Impact of single amino acid substitution on the structure and function of TANK-binding kinase-1

Tank-binding kinase 1 (TBK1) is a serine/threonine protein kinase involved in various signaling pathways and subsequently regulates cell proliferation, apoptosis, autophagy, antiviral and antitumor immunity. Dysfunction of TBK1 can cause many complex diseases, including autoimmunity, neurodegeneration, and cancer. This dysfunction of TBK1 may result from single amino acid substitutions and subsequent structural alterations. This study analyzed the effect of substituting amino acids on TBK1 structure, function, and subsequent disease using advanced computational methods and various tools. In the initial assessment, a total of 467 mutations were found to be deleterious. After that, in detailed structural and sequential analyses, 13 mutations were found to be pathogenic. Finally, based on the functional importance, two variants (K38D and S172A) of the TBK1 kinase domain were selected and studied in detail by utilizing all-atom molecular dynamics (MD) simulation for 200 ns. MD simulation, including correlation matrix and principal component analysis, helps to get deeper insights into the TBK1 structure at the atomic level. We observed a substantial change in variants' conformation, which may be possible for structural alteration and subsequent TBK1 dysfunction. However, substitution S172A shows a significant conformational change in TBK1 structure as compared to K38D. Thus, this study provides a structural basis to understand the effect of mutations on the kinase domain of TBK1 and its function associated with disease progression.

Identification of Amyloidogenic Regions in Pseudomonas aeruginosa Ribosomal S1 Protein

Bacterial S1 protein is a functionally important ribosomal protein. It is a part of the 30S ribosomal subunit and is also able to interact with mRNA and tmRNA. An important feature of the S1 protein family is a strong tendency towards aggregation. To study the amyloidogenic properties of S1, we isolated and purified the recombinant ribosomal S1 protein of Pseudomonas aeruginosa. Using the FoldAmyloid, Waltz, Pasta 2.0, and AGGRESCAN programs, amyloidogenic regions of the protein were predicted, which play a key role in its aggregation. The method of limited proteolysis in combination with high performance liquid chromatography and mass spectrometric analysis of the products, made it possible to identify regions of the S1 protein from P. aeruginosa that are protected from the action of proteinase K, trypsin, and chymotrypsin. Sequences of theoretically predicted and experimentally identified amyloidogenic regions were used to synthesize four peptides, three of which demonstrated the ability to form amyloid-like fibrils, as shown by electron microscopy and fluorescence spectroscopy. The identified amyloidogenic sites can further serve as a basis for the development of new antibacterial peptides against the pathogenic microorganism P. aeruginosa.

Exploring the sequence features determining amyloidosis in human antibody light chains

The light chain (AL) amyloidosis is caused by the aggregation of light chain of antibodies into amyloid fibrils. There are plenty of computational resources available for the prediction of short aggregation-prone regions within proteins. However, it is still a challenging task to predict the amyloidogenic nature of the whole protein using sequence/structure information. In the case of antibody light chains, common architecture and known binding sites can provide vital information for the prediction of amyloidogenicity at physiological conditions. Here, in this work, we have compared classical sequence-based, aggregation-related features (such as hydrophobicity, presence of gatekeeper residues, disorderness, β-propensity, etc.) calculated for the CDR, FR or V_L regions of amyloidogenic and non-amyloidogenic antibody light chains and implemented the insights gained in a machine learning-based webserver called "V_LAmY-Pred" ( https://web.iitm.ac.in/bioinfo2/vlamy-pred/ ). The model shows prediction accuracy of 79.7% (sensitivity: 78.7% and specificity: 79.9%) with a ROC value of 0.88 on a dataset of 1828 variable region sequences of the antibody light chains. This model will be helpful towards improved prognosis for patients that may likely suffer from diseases caused by light chain amyloidosis, understanding origins of aggregation in antibody-based biotherapeutics, large-scale in-silico analysis of antibody sequences generated by next generation sequencing, and finally towards rational engineering of aggregation resistant antibodies.

Computational Methods and Tools in Antimicrobial Peptide Research

The evolution of antibiotic-resistant bacteria is an ongoing and troubling development that has increased the number of diseases and infections that risk going untreated. There is an urgent need to develop alternative strategies and treatments to address this issue. One class of molecules that is attracting significant interest is that of antimicrobial peptides (AMPs). Their design and development has been aided considerably by the applications of molecular models, and we review these here. These methods include the use of tools to explore the relationships between their structures, dynamics, and functions and the increasing application of machine learning and molecular dynamics simulations. This review compiles resources such as AMP databases, AMP-related web servers, and commonly used techniques, together aimed at aiding researchers in the area toward complementing experimental studies with computational approaches.

[Antibacterial effects of peptides synthesized based on the sequence of ribosome protein S1]

Antibiotic resistance of bacteria is a topical problem on a global scale. Sometimes vigorous human activity leads to an increase in the number of bacteria carrying resistance genes in the environment. Antimicrobial peptides (AMPs) and similar compounds are potential candidates for combating antibiotic-resistant bacteria. Previously, we proposed and successfully tested on Thermus thermophilus a new mechanism of AMP action. This mechanism of directed coaggregation is based on the interaction of a peptide capable of forming fibrils with a target protein. In this work, we discuss the criteria for choosing a target for the targeted action of AMP, describe the features of the "parental" S1 ribosomal proteins T. thermophilus and Escherichia coli and the studied peptides using bioinformatic analysis methods, assess the antimicrobial effect of the synthesized peptides on a model organism of E. coli and cytotoxicity on cells of human fibroblasts. The obtained results will be important for the creation of new AMPs for pathogenic organisms.

AbsoluRATE: An in-silico method to predict the aggregation kinetics of native proteins

Protein aggregation has two aspects, namely, mechanistic and kinetics. Understanding protein aggregation kinetics is critical for prediction of progression of diseases caused by amyloidosis, accumulation of aggregates in biotherapeutics during storage and engineering commercial nano-biomaterials. In this work, we have collected experimentally determined absolute protein aggregation rates and developed an SVM based regression model to predict absolute rates of protein and peptide aggregation near-physiological conditions. The regression model achieved a correlation coefficient of 0.72 with MAE of 0.91 (natural log of k_app, where k_app is in hour^-1) using leave-one-out cross-validation on a dataset of 82 non-redundant proteins/peptides. The model accounts for the experimental conditions (such as temperature, pH, ionic and protein concentration) and sequence-based properties. The amino acid sequence features revealed by this model as being important for aggregation kinetics, are also associated with the aggregation mechanism. In particular, inherent aggregation propensity of the protein/peptide sequence and number of aggregation prone regions (APRs) unpunctuated by the gatekeeping residues, were found to play important roles in the prediction of the absolute aggregation rates. This analysis shows that mechanism and kinetics of protein aggregation are coupled via common sequence attributes. The aggregation kinetic prediction method developed in this work is available at https://web.iitm.ac.in/bioinfo2/absolurate-pred/index.html.

