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

Leech, potato, and tomato carboxypeptidase inhibitors against Anopheles stephensi carboxypeptidase B1 and B2

Carboxypeptidase B (CPB) in Anopheles spp. breaks down blood and releases free amino acids, which promote Plasmodium sexual development in the mosquito midgut. Our goal was to computationally assess the inhibitory effectiveness of carboxypeptidase inhibitors obtained from tomato, potato (CPiSt), and leech against the Anopheles stephensi CPBAs1 and CPBAs2 enzymes. The tertiary structures of CPB inhibitors were predicted and their interaction mode with CPBAs1 and CPBAs2 were examined using molecular docking. Next, this data was compared with four licensed medications that are known to reduce the Anopheles' CPB activity. Molecular dynamics simulations were used to evaluate the stability of complexes containing CPiSt and its mutant form. Both CPiSt and its mutant form showed promise as possible candidates for further evaluations in the paratransgenesis technique for malaria control, based on the similar bindings of CPiSt and CPiSt-Mut to the active sites of CPBAs1 and CPBAs2, as well as their binding affinity in comparison to the drugs.

Variant Impact Predictor database (VIPdb), version 2: trends from three decades of genetic variant impact predictors

BACKGROUND

Variant interpretation is essential for identifying patients' disease-causing genetic variants amongst the millions detected in their genomes. Hundreds of Variant Impact Predictors (VIPs), also known as Variant Effect Predictors (VEPs), have been developed for this purpose, with a variety of methodologies and goals. To facilitate the exploration of available VIP options, we have created the Variant Impact Predictor database (VIPdb).

RESULTS

The Variant Impact Predictor database (VIPdb) version 2 presents a collection of VIPs developed over the past three decades, summarizing their characteristics, ClinGen calibrated scores, CAGI assessment results, publication details, access information, and citation patterns. We previously summarized 217 VIPs and their features in VIPdb in 2019. Building upon this foundation, we identified and categorized an additional 190 VIPs, resulting in a total of 407 VIPs in VIPdb version 2. The majority of the VIPs have the capacity to predict the impacts of single nucleotide variants and nonsynonymous variants. More VIPs tailored to predict the impacts of insertions and deletions have been developed since the 2010s. In contrast, relatively few VIPs are dedicated to the prediction of splicing, structural, synonymous, and regulatory variants. The increasing rate of citations to VIPs reflects the ongoing growth in their use, and the evolving trends in citations reveal development in the field and individual methods.

CONCLUSIONS

VIPdb version 2 summarizes 407 VIPs and their features, potentially facilitating VIP exploration for various variant interpretation applications. VIPdb is available at  https://genomeinterpretation.org/vipdb.

Advanced vaccinomic, immunoinformatic, and molecular modeling strategies for designing Multi- epitope vaccines against the Enterobacter cloacae complex

The increasing and ongoing issue of antibiotic resistance in bacteria is of huge concern globally, mainly to healthcare facilities. It is now crucial to develop a vaccine for therapeutic and preventive purposes against the bacterial species causing hospital-based infections. Among the many antibiotic- resistant bacterial pathogens, the Enterobacter cloacae complex (ECC) including six species, E. Colcae, E. absuriae, E. kobie, E. hormaechei, E. ludwigii, and E. nimipressuralis, are dangerous to public health and may worsen the situation. Vaccination plays a vital role in the prevention of infections and infectious diseases. This research highlighted the construction and design of a multi-epitope vaccine for the E. cloacae complex by retrieving their complete sequenced proteome. The retrieved proteome was assessed to opt for potential vaccine candidates using immunoinformatic tools. Both B and T-cell epitopes were predicted in order to create both humoral and cellular immunity and further scrutinized for antigenicity, allergenicity, water solubility, and toxicity analysis. The final potential epitopes were subjected to population coverage analysis. Major histocompatibility complex (MHC) class combined, and MHC Class I and II world population coverage was obtained as 99.74%, and 98.55% respectively while a combined 81.81% was covered. A multi-epitope peptide-based vaccine construct consisting of the adjuvant, epitopes, and linkers was subjected to the ProtParam tool to calculate its physiochemical properties. The total amino acids were 236, the molecular weight was 27.64kd, and the vaccine construct was stable with an instability index of 27.01. The Grand Average of Hydropathy (GRAVY) (hydrophilicity) value obtained was -0.659, being more negative and depicting the hydrophilic character. It was non-allergen antigenic with an antigenicity of 0.8913. The vaccine construct was further validated for binding efficacy with immune cell receptors MHC-I, MHC-II, and Toll-like receptor (TLR)-4. The molecular docking results depict that the designed vaccine has good binding potency with immune receptors crucial for antigen presentation and processing. Among the Vaccine-MHC-I, Vaccine-MHC-II, and Vaccine-TLR-4 complexes, the best-docked poses were identified based on their lowest binding energy scores of -886.8, -995.6, and -883.6, respectively. Overall, we observed that the designed vaccine construct can evoke a proper immune response and the construct could help experimental researchers in the formulation of a vaccine against the targeted pathogens.

De Novo Rational Design of Peptide-Based Protein-Protein Inhibitors (Pep-PPIs) Approach by Mapping the Interaction Motifs of the PP Interface and Physicochemical Filtration: A Case on p25-Cdk5-Mediated Neurodegenerative Diseases

Protein-protein interactions, or PPIs, are a part of every biological activity and have been linked to a number of diseases, including cancer, infectious diseases, and neurological disorders. As such, targeting PPIs is considered a strategic and vital approach in the development of new medications. Nonetheless, the wide and flat contact interface makes it difficult to find small-molecule PP inhibitors. An alternative strategy would be to use the PPI interaction motifs as building blocks for the design of peptide-based inhibitors. Herein, we designed 12-mer peptide inhibitors to target p25-inducing-cyclin-dependent kinase (Cdk5) hyperregulation, a PPI that has been shown to perpetuate neuroinflammation, which is one of the major causal implications of neurodegenerative disorders such as Alzheimer's disease, Parkinson's disease, and frontotemporal dementia. We generated a library of 5 062 500 peptide combination sequences (PCS) derived from the interaction motif of Cdk5/p25 PP interface. The 20 amino acids were differentiated into six groups, namely, hydrophobic (aliphatic), aromatic, basic, acidic, unique, and polar uncharged, on the basis of their physiochemical properties. To preserve the interaction motif necessary for ideal binding, de novo modeling of all possible peptide sequence substitutions was considered. A set of filters, backed by the Support Vector Machine (SVM) algorithm, was then used to create a shortlisted custom peptide library that met specific bioavailability, toxicity, and therapeutic relevance, leading to a refined library of 15 PCS. A greedy algorithm and coarse-grained force field were used to predict peptide structure and folding before subsequent modeling studies. Molecular docking was performed to estimate the relative binding affinities, and out of the top hits, Pep15 was subjected to molecular dynamics simulations and binding free-energy calculations in comparison to a known peptide inhibitor with experimental data (template peptide). Interestingly, the identified peptide through our protocol, Pep15, was found to show a significantly higher binding affinity than the reference template peptide (-48.10 ± 0.23 kcal/mol and -17.53 ± 0.27 kcal/mol, respectively). In comparison to the template peptide, Pep15 was found to possess a more compact and buried surface area, tighter binding landscape, and reduced conformational variability, leading to enhanced structural and kinetic stability of the Cdk5/p25 complex. Notably, both peptide inhibitors were found to have a minimal impact on the architectural integrity of the Cdk5/p25 secondary structure. Herein, we propose Pep15 as a novel and potentially disruptive peptide drug for Cdk5/p25-mediated neurodegenerative phenotypes that require further clinical investigation. The systematic protocol and findings of this report would serve as a valuable tool in the identification of critical PPI interface reactive residues, designing of analogs, and identification of more potent peptide-based PPI inhibitors.

