influence of natural or engineered mutations on protein stability and solubility. The A3DyDB is freely available at http://biocomp.chem.uw.edu.pl/A3D2/yeast .

CONCLUSION

The A3DyDB addresses a gap in yeast resources by facilitating the exploration of correlations between structural aggregation propensity and diverse protein properties at the proteome level. We anticipate that this comprehensive database will become a standard tool in the modeling of protein aggregation and its implications in budding yeast.

Relationship between Changes in the Protein Folding Pathway and the Process of Amyloid Formation: The Case of Bovine Carbonic Anhydrase II

Many proteins form amyloid fibrils only under conditions when the probability of transition from a native (structured, densely packed) to an intermediate (labile, destabilized) state is increased. It implies the assumption that some structural intermediates are more convenient for amyloid formation than the others. Hence, if a mutation affects the protein folding pathway, one should expect that this mutation could affect the rate of amyloid formation as well. In the current work, we have compared the effects of amino acid substitutions of bovine carbonic anhydrase II on its unfolding pathway and on its ability to form amyloids at acidic pH and an elevated temperature. Wild-type protein and four mutant forms (L78A, L139A, I208A, and M239A) were studied. We analyzed the change of the protein unfolding pathway by the time-resolved fluorescence technique and the process of amyloid formation by thioflavin T fluorescence assay and electron microscopy. It was revealed that I208A substitution accelerates amyloid formation and affects the structure of the late (molten globule-like)-intermediate state of carbonic anhydrase, whereas the other mutations slow down the growth of amyloids and have either no effect on the unfolding pathway (L78A, L139A) or alter the conformational states arising at the early unfolding stage (M239A).

In silico characterization of fructosyl peptide oxidase properties from Eupenicillium terrenum

Fructosyl peptide oxidase (FPOX) enzyme from Eupenicillium terrenum has a high potential to be applied as a diagnostic enzyme. The aim of the present study is the characterization of FPOX from E. terrenum using different bioinformatics tools. The computational prediction of the RNA and protein secondary structures of FPOX, solubility profile in Escherichia coli, stability, domains, and functional properties were performed. In the FPOX protein, six motifs were detected. The d-amino acid oxidase motif was found as the most important motif that is a FAD-dependent oxidoreductase. The cysteines including 97, 154, 234, 280, and 360 showed a lower score than -10 that have a low possibility for participitation in the formation of the SS bond. The 56.52% of FPOX amino acids are nonpolar. Random coils are dominant in the FPOX sequence, followed by alpha-helix and extended strand. The fpox gene is capable of generating a stable RNA secondary structure (-423.90 kcal/mol) in E. coli. FPOX has a large number of hydrophobic amino acids. FPOX showed a low solubility in E. coli which has several aggregation-prone sites in its 3-D structure. According to the scores, the best mutation candidate for increasing solubility was the conversion of methionine 302 to arginine. The melting temperature of FPOX based on its amino acid sequence was 55°C to 65°C. The amounts of thermodynamic parameters for the FPOX enzyme were -137.4 kcal/mol, -3.59 kcal/(mol K), and -6.8 kcal/mol for standard folding enthalpy, heat capacity, and folding free energy, respectively. In conclusion, the in silico study of proteins can provide a valuable method for better understanding the protein properties and functions for use in our purposes.

SOD1 mutations associated with amyotrophic lateral sclerosis analysis of variant severity

Mutations in superoxide dismutase 1 gene (SOD1) are linked to amyotrophic lateral sclerosis (ALS), a neurodegenerative disorder predominantly affecting upper and lower motor neurons. The clinical phenotype of ALS shows inter- and intrafamilial heterogeneity. The aim of the study was to analyze the relations between individual SOD1 mutations and the clinical presentation using in silico methods to assess the SOD1 mutations severity. We identified SOD1 causative variants in a group of 915 prospectively tested consecutive Polish ALS patients from a neuromuscular clinical center, performed molecular modeling of mutated SOD1 proteins and in silico analysis of mutation impact on clinical phenotype and survival analysis of associations between mutations and hazard of clinical end-points. Fifteen SOD1 mutations were identified in 21.1% familial and 2.3% sporadic ALS cases. Their effects on SOD1 protein structure and functioning inferred from molecular modeling and in silico analyses correlate well with the clinical data. Molecular modeling results support the hypothesis that folding intermediates rather than mature SOD1 protein give rise to the source of cytotoxic conformations in ALS. Significant associations between type of mutation and clinical end-points were found.

Chemometric Study of the Relative Aggregation Propensity of Position 19 Mutants of Aβ(1-42)

Background:

The importance of aromaticity vs. hydrophobicity of the central hydrophobic core (CHC, residues 17-20) in governing fibril formation in Aβ(1-42) has been the focus of an ongoing debate in the literature.

Introduction:

Mutations in the CHC (especially at Phe19 and Phe20) have been used to examine the relative impact of hydrophobicity and aromaticity on the degree of aggregation of Aβ(1-42). However, the results have not been conclusive.

Methods:

Partial least squares (PLS) modeling of aggregation rates, using reduced properties of a series of position 19 mutants, was employed to identify the physicochemical properties that had the greatest impact on the extent of aggregation.

Results:

The PLS models indicate that hydrophobicity at position 19 of Aβ(1-42) appears to be the primary and dominant factor in controlling Aβ(1-42) aggregation, with aromaticity having little effect.

