A serendipitous survey of prediction algorithms for amyloidogenicity

Peptide Self-Assembly into Amyloid Fibrils: Unbiased All-Atom Simulations

Protein self-assembly plays an important role in biological systems, accounting for the formation of mesoscopic structures that can be highly symmetric as in the capsid of viruses or disordered as in molecular condensates or exhibit a one-dimensional fibrillar morphology as in amyloid fibrils. Deposits of the latter in tissues of individuals with degenerative diseases like Alzheimer's and Parkinson's has motivated extensive efforts to understand the sequence of molecular events accounting for their formation. These studies aim to identify on-pathway intermediates that may be the targets for therapeutic intervention. This detailed knowledge of fibril formation remains obscure, in part due to challenges with experimental analyses of these processes. However, important progress is being achieved for short amyloid peptides due to advances in our ability to perform completely unbiased all-atom simulations of the self-assembly process. This perspective discusses recent developments, their implications, and the hurdles that still need to be overcome to further advance the field.

Role of the mechanisms for antibody repertoire diversification in monoclonal light chain deposition disorders: when a friend becomes foe

The adaptive immune system of jawed vertebrates generates a highly diverse repertoire of antibodies to meet the antigenic challenges of a constantly evolving biological ecosystem. Most of the diversity is generated by two mechanisms: V(D)J gene recombination and somatic hypermutation (SHM). SHM introduces changes in the variable domain of antibodies, mostly in the regions that form the paratope, yielding antibodies with higher antigen binding affinity. However, antigen recognition is only possible if the antibody folds into a stable functional conformation. Therefore, a key force determining the survival of B cell clones undergoing somatic hypermutation is the ability of the mutated heavy and light chains to efficiently fold and assemble into a functional antibody. The antibody is the structural context where the selection of the somatic mutations occurs, and where both the heavy and light chains benefit from protective mechanisms that counteract the potentially deleterious impact of the changes. However, in patients with monoclonal gammopathies, the proliferating plasma cell clone may overproduce the light chain, which is then secreted into the bloodstream. This places the light chain out of the protective context provided by the quaternary structure of the antibody, increasing the risk of misfolding and aggregation due to destabilizing somatic mutations. Light chain-derived (AL) amyloidosis, light chain deposition disease (LCDD), Fanconi syndrome, and myeloma (cast) nephropathy are a diverse group of diseases derived from the pathologic aggregation of light chains, in which somatic mutations are recognized to play a role. In this review, we address the mechanisms by which somatic mutations promote the misfolding and pathological aggregation of the light chains, with an emphasis on AL amyloidosis. We also analyze the contribution of the variable domain (V_L) gene segments and somatic mutations on light chain cytotoxicity, organ tropism, and structure of the AL fibrils. Finally, we analyze the most recent advances in the development of computational algorithms to predict the role of somatic mutations in the cardiotoxicity of amyloidogenic light chains and discuss the challenges and perspectives that this approach faces.

On the Structural Diversity and Individuality of Polymorphic Amyloid Protein Assemblies

The prediction of highly ordered three-dimensional structures of amyloid protein fibrils from the amino acid sequences of their monomeric self-assembly precursors constitutes a challenging and unresolved aspect of the classical protein folding problem. Because of the polymorphic nature of amyloid assembly whereby polypeptide chains of identical amino acid sequences under identical conditions are capable of self-assembly into a spectrum of different fibril structures, the prediction of amyloid structures from an amino acid sequence requires a detailed and holistic understanding of its assembly free energy landscape. The full extent of the structure space accessible to the cross-β molecular architecture of amyloid must also be resolved. Here, we review the current understanding of the diversity and the individuality of amyloid structures, and how the polymorphic landscape of amyloid links to biology and disease phenotypes. We present a comprehensive review of structural models of amyloid fibrils derived by cryo-EM, ssNMR and AFM to date, and discuss the challenges ahead for resolving the structural basis and the biological consequences of polymorphic amyloid assemblies.

Native disulphide-linked dimers facilitate amyloid fibril formation by bovine milk αS2-casein

Bovine milk α_S2-casein, an intrinsically disordered protein, readily forms amyloid fibrils in vitro and is implicated in the formation of amyloid fibril deposits in mammary tissue. Its two cysteine residues participate in the formation of either intra- or intermolecular disulphide bonds, generating monomer and dimer species. X-ray solution scattering measurements indicated that both forms of the protein adopt large, spherical oligomers at 20 °C. Upon incubation at 37 °C, the disulphide-linked dimer showed a significantly greater propensity to form amyloid fibrils than its monomeric counterpart. Thioflavin T fluorescence, circular dichroism and infrared spectra were consistent with one or both of the dimer isomers (in a parallel or antiparallel arrangement) being predisposed toward an ordered, amyloid-like structure. Limited proteolysis experiments indicated that the region from Ala⁸¹ to Lys¹¹³ is incorporated into the fibril core, implying that this region, which is predicted by several algorithms to be amyloidogenic, initiates fibril formation of α_S2-casein. The partial conservation of the cysteine motif and the frequent occurrence of disulphide-linked dimers in mammalian milks despite the associated risk of mammary amyloidosis, suggest that the dimeric conformation of α_S2-casein is a functional, yet amyloidogenic, structure.

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

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

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.

