Structure-based machine-guided mapping of amyloid sequence space reveals uncharted sequence clusters with higher solubilities

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/.

Pathophysiology of Cardiac Amyloidosis

Amyloidosis refers to a heterogeneous group of disorders sharing common pathophysiological mechanisms characterized by the extracellular accumulation of fibrillar deposits consisting of the aggregation of misfolded proteins. Cardiac amyloidosis (CA), usually caused by deposition of misfolded transthyretin or immunoglobulin light chains, is an increasingly recognized cause of heart failure burdened by a poor prognosis. CA manifests with a restrictive cardiomyopathy which progressively leads to biventricular thickening, diastolic and then systolic dysfunction, arrhythmias, and valvular disease. The pathophysiology of CA is multifactorial and includes increased oxidative stress, mitochondrial damage, apoptosis, impaired metabolism, and modifications of intracellular calcium balance.

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.

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.

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/.

Clues to the Design of Aggregation-Resistant Insulin from Proline Scanning of Highly Amyloidogenic Peptides Derived from the N-Terminal Segment of the A-Chain

Insulin aggregation poses a significant problem in pharmacology and medicine as it occurs during prolonged storage of the hormone and in vivo at insulin injection sites. We have recently shown that dominant forces driving the self-assembly of insulin fibrils are likely to arise from intermolecular interactions involving the N-terminal segment of the A-chain (ACC1-13). Here, we study how proline substitutions within the pilot GIVEQ sequence of this fragment affect its propensity to aggregate in both neutral and acidic environments. In a reasonable agreement with in silico prediction based on the Cordax algorithm, proline substitutions at positions 3, 4, and 5 turn out to be very effective in preventing aggregation according to thioflavin T-fluorescence-based kinetic assay, infrared spectroscopy, and atomic force microscopy (AFM). Since the valine and glutamate side chains within this segment are strongly involved in the interactions with the insulin receptor, we have focused on the possible implications of the Q → P substitution for insulin's stability and interactions with the receptor. To this end, comparative molecular dynamics (MD) simulations of the Q5P mutant and wild-type insulin were carried out for both free and receptor-bound (site 1) monomers. The results point to a mild destabilization of the mutant vis à vis the wild-type monomer, as well as partial preservation of key contacts in the complex between Q5P insulin and the receptor. We discuss the implications of these findings in the context of the design of aggregation-resistant insulin analogues retaining hormonal activity.

Proteotoxic stress and the ubiquitin proteasome system

The ubiquitin proteasome system maintains protein homeostasis by regulating the breakdown of misfolded proteins, thereby preventing misfolded protein aggregates. The efficient elimination is vital for preventing damage to the cell by misfolded proteins, known as proteotoxic stress. Proteotoxic stress can lead to the collapse of protein homeostasis and can alter the function of the ubiquitin proteasome system. Conversely, impairment of the ubiquitin proteasome system can also cause proteotoxic stress and disrupt protein homeostasis. This review examines two impacts of proteotoxic stress, 1) disruptions to ubiquitin homeostasis (ubiquitin stress) and 2) disruptions to proteasome homeostasis (proteasome stress). Here, we provide a mechanistic description of the relationship between proteotoxic stress and the ubiquitin proteasome system. This relationship is illustrated by findings from several protein misfolding diseases, mainly neurodegenerative diseases, as well as from basic biology discoveries from yeast to mammals. In addition, we explore the importance of the ubiquitin proteasome system in endoplasmic reticulum quality control, and how proteotoxic stress at this organelle is alleviated. Finally, we highlight how cells utilize the ubiquitin proteasome system to adapt to proteotoxic stress and how the ubiquitin proteasome system can be genetically and pharmacologically manipulated to maintain protein homeostasis.

Local structural preferences in shaping tau amyloid polymorphism

Tauopathies encompass a group of neurodegenerative disorders characterised by diverse tau amyloid fibril structures. The persistence of polymorphism across tauopathies suggests that distinct pathological conditions dictate the adopted polymorph for each disease. However, the extent to which intrinsic structural tendencies of tau amyloid cores contribute to fibril polymorphism remains uncertain. Using a combination of experimental approaches, we here identify a new amyloidogenic motif, PAM4 (Polymorphic Amyloid Motif of Repeat 4), as a significant contributor to tau polymorphism. Calculation of per-residue contributions to the stability of the fibril cores of different pathologic tau structures suggests that PAM4 plays a central role in preserving structural integrity across amyloid polymorphs. Consistent with this, cryo-EM structural analysis of fibrils formed from a synthetic PAM4 peptide shows that the sequence adopts alternative structures that closely correspond to distinct disease-associated tau strains. Furthermore, in-cell experiments revealed that PAM4 deletion hampers the cellular seeding efficiency of tau aggregates extracted from Alzheimer's disease, corticobasal degeneration, and progressive supranuclear palsy patients, underscoring PAM4's pivotal role in these tauopathies. Together, our results highlight the importance of the intrinsic structural propensity of amyloid core segments to determine the structure of tau in cells, and in propagating amyloid structures in disease.

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.