Conclusions:

This study illustrates the value of using reduced properties of amino acids in conjunction with PLS modeling to investigate mutational effects in peptides and proteins, as the reduced properties can capture in a quantitative manner the different physicochemical properties of the amino acid side chains. In this particular study, hydrophobicity at position 19 was determined to be the dominant property controlling aggregation, while size, charge, and aromaticity had little impact.

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.

MILAMP: Multiple Instance Prediction of Amyloid Proteins

Amyloid proteins are implicated in several diseases such as Parkinson's, Alzheimer's, prion diseases, etc. In order to characterize the amyloidogenicity of a given protein, it is important to locate the amyloid forming hotspot regions within the protein as well as to analyze the effects of mutations on these proteins. The biochemical and biological assays used for this purpose can be facilitated by computational means. This paper presents a machine learning method that can predict hotspot amyloidogenic regions within proteins and characterize changes in their amyloidogenicity due to point mutations. The proposed method called MILAMP (Multiple Instance Learning of AMyloid Proteins) achieves high accuracy for identification of amyloid proteins, hotspot localization, and prediction of mutation effects on amyloidogenicity by integrating heterogenous data sources and exploiting common predictive patterns across these tasks through multiple instance learning. The paper presents comprehensive benchmarking experiments to test the predictive performance of MILAMP in comparison to previously published state of the art techniques for amyloid prediction. The python code for the implementation and webserver for MILAMP is available at the URL: http://faculty.pieas.edu.pk/fayyaz/software.html#MILAMP.

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.

Urea titration of a lipase from Pseudomonas sp. reveals four different conformational states, with a stable partially folded state explaining its high aggregation propensity

The conversion of soluble proteins into amyloid fibrils has importance in protein chemistry, biology, biotechnology and medicine. A novel lipase from Pseudomonas sp. was previously shown to have an extremely high aggregation propensity. It was therefore herein studied to elucidate the physicochemical and structural determinants of this extreme behaviour. Amyloid-like structures were found to form in samples up to 2.5-3.0 M using Thioflavin T fluorescence and Congo red binding assays. However, dynamic light scattering (DLS), static light scattering and turbidimetry revealed the existence of aggregates up to 4.0 M urea, without amyloid-like structure. Two monomeric conformational states were detected with intrinsic fluorescence, 8-anilinonaphthalene-1-sulfonate (ANS) binding and circular dichroism. These were further characterized in 7.5 M and 4.5 M urea using enzymatic activity measurements, tryptophan fluorescence quenching, DLS and nuclear magnetic resonance (NMR) and were found to consist of a largely disordered and a partially folded state, respectively, with the latter appearing stable, cooperative, fairly compact, non-active, α-helical, with largely buried hydrophobic residues. The persistence of a stable structure up to high concentrations of urea, in the absence of sequence characteristics typical of a high intrinsic aggregation propensity, explains the high tendency of this enzyme to form amyloid-like structures.

MIRRAGGE - Minimum Information Required for Reproducible AGGregation Experiments

Reports on phase separation and amyloid formation for multiple proteins and aggregation-prone peptides are recurrently used to explore the molecular mechanisms associated with several human diseases. The information conveyed by these reports can be used directly in translational investigation, e.g., for the design of better drug screening strategies, or be compiled in databases for benchmarking novel aggregation-predicting algorithms. Given that minute protocol variations determine different outcomes of protein aggregation assays, there is a strong urge for standardized descriptions of the different types of aggregates and the detailed methods used in their production. In an attempt to address this need, we assembled the Minimum Information Required for Reproducible Aggregation Experiments (MIRRAGGE) guidelines, considering first-principles and the established literature on protein self-assembly and aggregation. This consensus information aims to cover the major and subtle determinants of experimental reproducibility while avoiding excessive technical details that are of limited practical interest for non-specialized users. The MIRRAGGE table (template available in Supplementary Information) is useful as a guide for the design of new studies and as a checklist during submission of experimental reports for publication. Full disclosure of relevant information also enables other researchers to reproduce results correctly and facilitates systematic data deposition into curated databases.

Detection of Protein Aggregation in Live Plasmodium Parasites

The rapid evolution of resistance in the malaria parasite to every single drug developed against it calls for the urgent identification of new molecular targets. Using a stain specific for the detection of intracellular amyloid deposits in live cells, we have detected the presence of abundant protein aggregates in Plasmodium falciparum blood stages and female gametes cultured in vitro, in the blood stages of mice infected by Plasmodium yoelii, and in the mosquito stages of the murine malaria species Plasmodium berghei Aggregated proteins could not be detected in early rings, the parasite form that starts the intraerythrocytic cycle. A proteomics approach was used to pinpoint actual aggregating polypeptides in functional P. falciparum blood stages, which resulted in the identification of 369 proteins, with roles particularly enriched in nuclear import-related processes. Five aggregation-prone short peptides selected from this protein pool exhibited different aggregation propensity according to Thioflavin-T fluorescence measurements, and were observed to form amorphous aggregates and amyloid fibrils in transmission electron microscope images. The results presented suggest that generalized protein aggregation might have a functional role in malaria parasites. Future antimalarial strategies based on the upsetting of the pathogen's proteostasis and therefore affecting multiple gene products could represent the entry to new therapeutic approaches.

