The 3D profile method for identifying fibril-forming segments of proteins

Mutations in human prion-like domains: pathogenic but not always amyloidogenic

Heterogeneous nuclear ribonucleoproteins (hnRNPs) are multifunctional proteins with integral roles in RNA metabolism and the regulation of alternative splicing. These proteins typically contain prion-like domains of low complexity (PrLDs or LCDs) that govern their assembly into either functional or pathological amyloid fibrils. To date, over 60 mutations targeting the LCDs of hnRNPs have been identified and associated with a spectrum of neurodegenerative diseases including amyotrophic lateral sclerosis (ALS), frontotemporal dementia (FTD), and Alzheimer's disease (AD). The cryo-EM structures of pathological and functional fibrils formed by different hnRNPs have been recently elucidated, including those of hnRNPA1, hnRNPA2, hnRNPDL-2, TDP-43, and FUS. In this review, we discuss the structural features of these amyloid assemblies, placing particular emphasis on scrutinizing the impact of prevalent disease-associated mutations mapping within their LCDs. By performing systematic energy calculations, we reveal a prevailing trend of destabilizing effects induced by these mutations in the amyloid structure, challenging the traditionally assumed correlation between pathogenicity and amyloidogenic propensity. Understanding the molecular basis of this discrepancy might provide insights for developing targeted therapeutic strategies to combat hnRNP-associated diseases.

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.

Amyloidogenic regions in beta-strands II and III modulate the aggregation and toxicity of SOD1 in living cells

Mutations in the protein superoxide dismutase-1 (SOD1) promote its misfolding and aggregation, ultimately causing familial forms of the debilitating neurodegenerative disease amyotrophic lateral sclerosis (ALS). Currently, over 220 (mostly missense) ALS-causing mutations in the SOD1 protein have been identified, indicating that common structural features are responsible for aggregation and toxicity. Using in silico tools, we predicted amyloidogenic regions in the ALS-associated SOD1-G85R mutant, finding seven regions throughout the structure. Introduction of proline residues into β-strands II (I18P) or III (I35P) reduced the aggregation propensity and toxicity of SOD1-G85R in cells, significantly more so than proline mutations in other amyloidogenic regions. The I18P and I35P mutations also reduced the capability of SOD1-G85R to template onto previously formed non-proline mutant SOD1 aggregates as measured by fluorescence recovery after photobleaching. Finally, we found that, while the I18P and I35P mutants are less structurally stable than SOD1-G85R, the proline mutants are less aggregation-prone during proteasome inhibition, and less toxic to cells overall. Our research highlights the importance of a previously underappreciated SOD1 amyloidogenic region in β-strand II (15QGIINF20) to the aggregation and toxicity of SOD1 in ALS mutants, and suggests that β-strands II and III may be good targets for the development of SOD1-associated ALS therapies.

Tyrosine Mutation in the Characteristic Motif of the Amorphous Region of Spidroin for Self-Assembly Capability Enhancement

Spidroin, with robust mechanical performance and good biocompatibility, could fulfill broad applications in material science and biomedical fields. Development of miniature spidroin has made abundant fiber production economically feasible, but the mechanical properties of artificial silk still fall short of natural silk. The mechanism behind mechanical properties of spidroin usually focuses on β-microcrystalline regions; the effect of amorphous regions was barely studied. In this study, residue tyrosines (Y) were designed to replace asparagine (N)/glutamic acid (Q) in the characteristic motifs (GGX)n in amorphous regions for performance enhancement of spidroin; the mutants presented lower free energy and significantly exhibited stronger van der Waals and electrostatic interactions, which might result from π-π stacking interactions between the phenyl rings in the side chain of tyrosine. Additionally, the soluble expressions of wild-type spidroin and mutant spidroin were achieved when heterologously expressed in E. coli, with yields of 560 mg/L (2REP), 590 mg/L (2REPM), 240 mg/L (4REP), and 280 mg/L (4REPM). Significantly, secondary structure analysis confirmed that the mutant spidroin more avidly forms more β-sheets than the wild-type spidroin, and aggregation morphology suggested that mutant spidroin displayed better self-assembly capacity and was easier to form artificial spider silk fibers; in particular, self-assembled 4REPM nanofibrils had an average modulus of 11.2 ± 0.35 GPa, about 2 times higher than self-assembled B. mori silk nanofibrils and almost the same as that of native spider dragline silk fibers (10-15 GPa). Thus, we first demonstrated a new influence mechanism of the amorphous region's characteristic motif on the self-assembly and material properties of spidroin. Our study provides a reference for the design of high-performance material proteins and their heterologous preparation.

CORDAX web server: an online platform for the prediction and 3D visualization of aggregation motifs in protein sequences

MOTIVATION

Proteins, the molecular workhorses of biological systems, execute a multitude of critical functions dictated by their precise three-dimensional structures. In a complex and dynamic cellular environment, proteins can undergo misfolding, leading to the formation of aggregates that take up various forms, including amorphous and ordered aggregation in the shape of amyloid fibrils. This phenomenon is closely linked to a spectrum of widespread debilitating pathologies, such as Alzheimer's disease, Parkinson's disease, type-II diabetes, and several other proteinopathies, but also hampers the engineering of soluble agents, as in the case of antibody development. As such, the accurate prediction of aggregation propensity within protein sequences has become pivotal due to profound implications in understanding disease mechanisms, as well as in improving biotechnological and therapeutic applications.

RESULTS

We previously developed Cordax, a structure-based predictor that utilizes logistic regression to detect aggregation motifs in protein sequences based on their structural complementarity to the amyloid cross-beta architecture. Here, we present a dedicated web server interface for Cordax. This online platform combines several features including detailed scoring of sequence aggregation propensity, as well as 3D visualization with several customization options for topology models of the structural cores formed by predicted aggregation motifs. In addition, information is provided on experimentally determined aggregation-prone regions that exhibit sequence similarity to predicted motifs, scores, and links to other predictor outputs, as well as simultaneous predictions of relevant sequence propensities, such as solubility, hydrophobicity, and secondary structure propensity.

AVAILABILITY AND IMPLEMENTATION

The Cordax webserver is freely accessible at https://cordax.switchlab.org/.

