Insight into the structure of amyloid fibrils from the analysis of globular proteins

Zwitterionic peptides: Tunable next-generation stealth nanoparticle modifications

Adsorption of proteins to nanoparticles (NPs), a complex process that results in a protein corona, is controlled by NP surface properties that define NP interactions in vivo. Efforts to control adsorbed protein quantity through surface modification have led to improvements in circulation time or biodistribution. Still, current approaches have yet to be identified to control adsorbed protein identities within the corona. Here, we report the development and characterization of diverse zwitterionic peptides (ZIPs) for NP anti-fouling surface functionalization with specific and controllable affinity for protein adsorption profiles defined by ZIP sequence. Through serum exposure of ZIP-conjugated NPs and proteomics analysis of the resulting corona, we determined that protein adsorption profiles depend not on the exact composition of the ZIPs but on the sequence and order of charges along the sequence (charge motif). These findings pave the way for developing tunable ZIPs to orchestrate specific ZIP-NP protein adsorption profiles as a function of ZIP charge motif to better control cell and tissue specificity and pharmacokinetics and provide new tools for investigating relationships between protein corona and biological function. Furthermore, overall ZIP diversity enabled by the diversity of amino acids may ameliorate adaptive immune responses.

A highly sensitive hemicyanine-based near-infrared fluorescence sensor for detecting toxic amyloid aggregates in human serum

The development of an accurate and sensitive sensor for detecting amyloid plaques, which are responsible for many protein disorders like Alzheimer's disease, is crucial for early diagnosis. Recently, there has been a notable increase in the development of fluorescence probes that exhibit emission in the red region (>600 nm), aiming to effectively tackle the challenges encountered when working with complex biological matrices. In the current investigation, a hemicyanine-based probe, called LDS730, has been used for the sensing of amyloid fibrils, which belong to the Near-Infrared Fluorescence (NIRF) family of dyes. NIRF probes provide higher precision in detection, prevent photo-damage, and minimize the autofluorescence of biological specimens. The LDS730 sensor emits in the near-infrared region and shows a 110-fold increase in fluorescence turn-on emission when bound to insulin fibrils, making it a highly sensitive sensor. The sensor has an emission maximum of ~710 nm in a fibril-bound state, which shows a significant red shift along with a Stokes' shift of ~50 nm. The LDS730 sensor also displays excellent performance in the complicated human serum matrix, with a limit of detection (LOD) of 103 nM. Molecular docking calculations suggest that the most likely binding location of LDS730 in the fibrillar structure is the inner channels of amyloid fibrils along its long axis, and the sensor engages in several types of hydrophobic interactions with neighboring amino acid residues of the fibrillar structure. Overall, this new amyloid sensor has great potential for the early detection of amyloid plaques and for improving diagnostic accuracy.

Sequence-Based Prediction of Protein Phase Separation: The Role of Beta-Pairing Propensity

The formation of droplets of bio-molecular condensates through liquid-liquid phase separation (LLPS) of their component proteins is a key factor in the maintenance of cellular homeostasis. Different protein properties were shown to be important in LLPS onset, making it possible to develop predictors, which try to discriminate a positive set of proteins involved in LLPS against a negative set of proteins not involved in LLPS. On the other hand, the redundancy and multivalency of the interactions driving LLPS led to the suggestion that the large conformational entropy associated with non specific side-chain interactions is also a key factor in LLPS. In this work we build a LLPS predictor which combines the ability to form pi-pi interactions, with an unrelated feature, the propensity to stabilize the β-pairing interaction mode. The cross-β structure is formed in the amyloid aggregates, which are involved in degenerative diseases and may be the final thermodynamically stable state of protein condensates. Our results show that the combination of pi-pi and β-pairing propensity yields an improved performance. They also suggest that protein sequences are more likely to be involved in phase separation if the main chain conformational entropy of the β-pairing maintained droplet state is increased. This would stabilize the droplet state against the more ordered amyloid state. Interestingly, the entropic stabilization of the droplet state appears to proceed according to different mechanisms, depending on the fraction of "droplet-driving" proteins present in the positive set.

Phenylpropanoids on the Inhibition of β-Amyloid Aggregation and the Movement of These Molecules through the POPC Lipid Bilayer