Loss of angiogenin function is related to earlier ALS onset and a paradoxical increase in ALS duration

0.5-1% of ALS (Amyotrophic Lateral Sclerosis) and Parkinson's disease (PD) are associated with mutations in the angiogenin (ANG). These mutations are thought to cause disease through a loss of ANG function, but this hypothesis has not been evaluated statistically. In addition, the potential for ANG to promote disease has not been considered. With the goal of better defining the etiology of ANG-ALS, we assembled all clinical onset and disease duration data and determined if these were correlated with biochemical properties of ANG variants. Loss of ANG stability and ribonuclease activity were found to correlate with early ALS onset, confirming an aspect of the prevailing model of ANG-ALS. Conversely, loss of ANG stability and ribonuclease activity correlated with longer survival following diagnosis, which is inconsistent with the prevailing model. These results indicate that functional ANG appears to decrease the risk of developing ALS but exacerbate ALS once in progress. These findings are rationalized in terms of studies demonstrating that distinct mechanisms contribute to ALS onset and progression and propose that ANG replacement or stabilization would benefit pre-symptomatic ANG-ALS patients. However, this study challenges the prevailing hypothesis that augmenting ANG will benefit symptomatic ANG-ALS patients. Instead, our results suggest that silencing of ANG activity may be beneficial for symptomatic ALS patients. This study will serve as a call-to-arms for neurologists to consistently publish ALS and PD patient's clinical data-if all ANG-ALS patients' data were available our findings could be tested with considerable statistical power.

Molecular complementarity and structural heterogeneity within co-assembled peptide β-sheet nanofibers

Self-assembling peptides have garnered an increasing amount of interest as a functional biomaterial for medical and biotechnological applications. Recently, β-sheet peptide designs utilizing complementary pairs of peptides composed of charged amino acids positioned to impart co-assembly behavior have expanded the portfolio of peptide aggregate structures. Structural characterization of these charge-complementary peptide co-assemblies has been limited. Thus, it is not known how the complementary peptides organize on the molecular level. Through a combination of solid-state NMR measurements and discontinuous molecular dynamics simulations, we investigate the molecular organization of King-Webb peptide nanofibers. KW+ and KW- peptides co-assemble into near stoichiometric two-component β-sheet structures as observed by computational simulations and ¹³C-¹³C dipolar couplings. A majority of β-strands are aligned with antiparallel nearest neighbors within the β-sheet as previously suggested by Fourier transform infrared spectroscopy measurements. Surprisingly, however, a significant proportion of β-strand neighbors are parallel. While charge-complementary peptides were previously assumed to organize in an ideal (AB)_n pattern, dipolar recoupling measurements on isotopically diluted nanofiber samples reveal a non-negligible amount of self-associated (AA and BB) pairs. Furthermore, computational simulations predict these different structures can coexist within the same nanofiber. Our results highlight structural disorder at the molecular level in a charge-complementary peptide system with implications on co-assembling peptide designs.

Application of machine learning to predict monomer retention of therapeutic proteins after long term storage

An important aspect of initial developability assessments as well formulation development and selection of therapeutic proteins is the evaluation of data obtained under accelerated stress condition, i.e. at elevated temperatures. We propose the application of artificial neural networks (ANNs) to predict long term stability in real storage condition from accelerated stability studies and other high-throughput biophysical properties e.g. the first apparent temperature of unfolding (T_m). Our models have been trained on therapeutic relevant proteins, including monoclonal antibodies, in various pharmaceutically relevant formulations. Further, we developed network architectures with good prediction power using the least amount of input features, i.e. experimental effort to train the network. This provides an empiric means to highlight the most important parameters in the prediction of real-time protein stability. Further, several models were developed by a different validation means (i.e. leave-one-protein-out cross-validation) to test the robustness and the limitations of our approach. Finally, we apply surrogate machine learning algorithms (e.g. linear regression) to build trust in the ANNs decision making procedure and to highlight the connection between the leading inputs and the outputs.

pH-Dependent Aggregation in Intrinsically Disordered Proteins Is Determined by Charge and Lipophilicity

Protein aggregation is associated with an increasing number of human disorders and premature aging. Moreover, it is a central concern in the manufacturing of recombinant proteins for biotechnological and therapeutic applications. Nevertheless, the unique architecture of protein aggregates is also exploited by nature for functional purposes, from bacteria to humans. The relevance of this process in health and disease has boosted the interest in understanding and controlling aggregation, with the concomitant development of a myriad of algorithms aimed to predict aggregation propensities. However, most of these programs are blind to the protein environment and, in particular, to the influence of the pH. Here, we developed an empirical equation to model the pH-dependent aggregation of intrinsically disordered proteins (IDPs) based on the assumption that both the global protein charge and lipophilicity depend on the solution pH. Upon its parametrization with a model IDP, this simple phenomenological approach showed unprecedented accuracy in predicting the dependence of the aggregation of both pathogenic and functional amyloidogenic IDPs on the pH. The algorithm might be useful for diverse applications, from large-scale analysis of IDPs aggregation properties to the design of novel reversible nanofibrillar materials.

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.

