AGGRESCAN: a server for the prediction and evaluation of "hot spots" of aggregation in polypeptides

A machine learning strategy for the identification of key in silico descriptors and prediction models for IgG monoclonal antibody developability properties

Identification of favorable biophysical properties for protein therapeutics as part of developability assessment is a crucial part of the preclinical development process. Successful prediction of such properties and bioassay results from calculated in silico features has potential to reduce the time and cost of delivering clinical-grade material to patients, but nevertheless has remained an ongoing challenge to the field. Here, we demonstrate an automated and flexible machine learning workflow designed to compare and identify the most powerful features from computationally derived physiochemical feature sets, generated from popular commercial software packages. We implement this workflow with medium-sized datasets of human and humanized IgG molecules to generate predictive regression models for two key developability endpoints, hydrophobicity and poly-specificity. The most important features discovered through the automated workflow corroborate several previous literature reports, and newly discovered features suggest directions for further research and potential model improvement.

Characterisation of a novel crustin isoform from mud crab, Scylla serrata (Forsskål, 1775) and its functional analysis in silico

A 336-base pair (bp) sized mRNA sequence encoding 111 amino acid size crustin isoform (MC-crustin) was obtained from the gill sample of the green mud crab, Scylla serrata. MC-crustin possessed an N-terminal signal peptide region comprising of 21 amino acid residues, followed by a 90 amino acid mature peptide region having a molecular weight of 10.164 kDa, charge + 4.25 and theoretical pI of 8.27. Sequence alignment and phylogenetic tree analyses revealed the peptide to be a Type I crustin, with four conserved cysteine residues forming the cysteine rich region, followed by WAP domain. MC-crustin was cationic with cysteine/proline rich structure and was predicted with antimicrobial, anti-inflammatory, anti-angiogenic and anti-hypertensive property making it a potential molecule for possible therapeutic applications.

The role of heat shock proteins in preventing amyloid toxicity

The oligomerization of monomeric proteins into large, elongated, β-sheet-rich fibril structures (amyloid), which results in toxicity to impacted cells, is highly correlated to increased age. The concomitant decrease of the quality control system, composed of chaperones, ubiquitin-proteasome system and autophagy-lysosomal pathway, has been shown to play an important role in disease development. In the last years an increasing number of studies has been published which focus on chaperones, modulators of protein conformational states, and their effects on preventing amyloid toxicity. Here, we give a comprehensive overview of the current understanding of chaperones and amyloidogenic proteins and summarize the advances made in elucidating the impact of these two classes of proteins on each other, whilst also highlighting challenges and remaining open questions. The focus of this review is on structural and mechanistic studies and its aim is to bring novices of this field "up to speed" by providing insight into all the relevant processes and presenting seminal structural and functional investigations.

An in silico approach to study the role of epitope order in the multi-epitope-based peptide (MEBP) vaccine design

With different countries facing multiple waves, with some SARS-CoV-2 variants more deadly and virulent, the COVID-19 pandemic is becoming more dangerous by the day and the world is facing an even more dreadful extended pandemic with exponential positive cases and increasing death rates. There is an urgent need for more efficient and faster methods of vaccine development against SARS-CoV-2. Compared to experimental protocols, the opportunities to innovate are very high in immunoinformatics/in silico approaches, especially with the recent adoption of structural bioinformatics in peptide vaccine design. In recent times, multi-epitope-based peptide vaccine candidates (MEBPVCs) have shown extraordinarily high humoral and cellular responses to immunization. Most of the publications claim that respective reported MEBPVC(s) assembled using a set of in silico predicted epitopes, to be the computationally validated potent vaccine candidate(s) ready for experimental validation. However, in this article, for a given set of predicted epitopes, it is shown that the published MEBPVC is one among the many possible variants and there is high likelihood of finding more potent MEBPVCs than the published candidates. To test the same, a methodology is developed where novel MEBP variants are derived by changing the epitope order of the published MEBPVC. Further, to overcome the limitations of current qualitative methods of assessment of MEBPVC, to enable quantitative comparison and ranking for the discovery of more potent MEBPVCs, novel predictors, Percent Epitope Accessibility (PEA), Receptor specific MEBP vaccine potency (RMVP), MEBP vaccine potency (MVP) are introduced. The MEBP variants indeed showed varied MVP scores indicating varied immunogenicity. Further, the MEBP variants with IDs, SPVC_446 and SPVC_537, had the highest MVP scores indicating these variants to be more potent MEBPVCs than the published MEBPVC and hence should be preferred candidates for immediate experimental testing and validation. The method enables quicker selection and high throughput experimental validation of vaccine candidates. This study also opens the opportunity to develop new software tools for designing more potent MEBPVCs in less time.

Lysozyme amyloidosis-a report on a large German cohort and the characterisation of a novel amyloidogenic lysozyme gene variant

Lysozyme-derived (ALys) amyloidosis is a rare type of hereditary amyloidosis. Nine amyloidogenic variants and ∼30 affected families have been described worldwide. The most common manifestations are renal dysfunction, gastrointestinal tract symptoms, and sicca syndrome. We report on the clinical course of ten patients from six families representing one of the largest cohorts published so far. Seven patients carried the W64R variant showing the whole spectrum of ALys-associated symptoms. Two patients-a mother-son pair-carried a novel lysozyme variant, which was associated with nephropathy and peripheral polyneuropathy. In accordance with previous findings, the phenotype resembled within these families but did not correlate with the genotype. To gain insights into the effect of the variants at the molecular level, we analysed the structure of lysozyme and performed comparative computational predictions on aggregation propensity and conformational stability. Our study supports that decreased conformational stability is a key factor for lysozyme variants to be prone to aggregation. In summary, ALys amyloidosis is a very rare, but still heterogeneous disease that can manifest at an early age. Our newly identified lysozyme variant is associated with nephropathy and peripheral polyneuropathy. Further research is needed to understand its pathogenesis and to enable the development of new treatments.

The Increased Amyloidogenicity of Spike RBD and pH-Dependent Binding to ACE2 May Contribute to the Transmissibility and Pathogenic Properties of SARS-CoV-2 Omicron as Suggested by In Silico Study

SARS-CoV-2 is a rapidly evolving pathogen that has caused a global pandemic characterized by several consecutive waves. Based on epidemiological and NGS data, many different variants of SARS-CoV-2 were described and characterized since the original variant emerged in Wuhan in 2019. Notably, SARS-CoV-2 variants differ in transmissibility and pathogenicity in the human population, although the molecular basis for this difference is still debatable. A significant role is attributed to amino acid changes in the binding surface of the Spike protein to the ACE2 receptor, which may facilitate virus entry into the cell or contribute to immune evasion. We modeled in silico the interaction between Spike RBDs of Wuhan-Hu-1, Delta, and Omicron BA.1 variants and ACE2 at different pHs (pH 5 and pH 7) and showed that the strength of this interaction was higher for the Omicron BA.1 RBD compared to Wuhan-Hu-1 or Delta RBDs and that the effect was more profound at pH 5. This finding is strikingly related to the increased ability of Omicron variants to spread in the population. We also noted that during its spread in the population, SARS-CoV-2 evolved to a more charged, basic composition. We hypothesize that the more basic surface of the Omicron variant may facilitate its spread in the upper respiratory tract but not in the lower respiratory tract, where pH estimates are different. We calculated the amyloidogenic properties of Spike RBDs in different SARS-CoV-2 variants and found eight amyloidogenic regions in the Spike RBDs for each of the variants predicted by the FoldAmyloid program. Although all eight regions were almost identical in the Wuhan to Gamma variants, two of them were significantly longer in both Omicron variants, making the Omicron RBD more amyloidogenic. We discuss how the increased predicted amyloidogenicity of the Omicron variants RBDs may be important for protein stability, influence its interaction with ACE2 and contribute to immune evasion.