Solvent induced amyloid polymorphism and the uncovering of the elusive class 3 amyloid topology

Aggregation-prone-motifs (APRs) of proteins are short segments, which - as isolated peptides - form diverse amyloid-like crystals. We introduce two APRs - designed variants of the incretin mimetic Exendin-4 - that both display crystal-phase polymorphism. Crystallographic and spectroscopic analysis revealed that a single amino-acid substitution can greatly reduce topological variability: while LYIQWL can form both parallel and anti-parallel β-sheets, LYIQNL selects only the former. We also found that the parallel/anti-parallel switch of LYIQWL can be induced by simply changing the crystallization temperature. One crystal form of LYIQNL was found to belong to the class 3 topology, an arrangement previously not encountered among proteinogenic systems. We also show that subtle environmental changes lead to crystalline assemblies with different topologies, but similar interfaces. Spectroscopic measurements showed that polymorphism is already apparent in the solution state. Our results suggest that the temperature-, sequence- and environmental sensitivity of physiological amyloids is reflected in assemblies of the APR segments, which, complete with the new class 3 crystal form, effectively sample all the originally proposed basic topologies of amyloid-like aggregates.

Quercetin-Loaded Nanocarriers as Effective Inhibitors for Copper Metal Ion-Induced γD-Crystallin Aggregation

Cataract is one of the leading causes of blindness worldwide. Till date, the only solution for cataracts is surgery, which is a resource-intensive solution. A much simpler solution is to find a potential drug that could inhibit aggregation. It is well established that nonamyloid aggregates of eye lens protein result in cataract. γD-Crystallin, a thermodynamically stable protein, is one of the most abundant proteins in the core of the eye lens and is found to aggregate under stress conditions, leading to the cataract. It has also been found that in cataractous lens, the concentration of metals like copper is elevated significantly as compared to healthy eye lens, suggesting their role in inducing aggregation. In our present study, aggregation of γD-Crystallin was carried out in the presence of Cu (II). Using techniques like turbidity assay, CD spectroscopy, ANS binding assay, and microscopic studies like TEM, it could be confirmed that protein aggregates in the presence of Cu (II) and the nature of aggregates is amorphous. Various polyphenols were tested to suppress aggregation of the protein. Quercetin was observed to be the most efficient. To overcome the problems associated with the delivery of polyphenols, such as solubility and bioavailability, quercetin was encapsulated in two types of nanocarriers. Their characterization was done using TEM, DLS, and other techniques. The potency of quercetin-loaded CS-TPP/CS-PLGA NPs as inhibitors of γD-Crystallin aggregation was confirmed by various experiments.

A human-specific cytotoxic neopeptide generated by the deafness gene Cingulin

Accumulation of mutant proteins in cells can induce proteinopathies and cause functional damage to organs. Recently, the Cingulin (CGN) protein has been shown to maintain the morphology of cuticular plates of inner ear hair cells and a frameshift mutation in CGN causes autosomal dominant nonsyndromic hearing loss. Here, we find that the mutant CGN proteins form insoluble aggregates which accumulate intracellularly and lead to cell death. Expression of the mutant CGN in the inner ear results in severe hair cell death and hearing loss in mice, resembling the auditory phenotype in human patients. Interestingly, a human-specific residue (V1112) in the neopeptide generated by the frameshift mutation is critical for the aggregation and cytotoxicity of the mutant human CGN. Moreover, the expression of heat shock factor 1 (HSF1) decreases the accumulation of insoluble mutant CGN aggregates and rescues cell death. In summary, these findings identify mutant-specific toxic polypeptides as a disease-causing mechanism of the deafness mutation in CGN, which can be targeted by the expression of the cell chaperone response regulator HSF1.

Rapid classification of a novel ALS-causing I149S variant in superoxide dismutase-1

Variants of the oxygen free radical scavenging enzyme superoxide dismutase-1 (SOD1) are associated with the neurodegenerative disease amyotrophic lateral sclerosis (ALS). These variants occur in roughly 20% of familial ALS cases, and 1% of sporadic ALS cases. Here, we identified a novel SOD1 variant in a patient in their 50s who presented with movement deficiencies and neuropsychiatric features. The variant was heterozygous and resulted in the isoleucine at position 149 being substituted with a serine (I149S). In silico analysis predicted the variant to be destabilizing to the SOD1 protein structure. Expression of the SOD1I149S variant with a C-terminal EGFP tag in neuronal-like NSC-34 cells resulted in extensive inclusion formation and reduced cell viability. Immunoblotting revealed that the intramolecular disulphide between Cys57 and Cys146 was fully reduced for SOD1I149S. Furthermore, SOD1I149S was highly susceptible to proteolytic digestion, suggesting a large degree of instability to the protein fold. Finally, fluorescence correlation spectroscopy and native-PAGE of cell lysates showed that SOD1I149S was monomeric in solution in comparison to the dimeric SOD1WT. This experimental data was obtained within 3 months and resulted in the rapid re-classification of the variant from a variant of unknown significance (VUS) to a clinically actionable likely pathogenic variant.

Characterizing the amyloid core region of the tumor suppressor protein p16INK4a using a limited proteolysis and peptide-based approach

The human tumor suppressor p16INK4a is a small monomeric protein that can form amyloid structures. Formation of p16INK4a amyloid fibrils is induced by oxidation which creates an intermolecular disulfide bond. The conversion into amyloid is associated with a change from an all α-helical structure into β-sheet fibrils. Currently, structural insights into p16INK4a amyloid fibrils are lacking. Here, we investigate the amyloid-forming regions of this tumor suppressor using isotope-labeling limited-digestion mass spectrometry analysis. We discover two key regions that likely form the structured core of the amyloid. Further investigations using thioflavin-T fluorescence assays, electron microscopy, and solution nuclear magnetic resonance spectroscopy of shorter peptide regions confirm the self-assembly of the identified sequences that include methionine and leucine repeat regions. This work describes a simple approach for studying protein motifs involved in the conversion of monomeric species into aggregated fibril structures. It provides insight into the polypeptide sequence underlying the core structure of amyloid p16INK4a formed after a unique oxidation-driven structural transition.

Photocontrolled Reversible Amyloid Fibril Formation of Parathyroid Hormone-Derived Peptides

Peptide fibrillization is crucial in biological processes such as amyloid-related diseases and hormone storage, involving complex transitions between folded, unfolded, and aggregated states. We here employ light to induce reversible transitions between aggregated and nonaggregated states of a peptide, linked to the parathyroid hormone (PTH). The artificial light-switch 3-{[(4-aminomethyl)phenyl]diazenyl}benzoic acid (AMPB) is embedded into a segment of PTH, the peptide PTH25-37, to control aggregation, revealing position-dependent effects. Through in silico design, synthesis, and experimental validation of 11 novel PTH25-37-derived peptides, we predict and confirm the amyloid-forming capabilities of the AMPB-containing peptides. Quantum-chemical studies shed light on the photoswitching mechanism. Solid-state NMR studies suggest that β-strands are aligned parallel in fibrils of PTH25-37, while in one of the AMPB-containing peptides, β-strands are antiparallel. Simulations further highlight the significance of π-π interactions in the latter. This multifaceted approach enabled the identification of a peptide that can undergo repeated phototriggered transitions between fibrillated and defibrillated states, as demonstrated by different spectroscopic techniques. With this strategy, we unlock the potential to manipulate PTH to reversibly switch between active and inactive aggregated states, representing the first observation of a photostimulus-responsive hormone.

Interpretably deep learning amyloid nucleation by massive experimental quantification of random sequences

Insoluble amyloid aggregates are the hallmarks of more than fifty human diseases, including the most common neurodegenerative disorders. The process by which soluble proteins nucleate to form amyloid fibrils is, however, quite poorly characterized. Relatively few sequences are known that form amyloids with high propensity and this data shortage likely limits our capacity to understand, predict, engineer, and prevent the formation of amyloid fibrils. Here we quantify the nucleation of amyloids at an unprecedented scale and use the data to train a deep learning model of amyloid nucleation. In total, we quantify the nucleation rates of >100,000 20-amino-acid-long peptides. This large and diverse dataset allows us to train CANYA, a convolution-attention hybrid neural network. CANYA is fast and outperforms existing methods with stable performance across diverse prediction tasks. Interpretability analyses reveal CANYA's decision-making process and learned grammar, providing mechanistic insights into amyloid nucleation. Our results illustrate the power of massive experimental analysis of random sequence-spaces and provide an interpretable and robust neural network model to predict amyloid nucleation.