Brain clearance of protein aggregates: a close-up on astrocytes

Protein misfolding and accumulation defines a prevailing feature of many neurodegenerative disorders, finally resulting in the formation of toxic intra- and extracellular aggregates. Intracellular aggregates can enter the extracellular space and be subsequently transferred among different cell types, thus spreading between connected brain districts.Although microglia perform a predominant role in the removal of extracellular aggregated proteins, mounting evidence suggests that astrocytes actively contribute to the clearing process. However, the molecular mechanisms used by astrocytes to remove misfolded proteins are still largely unknown.Here we first provide a brief overview of the progressive transition from soluble monomers to insoluble fibrils that characterizes amyloid proteins, referring to α-Synuclein and Tau as archetypical examples. We then highlight the mechanisms at the basis of astrocyte-mediated clearance with a focus on their potential ability to recognize, collect, internalize and digest extracellular protein aggregates. Finally, we explore the potential of targeting astrocyte-mediated clearance as a future therapeutic approach for the treatment of neurodegenerative disorders characterized by protein misfolding and accumulation.

Expanding the Landscape of Amyloid Sequences with CARs-DB: A Database of Polar Amyloidogenic Peptides from Disordered Proteins

Several databases collecting amyloidogenic regions have been released to provide information on protein sequences able to form amyloid fibrils. However, most of these resources are built with data from experiments that detect highly hydrophobic stretches located within transiently exposed protein segments. We recently demonstrated that cryptic amyloidogenic regions (CARs) of polar nature have the potential to form amyloid fibrils in vitro. Given the underrepresentation of these types of sequences in current amyloid databases, we developed CARs-DB, the first repository that collects thousands of predicted CARs from intrinsically disordered regions. This protocol chapter describes how to use CARs-DB to search for sequences of interest that might be connected to disease or functional protein-protein interactions. In addition, we provide study cases to illustrate the database's features to users. The CARs-DB is readily accessible at http://carsdb.ppmclab.com/ .

Machine learning in Alzheimer's disease drug discovery and target identification

Alzheimer's disease (AD) stands as a formidable neurodegenerative ailment that poses a substantial threat to the elderly population, with no known curative or disease-slowing drugs in existence. Among the vital and time-consuming stages in the drug discovery process, disease modeling and target identification hold particular significance. Disease modeling allows for a deeper comprehension of disease progression mechanisms and potential therapeutic avenues. On the other hand, target identification serves as the foundational step in drug development, exerting a profound influence on all subsequent phases and ultimately determining the success rate of drug development endeavors. Machine learning (ML) techniques have ushered in transformative breakthroughs in the realm of target discovery. Leveraging the strengths of large dataset analysis, multifaceted data processing, and the exploration of intricate biological mechanisms, ML has become instrumental in the quest for effective AD treatments. In this comprehensive review, we offer an account of how ML methodologies are being deployed in the pursuit of drug discovery for AD. Furthermore, we provide an overview of the utilization of ML in uncovering potential intervention strategies and prospective therapeutic targets for AD. Finally, we discuss the principal challenges and limitations currently faced by these approaches. We also explore the avenues for future research that hold promise in addressing these challenges.

PACT - Prediction of amyloid cross-interaction by threading

Amyloid proteins are often associated with the onset of diseases, including Alzheimer's, Parkinson's and many others. However, there is a wide class of functional amyloids that are involved in physiological functions, e.g., formation of microbial biofilms or storage of hormones. Recent studies showed that an amyloid fibril could affect the aggregation of another protein, even from a different species. This may result in amplification or attenuation of the aggregation process. Insight into amyloid cross-interactions may be crucial for better understanding of amyloid diseases and the potential influence of microbial amyloids on human proteins. However, due to the demanding nature of the needed experiments, knowledge of such interactions is still limited. Here, we present PACT (Prediction of Amyloid Cross-interaction by Threading) - the computational method for the prediction of amyloid cross-interactions. The method is based on modeling of a heterogeneous fibril formed by two amyloidogenic peptides. The resulting structure is assessed by the structural statistical potential that approximates its plausibility and energetic stability. PACT was developed and first evaluated mostly on data collected in the AmyloGraph database of interacting amyloids and achieved high values of Area Under ROC (AUC=0.88) and F1 (0.82). Then, we applied our method to study the interactions of CsgA - a bacterial biofilm protein that was not used in our in-reference datasets, which is expressed in several bacterial species that inhabit the human intestines - with two human proteins. The study included alpha-synuclein, a human protein that is involved in Parkinson's disease, and human islet amyloid polypeptide (hIAPP), which is involved in type 2 diabetes. In both cases, PACT predicted the appearance of cross-interactions. Importantly, the method indicated specific regions of the proteins, which were shown to play a central role in both interactions. We experimentally confirmed the novel results of the indicated CsgA fragments interacting with hIAPP based on the kinetic characteristics obtained with the ThT assay. PACT opens the possibility of high-throughput studies of amyloid interactions. Importantly, it can work with fairly long protein fragments, and as a purely physicochemical approach, it relies very little on scarce training data. The tool is available as a web server at https://pact.e-science.pl/pact/ . The local version can be downloaded from https://github.com/KubaWojciechowski/PACT .

Mechanisms and pathology of protein misfolding and aggregation

Despite advances in machine learning-based protein structure prediction, we are still far from fully understanding how proteins fold into their native conformation. The conventional notion that polypeptides fold spontaneously to their biologically active states has gradually been replaced by our understanding that cellular protein folding often requires context-dependent guidance from molecular chaperones in order to avoid misfolding. Misfolded proteins can aggregate into larger structures, such as amyloid fibrils, which perpetuate the misfolding process, creating a self-reinforcing cascade. A surge in amyloid fibril structures has deepened our comprehension of how a single polypeptide sequence can exhibit multiple amyloid conformations, known as polymorphism. The assembly of these polymorphs is not a random process but is influenced by the specific conditions and tissues in which they originate. This observation suggests that, similar to the folding of native proteins, the kinetics of pathological amyloid assembly are modulated by interactions specific to cells and tissues. Here, we review the current understanding of how intrinsic protein conformational propensities are modulated by physiological and pathological interactions in the cell to shape protein misfolding and aggregation pathology.