Salt-Induced Dissolution of Protein Aggregates

Protein aggregation is mediated by a complex interplay of noncovalent interactions and is associated with a broad range of aspects from debilitating human diseases to the food industry and therapeutic biotechnology. Deciphering the intricate roles of noncovalent interactions is of paramount importance for the design of effective inhibitory and disaggregation strategies, which remains a formidable challenge. By using a combination of spectroscopic and microscopic tools, here we show that the surfactant-mediated protein aggregation can be modulated by an intriguing interplay of hydrophobic and electrostatic effects. Additionally, our results illuminate the unique role of salt as a potent disaggregation inducer that alters the protein-surfactant electrostatic interactions and triggers the dissolution of preformed protein aggregates resulting in restoring the native protein structure. This unusual salt-induced dissolution and refolding offers a unique approach to regulating the balance between protein self-assembly and disassembly and will offer a potent strategy to design electrostatically targeted inhibitors.

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

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

Succinct Amyloid and Nonamyloid Patterns in Hexapeptides

Hexapeptides are widely applied as a model system for studying the amyloid-forming properties of polypeptides, including proteins. Recently, large experimental databases have become publicly available with amyloidogenic labels. Using these data sets for training and testing purposes, one may build artificial intelligence (AI)-based classifiers for predicting the amyloid state of peptides. In our previous work (Biomolecules 2021, 11, 500), we described the Support Vector Machine (SVM)-based Budapest Amyloid Predictor (https://pitgroup.org/bap). Here, we apply the Budapest Amyloid Predictor for discovering numerous amyloidogenic and nonamyloidogenic hexapeptide patterns with accuracy between 80% and 84%, as surprising and succinct novel rules for further understanding the amyloid state of peptides. For example, we have shown that for any independently mutated residue (position marked by "x"), the patterns CxFLWx, FxFLFx, or xxIVIV are predicted to be amyloidogenic, while those of PxDxxx, xxKxEx, and xxPQxx are nonamyloidogenic. We note that each amyloidogenic pattern with two x's (e.g.,CxFLWx) describes succinctly 20² = 400 hexapeptides, while the nonamyloidogenic patterns comprising four point mutations (e.g.,PxDxxx) give 20⁴ = 160 000 hexapeptides in total. We also examine the restricted substitutions for positions "x" from subclasses of proteinogenic amino acid residues; for example, if "x" is substituted with hydrophobic amino acids, then there exist patterns containing three x's, like MxVVxx, predicted to be amyloidogenic. If we can choose for the x positions any hydrophobic amino acids, except the "structure breaker" proline, then we get amyloid patterns with five x positions, for example, xxxFxx, each corresponding to 32 768 hexapeptides. To our knowledge, no similar applications of artificial intelligence tools or succinct amyloid patterns were described before the present work.

Self-assembly and Hydrogelation Properties of Peptides Derived from Peptic Cleavage of Aggregation-prone Regions of Ovalbumin

Egg white protein hydrolysate generated with pepsin was investigated for the presence of peptides with self-assembly and hydrogelation properties. Incubation of the hydrolysates for 16 h resulted in aggregates with significantly (p < 0.05) lower free amino nitrogen and sulfhydryl contents, and higher particle diameter and surface hydrophobicity compared to the hydrolysates. LC-MS/MS analysis of the aggregates resulted in identification of 429 ovalbumin-derived peptides, among which the top-six aggregation-prone peptides IFYCPIAIM, NIFYCPIAIM, VLVNAIVFKGL, YCPIAIMSA, MMYQIGLF, and VYSFSLASRL were predicted using AGGRESCAN by analysis of the aggregation “Hot Spots”. NIFYCPIAIM had the highest thioflavin T fluorescence intensity, particle diameter (5611.3 nm), and polydispersity index (1.0) after 24 h, suggesting the formation of β-sheet structures with heterogeneous particle size distribution. Transmission electron microscopy of MMYQIGLF, and VYSFSLASRL demonstrated the most favorable peptide self-assembly, based on the formation of densely packed, intertwined fibrils. Rheological studies confirmed the viscoelastic and mechanical properties of the hydrogels, with IFYCPIAIM, NIFYCPIAIM, VLVNAIVFKGL, and VYSFSLASRL forming elastic solid hydrogels (tan δ < 1), while YCPIAIMSA and MMYQIGLF formed viscous liquid-like hydrogels (tan δ > 1). The results provide valuable insight into the influence of peptide sequence on hydrogelation and self-assembly progression, and prospects of food peptides in biomaterial applications.

Contribution of the 12–17 hydrophobic region of islet amyloid polypeptide in self-assembly and cytotoxicity

The islet amyloid polypeptide (IAPP) is a 37-residue aggregation-prone peptide hormone whose deposition as insoluble fibrils in the islets of Langerhans is associated with type II diabetes. Therapeutic interventions targeting IAPP amyloidogenesis, which contributes to pancreatic β-cell degeneration, remain elusive owing to the lack of understanding of the self-assembly mechanisms and of the quaternary proteospecies mediating toxicity. While countless studies have investigated the contributions of the 20–29 amyloidogenic core in self-assembly, IAPP central region, i.e. positions 11 to 19, has been less studied, notwithstanding its potential key role in oligomerization. In this context, the present study aimed at investigating the physicochemical and conformational properties driving IAPP self-assembly and associated cytotoxicity. Computational tools and all-atom molecular dynamics simulation suggested that the hydrophobic 12–17 segment promotes IAPP self-recognition and aggregation. Alanine scanning revealed that the hydrophobic side chains of Leu12, Phe15 and Val17 are critical for amyloid fibril formation. Destabilization of the α-helical folding by Pro substitution enhanced self-assembly when the pyrrolidine ring was successively introduced at positions Ala13, Asn14 and Phe15, in comparison to respective Ala-substituted counterparts. Modulating the peptide backbone flexibility at position Leu16 through successive incorporation of Pro, Gly and α-methylalanine, inhibited amyloid formation and reduced cytotoxicity, while the isobutyl side chain of Leu16 was not critical for self-assembly and IAPP-mediated toxicity. These results highlight the importance of the 12–17 hydrophobic region of IAPP for self-recognition, ultimately supporting the development of therapeutic approaches to prevent oligomerization and/or fibrillization.

Human RAD51 Protein Forms Amyloid-like Aggregates In Vitro

RAD51 is a central protein of homologous recombination and DNA repair processes that maintains genome stability and ensures the accurate repair of double-stranded breaks (DSBs). In this work, we assessed amyloid properties of RAD51 in vitro and in the bacterial curli-dependent amyloid generator (C-DAG) system. Resistance to ionic detergents, staining with amyloid-specific dyes, polarized microscopy, transmission electron microscopy (TEM), X-ray diffraction and other methods were used to evaluate the properties and structure of RAD51 aggregates. The purified human RAD51 protein formed detergent-resistant aggregates in vitro that had an unbranched cross-β fibrillar structure, which is typical for amyloids, and were stained with amyloid-specific dyes. Congo-red-stained RAD51 aggregates demonstrated birefringence under polarized light. RAD51 fibrils produced sharp circular X-ray reflections at 4.7 Å and 10 Å, demonstrating that they had a cross-β structure. Cytoplasmic aggregates of RAD51 were observed in cell cultures overexpressing RAD51. We demonstrated that a key protein that maintains genome stability, RAD51, has amyloid properties in vitro and in the C-DAG system and discussed the possible biological relevance of this observation.