Aggrescan4D: structure-informed analysis of pH-dependent protein aggregation

Protein aggregation is behind the genesis of incurable diseases and imposes constraints on drug discovery and the industrial production and formulation of proteins. Over the years, we have been advancing the Aggresscan3D (A3D) method, aiming to deepen our comprehension of protein aggregation and assist the engineering of protein solubility. Since its inception, A3D has become one of the most popular structure-based aggregation predictors because of its performance, modular functionalities, RESTful service for extensive screenings, and intuitive user interface. Building on this foundation, we introduce Aggrescan4D (A4D), significantly extending A3D's functionality. A4D is aimed at predicting the pH-dependent aggregation of protein structures, and features an evolutionary-informed automatic mutation protocol to engineer protein solubility without compromising structure and stability. It also integrates precalculated results for the nearly 500,000 jobs in the A3D Model Organisms Database and structure retrieval from the AlphaFold database. Globally, A4D constitutes a comprehensive tool for understanding, predicting, and designing solutions for specific protein aggregation challenges. The A4D web server and extensive documentation are available at https://biocomp.chem.uw.edu.pl/a4d/. This website is free and open to all users without a login requirement.

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

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

A new lysine biosynthetic enzyme from a bacterial endosymbiont shaped by genetic drift and genome reduction

The effect of population bottlenecks and genome reduction on enzyme function is poorly understood. Candidatus Liberibacter solanacearum is a bacterium with a reduced genome that is transmitted vertically to the egg of an infected psyllid-a population bottleneck that imposes genetic drift and is predicted to affect protein structure and function. Here, we define the function of Ca. L. solanacearum dihydrodipicolinate synthase (CLsoDHDPS), which catalyzes the committed branchpoint reaction in diaminopimelate and lysine biosynthesis. We demonstrate that CLsoDHDPS is expressed in Ca. L. solanacearum and expression is increased ~2-fold in the insect host compared to in planta. CLsoDHDPS has decreased thermal stability and increased aggregation propensity, implying mutations have destabilized the enzyme but are compensated for through elevated chaperone expression and a stabilized oligomeric state. CLsoDHDPS uses a ternary-complex kinetic mechanism, which is to date unique among DHDPS enzymes, has unusually low catalytic ability, but an unusually high substrate affinity. Structural studies demonstrate that the active site is more open, and the structure of CLsoDHDPS with both pyruvate and the substrate analogue succinic-semialdehyde reveals that the product is both structurally and energetically different and therefore evolution has in this case fashioned a new enzyme. Our study suggests the effects of genome reduction and genetic drift on the function of essential enzymes and provides insights on bacteria-host co-evolutionary associations. We propose that bacteria with endosymbiotic lifestyles present a rich vein of interesting enzymes useful for understanding enzyme function and/or informing protein engineering efforts.

Stability of Protein Pharmaceuticals: Recent Advances

There have been significant advances in the formulation and stabilization of proteins in the liquid state over the past years since our previous review. Our mechanistic understanding of protein-excipient interactions has increased, allowing one to develop formulations in a more rational fashion. The field has moved towards more complex and challenging formulations, such as high concentration formulations to allow for subcutaneous administration and co-formulation. While much of the published work has focused on mAbs, the principles appear to apply to any therapeutic protein, although mAbs clearly have some distinctive features. In this review, we first discuss chemical degradation reactions. This is followed by a section on physical instability issues. Then, more specific topics are addressed: instability induced by interactions with interfaces, predictive methods for physical stability and interplay between chemical and physical instability. The final parts are devoted to discussions how all the above impacts (co-)formulation strategies, in particular for high protein concentration solutions.'

Variant Impact Predictor database (VIPdb), version 2: Trends from 25 years of genetic variant impact predictors

Background

Variant interpretation is essential for identifying patients' disease-causing genetic variants amongst the millions detected in their genomes. Hundreds of Variant Impact Predictors (VIPs), also known as Variant Effect Predictors (VEPs), have been developed for this purpose, with a variety of methodologies and goals. To facilitate the exploration of available VIP options, we have created the Variant Impact Predictor database (VIPdb).

Results

The Variant Impact Predictor database (VIPdb) version 2 presents a collection of VIPs developed over the past 25 years, summarizing their characteristics, ClinGen calibrated scores, CAGI assessment results, publication details, access information, and citation patterns. We previously summarized 217 VIPs and their features in VIPdb in 2019. Building upon this foundation, we identified and categorized an additional 186 VIPs, resulting in a total of 403 VIPs in VIPdb version 2. The majority of the VIPs have the capacity to predict the impacts of single nucleotide variants and nonsynonymous variants. More VIPs tailored to predict the impacts of insertions and deletions have been developed since the 2010s. In contrast, relatively few VIPs are dedicated to the prediction of splicing, structural, synonymous, and regulatory variants. The increasing rate of citations to VIPs reflects the ongoing growth in their use, and the evolving trends in citations reveal development in the field and individual methods.

Conclusions

VIPdb version 2 summarizes 403 VIPs and their features, potentially facilitating VIP exploration for various variant interpretation applications.

Availability

VIPdb version 2 is available at https://genomeinterpretation.org/vipdb.

Protonation-State Dependent Modulation of Hen Egg-White Lysozyme Fibrillation under the Influence of a Short Synthetic Peptide

Under the influence of various conditions, misfolding of soluble proteins occurs, leading to the formation of toxic insoluble amyloids. The formation and deposition of such amyloids within the body are associated with detrimental biological consequences such as the onset of several amyloid-related diseases. Previously, we established a strategy for the rational design of peptide inhibitors against amyloid formation based on the amyloidogenic-prone region of the protein. In the current study, we have designed and identified an Asp-containing rationally designed hexapeptide (SqP4) as an excellent inhibitor of hen egg-white lysozyme (HEWL) amyloid progression in vitro. First, SqP4 showed strong affinity toward the native monomeric HEWL leading to the stabilization of the native form and restriction in the unfolding process of monomeric HEWL. Second, SqP4 was found to arrest the amyloidogenic misfolded structure of HEWL in a nonfibrillar monomer-like stage. We also observed the differential effect of the protonation state of the charged amino acid (Asp) within the peptide inhibitor on the amyloid formation of HEWL and explored the reason behind the observations. The findings of this study can be implemented in future strategies for the development of potent therapeutics against other amyloid-related diseases.

Cryo-EM and Solid State NMR Together Provide a More Comprehensive Structural Investigation of Protein Fibrils

The Tropomyosin 1 isoform I/C C-terminal domain (Tm1-LC) fibril structure is studied jointly with cryogenic electron microscopy (cryo-EM) and solid state nuclear magnetic resonance (NMR). This study demonstrates the complementary nature of these two structural biology techniques. Chemical shift assignments from solid state NMR are used to determine the secondary structure at the level of individual amino acids, which is faithfully seen in cryo-EM reconstructions. Additionally, solid state NMR demonstrates that the region not observed in the reconstructed cryo-EM density is primarily in a highly mobile random coil conformation rather than adopting multiple rigid conformations. Overall, this study illustrates the benefit of investigations combining cryo-EM and solid state NMR to investigate protein fibril structure.

Advanced computational approaches to understand protein aggregation

Protein aggregation is a widespread phenomenon implicated in debilitating diseases like Alzheimer's, Parkinson's, and cataracts, presenting complex hurdles for the field of molecular biology. In this review, we explore the evolving realm of computational methods and bioinformatics tools that have revolutionized our comprehension of protein aggregation. Beginning with a discussion of the multifaceted challenges associated with understanding this process and emphasizing the critical need for precise predictive tools, we highlight how computational techniques have become indispensable for understanding protein aggregation. We focus on molecular simulations, notably molecular dynamics (MD) simulations, spanning from atomistic to coarse-grained levels, which have emerged as pivotal tools in unraveling the complex dynamics governing protein aggregation in diseases such as cataracts, Alzheimer's, and Parkinson's. MD simulations provide microscopic insights into protein interactions and the subtleties of aggregation pathways, with advanced techniques like replica exchange molecular dynamics, Metadynamics (MetaD), and umbrella sampling enhancing our understanding by probing intricate energy landscapes and transition states. We delve into specific applications of MD simulations, elucidating the chaperone mechanism underlying cataract formation using Markov state modeling and the intricate pathways and interactions driving the toxic aggregate formation in Alzheimer's and Parkinson's disease. Transitioning we highlight how computational techniques, including bioinformatics, sequence analysis, structural data, machine learning algorithms, and artificial intelligence have become indispensable for predicting protein aggregation propensity and locating aggregation-prone regions within protein sequences. Throughout our exploration, we underscore the symbiotic relationship between computational approaches and empirical data, which has paved the way for potential therapeutic strategies against protein aggregation-related diseases. In conclusion, this review offers a comprehensive overview of advanced computational methodologies and bioinformatics tools that have catalyzed breakthroughs in unraveling the molecular basis of protein aggregation, with significant implications for clinical interventions, standing at the intersection of computational biology and experimental research.