Alzheimer's disease (AD), caused by Aβ aggregation, is a major concern in medical research. It is a neurodegenerative disorder, leading to a loss of cognitive abilities, which is still claiming the lives of many people all over the world. This poses a challenge before the scientific community to discover effective drugs which can prevent such toxic aggregation. Recent experimental findings suggest the potency of two naturally-occurring phenylpropanoids, Schizotenuin A (SCH) and Lycopic Acid B (LAB) which can effectively combat the deleterious effects of Aβ aggregation, although nothing is known about their mechanism of inhibition. In this work, we deal with an extensive computational study on the inhibitory effects of these inhibitors by using an all-atom molecular dynamics simulation to interpret the underlying mechanism of their inhibitory processes. A series of investigations is carried out while studying the various structural and conformational changes of the peptide chains in the absence and presence of inhibitors. To investigate the details of the interactions between the peptide residues and inhibitors, nonbonding energy calculations, the radial distribution function, the coordination number of water and inhibitor molecules around the peptide residues, and hydrogen-bonding interactions are calculated. The potential of mean force (PMF) is calculated to estimate aggregate formation from their free-energy profiles. It is seen that the hydrophobic core of the KLVFFAE undergoes aggregation and that these inhibitors show great promise in preventing the onset of AD in the future by preventing Aβ aggregation. Also, the translocation studies on these inhibitors through a model POPC lipid bilayer shed light on their permeation properties and biocompatibility.

General Principles Underpinning Amyloid Structure

Amyloid fibrils are a pathologically and functionally relevant state of protein folding, which is generally accessible to polypeptide chains and differs fundamentally from the globular state in terms of molecular symmetry, long-range conformational order, and supramolecular scale. Although amyloid structures are challenging to study, recent developments in techniques such as cryo-EM, solid-state NMR, and AFM have led to an explosion of information about the molecular and supramolecular organization of these assemblies. With these rapid advances, it is now possible to assess the prevalence and significance of proposed general structural features in the context of a diverse body of high-resolution models, and develop a unified view of the principles that control amyloid formation and give rise to their unique properties. Here, we show that, despite system-specific differences, there is a remarkable degree of commonality in both the structural motifs that amyloids adopt and the underlying principles responsible for them. We argue that the inherent geometric differences between amyloids and globular proteins shift the balance of stabilizing forces, predisposing amyloids to distinct molecular interaction motifs with a particular tendency for massive, lattice-like networks of mutually supporting interactions. This general property unites previously characterized structural features such as steric and polar zippers, and contributes to the long-range molecular order that gives amyloids many of their unique properties. The shared features of amyloid structures support the existence of shared structure-activity principles that explain their self-assembly, function, and pathogenesis, and instill hope in efforts to develop broad-spectrum modifiers of amyloid function and pathology.

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.

Statistical potentials from the Gaussian scaling behaviour of chain fragments buried within protein globules

Knowledge-based approaches use the statistics collected from protein data-bank structures to estimate effective interaction potentials between amino acid pairs. Empirical relations are typically employed that are based on the crucial choice of a reference state associated to the null interaction case. Despite their significant effectiveness, the physical interpretation of knowledge-based potentials has been repeatedly questioned, with no consensus on the choice of the reference state. Here we use the fact that the Flory theorem, originally derived for chains in a dense polymer melt, holds also for chain fragments within the core of globular proteins, if the average over buried fragments collected from different non-redundant native structures is considered. After verifying that the ensuing Gaussian statistics, a hallmark of effectively non-interacting polymer chains, holds for a wide range of fragment lengths, although with significant deviations at short spatial scales, we use it to define a ‘bona fide’ reference state. Notably, despite the latter does depend on fragment length, deviations from it do not. This allows to estimate an effective interaction potential which is not biased by the presence of correlations due to the connectivity of the protein chain. We show how different sequence-independent effective statistical potentials can be derived using this approach by coarse-graining the protein representation at varying levels. The possibility of defining sequence-dependent potentials is explored.

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.

Computational Identification and Design of Complementary β-Strand Sequences

The ß-sheet is a regular secondary structure element which consists of linear segments called ß-strands. They are involved in many important biological processes, and some are known to be related to serious diseases such as neurologic disorders and amyloidosis. The self-assembly of ß-sheet peptides also has practical applications in material sciences since they can be building blocks of repeated nanostructures. Therefore, computational algorithms for identification of ß-sheet formation can offer useful insight into the mechanism of disease-prone protein segments and the construction of biocompatible nanomaterials. Despite the recent advances in structure-based methods for the assessment of atomic interactions, identifying amyloidogenic peptides has proven to be extremely difficult since they are structurally very flexible. Thus, an alternative strategy is required to describe ß-sheet formation. It has been hypothesized and observed that there are certain amino acid propensities between ß-strand pairs. Based on this hypothesis, a database search algorithm, B-SIDER, is developed for the identification and design of ß-sheet forming sequences. Given a target sequence, the algorithm identifies exact or partial matches from the structure database and constructs a position-specific score matrix. The score matrix can be utilized to design novel sequences that can form a ß-sheet specifically with the target.

Inhibition of the formation of lysozyme fibrillar assemblies by the isoquinoline alkaloid coralyne

The isoquinoline alkaloid coralyne can efficiently attenuate fibrillogenesis in lysozyme.

Heterotypic amyloid interactions: Clues to polymorphic bias and selective cellular vulnerability?