Protein Design: From the Aspect of Water Solubility and Stability

Water solubility and structural stability are key merits for proteins defined by the primary sequence and 3D-conformation. Their manipulation represents important aspects of the protein design field that relies on the accurate placement of amino acids and molecular interactions, guided by underlying physiochemical principles. Emulated designer proteins with well-defined properties both fuel the knowledge-base for more precise computational design models and are used in various biomedical and nanotechnological applications. The continuous developments in protein science, increasing computing power, new algorithms, and characterization techniques provide sophisticated toolkits for solubility design beyond guess work. In this review, we summarize recent advances in the protein design field with respect to water solubility and structural stability. After introducing fundamental design rules, we discuss the transmembrane protein solubilization and de novo transmembrane protein design. Traditional strategies to enhance protein solubility and structural stability are introduced. The designs of stable protein complexes and high-order assemblies are covered. Computational methodologies behind these endeavors, including structure prediction programs, machine learning algorithms, and specialty software dedicated to the evaluation of protein solubility and aggregation, are discussed. The findings and opportunities for Cryo-EM are presented. This review provides an overview of significant progress and prospects in accurate protein design for solubility and stability.

HfaE Is a Component of the Holdfast Anchor Complex That Tethers the Holdfast Adhesin to the Cell Envelope

Bacteria use adhesins to colonize different surfaces and form biofilms. The species of the Caulobacterales order use a polar adhesin called holdfast, composed of polysaccharides, proteins, and DNA, to irreversibly adhere to surfaces. In Caulobacter crescentus, a freshwater Caulobacterales species, the holdfast is anchored at the cell pole via the holdfast anchor (Hfa) proteins HfaA, HfaB, and HfaD. HfaA and HfaD colocalize with holdfast and are thought to form amyloid-like fibers that anchor holdfast to the cell envelope. HfaB, a lipoprotein, is required for the translocation of HfaA and HfaD to the cell surface. Deletion of the anchor proteins leads to a severe defect in adherence resulting from holdfast not being properly attached to the cell and shed into the medium. This phenotype is greater in a ΔhfaB mutant than in a ΔhfaA ΔhfaD double mutant, suggesting that HfaB has other functions besides the translocation of HfaA and HfaD. Here, we identify an additional HfaB-dependent holdfast anchoring protein, HfaE, which is predicted to be a secreted protein. HfaE is highly conserved among Caulobacterales species, with no predicted function. In planktonic culture, hfaE mutants produce holdfasts and rosettes similar to those produced by the wild type. However, holdfasts from hfaE mutants bind to the surface but are unable to anchor cells, similarly to other anchor mutants. We showed that fluorescently tagged HfaE colocalizes with holdfast and that HfaE forms an SDS-resistant high-molecular-weight species consistent with amyloid fiber formation. We propose that HfaE is a novel holdfast anchor protein and that HfaE functions to link holdfast material to the cell envelope. IMPORTANCE For surface attachment and biofilm formation, bacteria produce adhesins that are composed of polysaccharides, proteins, and DNA. Species of the Caulobacterales produce a specialized polar adhesin, holdfast, which is required for permanent attachment to surfaces. In this study, we evaluate the role of a newly identified holdfast anchor protein, HfaE, in holdfast anchoring to the cell surface in two different members of the Caulobacterales with drastically different environments. We show that HfaE plays an important role in adhesion and biofilm formation in the Caulobacterales. Our results provide insights into bacterial adhesins and how they interact with the cell envelope and surfaces.

Mechanistic insights into the size-dependent effects of nanoparticles on inhibiting and accelerating amyloid fibril formation

The aggregation of peptides into amyloid fibrils has been linked to ageing-related diseases, such as Alzheimer's and type 2 diabetes. Interfaces, particularly those with large nanostructured surfaces, can affect the kinetics of peptide aggregation, which ranges from complete inhibition to strong acceleration. While a number of physiochemical parameters determine interfacial effects, we focus here on the role of nanoparticle (NP) size and curvature. We used thioflavin T (ThT) fluorescence assays to demonstrate the size-dependent effects of NPs on amyloid fibril formation for the peptides Aβ₄₀, NNFGAIL, GNNQQNY and VQIYVK. While 5 nm gold NPs (AuNP-5) retarded or inhibited the aggregation of all peptides except NNFGAIL, larger 20 nm gold NPs (AuNP-20) tended to accelerate or not influence peptide aggregation. Differences in the NP effects for the peptides resulted from the different peptide properties (size, tendency to aggregate) and associated surface binding affinities. Additional dynamic light scattering (DLS), electron microscopy, and atomic force microscopy (AFM) experiments with the Aβ₄₀ peptide confirmed size-dependent NP effects on peptide aggregation, and also suggested a structural influence on the formed fibrils. NPs can serve as a surface for the adsorption of peptide monomers and enable nucleation to oligomers and fibril formation. However, molecular dynamics (MD) simulations showed that peptide oligomers were less stable at smaller NPs. High surface curvatures destabilized prefibrillar structures, which provides a possible explanation for inhibitory effects on fibril growth, provided that peptide-NP surface binding was relevant for fibril formation. These mechanistic insights can support the design of future nanostructured materials.

Revisiting misfolding propensity of serum amyloid A1: Special focus on the signal peptide region

AA amyloidosis is the result of overproduction and aberrant processing of acute-phase serum amyloid A1 (SAA1) by hepatocytes. Proteolytic cleavage of SAA1 is believed to play a central role in AA amyloid formation. The SAA1 protein undergoes a cleavage of 18 residues consisting of the signal peptide at the N-terminal region. To better understand the mechanism behind systemic amyloidosis in the SAA1 protein, we studied the misfolding propensity of the signal peptide region. We first examined the signal peptide amino acid SAA derived from different animal species. A library of 16 peptides was designed to evaluate the propensity of aggregation. The amyloidogenic potential of each SAA1 signal peptide homolog was assessed using in silico Tango program, thioflavin T (ThT) fluorescence, transmission electron microscopy (TEM), and seeding with misfolded human SAA1 signal peptide. After 7 days of incubation, most of the SAA1 signal peptide fragments had the propensity to form fibrils at a concentration of 100 μM in 50 mM Tris buffer at 37 °C by TEM. All peptides were able to generate fibrils at a higher concentration, i.e 500 μM in 25 mM Tris buffer with 50% HFIP, by ThT. All SAA1 signal synthetic peptides designed from the different animal species had the propensity to misfold and form fibrils, particularly in species with low occurrence of systemic amyloidosis. The human SAA1 signal peptide region was capable to seed the SAA1 1–25 and 32–47 peptide regions. Characterizing fibrillar conformations are relevant for seeding intact and/or fragmented SAA, which may contribute, to the mechanism of protein misfolding. This research signifies the importance of the signal peptide region and its possible contribution to the misfolding of aggregation-prone proteins.

