Deep mutational scanning reveals the structural basis for α-synuclein activity

Revealing the principles of inter- and intra-domain regulation in a signaling enzyme via scanning mutagenesis

Multi-domain enzymes can be regulated by both inter-domain interactions and structural features intrinsic to the catalytic domain. The tyrosine phosphatase SHP2 is a quintessential example of a multi-domain protein that is regulated by inter-domain interactions. This enzyme has a protein tyrosine phosphatase (PTP) domain and two phosphotyrosine-recognition domains (N-SH2 and C-SH2) that regulate phosphatase activity through autoinhibitory interactions. SHP2 is canonically activated by phosphoprotein binding to the SH2 domains, which causes large inter-domain rearrangements, but autoinhibition can also be disrupted by disease-associated mutations. Many details of the SHP2 activation mechanism are still unclear, the physiologically-relevant active conformations remain elusive, and hundreds of human variants of SHP2 have not been functionally characterized. Here, we perform deep mutational scanning on both full-length SHP2 and its isolated PTP domain to examine mutational effects on inter-domain regulation and catalytic activity. Our experiments provide a comprehensive map of SHP2 mutational sensitivity, both in the presence and absence of inter-domain regulation. Coupled with molecular dynamics simulations, our investigation reveals novel structural features that govern the stability of the autoinhibited and active states of SHP2. Our analysis also identifies key residues beyond the SHP2 active site that control PTP domain dynamics and intrinsic catalytic activity. This work expands our understanding of SHP2 regulation and provides new insights into SHP2 pathogenicity.

PHACTboost: A Phylogeny-Aware Pathogenicity Predictor for Missense Mutations via Boosting

Most algorithms that are used to predict the effects of variants rely on evolutionary conservation. However, a majority of such techniques compute evolutionary conservation by solely using the alignment of multiple sequences while overlooking the evolutionary context of substitution events. We had introduced PHACT, a scoring-based pathogenicity predictor for missense mutations that can leverage phylogenetic trees, in our previous study. By building on this foundation, we now propose PHACTboost, a gradient boosting tree-based classifier that combines PHACT scores with information from multiple sequence alignments, phylogenetic trees, and ancestral reconstruction. By learning from data, PHACTboost outperforms PHACT. Furthermore, the results of comprehensive experiments on carefully constructed sets of variants demonstrated that PHACTboost can outperform 40 prevalent pathogenicity predictors reported in the dbNSFP, including conventional tools, metapredictors, and deep learning-based approaches as well as more recent tools such as AlphaMissense, EVE, and CPT-1. The superiority of PHACTboost over these methods was particularly evident in case of hard variants for which different pathogenicity predictors offered conflicting results. We provide predictions of 215 million amino acid alterations over 20,191 proteins. PHACTboost is available at https://github.com/CompGenomeLab/PHACTboost. PHACTboost can improve our understanding of genetic diseases and facilitate more accurate diagnoses.

α-Synuclein and Mitochondria: Probing the Dynamics of Disordered Membrane-protein Regions Using Solid-State Nuclear Magnetic Resonance

The characterization of intrinsically disordered regions (IDRs) in membrane-associated proteins is of crucial importance to elucidate key biochemical processes, including cellular signaling, drug targeting, or the role of post-translational modifications. These protein regions pose significant challenges to powerful analytical techniques of molecular structural investigations. We here applied magic angle spinning solid-state nuclear magnetic resonance to quantitatively probe the structural dynamics of IDRs of membrane-bound α-synuclein (αS), a disordered protein whose aggregation is associated with Parkinson's disease (PD). We focused on the mitochondrial binding of αS, an interaction that has functional and pathological relevance in neuronal cells and that is considered crucial for the underlying mechanisms of PD. Transverse and longitudinal 15N relaxation revealed that the dynamical properties of IDRs of αS bound to the outer mitochondrial membrane (OMM) are different from those of the cytosolic state, thus indicating that regions generally considered not to interact with the membrane are in fact affected by the spatial proximity with the lipid bilayer. Moreover, changes in the composition of OMM that are associated with lipid dyshomeostasis in PD were found to significantly perturb the topology and dynamics of IDRs in the membrane-bound state of αS. Taken together, our data underline the importance of characterizing IDRs in membrane proteins to achieve an accurate understanding of the role that these elusive protein regions play in numerous biochemical processes occurring on cellular surfaces.

