Intrinsically disordered proteins in various hypotheses on the pathogenesis of Alzheimer's and Parkinson's diseases

Beyond AlphaFold2: The Impact of AI for the Further Improvement of Protein Structure Prediction

Protein structure prediction is fundamental to molecular biology and has numerous applications in areas such as drug discovery and protein engineering. Machine learning techniques have greatly advanced protein 3D modeling in recent years, particularly with the development of AlphaFold2 (AF2), which can analyze sequences of amino acids and predict 3D structures with near experimental accuracy. Since the release of AF2, numerous studies have been conducted, either using AF2 directly for large-scale modeling or building upon the software for other use cases. Many reviews have been published discussing the impact of AF2 in the field of protein bioinformatics, particularly in relation to neural networks, which have highlighted what AF2 can and cannot do. It is evident that AF2 and similar approaches are open to further development and several new approaches have emerged, in addition to older refinement approaches, for improving the quality of predictions. Here we provide a brief overview, aimed at the general biologist, of how machine learning techniques have been used for improvement of 3D models of proteins following AF2, and we highlight the impacts of these approaches. In the most recent experiment on the Critical Assessment of Techniques for Protein Structure Prediction (CASP15), the most successful groups all developed their own tools for protein structure modeling that were based at least in some part on AF2. This improvement involved employing techniques such as generative modeling, changing parameters such as dropout to generate more AF2 structures, and data-driven approaches including using alternative templates and MSAs.

Accurate and Fast Prediction of Intrinsically Disordered Protein by Multiple Protein Language Models and Ensemble Learning

Intrinsically disordered proteins (IDPs) play a vital role in various biological processes and have attracted increasing attention in the past few decades. Predicting IDPs from the primary structures of proteins offers a rapid and facile means of protein analysis without necessitating crystal structures. In particular, machine learning methods have demonstrated their potential in this field. Recently, protein language models (PLMs) are emerging as a promising approach to extracting essential information from protein sequences and have been employed in protein modeling to utilize their advantages of precision and efficiency. In this article, we developed a novel IDP prediction method named IDP-ELM to predict the intrinsically disordered regions (IDRs) as well as their functions including disordered flexible linkers and disordered protein binding. This method utilizes high-dimensional representations extracted from several state-of-the-art PLMs and predicts IDRs by ensemble learning based on bidirectional recurrent neural networks. The performance of the method was evaluated on two independent test data sets from CAID (critical assessment of protein intrinsic disorder prediction) and CAID2, indicating notable improvements in terms of area under the receiver operating characteristic (AUC), Matthew's correlation coefficient (MCC), and F1 score. Moreover, IDP-ELM requires solely protein sequences as inputs and does not entail a time-consuming process of protein profile generation, which is a prerequisite for most existing state-of-the-art methods, enabling an accurate, fast, and convenient tool for proteome-level analysis. The corresponding reproducible source code and model weights are available at https://github.com/xu-shi-jie/idp-elm.

Chaotic aging: intrinsically disordered proteins in aging-related processes

The development of aging is associated with the disruption of key cellular processes manifested as well-established hallmarks of aging. Intrinsically disordered proteins (IDPs) and intrinsically disordered regions (IDRs) have no stable tertiary structure that provide them a power to be configurable hubs in signaling cascades and regulate many processes, potentially including those related to aging. There is a need to clarify the roles of IDPs/IDRs in aging. The dataset of 1702 aging-related proteins was collected from established aging databases and experimental studies. There is a noticeable presence of IDPs/IDRs, accounting for about 36% of the aging-related dataset, which is however less than the disorder content of the whole human proteome (about 40%). A Gene Ontology analysis of the used here aging proteome reveals an abundance of IDPs/IDRs in one-third of aging-associated processes, especially in genome regulation. Signaling pathways associated with aging also contain IDPs/IDRs on different hierarchical levels, revealing the importance of "structure-function continuum" in aging. Protein-protein interaction network analysis showed that IDPs present in different clusters associated with different aging hallmarks. Protein cluster with IDPs enrichment has simultaneously high liquid-liquid phase separation (LLPS) probability, "nuclear" localization and DNA-associated functions, related to aging hallmarks: genomic instability, telomere attrition, epigenetic alterations, and stem cells exhaustion. Intrinsic disorder, LLPS, and aggregation propensity should be considered as features that could be markers of pathogenic proteins. Overall, our analyses indicate that IDPs/IDRs play significant roles in aging-associated processes, particularly in the regulation of DNA functioning. IDP aggregation, which can lead to loss of function and toxicity, could be critically harmful to the cell. A structure-based analysis of aging and the identification of proteins that are particularly susceptible to disturbances can enhance our understanding of the molecular mechanisms of aging and open up new avenues for slowing it down.