Prediction of protein aggregation

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

Substitution of Met-38 to Ile in γ-synuclein found in two patients with amyotrophic lateral sclerosis induces aggregation into amyloid

α-, β-, and γ-Synuclein are intrinsically disordered proteins implicated in physiological processes in the nervous system of vertebrates. α-synuclein (αSyn) is the amyloidogenic protein associated with Parkinson's disease and certain other neurodegenerative disorders. Intensive research has focused on the mechanisms that cause αSyn to form amyloid structures, identifying its NAC region as being necessary and sufficient for amyloid assembly. Recent work has shown that a 7-residue sequence (P1) is necessary for αSyn amyloid formation. Although γ-synuclein (γSyn) is 55% identical in sequence to αSyn and its pathological deposits are also observed in association with neurodegenerative conditions, γSyn is resilient to amyloid formation in vitro. Here, we report a rare single nucleotide polymorphism (SNP) in the SNCG gene encoding γSyn, found in two patients with amyotrophic lateral sclerosis (ALS). The SNP results in the substitution of Met38 with Ile in the P1 region of the protein. These individuals also had a second, common and nonpathological, SNP in SNCG resulting in the substitution of Glu110 with Val. In vitro studies demonstrate that the Ile38 variant accelerates amyloid fibril assembly. Contrastingly, Val110 retards fibril assembly and mitigates the effect of Ile38. Substitution of residue 38 with Leu had little effect, while Val retards, and Ala increases the rate of amyloid formation. Ile38 γSyn also results in the formation of γSyn-containing inclusions in cells. The results show how a single point substitution can enhance amyloid formation of γSyn and highlight the P1 region in driving amyloid formation in another synuclein family member.

Protein-lipid interactions and protein anchoring modulate the modes of association of the globular domain of the Prion protein and Doppel protein to model membrane patches

The Prion protein is the molecular hallmark of the incurable prion diseases affecting mammals, including humans. The protein-only hypothesis states that the misfolding, accumulation, and deposition of the Prion protein play a critical role in toxicity. The cellular Prion protein (PrPC) anchors to the extracellular leaflet of the plasma membrane and prefers cholesterol- and sphingomyelin-rich membrane domains. Conformational Prion protein conversion into the pathological isoform happens on the cell surface. In vitro and in vivo experiments indicate that Prion protein misfolding, aggregation, and toxicity are sensitive to the lipid composition of plasma membranes and vesicles. A picture of the underlying biophysical driving forces that explain the effect of Prion protein - lipid interactions in physiological conditions is needed to develop a structural model of Prion protein conformational conversion. To this end, we use molecular dynamics simulations that mimic the interactions between the globular domain of PrPC anchored to model membrane patches. In addition, we also simulate the Doppel protein anchored to such membrane patches. The Doppel protein is the closest in the phylogenetic tree to PrPC, localizes in an extracellular milieu similar to that of PrPC, and exhibits a similar topology to PrPC even if the amino acid sequence is only 25% identical. Our simulations show that specific protein-lipid interactions and conformational constraints imposed by GPI anchoring together favor specific binding sites in globular PrPC but not in Doppel. Interestingly, the binding sites we found in PrPC correspond to prion protein loops, which are critical in aggregation and prion disease transmission barrier (β2-α2 loop) and in initial spontaneous misfolding (α2-α3 loop). We also found that the membrane re-arranges locally to accommodate protein residues inserted in the membrane surface as a response to protein binding.

Phase separations in oncogenesis, tumor progressions and metastasis: a glance from hallmarks of cancer

Liquid-liquid phase separation (LLPS) is a novel principle for interpreting precise spatiotemporal coordination in living cells through biomolecular condensate (BMC) formation via dynamic aggregation. LLPS changes individual molecules into membrane-free, droplet-like BMCs with specific functions, which coordinate various cellular activities. The formation and regulation of LLPS are closely associated with oncogenesis, tumor progressions and metastasis, the specific roles and mechanisms of LLPS in tumors still need to be further investigated at present. In this review, we comprehensively summarize the conditions of LLPS and identify mechanisms involved in abnormal LLPS in cancer processes, including tumor growth, metastasis, and angiogenesis from the perspective of cancer hallmarks. We have also reviewed the clinical applications of LLPS in oncologic areas. This systematic summary of dysregulated LLPS from the different dimensions of cancer hallmarks will build a bridge for determining its specific functions to further guide basic research, finding strategies to intervene in LLPS, and developing relevant therapeutic approaches.

PACT - Prediction of amyloid cross-interaction by threading

Amyloid proteins are often associated with the onset of diseases, including Alzheimer's, Parkinson's and many others. However, there is a wide class of functional amyloids that are involved in physiological functions, e.g., formation of microbial biofilms or storage of hormones. Recent studies showed that an amyloid fibril could affect the aggregation of another protein, even from a different species. This may result in amplification or attenuation of the aggregation process. Insight into amyloid cross-interactions may be crucial for better understanding of amyloid diseases and the potential influence of microbial amyloids on human proteins. However, due to the demanding nature of the needed experiments, knowledge of such interactions is still limited. Here, we present PACT (Prediction of Amyloid Cross-interaction by Threading) - the computational method for the prediction of amyloid cross-interactions. The method is based on modeling of a heterogeneous fibril formed by two amyloidogenic peptides. The resulting structure is assessed by the structural statistical potential that approximates its plausibility and energetic stability. PACT was developed and first evaluated mostly on data collected in the AmyloGraph database of interacting amyloids and achieved high values of Area Under ROC (AUC=0.88) and F1 (0.82). Then, we applied our method to study the interactions of CsgA - a bacterial biofilm protein that was not used in our in-reference datasets, which is expressed in several bacterial species that inhabit the human intestines - with two human proteins. The study included alpha-synuclein, a human protein that is involved in Parkinson's disease, and human islet amyloid polypeptide (hIAPP), which is involved in type 2 diabetes. In both cases, PACT predicted the appearance of cross-interactions. Importantly, the method indicated specific regions of the proteins, which were shown to play a central role in both interactions. We experimentally confirmed the novel results of the indicated CsgA fragments interacting with hIAPP based on the kinetic characteristics obtained with the ThT assay. PACT opens the possibility of high-throughput studies of amyloid interactions. Importantly, it can work with fairly long protein fragments, and as a purely physicochemical approach, it relies very little on scarce training data. The tool is available as a web server at https://pact.e-science.pl/pact/ . The local version can be downloaded from https://github.com/KubaWojciechowski/PACT .

Amyloid Aggregation Is Potently Slowed Down by Osmolytes Due to Compaction of Partially Folded State

Amyloid aggregation is a key process in amyloidoses and neurodegenerative diseases. Hydrophobicity is one of the major driving forces for this type of aggregation, as an increase in hydrophobicity generally correlates with aggregation susceptibility and rate. However, most experimental systems in vitro and prediction tools in silico neglect the contribution of protective osmolytes present in the cellular environment. Here, we assessed the role of hydrophobic mutations in amyloid aggregation in the presence of osmolytes. To achieve this goal, we used the model protein human muscle acylphosphatase (mAcP) and mutations to leucine that increased its hydrophobicity without affecting its thermodynamic stability. Osmolytes significantly slowed down the aggregation kinetics of the hydrophobic mutants, with an effect larger than that observed on the wild-type protein. The effect increased as the mutation site was closer to the middle of the protein sequence. We propose that the preferential exclusion of osmolytes from mutation-introduced hydrophobic side-chains quenches the aggregation potential of the ensemble of partially unfolded states of the protein by inducing its compaction and inhibiting its self-assembly with other proteins. Our results suggest that including the effect of the cellular environment in experimental setups and predictive softwares, for both mechanistic studies and drug design, is essential in order to obtain a more complete combination of the driving forces of amyloid aggregation.

