A simple algorithm locates beta-strands in the amyloid fibril core of alpha-synuclein, Abeta, and tau using the amino acid sequence alone

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.

AggreProt: a web server for predicting and engineering aggregation prone regions in proteins

Recombinant proteins play pivotal roles in numerous applications including industrial biocatalysts or therapeutics. Despite the recent progress in computational protein structure prediction, protein solubility and reduced aggregation propensity remain challenging attributes to design. Identification of aggregation-prone regions is essential for understanding misfolding diseases or designing efficient protein-based technologies, and as such has a great socio-economic impact. Here, we introduce AggreProt, a user-friendly webserver that automatically exploits an ensemble of deep neural networks to predict aggregation-prone regions (APRs) in protein sequences. Trained on experimentally evaluated hexapeptides, AggreProt compares to or outperforms state-of-the-art algorithms on two independent benchmark datasets. The server provides per-residue aggregation profiles along with information on solvent accessibility and transmembrane propensity within an intuitive interface with interactive sequence and structure viewers for comprehensive analysis. We demonstrate AggreProt efficacy in predicting differential aggregation behaviours in proteins on several use cases, which emphasize its potential for guiding protein engineering strategies towards decreased aggregation propensity and improved solubility. The webserver is freely available and accessible at https://loschmidt.chemi.muni.cz/aggreprot/.

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.

Flanking regions, amyloid cores, and polymorphism: the potential interplay underlying structural diversity

The β-sheet-rich amyloid core is the defining feature of protein aggregates associated with neurodegenerative disorders. Recent investigations have revealed that there exist multiple examples of the same protein, with the same sequence, forming a variety of amyloid cores with distinct structural characteristics. These structural variants, termed as polymorphs, are hypothesized to influence the pathological profile and the progression of different neurodegenerative diseases, giving rise to unique phenotypic differences. Thus, identifying the origin and properties of these structural variants remain a focus of studies, as a preliminary step in the development of therapeutic strategies. Here, we review the potential role of the flanking regions of amyloid cores in inducing polymorphism. These regions, adjacent to the amyloid cores, show a preponderance for being structurally disordered, imbuing them with functional promiscuity. The dynamic nature of the flanking regions can then manifest in the form of conformational polymorphism of the aggregates. We take a closer look at the sequences flanking the amyloid cores, followed by a review of the polymorphic aggregates of the well-characterized proteins amyloid-β, α-synuclein, Tau, and TDP-43. We also consider different factors that can potentially influence aggregate structure and how these regions can be viewed as novel targets for therapeutic strategies by utilizing their unique structural properties.

How can we discover developable antibody-based biotherapeutics?

Antibody-based biotherapeutics have emerged as a successful class of pharmaceuticals despite significant challenges and risks to their discovery and development. This review discusses the most frequently encountered hurdles in the research and development (R&D) of antibody-based biotherapeutics and proposes a conceptual framework called biopharmaceutical informatics. Our vision advocates for the syncretic use of computation and experimentation at every stage of biologic drug discovery, considering developability (manufacturability, safety, efficacy, and pharmacology) of potential drug candidates from the earliest stages of the drug discovery phase. The computational advances in recent years allow for more precise formulation of disease concepts, rapid identification, and validation of targets suitable for therapeutic intervention and discovery of potential biotherapeutics that can agonize or antagonize them. Furthermore, computational methods for de novo and epitope-specific antibody design are increasingly being developed, opening novel computationally driven opportunities for biologic drug discovery. Here, we review the opportunities and limitations of emerging computational approaches for optimizing antigens to generate robust immune responses, in silico generation of antibody sequences, discovery of potential antibody binders through virtual screening, assessment of hits, identification of lead drug candidates and their affinity maturation, and optimization for developability. The adoption of biopharmaceutical informatics across all aspects of drug discovery and development cycles should help bring affordable and effective biotherapeutics to patients more quickly.