AggBERT: Best in Class Prediction of Hexapeptide Amyloidogenesis with a Semi-Supervised ProtBERT Model

The prediction of peptide amyloidogenesis is a challenging problem in the field of protein folding. Large language models, such as the ProtBERT model, have recently emerged as powerful tools in analyzing protein sequences for applications, such as predicting protein structure and function. In this article, we describe the use of a semisupervised and fine-tuned ProtBERT model to predict peptide amyloidogenesis from sequences alone. Our approach, which we call AggBERT, achieved state-of-the-art performance, demonstrating the potential for large language models to improve the accuracy and speed of amyloid fibril prediction over simple heuristics or structure-based approaches. This work highlights the transformative potential of machine learning and large language models in the fields of chemical biology and biomedicine.

Census of exposed aggregation-prone regions in proteomes

Loss of solubility usually leads to the detrimental elimination of protein function. In some cases, the protein aggregation is also required for beneficial functions. Given the duality of this phenomenon, it remains a fundamental question how natural selection controls the aggregation. The exponential growth of genomic sequence data and recent progress with in silico predictors of the aggregation allows approaching this problem by a large-scale bioinformatics analysis. Most of the aggregation-prone regions are hidden within the 3D structure, rendering them inaccessible for the intermolecular interactions responsible for aggregation. Thus, the most realistic census of the aggregation-prone regions requires crossing aggregation prediction with information about the location of the natively unfolded regions. This allows us to detect so-called 'exposed aggregation-prone regions' (EARs). Here, we analyzed the occurrence and distribution of the EARs in 76 reference proteomes from the three kingdoms of life. For this purpose, we used a bioinformatics pipeline, which provides a consensual result based on several predictors of aggregation. Our analysis revealed a number of new statistically significant correlations about the presence of EARs in different organisms, their dependence on protein length, cellular localizations, co-occurrence with short linear motifs and the level of protein expression. We also obtained a list of proteins with the conserved aggregation-prone sequences for further experimental tests. Insights gained from this work led to a deeper understanding of the relationship between protein evolution and aggregation.

Sequence-targeted Peptides Divert Functional Bacterial Amyloid Towards Destabilized Aggregates and Reduce Biofilm Formation

Functional bacterial amyloid provides structural stability in biofilm, making it a promising target for anti-biofilm therapeutics. Fibrils formed by CsgA, the major amyloid component in E. coli are extremely robust and can withstand very harsh conditions. Like other functional amyloids, CsgA contains relatively short aggregation-prone regions (APR) which drive amyloid formation. Here, we demonstrate the use of aggregation-modulating peptides to knock down CsgA protein into aggregates with low stability and altered morphology. Remarkably, these CsgA-peptides also modulate fibrillation of the unrelated functional amyloid protein FapC from Pseudomonas, possibly through recognition of FapC segments with structural and sequence similarity with CsgA. The peptides also reduce the level of biofilm formation in E. coli and P. aeruginosa, demonstrating the potential for selective amyloid targeting to combat bacterial biofilm.

Accelerating therapeutic protein design with computational approaches toward the clinical stage

Therapeutic protein, represented by antibodies, is of increasing interest in human medicine. However, clinical translation of therapeutic protein is still largely hindered by different aspects of developability, including affinity and selectivity, stability and aggregation prevention, solubility and viscosity reduction, and deimmunization. Conventional optimization of the developability with widely used methods, like display technologies and library screening approaches, is a time and cost-intensive endeavor, and the efficiency in finding suitable solutions is still not enough to meet clinical needs. In recent years, the accelerated advancement of computational methodologies has ushered in a transformative era in the field of therapeutic protein design. Owing to their remarkable capabilities in feature extraction and modeling, the integration of cutting-edge computational strategies with conventional techniques presents a promising avenue to accelerate the progression of therapeutic protein design and optimization toward clinical implementation. Here, we compared the differences between therapeutic protein and small molecules in developability and provided an overview of the computational approaches applicable to the design or optimization of therapeutic protein in several developability issues.

Cell specialization in cyanobacterial biofilm development revealed by expression of a cell-surface and extracellular matrix protein

Cyanobacterial biofilms are ubiquitous and play important roles in diverse environments, yet, understanding of the processes underlying the development of these aggregates is just emerging. Here we report cell specialization in formation of Synechococcus elongatus PCC 7942 biofilms-a hitherto unknown characteristic of cyanobacterial social behavior. We show that only a quarter of the cell population expresses at high levels the four-gene ebfG-operon that is required for biofilm formation. Almost all cells, however, are assembled in the biofilm. Detailed characterization of EbfG4 encoded by this operon revealed cell-surface localization as well as its presence in the biofilm matrix. Moreover, EbfG1-3 were shown to form amyloid structures such as fibrils and are thus likely to contribute to the matrix structure. These data suggest a beneficial 'division of labor' during biofilm formation where only some of the cells allocate resources to produce matrix proteins-'public goods' that support robust biofilm development by the majority of the cells. In addition, previous studies revealed the operation of a self-suppression mechanism that depends on an extracellular inhibitor, which supresses transcription of the ebfG-operon. Here we revealed inhibitor activity at an early growth stage and its gradual accumulation along the exponential growth phase in correlation with cell density. Data, however, do not support a threshold-like phenomenon known for quorum-sensing in heterotrophs. Together, data presented here demonstrate cell specialization and imply density-dependent regulation thereby providing deep insights into cyanobacterial communal behavior.