Interplay of α-synuclein with Lipid Membranes: Cooperative Adsorption, Membrane Remodeling and Coaggregation

α-Synuclein is a small neuronal protein enriched at presynaptic termini. It is hypothesized to play a role in neurotransmitter release and synaptic vesicle cycling, while the formation of α-synuclein amyloid fibrils is associated with several neurodegenerative diseases, most notably Parkinson's Disease. The molecular mechanisms of both the physiological and pathological functions of α-synuclein remain to be fully understood, but in both cases, interactions with membranes play an important role. In this Perspective, we discuss several aspects of α-synuclein interactions with lipid membranes including cooperative adsorption, membrane remodeling and α-synuclein amyloid fibril formation in the presence of lipid membranes. We highlight the coupling between the different phenomena and their interplay in the context of physiological and pathological functions of α-synuclein.

Structure-Toxicity Relationship in Intermediate Fibrils from α-Synuclein Condensates

The aberrant aggregation of α-synuclein (αS) into amyloid fibrils is associated with a range of highly debilitating neurodegenerative conditions, including Parkinson's disease. Although the structural properties of mature amyloids of αS are currently understood, the nature of transient protofilaments and fibrils that appear during αS aggregation remains elusive. Using solid-state nuclear magnetic resonance (ssNMR), cryogenic electron microscopy (cryo-EM), and biophysical methods, we here characterized intermediate amyloid fibrils of αS forming during the aggregation from liquid-like spherical condensates to mature amyloids adopting the structure of pathologically observed aggregates. These transient amyloid intermediates, which induce significant levels of cytotoxicity when incubated with neuronal cells, were found to be stabilized by a small core in an antiparallel β-sheet conformation, with a disordered N-terminal region of the protein remaining available to mediate membrane binding. In contrast, mature amyloids that subsequently appear during the aggregation showed different structural and biological properties, including low levels of cytotoxicity, a rearranged structured core embedding also the N-terminal region, and a reduced propensity to interact with the membrane. The characterization of these two fibrillar forms of αS, and the use of antibodies and designed mutants, enabled us to clarify the role of critical structural elements endowing intermediate amyloid species with the ability to interact with membranes and induce cytotoxicity.

The role of α-synuclein in exocytosis

The pathogenesis of degeneration in Parkinson's disease (PD) remains poorly understood but multiple lines of evidence have converged on the presynaptic protein α-synuclein (αsyn). αSyn has been shown to regulate several cellular processes, however, its normal function remains poorly understood. In this review, we will specifically focus on its role in exocytosis.

Saturation mutagenesis of α-synuclein reveals monomer fold that modulates aggregation

α-Synuclein (aSyn) aggregation underlies neurodegenerative synucleinopathies. aSyn seeds are proposed to replicate and propagate neuronal pathology like prions. Seeding of aSyn can be recapitulated in cellular systems of aSyn aggregation; however, the mechanism of aSyn seeding and its regulation are not well understood. We developed an mEos-based aSyn seeding assay and performed saturation mutagenesis to identify with single-residue resolution positive and negative regulators of aSyn aggregation. We not only found the core regions that govern aSyn aggregation but also identified mutants outside of the core that enhance aggregation. We identified local structure within the N terminus of aSyn that hinders the fibrillization propensity of its aggregation-prone core. Based on the screen, we designed a minimal aSyn fragment that shows a ~4-fold enhancement in seeding activity and enabled discrimination of synucleinopathies. Our study expands the basic knowledge of aSyn aggregation and advances the design of cellular systems of aSyn aggregation to diagnose synucleinopathies based on protein conformation.