An Additive-Free Model for Tau Self-Assembly

Tau is a natively unfolded protein that contributes to the stability of microtubules. Under pathological conditions such as Alzheimer's disease (AD), tau protein misfolds and self-assembles to form paired helical filaments (PHFs) and straight filaments (SFs). Full-length tau protein assembles poorly and its self-assembly is enhanced with polyanions such as heparin and RNA in vitro, but a role for heparin or other polyanions in vivo remains unclear. Recently, a truncated form of tau (297-391) has been shown to self-assemble in the absence of additives which provides an alternative in vitro PHF model system. Here we describe methods to prepare in vitro PHFs and SFs from tau (297-391) named dGAE. We also discuss the range of biophysical/biochemical techniques used to monitor tau filament assembly and structure.

The Possible Mechanism of Amyloid Transformation Based on the Geometrical Parameters of Early-Stage Intermediate in Silico Model for Protein Folding

The specificity of the available experimentally determined structures of amyloid forms is expressed primarily by the two- and not three-dimensional forms of a single polypeptide chain. Such a flat structure is possible due to the β structure, which occurs predominantly. The stabilization of the fibril in this structure is achieved due to the presence of the numerous hydrogen bonds between the adjacent chains. Together with the different forms of twists created by the single R- or L-handed α-helices, they form the hydrogen bond network. The specificity of the arrangement of these hydrogen bonds lies in their joint orientation in a system perpendicular to the plane formed by the chain and parallel to the fibril axis. The present work proposes the possible mechanism for obtaining such a structure based on the geometric characterization of the polypeptide chain constituting the basis of our early intermediate model for protein folding introduced formerly. This model, being the conformational subspace of Ramachandran plot (the ellipse path), was developed on the basis of the backbone conformation, with the side-chain interactions excluded. Our proposal is also based on the results from molecular dynamics available in the literature leading to the unfolding of α-helical sections, resulting in the β-structural forms. Both techniques used provide a similar suggestion in a search for a mechanism of conformational changes leading to a formation of the amyloid form. The potential mechanism of amyloid transformation is presented here using the fragment of the transthyretin as well as amyloid Aβ.

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.

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.

Importance of Solvent in Guiding the Conformational Properties of an Intrinsically Disordered Peptide

Aggregated form of α-synuclein in the brain has been found to be the major component of Lewy bodies that are hallmarks of Parkinson's disease (PD), the second most devastating neurodegenerative disorder. We have carried out room-temperature all-atom molecular dynamics (MD) simulations of an ensemble of widely different α-synuclein_1-95 peptide monomer conformations in aqueous solution. Attempts have been made to obtain a generic understanding of the local conformational motions of different repeat unit segments, namely R1-R7, of the peptide and the correlated properties of the solvent at the interface. The analyses revealed relatively greater rigidity of the hydrophobic R6 unit as compared to the other repeat units of the peptide. Besides, water molecules around R6 have been found to be less structured and weakly interacting with the peptide. These are important observations as the R6 unit with reduced conformational motions can act as the nucleation site for the aggregation process, while less structured weakly interacting water around it can become displaced easily, thereby facilitating the hydrophobic collapse of the peptide monomers and their association during the nucleation phase at higher concentrations. In addition, we demonstrated presence of doubly coordinated highly ordered as well as triply coordinated relatively disordered water molecules at the interface. We believe that while the ordered water molecules can favor water-mediated interactions between different peptide monomers, the randomly ordered ones on the other hand are likely to be expelled easily from the interface, thereby facilitating direct peptide-peptide interactions during the aggregation process.

Capsicumicine, a New Bioinspired Peptide from Red Peppers Prevents Staphylococcal Biofilm In Vitro and In Vivo via a Matrix Anti-Assembly Mechanism of Action