Analyzing aggregation propensities of clinically relevant PTEN mutants: a new culprit in pathogenesis of cancer and other PTENopathies

While studies on pathological protein aggregation are largely limited to neurodegenerative disease, emerging evidence suggests that other diseases are also associated with pathogenic protein aggregation. For example, tumor suppressor protein p53, and its mutant conformers, undergo protein aggregation, exacerbating the cancer phenotype. These findings raise the possibility that inactivation of tumor suppressors via protein aggregation may participate in cancer and other disease pathologies. Since tumor suppressor protein PTEN has similar functions to p53, and is mutated in multiple diseases, we examined the aggregation propensity of PTEN wild-type and 1523 clinically relevant PTEN mutants. Applying computational tools to PTEN mutation databases revealed that PTEN wild-type protein can aggregate under physiological conditions, and 274 distinct PTEN mutants had increased aggregation propensity. To understand the mechanism underlying PTEN conformer aggregation, we analyzed the physicochemical properties of these 274 PTEN mutants and defined their aggregation potential. We conclude that increased aggregation propensity of select PTEN mutants may contribute to disease phenotypes. Our studies have built the foundation for interrogating the aggregation potential of these select mutants in cancers and in PTENopathies. Elucidating the pathogenic mechanisms associated with aggregation-prone PTEN conformers will aid in developing therapies that target PTEN-aggregates in multiple diseases.Communicated by Ramaswamy H. Sarma.

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.

Thermodynamic properties of amyloid fibrils in equilibrium

In this manuscript we use a two-dimensional coarse-grained model to study how amyloid fibrils grow towards an equilibrium state where they coexist with proteins dissolved in a solution. Free-energies to dissociate proteins from fibrils are estimated from the residual concentration of dissolved proteins. Consistent with experiments, the concentration of proteins in solution affects the growth rate of fibrils but not their equilibrium state. Also, studies of the temperature dependence of the equilibrium state can be used to estimate thermodynamic quantities, e.g., heat capacity and entropy.

Structural Studies of Amyloid Proteins at the Molecular Level

Dozens of proteins are known to convert to the aggregated amyloid state. These include fibrils associated with systemic and neurodegenerative diseases and cancer, functional amyloid fibrils in microorganisms and animals, and many denatured proteins. Amyloid fibrils can be much more stable than other protein assemblies. In contrast to globular proteins, a single protein sequence can aggregate into several distinctly different amyloid structures, termed polymorphs, and a given polymorph can reproduce itself by seeding. Amyloid polymorphs may be the molecular basis of prion strains. Whereas the Protein Data Bank contains some 100,000 globular protein and 3,000 membrane protein structures, only a few dozen amyloid protein structures have been determined, and most of these are short segments of full amyloid-forming proteins. Regardless, these amyloid structures illuminate the architecture of the amyloid state, including its stability and its capacity for formation of polymorphs.

Rapid α-oligomer formation mediated by the Aβ C terminus initiates an amyloid assembly pathway

Since early oligomeric intermediates in amyloid assembly are often transient and difficult to distinguish, characterize and quantify, the mechanistic basis of the initiation of spontaneous amyloid growth is often opaque. We describe here an approach to the analysis of the Aβ aggregation mechanism that uses Aβ-polyglutamine hybrid peptides designed to retard amyloid maturation and an adjusted thioflavin intensity scale that reveals structural features of aggregation intermediates. The results support an aggregation initiation mechanism for Aβ-polyQ hybrids, and by extension for full-length Aβ peptides, in which a modular Aβ C-terminal segment mediates rapid, non-nucleated formation of α-helical oligomers. The resulting high local concentration of tethered amyloidogenic segments within these α-oligomers facilitates transition to a β-oligomer population that, via further remodelling and/or elongation steps, ultimately generates mature amyloid. Consistent with this mechanism, an engineered Aβ C-terminal fragment delays aggregation onset by Aβ-polyglutamine peptides and redirects assembly of Aβ₄₂ fibrils.

The elucidation of amyloid nucleation mechanisms remains challenging as early oligomeric intermediates are transient and difficult to distinguish. Here the authors use Aβ- polyglutamine hybrid peptides designed to slow and limit amyloid maturation to provide insights into the structures of Aβ self-assembly intermediates.

Analysis of the Amyloidogenic Potential of Pufferfish (Takifugu rubripes) Islet Amyloid Polypeptide Highlights the Limitations of Thioflavin-T Assays and the Difficulties in Defining Amyloidogenicity

Islet amyloid polypeptide (IAPP, amylin) forms pancreatic amyloid in type-2 diabetes, a process that contributes to the loss of β-cell mass in the disease. IAPP has been found in all higher organisms examined, but not all species form amyloid and the ability to do so correlates with the primary sequence. The amyloidogenic potential of fish IAPPs has not been examined, although fish have been proposed as a source for xenobiotic transplantation. The sequence of pufferfish IAPP (Takifugu rubripes) is known and is the most divergent from human IAPP of any reported IAPP sequence, differing at 11 positions including seven located within residues 20-29, a segment of the molecule that is important for controlling amyloidogenicity. Several of the substitutions found in pufferfish IAPP are nonconservative including Ser to Pro, Asn to Thr, Ala to Tyr, and Leu to Tyr replacements, and several of these have not been reported in mammalian IAPP sequences. Amyloid prediction programs give conflicting results for pufferfish IAPP. CD spectroscopy, FTIR, and transmission electron microscopy reveal that pufferfish IAPP forms amyloid and does so more rapidly than human IAPP in tris buffer at pH 7.4, but does so more slowly in phosphate buffered saline (PBS) at pH 7.4. Molecular dynamics simulations indicate that the pufferfish sequence is compatible with models of IAPP amyloid. The fish polypeptide does not significantly bind to thioflavin-T in tris and does so only weakly in PBS. The results highlight difficulties with thioflavin-T assays and the ambiguity in defining amyloidogenicity.

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.

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/.

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.