Zika virus capsid anchor forms cytotoxic amyloid-like fibrils

Capsid-anchor (CA) of Zika virus (ZIKV) is a small, single-pass transmembrane sequence that separates the capsid (C) protein from downstream pre-membrane (PrM) protein. During polyprotein processing, CA is cleaved-off from C and PrM and left as a membrane-embedded peptide. CA plays an essential role in the assembly and maturation of the virus. However, its independent folding behavior is still unknown. Therefore, in this study, we investigated the amyloid-forming propensity of CA at physiological conditions. We observed the aggregation behavior of CA peptide using dye-binding assays and ThT kinetics. The morphological analysis of CA aggregates explored by high-resolution microscopy (TEM, AFM) and Far-UV CD spectroscopy revealed characteristic amyloid-like fibrils rich in β-sheet secondary structure. Further, the effect on mammalian cells exhibited the cytotoxic nature of the CA amyloid-fibrils. Our findings collectively shed light on the amyloidogenic phenomenon of flaviviral protein, which may contribute to their infection.

Variability of Amyloid Propensity in Imperfect Repeats of CsgA Protein of Salmonella enterica and Escherichia coli

CsgA is an aggregating protein from bacterial biofilms, representing a class of functional amyloids. Its amyloid propensity is defined by five fragments (R1–R5) of the sequence, representing non-perfect repeats. Gate-keeper amino acid residues, specific to each fragment, define the fragment’s propensity for self-aggregation and aggregating characteristics of the whole protein. We study the self-aggregation and secondary structures of the repeat fragments of Salmonella enterica and Escherichia coli and comparatively analyze their potential effects on these proteins in a bacterial biofilm. Using bioinformatics predictors, ATR-FTIR and FT-Raman spectroscopy techniques, circular dichroism, and transmission electron microscopy, we confirmed self-aggregation of R1, R3, R5 fragments, as previously reported for Escherichia coli, however, with different temporal characteristics for each species. We also observed aggregation propensities of R4 fragment of Salmonella enterica that is different than that of Escherichia coli. Our studies showed that amyloid structures of CsgA repeats are more easily formed and more durable in Salmonella enterica than those in Escherichia coli.

Comparison of the pH- and thermally-induced fluctuations of a therapeutic antibody Fab fragment by molecular dynamics simulation

Successful development of protein therapeutics depends critically on achieving stability under a range of conditions. A deeper understanding of the drivers of instability across different stress conditions, will enable the engineering of more robust protein scaffolds. We compared the impacts of low pH and high temperature stresses on the structure of a humanized antibody fragment (Fab) A33, using atomistic molecular dynamics simulations, using a recent 2.5 Å crystal structure. This revealed that low-pH induced the loss of native contacts in the domain C_L. By contrast, thermal stress led to 5-7% loss of native contacts in all four domains, and simultaneous loss of >30% of native contacts in the V_L-V_H and C_L-C_H interfaces. This revealed divergent destabilising pathways under the two different stresses. The underlying cause of instability was probed using FoldX and Rosetta mutation analysis, and packing density calculations. These agreed that mutations in the C_L domain, and C_L-C_H1 interface have the greatest potential for stabilisation of Fab A33. Several key salt bridge losses underpinned the conformational change in C_L at low pH, whereas at high temperature, salt bridges became more dynamic, thus contributing to an overall destabilization. Lastly, the unfolding events at the two stress conditions exposed different predicted aggregation-prone regions (APR) to solvent, which would potentially lead to different aggregation mechanisms. Overall, our results identified the early stages of unfolding and stability-limiting regions of Fab A33, and the V_H and C_L domains as interesting future targets for engineering stability to both pH- and thermal-stresses simultaneously.

MILAMP: Multiple Instance Prediction of Amyloid Proteins

Amyloid proteins are implicated in several diseases such as Parkinson's, Alzheimer's, prion diseases, etc. In order to characterize the amyloidogenicity of a given protein, it is important to locate the amyloid forming hotspot regions within the protein as well as to analyze the effects of mutations on these proteins. The biochemical and biological assays used for this purpose can be facilitated by computational means. This paper presents a machine learning method that can predict hotspot amyloidogenic regions within proteins and characterize changes in their amyloidogenicity due to point mutations. The proposed method called MILAMP (Multiple Instance Learning of AMyloid Proteins) achieves high accuracy for identification of amyloid proteins, hotspot localization, and prediction of mutation effects on amyloidogenicity by integrating heterogenous data sources and exploiting common predictive patterns across these tasks through multiple instance learning. The paper presents comprehensive benchmarking experiments to test the predictive performance of MILAMP in comparison to previously published state of the art techniques for amyloid prediction. The python code for the implementation and webserver for MILAMP is available at the URL: http://faculty.pieas.edu.pk/fayyaz/software.html#MILAMP.

Searching for universal model of amyloid signaling motifs using probabilistic context-free grammars

Background

Amyloid signaling motifs are a class of protein motifs which share basic structural and functional features despite the lack of clear sequence homology. They are hard to detect in large sequence databases either with the alignment-based profile methods (due to short length and diversity) or with generic amyloid- and prion-finding tools (due to insufficient discriminative power). We propose to address the challenge with a machine learning grammatical model capable of generalizing over diverse collections of unaligned yet related motifs.

Results

First, we introduce and test improvements to our probabilistic context-free grammar framework for protein sequences that allow for inferring more sophisticated models achieving high sensitivity at low false positive rates. Then, we infer universal grammars for a collection of recently identified bacterial amyloid signaling motifs and demonstrate that the method is capable of generalizing by successfully searching for related motifs in fungi. The results are compared to available alternative methods. Finally, we conduct spectroscopy and staining analyses of selected peptides to verify their structural and functional relationship.

Conclusions

While the profile HMMs remain the method of choice for modeling homologous sets of sequences, PCFGs seem more suitable for building meta-family descriptors and extrapolating beyond the seed sample.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-021-04139-y.

Bioinformatics methods for identification of amyloidogenic peptides show robustness to misannotated training data

Several disorders are related to amyloid aggregation of proteins, for example Alzheimer's or Parkinson's diseases. Amyloid proteins form fibrils of aggregated beta structures. This is preceded by formation of oligomers-the most cytotoxic species. Determining amyloidogenicity is tedious and costly. The most reliable identification of amyloids is obtained with high resolution microscopies, such as electron microscopy or atomic force microscopy (AFM). More frequently, less expensive and faster methods are used, especially infrared (IR) spectroscopy or Thioflavin T staining. Different experimental methods are not always concurrent, especially when amyloid peptides do not readily form fibrils but oligomers. This may lead to peptide misclassification and mislabeling. Several bioinformatics methods have been proposed for in-silico identification of amyloids, many of them based on machine learning. The effectiveness of these methods heavily depends on accurate annotation of the reference training data obtained from in-vitro experiments. We study how robust are bioinformatics methods to weak supervision, encountering imperfect training data. AmyloGram and three other amyloid predictors were applied. The results proved that a certain degree of misannotation in the reference data can be eliminated by the bioinformatics tools, even if they belonged to their training set. The computational results are supported by new experiments with IR and AFM methods.

