Identification of amyloid fibril-forming segments based on structure and residue-based statistical potential

Amyloids in bladder cancer hijack cancer-related proteins and are positive correlated to tumor stage

Despite the current diagnostic and therapeutic approaches to bladder cancer being widely accepted, there have been few significant advancements in this field over the past decades. This underscores the necessity for a paradigm shift in the approach to bladder cancer. The role of amyloids in cancer remains unclear despite their identification in several other pathologies. In this study, we present evidence of amyloids in bladder cancer, both in vitro and in vivo. In a murine model of bladder cancer, a positive correlation was observed between amyloids and tumor stage, indicating an association between amyloids and bladder cancer progression. Subsequently, the amyloid proteome of the RT4 non-invasive and HT1197 invasive bladder cancer cell lines was identified and included oncogenes, tumor suppressors, and highly expressed cancer-related proteins. It is proposed that amyloids function as structures that sequester key proteins. Therefore, amyloids should be considered in the study and diagnosis of bladder cancer.

Prediction and Evaluation of Protein Aggregation with Computational Methods

Protein and peptide aggregation has recently become one of the most studied biomedical problems due to its central role in several neurodegenerative disorders and of biotechnological importance. Multiple in silico methods, databases, tools, and algorithms have been developed to predict aggregation of proteins and peptides to better understand fundamental mechanisms of various aggregation diseases. Here, we attempt to provide a brief overview of bioinformatic methods and tools to better understand molecular mechanisms of aggregation disorders. Furthermore, through a better understanding of protein aggregation mechanisms, it might be possible to design novel therapeutic agents to treat and hopefully prevent protein aggregation diseases.

Revolutionizing viral disease treatment: Phase separation and lysosome/exosome targeting as new areas and new paradigms for antiviral drug research

With the advancement of globalization, our world is becoming increasingly interconnected. However, this interconnection means that once an infectious disease emerges, it can rapidly spread worldwide. Specifically, viral diseases pose a growing threat to human health. The COVID-19 pandemic has underscored the pressing need for expedited drug development to combat emerging viral diseases. Traditional drug discovery methods primarily rely on random screening and structure-based optimization, and new approaches are required to address more complex scenarios in drug discovery. Emerging antiviral strategies include phase separation and lysosome/exosome targeting. The widespread implementation of these innovative drug design strategies will contribute towards tackling existing viral infections and future outbreaks.

iAMY-RECMFF: Identifying amyloidgenic peptides by using residue pairwise energy content matrix and features fusion algorithm

Various diseases, including Huntington's disease, Alzheimer's disease, and Parkinson's disease, have been reported to be linked to amyloid. Therefore, it is crucial to distinguish amyloid from non-amyloid proteins or peptides. While experimental approaches are typically preferred, they are costly and time-consuming. In this study, we have developed a machine learning framework called iAMY-RECMFF to discriminate amyloidgenic from non-amyloidgenic peptides. In our model, we first encoded the peptide sequences using the residue pairwise energy content matrix. We then utilized Pearson's correlation coefficient and distance correlation to extract useful information from this matrix. Additionally, we employed an improved similarity network fusion algorithm to integrate features from different perspectives. The Fisher approach was adopted to select the optimal feature subset. Finally, the selected features were inputted into a support vector machine for identifying amyloidgenic peptides. Experimental results demonstrate that our proposed method significantly improves the identification of amyloidgenic peptides compared to existing predictors. This suggests that our method may serve as a powerful tool in identifying amyloidgenic peptides. To facilitate academic use, the dataset and codes used in the current study are accessible at https://figshare.com/articles/online_resource/iAMY-RECMFF/22816916.

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

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

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.