The number of atomic-resolution structures of disease-associated amyloids has greatly increased in recent years. These structures have confirmed not only the polymorphic nature of amyloids but also the association of specific polymorphs to particular proteinopathies. These observations are strengthening the view that amyloid polymorphism is a marker for specific pathological subtypes (e.g. in tauopathies or synucleinopathies). The nature of this association and how it relates to the selective cellular vulnerability of amyloid nucleation, propagation and toxicity are still unclear. Here, we provide an overview of the mechanistic insights provided by recent patient-derived amyloid structures. We discuss the framework organisation of amyloid polymorphism and how heterotypic amyloid interactions with the physiological environment could modify the solubility and assembly of amyloidogenic proteins. We conclude by hypothesising how such interactions could contribute to selective cellular vulnerability.

Illuminating amyloid fibrils: Fluorescence-based single-molecule approaches

The aggregation of proteins into insoluble filamentous amyloid fibrils is a pathological hallmark of neurodegenerative diseases that include Parkinson's disease and Alzheimer's disease. Since the identification of amyloid fibrils and their association with disease, there has been much work to describe the process by which fibrils form and interact with other proteins. However, due to the dynamic nature of fibril formation and the transient and heterogeneous nature of the intermediates produced, it can be challenging to examine these processes using techniques that rely on traditional ensemble-based measurements. Single-molecule approaches overcome these limitations as rare and short-lived species within a population can be individually studied. Fluorescence-based single-molecule methods have proven to be particularly useful for the study of amyloid fibril formation. In this review, we discuss the use of different experimental single-molecule fluorescence microscopy approaches to study amyloid fibrils and their interaction with other proteins, in particular molecular chaperones. We highlight the mechanistic insights these single-molecule techniques have already provided in our understanding of how fibrils form, and comment on their potential future use in studying amyloid fibrils and their intermediates.

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.

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

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

Defining the Neuropathological Aggresome across in Silico, in Vitro, and ex Vivo Experiments

The loss of proteostasis over the life course is associated with a wide range of debilitating degenerative diseases and is a central hallmark of human aging. When left unchecked, proteins that are intrinsically disordered can pathologically aggregate into highly ordered fibrils, plaques, and tangles (termed amyloids), which are associated with countless disorders such as Alzheimer's disease, Parkinson's disease, type II diabetes, cancer, and even certain viral infections. However, despite significant advances in protein folding and solution biophysics techniques, determining the molecular cause of these conditions in humans has remained elusive. This has been due, in part, to recent discoveries showing that soluble protein oligomers, not insoluble fibrils or plaques, drive the majority of pathological processes. This has subsequently led researchers to focus instead on heterogeneous and often promiscuous protein oligomers. Unfortunately, significant gaps remain in how to prepare, model, experimentally corroborate, and extract amyloid oligomers relevant to human disease in a systematic manner. This Review will report on each of these techniques and their successes and shortcomings in an attempt to standardize comparisons between protein oligomers across disciplines, especially in the context of neurodegeneration. By standardizing multiple techniques and identifying their common overlap, a clearer picture of the soluble neuropathological aggresome can be constructed and used as a baseline for studying human disease and aging.

A theoretical study of polymorphism in VQIVYK fibrils

The VQIVYK fragment from the Tau protein, also known as PHF6, is essential for aggregation of Tau into neurofibrillary lesions associated with neurodegenerative diseases. VQIVYK itself forms amyloid fibrils composed of paired β-sheets. Therefore, the full Tau protein and VQIVYK fibrils have been intensively investigated. A central issue in these studies is polymorphism, the ability of a protein to fold into more than one structure. Using all-atom molecular simulations, we generate five stable polymorphs of VQIVYK fibrils, establish their relative free energy with umbrella sampling methods, and identify the side chain interactions that provide stability. The two most stable polymorphs, which have nearly equal free energy, are formed by interdigitation of the mostly hydrophobic VIY "face" sides of the β-sheets. Another stable polymorph is formed by interdigitation of the QVK "back" sides. When we turn to examine structures from cryo-electron microscopy experiments on Tau filaments taken from diseased patients or generated in vitro, we find that the pattern of side chain interactions found in the two most stable face-to-face as well as the back-to-back polymorphs are recapitulated in amyloid structures of the full protein. Thus, our studies suggest that the interactions stabilizing PHF6 fibrils explain the amyloidogenicity of the VQIVYK motif within the full Tau protein and provide justification for the use of VQIVYK fibrils as a test bed for the design of molecules that identify or inhibit amyloid structures.