GAPR-1 Interferes with Condensate Formation of Beclin 1 in Saccharomyces cerevisiae

Golgi-Associated plant Pathogenesis Related protein 1 (GAPR-1) acts as a negative regulator of autophagy by interacting with Beclin 1 at Golgi membranes in mammalian cells. The molecular mechanism of this interaction is largely unknown. We recently showed that human GAPR-1 (hGAPR-1) has amyloidogenic properties resulting in the formation of protein condensates upon overexpression in Saccharomyces cerevisiae. Here we show that human Beclin 1 (hBeclin 1) has several predicted amyloidogenic regions and that overexpression of hBeclin 1-mCherry in yeast also results in the formation of fluorescent protein condensates. Surprisingly, co-expression of hGAPR-1-GFP and hBeclin 1-mCherry results in a strong reduction of hBeclin 1 condensates. Mutations of the known interaction site on the hGAPR-1 and hBeclin 1 surface abolished the effect on condensate formation during co-expression without affecting the condensate formation properties of the individual proteins. Similarly, a hBeclin 1-derived B18 peptide that is known to bind hGAPR-1 and to interfere with the interaction between hGAPR-1 and hBeclin 1, abolished the reduction of hBeclin 1 condensates by co-expression of hGAPR-1. These results indicate that the same type of protein-protein interactions interfere with condensate formation during co-expression of hGAPR-1 and hBeclin 1 as previously described for their interaction at Golgi membranes. The amyloidogenic properties of the B18 peptide were, however, important for the interaction with hGAPR-1, as mutant peptides with reduced amyloidogenic properties also showed reduced interaction with hGAPR-1 and reduced interference with hGAPR-1/hBeclin 1 condensate formation. We propose that amyloidogenic interactions take place between hGAPR-1 and hBeclin 1 prior to condensate formation.

A guide to studying protein aggregation

Disrupted protein folding or decreased protein stability can lead to the accumulation of (partially) un- or misfolded proteins, which ultimately cause the formation of protein aggregates. Much of the interest in protein aggregation is associated with its involvement in a wide range of human diseases and the challenges it poses for large-scale biopharmaceutical manufacturing and formulation of therapeutic proteins and peptides. On the other hand, protein aggregates can also be functional, as observed in nature, which triggered its use in the development of biomaterials or therapeutics as well as for the improvement of food characteristics. Thus, unmasking the various steps involved in protein aggregation is critical to obtain a better understanding of the underlying mechanism of amyloid formation. This knowledge will allow a more tailored development of diagnostic methods and treatments for amyloid-associated diseases, as well as applications in the fields of new (bio)materials, food technology and therapeutics. However, the complex and dynamic nature of the aggregation process makes the study of protein aggregation challenging. To provide guidance on how to analyse protein aggregation, in this review we summarize the most commonly investigated aspects of protein aggregation with some popular corresponding methods.

Connection between MHC class II binding and aggregation propensity: The antigenic peptide 10 of Paracoccidioides brasiliensis as a benchmark study

The aggregation of epitopes that are also able to bind major histocompatibility complex (MHC) alleles raises questions around the potential connection between the formation of epitope aggregates and their affinities to MHC receptors. We first performed a general bioinformatic assessment over a public dataset of MHC class II epitopes, finding that higher experimental binding correlates with higher aggregation-propensity predictors. We then focused on the case of P10, an epitope used as a vaccine candidate against Paracoccidioides brasiliensis that aggregates into amyloid fibrils. We used a computational protocol to design variants of the P10 epitope to study the connection between the binding stabilities towards human MHC class II alleles and their aggregation propensities. The binding of the designed variants was tested experimentally, as well as their aggregation capacity. High-affinity MHC class II binders in vitro were more disposed to aggregate forming amyloid fibrils capable of binding Thioflavin T and congo red, while low affinity MHC class II binders remained soluble or formed rare amorphous aggregates. This study shows a possible connection between the aggregation propensity of an epitope and its affinity for the MHC class II cleft.

Protein Design: From the Aspect of Water Solubility and Stability

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

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

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

Artificial intelligence and machine-learning approaches in structure and ligand-based discovery of drugs affecting central nervous system

CNS disorders are indications with a very high unmet medical needs, relatively smaller number of available drugs, and a subpar satisfaction level among patients and caregiver. Discovery of CNS drugs is extremely expensive affair with its own unique challenges leading to extremely high attrition rates and low efficiency. With explosion of data in information age, there is hardly any aspect of life that has not been touched by data driven technologies such as artificial intelligence (AI) and machine learning (ML). Drug discovery is no exception, emergence of big data via genomic, proteomic, biological, and chemical technologies has driven pharmaceutical giants to collaborate with AI oriented companies to revolutionise drug discovery, with the goal of increasing the efficiency of the process. In recent years many examples of innovative applications of AI and ML techniques in CNS drug discovery has been reported. Research on therapeutics for diseases such as schizophrenia, Alzheimer's and Parkinsonism has been provided with a new direction and thrust from these developments. AI and ML has been applied to both ligand-based and structure-based drug discovery and design of CNS therapeutics. In this review, we have summarised the general aspects of AI and ML from the perspective of drug discovery followed by a comprehensive coverage of the recent developments in the applications of AI/ML techniques in CNS drug discovery.