Staphylococci are pathogenic biofilm-forming bacteria and a source of multidrug resistance and/or tolerance causing a broad spectrum of infections. These bacteria are enclosed in a matrix that allows them to colonize medical devices, such as catheters and tissues, and that protects against antibiotics and immune systems. Advances in antibiofilm strategies for targeting this matrix are therefore extremely relevant. Here, we describe the development of the Capsicum pepper bioinspired peptide "capsicumicine." By using microbiological, microscopic, and nuclear magnetic resonance (NMR) approaches, we demonstrate that capsicumicine strongly prevents methicillin-resistant Staphylococcus epidermidis biofilm via an extracellular "matrix anti-assembly" mechanism of action. The results were confirmed in vivo in a translational preclinical model that mimics medical device-related infection. Since capsicumicine is not cytotoxic, it is a promising candidate for complementary treatment of infectious diseases. IMPORTANCE Pathogenic biofilms are a global health care concern, as they can cause extensive antibiotic resistance, morbidity, mortality, and thereby substantial economic loss. So far, no effective treatments targeting the bacteria in biofilms have been developed. Plants are constantly attacked by a wide range of pathogens and have protective factors, such as peptides, to defend themselves. These peptides are common components in Capsicum baccatum (red pepper). Here, we provide insights into an antibiofilm strategy based on the development of capsicumicine, a natural peptide that strongly controls biofilm formation by Staphylococcus epidermidis, the most prevalent pathogen in device-related infections.

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.

Investigating the Disordered and Membrane-Active Peptide A-Cage-C Using Conformational Ensembles

The driving forces and conformational pathways leading to amphitropic protein-membrane binding and in some cases also to protein misfolding and aggregation is the subject of intensive research. In this study, a chimeric polypeptide, A-Cage-C, derived from α-Lactalbumin is investigated with the aim of elucidating conformational changes promoting interaction with bilayers. From previous studies, it is known that A-Cage-C causes membrane leakages associated with the sporadic formation of amorphous aggregates on solid-supported bilayers. Here we express and purify double-labelled A-Cage-C and prepare partially deuterated bicelles as a membrane mimicking system. We investigate A-Cage-C in the presence and absence of these bicelles at non-binding (pH 7.0) and binding (pH 4.5) conditions. Using in silico analyses, NMR, conformational clustering, and Molecular Dynamics, we provide tentative insights into the conformations of bound and unbound A-Cage-C. The conformation of each state is dynamic and samples a large amount of overlapping conformational space. We identify one of the clusters as likely representing the binding conformation and conclude tentatively that the unfolding around the central W23 segment and its reorientation may be necessary for full intercalation at binding conditions (pH 4.5). We also see evidence for an overall elongation of A-Cage-C in the presence of model bilayers.

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.

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.

Protein aggregation: in silico algorithms and applications

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

Amyloid-Like Peptide Aggregates

Self-assembly of proteins and peptides into the amyloid fold is a widespread phenomenon in the natural world. The structural hallmark of self-assembly into amyloid fibrillar assemblies is the cross-beta motif, which conveys distinct morphological and mechanical properties. The amyloid fibril formation has contrasting results depending on the organism, in the sense that it can bestow an organism with the advantages of mechanical strength and improved functionality or, on the contrary, could give rise to pathological states. In this chapter we review the existing information on amyloid-like peptide aggregates, which could either be derived from protein sequences, but also could be rationally or de novo designed in order to self-assemble into amyloid fibrils under physiological conditions. Moreover, the development of self-assembled fibrillar biomaterials that are tailored for the desired properties towards applications in biomedical or environmental areas is extensively analyzed. We also review computational studies predicting the amyloid propensity of the natural amino acid sequences and the structure of amyloids, as well as designing novel functional amyloid materials.

Using protein engineering to understand and modulate aggregation

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

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.

Advances in the Prediction of Protein Aggregation Propensity

Background:

Protein aggregation into β-sheet-enriched insoluble assemblies is being found to be associated with an increasing number of debilitating human pathologies, such as Alzheimer’s disease or type 2 diabetes, but also with premature aging. Furthermore, protein aggregation represents a major bottleneck in the production and marketing of proteinbased therapeutics. Thus, the development of methods to accurately forecast the aggregation propensity of a certain protein is of much value.

Methods/Results:

A myriad of in vitro and in vivo aggregation studies have shown that the aggregation propensity of a certain polypeptide sequence is highly dependent on its intrinsic properties and, in most cases, driven by specific short regions of high aggregation propensity. These observations have fostered the development of a first generation of algorithms aimed to predict protein aggregation propensities from the protein sequence. A second generation of programs able to map protein aggregation on protein structures is emerging. Herein, we review the most representative online accessible predictive tools, emphasizing their main distinctive features and the range of applications.

Conclusion:

In this review, we describe representative biocomputational approaches to evaluate the aggregation properties of protein sequences and structures, while illustrating how they can become very useful tools to target protein aggregation in biomedicine and biotechnology.