Combined prediction and design reveals the target recognition mechanism of an intrinsically disordered protein interaction domain

An increasing number of protein interaction domains and their targets are being found to be intrinsically disordered proteins (IDPs). The corresponding target recognition mechanisms are mostly elusive because of challenges in performing detailed structural analysis of highly dynamic IDP-IDP complexes. Here, we show that by combining recently developed computational approaches with experiments, the structure of the complex between the intrinsically disordered C-terminal domain (CTD) of protein 4.1G and its target IDP region in NuMA can be dissected at high resolution. First, we carry out systematic mutational scanning using dihydrofolate reductase-based protein complementarity analysis to identify essential interaction regions and key residues. The results are found to be highly consistent with an α/β-type complex structure predicted by AlphaFold2 (AF2). We then design mutants based on the predicted structure using a deep learning protein sequence design method. The solved crystal structure of one mutant presents the same core structure as predicted by AF2. Further computational prediction and experimental assessment indicate that the well-defined core structure is conserved across complexes of 4.1G CTD with other potential targets. Thus, we reveal that an intrinsically disordered protein interaction domain uses an α/β-type structure module formed through synergistic folding to recognize broad IDP targets. Moreover, we show that computational prediction and experiment can be jointly applied to segregate true IDP regions from the core structural domains of IDP-IDP complexes and to uncover the structure-dependent mechanisms of some otherwise elusive IDP-IDP interactions.

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.

Genome-wide prediction of disease variant effects with a deep protein language model

Predicting the effects of coding variants is a major challenge. While recent deep-learning models have improved variant effect prediction accuracy, they cannot analyze all coding variants due to dependency on close homologs or software limitations. Here we developed a workflow using ESM1b, a 650-million-parameter protein language model, to predict all ~450 million possible missense variant effects in the human genome, and made all predictions available on a web portal. ESM1b outperformed existing methods in classifying ~150,000 ClinVar/HGMD missense variants as pathogenic or benign and predicting measurements across 28 deep mutational scan datasets. We further annotated ~2 million variants as damaging only in specific protein isoforms, demonstrating the importance of considering all isoforms when predicting variant effects. Our approach also generalizes to more complex coding variants such as in-frame indels and stop-gains. Together, these results establish protein language models as an effective, accurate and general approach to predicting variant effects.

Probing the drivers of Staphylococcus aureus biofilm protein amyloidogenesis and disrupting biofilms with engineered protein disaggregases

Phenol-soluble modulins (PSMs) are the primary proteinaceous component of Staphylococcus aureus biofilms. Residence in the protective environment of biofilms allows bacteria to rapidly evolve and acquire antimicrobial resistance, which can lead to persistent infections such as those caused by methicillin-resistant S. aureus (MRSA). In their soluble form, PSMs hinder the immune response of the host and can increase the virulence potential of MRSA. PSMs also self-assemble into insoluble functional amyloids that contribute to the structural scaffold of biofilms. The specific roles of PSM peptides in biofilms remain poorly understood. Here, we report the development of a genetically tractable yeast model system for studying the properties of PSMα peptides. Expression of PSMα peptides in yeast drives the formation of toxic insoluble aggregates that adopt vesicle-like structures. Using this system, we probed the molecular drivers of PSMα aggregation to delineate key similarities and differences among the PSMs and identified a crucial residue that drives PSM features. Biofilms are a major public health threat; thus, biofilm disruption is a key goal. To solubilize aggregates comprised of a diverse range of amyloid and amyloid-like species, we have developed engineered variants of Hsp104, a hexameric AAA+ protein disaggregase from yeast. Here, we demonstrate that potentiated Hsp104 variants counter the toxicity and aggregation of PSMα peptides. Further, we demonstrate that a potentiated Hsp104 variant can drive the disassembly of preformed S. aureus biofilms. We suggest that this new yeast model can be a powerful platform for screening for agents that disrupt PSM aggregation and that Hsp104 disaggregases could be a promising tool for the safe enzymatic disruption of biofilms. IMPORTANCE Biofilms are complex mixtures secreted by bacteria that form a material in which the bacteria can become embedded. This process transforms the properties of the bacteria, and they become more resistant to removal, which can give rise to multidrug-resistant strains, such as methicillin-resistant Staphylococcus aureus (MRSA). Here, we study phenol-soluble modulins (PSMs), which are amyloidogenic proteins secreted by S. aureus, that become incorporated into biofilms. Biofilms are challenging to study, so we have developed a new genetically tractable yeast model to study the PSMs. We used our system to learn about several key features of the PSMs. We also demonstrate that variants of an amyloid disaggregase, Hsp104, can disrupt the PSMs and, more importantly, dissolve preformed S. aureus biofilms. We propose that our system can be a powerful screening tool and that Hsp104 disaggregases may be a new avenue to explore for biofilm disruption agents.