•

Serum amyloid A1 (SAA1) signal peptide synthetic fragments have the propensity to form fibrils.

•

SAA1 signal peptide, 1–25, and 51–75 regions are prone-to-aggregate.

•

Human SAA1 signal peptide is capable to seeds human SAA1 1–25 and 32–47 fragments.

•

SAA1 signal peptide fibrils may have a role in seeding intact and/or fragmented SAA1.

Comparison of hydrophobicity scales for predicting biophysical properties of antibodies

While antibody-based therapeutics have grown to be one of the major classes of novel medicines, some antibody development candidates face significant challenges regarding expression levels, solubility, as well as stability and aggregation, under physiological and storage conditions. A major determinant of those properties is surface hydrophobicity, which promotes unspecific interactions and has repeatedly proven problematic in the development of novel antibody-based drugs. Multiple computational methods have been devised for in-silico prediction of antibody hydrophobicity, often using hydrophobicity scales to assign values to each amino acid. Those approaches are usually validated by their ability to rank potential therapeutic antibodies in terms of their experimental hydrophobicity. However, there is significant diversity both in the hydrophobicity scales and in the experimental methods, and consequently in the performance of in-silico methods to predict experimental results. In this work, we investigate hydrophobicity of monoclonal antibodies using hydrophobicity scales. We implement several scoring schemes based on the solvent-accessibility and the assigned hydrophobicity values, and compare the different scores and scales based on their ability to predict retention times from hydrophobic interaction chromatography. We provide an overview of the strengths and weaknesses of several commonly employed hydrophobicity scales, thereby improving the understanding of hydrophobicity in antibody development. Furthermore, we test several datasets, both publicly available and proprietary, and find that the diversity of the dataset affects the performance of hydrophobicity scores. We expect that this work will provide valuable guidelines for the optimization of biophysical properties in future drug discovery campaigns.

NOS1AP Interacts with α-Synuclein and Aggregates in Yeast and Mammalian Cells

The NOS1AP gene encodes a cytosolic protein that binds to the signaling cascade component neuronal nitric oxide synthase (nNOS). It is associated with many different disorders, such as schizophrenia, post-traumatic stress disorder, autism, cardiovascular disorders, and breast cancer. The NOS1AP (also known as CAPON) protein mediates signaling within a complex which includes the NMDA receptor, PSD-95, and nNOS. This adapter protein is involved in neuronal nitric oxide (NO) synthesis regulation via its association with nNOS (NOS1). Our bioinformatics analysis revealed NOS1AP as an aggregation-prone protein, interacting with α-synuclein. Further investigation showed that NOS1AP forms detergent-resistant non-amyloid aggregates when overproduced. Overexpression of NOS1AP was found in rat models for nervous system injury as well as in schizophrenia patients. Thus, we can assume for the first time that the molecular mechanisms underlying these disorders include misfolding and aggregation of NOS1AP. We show that NOS1AP interacts with α-synuclein, allowing us to suggest that this protein may be implicated in the development of synucleinopathies and that its aggregation may explain the relationship between Parkinson’s disease and schizophrenia.

Machine-learning analysis of intrinsically disordered proteins identifies key factors that contribute to neurodegeneration-related aggregation

Protein structure is determined by the amino acid sequence and a variety of post-translational modifications, and provides the basis for physiological properties. Not all proteins in the proteome attain a stable conformation; roughly one third of human proteins are unstructured or contain intrinsically disordered regions exceeding 40% of their length. Proteins comprising or containing extensive unstructured regions are termed intrinsically disordered proteins (IDPs). IDPs are known to be overrepresented in protein aggregates of diverse neurodegenerative diseases. We evaluated the importance of disordered proteins in the nematode Caenorhabditis elegans, by RNAi-mediated knockdown of IDPs in disease-model strains that mimic aggregation associated with neurodegenerative pathologies. Not all disordered proteins are sequestered into aggregates, and most of the tested aggregate-protein IDPs contribute to important physiological functions such as stress resistance or reproduction. Despite decades of research, we still do not understand what properties of a disordered protein determine its entry into aggregates. We have employed machine-learning models to identify factors that predict whether a disordered protein is found in sarkosyl-insoluble aggregates isolated from neurodegenerative-disease brains (both AD and PD). Machine-learning predictions, coupled with principal component analysis (PCA), enabled us to identify the physiochemical properties that determine whether a disordered protein will be enriched in neuropathic aggregates.

The germ plasm is anchored at the cleavage furrows through interaction with tight junctions in the early zebrafish embryo

The zebrafish germline is specified during early embryogenesis by inherited maternal RNAs and proteins collectively called germ plasm. Only the cells containing germ plasm will become part of the germline, whereas the other cells will commit to somatic cell fates. Therefore, proper localization of germ plasm is key for germ cell specification and its removal is crucial for the development of the soma. The molecular mechanism underlying this process in vertebrates is largely unknown. Here, we show that germ plasm localization in zebrafish is similar to that in Xenopus but distinct from Drosophila. We identified non muscle myosin II (NMII) and tight junction (TJ) components, such as ZO2 and claudin-d (Cldn-d) as interaction candidates of Bucky ball (Buc), which is the germ plasm organizer in zebrafish. Remarkably, we also found that TJ protein ZO1 colocalizes with germ plasm, and electron microscopy of zebrafish embryos uncovered TJ-like structures at the cleavage furrows where the germ plasm is anchored. In addition, injection of the TJ receptor Cldn-d produced extra germ plasm aggregates, whereas expression of a dominant-negative version inhibited germ plasm aggregate formation. Our findings support for the first time a role for TJs in germ plasm localization.

CARs-DB: A Database of Cryptic Amyloidogenic Regions in Intrinsically Disordered Proteins

Proteome-wide analyses suggest that most globular proteins contain at least one amyloidogenic region, whereas these aggregation-prone segments are thought to be underrepresented in intrinsically disordered proteins (IDPs). In recent work, we reported that intrinsically disordered regions (IDRs) indeed sustain a significant amyloid load in the form of cryptic amyloidogenic regions (CARs). CARs are widespread in IDRs, but they are necessarily exposed to solvent, and thus they should be more polar and have a milder aggregation potential than conventional amyloid regions protected inside globular proteins. CARs are connected with IDPs function and, in particular, with the establishment of protein-protein interactions through their IDRs. However, their presence also appears associated with pathologies like cancer or Alzheimer’s disease. Given the relevance of CARs for both IDPs function and malfunction, we developed CARs-DB, a database containing precomputed predictions for all CARs present in the IDPs deposited in the DisProt database. This web tool allows for the fast and comprehensive exploration of previously unnoticed amyloidogenic regions embedded within IDRs sequences and might turn helpful in identifying disordered interacting regions. It contains >8,900 unique CARs identified in a total of 1711 IDRs. CARs-DB is freely available for users and can be accessed at http://carsdb.ppmclab.com. To validate CARs-DB, we demonstrate that two previously undescribed CARs selected from the database display full amyloidogenic potential. Overall, CARs-DB allows easy access to a previously unexplored amyloid sequence space.