Inhibition of Amyloid Fibrillation of HEWL by 4-Methylcoumarin and 4-Methylthiocoumarin Derivatives

BACKGROUND

Several human diseases like Parkinson's, Alzheimer's disease, and systemic amyloidosis are associated with the misfolding and aggregation of protein molecules.

OBJECTIVE

The present study demonstrated the comparison of 4-methyl coumarin and 4-methylthiocoumarin derivative for their anti-amyloidogenic and disaggregation activities. The hen egg-white lysozyme is used as a model system to study protein aggregation and disaggregation under in vitro conditions.

METHODS

Techniques used in the study were Thioflavin T fluorescence assay, intrinsic fluorescence assay, circular dichroism, transmission electron microscopy, and molecular dynamics.

RESULTS

Fifteen compounds were screened for their anti-amyloidogenic and disaggregation potential. Six compounds significantly inhibited the fibril formation, whereas ten compounds showed disaggregation property of pre-formed fibrils. Under in vitro conditions, the compound C3 and C7 showed significant inhibition of fibril formation in a concentration-dependent manner as compared to control. C3 and C7 demonstrated 93% and 76% inhibition of fibril formation, respectively. Furthermore, C3 and C7 exhibited 83% and 76% disaggregation activity, respectively, of pre-formed HEWL fibrils at their highest concentration. These anti-amyloidogenic and disaggregation potential of C3 and C7 were validated by intrinsic fluorescence, CD, molecular dynamics, and TEM study.

DISCUSSION

4-methylthiocoumarins derivatives have shown better anti-amyloidogenic activity as compared to 4-methylcoumarin derivatives for both amyloid formation as well as disaggregation of preformed amyloid fibrils. Structurally, the derivatives of 4-methylthiocoumarins (C3 and C7) contain thio group on 2nd position that might be responsible for anti-amyloidogenic activity as compared to 4- methylcoumarin derivatives (C2 and C4).

CONCLUSION

C3 and C7 are novel 4-methylthiocoumarin derivatives that can be used as a lead for alleviation and symptoms associated with protein aggregation disorders.

Prediction and analysis of multiple protein lysine modified sites based on conditional wasserstein generative adversarial networks

BACKGROUND

Protein post-translational modification (PTM) is a key issue to investigate the mechanism of protein's function. With the rapid development of proteomics technology, a large amount of protein sequence data has been generated, which highlights the importance of the in-depth study and analysis of PTMs in proteins.

METHOD

We proposed a new multi-classification machine learning pipeline MultiLyGAN to identity seven types of lysine modified sites. Using eight different sequential and five structural construction methods, 1497 valid features were remained after the filtering by Pearson correlation coefficient. To solve the data imbalance problem, Conditional Generative Adversarial Network (CGAN) and Conditional Wasserstein Generative Adversarial Network (CWGAN), two influential deep generative methods were leveraged and compared to generate new samples for the types with fewer samples. Finally, random forest algorithm was utilized to predict seven categories.

RESULTS

In the tenfold cross-validation, accuracy (Acc) and Matthews correlation coefficient (MCC) were 0.8589 and 0.8376, respectively. In the independent test, Acc and MCC were 0.8549 and 0.8330, respectively. The results indicated that CWGAN better solved the existing data imbalance and stabilized the training error. Alternatively, an accumulated feature importance analysis reported that CKSAAP, PWM and structural features were the three most important feature-encoding schemes. MultiLyGAN can be found at https://github.com/Lab-Xu/MultiLyGAN .

CONCLUSIONS

The CWGAN greatly improved the predictive performance in all experiments. Features derived from CKSAAP, PWM and structure schemes are the most informative and had the greatest contribution to the prediction of PTM.

MAPRE2 mutations result in altered human cranial neural crest migration, underlying craniofacial malformations in CSC-KT syndrome

Circumferential skin creases (CSC-KT) is a rare polymalformative syndrome characterised by intellectual disability associated with skin creases on the limbs, and very characteristic craniofacial malformations. Previously, heterozygous and homozygous mutations in MAPRE2 were found to be causal for this disease. MAPRE2 encodes for a member of evolutionary conserved microtubule plus end tracking proteins, the end binding (EB) family. Unlike MAPRE1 and MAPRE3, MAPRE2 is not required for the persistent growth and stabilization of microtubules, but plays a role in other cellular processes such as mitotic progression and regulation of cell adhesion. The mutations identified in MAPRE2 all reside within the calponin homology domain, responsible to track and interact with the plus-end tip of growing microtubules, and previous data showed that altered dosage of MAPRE2 resulted in abnormal branchial arch patterning in zebrafish. In this study, we developed patient derived induced pluripotent stem cell lines for MAPRE2, together with isogenic controls, using CRISPR/Cas9 technology, and differentiated them towards neural crest cells with cranial identity. We show that changes in MAPRE2 lead to alterations in neural crest migration in vitro but also in vivo, following xenotransplantation of neural crest progenitors into developing chicken embryos. In addition, we provide evidence that changes in focal adhesion might underlie the altered cell motility of the MAPRE2 mutant cranial neural crest cells. Our data provide evidence that MAPRE2 is involved in cellular migration of cranial neural crest and offers critical insights into the mechanism underlying the craniofacial dysmorphisms and cleft palate present in CSC-KT patients. This adds the CSC-KT disorder to the growing list of neurocristopathies.

Aggregation hot spots in the SARS-CoV-2 proteome may constitute potential therapeutic targets for the suppression of the viral replication and multiplication

The emergence of novel coronavirus SARS-CoV-2 is responsible for causing coronavirus disease-19 (COVID-19) imposing serious threat to global public health. Infection of SARS-CoV-2 to the host cell is characterized by direct translation of positive single stranded (+ ss) RNA to form large polyprotein polymerase 1ab (pp1ab), which acts as precursor for a number of nonstructural and structural proteins that play vital roles in replication of viral genome and biosynthesis of new virus particles. The maintenance of viral protein homeostasis is essential for continuation of viral life cycle in the host cell. To test whether the protein homeostasis of SARS-CoV-2 can be disrupted by inducing specific protein aggregation, we made an effort to examine whether the viral proteome contains any aggregation prone regions (APRs) that can be explored for inducing toxic protein aggregation specifically in viral proteins and without affecting the host cell. This curiosity leads to the identification of several (> 70) potential APRs in SARS-CoV-2 proteome. The length of the APRs ranges from 5 to 25 amino acid residues. Nearly 70% of total APRs investigated are relatively smaller and found to be in the range of 5–10 amino acids. The maximum number of ARPs (> 50) was observed in pp1ab. On the other hand, the structural proteins such as, spike (S), nucleoprotein (N), membrane (M) and envelope (E) proteins also possess APRs in their primary structures which altogether constitute 30% of the total APRs identified. Our findings may provide new windows of opportunities to design specific peptide-based, anti-SARS-CoV-2 therapeutic molecules against COVID-19.