Protein engineering via sequence-performance mapping

Discovery and evolution of new and improved proteins has empowered molecular therapeutics, diagnostics, and industrial biotechnology. Discovery and evolution both require efficient screens and effective libraries, although they differ in their challenges because of the absence or presence, respectively, of an initial protein variant with the desired function. A host of high-throughput technologies-experimental and computational-enable efficient screens to identify performant protein variants. In partnership, an informed search of sequence space is needed to overcome the immensity, sparsity, and complexity of the sequence-performance landscape. Early in the historical trajectory of protein engineering, these elements aligned with distinct approaches to identify the most performant sequence: selection from large, randomized combinatorial libraries versus rational computational design. Substantial advances have now emerged from the synergy of these perspectives. Rational design of combinatorial libraries aids the experimental search of sequence space, and high-throughput, high-integrity experimental data inform computational design. At the core of the collaborative interface, efficient protein characterization (rather than mere selection of optimal variants) maps sequence-performance landscapes. Such quantitative maps elucidate the complex relationships between protein sequence and performance-e.g., binding, catalytic efficiency, biological activity, and developability-thereby advancing fundamental protein science and facilitating protein discovery and evolution.

Updated benchmarking of variant effect predictors using deep mutational scanning

The assessment of variant effect predictor (VEP) performance is fraught with biases introduced by benchmarking against clinical observations. In this study, building on our previous work, we use independently generated measurements of protein function from deep mutational scanning (DMS) experiments for 26 human proteins to benchmark 55 different VEPs, while introducing minimal data circularity. Many top-performing VEPs are unsupervised methods including EVE, DeepSequence and ESM-1v, a protein language model that ranked first overall. However, the strong performance of recent supervised VEPs, in particular VARITY, shows that developers are taking data circularity and bias issues seriously. We also assess the performance of DMS and unsupervised VEPs for discriminating between known pathogenic and putatively benign missense variants. Our findings are mixed, demonstrating that some DMS datasets perform exceptionally at variant classification, while others are poor. Notably, we observe a striking correlation between VEP agreement with DMS data and performance in identifying clinically relevant variants, strongly supporting the validity of our rankings and the utility of DMS for independent benchmarking.

Base editor screens for in situ mutational scanning at scale

A fundamental challenge in biology is understanding the molecular details of protein function. How mutations alter protein activity, regulation, and response to drugs is of critical importance to human health. Recent years have seen the emergence of pooled base editor screens for in situ mutational scanning: the interrogation of protein sequence-function relationships by directly perturbing endogenous proteins in live cells. These studies have revealed the effects of disease-associated mutations, discovered novel drug resistance mechanisms, and generated biochemical insights into protein function. Here, we discuss how this "base editor scanning" approach has been applied to diverse biological questions, compare it with alternative techniques, and describe the emerging challenges that must be addressed to maximize its utility. Given its broad applicability toward profiling mutations across the proteome, base editor scanning promises to revolutionize the investigation of proteins in their native contexts.

The landscape of tolerated genetic variation in humans and primates

Personalized genome sequencing has revealed millions of genetic differences between individuals, but our understanding of their clinical relevance remains largely incomplete. To systematically decipher the effects of human genetic variants, we obtained whole-genome sequencing data for 809 individuals from 233 primate species and identified 4.3 million common protein-altering variants with orthologs in humans. We show that these variants can be inferred to have nondeleterious effects in humans based on their presence at high allele frequencies in other primate populations. We use this resource to classify 6% of all possible human protein-altering variants as likely benign and impute the pathogenicity of the remaining 94% of variants with deep learning, achieving state-of-the-art accuracy for diagnosing pathogenic variants in patients with genetic diseases.

The landscape of tolerated genetic variation in humans and primates

Personalized genome sequencing has revealed millions of genetic differences between individuals, but our understanding of their clinical relevance remains largely incomplete. To systematically decipher the effects of human genetic variants, we obtained whole genome sequencing data for 809 individuals from 233 primate species, and identified 4.3 million common protein-altering variants with orthologs in human. We show that these variants can be inferred to have non-deleterious effects in human based on their presence at high allele frequencies in other primate populations. We use this resource to classify 6% of all possible human protein-altering variants as likely benign and impute the pathogenicity of the remaining 94% of variants with deep learning, achieving state-of-the-art accuracy for diagnosing pathogenic variants in patients with genetic diseases.