Trigger factor both holds and folds its client proteins

ATP-independent chaperones like trigger factor are generally assumed to play passive roles in protein folding by acting as holding chaperones. Here we show that trigger factor plays a more active role. Consistent with a role as an aggregation inhibiting chaperone, we find that trigger factor rapidly binds to partially folded glyceraldehyde 3-phosphate dehydrogenase (GAPDH) and prevents it from non-productive self-association by shielding oligomeric interfaces. In the traditional view of holding chaperone action, trigger factor would then be expected to transfer its client to a chaperone foldase system for complete folding. Unexpectedly, we noticed that GAPDH folds into a monomeric but otherwise rather native-like intermediate state while trigger factor-bound. Upon release from trigger factor, the mostly folded monomeric GAPDH rapidly self-associates into its native tetramer and acquires enzymatic activity without needing additional folding factors. The mechanism we propose here for trigger factor bridges the holding and folding activities of chaperone function.

Mycobacterium tuberculosis low molecular weight T-cell antigen Mtb8.4 has heme-binding and fiber-forming properties

Mtb8.4, a secretory T-cell antigen of Mycobacterium tuberculosis, is important for providing an antigen-specific immune response. In this study, we showed Mtb8.4 to have both heme-binding and fibril-forming properties, using experimental and in silico methods. High absorbance at 410 nm and interaction with hemin-agarose demonstrated its heme-binding nature. Titration of Mtb8.4 with heme resulted in 1:1 stoichiometry. The heme-binding pocket in Mtb8.4 was identified by molecular modeling, and binding residues were predicted using molecular docking. The molecular dynamics simulations of apo- and heme-bound Mtb8.4 confirmed that the heme group forms a stable complex. Transmission electron microscopy analyses and dye-binding assays showed that Mtb8.4 forms fibers. Computational studies predicted that the C-terminal sequence (⁹³ AAQYIGLVESV¹⁰³ ) is important for forming fibers. In silico analyses further anticipated the probable epitope (⁸² AMAAQLQAV⁹⁰ ) of Mtb8.4. The fiber-forming properties of Mtb8.4 could be advantageous from a vaccine perspective for aggregate/fibril-based vaccine delivery or it might influence the epitope presentation of Mtb8.4.

The Multivalent Polyampholyte Domain of Nst1, a P-Body-Associated Saccharomyces cerevisiae Protein, Provides a Platform for Interacting with P-Body Components

The condensation of nuclear promyelocytic leukemia bodies, cytoplasmic P-granules, P-bodies (PBs), and stress granules is reversible and dynamic via liquid–liquid phase separation. Although each condensate comprises hundreds of proteins with promiscuous interactions, a few key scaffold proteins are required. Essential scaffold domain sequence elements, such as poly-Q, low-complexity regions, oligomerizing domains, and RNA-binding domains, have been evaluated to understand their roles in biomolecular condensation processes. However, the underlying mechanisms remain unclear. We analyzed Nst1, a PB-associated protein that can intrinsically induce PB component condensations when overexpressed. Various Nst1 domain deletion mutants with unique sequence distributions, including intrinsically disordered regions (IDRs) and aggregation-prone regions, were constructed based on structural predictions. The overexpression of Nst1 deletion mutants lacking the aggregation-prone domain (APD) significantly inhibited self-condensation, implicating APD as an oligomerizing domain promoting self-condensation. Remarkably, cells overexpressing the Nst1 deletion mutant of the polyampholyte domain (PD) in the IDR region (Nst1_∆PD) rarely accumulate endogenous enhanced green fluorescent protein (EGFP)-tagged Dcp2. However, Nst1_∆PD formed self-condensates, suggesting that Nst1 requires PD to interact with Dcp2, regardless of its self-condensation. In Nst1_∆PD-overexpressing cells treated with cycloheximide (CHX), Dcp2, Xrn1, Dhh1, and Edc3 had significantly diminished condensation compared to those in CHX-treated Nst1-overexpressing cells. These observations suggest that the PD of the IDR in Nst1 functions as a hub domain interacting with other PB components.

A unified statistical potential reveals that amino acid stickiness governs nonspecific recruitment of client proteins into condensates

Membraneless organelles are cellular compartments that form by liquid-liquid phase separation of one or more components. Other molecules, such as proteins and nucleic acids, will distribute between the cytoplasm and the liquid compartment in accordance with the thermodynamic drive to lower the free energy of the system. The resulting distribution colocalizes molecular species to carry out a diversity of functions. Two factors could drive this partitioning: the difference in solvation between the dilute versus dense phase and intermolecular interactions between the client and scaffold proteins. Here, we develop a set of knowledge-based potentials that allow for the direct comparison between stickiness, which is dominated by desolvation energy, and pairwise residue contact propensity terms. We use these scales to examine experimental data from two systems: protein cargo dissolving within phase-separated droplets made from FG repeat proteins of the nuclear pore complex and client proteins dissolving within phase-separated FUS droplets. These analyses reveal a close agreement between the stickiness of the client proteins and the experimentally determined values of the partition coefficients (R > 0.9), while pairwise residue contact propensities between client and scaffold show weaker correlations. Hence, the stickiness of client proteins is sufficient to explain their differential partitioning within these two phase-separated systems without taking into account the composition of the condensate. This result implies that selective trafficking of client proteins to distinct membraneless organelles requires recognition elements beyond the client sequence composition. STATEMENT: Empirical potentials for amino acid stickiness and pairwise residue contact propensities are derived. These scales are unique in that they enable direct comparison of desolvation versus contact terms. We find that partitioning of a client protein to a condensate is best explained by amino acid stickiness.

Clustering and cross-linking of the wheat storage protein α-gliadin: A combined experimental and theoretical approach

Our aim was to understand mechanisms for clustering and cross-linking of gliadins, a wheat seed storage protein type, monomeric in native state, but incorporated in network while processed. The mechanisms were studied utilizing spectroscopy and high-performance liquid chromatography on a gliadin-rich fraction, in vitro produced α-gliadins, and synthetic gliadin peptides, and by coarse-grained modelling, Monte Carlo simulations and prediction algorithms. In solution, gliadins with α-helix structures (dip at 205 nm in CD) were primarily present as monomeric molecules and clusters of gliadins (peaks at 650- and 700-s on SE-HPLC). At drying, large polymers (R_g 90.3 nm by DLS) were formed and β-sheets increased (14% by FTIR). Trained algorithms predicted aggregation areas at amino acids 115-140, 150-179, and 250-268, and induction of liquid-liquid phase separation at P- and Poly-Q-sequences (Score = 1). Simulations showed that gliadins formed polymers by tail-to-tail or a hydrophobic core (Kratky plots and R_ee = 35 and 60 for C- and N-terminal). Thus, the N-terminal formed clusters while the C-terminal formed aggregates by disulphide and lanthionine bonds, with favoured hydrophobic clustering of similar/exact peptide sections (synthetic peptide mixtures on SE-HPLC). Mechanisms of clustering and cross-linking of the gliadins presented here, contribute ability to tailor processing results, using these proteins.

Scan-Find-Scan-Model: Discrete Site-Targeted Suppressor Design Strategy for Amyloid-β

Alzheimer's disease is undoubtedly the most well-studied neurodegenerative disease. Consequently, the amyloid-β (Aβ) protein ranks at the top in terms of getting attention from the scientific community for structural property-based characterization. Even after decades of extensive research, there is existing volatility in terms of understanding and hence the effective tackling procedures against the disease that arises due to the lack of knowledge of both specific target- and site-specific drugs. Here, we develop a multidimensional approach based on the characterization of the common static-dynamic-thermodynamic trait of the monomeric protein, which efficiently identifies a small target sequence that contains an inherent tendency to misfold and consequently aggregate. The robustness of the identification of the target sequence comes with an abundance of a priori knowledge about the length and sequence of the target and hence guides toward effective designing of the target-specific drug with a very low probability of bottleneck and failure. Based on the target sequence information, we further identified a specific mutant that showed the maximum potential to act as a destabilizer of the monomeric protein as well as enormous success as an aggregation suppressor. We eventually tested the drug efficacy by estimating the extent of modulation of binding affinity existing within the fibrillar form of the Aβ protein due to a single-point mutation and hence provided a proof of concept of the entire protocol.