A theoretical study of polymorphism in VQIVYK fibrils

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

Cryo-EM reveals structural breaks in a patient-derived amyloid fibril from systemic AL amyloidosis

Systemic AL amyloidosis is a debilitating and potentially fatal disease that arises from the misfolding and fibrillation of immunoglobulin light chains (LCs). The disease is patient-specific with essentially each patient possessing a unique LC sequence. In this study, we present two ex vivo fibril structures of a λ3 LC. The fibrils were extracted from the explanted heart of a patient (FOR005) and consist of 115-residue fibril proteins, mainly from the LC variable domain. The fibril structures imply that a 180° rotation around the disulfide bond and a major unfolding step are necessary for fibrils to form. The two fibril structures show highly similar fibril protein folds, differing in only a 12-residue segment. Remarkably, the two structures do not represent separate fibril morphologies, as they can co-exist at different z-axial positions within the same fibril. Our data imply the presence of structural breaks at the interface of the two structural forms.

Polyphenol binding disassembles glycation-modified bovine serum albumin amyloid fibrils

Glycation of protein results in the formation of advanced glycation end-products (AGEs) and leads to deposition as amyloid fibrils. Adhesive structural properties of polyphenols to aromatic amino acids draw significance in promoting, accelerating and/or stabilizing on-pathway and off-pathway folding intermediates, although the mechanistic action remains unclear. In this study, polyphenols remodeling mature AGEs modified amyloid fibrils were investigated through UV-visible spectroscopy, fluorescence spectroscopy, transmission electron microscopy, atomic force microscopy, circular dichroism spectroscopy, MALDI-MS/MS analysis and molecular docking studies. Our findings confirmed the glycation-mediated transformation of native protein into β-sheet rich amyloid fibrils. SDS-PAGE results suggested the presence of shorter peptide fragments ranging from ~10 kDa to ~40 kDa. MALDI-MS/MS results identified the plausible sequences to be His105-His181, Arg193-Lys242, Leu325-Tyr410, and Ala451-Tyr529. TEM and AFM results suggested that polyphenols binding mature amyloid fibrils remodel/disassemble them into distinct aggregate structures or non-amyloid fibrils. Circular dichroism studies suggested that polyphenols upon binding amyloid fibrils stabilizes and transforms the secondary structure towards helical or random coil-like conformation. Molecular modeling studies suggested high binding affinity and hydrophobic interaction to be the main driving force in remodeling perspective. Together, our findings suggest that polyphenols could differentially remodel mature AGEs-modified amyloid fibrils into distinct aggregate structures through non-covalent interactions and can alleviate AGEs-mediated amyloidosis.

Protein aggregation: in silico algorithms and applications

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

Computational design of enzymes for biotechnological applications

Enzymes are the natural catalysts that execute biochemical reactions upholding life. Their natural effectiveness has been fine-tuned as a result of millions of years of natural evolution. Such catalytic effectiveness has prompted the use of biocatalysts from multiple sources on different applications, including the industrial production of goods (food and beverages, detergents, textile, and pharmaceutics), environmental protection, and biomedical applications. Natural enzymes often need to be improved by protein engineering to optimize their function in non-native environments. Recent technological advances have greatly facilitated this process by providing the experimental approaches of directed evolution or by enabling computer-assisted applications. Directed evolution mimics the natural selection process in a highly accelerated fashion at the expense of arduous laboratory work and economic resources. Theoretical methods provide predictions and represent an attractive complement to such experiments by waiving their inherent costs. Computational techniques can be used to engineer enzymatic reactivity, substrate specificity and ligand binding, access pathways and ligand transport, and global properties like protein stability, solubility, and flexibility. Theoretical approaches can also identify hotspots on the protein sequence for mutagenesis and predict suitable alternatives for selected positions with expected outcomes. This review covers the latest advances in computational methods for enzyme engineering and presents many successful case studies.

Generation and functional characterization of a single-chain variable fragment (scFv) of the anti-FGF2 3F12E7 monoclonal antibody

Single-chain variable fragments (scFvs) are small-sized artificial constructs composed of the immunoglobulin heavy and light chain variable regions connected by a peptide linker. We have previously described an anti-fibroblast growth factor 2 (FGF2) immunoglobulin G (IgG) monoclonal antibody (mAb), named 3F12E7, with notable antitumor potential revealed by preclinical assays. FGF2 is a known angiogenesis-associated molecule implicated in tumor progression. In this report, we describe a recombinant scFv format for the 3F12E7 mAb. The results demonstrate that the generated 3F12E7 scFv, although prone to aggregation, comprises an active anti-FGF2 product that contains monomers and small oligomers. Functionally, the 3F12E7 scFv preparations specifically recognize FGF2 and inhibit tumor growth similar to the corresponding full-length IgG counterpart in an experimental model. In silico molecular analysis provided insights into the aggregation propensity and the antigen-recognition by scFv units. Antigen-binding determinants were predicted outside the most aggregation-prone hotspots. Overall, our experimental and prediction dataset describes an scFv scaffold for the 3F12E7 mAb and also provides insights to further engineer non-aggregated anti-FGF2 scFv-based tools for therapeutic and research purposes.

Bioactive Phytocompounds: Anti-amyloidogenic Effects Against Hen Egg-White Lysozyme Aggregation

Amyloidosis is the process of fibril formation responsible for causing several diseases in the human being that involve protein aggregation such as Alzheimer's, Parkinson's, Huntington's disease, and type II diabetes. Natural phytocompounds such as curcumin shown promising anti-amyloidogenic activity. In the present study, selective phytocompounds such as piperine, cinnamaldehyde, eugenol, and cuminaldehyde present in Piper nigrum L, Cinnamomum zeylanicum Blume, Eugenia caryophyllus Thumb, and Cuminum cyminum L, respectively were analyzed for anti-amyloidogenic activity using hen egg white-lysozyme (HEWL) as a model system. Out of the selected phytocompounds, piperine showed the most significant anti-amyloidogenic activity, as evident from in vitro assays that were validated by in silico molecular docking study. Piperine showed 64.7 ± 3.74% inhibition of amyloid formation at 50 μM concentration, as observed by Thioflavin T assay. Subsequently, the anti-amyloidogenic activity of piperine was further validated by congo red, intrinsic fluorescence assay, and transmission electron microscopy analysis. The in silico molecular binding interaction showed piperine with the highest docking score and glide energy. Piperine was found to be interacting with amyloidogenic region residues and Trp62, the most important residue involved in the amyloidogenesis process. In conclusion, piperine can be used as a positive lead for a potential therapeutic role in targeting diseases involved amyloidogenesis.