Amyloidogenic regions in beta-strands II and III modulate the aggregation and toxicity of SOD1 in living cells

Mutations in the protein superoxide dismutase-1 (SOD1) promote its misfolding and aggregation, ultimately causing familial forms of the debilitating neurodegenerative disease amyotrophic lateral sclerosis (ALS). Currently, over 220 (mostly missense) ALS-causing mutations in the SOD1 protein have been identified, indicating that common structural features are responsible for aggregation and toxicity. Using in silico tools, we predicted amyloidogenic regions in the ALS-associated SOD1-G85R mutant, finding seven regions throughout the structure. Introduction of proline residues into β-strands II (I18P) or III (I35P) reduced the aggregation propensity and toxicity of SOD1-G85R in cells, significantly more so than proline mutations in other amyloidogenic regions. The I18P and I35P mutations also reduced the capability of SOD1-G85R to template onto previously formed non-proline mutant SOD1 aggregates as measured by fluorescence recovery after photobleaching. Finally, we found that, while the I18P and I35P mutants are less structurally stable than SOD1-G85R, the proline mutants are less aggregation-prone during proteasome inhibition, and less toxic to cells overall. Our research highlights the importance of a previously underappreciated SOD1 amyloidogenic region in β-strand II (15QGIINF20) to the aggregation and toxicity of SOD1 in ALS mutants, and suggests that β-strands II and III may be good targets for the development of SOD1-associated ALS therapies.

Hotspot site microenvironment in the deubiquitinase OTUB1 drives its stability and aggregation

Lewy bodies (LB) are aberrant protein accumulations observed in the brain cells of individuals affected by Parkinson's disease (PD). A comprehensive analysis of LB proteome identified over a hundred proteins, many co-enriched with α-synuclein, a major constituent of LB. Within this context, OTUB1, a deubiquitinase detected in LB, exhibits amyloidogenic properties, yet the mechanisms underlying its aggregation remain elusive. In this study, we identify two critical sites in OTUB1-namely, positions 133 and 173-that significantly impact its amyloid aggregation. Substituting alanine at position 133 and lysine at position 173 enhances both thermodynamic and kinetic stability, effectively preventing amyloid aggregation. Remarkably, lysine at position 173 demonstrates the highest stability without compromising enzymatic activity. The increased stability and inhibition of amyloid aggregation are attributed mainly to the changes in the specific microenvironment at the hotspot. In our exploration of the in-vivo co-occurrence of α-synuclein and OTUB1 in LB, we observed a synergistic modulation of each other's aggregation. Collectively, our study unveils the molecular determinants influencing OTUB1 aggregation, shedding light on the role of specific residues in modulating aggregation kinetics and structural transition. These findings contribute valuable insights into the complex interplay of amino acid properties and protein aggregation, with potential implications for understanding broader aspects of protein folding and aggregation phenomena.

PACSAB Server: A Web-Based Tool for the Study of Aggregation and the Conformational Ensemble of Disordered and Folded Proteins

We present in this article the PACSAB server, which is designed to provide information about the structural ensemble and interactions of both stable and disordered proteins to researchers in the field of molecular biology. The use of this tool does not require any computational skills as the user just needs to upload the structure of the protein to be studied; the server runs a simulation with the PACSAB model, a highly accurate coarse-grained model that is much more efficient than standard molecular dynamics for the exploration of the conformational space of multiprotein systems. The trajectories generated by the simulations based on this model reveal the propensity of the protein under study for aggregation, identify the residues playing a central role in the aggregation process, and reproduce the whole conformational space of disordered proteins. All of this information is shown and can be downloaded from the web page.

Recombinant multiepitope proteins expressed in Escherichia coli cells and their potential for immunodiagnosis

Recombinant multiepitope proteins (RMPs) are a promising alternative for application in diagnostic tests and, given their wide application in the most diverse diseases, this review article aims to survey the use of these antigens for diagnosis, as well as discuss the main points surrounding these antigens. RMPs usually consisting of linear, immunodominant, and phylogenetically conserved epitopes, has been applied in the experimental diagnosis of various human and animal diseases, such as leishmaniasis, brucellosis, cysticercosis, Chagas disease, hepatitis, leptospirosis, leprosy, filariasis, schistosomiasis, dengue, and COVID-19. The synthetic genes for these epitopes are joined to code a single RMP, either with spacers or fused, with different biochemical properties. The epitopes' high density within the RMPs contributes to a high degree of sensitivity and specificity. The RMPs can also sidestep the need for multiple peptide synthesis or multiple recombinant proteins, reducing costs and enhancing the standardization conditions for immunoassays. Methods such as bioinformatics and circular dichroism have been widely applied in the development of new RMPs, helping to guide their construction and better understand their structure. Several RMPs have been expressed, mainly using the Escherichia coli expression system, highlighting the importance of these cells in the biotechnological field. In fact, technological advances in this area, offering a wide range of different strains to be used, make these cells the most widely used expression platform. RMPs have been experimentally used to diagnose a broad range of illnesses in the laboratory, suggesting they could also be useful for accurate diagnoses commercially. On this point, the RMP method offers a tempting substitute for the production of promising antigens used to assemble commercial diagnostic kits.

Gut microbiota produces biofilm-associated amyloids with potential for neurodegeneration

Age-related neurodegenerative diseases involving amyloid aggregation remain one of the biggest challenges of modern medicine. Alterations in the gastrointestinal microbiome play an active role in the aetiology of neurological disorders. Here, we dissect the amyloidogenic properties of biofilm-associated proteins (BAPs) of the gut microbiota and their implications for synucleinopathies. We demonstrate that BAPs are naturally assembled as amyloid-like fibrils in insoluble fractions isolated from the human gut microbiota. We show that BAP genes are part of the accessory genomes, revealing microbiome variability. Remarkably, the abundance of certain BAP genes in the gut microbiome is correlated with Parkinson's disease (PD) incidence. Using cultured dopaminergic neurons and Caenorhabditis elegans models, we report that BAP-derived amyloids induce α-synuclein aggregation. Our results show that the chaperone-mediated autophagy is compromised by BAP amyloids. Indeed, inoculation of BAP fibrils into the brains of wild-type mice promote key pathological features of PD. Therefore, our findings establish the use of BAP amyloids as potential targets and biomarkers of α-synucleinopathies.

Structural effects of charge destabilization and amino acid substitutions in amyloid fragments of CsgA

The most studied functional amyloid is the CsgA, major curli subunit protein, which is produced by numerous strains of Enterobacteriaceae. Although CsgA sequences are highly conserved, they exhibit species diversity, which reflects the specific evolutionary and functional adaptability of the major curli subunit. Herein, we performed bioinformatics analyses to uncover the differences in the amyloidogenic properties of the R4 fragments in Escherichia coli and Salmonella enterica and proposed four mutants for more detailed studies: M1, M2, M3, and M4. The mutated sequences were characterized by various experimental techniques, such as circular dichroism, ATR-FTIR, FT-Raman, thioflavin T, transmission electron microscopy and confocal microscopy. Additionally, molecular dynamics simulations were performed to determine the role of buffer ions in the aggregation process. Our results demonstrated that the aggregation kinetics, fibril morphology, and overall structure of the peptide were significantly affected by the positions of charged amino acids within the repeat sequences of CsgA. Notably, substituting glycine with lysine resulted in the formation of distinctive spherically packed globular aggregates. The differences in morphology observed are attributed to the influence of phosphate ions, which disrupt the local electrostatic interaction network of the polypeptide chains. This study provides knowledge on the preferential formation of amyloid fibrils based on charge states within the polypeptide chain.