Protein misfolding and clearance in the pathogenesis of a new infantile onset ataxia caused by mutations in PRDX3

Peroxiredoxin 3 (PRDX3) encodes a mitochondrial antioxidant protein which is essential for the control of reactive oxidative species (ROS) homeostasis. So far, PRDX3 mutations are involved in mild-to-moderate progressive juvenile onset cerebellar ataxia. We aimed to unravel the molecular bases underlying the disease in an infant suffering from cerebellar ataxia that started at 19 months old and presented severe cerebellar atrophy and peripheral neuropathy early in the course of disease. By whole exome sequencing, we identified a novel homozygous mutation, PRDX3 p.D163E, which impaired the mitochondrial ROS defense system. In mouse primary cortical neurons, the exogenous expression of PRDX3 p.D163E was reduced and triggered alterations in neurite morphology and in mitochondria. Mitochondrial computational parameters showed that p.D163E led to serious mitochondrial alterations. In transfected HeLa cells expressing the mutation, mitochondria accumulation was detected by correlative light electron microscopy (CLEM). Mitochondrial morphology showed severe changes, including extremely damaged outer and inner membranes with a notable cristae disorganization. Moreover, spherical structures compatible with lipid droplets were identified, which can be associated with a generalized response to stress and can be involved in the removal of unfolded proteins. In the patient's fibroblasts, PRDX3 expression was nearly absent. The biochemical analysis suggested that the mutation p.D163E would result in an unstable structure tending to form aggregates that trigger unfolded protein responses via mitochondria and endoplasmic reticulum. Altogether, our findings broaden the clinical spectrum of the recently described PRDX3-associated neurodegeneration and provide new insight into the pathological mechanisms underlying this new form of cerebellar ataxia.

An Efficient Evaluation System Accelerates α-Helical Antimicrobial Peptide Discovery and Its Application to Global Human Genome Mining

Antimicrobial peptides (AMPs), as an important part of the innate immune system of an organism, is a kind of promising drug candidate for novel antibiotics due to their unique antibacterial mechanism. However, the discovery of novel AMPs is facing a great challenge due to the complexity of systematic experiments and the poor predictability of antimicrobial activity. Here, a novel and comprehensive screening system, the Multiple Descriptor Multiple Strategy (MultiDS), was proposed based on 59 physicochemical and structural parameters, three strategies, and four algorithms for the mining of α-helical AMPs. This approach was applied to mine the encrypted peptide antibiotics from the global human genome, including introns and exons. A library of approximately 70 billion peptides with 15–25 amino acid residues was screened by the MultiDS system and generated a list of peptides with the Multiple Descriptor Index (MD index) scores, which was the core part of the MultiDS system. Sixty peptides with top MD scores were chemically synthesized and experimentally tested their antimicrobial activity against 10 kinds of Gram-positive bacteria, Gram-negative bacteria (including drug-resistant pathogens). A total of fifty-nine out of 60 (98.3%) peptides exhibited antimicrobial activity (MIC ≤ 64 μg/mL), and 24 out of 60 (40%) peptides showed high activity (MIC ≤ 2 μg/mL), validating the MultiDS system was an effective and predictive screening tool with high hit rate and superior antimicrobial activity. For further investigation, AMPs S1, S2, and S3 with the highest MD scores were used to treat the skin infection mouse models in vivo caused by Escherichia coli, drug-resistance Escherichia coli, and Staphylococcus aureus, respectively. All of S1, S2, and S3 showed comparable therapeutic effects on promoting infection healing to or even better than the positive drug levofloxacin. A mechanism study discovered that rapid bactericidal action was caused by cell membrane disruption and content leakage. The MultiDS system not only provides a high-throughput approach that allows for the mining of candidate AMPs from the global genome sequence but also opens up a new route to accelerate the discovery of peptide antibiotics.

Amyloidogenic Peptides: New Class of Antimicrobial Peptides with the Novel Mechanism of Activity

Antibiotic-resistant bacteria are recognized as one of the leading causes of death in the world. We proposed and successfully tested peptides with a new mechanism of antimicrobial action “protein silencing” based on directed co-aggregation. The amyloidogenic antimicrobial peptide (AAMP) interacts with the target protein of model or pathogenic bacteria and forms aggregates, thereby knocking out the protein from its working condition. In this review, we consider antimicrobial effects of the designed peptides on two model organisms, E. coli and T. thermophilus, and two pathogenic organisms, P. aeruginosa and S. aureus. We compare the amino acid composition of proteomes and especially S1 ribosomal proteins. Since this protein is inherent only in bacterial cells, it is a good target for studying the process of co-aggregation. This review presents a bioinformatics analysis of these proteins. We sum up all the peptides predicted as amyloidogenic by several programs and synthesized by us. For the four organisms we studied, we show how amyloidogenicity correlates with antibacterial properties. Let us especially dwell on peptides that have demonstrated themselves as AMPs for two pathogenic organisms that cause dangerous hospital infections, and in which the minimal inhibitory concentration (MIC) turned out to be comparable to the MIC of gentamicin sulfate. All this makes our study encouraging for the further development of AAMP. The hybrid peptides may thus provide a starting point for the antibacterial application of amyloidogenic peptides.

AMYPred-FRL is a novel approach for accurate prediction of amyloid proteins by using feature representation learning

Amyloid proteins have the ability to form insoluble fibril aggregates that have important pathogenic effects in many tissues. Such amyloidoses are prominently associated with common diseases such as type 2 diabetes, Alzheimer's disease, and Parkinson's disease. There are many types of amyloid proteins, and some proteins that form amyloid aggregates when in a misfolded state. It is difficult to identify such amyloid proteins and their pathogenic properties, but a new and effective approach is by developing effective bioinformatics tools. While several machine learning (ML)-based models for in silico identification of amyloid proteins have been proposed, their predictive performance is limited. In this study, we present AMYPred-FRL, a novel meta-predictor that uses a feature representation learning approach to achieve more accurate amyloid protein identification. AMYPred-FRL combined six well-known ML algorithms (extremely randomized tree, extreme gradient boosting, k-nearest neighbor, logistic regression, random forest, and support vector machine) with ten different sequence-based feature descriptors to generate 60 probabilistic features (PFs), as opposed to state-of-the-art methods developed by a single feature-based approach. A logistic regression recursive feature elimination (LR-RFE) method was used to find the optimal m number of 60 PFs in order to improve the predictive performance. Finally, using the meta-predictor approach, the 20 selected PFs were fed into a logistic regression method to create the final hybrid model (AMYPred-FRL). Both cross-validation and independent tests showed that AMYPred-FRL achieved superior predictive performance than its constituent baseline models. In an extensive independent test, AMYPred-FRL outperformed the existing methods by 5.5% and 16.1%, respectively, with accuracy and MCC of 0.873 and 0.710. To expedite high-throughput prediction, a user-friendly web server of AMYPred-FRL is freely available at http://pmlabstack.pythonanywhere.com/AMYPred-FRL. It is anticipated that AMYPred-FRL will be a useful tool in helping researchers to identify new amyloid proteins.