Prediction of Protein Aggregation Propensity via Data-Driven Approaches

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

Incorporating physics to overcome data scarcity in predictive modeling of protein function: A case study of BK channels

Machine learning has played transformative roles in numerous chemical and biophysical problems such as protein folding where large amount of data exists. Nonetheless, many important problems remain challenging for data-driven machine learning approaches due to the limitation of data scarcity. One approach to overcome data scarcity is to incorporate physical principles such as through molecular modeling and simulation. Here, we focus on the big potassium (BK) channels that play important roles in cardiovascular and neural systems. Many mutants of BK channel are associated with various neurological and cardiovascular diseases, but the molecular effects are unknown. The voltage gating properties of BK channels have been characterized for 473 site-specific mutations experimentally over the last three decades; yet, these functional data by themselves remain far too sparse to derive a predictive model of BK channel voltage gating. Using physics-based modeling, we quantify the energetic effects of all single mutations on both open and closed states of the channel. Together with dynamic properties derived from atomistic simulations, these physical descriptors allow the training of random forest models that could reproduce unseen experimentally measured shifts in gating voltage, ∆V1/2, with a RMSE ~ 32 mV and correlation coefficient of R ~ 0.7. Importantly, the model appears capable of uncovering nontrivial physical principles underlying the gating of the channel, including a central role of hydrophobic gating. The model was further evaluated using four novel mutations of L235 and V236 on the S5 helix, mutations of which are predicted to have opposing effects on V1/2 and suggest a key role of S5 in mediating voltage sensor-pore coupling. The measured ∆V1/2 agree quantitatively with prediction for all four mutations, with a high correlation of R = 0.92 and RMSE = 18 mV. Therefore, the model can capture nontrivial voltage gating properties in regions where few mutations are known. The success of predictive modeling of BK voltage gating demonstrates the potential of combining physics and statistical learning for overcoming data scarcity in nontrivial protein function prediction.

Synthesis of robust underwater glues from common proteins via unfolding-aggregating strategy

Underwater adhesive proteins secreted by organisms greatly inspires the development of underwater glue. However, except for specific proteins such as mussel adhesive protein, barnacle cement proteins, curli protein and their related recombinant proteins, it is believed that abundant common proteins cannot be converted into underwater glue. Here, we demonstrate that unfolded common proteins exhibit high affinity to surfaces and strong internal cohesion via amyloid-like aggregation in water. Using bovine serum albumin (BSA) as a model protein, we obtain a stable unfolded protein by cleaving the disulfide bonds and maintaining the unfolded state by means of stabilizing agents such as trifluoroethanol (TFE) and urea. The diffusion of stabilizing agents into water exposes the hydrophobic residues of an unfolded protein and initiates aggregation of the unfolded protein into a solid block. A robust and stable underwater glue can thus be prepared from tens of common proteins. This strategy deciphers a general code in common proteins to construct robust underwater glue from abundant biomass.

Census of exposed aggregation-prone regions in proteomes

Loss of solubility usually leads to the detrimental elimination of protein function. In some cases, the protein aggregation is also required for beneficial functions. Given the duality of this phenomenon, it remains a fundamental question how natural selection controls the aggregation. The exponential growth of genomic sequence data and recent progress with in silico predictors of the aggregation allows approaching this problem by a large-scale bioinformatics analysis. Most of the aggregation-prone regions are hidden within the 3D structure, rendering them inaccessible for the intermolecular interactions responsible for aggregation. Thus, the most realistic census of the aggregation-prone regions requires crossing aggregation prediction with information about the location of the natively unfolded regions. This allows us to detect so-called 'exposed aggregation-prone regions' (EARs). Here, we analyzed the occurrence and distribution of the EARs in 76 reference proteomes from the three kingdoms of life. For this purpose, we used a bioinformatics pipeline, which provides a consensual result based on several predictors of aggregation. Our analysis revealed a number of new statistically significant correlations about the presence of EARs in different organisms, their dependence on protein length, cellular localizations, co-occurrence with short linear motifs and the level of protein expression. We also obtained a list of proteins with the conserved aggregation-prone sequences for further experimental tests. Insights gained from this work led to a deeper understanding of the relationship between protein evolution and aggregation.

AB-Amy: machine learning aided amyloidogenic risk prediction of therapeutic antibody light chains

Over 120 FDA-approved antibody-based therapeutics are used to treat a variety of diseases.However, many candidates could fail because of unfavorable physicochemical properties. Light-chain amyloidosis is one form of aggregation that can lead to severe safety risks in clinical development. Therefore, screening candidates with a less amyloidosis risk at the early stage can not only save the time and cost of antibody development but also improve the safety of antibody drugs. In this study, based on the dipeptide composition of 742 amyloidogenic and 712 non-amyloidogenic antibody light chains, a support vector machine-based model, AB-Amy, was trained to predict the light-chain amyloidogenic risk. The AUC of AB-Amy reaches 0.9651. The excellent performance of AB-Amy indicates that it can be a useful tool for the in silico evaluation of the light-chain amyloidogenic risk to ensure the safety of antibody therapeutics under clinical development. A web server is freely available at http://i.uestc.edu.cn/AB-Amy/.

Incorporating physics to overcome data scarcity in predictive modeling of protein function: a case study of BK channels

Machine learning has played transformative roles in numerous chemical and biophysical problems such as protein folding where large amount of data exists. Nonetheless, many important problems remain challenging for data-driven machine learning approaches due to the limitation of data scarcity. One approach to overcome data scarcity is to incorporate physical principles such as through molecular modeling and simulation. Here, we focus on the big potassium (BK) channels that play important roles in cardiovascular and neural systems. Many mutants of BK channel are associated with various neurological and cardiovascular diseases, but the molecular effects are unknown. The voltage gating properties of BK channels have been characterized for 473 site-specific mutations experimentally over the last three decades; yet, these functional data by themselves remain far too sparse to derive a predictive model of BK channel voltage gating. Using physics-based modeling, we quantify the energetic effects of all single mutations on both open and closed states of the channel. Together with dynamic properties derived from atomistic simulations, these physical descriptors allow the training of random forest models that could reproduce unseen experimentally measured shifts in gating voltage, ΔV 1/2 , with a RMSE ∼ 32 mV and correlation coefficient of R ∼ 0.7. Importantly, the model appears capable of uncovering nontrivial physical principles underlying the gating of the channel, including a central role of hydrophobic gating. The model was further evaluated using four novel mutations of L235 and V236 on the S5 helix, mutations of which are predicted to have opposing effects on V 1/2 and suggest a key role of S5 in mediating voltage sensor-pore coupling. The measured ΔV 1/2 agree quantitatively with prediction for all four mutations, with a high correlation of R = 0.92 and RMSE = 18 mV. Therefore, the model can capture nontrivial voltage gating properties in regions where few mutations are known. The success of predictive modeling of BK voltage gating demonstrates the potential of combining physics and statistical learning for overcoming data scarcity in nontrivial protein function prediction.