The Amyloid as a Ribbon-Like Micelle in Contrast to Spherical Micelles Represented by Globular Proteins

Selected amyloid structures available in the Protein Data Bank have been subjected to a comparative analysis. Classification is based on the distribution of hydrophobicity in amyloids that differ with respect to sequence, chain length, the distribution of beta folds, protofibril structure, and the arrangement of protofibrils in each superfibril. The study set includes the following amyloids: Aβ (1-42), which is listed as Aβ (15-40) and carries the D23N mutation, and Aβ (11-42) and Aβ (1-40), both of which carry the E22Δ mutation, tau amyloid, and α-synuclein. Based on the fuzzy oil drop model (FOD), we determined that, despite their conformational diversity, all presented amyloids adopt a similar structural pattern that can be described as a ribbon-like micelle. The same model, when applied to globular proteins, results in structures referred to as "globular micelles," emerging as a result of interactions between the proteins' constituent residues and the aqueous solvent. Due to their composition, amyloids are unable to attain entropically favorable globular forms and instead attempt to limit contact between hydrophobic residues and water by producing elongated structures. Such structures typically contain quasi hydrophobic cores that stretch along the fibril's long axis. Similar properties are commonly found in ribbon-like micelles, with alternating bands of high and low hydrophobicity emerging as the fibrils increase in length. Thus, while globular proteins are generally consistent with a 3D Gaussian distribution of hydrophobicity, the distribution instead conforms to a 2D Gaussian distribution in amyloid fibrils.

Identification of Novel 1,3,5-Triphenylbenzene Derivative Compounds as Inhibitors of Hen Lysozyme Amyloid Fibril Formation

Deposition of soluble proteins as insoluble amyloid fibrils is associated with a number of pathological states. There is a growing interest in the identification of small molecules that can prevent proteins from undergoing amyloid fibril formation. In the present study, a series of small aromatic compounds with different substitutions of 1,3,5-triphenylbenzene have been synthesized and their possible effects on amyloid fibril formation by hen egg white lysozyme (HEWL), a model protein for amyloid formation, and of their resulting toxicity were examined. The inhibitory effect of the compounds against HEWL amyloid formation was analyzed using thioflavin T and Congo red binding assays, atomic force microscopy, Fourier-transform infrared spectroscopy, and cytotoxicity assays, such as the 3-(4,5-Dimethylthiazol)-2,5-Diphenyltetrazolium Bromide (MTT) reduction assay and caspase-3 activity measurements. We found that all compounds in our screen were efficient inhibitors of HEWL fibril formation and their associated toxicity. We showed that electron-withdrawing substituents such as –F and –NO₂ potentiated the inhibitory potential of 1,3,5-triphenylbenzene, whereas electron-donating groups such as –OH, –OCH₃, and –CH₃ lowered it. These results may ultimately find applications in the development of potential inhibitors against amyloid fibril formation and its biologically adverse effects.

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.

Computational Assessment of Bacterial Protein Structures Indicates a Selection Against Aggregation

The aggregation of proteins compromises cell fitness, either because it titrates functional proteins into non-productive inclusions or because it results in the formation of toxic assemblies. Accordingly, computational proteome-wide analyses suggest that prevention of aggregation upon misfolding plays a key role in sequence evolution. Most proteins spend their lifetimes in a folded state; therefore, it is conceivable that, in addition to sequences, protein structures would have also evolved to minimize the risk of aggregation in their natural environments. By exploiting the AGGRESCAN3D structure-based approach to predict the aggregation propensity of >600 Escherichia coli proteins, we show that the structural aggregation propensity of globular proteins is connected with their abundance, length, essentiality, subcellular location and quaternary structure. These data suggest that the avoidance of protein aggregation has contributed to shape the structural properties of proteins in bacterial cells.

Aggregation rate of amyloid beta peptide is controlled by beta-content in monomeric state

Understanding the key factors that govern the rate of protein aggregation is of immense interest since protein aggregation is associated with a number of neurodegenerative diseases. Previous experimental and theoretical studies have revealed that the hydrophobicity, charge, and population of the fibril-prone monomeric state control the fibril formation rate. Because the fibril structures consist of cross beta sheets, it is widely believed that those sequences that have a high beta content (β) in the monomeric state should have high aggregation rates as the monomer can serve as a template for fibril growth. However, this important fact has never been explicitly proven, motivating us to carry out this study. Using replica exchange molecular dynamics simulation with implicit water, we have computed β of 19 mutations of amyloid beta peptide of 42 residues (Aβ42) for which the aggregation rate κ has been measured experimentally. We have found that κ depends on β in such a way that the higher the propensity to aggregation, the higher the beta content in the monomeric state. Thus, we have solved a long-standing problem of the dependence of fibril formation time of the β-structure on a quantitative level.

SolubiS: Optimizing Protein Solubility by Minimal Point Mutations

Protein solubility is adapted to endogeneous protein abundance in the cell where protein folding is also assisted by multiple chaperones. During recombinant protein production, purification and storage proteins are frequently handled at concentrations that are several orders of magnitude above their physiological concentration, often resulting in protein aggregation. Here we describe SolubiS, a method allowing for (1) detection of aggregation prone linear segments within a protein sequence and (2) identification of mutations that abolish the aggregation propensity of these segments without affecting the thermodynamic stability of the protein. Provided the availability of structural information this method is applicable to all globular proteins including antibodies, resulting both in increased in vitro protein solubility and in better protein production yields.