Structural and functional insights into the DNA damage-inducible protein 1 (Ddi1) from protozoa

Ddi1 is a multidomain protein that belongs to the ubiquitin receptor family of proteins. The Ddi1 proteins contain a highly conserved retroviral protease (RVP)-like domain along with other domains. The severity of opportunistic infections, caused by parasitic protozoa in AIDS patients, was found to decline when HIV protease inhibitors were used in antiretroviral therapy. Parasite growth was shown to be suppressed by a few of the inhibitors targeting Ddi1 present in these parasites. In this study, the binding of HIV protease inhibitors to the RVP domain of Ddi1 from Toxoplasma gondii and Cryptosporidium hominis; and the binding of ubiquitin to the ubiquitin-associated domain of Ddi1 from these two parasites were established using Biolayer Interferometry. The crystal structures of the RVP domains of Ddi1 from T. gondii and C. hominis were determined; they form homodimers similar to those observed in HIV protease and the reported structures of the same domain from Saccharomyces cerevisiae, Leishmania major and humans. The native form of the domain showed an open dimeric structure and a normal mode analysis revealed that it can take up a closed conformation resulting from relative movements of the subunits. Based on the crystal structure of the RVP domain of Ddi1 from L. major, a seven residue peptide inhibitor was designed and it was shown to bind to the RVP domain of Ddi1 from L. major by Biolayer Interferometry. This peptide was modified using computational methods and was shown to have a better affinity than the initial peptide.

•

Crystal structures of the retroviral protease (RVP)-like domains of DNA damage inducible protein 1 (Ddi1) from Toxoplasma gondii and Cryptosporidium hominis have been determined.

•

ToxoDdi1-RVP and CrypDdi1-RVP crystal structures illustrate the conserved structural features with HIV protease.

•

HIV protease inhibitors were found to bind to ToxoDdi1-RVP and CrypDdi1-RVP in solution showing the potential to be developed as drugs for protozoal diseases.

Proteome-wide landscape of solubility limits in a bacterial cell

Proteins are prone to aggregate when expressed above their solubility limits. Aggregation may occur rapidly, potentially as early as proteins emerge from the ribosome, or slowly, following synthesis. However, in vivo data on aggregation rates are scarce. Here, we classified the Escherichia coli proteome into rapidly and slowly aggregating proteins using an in vivo image-based screen coupled with machine learning. We find that the majority (70%) of cytosolic proteins that become insoluble upon overexpression have relatively low rates of aggregation and are unlikely to aggregate co-translationally. Remarkably, such proteins exhibit higher folding rates compared to rapidly aggregating proteins, potentially implying that they aggregate after reaching their folded states. Furthermore, we find that a substantial fraction (~ 35%) of the proteome remain soluble at concentrations much higher than those found naturally, indicating a large margin of safety to tolerate gene expression changes. We show that high disorder content and low surface stickiness are major determinants of high solubility and are favored in abundant bacterial proteins. Overall, our study provides a global view of aggregation rates and hence solubility limits of proteins in a bacterial cell.

Improved Stability of Human CGI-58 Induced by Phosphomimetic S237E Mutation

In lipolysis, the activating function of CGI-58 is regulated by its interaction with perilipin 1 (PLIN1) localized on the lipid droplet (LD), and its release is controlled by phosphorylation. Once lipolysis is stimulated by catecholamines, protein kinase A (PKA)-mediated phosphorylation enables the dissociation of the CGI-58/PLIN1 complex, thereby recruiting adipose triglyceride lipase (ATGL) and hormone-sensitive lipase (HSL) to initiate fatty acid release. It has been shown that mouse CGI-58 mutant S239E, which mimics the phosphorylation of this residue, is able to dissociate from the CGI-58/PLIN1 complex and activate ATGL. Here, we analyze the stabilizing effect on human CGI-58 of a triple tryptophan to alanine mutant (3WA) on the LD-binding motif, as well as a quadruple mutant in which the phosphomimetic S237E substitution was introduced to the 3WA construct (3WA/S237E). We found that tryptophan residues promote wild-type (WT) protein aggregation in solution since their substitution for alanine residues favors the presence of the monomer. Our experimental data showed increased thermal stability and solubility of 3WA/S237E protein compared to the 3WA mutant. Moreover, the 3WA/S237E protein showed proper folding and a functional binding site for oleoyl-CoA. The analysis of a bioinformatic three-dimensional (3D) model suggests an intramolecular interaction between the phosphomimetic glutamic acid and a residue of the α/β hydrolase core. This could explain the increased solubility and stability observed in the 3WA/S237E mutant and evidences the possible role of serine 237 phosphorylation.

Isolation and characterisation of milk-derived amyloid-like protein aggregates (MAPA) from cottage cheese

Cottage cheese, extensively consumed worldwide, contains coagulated milk protein (casein), produced through boiling and acidification of milk. Casein forms amyloid or amyloid-like structures at high temperatures and low pH. Due to the similarities in the preparation of casein amyloids and cottage cheese, we hypothesized the presence of amyloid or amyloid-like protein aggregates in cottage cheese. To examine this hypothesis, cottage cheese was prepared from cow (Bos indicus) milk and isolated amyloids through a water extraction method. The isolated protein aggregates displayed typical characteristics of amyloids, such as a bathochromic shift in the wavelength of maximum absorption (λ_max) of Congo red (CR), high thioflavin T (ThT) binding, increased surface hydrophobicity, and high β-sheet structure. However, they did not show antibacterial activity and toxic properties against erythrocytes. Our study revealed that the heat-treatment and subsequent acidification during cottage cheese preparation lead to the formation of non-toxic amyloid-like aggregates.

Molecular Docking, Dynamics, and WaterSwap Analysis to Identify Anti-aggregating Agents of Insulin and IFN-β

There are several challenges in the development, and formulation of biologics, particularly concerning their physical stabilities. The self-assembly of peptides like human insulin and interferon beta (IFN-β) has potential to form aggregates in pharmaceutical formulation. Therefore, it is a significant problem in the manufacturing, storage, and delivery of insulin and IFN-β formulations. Amino acids as aggregation suppressing additives have been used to stabilize proteins during manufacturing and storage. Several changes to the B chain's C-terminus have been proposed in an attempt to improve insulin formulation. The core segments of the A and B chains (SLYQLENY and LVEALYLV) have recently been identified as sheet-forming areas, and their microcrystalline structures have been exploited to construct a high-resolution insulin amyloid fibril model. Here, we have chosen twenty-one amino acids to develop as additives in rendering the insulin and IFN-β aggregations. Thereafter, integrated molecular docking studies of single layer monomers of full-length insulin and IFN-β have been performed to identify structural elements (amino acids) that can act as disaggregating agents. The stability of the best-docked amino acid complexes was judged using molecular dynamics studies. Finally, phenylalanine was identified as a disaggregation agent for insulin, and lysine, tyrosine, phenylalanine, and tryptophan were identified as disaggregation agents for IFN-β from the molecular dynamics study. These findings may open a novel proposal to explore further in vitro studies to increase the stability of the insulin and IFN-β formulation.