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

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

In Silico Insights into HIV-1 Vpu-Tetherin Interactions and Its Mutational Counterparts

Tetherin, an interferon-induced host protein encoded by the bone marrow stromal antigen 2 (BST2/CD317/HM1.24) gene, is involved in obstructing the release of many retroviruses and other enveloped viruses by cross-linking the budding virus particles to the cell surface. This activity is antagonized in the case of human immunodeficiency virus (HIV)-1 wherein its accessory protein Viral Protein U (Vpu) interacts with tetherin, causing its downregulation from the cell surface. Vpu and tetherin connect through their transmembrane (TM) domains, culminating into events leading to tetherin degradation by recruitment of β-TrCP2. However, mutations in the TM domains of both proteins are reported to act as a resistance mechanism to Vpu countermeasure impacting tetherin's sensitivity towards Vpu but retaining its antiviral activity. Our study illustrates the binding aspects of blood-derived, brain-derived, and consensus HIV-1 Vpu with tetherin through protein-protein docking. The analysis of the bound complexes confirms the blood-derived Vpu-tetherin complex to have the best binding affinity as compared to other two. The mutations in tetherin and Vpu are devised computationally and are subjected to protein-protein interactions. The complexes are tested for their binding affinities, residue connections, hydrophobic forces, and, finally, the effect of mutation on their interactions. The single point mutations in tetherin at positions L23Y, L24T, and P40T, and triple mutations at {L22S, F44Y, L37I} and {L23T, L37T, T45I}, while single point mutations in Vpu at positions A19H and W23Y and triplet of mutations at {V10K, A11L, A19T}, {V14T, I18T, I26S}, and {A11T, V14L, A15T} have revealed no polar contacts with minimal hydrophobic interactions between Vpu and tetherin, resulting in reduced binding affinity. Additionally, we have explored the aggregation potential of tetherin and its association with the brain-derived Vpu protein. This work is a possible step toward an understanding of Vpu-tetherin interactions.

Acid-denatured small heat shock protein HdeA from Escherichia coli forms reversible fibrils with an atypical secondary structure

The periplasmic small heat shock protein HdeA from Escherichia coli is inactive under normal growth conditions (at pH 7) and activated only when E. coli cells are subjected to a sudden decrease in pH, converting HdeA into an acid-denatured active state. Here, using in vitro fibrillation assays, transmission EM, atomic-force microscopy, and CD analyses, we found that when HdeA is active as a molecular chaperone, it is also capable of forming inactive aggregates that, at first glance, resemble amyloid fibrils. We noted that the molecular chaperone activity of HdeA takes precedence over fibrillogenesis under acidic conditions, as the presence of denatured substrate protein was sufficient to suppress HdeA fibril formation. Further experiments suggested that the secondary structure of HdeA fibrils deviates somewhat from typical amyloid fibrils and contains α-helices. Strikingly, HdeA fibrils that formed at pH 2 were immediately resolubilized by a simple shift to pH 7 and from there could regain molecular chaperone activity upon a return to pH 1. HdeA, therefore, provides an unusual example of a "reversible" form of protein fibrillation with an atypical secondary structure composition. The competition between active assistance of denatured polypeptides (its "molecular chaperone" activity) and the formation of inactive fibrillary deposits (its "fibrillogenic" activity) provides a unique opportunity to probe the relationship among protein function, structure, and aggregation in detail.

Structural discordance in HIV-1 Vpu from brain isolate alarms amyloid fibril forming behavior- a computational perspective