When safeguarding goes wrong: Impact of oxidative stress on protein homeostasis in health and neurodegenerative disorders

Cellular redox status is an established player in many different cellular functions. The buildup of oxidants within the cell is tightly regulated to maintain a balance between the positive and negative outcomes of cellular oxidants. Proteins are highly sensitive to oxidation, since modification can cause widespread unfolding and the formation of toxic aggregates. In response, cells have developed highly regulated systems that contribute to the maintenance of both the global redox status and protein homeostasis at large. Changes to these systems have been found to correlate with aging and age-related disorders, such as neurodegenerative pathologies. This raises intriguing questions as to the source of the imbalance in the redox and protein homeostasis systems, their interconnectivity, and their role in disease progression. Here we focus on the crosstalk between the redox and protein homeostasis systems in neurodegenerative diseases, specifically in Alzheimer's, Parkinson's, and ALS. We elaborate on some of the main players of the stress response systems, including the master regulators of oxidative stress and the heat shock response, Nrf2 and Hsf1, which are essential features of protein folding, and mediators of protein turnover. We illustrate the elegant mechanisms used by these components to provide an immediate response, including protein plasticity controlled by redox-sensing cysteines and the recruitment of naive proteins to the redox homeostasis array that act as chaperons in an ATP-independent manner.

Proteome-scale understanding of relationship between homo-repeat enrichments and protein aggregation properties

Expansion of homo-repeats is a molecular basis for human neurological diseases. We are the first who studied the influence of homo-repeats with lengths larger than four amino acid residues on the aggregation properties of 1449683 proteins across 122 eukaryotic and bacterial proteomes. Only 15% of proteins (215481) include homo-repeats of such length. We demonstrated that RNA-binding proteins with a prion-like domain are enriched with homo-repeats in comparison with other non-redundant protein sequences and those in the PDB. We performed a bioinformatics analysis for these proteins and found that proteins with homo-repeats are on average two times longer than those in the whole database. Moreover, we are first to discover that as a rule, homo-repeats appear in proteins not alone but in pairs: hydrophobic and aromatic homo-repeats appear with similar ones, while homo-repeats with small, polar and charged amino acids appear together with different preferences. We elaborated a new complementary approach to demonstrate the influence of homo-repeats on their host protein aggregation properties. We have shown that addition of artificial homo-repeats to natural and random proteins results in intensification of aggregation properties of the proteins. The maximal effect is observed for the insertion of artificial homo-repeats with 5–6 residues, which is consistent with the minimal length of an amyloidogenic region. We have also demonstrated that the ability of proteins with homo-repeats to aggregate cannot be explained only by the presence of long homo-repeats in them. There should be other characteristics of proteins intensifying the aggregation property including such as the appearance of homo-repeats in pairs in the same protein. We are the first who elaborated a new approach to study the influence of homo-repeats present in proteins on their aggregation properties and performed an appropriate analysis of the large number of proteomes and proteins.

AGGRESCAN3D: Toward the Prediction of the Aggregation Propensities of Protein Structures

Protein aggregation is responsible for the onset and spread of many human diseases, ranging from neurodegenerative disorders to cancer and diabetes. Moreover, it is one of the major bottlenecks for the production of protein-based therapeutics such as antibodies or enzymes. AGGRESCAN3D (A3D) is a web server aimed to identify and evaluate structural aggregation prone regions, overcoming the limitations of sequence-based algorithms in the prediction of the aggregation propensity of globular proteins. A3D allows the redesign of protein solubility by predicting in silico the impact of mutations and protein conformational fluctuations on the aggregation of native polypeptides.

sw ApoMb Amyloid Aggregation under Nondenaturing Conditions: The Role of Native Structure Stability

Investigation of the molecular mechanisms underlying amyloid-related human diseases attracts close attention. These diseases, the number of which currently is above 40, are characterized by formation of peptide or protein aggregates containing a cross-β structure. Most of the amyloidogenesis mechanisms described so far are based on experimental studies of aggregation of short peptides, intrinsically disordered proteins, or proteins under denaturing conditions, and studies of amyloid aggregate formations by structured globular proteins under conditions close to physiological ones are still in the initial stage. We investigated the effect of amino acid substitutions on propensity of the completely helical protein sperm whale apomyoglobin (sw ApoMb) for amyloid formation from its structured state in the absence of denaturing agents. Stability and aggregation of mutated sw ApoMb were studied using circular dichroism, Fourier transform infrared spectroscopy, x-ray diffraction, native electrophoresis, and electron microscopy techniques. Here, we demonstrate that stability of the protein native state determines both protein aggregation propensity and structural peculiarities of formed aggregates. Specifically, structurally stable mutants show low aggregation propensity and moderately destabilized sw ApoMb variants form amyloids, whereas their strongly destabilized mutants form both amyloids and nonamyloid aggregates.

Mining databases for protein aggregation: a review

Protein aggregation is an active area of research in recent decades, since it is the most common and troubling indication of protein instability. Understanding the mechanisms governing protein aggregation and amyloidogenesis is a key component to the aetiology and pathogenesis of many devastating disorders, including Alzheimer's disease or type 2 diabetes. Protein aggregation data are currently found "scattered" in an increasing number of repositories, since advances in computational biology greatly influence this field of research. This review exploits the various resources of aggregation data and attempts to distinguish and analyze the biological knowledge they contain, by introducing protein-based, fragment-based and disease-based repositories, related to aggregation. In order to gain a broad overview of the available repositories, a novel comprehensive network maps and visualizes the current association between aggregation databases and other important databases and/or tools and discusses the beneficial role of community annotation. The need for unification of aggregation databases in a common platform is also addressed.