Activation of phospholipase A2 by prostaglandin in vitro

Prostaglandins are a diverse family of biological active molecules that are synthesized after liberation of arachnidonic or linolenic acid from the plasma membrane by phospholipase A2 (PLA2). Specific prostaglandins may be pro-inflammatory or anti-inflammatory due to a poorly understood biochemical equilibrium. Some of the anti-inflammatory prostaglandins namely, prostaglandin A1 (PGA1) and prostaglandin E1 (PGE1) have a cyclopentenone moiety that can react and modify a protein's activity. These two prostaglandins are electrophilic reactive lipid species and are formed as a result of the reaction cascade initiated by PLA2. It was of interest to study the interaction with these prostaglandins as they could either amplify or block this enzyme's activity. We found that the former is true initially as there is a shorter time to activate the protein on the lipid membrane and an overall increase in hydrolysis was observed when PGA1 and PGE1 prostaglandin was added with PLA2 and liposomes. The interfacial activation model was further explored in which there is a modification of the enzyme rather than a modifcation of the substrate. However, after a time the protein was shown to form amyloid like fibrils thereby blocking further hydrolysis. The fibrillization kinetics in the presence of the one of the prostaglandins was monitored using thioflavin T (ThT) and the resulting fibrils were characterized using transmission electron microscopy (TEM) and atomic force microscopy (AFM). Modification of PLA2 by these prostaglandins leading to amyloid like fibrils gives an additional perspective of control of the interfacial activation mechanism of this enzyme.

Computational prediction of protein aggregation: Advances in proteomics, conformation-specific algorithms and biotechnological applications

Protein aggregation is a widespread phenomenon that stems from the establishment of non-native intermolecular contacts resulting in protein precipitation. Despite its deleterious impact on fitness, protein aggregation is a generic property of polypeptide chains, indissociable from protein structure and function. Protein aggregation is behind the onset of neurodegenerative disorders and one of the serious obstacles in the production of protein-based therapeutics. The development of computational tools opened a new avenue to rationalize this phenomenon, enabling prediction of the aggregation propensity of individual proteins as well as proteome-wide analysis. These studies spotted aggregation as a major force driving protein evolution. Actual algorithms work on both protein sequences and structures, some of them accounting also for conformational fluctuations around the native state and the protein microenvironment. This toolbox allows to delineate conformation-specific routines to assist in the identification of aggregation-prone regions and to guide the optimization of more soluble and stable biotherapeutics. Here we review how the advent of predictive tools has change the way we think and address protein aggregation.

The hydrophobic effect characterises the thermodynamic signature of amyloid fibril growth

Many proteins have the potential to aggregate into amyloid fibrils, protein polymers associated with a wide range of human disorders such as Alzheimer’s and Parkinson’s disease. The thermodynamic stability of amyloid fibrils, in contrast to that of folded proteins, is not well understood: the balance between entropic and enthalpic terms, including the chain entropy and the hydrophobic effect, are poorly characterised. Using a combination of theory, in vitro experiments, simulations of a coarse-grained protein model and meta-data analysis, we delineate the enthalpic and entropic contributions that dominate amyloid fibril elongation. Our prediction of a characteristic temperature-dependent enthalpic signature is confirmed by the performed calorimetric experiments and a meta-analysis over published data. From these results we are able to define the necessary conditions to observe cold denaturation of amyloid fibrils. Overall, we show that amyloid fibril elongation is associated with a negative heat capacity, the magnitude of which correlates closely with the hydrophobic surface area that is buried upon fibril formation, highlighting the importance of hydrophobicity for fibril stability.

Most proteins fold in the cell into stable, compact structures. Nevertheless, many proteins also have the ability to stick together, forming long fibrillar structures that are associated with a wide range of human disorders including Alzheimer’s and Parkinson’s disease. The exact nature of the amyloid-causing stickiness is not well understood, nevertheless amyloid fibrils show some very specific thermodynamic characteristics. Some fibrils even destabilise at low temperatures. In this work we translate hydrophobic theory previously used to model protein folding to fibril formation. We combine this theory with experimental measurements, simulations and meta-data analysis of different types of fibrils. This allowed us to unravel the nature of the stickiness in amyloid fibrils by observing the effect of temperature changes, specifically at low temperatures, on hydrophobicity.

Protein Aggregation in a Nutshell: The Splendid Molecular Architecture of the Dreaded Amyloid Fibrils

The recent high-resolution structures of amyloid fibrils show that the organization of peptide segments into amyloid aggregate architecture is a general process, though the morphology is more complex and intricate than suspected previously. The amyloid fibrils are often cytotoxic, accumulating as intracellular inclusions or extracellular plaques and have the ability to interfere with cellular physiology causing various cellular malfunctions. At the same time, the highly ordered amyloid structures also present an opportunity for nature to store and protect peptide chains under extreme conditions - something that might be used for designing storage, formulation, and delivery of protein medications or for contriving bio-similar materials of great resistance or structure-ordering capacity. Here we summarize amyloid characteristics; discussing the basic morphologies, sequential requirements and 3D-structure that are required for the understanding of this newly (re)discovered protein structure - a prerequisite for developing either inhibitors or promoters of amyloid-forming processes.