HIV-1 being the most widespread type worldwide, its accounts for almost 95% of all infections including HIV associated dementia (HAD) that triggers neurological dysfunction and neurodegeneration in patients. The common features associated with HAD and other neurodegenerative diseases are accumulation of amyloid plaques, neuronal loss and deterioration of cognitive abilities, amongst which amyloid fibrillation is considered to be a hallmark. The success of effective therapeutics lies in the understanding of mechanisms leading to neurotoxicity. Few viral proteins like gp-120 are known to be involved in aggregation and enhancement of viral infectivity while comprehending the neurotoxic role of some other proteins is still underway. In the current study, amyloidogenic potential of HIV-1 Vpu protein from brain isolate is investigated through computational approaches. The aggregation propensity of brain derived HIV-1 Vpu was assessed by several amyloid prediction servers that projected the region 4-35 to be amyloidogenic. The protein structure was modeled and subjected to 70 ns molecular dynamics (MD) simulation to investigate the transformation of α-helical conformation of the predicted aggregate region into β-sheet, proposing the protein's ability to initiate fibril formation that is central to amyloidogenic proteins. The structural features of brain derived HIV-1 Vpu were consistent with the in silico amyloid prediction results that depicts the conformational change in the region 8-28 of which residues Ala8, Ile9, Val10, Ala19, Ile20 and Val21 constitutes β-sheet formation. The α-helix/β-sheet discordance of the predicted region was reflected in the simulation study highlighting the possible structural transition associated with HIV-1 Vpu protein of brain isolate.

The Associative Memory, Water Mediated, Structure and Energy Model (AWSEM)-Amylometer: Predicting Amyloid Propensity and Fibril Topology Using an Optimized Folding Landscape Model

Amyloids are fibrillar protein aggregates with simple repeated structural motifs in their cores, usually β-strands but sometimes α-helices. Identifying the amyloid-prone regions within protein sequences is important both for understanding the mechanisms of amyloid-associated diseases and for understanding functional amyloids. Based on the crystal structures of seven cross-β amyloidogenic peptides with different topologies and one recently solved cross-α fiber structure, we have developed a computational approach for identifying amyloidogenic segments in protein sequences using the Associative memory, Water mediated, Structure and Energy Model (AWSEM). The AWSEM-Amylometer performs favorably in comparison with other predictors in predicting aggregation-prone sequences in multiple data sets. The method also predicts well the specific topologies (the relative arrangement of β-strands in the core) of the amyloid fibrils. An important advantage of the AWSEM-Amylometer over other existing methods is its direct connection with an efficient, optimized protein folding simulation model, AWSEM. This connection allows one to combine efficient and accurate search of protein sequences for amyloidogenic segments with the detailed study of the thermodynamic and kinetic roles that these segments play in folding and aggregation in the context of the entire protein sequence. We present new simulation results that highlight the free energy landscapes of peptides that can take on multiple fibril topologies. We also demonstrate how the Amylometer methodology can be straightforwardly extended to the study of functional amyloids that have the recently discovered cross-α fibril architecture.

The diversity and utility of amyloid fibrils formed by short amyloidogenic peptides

Amyloidogenic peptides are well known for their involvement in diseases such as type 2 diabetes and Alzheimer's disease. However, more recently, amyloid fibrils have been shown to provide scaffolding and protection as functional materials in a range of organisms from bacteria to humans. These roles highlight the incredible tensile strength of the cross-β amyloid architecture. Many amino acid sequences are able to self-assemble to form amyloid with a cross-β core. Here we describe our recent advances in understanding how sequence contributes to amyloidogenicity and structure. For example, we describe penta- and hexapeptides that assemble to form different morphologies; a 12mer peptide that forms fibrous crystals; and an eight-residue peptide originating from α-synuclein that has the ability to form nanotubes. This work provides a wide range of peptides that may be exploited as fibrous bionanomaterials. These fibrils provide a scaffold upon which functional groups may be added, or templated assembly may be performed.

Factors affecting the physical stability (aggregation) of peptide therapeutics

The number of biological therapeutic agents in the clinic and development pipeline has increased dramatically over the last decade and the number will undoubtedly continue to increase in the coming years. Despite this fact, there are considerable challenges in the development, production and formulation of such biologics particularly with respect to their physical stabilities. There are many cases where self-association to form either amorphous aggregates or highly structured fibrillar species limits their use. Here, we review the numerous factors that influence the physical stability of peptides including both intrinsic and external factors, wherever possible illustrating these with examples that are of therapeutic interest. The effects of sequence, concentration, pH, net charge, excipients, chemical degradation and modification, surfaces and interfaces, and impurities are all discussed. In addition, the effects of physical parameters such as pressure, temperature, agitation and lyophilization are described. We provide an overview of the structures of aggregates formed, as well as our current knowledge of the mechanisms for their formation.