Author Summary

Deep machine learning has brought many exciting breakthroughs in chemistry, physics and biology. These models require large amount of training data and struggle when the data is scarce. The latter is true for predictive modeling of the function of complex proteins such as ion channels, where only hundreds of mutational data may be available. Using the big potassium (BK) channel as a biologically important model system, we demonstrate that a reliable predictive model of its voltage gating property could be derived from only 473 mutational data by incorporating physics-derived features, which include dynamic properties from molecular dynamics simulations and energetic quantities from Rosetta mutation calculations. We show that the final random forest model captures key trends and hotspots in mutational effects of BK voltage gating, such as the important role of pore hydrophobicity. A particularly curious prediction is that mutations of two adjacent residues on the S5 helix would always have opposite effects on the gating voltage, which was confirmed by experimental characterization of four novel mutations. The current work demonstrates the importance and effectiveness of incorporating physics in predictive modeling of protein function with scarce data.

Energy Landscapes and Heat Capacity Signatures for Monomers and Dimers of Amyloid-Forming Hexapeptides

Amyloid formation is a hallmark of various neurodegenerative disorders. In this contribution, energy landscapes are explored for various hexapeptides that are known to form amyloids. Heat capacity (CV) analysis at low temperature for these hexapeptides reveals that the low energy structures contributing to the first heat capacity feature above a threshold temperature exhibit a variety of backbone conformations for amyloid-forming monomers. The corresponding control sequences do not exhibit such structural polymorphism, as diagnosed via end-to-end distance and a dihedral angle defined for the monomer. A similar heat capacity analysis for dimer conformations obtained using basin-hopping global optimisation shows clear features in end-to-end distance versus dihedral correlation plots, where amyloid-forming sequences exhibit a preference for larger end-to-end distances and larger positive dihedrals. These results hold true for sequences taken from tau, amylin, insulin A chain, a de novo designed peptide, and various control sequences. While there is a little overall correlation between the aggregation propensity and the temperature at which the low-temperature CV feature occurs, further analysis suggests that the amyloid-forming sequences exhibit the key CV feature at a lower temperature compared to control sequences derived from the same protein.

Phosphorylation sites are evolutionary checkpoints against liquid-solid transition in protein condensates

Assemblies of multivalent RNA-binding protein fused in sarcoma (FUS) can exist in the functional liquid-like state as well as less dynamic and potentially toxic amyloid- and hydrogel-like states. How could then cells form liquid-like condensates while avoiding their transformation to amyloids? Here, we show how posttranslational phosphorylation can provide a "handle" that prevents liquid-solid transition of intracellular condensates containing FUS. Using residue-specific coarse-grained simulations, for 85 different mammalian FUS sequences, we show how the number of phosphorylation sites and their spatial arrangement affect intracluster dynamics preventing conversion to amyloids. All atom simulations further confirm that phosphorylation can effectively reduce the β-sheet propensity in amyloid-prone fragments of FUS. A detailed evolutionary analysis shows that mammalian FUS PLDs are enriched in amyloid-prone stretches compared to control neutrally evolved sequences, suggesting that mammalian FUS proteins evolved to self-assemble. However, in stark contrast to proteins that do not phase-separate for their function, mammalian sequences have phosphosites in close proximity to these amyloid-prone regions. These results suggest that evolution uses amyloid-prone sequences in prion-like domains to enhance phase separation of condensate proteins while enriching phosphorylation sites in close proximity to safeguard against liquid-solid transitions.

A guide to studying protein aggregation

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

Exploring cryptic amyloidogenic regions in prion-like proteins from plants

Prion-like domains (PrLDs) are intrinsically disordered regions (IDRs) of low sequence complexity with a similar composition to yeast prion domains. PrLDs-containing proteins have been involved in different organisms' regulatory processes. Regions of moderate amyloid propensity within IDRs have been shown to assemble autonomously into amyloid fibrils. These sequences tend to be rich in polar amino acids and often escape from the detection of classical bioinformatics screenings that look for highly aggregation-prone hydrophobic sequence stretches. We defined them as cryptic amyloidogenic regions (CARs) and recently developed an integrated database that collects thousands of predicted CARs in IDRs. CARs seem to be evolutionary conserved among disordered regions because of their potential to stablish functional contacts with other biomolecules. Here we have focused on identifying and characterizing CARs in prion-like proteins (pCARs) from plants, a lineage that has been poorly studied in comparison with other prionomes. We confirmed the intrinsic amyloid potential for a selected pCAR from Arabidopsis thaliana and explored functional enrichments and compositional bias of pCARs in plant prion-like proteins.

Cross-Linking Mass Spectrometry Analysis of Metastable Compact Structures in Intrinsically Disordered Proteins

Protein assembly into beta-sheet-rich amyloids is a common phenomenon in neurodegenerative diseases including Alzheimer's (AD) and Parkinson's (PD). The proteins implicated in amyloid deposition are often intrinsically disordered proteins (IDPs) and are characterized by not folding into a defined globular conformation. The amyloidogenic properties of IDPs are determined by the presence of short sequence elements, referred to as amyloid motifs, that drive ordered aggregation (Thompson MJ, Sievers SA, Karanicolas J et al. Proc Natl Acad Sci USA 103(11):4074-8, 2006; Goldschmidt L, Teng PK, Riek R et al. Proc Natl Acad Sci USA 107(8):3487-92, 2010]. The microtubule-associated protein tau adopts amyloid assemblies in over 20 different diseases commonly referred to as tauopathies. However, native tau is aggregation-resistant despite encoding at least three amyloid motifs (Chen D, Drombosky KW, Hou Z et al. Nat Commun 10(1):2493, 2019). Recent cryogenic electron microscopy (cryo-EM) structures of tau amyloid fibrils isolated from patient brains showed the involvement of amyloid motifs in the fibril core (Fitzpatrick AWP, Falcon B, He S et al. Nature 547(7662):185-90, 2017; Falcon B, Zhang W, Murzin AG et al. Nature 561(7721):137-40, 2018; Zhang W, Tarutani A, Newell KL et al. Nature 580(7802):283-7, 2020). How does tau change from an aggregation-resistant state to an aggregation-prone state? Consistent with the fibril structures, we hypothesize that tau must change conformation to expose the amyloid motifs that allow self-association into beta-sheet-rich aggregates. This would suggest that the amyloid motifs are likely buried in natively folded tau to prevent self-assembly. We developed an approach that couples cross-linking mass spectrometry (XL-MS) with temperature denaturation to probe the loss of contacts as a proxy to measure protein unfolding with sequence resolution. Using this method, we demonstrated that disease-associated mutations in tau located near an amyloid motif disrupt the protective local structure, promote amyloid motif exposure, and thus lead to aggregation (Chen D, Drombosky KW, Hou Z et al. Nat Commun 10(1):2493, 2019). In this chapter, we describe the detailed protocol for this approach. We anticipate that our protocol can be generalized to other IDPs and will help discover critical structural elements to better understand important biological questions including protein aggregation.