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

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

Author Summary

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

Insights into Network of Hot Spots of Aggregation in Nucleophosmin 1

In a protein, point mutations associated with diseases can alter the native structure and provide loss or alteration of functional levels, and an internal structural network defines the connectivity among domains, as well as aggregate/soluble states' equilibria. Nucleophosmin (NPM)1 is an abundant nucleolar protein, which becomes mutated in acute myeloid leukemia (AML) patients. NPM1-dependent leukemogenesis, which leads to its aggregation in the cytoplasm (NPMc+), is still obscure, but the investigations have outlined a direct link between AML mutations and amyloid aggregation. Protein aggregation can be due to the cooperation among several hot spots located within the aggregation-prone regions (APR), often predictable with bioinformatic tools. In the present study, we investigated potential APRs in the entire NPM1 not yet investigated. On the basis of bioinformatic predictions and experimental structures, we designed several protein fragments and analyzed them through typical aggrsegation experiments, such as Thioflavin T (ThT), fluorescence and scanning electron microscopy (SEM) experiments, carried out at different times; in addition, their biocompatibility in SHSY5 cells was also evaluated. The presented data clearly demonstrate the existence of hot spots of aggregation located in different regions, mostly in the N-terminal domain (NTD) of the entire NPM1 protein, and provide a more comprehensive view of the molecular details potentially at the basis of NPMc+-dependent AML.

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

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

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.

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

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

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.

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.

On the Conformational Dynamics of β-Amyloid Forming Peptides: A Computational Perspective

Understanding the conformational dynamics of proteins and peptides involved in important functions is still a difficult task in computational structural biology. Because such conformational transitions in β-amyloid (Aβ) forming peptides play a crucial role in many neurological disorders, researchers from different scientific fields have been trying to address issues related to the folding of Aβ forming peptides together. Many theoretical models have been proposed in the recent years for studying Aβ peptides using mathematical, physicochemical, and molecular dynamics simulation, and machine learning approaches. In this article, we have comprehensively reviewed the developmental advances in the theoretical models for Aβ peptide folding and interactions, particularly in the context of neurological disorders. Furthermore, we have extensively reviewed the advances in molecular dynamics simulation as a tool used for studying the conversions between polymorphic amyloid forms and applications of using machine learning approaches in predicting Aβ peptides and aggregation-prone regions in proteins. We have also provided details on the theoretical advances in the study of Aβ peptides, which would enhance our understanding of these peptides at the molecular level and eventually lead to the development of targeted therapies for certain acute neurological disorders such as Alzheimer's disease in the future.

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.

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.

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.

Antibody Aggregation: Insights from Sequence and Structure

Monoclonal antibodies (mAbs) are the fastest-growing biological therapeutics with important applications ranging from cancers, autoimmunity diseases and metabolic disorders to emerging infectious diseases. Aggregation of mAbs continues to be a major problem in their developability. Antibody aggregation could be triggered by partial unfolding of its domains, leading to monomer-monomer association followed by nucleation and growth. Although the aggregation propensities of antibodies and antibody-based proteins can be affected by the external experimental conditions, they are strongly dependent on the intrinsic antibody properties as determined by their sequences and structures. In this review, we describe how the unfolding and aggregation susceptibilities of IgG could be related to their cognate sequences and structures. The impact of antibody domain structures on thermostability and aggregation propensities, and effective strategies to reduce aggregation are discussed. Finally, the aggregation of antibody-drug conjugates (ADCs) as related to their sequence/structure, linker payload, conjugation chemistry and drug-antibody ratio (DAR) is reviewed.