Mining databases for protein aggregation: a review

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

Zinc binding to RNA recognition motif of TDP-43 induces the formation of amyloid-like aggregates

Aggregation of TDP-43 (transactive response DNA binding protein 43 kDa) is a hallmark of certain forms of amyotrophic lateral sclerosis (ALS) and frontotemporal lobar degeneration (FTLD). Moreover, intracellular TDP-43-positive inclusions are often found in other neurodegenerative diseases. Recently it was shown that zinc ions can provoke the aggregation of endogenous TDP-43 in cells, allowing to assume a direct interaction of TDP-43 with zinc ions. In this work, we investigated zinc binding to the 102-269 TDP-43 fragment, which comprise the two RNA recognition motifs. Using isothermal titration calorimetry, mass spectrometry, and differential scanning fluorimetry, we showed that zinc binds to this TDP-43 domain with a dissociation constant in the micromolar range and modifies its tertiary structure leading to a decrease of its thermostability. Moreover, the study by dynamic light scattering and negative stain electron microscopy demonstrated that zinc ions induce auto-association process of this TDP-43 fragment into rope-like structures. These structures are thioflavin-T-positive allowing to hypothesize the direct implication of zinc ions in pathological aggregation of TDP-43.

Can Simple Interaction Models Explain Sequence-Dependent Effects in Peptide Homodimerization?

The development of rapid methods to explain and predict peptide interactions, aggregation, and self-assembly has become important to understanding amyloid disease pathology, the shelf stability of peptide therapeutics, and the design of novel peptide materials. Although experimental aggregation databases have been used to develop correlative and statistical models, molecular simulations offer atomic-level details that potentially provide greater physical insight and allow one to single out the most explanatory simple models. Here, we outline one such approach using a case study that develops homodimerization models for serine-glycine peptides with various hydrophobic leucine mutations. Using detailed all-atom simulations, we calculate reference dimerization free energy profiles and binding constants for a small peptide library. We then use statistical methods to systematically assess whether simple interaction models, which do not require expensive simulations and free energy calculation, can capture them. Surprisingly, some combinations of a few simple scaling laws well recapitulate the detailed, all-atom results with high accuracy. Specifically, we find that a recently proposed phenomenological hydrophobic force law and coarse measures of entropic effects in binding offer particularly high explanatory power, underscoring the physical relevance to association that these driving forces can play.

Catalytically-active inclusion bodies-Carrier-free protein immobilizates for application in biotechnology and biomedicine

Bacterial inclusion bodies (IBs) consist of unfolded protein aggregates and represent inactive waste products often accumulating during heterologous overexpression of recombinant genes in Escherichia coli. This general misconception has been challenged in recent years by the discovery that IBs, apart from misfolded polypeptides, can also contain substantial amounts of active and thus correctly or native-like folded protein. The corresponding catalytically-active inclusion bodies (CatIBs) can be regarded as a biologically-active sub-micrometer sized biomaterial or naturally-produced carrier-free protein immobilizate. Fusion of polypeptide (protein) tags can induce CatIB formation paving the way towards the wider application of CatIBs in synthetic chemistry, biocatalysis and biomedicine. In the present review we summarize the history of CatIBs, present the molecular-biological tools that are available to induce CatIB formation, and highlight potential lines of application. In the second part findings regarding the formation, architecture, and structure of (Cat)IBs are summarized. Finally, an overview is presented about the available bioinformatic tools that potentially allow for the prediction of aggregation and thus (Cat)IB formation. This review aims at demonstrating the potential of CatIBs for biotechnology and hopefully contributes to a wider acceptance of this promising, yet not widely utilized, protein preparation.