Functional Bacterial Amyloids: Understanding Fibrillation, Regulating Biofilm Fibril Formation and Organizing Surface Assemblies

Functional amyloid is produced by many organisms but is particularly well understood in bacteria, where proteins such as CsgA (E. coli) and FapC (Pseudomonas) are assembled as functional bacterial amyloid (FuBA) on the cell surface in a carefully optimized process. Besides a host of helper proteins, FuBA formation is aided by multiple imperfect repeats which stabilize amyloid and streamline the aggregation mechanism to a fast-track assembly dominated by primary nucleation. These repeats, which are found in variable numbers in Pseudomonas, are most likely the structural core of the fibrils, though we still lack experimental data to determine whether the repeats give rise to β-helix structures via stacked β-hairpins (highly likely for CsgA) or more complicated arrangements (possibly the case for FapC). The response of FuBA fibrillation to denaturants suggests that nucleation and elongation involve equal amounts of folding, but protein chaperones preferentially target nucleation for effective inhibition. Smart peptides can be designed based on these imperfect repeats and modified with various flanking sequences to divert aggregation to less stable structures, leading to a reduction in biofilm formation. Small molecules such as EGCG can also divert FuBA to less organized structures, such as partially-folded oligomeric species, with the same detrimental effect on biofilm. Finally, the strong tendency of FuBA to self-assemble can lead to the formation of very regular two-dimensional amyloid films on structured surfaces such as graphite, which strongly implies future use in biosensors or other nanobiomaterials. In summary, the properties of functional amyloid are a much-needed corrective to the unfortunate association of amyloid with neurodegenerative disease and a testimony to nature’s ability to get the best out of a protein fold.

Expression of Huntingtin and TDP-43 Derivatives in Fission Yeast Can Cause Both Beneficial and Toxic Effects

Many neurodegenerative disorders display protein aggregation as a hallmark, Huntingtin and TDP-43 aggregates being characteristic of Huntington disease and amyotrophic lateral sclerosis, respectively. However, whether these aggregates cause the diseases, are secondary by-products, or even have protective effects, is a matter of debate. Mutations in both human proteins can modulate the structure, number and type of aggregates, as well as their toxicity. To study the role of protein aggregates in cellular fitness, we have expressed in a highly tractable unicellular model different variants of Huntingtin and TDP-43. They each display specific patterns of aggregation and toxicity, even though in both cases proteins have to be very highly expressed to affect cell fitness. The aggregation properties of Huntingtin, but not of TDP-43, are affected by chaperones such as Hsp104 and the Hsp40 couple Mas5, suggesting that the TDP-43, but not Huntingtin, derivatives have intrinsic aggregation propensity. Importantly, expression of the aggregating form of Huntingtin causes a significant extension of fission yeast lifespan, probably as a consequence of kidnapping chaperones required for maintaining stress responses off. Our study demonstrates that in general these prion-like proteins do not cause toxicity under normal conditions, and in fact they can protect cells through indirect mechanisms which up-regulate cellular defense pathways.

Mapping the sequence specificity of heterotypic amyloid interactions enables the identification of aggregation modifiers

Heterotypic amyloid interactions between related protein sequences have been observed in functional and disease amyloids. While sequence homology seems to favour heterotypic amyloid interactions, we have no systematic understanding of the structural rules determining such interactions nor whether they inhibit or facilitate amyloid assembly. Using structure-based thermodynamic calculations and extensive experimental validation, we performed a comprehensive exploration of the defining role of sequence promiscuity in amyloid interactions. Using tau as a model system we demonstrate that proteins with local sequence homology to tau amyloid nucleating regions can modify fibril nucleation, morphology, assembly and spreading of aggregates in cultured cells. Depending on the type of mutation such interactions inhibit or promote aggregation in a manner that can be predicted from structure. We find that these heterotypic amyloid interactions can result in the subcellular mis-localisation of these proteins. Moreover, equilibrium studies indicate that the critical concentration of aggregation is altered by heterotypic interactions. Our findings suggest a structural mechanism by which the proteomic background can modulate the aggregation propensity of amyloidogenic proteins and we discuss how such sequence-specific proteostatic perturbations could contribute to the selective cellular susceptibility of amyloid disease progression.

In this work, Louros et al. uncover a rule book for interactions of amyloids with other proteins. This grammar was shown to promote cellular spreading of tau aggregates in cells, but can also be harvested to develop structure-based aggregation blockers.

Computational methods to predict protein aggregation

In most cases, protein aggregation stems from the establishment of non-native intermolecular contacts. The formation of insoluble protein aggregates is associated with many human diseases and is a major bottleneck for the industrial production of protein-based therapeutics. Strikingly, fibrillar aggregates are naturally exploited for structural scaffolding or to generate molecular switches and can be artificially engineered to build up multi-functional nanomaterials. Thus, there is a high interest in rationalizing and forecasting protein aggregation. Here, we review the available computational toolbox to predict protein aggregation propensities, identify sequential or structural aggregation-prone regions, evaluate the impact of mutations on aggregation or recognize prion-like domains. We discuss the strengths and limitations of these algorithms and how they can evolve in the next future.