Dissociation of energy connectivity and functional connectivity in Alzheimer's disease is associated with maintenance of cognitive performance

The correlation between functional connectivity (FC) network segregation, glucose metabolism and cognitive decline has been recently identified. The coupling relationship between glucose metabolism and the intensity of neuronal activity obtained using hybrid PET/MRI techniques can provide additional information on the physiological state of the brain in patients with AD and mild cognitive impairment (MCI). It is a valuable task to use the above rules for constructing biomarkers that are closely related to the cognitive ability of individuals to monitor the pathological status of patients. This study proposed the concept of the energy connectivity (EC) network and its construction method. We hypothesized that the dissociation between energy connectivity and functional connectivity of brain regions is a valid indicator of cognitive ability in patients with dementia. The number of EC-attenuated brain regions (EC-AR) and the number of FC-attenuated brain regions (FC-AR) are obtained by comparison with the normal group, and the dissociation between functional connectivity and energy connectivity is indicated using the ratio of FC-AR to EC-AR for individuals in the disease group. The findings suggest that FC-AR/EC-AR values are accurate predictors of cognitive performance, while taking into account the cognitive recovery due to compensatory effects of the brain. The cognitive ability of some patients with cognitive recovery can also be predicted more accurately. This also indicates that lower functional connectivity and higher energy connectivity between network modules may be one of the important features that maintain cognitive performance. The concept of energy connectivity also has potential to help explore the pathological state of AD.

Paving the Way for Synthetic Intrinsically Disordered Polymers for Soft Robotics

Nature is full of examples of processes that, through evolution, have been perfected over the ages to effectively use matter and sustain life. Here, we present our strategies for designing intrinsically disordered smart polymers for soft robotics applications that are bio-inspired by intrinsically disordered proteins. Bio-inspired intrinsically disordered smart and soft polymers designed using our deep understanding of intrinsically disordered proteins have the potential to open new avenues in soft robotics. Together with other desirable traits, such as robustness, dynamic self-organization, and self-healing abilities, these systems possess ideal characteristics that human-made formations strive for but often fail to achieve. Our main aim is to develop materials for soft robotics applications bio-inspired by intrinsically disordered proteins to address what we see as the largest current barriers in the practical deployment of future soft robotics in various areas, including defense. Much of the current literature has focused on the de novo synthesis of tailor-made polymers to perform specific functions. With bio-inspired polymers, the complexity of protein folding mechanisms has limited the ability of researchers to reliably engineer specific structures. Unlike existing studies, our work is focused on utilizing the high flexibility of intrinsically disordered proteins and their self-organization characteristics using synthetic quasi-foldamers.