Antimicrobial and Amyloidogenic Activity of Peptides. Can Antimicrobial Peptides Be Used against SARS-CoV-2?

At present, much attention is paid to the use of antimicrobial peptides (AMPs) of natural and artificial origin to combat pathogens. AMPs have several points that determine their biological activity. We analyzed the structural properties of AMPs, as well as described their mechanism of action and impact on pathogenic bacteria and viruses. Recently published data on the development of new AMP drugs based on a combination of molecular design and genetic engineering approaches are presented. In this article, we have focused on information on the amyloidogenic properties of AMP. This review examines AMP development strategies from the perspective of the current high prevalence of antibiotic-resistant bacteria, and the potential prospects and challenges of using AMPs against infection caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2).

Effect of urea concentration on instant refolding of Nuclear Export Protein (NEP) from Influenza-A virus H1N1: A solution NMR based investigation

Nuclear-export-protein (NEP) plays multiple-functions during influenza virus replication-cycle and shows unique pattern of conserved residues, which altogether make NEP a potential target for developing novel anti-influenza drugs. However, the mechanistic structural biology of NEP has not been fully characterized so far owing to its tendency to aggregate in solution. As structural information is important to guide rational drug-discovery process; therefore, procedural optimization efforts are going on to achieve properly folded NEP in sub-millimolar concentrations for solution-NMR investigations. As a first step in this direction, the refolding-cum-aggregation behavior of recombinant-NEP with N-terminal purification-tag (referred here as NEPN) at different urea-concentrations has been investigated here by NMR-based methods. Several attempts were made to refold denatured NEP-N through step-dialysis. However, owing to its strong tendency to aggregate, excessive precipitation was observed at sub-higher levels of urea concentration (5.0 ± 1.0 M). Finally, we used drip-dilution method with 10.5 M urea-denatured NEP-N and were able to refold NEP-N instantly. The amide ¹H dispersion of 3.6 ppm (6.6-10.2 ppm) in the ¹⁵N-HSQC-spectra of instantly refolded NEP-N confirmed the folded state. This successful instant-refolding of NEP-N has been reported for the first-time and the underlying mechanism has been rationalized through establishing the complete backbone-resonance-assignments of NEP-N at 9.7 M urea-denatured state.

Synthesis of novel 4-methylthiocoumarin and comparison with conventional coumarin derivative as a multi-target-directed ligand in Alzheimer's disease

Alzheimer's disease (AD) is a multifactorial disorder characterized by cognitive deficit and memory loss. The pathological feature of the disease involves β-amyloid senile plaques, reduced levels of acetylcholine neurotransmitter, oxidative stress and neurofibrillary tangles formation within the brain of AD patients. The present study aims to screen the inhibitory activity of newly synthesized and existing novel 4-methylthiocoumarin derivative against acetylcholinesterase, butyrylcholinesterase, BACE1, β-amyloid aggregation and oxidative stress involved in the AD pathogenesis. The in vitro assays used in this study were Ellman's assay, FRET assays, Thioflavin T, transmission electron microscopy, circular dichroism, FRAP, and TEAC. Molecular docking and dynamics studies were performed to correlate the results. C3 and C7 (thiocoumarin derivatives) were found to be the most potent inhibitors of acetylcholinesterase (IC50-5.63 µM) and butyrylcholinesterase (IC50-3.40 µM) using Ellman's assays. Enzyme kinetic studies showed that C3 and C7 compounds followed by the mixed mode of inhibition using LB plot. C3 also moderately inhibited the BACE1 using FRET assay. C3 inhibited the fibrillization of β-amyloid peptides in a concentration-dependent manner as observed by Thioflavin T, TEM studies and Circular dichroism data. Molecular modeling studies were performed to understand the probable mode of binding of C3 and C7 in the binding pocket of acetylcholinesterase, butyrylcholinesterase, BACE1 and amyloid β peptides. This indicates the important role of hydrophobic interactions between C3 and acetylcholinesterase. C3 also exhibited significant antioxidant potential by FRAP and TEAC assays. Hence, C3 might serve as a promising lead for developing novel multi target-directed ligand for the treatment of AD.

Inhibition of BACE1, MAO-B, cholinesterase enzymes, and anti-amyloidogenic potential of selected natural phytoconstituents: Multi-target-directed ligand approach

Alzheimer's disease (AD) is a neurodegenerative disorder, and multiple factors are involved in disease progression. This is why there is an urgent need to develop novel molecules with multi-target-directed ligands (MTDLs) potential. The current study explores the active phytoconstituents from traditionally used medicinal spices, namely piperine, cinnamaldehyde, eugenol, cuminaldehyde, and alpha-terpinyl acetate for the inhibition of β-secretase, monoamine oxidase, cholinesterase enzymes, anti-aggregation of amyloid β (Aβ) fibrils, and their protective effect against hydrogen peroxide (H₂ O₂ ) and Aβ-induced toxicity. Eugenol showed inhibitory activity against MAO-B enzyme, free radical scavenging activity, and anti-aggregation activity against Aβ peptides than other phytoconstituents. It also demonstrated a significant cytoprotective effect against H₂ O₂ -induced oxidative stress and Aβ-induced cytotoxicity in pheochromocytoma (PC) 12 cells. A molecular docking study of eugenol showed interactions with active site residue of the target enzymes. The study successfully demonstrated that eugenol could have an MTDLs potential better than synthesized drugs used in the treatment of AD. PRACTICAL APPLICATIONS: The present study demonstrated multi-target-directed ligand potential of eugenol and can be developed to treat complex diseases like Alzheimer's. Eugenol can bind to different Alzheimer's targets such as β-secretase (BACE1), Monoamine oxidase B (MAO-B), Cholinesterase's, and amyloid β_1-42 fibrils and might have a disease-modifying potential. The other natural phytoconstituents such as piperine, cinnamaldehyde, cuminaldehyde, and alpha-terpinyl acetate also demonstrated MTDL potential could also be used for developing novel molecules for disease-modifying effect. It also protects against oxidative stress.

Non enzymatic covalent modification by glycolysis end product converts hemoglobin into its oxidative stress potency state

The effect of glycation by Pyruvic acid (PA) on the early and advanced conformational changes in Hemoglobin (Hb) was studied. Multi Spectroscopic measurement revealed that Hb undergoes structural conformational changes and unbound heme upon incubation with PA. These covalent modifications were followed by the reduction of heme centre and these reduction processes initiates its peroxidase-like activity. An extended PA glycation resulted in the appearance of advanced glycation end products fluorescence, with notable changes in compositions of secondary structure. The amyloidogenic state was confirmed by SEM, fluorescence microscope observation. This study reveals an insight to the role of pyruvic acid which increases the oxidative stress due to the heme reduction and diabetic complication.