M. Brito R. Structure and Aggregation Mechanisms in Amyloids

The aggregation of a polypeptide chain into amyloid fibrils and their accumulation and deposition into insoluble plaques and intracellular inclusions is the hallmark of several misfolding diseases known as amyloidoses. Alzheimer′s, Parkinson′s and Huntington’s diseases are some of the approximately 50 amyloid diseases described to date. The identification and characterization of the molecular species critical for amyloid formation and disease development have been the focus of intense scrutiny. Methods such as X-ray and electron diffraction, solid-state nuclear magnetic resonance spectroscopy (ssNMR) and cryo-electron microscopy (cryo-EM) have been extensively used and they have contributed to shed a new light onto the structure of amyloid, revealing a multiplicity of polymorphic structures that generally fit the cross-β amyloid motif. The development of rational therapeutic approaches against these debilitating and increasingly frequent misfolding diseases requires a thorough understanding of the molecular mechanisms underlying the amyloid cascade. Here, we review the current knowledge on amyloid fibril formation for several proteins and peptides from a kinetic and thermodynamic point of view, the structure of the molecular species involved in the amyloidogenic process, and the origin of their cytotoxicity.

Accurate prediction of protein beta-aggregation with generalized statistical potentials

Motivation

Protein beta-aggregation is an important but poorly understood phenomena involved in diseases as well as in beneficial physiological processes. However, while this task has been investigated for over 50 years, very little is known about its mechanisms of action. Moreover, the identification of regions involved in aggregation is still an open problem and the state-of-the-art methods are often inadequate in real case applications.

Results

In this article we present AgMata, an unsupervised tool for the identification of such regions from amino acidic sequence based on a generalized definition of statistical potentials that includes biophysical information. The tool outperforms the state-of-the-art methods on two different benchmarks. As case-study, we applied our tool to human ataxin-3, a protein involved in Machado–Joseph disease. Interestingly, AgMata identifies aggregation-prone residues that share the very same structural environment. Additionally, it successfully predicts the outcome of in vitro mutagenesis experiments, identifying point mutations that lead to an alteration of the aggregation propensity of the wild-type ataxin-3.

Availability and implementation

A python implementation of the tool is available at https://bitbucket.org/bio2byte/agmata.

Supplementary information

Supplementary data are available at Bioinformatics online.

B-SIDER: Computational Algorithm for the Design of Complementary β-Sheet Sequences

The β-sheet is an element of protein secondary structure, and intra-/intermolecular β-sheet interactions play pivotal roles in biological regulatory processes including scaffolding, transporting, and oligomerization. In nature, a β-sheet formation is tightly regulated because dysregulated β-stacking often leads to severe diseases such as Alzheimer's, Parkinson's, systemic amyloidosis, or diabetes. Thus, the identification of intrinsic β-sheet-forming propensities can provide valuable insight into protein designs for the development of novel therapeutics. However, structure-based design methods may not be generally applicable to such amyloidogenic peptides mainly owing to high structural plasticity and complexity. Therefore, an alternative design strategy based on complementary sequence information is of significant importance. Herein, we developed a database search method called β-Stacking Interaction DEsign for Reciprocity (B-SIDER) for the design of complementary β-strands. This method makes use of the structural database information and generates target-specific score matrices. The discriminatory power of the B-SIDER score function was tested on representative amyloidogenic peptide substructures against a sequence-based score matrix (PASTA 2.0) and two popular ab initio protein design score functions (Rosetta and FoldX). B-SIDER is able to distinguish wild-type amyloidogenic β-strands as favored interactions in a more consistent manner than other methods. B-SIDER was prospectively applied to the design of complementary β-strands for a splitGFP scaffold. Three variants were identified to have stronger interactions than the original sequence selected through a directed evolution, emitting higher fluorescence intensities. Our results indicate that B-SIDER can be applicable to the design of other β-strands, assisting in the development of therapeutics against disease-related amyloidogenic peptides.

Cryo-EM of amyloid fibrils and cellular aggregates

Neurodegenerative and other protein misfolding diseases are associated with the aggregation of a protein, which may be mutated in genetic forms of disease, or the wild type form in late onset sporadic disease. A wide variety of proteins and peptides can be involved, with aggregation originating from a natively folded or a natively unstructured species. Large deposits of amyloid fibrils are typically associated with cell death in late stage pathology. In this review, we illustrate the contributions of cryo-EM and related methods to the structure determination of amyloid fibrils extracted post mortem from patient brains or formed in vitro. We also discuss cell models of protein aggregation and the contributions of electron tomography to understanding the cellular context of aggregation.

Amyloid Oligomers, Protofibrils and Fibrils

Amyloid diseases are of major concern all over the world due to a number of factors including: (i) aging population, (ii) increasing life span and (iii) lack of effective pharmacotherapy options. The past decade has seen intense research in discovering disease-modifying multi-targeting small molecules as therapeutic options. In recent years, targeting the amyloid cascade has emerged as an attractive strategy to discover novel neurotherapeutics. Formation of amyloid species, with different degrees of solubility and neurotoxicity is associated with the gradual decline in cognition leading to dementia/cell dysfunction. Here, in this chapter, we have described the recent scenario of amyloid diseases with a great deal of information about the structural features of oligomers, protofibrils and fibrils. Also, comprehensive details have been provided to differentiate the degree of toxicity associated with prefibrillar aggregates. Moreover, a review of the technologies that aid characterisation of oligomer, protofibrils and fibrils as well as various inhibition strategies to overcome protein fibrillation are also discussed.