Protein Misfolding, Amyloid Formation, and Human Disease: A Summary of Progress Over the Last Decade

Peptides and proteins have been found to possess an inherent tendency to convert from their native functional states into intractable amyloid aggregates. This phenomenon is associated with a range of increasingly common human disorders, including Alzheimer and Parkinson diseases, type II diabetes, and a number of systemic amyloidoses. In this review, we describe this field of science with particular reference to the advances that have been made over the last decade in our understanding of its fundamental nature and consequences. We list the proteins that are known to be deposited as amyloid or other types of aggregates in human tissues and the disorders with which they are associated, as well as the proteins that exploit the amyloid motif to play specific functional roles in humans. In addition, we summarize the genetic factors that have provided insight into the mechanisms of disease onset. We describe recent advances in our knowledge of the structures of amyloid fibrils and their oligomeric precursors and of the mechanisms by which they are formed and proliferate to generate cellular dysfunction. We show evidence that a complex proteostasis network actively combats protein aggregation and that such an efficient system can fail in some circumstances and give rise to disease. Finally, we anticipate the development of novel therapeutic strategies with which to prevent or treat these highly debilitating and currently incurable conditions.

Exploring the relationships between protein sequence, structure and solubility

Aggregation can be thought of as a form of protein folding in which intermolecular associations lead to the formation of large, insoluble assemblies. Various types of aggregates can be differentiated by their internal structures and gross morphologies (e.g., fibrillar or amorphous), and the ability to accurately predict the likelihood of their formation by a given polypeptide is of great practical utility in the fields of biology (including the study of disease), biotechnology, and biomaterials research. Here we review aggregation/solubility prediction methods and selected applications thereof. The development of increasingly sophisticated methods that incorporate knowledge of conformations possibly adopted by aggregating polypeptide monomers and predict the internal structure of aggregates is improving the accuracy of the predictions and continually expanding the range of applications.

Peptide binding landscapes: Specificity and homophilicity across sequence space in a lattice model

Peptide aggregation frequently involves sequences with strong homophilic binding character, i.e., sequences that self-assemble with like species in a crowded cellular environment, in the face of a multitude of other peptides or proteins as potential heterophilic binding partners. What kinds of sequences display a strong tendency towards homophilic binding and self-assembly, and what are the origins of this behavior? Here, we consider how sequence specificity in oligomerization processes plays out in a simple two-dimensional (2D) lattice statistical-thermodynamic peptide model that permits exhaustive examination of the entire sequence and configurational landscapes. We find that sequences with strong self-specificities have either alternating hydrophobic and hydrophilic residues or short patches of hydrophobic residues, both which minimize intramolecular hydrophobic interactions in part due to the constraints of the 2D lattice. We also find that these specificities are highly sensitive to entropic and free energetic features of the unbound conformational state, such that direct binding interaction energies alone do not capture the complete behavior. These results suggest that the ability of particular peptide sequences to self-assemble and aggregate in a many-protein environment reflects a precise balance of direct binding interactions and behavior in the unbound (monomeric) state.

The effect of protein acetylation on the formation and processing of inclusion bodies and endogenous protein aggregates in Escherichia coli cells

Background

Acetylation of lysine residues is a reversible post-translational modification conserved from bacteria to humans. Several recent studies have revealed hundreds of lysine-acetylated proteins in various bacteria; however, the physiological role of these modifications remains largely unknown. Since lysine acetylation changes the size and charge of proteins and thereby may affect their conformation, we assumed that lysine acetylation can stimulate aggregation of proteins, especially for overproduced recombinant proteins that form inclusion bodies.

Results

To verify this assumption, we used Escherichia coli strains that overproduce aggregation-prone VP1GFP protein. We found that in ΔackA-pta cells, which display diminished protein acetylation, inclusion bodies were formed with a delay and processed faster than in the wild-type cells. Moreover, in ΔackA-pta cells, inclusion bodies exhibited significantly increased specific GFP fluorescence. In CobB deacetylase-deficient cells, in which protein acetylation was enhanced, the formation of inclusion bodies was increased and their processing was significantly inhibited. Similar results were obtained with regard to endogenous protein aggregates formed during the late stationary phase in ΔackA-pta and ΔcobB cells.

Conclusions

Our studies revealed that protein acetylation affected the aggregation of endogenous E. coli proteins and the yield, solubility, and biological activity of a model recombinant protein. In general, decreased lysine acetylation inhibited the formation of protein aggregates, whereas increased lysine acetylation stabilized protein aggregates. These findings should be considered during the designing of efficient strategies for the production of recombinant proteins in E. coli cells.

Electronic supplementary material

The online version of this article (doi:10.1186/s12934-016-0590-8) contains supplementary material, which is available to authorized users.