Amyloid-Like Peptide Aggregates

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

PredAmyl-MLP: Prediction of Amyloid Proteins Using Multilayer Perceptron

Amyloid is generally an aggregate of insoluble fibrin; its abnormal deposition is the pathogenic mechanism of various diseases, such as Alzheimer's disease and type II diabetes. Therefore, accurately identifying amyloid is necessary to understand its role in pathology. We proposed a machine learning-based prediction model called PredAmyl-MLP, which consists of the following three steps: feature extraction, feature selection, and classification. In the step of feature extraction, seven feature extraction algorithms and different combinations of them are investigated, and the combination of SVMProt-188D and tripeptide composition (TPC) is selected according to the experimental results. In the step of feature selection, maximum relevant maximum distance (MRMD) and binomial distribution (BD) are, respectively, used to remove the redundant or noise features, and the appropriate features are selected according to the experimental results. In the step of classification, we employed multilayer perceptron (MLP) to train the prediction model. The 10-fold cross-validation results show that the overall accuracy of PredAmyl-MLP reached 91.59%, and the performance was better than the existing methods.

Predicted aggregation-prone region (APR) in βB1-crystallin forms the amyloid-like structure and induces aggregation of soluble proteins isolated from human cataractous eye lens

The aggregation of β-crystallins in the human eye lens constitutes a critical step during the development of cataract. We anticipated that the presence of Aggregation-Prone Regions (APRs) in their primary structure, which might be responsible for conformational change required for the self-assembly. To examine the presence of APRs, we systematically analyzed the primary structures of β-crystallins. Out of seven subtypes, the βB1-crystallin found to possess the highest aggregation score with 9 APRs in its primary structure. To confirm the amyloidogenic nature of these newly identified APRs, we further studied the aggregation behavior of one of the APRs spanning from 174 to 180 residues (¹⁷⁴LWVYGFS¹⁸⁰) of βB1-crystallin, which is referred as βB1_(174-180). Under in vitro conditions, the synthetic analogue of βB1_(174-180) peptide formed visible aggregates and displayed high Congo red (CR) bathochromic shift, Thioflavin T (ThT) binding and fibrilar morphology under transmission electron microscopy, which are the typical characteristics of amyloids. Further, the aggregated βB1_(174-180) was found to induce aggregation of the soluble fraction of proteins isolated from the human cataractous lens. This observation suggests that the presence of APRs in βB1-crystallin might be serving as one of the intrinsic supplementary factors responsible for constitutive aggregation behavior of βB1-crystallin and development of cataract.

New Model for Stacking Monomers in Filamentous Actin from Skeletal Muscles of Oryctolagus cuniculus

To date, some scientific evidence (limited proteolysis, mass spectrometry analysis, electron microscopy (EM)) has accumulated, which indicates that the generally accepted model of double-stranded of filamentous actin (F-actin) organization in eukaryotic cells is not the only one. This entails an ambiguous understanding of many of the key cellular processes in which F-actin is involved. For a detailed understanding of the mechanism of F-actin assembly and actin interaction with its partners, it is necessary to take into account the polymorphism of the structural organization of F-actin at the molecular level. Using electron microscopy, limited proteolysis, mass spectrometry, X-ray diffraction, and structural modeling we demonstrated that F-actin presented in the EM images has no double-stranded organization, the regions of protease resistance are accessible for action of proteases in F-actin models. Based on all data, a new spatial model of filamentous actin is proposed, and the F-actin polymorphism is discussed.

Extremely Amyloidogenic Single-Chain Analogues of Insulin's H-Fragment: Structural Adaptability of an Amyloid Stretch

Relatively short amino acid sequences often play a pivotal role in triggering protein aggregation leading to the formation of amyloid fibrils. In the case of insulin, various regions of A- and B-chains have been implicated as the most relevant to the protein's amyloidogenicity. Here, we focus on the highly amyloidogenic H-fragment of insulin comprising the disulfide-bonded N-terminal parts of both chains. Analysis of the aggregation behavior of single-chain peptide derivatives of the H-fragment suggests that the A-chain's part initiates the aggregation process while the disulfide-tethered B-chain reluctantly adapts to amyloid structure. Merging of both A- and B-parts into single-chain continuous peptides (A-B and B-A) results in extreme amyloidogenicity exceeding that of the double-chain H-fragment as reflected by almost instantaneous de novo fibrillization. Amyloid fibrils of A-B and B-A present distinct morphological and infrared traits and do not cross-seed insulin. Our study suggests that the N-terminal part of insulin's A-chain containing the intact Cys6-Cys11 intrachain disulfide bond may constitute insulin's major amyloid stretch which, through its bent conformation, enforces a parallel in-register alignment of β-strands. Comparison of the self-association behavior of H, A-B, and B-A peptides suggests that A-chain's N-terminal amyloid stretch is very versatile and adaptive to various structural contexts.

iAMY-SCM: Improved prediction and analysis of amyloid proteins using a scoring card method with propensity scores of dipeptides

Fast, accurate identification and characterization of amyloid proteins at a large-scale is essential for understating their role in therapeutic intervention strategies. As a matter of fact, there exist only one in silico model for amyloid protein identification using the random forest (RF) model in conjunction with various feature types namely the RFAmy. However, it suffers from low interpretability for biologists. Thus, it is highly desirable to develop a simple and easily interpretable prediction method with robust accuracy as compared to the existing complicated model. In this study, we propose iAMY-SCM, the first scoring card method-based predictor for predicting and analyzing amyloid proteins. Herein, the iAMY-SCM made use of a simple weighted-sum function in conjunction with the propensity scores of dipeptides for the amyloid protein identification. Cross-validation results indicated that iAMY-SCM provided an accuracy of 0.895 that corresponded to 10-22% higher performance than that of widely used machine learning models. Furthermore, iAMY-SCM achieving an accuracy of 0.827 as evaluated by an independent test, which was found to be comparable to that of RFAmy and was approximately 9-13% higher than widely used machine learning models. Furthermore, the analysis of estimated propensity scores of amino acids and dipeptides were performed to provide insights into the biophysical and biochemical properties of amyloid proteins. As such, this demonstrates that the proposed iAMY-SCM is efficient and reliable in terms of simplicity, interpretability and implementation. To facilitate ease of use of the proposed iAMY-SCM, a user-friendly and publicly accessible web server at http://camt.pythonanywhere.com/iAMY-SCM has been established. We anticipate that that iAMY-SCM will be an important tool for facilitating the large-scale prediction and characterization of amyloid protein.