Formation and properties of amyloid fibrils of prion protein

Amyloid fibrils formed from prion protein (PrP) are associated with prion diseases. In this review we discuss a number of extrinsic and intrinsic experimental factors related to the formation of PrP amyloid fibrils in vitro. We first examined the effects of ultrasonic power on the induction of amyloid fibrillation from PrP. The most important conclusion drawn from the results is that an applied ultrasonic power of approximately 2 W enhanced the nucleation of amyloid fibrils efficiently but that more powerful ultrasonication led to retardation of growth. We also reviewed evidence on the amyloidogenic regions of PrP based on peptide screening throughout the polypeptide sequence. These results showed that helix 2 (H2) peptides of PrP were capable of both the fibrillation and propagation of straight, long fibrils. Moreover, the conformation of preformed H2 fibrils changed reversibly depending on the pH of the solution, implying that interactions between side-chains modulated the conformation of amyloid fibrils. The evidence discussed in this review relates specifically to PrP but may be relevant to other amyloidogenic proteins.

Clarifying the role of cryo- and lyo-protectants in the biopreservation of proteins

Biopharmaceuticals are frequently stored in the frozen state to avoid rapid degradation. Moreover, therapeutic proteins are frequently made into a dried form to provide long-term storage. However, both freezing and drying stresses can result in protein unfolding and aggregation. Thus, a proper formulation, containing suitable excipients, must be used to avoid loss of activity. Here, the conformational stability of a model protein, human growth hormone, is studied during freezing, and in the dried state as well, using molecular dynamics. The impact of the ice-water interface and of water removal is deeply investigated, and the role of protectants in preventing denaturation phenomena is addressed. We found that good cryo-protectants not always are equally effective as lyo-protectants, and experimental data confirmed simulation results. From this analysis, we also discovered that the interaction of stabilizers with specific amino acid sequences of the protein, rather than with the molecule as a whole, seems to be a crucial issue in the preservation of protein structure. This finding was confirmed for another protein, i.e., lactate dehydrogenase, thus suggesting that it is a generally applicable result. Remarkably, those sequences which unfolded during freezing and drying, generally coincided with the aggregation prone regions of the protein.

Theory of Sequence Effects in Amyloid Aggregation

We present a simple model for the effect of amino acid sequences on amyloid fibril formation. Using the HP model we find the binding lifetimes of four simple sequences by solving the first passage time for the intermolecular H-bond reaction coordinate. We find that sequences with identical binding energies have widely varying binding times depending on where the aggregation prone amino acids are located in the sequence. In general, longer binding times occur when the aggregation prone amino acids are clustered in a single "hot spot". Similarly, binding times are shortened by clustering weakly bound residues. Both of these effects are explained by an increase in the multiplicity of unbinding trajectories that comes from adding weak binding residues. Our model predicts a transition from ordered to disordered fibrils as the concentration of monomers increases. We apply our model to Aβ, IAPP, and apomyoglobin using binding energy estimates derived from bioinformatics. We find that these sequences are highly selective of the in-register state. This selectivity arises from the having strongly bound segments of varying length and separation.

C-Terminal sequence is involved in the incorporation of Bgl2p glucanosyltransglycosylase in the cell wall of Saccharomyces cerevisiae

A cell wall (CW) provides a protective barrier for a yeast cell and is a firm structure that nevertheless dynamically changes during cell's growth. Bgl2p is a non-covalently anchored glucanosyltransglycosylase in the CW of the yeast Saccharomyces cerevisiae. The mode of its anchorage is poorly understood, while its association with CW components is tight and resistant to 1-h treatment with 1% SDS at 37°C. In order to demarcate the potential structural block responsible for incorporation of Bgl2p into the CW, bioinformatics analysis of its sequence was performed, and a conservative structural region was identified in the C-terminal region of Bgl2p, which was absent in its homologues in S. cerevisiae, the Scw4p and Scw10p. Deletion of this region disrupted the incorporation of Bgl2p into the CW and led to release of this protein through the CW into the culture medium. Two left-handed polyproline-II helices were identified in the C-terminal region of the structure model of a wild-type Bgl2p. These helices potentially formed binding sites, which were absent in the truncated protein. Using immune fluorescence microscopy, we demonstrated that C-truncated Bgl2p was exported into culture medium and lost its ability to form fibrils described earlier. It was also shown that the C-terminal truncation of Bgl2p led to a more severe decrease of cell survivability in extreme conditions than BGL2 deletion.