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

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

Multiple Antimicrobial Effects of Hybrid Peptides Synthesized Based on the Sequence of Ribosomal S1 Protein from Staphylococcus aureus

The need to develop new antimicrobial peptides is due to the high resistance of pathogenic bacteria to traditional antibiotics now and in the future. The creation of synthetic peptide constructs is a common and successful approach to the development of new antimicrobial peptides. In this work, we use a simple, flexible, and scalable technique to create hybrid antimicrobial peptides containing amyloidogenic regions of the ribosomal S1 protein from Staphylococcus aureus. While the cell-penetrating peptide allows the peptide to enter the bacterial cell, the amyloidogenic site provides an antimicrobial effect by coaggregating with functional bacterial proteins. We have demonstrated the antimicrobial effects of the R23F, R23DI, and R23EI hybrid peptides against Staphylococcus aureus, methicillin-resistant S. aureus (MRSA), Pseudomonas aeruginosa, Escherichia coli, and Bacillus cereus. R23F, R23DI, and R23EI can be used as antimicrobial peptides against Gram-positive and Gram-negative bacteria resistant to traditional antibiotics.

Dynamic protein structures in normal function and pathologic misfolding in systemic amyloidosis

Dynamic and disordered regions in native proteins are often critical for their function, particularly in ligand binding and signaling. In certain proteins, however, such regions can contribute to misfolding and pathologic deposition as amyloid fibrils in vivo. For example, dynamic and disordered regions can promote amyloid formation by destabilizing the native structure, by directly triggering the aggregation, by promoting protein condensation, or by acting as sites of early proteolytic cleavage that favor a release of aggregation-prone fragments or facilitate fibril maturation. At the same time, enhanced dynamics in the native protein state accelerates proteolytic degradation that counteracts amyloid accumulation in vivo. Therefore, the functional need for dynamic protein regions must be balanced against their inherently labile nature. How exactly this balance is achieved and how is it shifted upon amyloidogenic mutations or post-translational modifications? To illustrate possible scenarios, here we review the beneficial and pathologic roles of dynamic and disordered regions in the native states of three families of human plasma proteins that form amyloid precursors in systemic amyloidoses: immunoglobulin light chain, apolipoproteins, and serum amyloid A. Analysis of structure, stability and local dynamics of these diverse proteins and their amyloidogenic variants exemplifies how disordered/dynamic regions can provide a functional advantage as well as an Achilles heel in pathologic amyloid formation.

Heterotypic amyloid interactions: Clues to polymorphic bias and selective cellular vulnerability?

The number of atomic-resolution structures of disease-associated amyloids has greatly increased in recent years. These structures have confirmed not only the polymorphic nature of amyloids but also the association of specific polymorphs to particular proteinopathies. These observations are strengthening the view that amyloid polymorphism is a marker for specific pathological subtypes (e.g. in tauopathies or synucleinopathies). The nature of this association and how it relates to the selective cellular vulnerability of amyloid nucleation, propagation and toxicity are still unclear. Here, we provide an overview of the mechanistic insights provided by recent patient-derived amyloid structures. We discuss the framework organisation of amyloid polymorphism and how heterotypic amyloid interactions with the physiological environment could modify the solubility and assembly of amyloidogenic proteins. We conclude by hypothesising how such interactions could contribute to selective cellular vulnerability.

Peptides derived from gp43, the most antigenic protein from Paracoccidioides brasiliensis, form amyloid fibrils in vitro: implications for vaccine development

Fungal infection is an important health problem in Latin America, and in Brazil in particular. Paracoccidioides (mainly P. brasiliensis and P. lutzii) is responsible for paracoccidioidomycosis, a disease that affects mainly the lungs. The glycoprotein gp43 is involved in fungi adhesion to epithelial cells, which makes this protein an interesting target of study. A specific stretch of 15 amino acids that spans the region 181–195 (named P10) of gp43 is an important epitope of gp43 that is being envisioned as a vaccine candidate. Here we show that synthetic P10 forms typical amyloid aggregates in solution in very short times, a property that could hamper vaccine development. Seeds obtained by fragmentation of P10 fibrils were able to induce the aggregation of P4, but not P23, two other peptides derived from gp43. In silico analysis revealed several regions within the P10 sequence that can form amyloid with steric zipper architecture. Besides, in-silico proteolysis studies with gp43 revealed that aggregation-prone, P10-like peptides could be generated by several proteases, which suggests that P10 could be formed under physiological conditions. Considering our data in the context of a potential vaccine development, we redesigned the sequence of P10, maintaining the antigenic region (HTLAIR), but drastically reducing its aggregation propensity.

Precision Psychiatry: Machine learning as a tool to find new pharmacological targets

There is an increasing amount of data arising from neurobehavioral sciences and medical records that cannot be adequately analyzed by traditional research methods. New drugs develop at a slow rate and seem unsatisfactory for the majority of neurobehavioral disorders. Machine learning (ML) techniques, instead, can incorporate psychopathological, computational, cognitive, and neurobiological underpinning knowledge leading to a refinement of detection, diagnosis, prognosis, treatment, research, and support. Machine and deep learning methods are currently used to accelerate the process of discovering new pharmacological targets and drugs.

OBJECTIVE

The present work reviews current evidence regarding the contribution of machine learning to the discovery of new drug targets.

METHODS

Scientific articles from PubMed, SCOPUS, EMBASE, and Web of Science Core Collection published until May 2021 were included in this review.

RESULTS

The most significant areas of research are schizophrenia, depression and anxiety, Alzheimer´s disease, and substance use disorders. ML techniques have pinpointed target gene candidates and pathways, new molecular substances, and several biomarkers regarding psychiatric disorders. Drug repositioning studies using ML have identified multiple drug candidates as promising therapeutic agents.