New Human Islet Amyloid Polypeptide Fragments Susceptible to Aggregation

Human Islet Amyloid Polypeptide (hIAPP) plays a key role in the pathogenesis of type II diabetes. The aim of this research was to search for new amyloidogenic fragments of hIAPP. An initial attempt to predict the amyloidogenic cores of polypeptides/proteins using five different computer programs did not provide conclusive results. Therefore, we synthesized hIAPP fragments covering the entire hormone. The fragments were assessed for their aggregation ability, using recommended methods to search for the amyloidogenic fragments of the polypeptides/proteins. It was found that fragments (18-22) H-HSSNN-OH and (33-37) H-GSNTY-NH₂ aggregate and form stable amyloid-like structures. Both of these fragments have a much higher antiproliferative activity relative to the RIN-5F cell compared to the (23-27) H-FGAIL-OH fragment widely regarded as the amyloidogenic core of amylin. The analog of (33-37) H-GSNTY-NH₂ containing a free carboxy group on the C-terminal amino acid (H-GSNTY-OH) does not have amyloidogenic properties and can therefore be considered as a potential inhibitor of amylin aggregation. Research on the use of non-aggregating amylin fragments as potential hormone aggregation inhibitors is ongoing.

Antimicrobial and Amyloidogenic Activity of Peptides Synthesized on the Basis of the Ribosomal S1 Protein from Thermus Thermophilus

Controlling the aggregation of vital bacterial proteins could be one of the new research directions and form the basis for the search and development of antibacterial drugs with targeted action. Such approach may be considered as an alternative one to antibiotics. Amyloidogenic regions can, like antibacterial peptides, interact with the "parent" protein, for example, ribosomal S1 protein (specific only for bacteria), and interfere with its functioning. The aim of the work was to search for peptides based on the ribosomal S1 protein from T. thermophilus, exhibiting both aggregation and antibacterial properties. The biological system of the response of Gram-negative bacteria T. thermophilus to the action of peptides was characterized. Among the seven studied peptides, designed based on the S1 protein sequence, the R23I (modified by the addition of HIV transcription factor fragment for bacterial cell penetration), R23T (modified), and V10I (unmodified) peptides have biological activity that inhibits the growth of T. thermophilus cells, that is, they have antimicrobial activity. But, only the R23I peptide had the most pronounced activity comparable with the commercial antibiotics. We have compared the proteome of peptide-treated and intact T. thermophilus cells. These important data indicate a decrease in the level of energy metabolism and anabolic processes, including the processes of biosynthesis of proteins and nucleic acids. Under the action of 20 and 50 μg/mL R23I, a decrease in the number of proteins in T. thermophilus cells was observed and S1 ribosomal protein was absent. The obtained results are important for understanding the mechanism of amyloidogenic peptides with antimicrobial activity and can be used to develop new and improved analogues.

Shear-induced aggregation of amyloid β (1-40) in a parallel plate geometry

Protein aggregation is induced by various environmental or external factors and associated with various neurodegenerative diseases. Among various external factors, shear stress is inevitable for both in vivo and in vitro applications of proteins. In this study, Aβ (1-40) peptide, a derivative of the amyloid precursor protein, was subjected to constant (300, 500, 700 s^-1) and varying (ramp) shear in a parallel plate geometry to explore the implications of shear in terms of macro (viscosity) and micro (secondary structure, morphology) characteristics. Aβ (1-40) solution followed a shear thickening flow behaviour with performance index value 'n' of 2.12. The fibrillation process resulting from the shear force was evaluated in terms of dissipation energy, which was found to exceed the free energy of unfolding. This resulted in the formation of β-sheet rich structures, which were confirmed by CD and FTIR analyses and enhanced Th-T fluorescence. The apparent rate of aggregation (k) was found to increase with the shear rate, and inversely related to the solution viscosity. The maximum k value was 0.21 ± 0.3 min^-1 at 700 s^-1. The molecular weights of aggregates were determined using gel filtration, which were proportionally related to the solution viscosity. The average molecular weights were estimated to be 70, 62 and 52 KDa for samples sheared at 300, 500 and 700 s^-1, respectively. The present study has deciphered the interplay of viscosity, a fluid property, with the aggregation process and its corresponding change in the secondary structures of the peptide. These findings provide useful insights for understanding various proteopathies under shear force.Communicated by Ramaswamy H. Sarma.

Amyloidogenic Propensities of Ribosomal S1 Proteins: Bioinformatics Screening and Experimental Checking

Structural S1 domains belong to the superfamily of oligosaccharide/oligonucleotide-binding fold domains, which are highly conserved from prokaryotes to higher eukaryotes and able to function in RNA binding. An important feature of this family is the presence of several copies of the structural domain, the number of which is determined in a strictly limited range from one to six. Despite the strong tendency for the aggregation of several amyloidogenic regions in the family of the ribosomal S1 proteins, their fibril formation process is still poorly understood. Here, we combined computational and experimental approaches for studying some features of the amyloidogenic regions in this protein family. The FoldAmyloid, Waltz, PASTA 2.0 and Aggrescan programs were used to assess the amyloidogenic propensities in the ribosomal S1 proteins and to identify such regions in various structural domains. The thioflavin T fluorescence assay and electron microscopy were used to check the chosen amyloidogenic peptides' ability to form fibrils. The bioinformatics tools were used to study the amyloidogenic propensities in 1331 ribosomal S1 proteins. We found that amyloidogenicity decreases with increasing sizes of proteins. Inside one domain, the amyloidogenicity is higher in the terminal parts. We selected and synthesized 11 amyloidogenic peptides from the Escherichia coli and Thermus thermophilus ribosomal S1 proteins and checked their ability to form amyloids using the thioflavin T fluorescence assay and electron microscopy. All 11 amyloidogenic peptides form amyloid-like fibrils. The described specific amyloidogenic regions are actually responsible for the fibrillogenesis process and may be potential targets for modulating the amyloid properties of bacterial ribosomal S1 proteins.

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

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

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

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

CPAD 2.0: a repository of curated experimental data on aggregating proteins and peptides

The Curated Protein Aggregation Database (CPAD) is a manually curated and open-access database dedicated to providing comprehensive information related to mechanistic, kinetic and structural aspects of protein and peptide aggregation. The database has been updated to CPAD 2.0 by significantly expanding datasets and improving the user-interface. Key features of CPAD 2.0 are (i) 83,098 data points on aggregation kinetics experiments, (ii) 565 structures related to aggregation, which are classified into proteins, fibrils, and protein-ligand complexes, (iii) 2031 aggregating/non-aggregating peptides with pre-calculated aggregation properties, and (iv) 912 aggregation-prone regions in amyloidogenic proteins. This database will help the scientific community (a) by facilitating research leading to improved understanding of protein aggregation, (b) by helping develop, validate and benchmark mechanistic and kinetic models of protein aggregation, and (c) by assisting experimentalists with design of their investigations and dissemination of data generated by their studies. CPAD 2.0 can be accessed at https://web.iitm.ac.in/bioinfo2/cpad2/index.html.