From the Evolution of Protein Sequences Able to Resist Self-Assembly to the Prediction of Aggregation Propensity

Folding of polypeptide chains into biologically active entities is an astonishingly complex process, determined by the nature and the sequence of residues emerging from ribosomes. While it has been long believed that evolution has pressed genomes so that specific sequences could adopt unique, functional three-dimensional folds, it is now clear that complex protein machineries act as quality control system and supervise folding. Notwithstanding that, events such as erroneous folding, partial folding, or misfolding are frequent during the life of a cell or a whole organism, and they can escape controls. One of the possible outcomes of this misbehavior is cross-β aggregation, a super secondary structure which represents the hallmark of self-assembled, well organized, and extremely ordered structures termed amyloid fibrils. What if evolution would have not taken into account such possibilities? Twenty years of research point toward the idea that, in fact, evolution has constantly supervised the risk of errors and minimized their impact. In this review we tried to survey the major findings in the amyloid field, trying to describe what the real pitfalls of protein folding are-from an evolutionary perspective-and how sequence and structural features have evolved to balance the need for perfect, dynamic, functionally efficient structures, and the detrimental effects implicit in the dangerous process of folding. We will discuss how the knowledge obtained from these studies has been employed to produce computational methods able to assess, predict, and discriminate the aggregation properties of protein sequences.

Amyloid-β 1-24 C-terminal truncated fragment promotes amyloid-β 1-42 aggregate formation in the healthy brain

Substantial data indicate that amyloid-β (Aβ), the major component of senile plaques, plays a central role in Alzheimer’s Disease and indeed the assembly of naturally occurring amyloid peptides into cytotoxic aggregates is linked to the disease pathogenesis. Although Aβ42 is a highly aggregating form of Aβ, the co-occurrence of shorter Aβ peptides might affect the aggregation potential of the Aβ pool. In this study we aimed to assess whether the structural behavior of human Aβ42 peptide inside the brain is influenced by the concomitant presence of N-terminal fragments produced by the proteolytic activity of glial cells. We show that the occurrence of the human C-terminal truncated 1–24 Aβ fragment impairs Aβ42 clearance through blood brain barrier and promotes the formation of Aβ42 aggregates even in the healthy brain. By showing that Aβ1-24 has seeding properties for aggregate formation in intracranially injected wild type mice, our study provide the proof-of-concept that peptides produced upon Aβ42 cleavage by activated glial cells may cause phenotypic defects even in the absence of genetic mutations associated with Alzheimer’s Disease, possibly contributing to the development of the sporadic form of the pathology.

Mechanisms for the inhibition of amyloid aggregation by small ligands

This work investigates by biochemical, biophysical and MD techniques the opposite anti-amyloid properties of resveratrol and rosmarinic acid on the aggregation of hen egg white lysozyme (HEWL). Differences in association energy and contact maps were found that explain the different behaviours.

The formation of amyloid aggregates is the hallmark of systemic and neurodegenerative disorders, also known as amyloidoses. Many proteins have been found to aggregate into amyloid-like fibrils and this process is recognized as a general tendency of polypeptides. Lysozyme, an antibacterial protein, is a well-studied model since it is associated in human with systemic amyloidosis and that is widely available from chicken eggs (HEWL, hen egg white lysozyme). In the present study we investigated the mechanism of interaction of aggregating HEWL with rosmarinic acid and resveratrol, that we verified to be effective and ineffective, respectively, in inhibiting aggregate formation. We used a multidisciplinary strategy to characterize such effects, combining biochemical and biophysical methods with molecular dynamics (MD) simulations on the HEWL peptide 49–64 to gain insights into the mechanisms and energy variations associated to amyloid formation and inhibition. MD revealed that neither resveratrol nor rosmarinic acid were able to compete with the initial formation of the β-sheet structure. We then tested the association of two β-sheets, representing the model of an amyloid core structure. MD showed that rosmarinic acid displayed an interaction energy and a contact map comparable to that of sheet pairings. On the contrary, resveratrol association energy was found to be much lower and its contact map largely different than that of sheet pairings. The overall characterization elucidated a possible mechanism explaining why, in this model, resveratrol is inactive in blocking fibril formation, whereas rosmarinic acid is instead a powerful inhibitor.

Understanding and predicting protein misfolding and aggregation: Insights from proteomics

Protein misfolding and aggregation are being found to be associated with an increasing number of human diseases and premature aging, either because they promote a loss of protein function or, more frequently, because the aggregated species gain a toxic activity. Despite potentially harmful, aggregation seems to be a generic property of polypeptide chains and aggregation-prone protein sequences seem to be ubiquitous, which, counterintuitively, suggests that they serve evolutionary conserved functions. The in vitro study of individual aggregation reactions of a large number of proteins has provided important insights on the structural and sequential determinants of this process. However, it is clear that understanding the role played by protein aggregation and its regulation in health and disease at the cellular, developmental, and evolutionary levels require more global approaches. The use of model organisms and their proteomic analysis hold the power to provide answers to such issues. In the present review, we address how, initially, computational large-scale analysis and, more recently, experimental proteomics are helping us to rationalize how, why and when proteins aggregate, as well as to decipher the strategies organisms have developed to control proteins aggregation propensities.

Solubis: a webserver to reduce protein aggregation through mutation

Protein aggregation is a major factor limiting the biotechnological and therapeutic application of many proteins, including enzymes and monoclonal antibodies. The molecular principles underlying aggregation are by now sufficiently understood to allow rational redesign of natural polypeptide sequences for decreased aggregation tendency, and hence potentially increased expression and solubility. Given that aggregation-prone regions (APRs) tend to contribute to the stability of the hydrophobic core or to functional sites of the protein, mutations in these regions have to be carefully selected in order not to disrupt protein structure or function. Therefore, we here provide access to an automated pipeline to identify mutations that reduce protein aggregation by reducing the intrinsic aggregation propensity of the sequence (using the TANGO algorithm), while taking care not to disrupt the thermodynamic stability of the native structure (using the empirical force-field FoldX). Moreover, by providing a plot of the intrinsic aggregation propensity score of APRs corrected by the local stability of that region in the folded structure, we allow users to prioritize those regions in the protein that are most in need of improvement through protein engineering. The method can be accessed at http://solubis.switchlab.org/.