Reorganization of the outer layer of a model of the plasma membrane induced by a neuroprotective aminosterol

Trodusquemine is an amphipathic aminosterol that has recently shown therapeutic benefit in neurodegenerative diseases altering the binding of misfolded proteins to the cell membrane. To unravel the underlying mechanism, we studied the interactions between Trodusquemine (TRO) and lipid monolayers simulating the outer layer of the plasma membrane. We selected two different compositions of dioleoylphosphatidylcholine (DOPC), sphingomyelin (SM), cholesterol (Chol) and monosialotetrahexosylganglioside (GM1) lipid mixture mimicking either a lipid-raft containing membrane (L_d+S_o phases) or a single-phase disordered membrane (L_d phase). Surface pressure-area isotherms and surface compressional modulus-area combined with Brewster Angle Microscopy (BAM) provided the thermodynamic and morphological information on the lipid monolayer in the presence of increasing amounts of TRO in the monolayer. Experiments revealed that TRO forms stable spreading monolayers at the buffer-air interface where it undergoes multiple reversible phase transitions to bi- and tri-layers at the interface. When TRO was spread at the interface with the lipid mixtures, we found that it distributes in the lipid monolayer for both the selected lipid compositions, but a maximum TRO uptake in the rafts-containing monolayer was observed for a Lipid/TRO molar ratio equal to 3:2. Statistical analysis of BAM images revealed that TRO induces a decrease in the size of the condensed domains, an increase in their number and in the thickness mismatch between the L_d and S_o phase. Experiments and MD simulations converge to indicate that TRO adsorbs preferentially at the border of the S_o domains. Removal of GM1 from the lipid L_d+S_o mixture resulted in an even greater TRO-mediated reduction of the size of the S_o domains suggesting that the presence of GM1 hinders the localization of TRO at the S_o domains boundaries. Taken together these observations suggest that Trodusquemine influences the organization of lipid rafts within the neuronal membrane in a dose-dependent manner whereas it evenly distributes in disordered expanded phases of the membrane model.

Pre-Molten, Wet, and Dry Molten Globules en Route to the Functional State of Proteins

Transitions between the unfolded and native states of the ordered globular proteins are accompanied by the accumulation of several intermediates, such as pre-molten globules, wet molten globules, and dry molten globules. Structurally equivalent conformations can serve as native functional states of intrinsically disordered proteins. This overview captures the characteristics and importance of these molten globules in both structured and intrinsically disordered proteins. It also discusses examples of engineered molten globules. The formation of these intermediates under conditions of macromolecular crowding and their interactions with nanomaterials are also reviewed.

Intrinsically Disordered Proteins: An Overview

Many proteins and protein segments cannot attain a single stable three-dimensional structure under physiological conditions; instead, they adopt multiple interconverting conformational states. Such intrinsically disordered proteins or protein segments are highly abundant across proteomes, and are involved in various effector functions. This review focuses on different aspects of disordered proteins and disordered protein regions, which form the basis of the so-called "Disorder-function paradigm" of proteins. Additionally, various experimental approaches and computational tools used for characterizing disordered regions in proteins are discussed. Finally, the role of disordered proteins in diseases and their utility as potential drug targets are explored.

AlphaFold illuminates half of the dark human proteins

We investigate the use of confidence scores to evaluate the accuracy of a given AlphaFold (AF2) protein model for drug discovery. Prediction of accuracy is improved by not considering confidence scores below 80 due to the effects of disorder. On a set of recent crystal structures, 95% are likely to have accurate folds. Conformational discordance in the training set has a much more significant effect on accuracy than sequence divergence. We propose criteria for models and residues that are possibly useful for virtual screening. Based on these criteria, AF2 provides models for half of understudied (dark) human proteins and two-thirds of residues in those models.

Therapeutic potential of pluripotent stem cell-derived dopaminergic progenitors in Parkinson's disease: a systematic review protocol

BACKGROUND

Parkinson's disease (PD) is the second most common age-dependent neurodegenerative disease that causes motor and cognitive disabilities. This disease is associated with a loss of dopamine content within the putamen, which stems from the degeneration of dopaminergic (DA) neurons in the substantia nigra pars compacta (SNc). Several approved drugs are available that can effectively treat symptoms of PD. However, long-term medical management is often complicated and does not delay or halt disease progression. Alternatively, cell replacement strategies can address these shortcomings and provide dopamine where it is needed. Although using human pluripotent stem cells (hPSCs) for treatment of PD is a promising alternative, no consensus in the literature pertains to efficacy concerns of hPSC-based therapy for PD. This systematic review aims to investigate the efficacy of primate PSC-derived DA progenitor transplantation to treat PD in preclinical studies.