PATH - Prediction of Amyloidogenicity by Threading and Machine Learning

Amyloids are protein aggregates observed in several diseases, for example in Alzheimer's and Parkinson's diseases. An aggregate has a very regular beta structure with a tightly packed core, which spontaneously assumes a steric zipper form. Experimental methods enable studying such peptides, however they are tedious and costly, therefore inappropriate for genomewide studies. Several bioinformatic methods have been proposed to evaluate protein propensity to form an amyloid. However, the knowledge of aggregate structures is usually not taken into account. We propose PATH (Prediction of Amyloidogenicity by THreading) - a novel structure-based method for predicting amyloidogenicity and show that involving available structures of amyloidogenic fragments enhances classification performance. Experimental aggregate structures were used in templatebased modeling to recognize the most stable representative structural class of a query peptide. Several machine learning methods were then applied on the structural models, using their energy terms. Finally, we identified the most important terms in classification of amyloidogenic peptides. The proposed method outperforms most of the currently available methods for predicting amyloidogenicity, with its area under ROC curve equal to 0.876. Furthermore, the method gave insight into significance of selected structural features and the potentially most stable structural class of a peptide fragment if subjected to crystallization.

The presence of cross-β-structure as a key determinant of carbonic anhydrase amyloid fibrils cytotoxicity

In most cases high cytotoxicity is characteristic of aggregates formed during lag phase of amyloid formation, whereas mature fibrils represent the depot of protein molecules incapable of damaging cell membranes. However, new experimental data show that in cases of some proteins the fibrils are the most toxic type of aggregates. Meanwhile, structural characteristics of cytotoxic fibrils and mechanisms of their cell damaging action are insufficiently explored. This work is dedicated to studying amyloid aggregation of bovine carbonic anhydrase (BCA) and effect of aggregates formed at different stages of amyloid formation on viability of the cells. Here we demonstrate that oligomers formed during lag phase do not decrease cell viability, whereas protofibrils and amyloids of BCA are cytotoxic. Obtained results allow concluding that toxicity of BCA aggregates is associated with the presence of amyloid cross-β-structure, which signature is absorbance peak at low wavenumbers at FTIR spectra (1615-1630 cm^-1). Our data suppose that cross-β-core of ВСА amyloid fibrils is responsible for their cytotoxicity.

The biofilm-associated surface protein Esp of Enterococcus faecalis forms amyloid-like fibers

Functional amyloids are considered as common building block structures of the biofilm matrix in different bacteria. In previous work, we have shown that the staphylococcal surface protein Bap, a member of the Biofilm-Associated Proteins (BAP) family, is processed and the fragments containing the N-terminal region become aggregation-prone and self-assemble into amyloid-like structures. Here, we report that Esp, a Bap-orthologous protein produced by Enterococcus faecalis, displays a similar amyloidogenic behavior. We demonstrate that at acidic pH the N-terminal region of Esp forms aggregates with an amyloid-like conformation, as evidenced by biophysical analysis and the binding of protein aggregates to amyloid-indicative dyes. Expression of a chimeric protein, with its Esp N-terminal domain anchored to the cell wall through the R domain of clumping factor A, showed that the Esp N-terminal region is sufficient to confer multicellular behavior through the formation of an extracellular amyloid-like material. These results suggest that the mechanism of amyloid-like aggregation to build the biofilm matrix might be widespread among BAP-like proteins. This amyloid-based mechanism may not only have strong relevance for bacteria lifestyle but could also contribute to the amyloid burden to which the human physiology is potentially exposed.

M. Brito R. Structure and Aggregation Mechanisms in Amyloids

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

Using protein engineering to understand and modulate aggregation

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

Oligomers Are Promising Targets for Drug Development in the Treatment of Proteinopathies

Currently, there is no effective treatment of proteinopathies, as well as their diagnosis in the early stages of the disease until the first clinical symptoms appear. The proposed model of fibrillation of the Aβ peptide and its fragments not only describes molecular rearrangements, but also offers models of processes that occur during the formation of amyloid aggregates. Since this model is also characteristic of other proteins and peptides, a new potential target for drug development in the treatment of Alzheimer’s disease (AD) and other proteinopathies is proposed on the basis of this model. In our opinion, it is oligomers that are promising targets for innovative developments in the treatment of these diseases.

pH-Dependent Aggregation in Intrinsically Disordered Proteins Is Determined by Charge and Lipophilicity

Protein aggregation is associated with an increasing number of human disorders and premature aging. Moreover, it is a central concern in the manufacturing of recombinant proteins for biotechnological and therapeutic applications. Nevertheless, the unique architecture of protein aggregates is also exploited by nature for functional purposes, from bacteria to humans. The relevance of this process in health and disease has boosted the interest in understanding and controlling aggregation, with the concomitant development of a myriad of algorithms aimed to predict aggregation propensities. However, most of these programs are blind to the protein environment and, in particular, to the influence of the pH. Here, we developed an empirical equation to model the pH-dependent aggregation of intrinsically disordered proteins (IDPs) based on the assumption that both the global protein charge and lipophilicity depend on the solution pH. Upon its parametrization with a model IDP, this simple phenomenological approach showed unprecedented accuracy in predicting the dependence of the aggregation of both pathogenic and functional amyloidogenic IDPs on the pH. The algorithm might be useful for diverse applications, from large-scale analysis of IDPs aggregation properties to the design of novel reversible nanofibrillar materials.

Changes in redox and endoplasmic reticulum homeostasis are related to congenital generalized lipodystrophy type 2

CGL type 2 is a rare autosomal recessive syndrome characterized by an almost complete lack of body fat. CGL is caused by loss-of-function mutations in both alleles of the BSCL2 gene that codifies to seipin. Subjects often show hyperglycemia, decreased HDL-c, and hypoadiponectinemia. These laboratory findings are important triggers for changes in redox and ER homeostasis. Therefore, our aim was to investigate whether these intracellular mechanisms are associated with this syndrome. We collected blood from people from Northeastern Brazil with 0, 1, and 2 mutant alleles for the rs786205071 in the BSCL2 gene. Through the qPCR technique, we evaluated the expression of genes responsible for triggering the antioxidant response, DNA repair, and ER stress in leukocytes. Colorimetric tests were applied to quantify lipid peroxidation and to evaluate the redox status of glutathione, as well as to access the panorama of energy metabolism. Long extension PCR was performed to observe leukocyte mitochondrial DNA lesions, and the immunoblot technique to investigate plasma adiponectin concentrations. Subjects with the rs786205071 in both BSCL2 alleles showed increased transcription of NFE2L2, APEX1, and OGG1 in leukocytes, as well as high concentrations of malondialdehyde and the GSSG:GSH ratio in plasma. We also observed increase of mitochondrial DNA lesions and XBP1 splicing, as well as a decrease in adiponectin and HDL-c. Our data suggest the presence of lipid lesions due to changes in redox homeostasis in that group, associated with increased levels of mitochondrial DNA damage and transcriptional activation of genes involved with antioxidant response and DNA repair.