Usage of a dataset of NMR resolved protein structures to test aggregation versus solubility prediction algorithms

There has been an increased interest in computational methods for amyloid and (or) aggregate prediction, due to the prevalence of these aggregates in numerous diseases and their recently discovered functional importance. To evaluate these methods, several datasets have been compiled. Typically, aggregation-prone regions of proteins, which form aggregates or amyloids in vivo, are more than 15 residues long and intrinsically disordered. However, the number of such experimentally established amyloid forming and non-forming sequences are limited, not exceeding one hundred entries in existing databases. In this work, we parsed all available NMR-resolved protein structures from the PDB and assembled a new, sevenfold larger, dataset of unfolded sequences, soluble at high concentrations. We proposed to use these sequences as a negative set for evaluating methods for predicting aggregation in vivo. We also present the results of benchmarking cutting edge tools for the prediction of aggregation versus solubility propensity.

The Solution Assembly of Biological Molecules Using Ion Mobility Methods: From Amino Acids to Amyloid β-Protein

Ion mobility spectrometry-mass spectrometry (IMS-MS) methods are increasingly used to study noncovalent assemblies of peptides and proteins. This review focuses on the noncovalent self-assembly of amino acids and peptides, systems at the heart of the amyloid process that play a central role in a number of devastating diseases. Three different systems are discussed in detail: the 42-residue peptide amyloid-β42 implicated in the etiology of Alzheimer's disease, several amyloid-forming peptides with 6-11 residues, and the assembly of individual amino acids. We also discuss from a more fundamental perspective the processes that determine how quickly proteins and their assemblies denature when the analyte ion has been stripped of its solvent in an IMS-MS measurement and how to soften the measurement so that biologically meaningful data can be recorded.

Reversible Morphological Control of Cholecystokinin Tetrapeptide Amyloid Assemblies as a Function of pH

Most amyloid assemblies are seen as irreversible and exhibit polymorphism because their assembly is kinetically controlled and different structures are trapped during the aggregation process. However, in the specific case of peptide hormones, formation of amyloid assemblies for storage purposes has been reported. This suggests a strict control of assembly and the ability to disassemble upon hormone secretion. In the present work, we have sought to test these assertions with a short peptide, the cholecystokinin (or gastrin) tetrapeptide (CCK-4), that has been found in both gastrointestinal tract and central nervous system, and whose sequence is shared by a large number of hormones. We have thus studied in vitro this peptide's self-assembling properties in dense phases at different pH levels, thus mimicking in vivo storage conditions. The solubility and morphology of the supramolecular assemblies have been shown to vary with the pH. At low pH, the tetrapeptide exhibits a low solubility and forms microcrystals. At higher pH levels, peptide solubility increases and above a high enough concentration, peptide monomers self-assemble into typical amyloid fibrils of 10-20 nm diameter. The physical network formed by these fibrils results in a birefringent hydrogel phase. Despite the different morphological features exhibited at different pH, structural analysis shows strong similarities. Both supramolecular assemblies-microcrystals and fibrils-are structured by β-sheets. We also show that all these morphologies are reversible and can be either dissolved or changed into one another by switching the pH. In addition, we demonstrate that a modification in the charge sequence of the peptide by amino acid mutation modifies its self-assembly properties. In conclusion, just as the CCK-4 sequence is the minimal sequence required for a complete biological activity at CCK_B receptors in the brain, it is also sufficient to form amyloid fibers whose properties can be related to hormone storage and release purposes in vivo.

The Three-Dimensional Structures of Amyloids

Amyloids are highly ordered protein aggregates that are associated with both disease (including PrP prion, Alzheimer's, and Parkinson's) and biological function. The amyloid structure is composed of the cross-β-sheet entity, which is an almost indefinitely repeating two-layered intermolecular β-sheet motif. The three-dimensional (3D) structure is unique among protein folds because it folds only upon intermolecular contacts (for a folding to occur, only short sequences of amino acid residues are required), and the structure repeats itself at the atomic level (i.e., every 4.7 Å). As a consequence of this structure, among others, it can grow by recruiting corresponding amyloid peptide/protein and thus has the capacity to be an infectious protein (i.e., a prion). Furthermore, its repetitiveness can translate what would be a nonspecific activity as monomer into a potent one through cooperativity. Because of these and other properties, the activities of amyloids are manifold and include peptide storage, template assistance, loss of function, gain of function, generation of toxicity, membrane binding, infectivity, and more. This review summarizes the structural nature of the cross-β-sheet motif on the basis of a few high-resolution structural studies of amyloids in the context of potential biological activities.

Detection, inhibition and disintegration of amyloid fibrils: the role of optical probes and macrocyclic receptors

This article provides a brief account of the recent reports on the early detection of amyloid fibril formation using fluorescent dyes and inhibition and disintegration of fibrils using macrocyclic receptors, which find applications in the treatment of fibril associated neurodegenerative diseases.