One Sentence Summary

Deep learning classifier trained on 4.3 million common primate missense variants predicts variant pathogenicity in humans.

Deep mutational scan of a drug efflux pump reveals its structure-function landscape

Drug efflux is a common resistance mechanism found in bacteria and cancer cells, but studies providing comprehensive functional insights are scarce. In this study, we performed deep mutational scanning (DMS) on the bacterial ABC transporter EfrCD to determine the drug efflux activity profile of more than 1,430 single variants. These systematic measurements revealed that the introduction of negative charges at different locations within the large substrate binding pocket results in strongly increased efflux activity toward positively charged ethidium, whereas additional aromatic residues did not display the same effect. Data analysis in the context of an inward-facing cryogenic electron microscopy structure of EfrCD uncovered a high-affinity binding site, which releases bound drugs through a peristaltic transport mechanism as the transporter transits to its outward-facing conformation. Finally, we identified substitutions resulting in rapid Hoechst influx without affecting the efflux activity for ethidium and daunorubicin. Hence, single mutations can convert EfrCD into a drug-specific ABC importer.

Conformational dynamics of A30G α-synuclein that causes familial Parkinson disease

The first gene shown to be responsible for autosomal-dominant Parkinson's disease (PD) is the SNCA gene, which encodes for alpha synuclein (α-Syn). Recently, a novel heterozygous A30G mutation of the SNCA gene associated with familial PD has been reported. However, little research has been done on how the A30G mutation affects the structure of α-Syn. So, using atomistic molecular dynamics (MD) simulation, we demonstrate here the key structural characteristics of A30G α-Syn in the free monomer form and membrane associated state. From the MD trajectory analysis, the structure of A30G α-Syn was noticed to exhibit rapid conformational change, increase in backbone flexibility near the site of mutation and decrease in α-helical propensity. The typical torsion angles in residues (Val26 and Glu28) near the mutation site were observed to deviate significantly in A30G α-Syn. In the case of membrane bound A30G α-Syn, the regions that were submerged in the lipid bilayer (N-helix (3-37) and turn region (38-44)) found to contain higher helical content than the elevated region above the lipid surface. The bending angle in the helix-N and helix-C regions were noticed to be relatively higher in the free form of A30G α-Syn (38.50) than in the membrane bound form (370). The A30G mutation in α-Syn was predicted to have an impact on the stability and function of the protein based on ΔΔG values obtained from the online servers. Our results demonstrate that the A30G mutation in α-Syn altered the protein's α-helical structure and slightly altered the membrane binding.Communicated by Ramaswamy H. Sarma.

Mutational scan inferred binding energetics and structure in intrinsically disordered protein CcdA

Unlike globular proteins, mutational effects on the function of Intrinsically Disordered Proteins (IDPs) are not well-studied. Deep Mutational Scanning of a yeast surface displayed mutant library yields insights into sequence-function relationships in the CcdA IDP. The approach enables facile prediction of interface residues and local structural signatures of the bound conformation. In contrast to previous titration-based approaches which use a number of ligand concentrations, we show that use of a single rationally chosen ligand concentration can provide quantitative estimates of relative binding constants for large numbers of protein variants. This is because the extended interface of IDP ensures that energetic effects of point mutations are spread over a much smaller range than for globular proteins. Our data also provides insights into the much-debated role of helicity and disorder in partner binding of IDPs. Based on this exhaustive mutational sensitivity dataset, a rudimentary model was developed in an attempt to predict mutational effects on binding affinity of IDPs that form alpha-helical structures upon binding.