Furosemide Derails Human Lysozyme Fibrillation by Interacting with Aggregation Hot Spots: A Biophysical Comprehension

Kidney-associated human lysozyme amyloidosis leads to renal impairments;thus, patients are often prescribed furosemide. Based on this fact, the effect of furosemide on induced human lysozyme fibrillation, in vitro, is evaluated by spectroscopic, calorimetric, computational, and cellular-based assays/methods. Results show that furosemide increases the lag phase and decreases the apparent rate of aggregation of human lysozyme, thereby decelerating the nucleation phase and amyloid fibril formation, as confirmed by the decrease in the level of Thioflavin-T fluorescence. Fewer entities of hydrodynamic radii of ∼171 nm instead of amyloid fibrils (∼412 nm) are detected in human lysozyme in the presence of furosemide by dynamic light scattering. Moreover, furosemide decreases the extent of conversion of the α/β structure of human lysozyme into a predominant β-sheet. The isothermal titration calorimetry established that furosemide forms a complex with human lysozyme, which was also confirmed through fluorescence quenching and computational studies. Also, human lysozyme lytic activity is inhibited competitively by furosemide due to the involvement of amino acid residues of the active site in catalysis, as well as complex formation. Conclusively, furosemide interacts with Gln58, Ile59, Asn60, Ala108, and Trp109 of aggregation-prone regions 2 and 4 of human lysozyme, thereby masking its sites of aggregation and generating only lower-order entities that are less toxic to red blood cells than the fibrils. Thus, furosemide slows the progression of amyloid fibrillation in human lysozyme.

CORDAX web server: an online platform for the prediction and 3D visualization of aggregation motifs in protein sequences

MOTIVATION

Proteins, the molecular workhorses of biological systems, execute a multitude of critical functions dictated by their precise three-dimensional structures. In a complex and dynamic cellular environment, proteins can undergo misfolding, leading to the formation of aggregates that take up various forms, including amorphous and ordered aggregation in the shape of amyloid fibrils. This phenomenon is closely linked to a spectrum of widespread debilitating pathologies, such as Alzheimer's disease, Parkinson's disease, type-II diabetes, and several other proteinopathies, but also hampers the engineering of soluble agents, as in the case of antibody development. As such, the accurate prediction of aggregation propensity within protein sequences has become pivotal due to profound implications in understanding disease mechanisms, as well as in improving biotechnological and therapeutic applications.

RESULTS

We previously developed Cordax, a structure-based predictor that utilizes logistic regression to detect aggregation motifs in protein sequences based on their structural complementarity to the amyloid cross-beta architecture. Here, we present a dedicated web server interface for Cordax. This online platform combines several features including detailed scoring of sequence aggregation propensity, as well as 3D visualization with several customization options for topology models of the structural cores formed by predicted aggregation motifs. In addition, information is provided on experimentally determined aggregation-prone regions that exhibit sequence similarity to predicted motifs, scores, and links to other predictor outputs, as well as simultaneous predictions of relevant sequence propensities, such as solubility, hydrophobicity, and secondary structure propensity.

AVAILABILITY AND IMPLEMENTATION

The Cordax webserver is freely accessible at https://cordax.switchlab.org/.

Intrinsically disordered Pseudomonas chaperone FapA slows down the fibrillation of major biofilm-forming functional amyloid FapC

Functional bacterial amyloids play a crucial role in the formation of biofilms, which mediate chronic infections and contribute to antimicrobial resistance. This study focuses on the FapC amyloid fibrillar protein from Pseudomonas, a major contributor to biofilm formation. We investigate the initial steps of FapC amyloid formation and the impact of the chaperone-like protein FapA on this process. Using solution nuclear magnetic resonance (NMR), we recently showed that both FapC and FapA are intrinsically disordered proteins (IDPs). Here, the secondary structure propensities (SSPs) are compared to alphafold (DeepMind, protein structure prediction tool/algorithm: https://alphafold.ebi.ac.uk/) models. We further demonstrate that the FapA chaperone interacts with FapC and significantly slows down the formation of FapC fibrils. Our NMR titrations reveal ~ 18% of the resonances show FapA-induced chemical shift perturbations (CSPs), which has not been previously observed, the largest being for A82, N201, C237, C240, A241, and G245. These sites may suggest a specific interaction site and/or hotspots of fibrillation inhibition/control interface at the repeat-1 (R1)/loop-2 (L2) and L2/R3 transition areas and at the C-terminus of FapC. Remarkably, ~ 90% of FapA NMR signals exhibit substantial CSPs upon titration with FapC, the largest being for S63, A69, A80, and I92. A temperature-dependent effect of FapA was observed on FapC by thioflavin T (ThT) and NMR experiments. This study provides a detailed understanding of the interaction between the FapA and FapC, shedding light on the regulation and slowing down of amyloid formation, and has important implications for the development of therapeutic strategies targeting biofilms and associated infections.

A Rationally Designed Synthetic Antiviral Peptide Binder Targeting the Receptor-Binding Domain of SARS-CoV-2

The severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), a novel coronavirus, is the causative agent responsible for the spread of the COVID19 pandemic across the globe. The global impact of the COVID19 pandemic, the successful approval of vaccines for controlling the pandemic, and the further resurgence of COVID19 necessitate the exploration and validation of alternative therapeutic avenues targeting SARS-CoV-2. The initial entry and further invasion by SARS-CoV-2 require strong protein-protein interactions (PPIs) between the receptor-binding domain (RBD) of the SARS-CoV-2 spike protein and the human angiotensin-converting enzyme 2 (ACE2) receptors expressed on the cell surfaces of various tissues. In principle, disruption of the PPIs between the RBD of SARS-CoV-2 and the ACE2 receptor by designer peptides with optimized pharmacology appears to be an ideal choice for potentially preventing viral entry with minimal immunogenicity. In this context, the current study describes a short, synthetic designer peptide (codenamed SR16, ≤18 aa, molecular weight ≤2.5 kDa), which has a few noncoded amino acids, demonstrates a helical conformation in solution, and also engages the RBD of SARS-CoV-2 through a high-affinity interaction, as judged from a battery of biophysical studies. Further, the designer peptide demonstrates resistance to trypsin degradation, appears to be nontoxic to mammalian cells, and also does not induce hemolysis in freshly isolated human erythrocytes. In summary, SR16 appears to be an ideal peptide binder targeting the RBD of SARS-CoV-2, which has the potential for further optimization and development as an antiviral agent targeting SARS-CoV-2.

Click, Compute, Create: A Review of Web-based Tools for Enzyme Engineering

Enzyme engineering, though pivotal across various biotechnological domains, is often plagued by its time-consuming and labor-intensive nature. This review aims to offer an overview of supportive in silico methodologies for this demanding endeavor. Starting from methods to predict protein structures, to classification of their activity and even the discovery of new enzymes we continue with describing tools used to increase thermostability and production yields of selected targets. Subsequently, we discuss computational methods to modulate both, the activity as well as selectivity of enzymes. Last, we present recent approaches based on cutting-edge machine learning methods to redesign enzymes. With exception of the last chapter, there is a strong focus on methods easily accessible via web-interfaces or simple Python-scripts, therefore readily useable for a diverse and broad community.

Prediction of protein aggregation

The scientific community is very interested in protein aggregation because of its involvement in several neurodegenerative diseases and its significance in industry. Remarkably, fibrillar aggregates are utilized naturally for constructing structural scaffolds or creating biological switches and may be intentionally designed to construct versatile nanomaterials. Consequently, there is a significant need to rationalize and predict protein aggregation. Researchers have developed various computational methodologies and algorithms to predict protein aggregation and understand its underlying mechanics. This chapter aims to summarize the significant advancements in computational methods, accessible resources, and prospective developments in the field of in silico research. We assess the existing computational tools for predicting protein aggregation propensities, detecting areas that are prone to sequential and structural aggregation, analyzing the effects of mutations on protein aggregation, or identifying prion-like domains.

Pathways of amyloid fibril formation and protein aggregation

The main cause of many neurodegenerative diseases and systemic amyloidoses is protein and peptide aggregation and the formation of amyloid fibrils. The study of aggregation mechanisms, the discovery and description of aggregate structures, and a comprehensive understanding of the molecular mechanisms of amyloid formation are of great importance for the diagnostic processes at the molecular level and for the development of therapeutic strategies to counter aggregation-associated disorders. Given that understanding protein misfolding phenomena is directly related to the protein folding process, we will briefly explain the protein folding mechanism and then discuss the important factors involved in protein aggregation. In the following, we review different mechanisms of amyloid formation and finally represent the current knowledge on how amyloid fibrils are formed based on kinetic and thermodynamic factors.