Design of Surfactant Protein B Peptide Mimics Based on the Saposin Fold for Synthetic Lung Surfactants

Surfactant protein (SP)-B is a 79-residue polypeptide crucial for the biophysical and physiological function of endogenous lung surfactant. SP-B is a member of the saposin or saposin-like proteins (SAPLIP) family of proteins that share an overall three-dimensional folding pattern based on secondary structures and disulfide connectivity and exhibit a wide diversity of biological functions. Here, we review the synthesis, molecular biophysics and activity of synthetic analogs of saposin proteins designed to mimic those interactions of the parent proteins with lipids that enhance interfacial activity. Saposin proteins generally interact with target lipids as either monomers or multimers via well-defined amphipathic helices, flexible hinge domains, and insertion sequences. Based on the known 3D-structural motif for the saposin family, we show how bioengineering techniques may be used to develop minimal peptide constructs that maintain desirable structural properties and activities in biomedical applications. One important application is the molecular design, synthesis and activity of Saposin mimics based on the SP-B structure. Synthetic lung surfactants containing active SP-B analogs may be potentially useful in treating diseases of surfactant deficiency or dysfunction including the neonatal respiratory distress syndrome and acute lung injury/acute respiratory distress syndrome.

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.

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.

Understanding curcumin-induced modulation of protein aggregation

Curcumin, a diarylheptanoid compound, found in spice turmeric is known to alter the aggregation of proteins and reduce the toxicity of the aggregates. This review looks at the molecular basis of modulating protein aggregation and toxicity of the aggregates. Foremost, we identify the interaction of curcumin and its derivatives with proteins/peptides and the effect of their interaction on the conformational stability and unfolding/folding pathway(s). The unfolding/folding processes generate partially folded/unfolded intermediate, which serve as aggregation precursor state. Secondly, we discuss the effect of curcumin binding on the kinetics parameters of the aggregation process, which give information about the mechanism of the aggregation inhibition. We describe, in addition, that curcumin can accelerate/promote fibril formation by binding to oligomeric intermediate(s) accumulated in the aggregation pathway. Finally, we discuss the correlation of curcumin-induced monomeric and/or oligomeric precursor states with aggregate structure and toxicity. On the basis of these discussions, we propose a model describing curcumin-induced inhibition/promotion of formation of amyloid-like fibrils.

Amyloid-β peptides time-dependent structural modifications: AFM and voltammetric characterization

The human amyloid beta (Aβ) peptides, Aβ1-40 and Aβ1-42, structural modifications, from soluble monomers to fully formed fibrils through intermediate structures, were investigated, and the results were compared with those obtained for the inverse Aβ40-1 and Aβ42-1, mutant Aβ1-40Phe(10) and Aβ1-40Nle(35), and rat Aβ1-40Rat peptide sequences. The aggregation was followed at a slow rate, in chloride free media and room temperature, and revealed to be a sequence-structure process, dependent on the physicochemical properties of each Aβ peptide isoforms, and occurring at different rates and by different pathways. The fibrilization process was investigated by atomic force microscopy (AFM), via changes in the adsorption morphology from: (i) initially random coiled structures of ∼0.6 nm height, corresponding to the Aβ peptide monomers in random coil or in α-helix conformations, to (ii) aggregates and protofibrils of 1.5-6.0 nm height and (iii) two types of fibrils, corresponding to the Aβ peptide in a β-sheet configuration. The reactivity of the carbon electrode surface was considered. The hydrophobic surface induced rapid changes of the Aβ peptide conformations, and differences between the adsorbed fibrils, formed at the carbon surface (beaded, thin, <2.0 nm height) or in solution (long, smooth, thick, >2.0 nm height), were detected. Differential pulse voltammetry showed that, according to their primary structure, the Aβ peptides undergo oxidation in one or two steps, the first step corresponding to the tyrosine amino acids oxidation, and the second one to the histidine and methionine amino acids oxidation. The fibrilization process was electrochemically detected via the decrease of the Aβ peptide oxidation peak currents that occurred in a time dependent manner.

Nucleation of polymorphic amyloid fibrils

One and the same protein can self-assemble into amyloid fibrils with different morphologies. The phenomenon of fibril polymorphism is relevant biologically because different fibril polymorphs can have different toxicity, but there is no tool for predicting which polymorph forms and under what conditions. Here, we consider the nucleation of polymorphic amyloid fibrils occurring by direct polymerization of monomeric proteins into fibrils. We treat this process within the framework of our newly developed nonstandard nucleation theory, which allows prediction of the concentration dependence of the nucleation rate for different fibril polymorphs. The results highlight that the concentration dependence of the nucleation rate is closely linked with the protein solubility and a threshold monomer concentration below which fibril formation becomes biologically irrelevant. The relation between the nucleation rate, the fibril solubility, the threshold concentration, and the binding energies of the fibril building blocks within fibrils might prove a valuable tool for designing new experiments to control the formation of particular fibril polymorphs.