Amyloid fibril formation by αS1- and β-casein implies that fibril formation is a general property of casein proteins

Caseins are a diverse family of intrinsically disordered proteins present in the milks of all mammals. A property common to two cow paralogues, α_S2- and κ-casein, is their propensity in vitro to form amyloid fibrils, the highly ordered protein aggregates associated with many age-related, including neurological, diseases. In this study, we explored whether amyloid fibril-forming propensity is a general feature of casein proteins by examining the other cow caseins (α_S1 and β) as well as β-caseins from camel and goat. Small-angle X-ray scattering measurements indicated that cow α_S1- and β-casein formed large spherical aggregates at neutral pH and 20°C. Upon incubation at 65°C, α_S1- and β-casein underwent conversion to amyloid fibrils over the course of ten days, as shown by thioflavin T binding, transmission electron microscopy, and X-ray fibre diffraction. At the lower temperature of 37°C where fibril formation was more limited, camel β-casein exhibited a greater fibril-forming propensity than its cow or goat orthologues. Limited proteolysis of cow and camel β-casein fibrils and analysis by mass spectrometry indicated a common amyloidogenic sequence in the proline, glutamine-rich, C-terminal region of β-casein. These findings highlight the persistence of amyloidogenic sequences within caseins, which likely contribute to their functional, heterotypic self-assembly; in all mammalian milks, at least two caseins coalesce to form casein micelles, implying that caseins diversified partly to avoid dysfunctional amyloid fibril formation.

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.

Computationally-Aided Modeling of Hsp70-Client Interactions: Past, Present, and Future

Hsp70 molecular chaperones play central roles in maintaining a healthy cellular proteome. Hsp70s function by binding to short peptide sequences in incompletely folded client proteins, thus preventing them from misfolding and/or aggregating, and in many cases holding them in a state that is competent for subsequent processes like translocation across membranes. There is considerable interest in predicting the sites where Hsp70s may bind their clients, as the ability to do so sheds light on the cellular functions of the chaperone. In addition, the capacity of the Hsp70 chaperone family to bind to a broad array of clients and to identify accessible sequences that enable discrimination of those that are folded from those that are not fully folded, which is essential to their cellular roles, is a fascinating puzzle in molecular recognition. In this article we discuss efforts to harness computational modeling with input from experimental data to develop a predictive understanding of the promiscuous yet selective binding of Hsp70 molecular chaperones to accessible sequences within their client proteins. We trace how an increasing understanding of the complexities of Hsp70-client interactions has led computational modeling to new underlying assumptions and design features. We describe the trend from purely data-driven analysis toward increased reliance on physics-based modeling that deeply integrates structural information and sequence-based functional data with physics-based binding energies. Notably, new experimental insights are adding to our understanding of the molecular origins of "selective promiscuity" in substrate binding by Hsp70 chaperones and challenging the underlying assumptions and design used in earlier predictive models. Taking the new experimental findings together with exciting progress in computational modeling of protein structures leads us to foresee a bright future for a predictive understanding of selective-yet-promiscuous binding exploited by Hsp70 molecular chaperones; the resulting new insights will also apply to substrate binding by other chaperones and by signaling proteins.

Single residue modulators of amyloid formation in the N-terminal P1-region of α-synuclein

Alpha-synuclein (αSyn) is a protein involved in neurodegenerative disorders including Parkinson’s disease. Amyloid formation of αSyn can be modulated by the ‘P1 region’ (residues 36-42). Here, mutational studies of P1 reveal that Y39A and S42A extend the lag-phase of αSyn amyloid formation in vitro and rescue amyloid-associated cytotoxicity in C. elegans. Additionally, L38I αSyn forms amyloid fibrils more rapidly than WT, L38A has no effect, but L38M does not form amyloid fibrils in vitro and protects from proteotoxicity. Swapping the sequence of the two residues that differ in the P1 region of the paralogue γSyn to those of αSyn did not enhance fibril formation for γSyn. Peptide binding experiments using NMR showed that P1 synergises with residues in the NAC and C-terminal regions to initiate aggregation. The remarkable specificity of the interactions that control αSyn amyloid formation, identifies this region as a potential target for therapeutics, despite their weak and transient nature.

The authors of this work characterize the effect of amino acid substitution on α-synuclein (α-Syn) aggregation. Residues 38 and 42 (in addition to 39) within the P1 region of α-Syn affect amyloid formation. The effect of substitution at position 38 is dependent on the amino-acid introduced, suggesting that specific interactions control α -Syn aggregation.

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

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

The interactions that shape amyloid fibrils in disease

In this issue of Structure, van der Kant and colleagues use a computational approach to uncover what dictates assembly of proteins into amyloid fibrils. Structurally distinct amyloids have about 30% of their residues predisposed to cross-β conformation, while less favorable regions may be the source of polymorphism by interacting with stabilizing cofactors.

DES-Amyloidoses “Amyloidoses through the looking-glass”: A knowledgebase developed for exploring and linking information related to human amyloid-related diseases

More than 30 types of amyloids are linked to close to 50 diseases in humans, the most prominent being Alzheimer’s disease (AD). AD is brain-related local amyloidosis, while another amyloidosis, such as AA amyloidosis, tends to be more systemic. Therefore, we need to know more about the biological entities’ influencing these amyloidosis processes. However, there is currently no support system developed specifically to handle this extraordinarily complex and demanding task. To acquire a systematic view of amyloidosis and how this may be relevant to the brain and other organs, we needed a means to explore "amyloid network systems" that may underly processes that leads to an amyloid-related disease. In this regard, we developed the DES-Amyloidoses knowledgebase (KB) to obtain fast and relevant information regarding the biological network related to amyloid proteins/peptides and amyloid-related diseases. This KB contains information obtained through text and data mining of available scientific literature and other public repositories. The information compiled into the DES-Amyloidoses system based on 19 topic-specific dictionaries resulted in 796,409 associations between terms from these dictionaries. Users can explore this information through various options, including enriched concepts, enriched pairs, and semantic similarity. We show the usefulness of the KB using an example focused on inflammasome-amyloid associations. To our knowledge, this is the only KB dedicated to human amyloid-related diseases derived primarily through literature text mining and complemented by data mining that provides a novel way of exploring information relevant to amyloidoses.