Rational design of antibody-like peptides for targeting the human complement fragment protein C5a

Human complement fragment 5a (C5a) is one of the most potent glycoproteins generated downstream of C3a and C4a during late-stage activation of the complement signaling cascade. C5a recruits receptors like C5aR1 and C5aR2 and is established to play a critical role in complement-mediated inflammation. Thus, excessive C5a in the plasma due to aberrant activation of the complement contributes to the pathophysiology of several chronic inflammatory diseases. Therefore, restricting the excessive interaction of C5a with its receptors by neutralizing C5a has been one of the most effective therapeutic strategies for the management of inflammatory diseases. Indeed, antibodies targeting C5 (Eculizumab), the precursor of C5a, and C5a (Vilobelimab) have already been approved by the FDA. Still, small designer peptides that work like antibodies and can target and stop C5a from interacting with its receptors seem to be a possible therapeutic alternative to antibodies because they are smaller, cheaper to make, more specific to their target, and can get through membrane barriers. As a proof-of-principle, the current study describes the computational design and evaluation of a pair of peptides that are able to form stable high-affinity complexes with the epitope regions of C5a that are important for the recruitment of C5aR1 and C5aR2. The computational data further supports the potential of designer peptides for mimicking the function of antibodies targeting C5a. However, further experimental studies will be required to establish the structure-function relationship of the designer peptides and also to establish the hypothesis of antibody-like peptides targeting C5a.

Interaction and simulation studies suggest the possible molecular targets of intrinsically disordered amyloidogenic antimicrobial peptides in Acinetobacter baumannii

Acinetobacter baumannii is one of the causing agents of nosocomial infections. A wide range of antibiotics fails to work against these pathogens. Hence, there is an urgent requirement to develop other therapeutics to solve this problem. Antimicrobial peptides (AMPs) are a diverse group of naturally occurring peptides that have the ability to kill diverse groups of microorganisms. The major challenge of using AMPs as therapeutics is their unstable nature and the fact that most of their molecular targets are still unknown. In this study, we have selected intrinsically disordered and amyloidogenic AMPs, showing activity against A. baumannii, that is, Bactenecin, Cath BF, Citropin 1.1, DP7, NA-CATH, Tachyplesin, and WAM-1. To identify the probable target of these AMPs in A. baumannii, calculation of docking score, binding energy, dissociation constant, and molecular dynamics analysis was performed with selected seventeen possible molecular targets. The result showed that the most probable molecular targets of most of the intrinsically disordered amyloidogenic AMPs were UDP-N-acetylenol-pyruvoyl-glucosamine reductase (MurB), followed by 33-36 kDa outer membrane protein (Omp 33-36), UDP-N-acetylmuramoyl-l-alanyl-d-glutamate-2,6-diaminopimelate ligase (MurE), and porin Subfamily Protein (PorinSubF). Further, molecular dynamics analysis concluded that the target of antimicrobial peptide Bactenecin is MurB of A. baumannii, and identified other molecular targets of selected AMPs. Additionally, the oligomerization capacity of the selected AMPs was also investigated, and it was shown that the selected AMPs form oligomeric states, and interact with their molecular targets in that state. Experimental validation using purified AMPs and molecular targets needs to be done to confirm the interaction.Communicated by Ramaswamy H. Sarma.

Structural determinants of odorant-binding proteins affecting their ability to form amyloid fibrils

The formation of amyloid fibrils is associated with many severe pathologies as well as the execution of essential physiological functions by proteins. Despite the diversity, all amyloids share a similar morphology and consist of stacked β-strands, suggesting high amyloidogenicity of native proteins enriched with β-structure. Such proteins include those with a β-barrel-like structure with β-strands arranged into a cylindrical β-sheet. However, the mechanisms responsible for destabilization of the native state and triggering fibrillogenesis have not thoroughly explored yet. Here we analyze the structural determinants of fibrillogenesis in proteins with β-barrel structures on the example of odorant-binding protein (OBP), whose amyloidogenicity was recently demonstrated in vitro. We reveal a crucial role in the fibrillogenesis of OBPs for the "open" conformation of the molecule. This conformation is achieved by disrupting the interaction between the β-barrel and the C-terminus of protein monomers or dimers, which exposes "sticky" amyloidogenic sites for interaction. The data suggest that the "open" conformation of OBPs can be induced by destabilizing the native β-barrel structure through the disruption of: 1) intramolecular disulfide cross-linking and non-covalent contacts between the C-terminal fragment and β-barrel in the protein's monomeric form, or 2) intermolecular contacts involved in domain swapping in the protein's dimeric form.

Sulfation of hyaluronic acid reconfigures the mechanistic pathway of bone morphogenetic protein-2 aggregation

Bone morphogenetic protein-2 (BMP-2) is a critical growth factor of bone extracellular matrix (ECM), pivotal for osteogenesis. Glycosaminoglycans (GAGs), another vital ECM biomolecules, interact with growth factors, affecting signal transduction. Our study primarily focused on hyaluronic acid (HA), a prevalent GAG, and its sulfated derivative (SHA). We explored their impact on BMP-2's conformation, aggregation, and mechanistic pathways of aggregation using diverse optical and rheological methods. In the presence of HA and SHA, the secondary structure of BMP-2 underwent a structured transformation, characterized by a substantial increase in beta sheet content, and a detrimental alteration, manifesting as a shift towards unstructured content, respectively. Although both HA and SHA induced BMP-2 aggregation, their mechanisms differed. SHA led to rapid amorphous aggregates, while HA promoted amyloid fibrils with a lag phase and sigmoidal kinetics. Aggregate size and shape varied; HA produced larger structures, SHA smaller. Each aggregation type followed distinct pathways influenced by viscosity and excluded volume. Higher viscosity, low diffusivity of protein and higher excluded volume In the presence of HA promotes fibrillation having size in micrometer range. Low viscosity, high diffusivity of protein and lesser excluded volume leads to amorphous aggregate of size in nanometer range.

Short Peptide Amyloids Are a Potential Sequence Pool for the Emergence of Proteins

Under prebiotic conditions, peptides are capable of self-replication through a structure-based template-assisted mechanism when they form amyloids. Furthermore, peptide amyloids can spontaneously form inside fatty acid vesicles creating membrane enclosed complex structures of variable morphologies. This is possible because fatty acid vesicle membranes act as filters allowing passage of activated amino acids while some amino acids derived from the activated species become non-permeable and trapped in the vesicles. Similarly, nascent peptides derived from the condensation of the activated amino acids are also trapped in the vesicles. It is hypothesized that such preselected peptide amyloids become a sequence pool for the emergence of proteins in life and that after billions of years of cellular evolution, the sequences in the current proteome have diverged significantly from these original seed peptides. If this hypothesis is correct, it could be possible to detect the traces of these seed sequences in current proteomes. Here, we show for all possible 3, 6, 7, 8 or 9 residue sequence motifs that those motifs that are most amyloidogenic/aggregation prone are over-represented in extant proteomes compared to a sequence-randomized proteome. Furthermore, we find that there is a greater proportion of amyloidogenic sequence motifs in archaea proteomes than in the larger primate proteomes. This suggests that the evolution towards larger proteomes leads to smaller proportion of amyloidogenic sequences.

AGRAMP: machine learning models for predicting antimicrobial peptides against phytopathogenic bacteria

Introduction

Antimicrobial peptides (AMPs) are promising alternatives to traditional antibiotics for combating plant pathogenic bacteria in agriculture and the environment. However, identifying potent AMPs through laborious experimental assays is resource-intensive and time-consuming. To address these limitations, this study presents a bioinformatics approach utilizing machine learning models for predicting and selecting AMPs active against plant pathogenic bacteria.

Methods

N-gram representations of peptide sequences with 3-letter and 9-letter reduced amino acid alphabets were used to capture the sequence patterns and motifs that contribute to the antimicrobial activity of AMPs. A 5-fold cross-validation technique was used to train the machine learning models and to evaluate their predictive accuracy and robustness.