The C-terminal amyloidogenic peptide contributes to self-assembly of Avibirnavirus viral protease

Unlike other viral protease, Avibirnavirus infectious bursal disease virus (IBDV)-encoded viral protease VP4 forms unusual intracellular tubule-like structures during viral infection. However, the formation mechanism and potential biological functions of intracellular VP4 tubules remain largely elusive. Here, we show that VP4 can assemble into tubules in diverse IBDV-infected cells. Dynamic analysis show that VP4 initiates the assembly at early stage of IBDV infection, and gradually assembles into larger size of fibrils within the cytoplasm and nucleus. Intracellular assembly of VP4 doesn't involve the host cytoskeleton, other IBDV-encoded viral proteins or vital subcellular organelles. Interestingly, the last C-terminal hydrophobic and amyloidogenic stretch (238)YHLAMA(243) with two "aggregation-prone" alanine residues was found to be essential for its intracellular self-assembly. The assembled VP4 fibrils show significantly low solubility, subsequently, the deposition of highly assembled VP4 structures ultimately deformed the host cytoskeleton and nucleus, which was potentially associated with IBDV lytic infection. Importantly, the assembly of VP4 significantly reduced the cytotoxicity of protease activity in host cells which potentially prevent the premature cell death and facilitate viral replication. This study provides novel insights into the formation mechanism and biological functions of the Avibirnavirus protease-related fibrils.

Aspects of structural landscape of human islet amyloid polypeptide

The human islet amyloid polypeptide (hIAPP) co-operates with insulin to maintain glycemic balance. It also constitutes the amyloid plaques that aggregate in the pancreas of type-II diabetic patients. We have performed extensive in silico investigations to analyse the structural landscape of monomeric hIAPP, which is presumed to be intrinsically disordered. For this, we construct from first principles a highly predictive energy function that describes a monomeric hIAPP observed in a nuclear magnetic resonance experiment, as a local energy minimum. We subject our theoretical model of hIAPP to repeated heating and cooling simulations, back and forth between a high temperature regime where the conformation resembles a random walker and a low temperature limit where no thermal motions prevail. We find that the final low temperature conformations display a high level of degeneracy, in a manner which is fully in line with the presumed intrinsically disordered character of hIAPP. In particular, we identify an isolated family of α-helical conformations that might cause the transition to amyloidosis, by nucleation.

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.

Molecular understanding of a potential functional link between antimicrobial and amyloid peptides

Antimicrobial and amyloid peptides do not share common sequences, typical secondary structures, or normal biological activity but both the classes of peptides exhibit membrane-disruption ability to induce cell toxicity. Different membrane-disruption mechanisms have been proposed for antimicrobial and amyloid peptides, individually, some of which are not exclusive to either peptide type, implying that certain common principles may govern the folding and functions of different cytolytic peptides and associated membrane disruption mechanisms. Particularly, some antimicrobial and amyloid peptides have been identified to have dual complementary amyloid and antimicrobial properties, suggesting a potential functional link between amyloid and antimicrobial peptides. Given that some similar structural and membrane-disruption characteristics exist between the two classes of peptides, this review summarizes major findings, recent advances, and future challenges related to antimicrobial and amyloid peptides and strives to illustrate the similarities, differences, and relationships in the sequences, structures, and membrane interaction modes between amyloid and antimicrobial peptides, with a special focus on direct interactions of the peptides with the membranes. We hope that this review will stimulate further research at the interface of antimicrobial and amyloid peptides - which has been studied less intensively than either type of peptides - to decipher a possible link between both amyloid pathology and antimicrobial activity, which can guide drug design and peptide engineering to influence peptide-membrane interactions important in human health and diseases.

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/.

Computational approaches to understanding protein aggregation in neurodegeneration

The generation of toxic non-native protein conformers has emerged as a unifying thread among disorders such as Alzheimer's disease, Parkinson's disease, and amyotrophic lateral sclerosis. Atomic-level detail regarding dynamical changes that facilitate protein aggregation, as well as the structural features of large-scale ordered aggregates and soluble non-native oligomers, would contribute significantly to current understanding of these complex phenomena and offer potential strategies for inhibiting formation of cytotoxic species. However, experimental limitations often preclude the acquisition of high-resolution structural and mechanistic information for aggregating systems. Computational methods, particularly those combine both all-atom and coarse-grained simulations to cover a wide range of time and length scales, have thus emerged as crucial tools for investigating protein aggregation. Here we review the current state of computational methodology for the study of protein self-assembly, with a focus on the application of these methods toward understanding of protein aggregates in human neurodegenerative disorders.