METHODS

This is a systematic review of preclinical studies in animal models of PD. We intend to use the following databases as article sources: MEDLINE (via PubMed), Web of Science, and SCOPUS without any restrictions on language or publication status for all related articles published until the end of April 2021. Two independent reviewers will select the titles and abstracts, extract data from qualifying studies, and assess the risk of bias using the SYstematic Review Centre for Laboratory animal Experimentation (SYRCLE) risk of bias tool and the Collaborative Approach to Meta-Analysis and Review of Animal Data from Experimental Studies (CAMARADES) checklist. Apomorphine-induced rotation test (APO-IR) and amphetamine-induced rotation test (AMP-IR) are defined as the primary outcomes. The standardized mean difference (SMD) by Hedges' g method (r) and odds ratio (OR) and related 95% confidence interval (CI) will be calculated to determine the size effect of the treatment. The heterogeneity between studies will be calculated by "I2 inconsistency of values and Cochran's Q statistical test," where I2 > 50% and/or p < 0.10 suggests high heterogeneity. Meta-analyses of random effects will be run when appropriate.

DISCUSSION

This study will present an overview of preclinical research on PSCs and their therapeutic effects in PD animal models. This systematic review will point out the strengths and limitations of studies in the current literature while encouraging the funding of new studies by public health managers and governmental bodies.

Special Issue-The Conformational Universe of Proteins and Peptides: Tales of Order and Disorder

Among biological macromolecules, proteins hold prominent roles in a vast array of physiological and pathological processes [...].

Stoichiometry of multi-specific immune checkpoint RNA Abs for T cell activation and tumor inhibition using ultra-stable RNA nanoparticles

Immunotherapy has become a revolutionary subject in cancer therapy during the past few years. Immune checkpoint-targeting antibodies (Abs) could boost anticancer immune responses. However, certain protein-based immunotherapies revealed side effects and unfavorable biodistribution, so effective non-protein options with lower side effects are highly sought after. RNA's ability to form various three-dimensional configurations allows for the creation of a variety of ligands to bind different cell receptors. The rubber-like properties of RNA nanoparticles (NPs) allow for swift lodging to cancer vasculature with little accumulation in vital organs, resulting in a favorable pharmacokinetic/pharmacodynamic (PK/PD) profile and safe pharmacological parameters. Multi-specific drugs are expected to be the fourth wave of biopharmaceutical innovation. Herein, we report the development of multi-specific Ab-like RNA NPs carrying multiple ligands for immunotherapy. The stoichiometries and stereo conformations of the checkpoint-activating RNA NPs were optimized for T cell activation. When compared to mono- and bi-specific RNA NPs, the tri-specific Ab-like RNA NPs bound to the trimeric T cell receptor with the highest efficiency, showed the optimal T cell activation, and promoted the strongest anti-tumor function of immune cells. Animal trials demonstrated that the tri-specific RNA NPs inhibited cancer growth. This Ab-like RNA NP platform represents an alternative to protein Abs for tumor immunotherapy.