Results

The models were applied to predict putative AMPs encoded by intergenic regions and small open reading frames (ORFs) of the citrus genome. Approximately 7% of the 10,000-peptide dataset from the intergenic region and 7% of the 685,924-peptide dataset from the whole genome were predicted as probable AMPs. The prediction accuracy of the reported models range from 0.72 to 0.91. A subset of the predicted AMPs was selected for experimental test against Spiroplasma citri, the causative agent of citrus stubborn disease. The experimental results confirm the antimicrobial activity of the selected AMPs against the target bacterium, demonstrating the predictive capability of the machine learning models.

Discussion

Hydrophobic amino acid residues and positively charged amino acid residues are among the key features in predicting AMPs by the Random Forest Algorithm. Aggregation propensity appears to be correlated with the effectiveness of the AMPs. The described models would contribute to the development of effective AMP-based strategies for plant disease management in agricultural and environmental settings. To facilitate broader accessibility, our model is publicly available on the AGRAMP (Agricultural Ngrams Antimicrobial Peptides) server.

Effect of the aggregated protein dye YAT2150 on Leishmania parasite viability

The problems associated with the drugs currently used to treat leishmaniasis, including resistance, toxicity, and the high cost of some formulations, call for the urgent identification of new therapeutic agents with novel modes of action. The aggregated protein dye YAT2150 has been found to be a potent antileishmanial compound, with a half-maximal inhibitory concentration (IC50) of approximately 0.5 µM against promastigote and amastigote stages of Leishmania infantum. The encapsulation in liposomes of YAT2150 significantly improved its in vitro IC50 to 0.37 and 0.19 µM in promastigotes and amastigotes, respectively, and increased the half-maximal cytotoxic concentration in human umbilical vein endothelial cells to >50 µM. YAT2150 became strongly fluorescent when binding intracellular protein deposits in Leishmania cells. This fluorescence pattern aligns with the proposed mode of action of this drug in the malaria parasite Plasmodium falciparum, the inhibition of protein aggregation. In Leishmania major, YAT2150 rapidly reduced ATP levels, suggesting an alternative antileishmanial mechanism. To the best of our knowledge, this first-in-class compound is the only one described so far having significant activity against both Plasmodium and Leishmania, thus being a potential drug for the treatment of co-infections of both parasites.

Aggregation of Disrupted in Schizophrenia 1 arises from a central region of the protein

An emerging approach to studying major mental illness is through proteostasis, with the identification of several proteins that form insoluble aggregates in the brains of patients. One of these is Disrupted in Schizophrenia 1 (DISC1), a neurodevelopmentally-important scaffold protein, and product of a classic schizophrenia risk gene. DISC1 aggregates have been detected in post mortem brain tissue from patients with schizophrenia, bipolar disorder and major depressive disorder, as well as various model systems, although the mechanism by which it aggregates is still unclear. Aggregation of two other proteins implicated in mental illness, TRIOBP-1 and NPAS3, was shown to be dependent on very specific structural regions of the protein. We therefore looked at the domain structure of DISC1, and investigated which structural elements are key for its aggregation. While none of the known structured DISC1 regions (named D, I, S and C respectively) formed aggregates individually when expressed in neuroblastoma cells, the combination of the D and I regions, plus the linker region between them, formed visible aggregates. Further refinement revealed that a region of approximately 30 amino acids between these two regions is critical for aggregation, and deletion of this region is sufficient to abolish the aggregation propensity of DISC1. This finding from mammalian cell culture contrasts with the recent determination that the C-region of DISC1 can aggregate in vitro, although some variations of the C-terminal of DISC1 could aggregate in our system. It therefore appears likely that DISC1 aggregation, implicated in mental illness, can occur through at least two distinct mechanisms.

A structural and dynamic visualization of the interaction between MAP7 and microtubules

Microtubules (MTs) are key components of the eukaryotic cytoskeleton and are essential for intracellular organization, organelle trafficking and mitosis. MT tasks depend on binding and interactions with MT-associated proteins (MAPs). MT-associated protein 7 (MAP7) has the unusual ability of both MT binding and activating kinesin-1-mediated cargo transport along MTs. Additionally, the protein is reported to stabilize MTs with its 112 amino-acid long MT-binding domain (MTBD). Here we investigate the structural basis of the interaction of MAP7 MTBD with the MT lattice. Using a combination of solid and solution-state nuclear magnetic resonance (NMR) spectroscopy with electron microscopy, fluorescence anisotropy and isothermal titration calorimetry, we shed light on the binding mode of MAP7 to MTs at an atomic level. Our results show that a combination of interactions between MAP7 and MT lattice extending beyond a single tubulin dimer and including tubulin C-terminal tails contribute to formation of the MAP7-MT complex.

A sequence-based model for identifying proteins undergoing liquid-liquid phase separation/forming fibril aggregates via machine learning

Liquid-liquid phase separation (LLPS) and the solid aggregate (also referred to as amyloid aggregates) formation of proteins, have gained significant attention in recent years due to their associations with various physiological and pathological processes in living organisms. The systematic investigation of the differences and connections between proteins undergoing LLPS and those forming amyloid fibrils at the sequence level has not yet been explored. In this research, we aim to address this gap by comparing the two types of proteins across 36 features using collected data available currently. The statistical comparison results indicate that, 24 of the selected 36 features exhibit significant difference between the two protein groups. A LLPS-Fibrils binary classification model built on these 24 features using random forest reveals that the fraction of intrinsically disordered residues (FIDR ) is identified as the most crucial feature. While, in the further three-class LLPS-Fibrils-Background classification model built on the same screened features, the composition of cysteine and that of leucine show more significant contributions than others. Through feature ablation analysis, we finally constructed a model FLFB (Feature-based LLPS-Fibrils-Background protein predictor) using six refined features, with an average area under the receiver operating characteristics of 0.83. This work indicates using sequence features and a machine learning model, proteins undergoing LLPS or forming amyloid fibrils can be identified.

Accelerating Antimicrobial Peptide Discovery for WHO Priority Pathogens through Predictive and Interpretable Machine Learning Models

The escalating menace of multidrug-resistant (MDR) pathogens necessitates a paradigm shift from conventional antibiotics to innovative alternatives. Antimicrobial peptides (AMPs) emerge as a compelling contender in this arena. Employing in silico methodologies, we can usher in a new era of AMP discovery, streamlining the identification process from vast candidate sequences, thereby optimizing laboratory screening expenditures. Here, we unveil cutting-edge machine learning (ML) models that are both predictive and interpretable, tailored for the identification of potent AMPs targeting World Health Organization's (WHO) high-priority pathogens. Furthermore, we have developed ML models that consider the hemolysis of human erythrocytes, emphasizing their therapeutic potential. Anchored in the nuanced physical-chemical attributes gleaned from the three-dimensional (3D) helical conformations of AMPs, our optimized models have demonstrated commendable performance-boasting an accuracy exceeding 75% when evaluated against both low-sequence-identified peptides and recently unveiled AMPs. As a testament to their efficacy, we deployed these models to prioritize peptide sequences stemming from PEM-2 and subsequently probed the bioactivity of our algorithm-predicted peptides vis-à-vis WHO's priority pathogens. Intriguingly, several of these new AMPs outperformed the native PEM-2 in their antimicrobial prowess, thereby underscoring the robustness of our modeling approach. To elucidate ML model outcomes, we probe via Shapley Additive exPlanations (SHAP) values, uncovering intricate mechanisms guiding diverse actions against bacteria. Our state-of-the-art predictive models expedite the design of new AMPs, offering a robust countermeasure to antibiotic resistance. Our prediction tool is available to the public at https://ai-meta.chem.ncu.edu.tw/amp-meta.

Disaggregation of amyloid-beta fibrils via natural metabolites using long timescale replica exchange molecular dynamics simulation studies

CONTEXT

Amyloid fibrils are self-assembled fibrous protein aggregates that are associated with several presently incurable diseases such as Alzheimer's. disease that is characterized by the accumulation of amyloid fibrils in the brain, which leads to the formation of plaques and the death of brain cells. Disaggregation of amyloid fibrils is considered a promising approach to cure Alzheimer's disease. The mechanism of amyloid fibril formation is complex and not fully understood, making it difficult to develop drugs that can target the process. Diacetonamine and cystathionine are potential lead compounds to induce disaggregation of amyloid fibrils.