Sequence complexity of amyloidogenic regions in intrinsically disordered human proteins

An amyloidogenic region (AR) in a protein sequence plays a significant role in protein aggregation and amyloid formation. We have investigated the sequence complexity of AR that is present in intrinsically disordered human proteins. More than 80% human proteins in the disordered protein databases (DisProt+IDEAL) contained one or more ARs. With decrease of protein disorder, AR content in the protein sequence was decreased. A probability density distribution analysis and discrete analysis of AR sequences showed that ∼8% residue in a protein sequence was in AR and the region was in average 8 residues long. The residues in the AR were high in sequence complexity and it seldom overlapped with low complexity regions (LCR), which was largely abundant in disorder proteins. The sequences in the AR showed mixed conformational adaptability towards α-helix, β-sheet/strand and coil conformations.

FISH Amyloid - a new method for finding amyloidogenic segments in proteins based on site specific co-occurrence of aminoacids

BACKGROUND

Amyloids are proteins capable of forming fibrils whose intramolecular contact sites assume densely packed zipper pattern. Their oligomers can underlie serious diseases, e.g. Alzheimer's and Parkinson's diseases. Recent studies show that short segments of aminoacids can be responsible for amyloidogenic properties of a protein. A few hundreds of such peptides have been experimentally found but experimental testing of all candidates is currently not feasible. Here we propose an original machine learning method for classification of aminoacid sequences, based on discovering a segment with a discriminative pattern of site-specific co-occurrences between sequence elements. The pattern is based on the positions of residues with correlated occurrence over a sliding window of a specified length. The algorithm first recognizes the most relevant training segment in each positive training instance. Then the classification is based on maximal distances between co-occurrence matrix of the relevant segments in positive training sequences and the matrix from negative training segments. The method was applied for studying sequences of aminoacids with regard to their amyloidogenic properties.

RESULTS

Our method was first trained on available datasets of hexapeptides with the amyloidogenic classification, using 5 or 6-residue sliding windows. Depending on the choice of training and testing datasets, the area under ROC curve obtained the value up to 0.80 for experimental, and 0.95 for computationally generated (with 3D profile method) datasets. Importantly, the results on 5-residue segments were not significantly worse, although the classification required that algorithm first recognized the most relevant training segments. The dataset of long sequences, such as sup35 prion and a few other amyloid proteins, were applied to test the method and gave encouraging results. Our web tool FISH Amyloid was trained on all available experimental data 4-10 residues long, offers prediction of amyloidogenic segments in protein sequences.

CONCLUSIONS

We proposed a new original classification method which recognizes co-occurrence patterns in sequences. The method reveals characteristic classification pattern of the data and finds the segments where its scoring is the strongest, also in long training sequences. Applied to the problem of amyloidogenic segments recognition, it showed a good potential for classification problems in bioinformatics.

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.

Carnosine's effect on amyloid fibril formation and induced cytotoxicity of lysozyme

Carnosine, a common dipeptide in mammals, has previously been shown to dissemble alpha-crystallin amyloid fibrils. To date, the dipeptide's anti-fibrillogensis effect has not been thoroughly characterized in other proteins. For a more complete understanding of carnosine's mechanism of action in amyloid fibril inhibition, we have investigated the effect of the dipeptide on lysozyme fibril formation and induced cytotoxicity in human neuroblastoma SH-SY5Y cells. Our study demonstrates a positive correlation between the concentration and inhibitory effect of carnosine against lysozyme fibril formation. Molecular docking results show carnosine's mechanism of fibrillogenesis inhibition may be initiated by binding with the aggregation-prone region of the protein. The dipeptide attenuates the amyloid fibril-induced cytotoxicity of human neuronal cells by reducing both apoptotic and necrotic cell deaths. Our study provides solid support for carnosine's amyloid fibril inhibitory property and its effect against fibril-induced cytotoxicity in SH-SY5Y cells. The additional insights gained herein may pave way to the discovery of other small molecules that may exert similar effects against amyloid fibril formation and its associated neurodegenerative diseases.