Methodological advances and strategies for high resolution structure determination of cellular protein aggregates

Aggregation of proteins is at the nexus of molecular processes crucial to aging, disease, and employing proteins for biotechnology and medical applications. There has been much recent progress in determining the structural features of protein aggregates that form in cells; yet, owing to prevalent heterogeneity in aggregation, many aspects remain obscure and often experimentally intractable to define. Here we review recent results of structural studies for cell-derived aggregates of normally globular proteins, with a focus on high resolution methods for their analysis and prediction. Complementary results obtained by solid-state NMR spectroscopy, Fourier transform infrared (FTIR) spectroscopy and microspectroscopy, cryo-electron microscopy, and amide hydrogen/deuterium exchange measured by NMR and mass spectrometry (MS), applied to bacterial inclusion bodies and disease inclusions, are uncovering novel information on in-cell aggregation patterns as well as great diversity in the structural features of useful and aberrant protein aggregates. Using these advances as a guide, this review aims to advise the reader on which combination of approaches may be the most appropriate to apply to their unique system.

Neurotoxic amyloidogenic peptides in the proteome of SARS-COV2: potential implications for neurological symptoms in COVID-19

COVID-19 is primarily known as a respiratory disease caused by SARS-CoV-2. However, neurological symptoms such as memory loss, sensory confusion, severe headaches, and even stroke are reported in up to 30% of cases and can persist even after the infection is over (long COVID). These neurological symptoms are thought to be produced by the virus infecting the central nervous system, however we don’t understand the molecular mechanisms triggering them. The neurological effects of COVID-19 share similarities to neurodegenerative diseases in which the presence of cytotoxic aggregated amyloid protein or peptides is a common feature. Following the hypothesis that some neurological symptoms of COVID-19 may also follow an amyloid etiology we identified two peptides from the SARS-CoV-2 proteome that self-assemble into amyloid assemblies. Furthermore, these amyloids were shown to be highly toxic to neuronal cells. We suggest that cytotoxic aggregates of SARS-CoV-2 proteins may trigger neurological symptoms in COVID-19.

Here the authors report the formation of toxic clumps of protein, similar to amyloid assemblies found in Alzheimer’s disease and suggest their possible role for some of the neurological symptoms of long-COVID.

High-Resolution NMR H/D Exchange of Human Superoxide Dismutase Inclusion Bodies Reveals Significant Native Features Despite Structural Heterogeneity

Protein aggregation is central to aging, disease and biotechnology. While there has been recent progress in defining structural features of cellular protein aggregates, many aspects remain unclear due to heterogeneity of aggregates presenting obstacles to characterization. Here we report high-resolution analysis of cellular inclusion bodies (IBs) of immature human superoxide dismutase (SOD1) mutants using NMR quenched amide hydrogen/deuterium exchange (qHDX), FTIR and Congo red binding. The extent of aggregation is correlated with mutant global stability and, notably, the free energy of native dimer dissociation, indicating contributions of native-like monomer associations to IB formation. This is further manifested by a common pattern of extensive protection against H/D exchange throughout nine mutant SOD1s despite their diverse characteristics. These results reveal multiple aggregation-prone regions in SOD1 and illuminate how aggregation may occur via an ensemble of pathways.

Identification of a Steric Zipper Motif in the Amyloidogenic Core of Human Cystatin C and Its Use for the Design of Self-Assembling Peptides

Amyloid fibrils have been known for many years. Unfortunately, their fame stems from negative aspects related to amyloid diseases. Nevertheless, due to their properties, they can be used as interesting nanomaterials. Apart from their remarkable stability, amyloid fibrils may be regarded as a kind of a storage medium and as a source of active peptides. In many cases, their structure may guarantee a controlled and slow release of peptides in their active form; therefore, they can be used as a potential nanomaterial in drug delivery systems. In addition, amyloid fibrils display controllable stiffness, flexibility, and satisfactory mechanical strength. In addition, they can be modified and functionalized very easily. Understanding the structure and genesis of amyloid assemblies derived from a broad range of amyloidogenic proteins could help to better understand and use this unique material. One of the factors responsible for amyloid aggregation is the steric zipper. Here, we report the discovery of steric zipper-forming peptides in the sequence of the amyloidogenic protein, human cystatin C (HCC). The ability of short peptides derived from this fragment of HCC to form fibrillar structures with defined self-association characteristics and the factors influencing this aggregation are also presented in this paper.

Computational methods to predict protein aggregation

In most cases, protein aggregation stems from the establishment of non-native intermolecular contacts. The formation of insoluble protein aggregates is associated with many human diseases and is a major bottleneck for the industrial production of protein-based therapeutics. Strikingly, fibrillar aggregates are naturally exploited for structural scaffolding or to generate molecular switches and can be artificially engineered to build up multi-functional nanomaterials. Thus, there is a high interest in rationalizing and forecasting protein aggregation. Here, we review the available computational toolbox to predict protein aggregation propensities, identify sequential or structural aggregation-prone regions, evaluate the impact of mutations on aggregation or recognize prion-like domains. We discuss the strengths and limitations of these algorithms and how they can evolve in the next future.

Bioinformatics Methods in Predicting Amyloid Propensity of Peptides and Proteins

Several computational methods have been developed to predict amyloid propensity of a protein or peptide. These bioinformatics tools are time- and cost-saving alternatives to expensive and laborious experimental methods which are used to confirm self-aggregation of a protein. Computational approaches not only allow preselection of reliable candidates for amyloids but, most importantly, are capable of a thorough and informative analysis of a protein, indicating the sequence determinants of protein aggregation, identifying the potential causal mutations and likely mechanisms. Bioinformatics modeling applies several different approaches, which most typically include physicochemical or structure-based modeling, machine learning, or statistics based modeling. Bioinformatics methods typically use the amino acid sequence of a protein as an input, some also include additional information, for example, an available structure. This chapter describes the methods currently used to computationally predict amyloid propensity of a protein or peptide. Since the accuracy of bioinformatics methods may be highly dependent on reference data used to develop and evaluate the predictors, we also briefly present the main databases of amyloids used by the authors of bioinformatics tools.

TAPASS: Tool for Annotation of Protein Amyloidogenicity in the context of other Structural States

Numerous studies have demonstrated that the propensity of a protein to form amyloids or amorphous aggregates is encoded by its amino acid sequence. This led to the emergence of several computational programs to predict amyloidogenicity from amino acid sequences. However, a growing number of studies indicate that an accurate prediction of the protein aggregation can only be achieved when also accounting for the overall structural context of the protein, and the likelihood of transition between the initial state and the aggregate. Here, we describe a computational pipeline called TAPASS, which was designed to do just that. The pipeline assigns each residue of a protein as belonging to a structured region or an intrinsically disordered region (IDR). For this purpose, TAPASS uses either several state-of-the-art programs for prediction of IDRs, of transmembrane regions and of structured domains or the artificial intelligence program AlphaFold. In the next step, this assignment is crossed with amyloidogenicity prediction. As a result, TAPASS allows the detection of Exposed Amyloidogenic Regions (EARs) located within intrinsically disordered regions (IDRs) and carrying high amyloidogenic potential. TAPASS can substantially improve the prediction of amyloids and be used in proteome-wide analysis to discover new amyloid-forming proteins. Its results, combined with clinical data, can create individual risk profiles for different amyloidoses, opening up new opportunities for personalised medicine. The architecture of the pipeline is designed so that it makes it easy to add new individual predictors as they become available. TAPASS can be used through the web interface (https://bioinfo.crbm.cnrs.fr/index.php?route=tools&tool=32).