METHODS

In the current research, we have used long timescale molecular simulation studies and replica exchange molecular dynamics (REMD) for 1000 ns (1 μs) to examine the mechanisms by which natural metabolites can disaggregate amyloid-beta fibrils. Molecular docking was carried out using Glide and with prior protein minimization and ligand preparation. We focused on a screening a database of natural metabolites, as potential candidates for disaggregating amyloid fibrils. We used Desmond with OPLS 3e as a force field. MM-GBSA calculations were performed. Blood-brain barrier permeability, SASA, and radius of gyration parameters were calculated.

Occurrence and evolutionary conservation analysis of α-helical cationic amphiphilic segments in the human proteome

The existence of encrypted fragments with antimicrobial activity in human proteins has been thoroughly demonstrated in the literature. Recently, algorithms for the large-scale identification of these segments in whole proteomes were developed, and the pervasiveness of this phenomenon was stated. These algorithms typically mine encrypted cationic and amphiphilic segments of proteins, which, when synthesized as individual polypeptide sequences, exert antimicrobial activity by membrane disruption. In the present report, the human reference proteome was submitted to the software kamal for the uncovering of protein segments that correspond to putative intragenic antimicrobial peptides (IAPs). The assessment of the identity of these segments, frequency, functional classes of parent proteins, structural relevance, and evolutionary conservation of amino acid residues within their corresponding proteins was conducted in silico. Additionally, the antimicrobial and anticancer activity of six selected synthetic peptides was evaluated. Our results indicate that cationic and amphiphilic segments can be found in 2% of all human proteins, but are more common in transmembrane and peripheral membrane proteins. These segments are surface-exposed basic patches whose amino acid residues present similar conservation scores to other residues with similar solvent accessibility. Moreover, the antimicrobial and anticancer activity of the synthetic putative IAP sequences was irrespective to whether these are associated to membranes in the cellular setting. Our study discusses these findings in light of the current understanding of encrypted peptide sequences, offering some insights into the relevance of these segments to the organism in the context of their harboring proteins or as separate polypeptide sequences.

Structural similarity of human papillomavirus E4 and polyomaviral VP4 exhibited by genomic analysis of the common kestrel (Falco tinnunculus) polyomavirus

Polyomaviruses are widely distributed viruses of birds that may induce developmental deformities and internal organ disorders primarily in nestlings. In this study, polyomavirus sequence was detected in kidney and liver samples of a common kestrel (Falco tinnunculus) that succumbed at a rescue station in Hungary. The amplified 5025 nucleotide (nt) long genome contained the early (large and small T antigen, LTA and STA) and late (viral proteins, VP1, VP2, VP3) open reading frames (ORFs) typical for polyomaviruses. One of the additional putative ORFs (named VP4) showed identical localization with the VP4 and ORF-X of gammapolyomaviruses, but putative splicing sites could not be found in its sequence. Interestingly, the predicted 123 amino acid (aa) long protein sequence showed the highest similarity with human papillomavirus E4 early proteins in respect of the aa distribution and motif arrangement implying similar functions. The LTA of the kestrel polyomavirus shared <59.2% nt and aa pairwise identity with the LTA sequence of other polyomaviruses and formed a separated branch in the phylogenetic tree among gammapolyomaviruses. Accordingly, the kestrel polyomavirus may be the first member of a novel species within the Gammapolyomavirus genus, tentatively named Gammapolyomavirus faltin.

Amyloid-α Peptide Formed through Alternative Processing of the Amyloid Precursor Protein Attenuates Alzheimer's Amyloid-β Toxicity via Cross-Chaperoning

Amyloid aggregation is a key feature of Alzheimer's disease (AD) and a primary target for past and present therapeutic efforts. Recent research is making it increasingly clear that the heterogeneity of amyloid deposits, extending past the commonly targeted amyloid-β (Aβ), must be considered for successful therapy. We recently demonstrated that amyloid-α (Aα or p3), a C-terminal peptidic fragment of Aβ, aggregates rapidly to form amyloids and can expedite the aggregation of Aβ through seeding. Here, we advance the understanding of Aα biophysics and biology in several important ways. We report the first cryogenic electron microscopy (cryo-EM) structure of an Aα amyloid fibril, proving unambiguously that the peptide is fibrillogenic. We demonstrate that Aα induces Aβ to form amyloid aggregates that are less toxic than pure Aβ aggregates and use nuclear magnetic resonance spectroscopy (NMR) to provide insights into specific interactions between Aα and Aβ in solution. This is the first evidence that Aα can coassemble with Aβ and alter its biological effects at relatively low concentrations. Based on the above, we urge researchers in the field to re-examine the significance of Aα in AD.

The blobulator: a webtool for identification and visual exploration of hydrophobic modularity in protein sequences

Motivation

Clusters of hydrophobic residues are known to promote structured protein stability and drive protein aggregation. Recent work has shown that identifying contiguous hydrophobic residue clusters (termed "blobs") has proven useful in both intrinsically disordered protein (IDP) simulation and human genome studies. However, a graphical interface was unavailable.

Results

Here, we present the blobulator: an interactive and intuitive web interface to detect intrinsic modularity in any protein sequence based on hydrophobicity. We demonstrate three use cases of the blobulator and show how identifying blobs with biologically relevant parameters provides useful information about a globular protein, two orthologous membrane proteins, and an IDP. Other potential applications are discussed, including: predicting protein segments with critical roles in tertiary interactions, providing a definition of local order and disorder with clear edges, and aiding in predicting protein features from sequence.

Availability

The blobulator GUI can be found at www.blobulator.branniganlab.org, and the source code with pip installable command line tool can be found on GitHub at www.GitHub.com/BranniganLab/blobulator.

Cryo-EM observation of the amyloid key structure of polymorphic TDP-43 amyloid fibrils

The transactive response DNA-binding protein-43 (TDP-43) is a multi-facet protein involved in phase separation, RNA-binding, and alternative splicing. In the context of neurodegenerative diseases, abnormal aggregation of TDP-43 has been linked to amyotrophic lateral sclerosis and frontotemporal lobar degeneration through the aggregation of its C-terminal domain. Here, we report a cryo-electron microscopy (cryo-EM)-based structural characterization of TDP-43 fibrils obtained from the full-length protein. We find that the fibrils are polymorphic and contain three different amyloid structures. The structures differ in the number and relative orientation of the protofilaments, although they share a similar fold containing an amyloid key motif. The observed fibril structures differ from previously described conformations of TDP-43 fibrils and help to better understand the structural landscape of the amyloid fibril structures derived from this protein.

Optimizing Excipient Properties to Prevent Aggregation in Biopharmaceutical Formulations

Excipients are included within protein biotherapeutic solution formulations to improve colloidal and conformational stability but are generally not designed for the specific purpose of preventing aggregation and improving cryoprotection in solution. In this work, we have explored the relationship between the structure and antiaggregation activity of excipients by utilizing coarse-grained molecular dynamics modeling of protein-excipient interaction. We have studied human serum albumin as a model protein, and we report the interaction of 41 excipients (polysorbates, fatty alcohol ethoxylates, fatty acid ethoxylates, phospholipids, glucosides, amino acids, and others) in terms of the reduction of solvent accessible surface area of aggregation-prone regions, proposed as a mechanism of aggregation prevention. Polyoxyethylene sorbitan had the greatest degree of interaction with aggregation-prone regions, decreasing the solvent accessible surface area of APRs by 20.7 nm2 (40.1%). Physicochemical descriptors generated by Mordred are employed to probe the structure-property relationship using partial least-squares regression. A leave-one-out cross-validated model had a root-mean-square error of prediction of 4.1 nm2 and a mean relative error of prediction of 0.077. Generally, longer molecules with a large number of alcohol-terminated PEG units tended to interact more, with qualitatively different protein interactions, wrapping around the protein. Shorter or less ethoxylated compounds tend to form hemimicellar clusters at the protein surface. We propose that an improved design would feature many short chains of 5 to 10 PEG units in many distinct branches and at least some hydrophobic content in the form of medium-length or greater aliphatic chains (i.e., six or more carbon atoms). The combination of molecular dynamics simulation and quantitative modeling is an important first step in an all-purpose protein-independent model for the computer-aided design of stabilizing excipients.