A Population Shift between Sparsely Populated Folding Intermediates Determines Amyloidogenicity

The balance between protein folding and misfolding is a crucial determinant of amyloid assembly. Transient intermediates that are sparsely populated during protein folding have been identified as key players in amyloid aggregation. However, due to their ephemeral nature, structural characterization of these species remains challenging. Here, using the power of nonuniformly sampled NMR methods we investigate the folding pathway of amyloidogenic and nonamyloidogenic variants of β2-microglobulin (β2m) in atomic detail. Despite folding via common intermediate states, we show that the decreased population of the aggregation-prone ITrans state and population of a less stable, more dynamic species ablate amyloid formation by increasing the energy barrier for amyloid assembly. The results show that subtle changes in conformational dynamics can have a dramatic effect in determining whether a protein is amyloidogenic, without perturbation of the mechanism of protein folding.

Critical Evaluation and Compilation of Physicochemical Determinants and Membrane Interactions of MMGP1 Antifungal Peptide

A growing issue of pathogen resistance to antibiotics has fostered the development of innovative approaches for novel drug development. Here, we report the physicochemical and biological properties of an antifungal peptide, MMGP1, based on computational analysis. Computation of physicochemical properties has revealed that the natural biological activities of MMGP1 are coordinated by its intrinsic properties such as net positive charge (+5.04), amphipathicity, high hydrophobicity, low hydrophobic moment, and higher isoelectric point (11.915). Prediction of aggregation hot spots in MMGP1 had revealed the presence of potentially aggregation-prone segments that can nucleate in vivo aggregation (on the membrane), whereas no aggregating regions were predicted for in vitro aggregation (in solutions) of MMGP1. This ability of MMGP1 to form oligomeric aggregates on membrane further substantiates its direct-cell penetrating potency. Monte Carlo simulation of the interactions of MMGP1 in the aqueous phase and different membrane environments revealed that increasing the proportion of acidic lipids on membrane had led to increase in the peptide helicity. Furthermore, the peptide adopts energetically favorable transmembrane configuration, by inserting peptide loop and helix termini into the membrane containing >60% of anionic lipids. The charged lipid-based insertion of MMGP1 into membrane might be responsible for the selectivity of peptide toward fungal cells. Additionally, MMGP1 possessed DNA-binding property. Computational docking has identified DNA-binding residues (TRP3, SER4, MET7, ARG8, PHE10, ALA11, GLY20, THR21, ARG22, MET23, TRP34, and LYS36) in MMGP1 crucial for its DNA-binding property. Furthermore, computational mutation analysis revealed that aromatic amino acids are crucial for in vivo aggregation, membrane insertion, and DNA-binding property of MMGP1. These data provide new insight into the molecular determinants of MMGP1 antifungal activity and also serves as the template for the design of novel peptide antibiotics.

Amyloids or prions? That is the question

Despite major efforts devoted to understanding the phenomenon of prion transmissibility, it is still poorly understood how this property is encoded in the amino acid sequence. In recent years, experimental data on yeast prion domains allow to start at least partially decrypting the sequence requirements of prion formation. These experiments illustrate the need for intrinsically disordered sequence regions enriched with a particularly high proportion of glutamine and asparagine. Bioinformatic analysis suggests that these regions strike a balance between sufficient amyloid nucleation propensity on the one hand and disorder on the other, which ensures availability of the amyloid prone regions but entropically prevents unwanted nucleation and facilitates brittleness required for propagation.

Amyloid properties of the leader peptide of variant B cystatin C: implications for Alzheimer and macular degeneration

Variant B (VB) of cystatin C has a mutation in its signal peptide (A25T), which interferes with its processing leading to reduced secretion and partial retention in the vicinity of the mitochondria. There are genetic evidences of the association of VB with Alzheimer's disease (AD) and age-related macular degeneration (AMD). Here, we investigated aggregation and amyloid propensities of unprocessed VB combining computational and in vitro studies. Aggregation predictors revealed the presence of four aggregation-prone regions, with a strong one at the level of the signal peptide, which indeed formed toxic aggregates and mature amyloid fibrils in solution. In light of these results, we propose for the first time the role of the signal peptide in pathogenesis of AD and AMD.

Aggregation risk prediction for antibodies and its application to biotherapeutic development

Aggregation is a common problem affecting biopharmaceutical development that can have a significant effect on the quality of the product, as well as the safety to patients, particularly because of the increased risk of immune reactions. Here, we describe a new high-throughput screening algorithm developed to classify antibody molecules based on their propensity to aggregate. The tool, constructed and validated on experimental aggregation data for over 500 antibodies, is able to discern molecules with a high aggregation propensity as defined by experimental criteria relevant to bioprocessing and manufacturing of these molecules. Furthermore, we show how this tool can be combined with other computational approaches during early drug development to select molecules with reduced risk of aggregation and optimal developability properties.