Chaperoning shape-shifting tau in disease

Many neurodegenerative diseases, including Alzheimer's, originate from the conversion of proteins into pathogenic conformations. The microtubule-associated protein tau converts into β-sheet-rich amyloid conformations, which underlie pathology in over 25 related tauopathies. Structural studies of tau amyloid fibrils isolated from human tauopathy tissues have revealed that tau adopts diverse structural polymorphs, each linked to a different disease. Molecular chaperones play central roles in regulating tau function and amyloid assembly in disease. New data supports the model that chaperones selectively recognize different conformations of tau to limit the accumulation of proteotoxic species. The challenge now is to understand how chaperones influence disease processes across different tauopathies, which will help guide the development of novel conformation-specific diagnostic and therapeutic strategies.

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.

Design and Testing of Synthetic Catalytic Amyloids Based on the Active Site of Enzymes

The amyloid fold is nowadays recognized as an alternative conformation accessible to different proteins and peptides. The highly stable and ordered structural organization of amyloid fibrils can be exploited for the design of novel nanomaterials with emergent properties. Recent works have demonstrated that the functional features of the active site of enzymes can be partially recreated using this fold as a scaffold to develop catalytically active amyloids. We describe in this chapter a protocol to design functionally active amyloids that emerge from the self-assembly in vitro of synthetic peptides with sequences based on the active site of enzymes. Using this protocol, we show the development of amyloids that catalyze the metal-dependent hydrolysis of the phosphoanhydride bonds of nucleoside triphosphates.

Modifying Amyloid Motif Aggregation Through Local Structure

Protein assembly into β-sheet-rich amyloid structures is a general biophysical phenomenon that has significant biological consequences, most notable for their prominent association with neurodegenerative diseases, including Alzheimer's, Huntington's, or Parkinson's diseases. The assembly of amyloid structures is driven by short sequences called amyloid motifs. In many neurodegenerative diseases, intrinsically disordered proteins (IDPs) self-assemble through amyloid motifs, but these motifs are present in all proteins, including folded globular proteins. Importantly, mechanistic knowledge is lacking for how IDPs, which do not adopt a stable tertiary structure, mask these amyloidogenic motifs to mitigate or slow the formation of β-sheet-rich amyloid structures that cause disease. Our recent work has shown that local structural elements can modify the aggregation propensity of amyloid motifs in the intrinsically disordered microtubule-associated protein tau by adopting metastable β-hairpin-like structures that shield the amyloid motif, and disease-causing mutations change the conformation, thus increase aggregation propensity (Chen, Nat Commun 10:2493, 2019). Here we describe a protocol that correlates experimentally determined aggregation propensities for peptides measured by the Thioflavin T (ThT) fluorescence aggregation assay with their conformational ensembles derived from Groningen machine chemical simulations (GROMACS). Integration of experiment and simulation will help uncover structural rules behind changes in conformation that modulate protein aggregation. We anticipate that our general protocol will help identify key interactions in local structures that engage amyloid-forming motifs in IDPs which influence aggregation behavior.

Delineating the Aggregation-Prone Hotspot Regions (Peptides) in the Human Cu/Zn Superoxide Dismutase 1

Amyotrophic lateral sclerosis (ALS) is a fatal, incurable neurodegenerative disease described by progressive degeneration of motor neurons. The most common familial form of ALS (fALS) has been associated with mutations in the Cu/Zn superoxide dismutase (SOD1) gene. Mutation-induced misfolding and aggregation of SOD1 is often found in ALS patients. In this work, we probe the aggregation properties of peptides derived from the SOD1. To examine the source of SOD1 aggregation, we have employed a computational algorithm to identify four peptides from the SOD1 protein sequence that aggregates into a fibril. Aided by computational algorithms, we identified four peptides likely involved in SOD1 fibrillization. These four aggregation-prone peptides were 14VQGIINFE21, 30KVWGSIKGL38, 101DSVISLS107, and 147GVIGIAQ153. In addition, the formation of fibril propensities from the identified peptides was investigated through different biophysical techniques. The atomic structures of two fibril-forming peptides from the C-terminal SOD1 showed that the steric zippers formed by 101DSVISLS107 and 147GVIGIAQ153 vary in their arrangement. We also discovered that fALS mutations in the peptide 147GVIGIAQ153 increased the fibril-forming propensity and altered the steric zipper's packing. Thus, our results suggested that the C-terminal peptides of SOD1 have a central role in amyloid formation and might be involved in forming the structural core of SOD1 aggregation observed in vivo.

Peptides derived from gp43, the most antigenic protein from Paracoccidioides brasiliensis, form amyloid fibrils in vitro: implications for vaccine development

Fungal infection is an important health problem in Latin America, and in Brazil in particular. Paracoccidioides (mainly P. brasiliensis and P. lutzii) is responsible for paracoccidioidomycosis, a disease that affects mainly the lungs. The glycoprotein gp43 is involved in fungi adhesion to epithelial cells, which makes this protein an interesting target of study. A specific stretch of 15 amino acids that spans the region 181–195 (named P10) of gp43 is an important epitope of gp43 that is being envisioned as a vaccine candidate. Here we show that synthetic P10 forms typical amyloid aggregates in solution in very short times, a property that could hamper vaccine development. Seeds obtained by fragmentation of P10 fibrils were able to induce the aggregation of P4, but not P23, two other peptides derived from gp43. In silico analysis revealed several regions within the P10 sequence that can form amyloid with steric zipper architecture. Besides, in-silico proteolysis studies with gp43 revealed that aggregation-prone, P10-like peptides could be generated by several proteases, which suggests that P10 could be formed under physiological conditions. Considering our data in the context of a potential vaccine development, we redesigned the sequence of P10, maintaining the antigenic region (HTLAIR), but drastically reducing its aggregation propensity.

Transient disorder along pathways to amyloid

High-resolution structures of amyloid fibrils formed from normally-folded proteins have revealed non-native conformations of the polypeptide chains. Attaining these conformations apparently requires transition from the native state via a highly disordered conformation, in contrast to earlier models that posited a role for assembly of partially folded proteins. Modifications or interactions that extend the lifetime or constrain the conformations of these disordered states could act to enhance or suppress amyloid formation. Understanding how the properties of both the folded and transiently disordered structural ensembles influence the process of amyloid formation is a substantial challenge, but research into the properties of intrinsically disordered proteins will deliver important insights.