Small molecules disaggregate alpha-synuclein and prevent seeding from patient brain-derived fibrils

The amyloid aggregation of alpha-synuclein within the brain is associated with the pathogenesis of Parkinson's disease (PD) and other related synucleinopathies, including multiple system atrophy (MSA). Alpha-synuclein aggregates are a major therapeutic target for treatment of these diseases. We identify two small molecules capable of disassembling preformed alpha-synuclein fibrils. The compounds, termed CNS-11 and CNS-11g, disaggregate recombinant alpha-synuclein fibrils in vitro, prevent the intracellular seeded aggregation of alpha-synuclein fibrils, and mitigate alpha-synuclein fibril cytotoxicity in neuronal cells. Furthermore, we demonstrate that both compounds disassemble fibrils extracted from MSA patient brains and prevent their intracellular seeding. They also reduce in vivo alpha-synuclein aggregates in C. elegans. Both compounds also penetrate brain tissue in mice. A molecular dynamics-based computational model suggests the compounds may exert their disaggregating effects on the N terminus of the fibril core. These compounds appear to be promising therapeutic leads for targeting alpha-synuclein for the treatment of synucleinopathies.

Using human genetics to improve safety assessment of therapeutics

Human genetics research has discovered thousands of proteins associated with complex and rare diseases. Genome-wide association studies (GWAS) and studies of Mendelian disease have resulted in an increased understanding of the role of gene function and regulation in human conditions. Although the application of human genetics has been explored primarily as a method to identify potential drug targets and support their relevance to disease in humans, there is increasing interest in using genetic data to identify potential safety liabilities of modulating a given target. Human genetic variants can be used as a model to anticipate the effect of lifelong modulation of therapeutic targets and identify the potential risk for on-target adverse events. This approach is particularly useful for non-clinical safety evaluation of novel therapeutics that lack pharmacologically relevant animal models and can contribute to the intrinsic safety profile of a drug target. This Review illustrates applications of human genetics to safety studies during drug discovery and development, including assessing the potential for on- and off-target associated adverse events, carcinogenicity risk assessment, and guiding translational safety study designs and monitoring strategies. A summary of available human genetic resources and recommended best practices is provided. The challenges and future perspectives of translating human genetic information to identify risks for potential drug effects in preclinical and clinical development are discussed.

Fibril treatment changes protein interactions of tau and α-synuclein in human neurons

In several neurodegenerative disorders, the neuronal proteins tau and α-synuclein adopt aggregation-prone conformations capable of replicating within and between cells. To better understand how these conformational changes drive neuropathology, we compared the interactomes of tau and α-synuclein in the presence or the absence of recombinant fibril seeds. Human embryonic stem cells with an inducible neurogenin-2 transgene were differentiated into glutamatergic neurons expressing (1) WT 0N4R tau, (2) mutant (P301L) 0N4R tau, (3) WT α-synuclein, or (4) mutant (A53T) α-synuclein, each genetically fused to a promiscuous biotin ligase (BioID2). Neurons expressing unfused BioID2 served as controls. After treatment with fibrils or PBS, interacting proteins were labeled with biotin in situ and quantified using mass spectrometry via tandem mass tag labeling. By comparing interactions in mutant versus WT neurons and in fibril- versus PBS-treated neurons, we observed changes in protein interactions that are likely relevant to disease progression. We identified 45 shared interactors, suggesting that tau and α-synuclein function within some of the same pathways. Potential loci of shared interactions include microtubules, Wnt signaling complexes, and RNA granules. Following fibril treatment, physiological interactions decreased, whereas other interactions, including those between tau and 14-3-3 η, increased. We confirmed that 14-3-3 proteins, which are known to colocalize with protein aggregates during neurodegeneration, can promote or inhibit tau aggregation in vitro depending on the specific combination of 14-3-3 isoform and tau sequence.

Metal interactions of α-synuclein probed by NMR amide-proton exchange

The aberrant aggregation of α-synuclein (αS), a disordered protein primarily expressed in neuronal cells, is strongly associated with the underlying mechanisms of Parkinson's disease. It is now established that αS has a weak affinity for metal ions and that these interactions alter its conformational properties by generally promoting self-assembly into amyloids. Here, we characterised the nature of the conformational changes associated with metal binding by αS using nuclear magnetic resonance (NMR) to measure the exchange of the backbone amide protons at a residue specific resolution. We complemented these experiments with ¹⁵N relaxation and chemical shift perturbations to obtain a comprehensive map of the interaction between αS and divalent (Ca²⁺, Cu²⁺, Mn²⁺, and Zn²⁺) and monovalent (Cu⁺) metal ions. The data identified specific effects that the individual cations exert on the conformational properties of αS. In particular, binding to calcium and zinc generated a reduction of the protection factors in the C-terminal region of the protein, whereas both Cu(II) and Cu(I) did not alter the amide proton exchange along the αS sequence. Changes in the R₂/R₁ ratios from ¹⁵N relaxation experiments were, however, detected as a result of the interaction between αS and Cu⁺ or Zn²⁺, indicating that binding to these metals induces conformational perturbations in distinctive regions of the protein. Collectively our data suggest that multiple mechanisms of enhanced αS aggregation are associated with the binding of the analysed metals.