On the amyloid datasets used for training PAFIG--how (not) to extend the experimental dataset of hexapeptides

BACKGROUND

Amyloids are proteins capable of forming aberrant intramolecular contact sites, characteristic of beta zipper configuration. Amyloids can underlie serious health conditions, e.g. Alzheimer's or Parkinson's diseases. It has been proposed that short segments of amino acids can be responsible for protein amyloidogenicity, but no more than two hundred such hexapeptides have been experimentally found. The authors of the computational tool Pafig published in BMC Bioinformatics a method for extending the amyloid hexapeptide dataset that could be used for training and testing models. They assumed that all hexapeptides belonging to an amyloid protein can be regarded as amylopositive, while those from proteins never reported as amyloid are always amylonegative. Here we show why the above described method of extending datasets is wrong and discuss the reasons why the incorrect data could lead to falsely correct classification.

RESULTS

The amyloid classification of hexapeptides by Pafig was confronted with the classification results from different state of the art computational methods and the outputs of all methods were studied by clustering analysis. The clustering methods show that Pafig is an outlier with regard to other approaches. Our study of the statistical patterns of its training and testing datasets showed a strong bias towards STVIIE hexapeptide in their positive part. Different statistical patterns of seemingly amylo-positive and -negative hexapeptides allow for a repeatable classification, which is not related to amyloid propensity of the hexapetides.

CONCLUSIONS

Our study on recognition of amyloid hexapeptides showed that occurrence of incidental patterns in wrongly selected datasets can produce falsely correct results of classification. The assumption that all hexapeptides belonging to amyloid protein can be regarded as amylopositive and those from proteins never reported as amyloid are always amylonegative is not supported by any other computational method. This is in line with experimental observations that amyloid propensity of a full protein can result from only one amyloidogenic fragment in this protein, while the occurrence of amyliodogenic part that is well hidden inside the protein may never lead to fibril formation. This leads to the conclusion that Pafig does not provide correct classification with regard to amyloidogenicity.

MetAmyl: a METa-predictor for AMYLoid proteins

The aggregation of proteins or peptides in amyloid fibrils is associated with a number of clinical disorders, including Alzheimer's, Huntington's and prion diseases, medullary thyroid cancer, renal and cardiac amyloidosis. Despite extensive studies, the molecular mechanisms underlying the initiation of fibril formation remain largely unknown. Several lines of evidence revealed that short amino-acid segments (hot spots), located in amyloid precursor proteins act as seeds for fibril elongation. Therefore, hot spots are potential targets for diagnostic/therapeutic applications, and a current challenge in bioinformatics is the development of methods to accurately predict hot spots from protein sequences. In this paper, we combined existing methods into a meta-predictor for hot spots prediction, called MetAmyl for METapredictor for AMYLoid proteins. MetAmyl is based on a logistic regression model that aims at weighting predictions from a set of popular algorithms, statistically selected as being the most informative and complementary predictors. We evaluated the performances of MetAmyl through a large scale comparative study based on three independent datasets and thus demonstrated its ability to differentiate between amyloidogenic and non-amyloidogenic polypeptides. Compared to 9 other methods, MetAmyl provides significant improvement in prediction on studied datasets. We further show that MetAmyl is efficient to highlight the effect of point mutations involved in human amyloidosis, so we suggest this program should be a useful complementary tool for the diagnosis of these diseases.