Pep-Calc.com: a set of web utilities for the calculation of peptide and peptoid properties and automatic mass spectral peak assignment

The ability to calculate molecular properties such as molecular weights, isoelectric points, and extinction coefficients is vital for scientists using and/or synthesizing peptides and peptoids for research. A suite of two web utilities: Peptide Calculator and Peptoid Calculator, available free at http://www.pep-calc.com, are presented. Both tools allow the calculation of peptide/peptoid chemical formulae and molecular weight, ChemDraw structure file export and automatic assignment of mass spectral peaks to deletion sequences and metal/protecting group adducts. Peptide Calculator also provides a calculated isoelectric point, molar extinction coefficient, graphical peptide charge summary and β-strand contiguity profile (for aggregation-prone sequences), indicating potential regions of synthesis difficulty. In addition to the unique automatic spectral assignment features offered across both utilities, Peptoid Calculator represents a first-of-a-kind resource for researchers in the field of peptoid science. With a constantly expanding database of over 120 amino acids, non-natural peptide building blocks and peptoid building blocks, it is anticipated that Pep-Calc.com will act as a valuable asset to those working on the synthesis and/or application of peptides and peptoids in the biophysical and life sciences fields.

Transient β-hairpin formation in α-synuclein monomer revealed by coarse-grained molecular dynamics simulation

Parkinson's disease, originating from the intrinsically disordered peptide α-synuclein, is a common neurodegenerative disorder that affects more than 5% of the population above age 85. It remains unclear how α-synuclein monomers undergo conformational changes leading to aggregation and formation of fibrils characteristic for the disease. In the present study, we perform molecular dynamics simulations (over 180 μs in aggregated time) using a hybrid-resolution model, Proteins with Atomic details in Coarse-grained Environment (PACE), to characterize in atomic detail structural ensembles of wild type and mutant monomeric α-synuclein in aqueous solution. The simulations reproduce structural properties of α-synuclein characterized in experiments, such as secondary structure content, long-range contacts, chemical shifts, and (3)J(HNHCα )-coupling constants. Most notably, the simulations reveal that a short fragment encompassing region 38-53, adjacent to the non-amyloid-β component region, exhibits a high probability of forming a β-hairpin; this fragment, when isolated from the remainder of α-synuclein, fluctuates frequently into its β-hairpin conformation. Two disease-prone mutations, namely, A30P and A53T, significantly accelerate the formation of a β-hairpin in the stated fragment. We conclude that the formation of a β-hairpin in region 38-53 is a key event during α-synuclein aggregation. We predict further that the G47V mutation impedes the formation of a turn in the β-hairpin and slows down β-hairpin formation, thereby retarding α-synuclein aggregation.

Predicting aggregation-prone sequences in proteins

Owing to its association with a diverse range of human diseases, the determinants of protein aggregation are studied intensively. It is generally accepted that the effective aggregation tendency of a protein depends on many factors such as folding efficiency towards the native state, thermodynamic stability of that conformation, intrinsic aggregation propensity of the polypeptide sequence and its ability to be recognized by the protein quality control system. The intrinsic aggregation propensity of a polypeptide sequence is related to the presence of short APRs (aggregation-prone regions) that self-associate to form intermolecular β-structured assemblies. These are typically short sequence segments (5-15 amino acids) that display high hydrophobicity, low net charge and a high tendency to form β-structures. As the presence of such APRs is a prerequisite for aggregation, a plethora of methods have been developed to identify APRs in amino acid sequences. In the present chapter, the methodological basis of these approaches is discussed, as well as some practical applications.

Predicting the aggregation propensity of prion sequences

The presence of prions can result in debilitating and neurodegenerative diseases in mammals and protein-based genetic elements in fungi. Prions are defined as a subclass of amyloids in which the self-aggregation process becomes self-perpetuating and infectious. Like all amyloids, prions polymerize into fibres with a common core formed of β-sheet structures oriented perpendicular to the fibril axes which form a structure known as a cross-β structure. The intermolecular β-sheet propensity, a characteristic of the amyloid pattern, as well as other key parameters of amyloid fibril formation can be predicted. Mathematical algorithms have been proposed to predict both amyloid and prion propensities. However, it has been shown that the presence of amyloid-prone regions in a polypeptide sequence could be insufficient for amyloid formation. It has also often been stated that the formation of amyloid fibrils does not imply that these are prions. Despite these limitations, in silico prediction of amyloid and prion propensities should help detect potential new prion sequences in mammals. In addition, the determination of amyloid-prone regions in prion sequences could be very useful in understanding the effect of sporadic mutations and polymorphisms as well as in the search for therapeutic targets.