Integrating deep mutational scanning and low-throughput mutagenesis data to predict the impact of amino acid variants

BACKGROUND

Evaluating the impact of amino acid variants has been a critical challenge for studying protein function and interpreting genomic data. High-throughput experimental methods like deep mutational scanning (DMS) can measure the effect of large numbers of variants in a target protein, but because DMS studies have not been performed on all proteins, researchers also model DMS data computationally to estimate variant impacts by predictors.

RESULTS

In this study, we extended a linear regression-based predictor to explore whether incorporating data from alanine scanning (AS), a widely used low-throughput mutagenesis method, would improve prediction results. To evaluate our model, we collected 146 AS datasets, mapping to 54 DMS datasets across 22 distinct proteins.

CONCLUSIONS

We show that improved model performance depends on the compatibility of the DMS and AS assays, and the scale of improvement is closely related to the correlation between DMS and AS results.

Effects of Mutations and Post-Translational Modifications on α-Synuclein In Vitro Aggregation

Fibrillar aggregates of the α-synuclein (αS) protein are the hallmark of Parkinson's Disease and related neurodegenerative disorders. Characterization of the effects of mutations and post-translational modifications (PTMs) on the αS aggregation rate can provide insight into the mechanism of fibril formation, which remains elusive in spite of intense study. A comprehensive collection (375 examples) of mutant and PTM aggregation rate data measured using the fluorescent probe thioflavin T is presented, as well as a summary of the effects of fluorescent labeling on αS aggregation (20 examples). A curated set of 131 single mutant de novo aggregation experiments are normalized to wild type controls and analyzed in terms of structural data for the monomer and fibrillar forms of αS. These tabulated data serve as a resource to the community to help in interpretation of aggregation experiments and to potentially be used as inputs for computational models of aggregation.

Synucleins: New Data on Misfolding, Aggregation and Role in Diseases

The synucleins are a family of natively unfolded (or intrinsically unstructured) proteins consisting of α-, β-, and γ-synuclein involved in neurodegenerative diseases and cancer. The current number of publications on synucleins has exceeded 16.000. They remain the subject of constant interest for over 35 years. Two reasons explain this unchanging attention: synuclein's association with several severe human diseases and the lack of understanding of the functional roles under normal physiological conditions. We analyzed recent publications to look at the main trends and developments in synuclein research and discuss possible future directions. Traditional areas of peak research interest which still remain high among last year's publications are comparative studies of structural features as well as functional research on of three members of the synuclein family. Another popular research topic in the area is a mechanism of α-synuclein accumulation, aggregation, and fibrillation. Exciting fast-growing area of recent research is α-synuclein and epigenetics. We do not present here a broad and comprehensive review of all directions of studies but summarize only the most significant recent findings relevant to these topics and outline potential future directions.

Scalable Functional Assays for the Interpretation of Human Genetic Variation

Scalable sequence-function studies have enabled the systematic analysis and cataloging of hundreds of thousands of coding and noncoding genetic variants in the human genome. This has improved clinical variant interpretation and provided insights into the molecular, biophysical, and cellular effects of genetic variants at an astonishing scale and resolution across the spectrum of allele frequencies. In this review, we explore current applications and prospects for the field and outline the principles underlying scalable functional assay design, with a focus on the study of single-nucleotide coding and noncoding variants.