Molecular dynamics studies on 3D structures of the hydrophobic region PrP(109-136)

Prion diseases, traditionally referred to as transmissible spongiform encephalopathies, are invariably fatal and highly infectious neurodegenerative diseases that affect a wide variety of mammalian species, manifesting as scrapie in sheep, bovine spongiform encephalopathy (or 'mad-cow' disease) in cattle, and Creutzfeldt-Jakob disease, Gerstmann-Strussler-Scheinker syndrome, fatal familial insomnia (FFI), and Kulu in humans, etc. These neurodegenerative diseases are caused by the conversion from a soluble normal cellular prion protein (PrP(C)) into insoluble abnormally folded infectious prions (PrP(Sc)). The hydrophobic region PrP(109-136) controls the formation of diseased prions: the normal PrP(113-120) AGAAAAGA palindrome is an inhibitor/blocker of prion diseases and the highly conserved glycine-xxx-glycine motif PrP(119-131) can inhibit the formation of infectious prion proteins in cells. This article gives detailed reviews on the PrP(109-136) region and presents the studies of its three-dimensional structures and structural dynamics.

Amylin uncovered: a review on the polypeptide responsible for type II diabetes

Amylin is primarily responsible for classifying type II diabetes as an amyloid (protein misfolding) disease as it has great potential to aggregate into toxic nanoparticles, thereby resulting in loss of pancreatic β-cells. Although type II diabetes is on the increase each year, possibly due to bad eating habits of modern society, research on the culprit for this disease is still in its early days. In addition, unlike the culprit for Alzheimer's disease, amyloid β-peptide, amylin has failed to receive attention worthy of being featured in an abundance of review articles. Thus, the aim of this paper is to shine the spotlight on amylin in an attempt to put it onto the top of researchers' to-do list since the secondary complications of type II diabetes have far-reaching and severe consequences on public health both in developing and fully developed countries alike. This paper will cover characteristics of the amylin aggregates, mechanisms of toxicity, and a particular focus on inhibitors of toxicity and techniques used to assess these inhibitors.

Machine learning methods can replace 3D profile method in classification of amyloidogenic hexapeptides

Background

Amyloids are proteins capable of forming fibrils. Many of them underlie serious diseases, like Alzheimer disease. The number of amyloid-associated diseases is constantly increasing. Recent studies indicate that amyloidogenic properties can be associated with short segments of aminoacids, which transform the structure when exposed. A few hundreds of such peptides have been experimentally found. Experimental testing of all possible aminoacid combinations is currently not feasible. Instead, they can be predicted by computational methods. 3D profile is a physicochemical-based method that has generated the most numerous dataset - ZipperDB. However, it is computationally very demanding. Here, we show that dataset generation can be accelerated. Two methods to increase the classification efficiency of amyloidogenic candidates are presented and tested: simplified 3D profile generation and machine learning methods.

Results

We generated a new dataset of hexapeptides, using more economical 3D profile algorithm, which showed very good classification overlap with ZipperDB (93.5%). The new part of our dataset contains 1779 segments, with 204 classified as amyloidogenic. The dataset of 6-residue sequences with their binary classification, based on the energy of the segment, was applied for training machine learning methods. A separate set of sequences from ZipperDB was used as a test set. The most effective methods were Alternating Decision Tree and Multilayer Perceptron. Both methods obtained area under ROC curve of 0.96, accuracy 91%, true positive rate ca. 78%, and true negative rate 95%. A few other machine learning methods also achieved a good performance. The computational time was reduced from 18-20 CPU-hours (full 3D profile) to 0.5 CPU-hours (simplified 3D profile) to seconds (machine learning).

Conclusions

We showed that the simplified profile generation method does not introduce an error with regard to the original method, while increasing the computational efficiency. Our new dataset proved representative enough to use simple statistical methods for testing the amylogenicity based only on six letter sequences. Statistical machine learning methods such as Alternating Decision Tree and Multilayer Perceptron can replace the energy based classifier, with advantage of very significantly reduced computational time and simplicity to perform the analysis. Additionally, a decision tree provides a set of very easily interpretable rules.