Molecular simulations of IDPs: From ensemble generation to IDP interactions leading to disorder-to-order transitions

Intrinsically disordered proteins (IDPs) lack a well-defined three-dimensional structure but do exhibit some dynamical and structural ordering. The structural plasticity of IDPs indicates that entropy-driven motions are crucial for their function. Many IDPs undergo function-related disorder-to-order transitions upon by their interaction with specific binding partners. Approaches that are based on both experimental and theoretical tools enable the biophysical characterization of IDPs. Molecular simulations provide insights into IDP structural ensembles and disorder-to-order transition mechanisms. However, such studies depend strongly on the chosen force field parameters and simulation techniques. In this chapter, we provide an overview of IDP characteristics, review all-atom force fields recently developed for IDPs, and present molecular dynamics-based simulation methods that allow IDP ensemble generation as well as the characterization of disorder-to-order transitions. In particular, we introduce metadynamics, replica exchange molecular dynamics simulations, and also kinetic models resulting from Markov State modeling, and provide various examples for the successful application of these simulation methods to IDPs.

Mapping of Photochemically-Derived Dityrosine across Fe-Bound N-Acetylated α-Synuclein

Parkinson’s disease (PD) is the second most common neurological disease and belongs to a group of neurodegenerative disorders called synucleinopathies in which pathological aggregates of N-terminally acetylated α-synuclein (^NAcα-Syn) accumulate in various regions of the brain. In PD, these ^NAcα-Syn aggregates have been found to contain covalent dityrosine crosslinks, which can occur either intermolecularly or intramolecularly. Cerebral metal imbalance is also a hallmark of PD, warranting investigations into the effects of brain biometals on ^NAcα-Syn. ^NAcα-Syn is an intrinsically disordered protein, and metal-mediated conformational modifications of this structurally dynamic protein have been demonstrated to influence its propensity for dityrosine formation. In this study, a library of tyrosine-to-phenylalanine (Y-to-F) ^NAcα-Syn constructs were designed in order to elucidate the nature and the precise residues involved in dityrosine crosslinking of Fe-bound ^NAcα-Syn. The structural capacity of each mutant to form dityrosine crosslinks was assessed using Photo-Induced Cross-Linking of Unmodified Proteins (PICUP), demonstrating that coordination of either Fe^III or Fe^II to ^NAcα-Syn inhibits dityrosine crosslinking among the C-terminal residues. We further demonstrate that Y39 is the main contributor to dityrosine formation of Fe-bound ^NAcα-Syn, while Y125 is the main residue involved in dityrosine crosslinks in unmetalated ^NAcα-Syn. Our results confirm that iron coordination has a global effect on ^NAcα-Syn structure and reactivity.

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

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

Prions and Prion-like assemblies in neurodegeneration and immunity: The emergence of universal mechanisms across health and disease

Prion-like behaviour is an abrupt process, an "all-or-nothing" transition between a monomeric species and an "infinite" fibrillated form. Once a nucleation point is formed, the process is unstoppable as fibrils self-propagate by recruiting and converting all monomers into the amyloid fold. After the "mad cow" episode, prion diseases have made the headlines, but more and more prion-like behaviours have emerged in neurodegenerative diseases, where formation of fibrils and large conglomerates of proteins deeply disrupt the cell homeostasis. More interestingly, in the last decade, examples emerged to suggest that prion-like conversion can be used as a positive gain of function, for memory storage or structural scaffolding. More recent experiments show that we are only seeing the tip of the iceberg and that, for example, prion-like amplification is found in many pathways of the immune response. In innate immunity, receptors on the cellular surface or within the cells 'sense' danger and propagate this information as signal, through protein-protein interactions (PPIs) between 'receptor', 'adaptor' and 'effector' proteins. In innate immunity, the smallest signal of a foreign element or pathogen needs to trigger a macroscopic signal output, and it was found that adaptor polymerize to create an extreme signal amplification. Interestingly, our body uses multiple structural motifs to create large signalling platform; a few innate proteins use amyloid scaffolds but most of the polymers discovered are composed by self-assembly in helical filaments. Some of these helical assemblies even have intercellular "contamination" in a "true" prion action, as demonstrated for ASC specks and MyD88 filaments. Here, we will describe the current knowledge in neurodegenerative diseases and innate immunity and show how these two very different fields can cross-seed discoveries.