CONCLUSION

Next-generation ML techniques and subsequent deep learning may power new findings regarding the discovery of new pharmacological agents by bridging the gap between biological data and chemical drug information.

Protein structure and aggregation: a marriage of necessity ruled by aggregation gatekeepers

Protein aggregation propensity is a pervasive and seemingly inescapable property of proteomes. Strikingly, a significant fraction of the proteome is supersaturated, meaning that, for these proteins, their native conformation is less stable than the aggregated state. Maintaining the integrity of a proteome under such conditions is precarious and requires energy-consuming proteostatic regulation. Why then is aggregation propensity maintained at such high levels over long evolutionary timescales? Here, we argue that the conformational stability of the native and aggregated states are correlated thermodynamically and that codon usage strengthens this correlation. As a result, the folding of stable proteins requires kinetic control to avoid aggregation, provided by aggregation gatekeepers. These unique residues are evolutionarily selected to kinetically favor native folding, either on their own or by coopting chaperones.

Cryptic amyloidogenic regions in intrinsically disordered proteins: Function and disease association

The amyloid conformation is considered a fundamental state of proteins and the propensity to populate it a generic property of polypeptides. Multiple proteome-wide analyses addressed the presence of amyloidogenic regions in proteins, nurturing our understanding of their nature and biological implications. However, these analyses focused on highly aggregation-prone and hydrophobic stretches that are only marginally found in intrinsically disordered regions (IDRs). Here, we explore the prevalence of cryptic amyloidogenic regions (CARs) of polar nature in IDRs. CARs are widespread in IDRs and associated with IDPs function, with particular involvement in protein–protein interactions, but their presence is also connected to a risk of malfunction. By exploring this function/malfunction dichotomy, we speculate that ancestral CARs might have evolved into functional interacting regions playing a significant role in protein evolution at the origins of life.

Biomolecular simulation based machine learning models accurately predict sites of tolerability to the unnatural amino acid acridonylalanine

The incorporation of unnatural amino acids (Uaas) has provided an avenue for novel chemistries to be explored in biological systems. However, the successful application of Uaas is often hampered by site-specific impacts on protein yield and solubility. Although previous efforts to identify features which accurately capture these site-specific effects have been unsuccessful, we have developed a set of novel Rosetta Custom Score Functions and alternative Empirical Score Functions that accurately predict the effects of acridon-2-yl-alanine (Acd) incorporation on protein yield and solubility. Acd-containing mutants were simulated in PyRosetta, and machine learning (ML) was performed using either the decomposed values of the Rosetta energy function, or changes in residue contacts and bioinformatics. Using these feature sets, which represent Rosetta score function specific and bioinformatics-derived terms, ML models were trained to predict highly abstract experimental parameters such as mutant protein yield and solubility and displayed robust performance on well-balanced holdouts. Model feature importance analyses demonstrated that terms corresponding to hydrophobic interactions, desolvation, and amino acid angle preferences played a pivotal role in predicting tolerance of mutation to Acd. Overall, this work provides evidence that the application of ML to features extracted from simulated structural models allow for the accurate prediction of diverse and abstract biological phenomena, beyond the predictivity of traditional modeling and simulation approaches.

Sequence Determinants of the Aggregation of Proteins Within Condensates Generated by Liquid-liquid Phase Separation

The transition between the native and amyloid states of proteins can proceed via a deposition pathway via oligomeric intermediates or via a condensation pathway involving liquid droplet intermediates generated through liquid-liquid phase separation. While several computational methods are available to perform sequence-based predictions of the propensity of proteins to aggregate via the deposition pathway, much less is known about the physico-chemical principles that underlie aggregation within condensates. Here we investigate the sequence determinants of aggregation via the condensation pathway, and identify three relevant features: droplet-promoting propensity, aggregation-promoting propensity and multimodal interactions quantified by the binding mode entropy. By using this approach, we show that it is possible to predict aggregation-promoting mutations in droplet-forming proteins associated with amyotrophic lateral sclerosis (ALS). This analysis provides insights into the amino acid code for the conversion of proteins between liquid-like and solid-like condensates.

Exploring amyloid oligomers with peptide model systems

The assembly of amyloidogenic peptides and proteins, such as the β-amyloid peptide, α-synuclein, huntingtin, tau, and islet amyloid polypeptide, into amyloid fibrils and oligomers is directly linked to amyloid diseases, such as Alzheimer's, Parkinson's, and Huntington's diseases, frontotemporal dementias, and type II diabetes. Although amyloid oligomers have emerged as especially important in amyloid diseases, high-resolution structures of the oligomers formed by full-length amyloidogenic peptides and proteins have remained elusive. Investigations of oligomers assembled from fragments or stabilized β-hairpin segments of amyloidogenic peptides and proteins have allowed investigators to illuminate some of the structural, biophysical, and biological properties of amyloid oligomers. Here, we summarize recent advances in the application of these peptide model systems to investigate and understand the structures, biological properties, and biophysical properties of amyloid oligomers.