Contiguously hydrophobic sequences are functionally significant throughout the human exome

SignificanceProteins rely on the hydrophobic effect to maintain structure and interactions with the environment. Surprisingly, natural selection on amino acid hydrophobicity has not been detected using modern genetic data. Analyses that treat each amino acid separately do not reveal significant results, which we confirm here. However, because the hydrophobic effect becomes more powerful as more hydrophobic molecules are introduced, we tested whether unbroken stretches of hydrophobic amino acids are under selection. Using genetic variant data from across the human genome, we find evidence that selection increases with the length of the unbroken hydrophobic sequence. These results could lead to improvements in a wide range of genomic tools as well as insights into protein-aggregation disease etiology and protein evolutionary history.

Computational models for studying physical instabilities in high concentration biotherapeutic formulations

Computational prediction of the behavior of concentrated protein solutions is particularly advantageous in early development stages of biotherapeutics when material availability is limited and a large set of formulation conditions needs to be explored. This review provides an overview of the different computational paradigms that have been successfully used in modeling undesirable physical behaviors of protein solutions with a particular emphasis on high-concentration drug formulations. This includes models ranging from all-atom simulations, coarse-grained representations to macro-scale mathematical descriptions used to study physical instability phenomena of protein solutions such as aggregation, elevated viscosity, and phase separation. These models are compared and summarized in the context of the physical processes and their underlying assumptions and limitations. A detailed analysis is also given for identifying protein interaction processes that are explicitly or implicitly considered in the different modeling approaches and particularly their relations to various formulation parameters. Lastly, many of the shortcomings of existing computational models are discussed, providing perspectives and possible directions toward an efficient computational framework for designing effective protein formulations.

A Descriptor set for Quantitative Structure-Property Relationship Prediction in Biologics

In addition to attaining the desired binding to their targets, a crucial aspect in the development of biotherapeutics is 'developability', which includes several desirable properties such as high solubility, low viscosity and aggregation, physico-chemical stability and low immunogenicity. The lack of any of these properties can lead to significant obstacles in advancing them to clinic; thus in silico methods capable of raising warning flags in earlier stages of development are highly beneficial. We have developed a computational framework based on a large and diverse set of protein specific descriptors ideal for making liability predictions using a machine-learning approach. This set offers a high degree of feature diversity classifiable by sequence, structure and surface patches. We assess the sensitivity and applicability of these descriptors in four dedicated case studies that are believed to be representative of biophysical characterizations commonly employed during the development process. In addition to data sets obtained from public sources, we have validated the descriptors on novel experimental data sets in order to address antibody developability and to generate prospective predictions on Adnectins. The results demonstrate that the descriptors are well suited to assist in the improvement of properties of systems that exhibit poor solubility or aggregation.

Binary Matrix Method to Enumerate, Hierarchically Order, and Structurally Classify Peptide Aggregation

Protein aggregation is a common and complex phenomenon in biological processes, yet a robust analysis of this aggregation process remains elusive. The commonly used methods such as center-of-mass to center-of-mass (COM-COM) distance, the radius of gyration (R_g), hydrogen bonding (HB), and solvent accessible surface area do not quantify the aggregation accurately. Herein, a new and robust method that uses an aggregation matrix (AM) approach to investigate peptide aggregation in a MD simulation trajectory is presented. An nxn two-dimensional AM is created by using the interpeptide C_α-C_α cutoff distances, which are binarily encoded (0 or 1). These aggregation matrices are analyzed to enumerate, hierarchically order, and structurally classify the aggregates. Comparison of the present AM method suggests that it is superior to the HB method since it can incorporate nonspecific interactions and the R_g and COM-COM methods since the cutoff distance is independent of the length of the peptide. More importantly, the present method can structurally classify the peptide aggregates, which the conventional R_g, COM-COM, and HB methods fail to do. The unique selling point of this method is its ability to structurally classify peptide aggregates using two-dimensional matrices.

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.

Determination of the Most Stable Packing of Peptides from Ribosomal S1 Protein, Protein Bgl2p, and Aβ peptide in β-layers During Molecular Dynamics Simulations

Our task was to determine the most stable packing of peptides in β-layers to construct an oligomer structure for fibril growth. The β-layers consisting of eight short peptides with the amino acid sequences IVRGVVVAID, VDSWNVLVAG (VESWNVLVAG), KLVFFAEDVG, and IIGLMVGGVV were built. These sequences correspond to the amyloidogenic regions of ribosomal S1 protein from E. coli, protein glucantransferase Bgl2p from the yeast cell wall, and Aβ peptide. First, the amyloidogenic regions were predicted theoretically, and then were confirmed experimentally. Four β-layers with different orientation of the peptides in the layers and the layers relative to each other were constructed. To determine the most stable packing of β-strands, the molecular dynamic (MD) simulations in explicit water were carried out. Two charge states (pH3 and pH5) for each β-layer were considered. The fraction of the secondary structure was a measure of stability for β-layers. β-Layers, in which β-strands are antiparallel relative to each other, were the most stable. Using this packing for β-strands, we constructed the oligomer structures and also checked their stability by using MD simulations.

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.

Dudás E, Fantini M, Puglisi R, Louka A, Marcatili P, Cattaneo A, Pastore A. Selection and Modelling of a New Single-Domain Intrabody Against TDP-43

Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disorder associated to deteriorating motor and cognitive functions, and short survival. The disease is caused by neuronal death which results in progressive muscle wasting and weakness, ultimately leading to lethal respiratory failure. The misbehaviour of a specific protein, TDP-43, which aggregates and becomes toxic in ALS patient’s neurons, is supposed to be one of the causes. TDP-43 is a DNA/RNA-binding protein involved in several functions related to nucleic acid metabolism. Sequestration of TDP-43 aggregates is a possible therapeutic strategy that could alleviate or block pathology. Here, we describe the selection and characterization of a new intracellular antibody (intrabody) against TDP-43 from a llama nanobody library. The structure of the selected intrabody was predicted in silico and the model was used to suggest mutations that enabled to improve its expression yield, facilitating its experimental validation. We showed how coupling experimental methodologies with in silico design may allow us to obtain an antibody able to recognize the RNA binding regions of TDP-43. Our findings illustrate a strategy for the mitigation of TDP-43 proteinopathy in ALS and provide a potential new tool for diagnostics.

Electrostatically Driven Protein-Protein Interactions: Quantitative Prediction of Second Osmotic Virial Coefficients to Aid Antibody Design

Electrostatically driven attractions between proteins can result in issues for therapeutic protein formulations such as solubility limits, aggregation, and high solution viscosity. Previous work showed that a model monoclonal antibody displayed large and potentially problematic electrostatically driven attractions at typical pH (5-8) and ionic strength conditions (∼10-100 mM). Molecular simulations of a hybrid coarse-grained model (1bC/D, one bead per charged site and per domain) were used to predict potential point mutations to identify key charge changes (charge-to-neutral or charge-swap) that could greatly reduce the net attractive protein-protein self-interactions. A series of variants were tested experimentally with static and dynamic light scattering to quantify interactions and compared to model predictions at low and intermediate ionic strength. Differential scanning calorimetry and circular dichroism confirmed minimal impact on structural or thermal stability of the variants. The model provided quantitative/semiquantitative predictions of protein self-interactions compared to experimental results as well as showed which amino acid pairings or groups had the most impact.

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

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