Structural and Functional Insights into α-Synuclein Fibril Polymorphism

Abnormal accumulation of aggregated α-synuclein (α-Syn) is seen in a variety of neurodegenerative diseases, including Parkinson's disease (PD), multiple system atrophy (MSA), dementia with Lewy body (DLB), Parkinson's disease dementia (PDD), and even subsets of Alzheimer's disease (AD) showing Lewy-body-like pathology. These synucleinopathies exhibit differences in their clinical and pathological representations, reminiscent of prion disorders. Emerging evidence suggests that α-Syn self-assembles and polymerizes into conformationally diverse polymorphs in vitro and in vivo, similar to prions. These α-Syn polymorphs arising from the same precursor protein may exhibit strain-specific biochemical properties and the ability to induce distinct pathological phenotypes upon their inoculation in animal models. In this review, we discuss clinical and pathological variability in synucleinopathies and several aspects of α-Syn fibril polymorphism, including the existence of high-resolution molecular structures and brain-derived strains. The current review sheds light on the recent advances in delineating the structure-pathogenic relationship of α-Syn and how diverse α-Syn molecular polymorphs contribute to the existing clinical heterogeneity in synucleinopathies.

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.

Controlling the self-assembly of human calcitonin: a theoretical approach using molecular dynamics simulations

Human calcitonin (hCT) is a 32-residue amino acid poly-peptide hormone which is secreted by the C-cells (also known as parafollicular cells) of thyroid glands. It acts to inhibit osteoclast cell hormones by reducing the cell function and regulating calcium and phosphate in blood. hCT has a high tendency to assemble into protofilaments with β-sheet conformations. Amyloid fibril formation of hCT reduces its bio-activity and limits its application as a therapeutic drug. Salmon calcitonin (sCT), which also carries the same disulfide bridge at the N and C-terminus, but differs at the 16 residue position from hCT, has less propensity to aggregate than hCT. Human calcitonin has much higher bio-activity than sCT if its aggregation propensity is reduced. Substituting the key residues which are responsible for the aggregation of hCT, is one of the ways to reduce its aggregation and fibril formation. hCT analogues with less aggregation tendency can be exploited as therapeutic drugs. In this work, we study the amyloidogenic behavior of hCT and its peptide based derivatives i.e., sCT, phCT, N17H hCT, Y12L hCT and DM hCT, through classical molecular dynamics (MD) simulations. Our study reveals that sCT is the least aggregation prone derivative, and the double mutation at position 12 and 17 can reduce the aggregation propensity of this peptide. Also, we have applied these mutant variants of hCT as peptide inhibitors in the self-aggregation of hCT. This study could help in understanding and preparing peptide-based inhibitors for hCT fibrillation and their applications as therapeutic drugs.

Carvedilol inhibits Aβ25-35 fibrillation by intervening the early stage helical intermediate formation: A biophysical investigation

Self-assembly of disordered amyloid-beta (Aβ) peptides results in highly ordered amyloid fibrils. The structural information of the early-stage events and also in the presence of inhibitors is of great significance. It is challenging to acquire due to the nature of the amyloids and experimental constraints. Here, we demonstrate the cascade of aggregation (early to late) of the Aβ_25-35 peptide in the absence and presence of carvedilol, a nonselective β-adrenergic receptor blocker. The aggregation process of Aβ_25-35 peptide is monitored using Thioflavin T (ThT) fluorescence, dynamic light scattering (DLS), circular dichroism (CD), Raman spectroscopic techniques, and imaging experiments. We find that the Aβ_25-35 peptide undergoes an early-stage (3-6 h) helical intermediate formation across the fibrillation pathway using CD and Raman measurements. Carvedilol obstructs the helical intermediate formation of Aβ_25-35 peptide resulting in inhibition. CD spectra and deconvolution of the Raman bands suggest the β-sheet formation (24-100 h) in the absence of carvedilol. Spectroscopic results indicate a disordered structure for the peptide in the presence of carvedilol (24-100 h). Electron microscopy (EM) shows the formation of polymorphic fibrils for the peptide alone and non-amyloidal aggregates in the presence of carvedilol. Molecular docking study suggests that the plausible mode of interaction with carvedilol involves the C-terminal residues of the peptide.

Rational optimization of a monoclonal antibody improves the aggregation propensity and enhances the CMC properties along the entire pharmaceutical process chain

The discovery of therapeutic monoclonal antibodies (mAbs) primarily focuses on their biological activity favoring the selection of highly potent drug candidates. These candidates, however, may have physical or chemical attributes that lead to unfavorable chemistry, manufacturing, and control (CMC) properties, such as low product titers, conformational and colloidal instabilities, or poor solubility, which can hamper or even prevent development and manufacturing. Hence, there is an urgent need to consider the developability of mAb candidates during lead identification and optimization. This work provides a comprehensive proof of concept study for the significantly improved developability of a mAb variant that was optimized with the help of sophisticated in silico tools relative to its difficult-to-develop parental counterpart. Interestingly, a single amino acid substitution in the variable domain of the light chain resulted in a three-fold increased product titer after stable expression in Chinese hamster ovary cells. Microscopic investigations revealed that wild type mAb-producing cells displayed potential antibody inclusions, while the in silico optimized variant-producing cells showed a rescued phenotype. Notably, the drug substance of the in silico optimized variant contained substantially reduced levels of aggregates and fragments after downstream process purification. Finally, formulation studies unraveled a significantly enhanced colloidal stability of the in silico optimized variant while its folding stability and potency were maintained. This study emphasizes that implementation of bioinformatics early in lead generation and optimization of biotherapeutics reduces failures during subsequent development activities and supports the reduction of project timelines and resources.

Machine learning analyses of antibody somatic mutations predict immunoglobulin light chain toxicity

In systemic light chain amyloidosis (AL), pathogenic monoclonal immunoglobulin light chains (LC) form toxic aggregates and amyloid fibrils in target organs. Prompt diagnosis is crucial to avoid permanent organ damage, but delayed diagnosis is common because symptoms usually appear only after strong organ involvement. Here we present LICTOR, a machine learning approach predicting LC toxicity in AL, based on the distribution of somatic mutations acquired during clonal selection. LICTOR achieves a specificity and a sensitivity of 0.82 and 0.76, respectively, with an area under the receiver operating characteristic curve (AUC) of 0.87. Tested on an independent set of 12 LCs sequences with known clinical phenotypes, LICTOR achieves a prediction accuracy of 83%. Furthermore, we are able to abolish the toxic phenotype of an LC by in silico reverting two germline-specific somatic mutations identified by LICTOR, and by experimentally assessing the loss of in vivo toxicity in a Caenorhabditis elegans model. Therefore, LICTOR represents a promising strategy for AL diagnosis and reducing high mortality rates in AL.