Amyloid-Forming Properties of Human Apolipoproteins: Sequence Analyses and Structural Insights

Apolipoproteins are protein constituents of lipoproteins that transport cholesterol and fat in circulation and are central to cardiovascular health and disease. Soluble apolipoproteins can transiently dissociate from the lipoprotein surface in a labile free form that can misfold, potentially leading to amyloid disease. Misfolding of apoA-I, apoA-II, and serum amyloid A (SAA) causes systemic amyloidoses, apoE4 is a critical risk factor in Alzheimer's disease, and apolipoprotein misfolding is also implicated in cardiovascular disease. To explain why apolipoproteins are over-represented in amyloidoses, it was proposed that the amphipathic α-helices, which form the lipid surface-binding motif in this protein family, have high amyloid-forming propensity. Here, we use 12 sequence-based bioinformatics approaches to assess amyloid-forming potential of human apolipoproteins and to identify segments that are likely to initiate β-aggregation. Mapping such segments on the available atomic structures of apolipoproteins helps explain why some of them readily form amyloid while others do not. Our analysis shows that nearly all amyloidogenic segments: (i) are largely hydrophobic, (ii) are located in the lipid-binding amphipathic α-helices in the native structures of soluble apolipoproteins, (iii) are predicted in both native α-helices and β-sheets in the insoluble apoB, and (iv) are predicted to form parallel in-register β-sheet in amyloid. Most of these predictions have been verified experimentally for apoC-II, apoA-I, apoA-II and SAA. Surprisingly, the rank order of the amino acid sequence propensity to form amyloid (apoB>apoA-II>apoC-II≥apoA-I, apoC-III, SAA, apoC-I>apoA-IV, apoA-V, apoE) does not correlate with the proteins' involvement in amyloidosis. Rather, it correlates directly with the strength of the protein-lipid association, which increases with increasing protein hydrophobicity. Therefore, the lipid surface-binding function and the amyloid-forming propensity are both rooted in apolipoproteins' hydrophobicity, suggesting that functional constraints make it difficult to completely eliminate pathogenic apolipoprotein misfolding. We propose that apolipoproteins have evolved protective mechanisms against misfolding, such as the sequestration of the amyloidogenic segments via the native protein-lipid and protein-protein interactions involving amphipathic α-helices and, in case of apoB, β-sheets.

PASTA 2.0: an improved server for protein aggregation prediction

The formation of amyloid aggregates upon protein misfolding is related to several devastating degenerative diseases. The propensities of different protein sequences to aggregate into amyloids, how they are enhanced by pathogenic mutations, the presence of aggregation hot spots stabilizing pathological interactions, the establishing of cross-amyloid interactions between co-aggregating proteins, all rely at the molecular level on the stability of the amyloid cross-beta structure. Our redesigned server, PASTA 2.0, provides a versatile platform where all of these different features can be easily predicted on a genomic scale given input sequences. The server provides other pieces of information, such as intrinsic disorder and secondary structure predictions, that complement the aggregation data. The PASTA 2.0 energy function evaluates the stability of putative cross-beta pairings between different sequence stretches. It was re-derived on a larger dataset of globular protein domains. The resulting algorithm was benchmarked on comprehensive peptide and protein test sets, leading to improved, state-of-the-art results with more amyloid forming regions correctly detected at high specificity. The PASTA 2.0 server can be accessed at http://protein.bio.unipd.it/pasta2/.

Hot spots in apolipoprotein A-II misfolding and amyloidosis in mice and men

ApoA-II is the second-major protein of high-density lipoproteins. C-terminal extension in human apoA-II or point substitutions in murine apoA-II cause amyloidosis. The molecular mechanism of apolipoprotein misfolding, from the native predominantly α-helical conformation to cross-β-sheet in amyloid, is unknown. We used 12 sequence-based prediction algorithms to identify two ten-residue segments in apoA-II that probably initiate β-aggregation. Previous studies of apoA-II fragments experimentally verify this prediction. Together, experimental and bioinformatics studies explain why the C-terminal extension in human apoA-II causes amyloidosis and why, unlike murine apoA-II, human apoA-II normally does not cause amyloidosis despite its unusually high sequence propensity for β-aggregation.