Aggregation is a Context-Dependent Constraint on Protein Evolution

Solubility is a requirement for many cellular processes. Loss of solubility and aggregation can lead to the partial or complete abrogation of protein function. Thus, understanding the relationship between protein evolution and aggregation is an important goal. Here, we analysed two deep mutational scanning experiments to investigate the role of protein aggregation in molecular evolution. In one data set, mutants of a protein involved in RNA biogenesis and processing, human TAR DNA binding protein 43 (TDP-43), were expressed in S. cerevisiae. In the other data set, mutants of a bacterial enzyme that controls resistance to penicillins and cephalosporins, TEM-1 beta-lactamase, were expressed in E. coli under the selective pressure of an antibiotic treatment. We found that aggregation differentiates the effects of mutations in the two different cellular contexts. Specifically, aggregation was found to be associated with increased cell fitness in the case of TDP-43 mutations, as it protects the host from aberrant interactions. By contrast, in the case of TEM-1 beta-lactamase mutations, aggregation is linked to a decreased cell fitness due to inactivation of protein function. Our study shows that aggregation is an important context-dependent constraint of molecular evolution and opens up new avenues to investigate the role of aggregation in the cell.

Bacterial Protein Homeostasis Disruption as a Therapeutic Intervention

Cells have evolved a complex molecular network, collectively called the protein homeostasis (proteostasis) network, to produce and maintain proteins in the appropriate conformation, concentration and subcellular localization. Loss of proteostasis leads to a reduction in cell viability, which occurs to some degree during healthy ageing, but is also the root cause of a group of diverse human pathologies. The accumulation of proteins in aberrant conformations and their aggregation into specific beta-rich assemblies are particularly detrimental to cell viability and challenging to the protein homeostasis network. This is especially true for bacteria; it can be argued that the need to adapt to their changing environments and their high protein turnover rates render bacteria particularly vulnerable to the disruption of protein homeostasis in general, as well as protein misfolding and aggregation. Targeting bacterial proteostasis could therefore be an attractive strategy for the development of novel antibacterial therapeutics. This review highlights advances with an antibacterial strategy that is based on deliberately inducing aggregation of target proteins in bacterial cells aiming to induce a lethal collapse of protein homeostasis. The approach exploits the intrinsic aggregation propensity of regions residing in the hydrophobic core regions of the polypeptide sequence of proteins, which are genetically conserved because of their essential role in protein folding and stability. Moreover, the molecules were designed to target multiple proteins, to slow down the build-up of resistance. Although more research is required, results thus far allow the hope that this strategy may one day contribute to the arsenal to combat multidrug-resistant bacterial infections.

Artificial intelligence and machine learning-aided drug discovery in central nervous system diseases: State-of-the-arts and future directions

Neurological disorders significantly outnumber diseases in other therapeutic areas. However, developing drugs for central nervous system (CNS) disorders remains the most challenging area in drug discovery, accompanied with the long timelines and high attrition rates. With the rapid growth of biomedical data enabled by advanced experimental technologies, artificial intelligence (AI) and machine learning (ML) have emerged as an indispensable tool to draw meaningful insights and improve decision making in drug discovery. Thanks to the advancements in AI and ML algorithms, now the AI/ML-driven solutions have an unprecedented potential to accelerate the process of CNS drug discovery with better success rate. In this review, we comprehensively summarize AI/ML-powered pharmaceutical discovery efforts and their implementations in the CNS area. After introducing the AI/ML models as well as the conceptualization and data preparation, we outline the applications of AI/ML technologies to several key procedures in drug discovery, including target identification, compound screening, hit/lead generation and optimization, drug response and synergy prediction, de novo drug design, and drug repurposing. We review the current state-of-the-art of AI/ML-guided CNS drug discovery, focusing on blood-brain barrier permeability prediction and implementation into therapeutic discovery for neurological diseases. Finally, we discuss the major challenges and limitations of current approaches and possible future directions that may provide resolutions to these difficulties.

The Budapest Amyloid Predictor and Its Applications

The amyloid state of proteins is widely studied with relevance to neurology, biochemistry, and biotechnology. In contrast with nearly amorphous aggregation, the amyloid state has a well-defined structure, consisting of parallel and antiparallel β-sheets in a periodically repeated formation. The understanding of the amyloid state is growing with the development of novel molecular imaging tools, like cryogenic electron microscopy. Sequence-based amyloid predictors were developed, mainly using artificial neural networks (ANNs) as the underlying computational technique. From a good neural-network-based predictor, it is a very difficult task to identify the attributes of the input amino acid sequence, which imply the decision of the network. Here, we present a linear Support Vector Machine (SVM)-based predictor for hexapeptides with correctness higher than 84%, i.e., it is at least as good as the best published ANN-based tools. Unlike artificial neural networks, the decisions of the linear SVMs are much easier to analyze and, from a good predictor, we can infer rich biochemical knowledge. In the Budapest Amyloid Predictor webserver the user needs to input a hexapeptide, and the server outputs a prediction for the input plus the 6 × 19 = 114 distance-1 neighbors of the input hexapeptide.

Heterotypic interactions in amyloid function and disease

Amyloid aggregation results from the self-assembly of identical aggregation-prone sequences into cross-beta-sheet structures. The process is best known for its association with a wide range of human pathologies but also as a functional mechanism in all kingdoms of life. Less well elucidated is the role of heterotypic interactions between amyloids and other proteins and macromolecules and how this contributes to disease. We here review current data with a focus on neurodegenerative amyloid-associated diseases. Evidence indicates that heterotypic interactions occur in a wide range of amyloid processes and that these interactions modify fundamental aspects of amyloid aggregation including seeding, aggregation rates and toxicity. More work is required to understand the mechanistic origin of these interactions, but current understanding suggests that both supersaturation and sequence-specific binding can contribute to heterotypic amyloid interactions. Further unravelling these mechanisms may help to answer outstanding questions in the field including the selective vulnerability of cells types and tissues and the stereotypical spreading patterns of amyloids in disease.