Phenotypic screening models for rapid diagnosis of genetic variants and discovery of personalized therapeutics

Precision medicine strives for highly individualized treatments for disease under the notion that each individual's unique genetic makeup and environmental exposures imprints upon them not only a disposition to illness, but also an optimal therapeutic approach. In the realm of rare disorders, genetic predisposition is often the predominant mechanism driving disease presentation. For such, mostly, monogenic disorders, a causal gene to phenotype association is likely. As a result, it becomes important to query the patient's genome for the presence of pathogenic variations that are likely to cause the disease. Determining whether a variant is pathogenic or not is critical to these analyses and can be challenging, as many disease-causing variants are novel and, ergo, have no available functional data to help categorize them. This problem is exacerbated by the need for rapid evaluation of pathogenicity, since many genetic diseases present in young children who will experience increased morbidity and mortality without rapid diagnosis and therapeutics. Here, we discuss the utility of animal models, with a focus mainly on C. elegans, as a contrast to tissue culture and in silico approaches, with emphasis on how these systems are used in determining pathogenicity of variants with uncertain significance and then used to screen for novel therapeutics.

Combining cysteine scanning with chemical labeling to map protein-protein interactions and infer bound structure in an intrinsically disordered region

The Mycobacterium tuberculosis genome harbours nine toxin-antitoxin (TA) systems of the mazEF family. These consist of two proteins, a toxin and an antitoxin, encoded in an operon. While the toxin has a conserved fold, the antitoxins are structurally diverse and the toxin binding region is typically intrinsically disordered before binding. We describe high throughput methodology for accurate mapping of interfacial residues and apply it to three MazEF complexes. The method involves screening one partner protein against a panel of chemically masked single cysteine mutants of its interacting partner, displayed on the surface of yeast cells. Such libraries have much lower diversity than those generated by saturation mutagenesis, simplifying library generation and data analysis. Further, because of the steric bulk of the masking reagent, labeling of virtually all exposed epitope residues should result in loss of binding, and buried residues are inaccessible to the labeling reagent. The binding residues are deciphered by probing the loss of binding to the labeled cognate partner by flow cytometry. Using this methodology, we have identified the interfacial residues for MazEF3, MazEF6 and MazEF9 TA systems of M. tuberculosis. In the case of MazEF9, where a crystal structure was available, there was excellent agreement between our predictions and the crystal structure, superior to those with AlphaFold2. We also report detailed biophysical characterization of the MazEF3 and MazEF9 TA systems and measured the relative affinities between cognate and non-cognate toxin–antitoxin partners in order to probe possible cross-talk between these systems.

High-throughput approaches to functional characterization of genetic variation in yeast

Expansion of sequencing efforts to include thousands of genomes is providing a fundamental resource for determining the genetic diversity that exists in a population. Now, high-throughput approaches are necessary to begin to understand the role these genotypic changes play in affecting phenotypic variation. Saccharomyces cerevisiae maintains its position as an excellent model system to determine the function of unknown variants with its exceptional genetic diversity, phenotypic diversity, and reliable genetic manipulation tools. Here, we review strategies and techniques developed in yeast that scale classic approaches of assessing variant function. These approaches improve our ability to better map quantitative trait loci at a higher resolution, even for rare variants, and are already providing greater insight into the role that different types of mutations play in phenotypic variation and evolution not just in yeast but across taxa.

Single residue modulators of amyloid formation in the N-terminal P1-region of α-synuclein

Alpha-synuclein (αSyn) is a protein involved in neurodegenerative disorders including Parkinson’s disease. Amyloid formation of αSyn can be modulated by the ‘P1 region’ (residues 36-42). Here, mutational studies of P1 reveal that Y39A and S42A extend the lag-phase of αSyn amyloid formation in vitro and rescue amyloid-associated cytotoxicity in C. elegans. Additionally, L38I αSyn forms amyloid fibrils more rapidly than WT, L38A has no effect, but L38M does not form amyloid fibrils in vitro and protects from proteotoxicity. Swapping the sequence of the two residues that differ in the P1 region of the paralogue γSyn to those of αSyn did not enhance fibril formation for γSyn. Peptide binding experiments using NMR showed that P1 synergises with residues in the NAC and C-terminal regions to initiate aggregation. The remarkable specificity of the interactions that control αSyn amyloid formation, identifies this region as a potential target for therapeutics, despite their weak and transient nature.

The authors of this work characterize the effect of amino acid substitution on α-synuclein (α-Syn) aggregation. Residues 38 and 42 (in addition to 39) within the P1 region of α-Syn affect amyloid formation. The effect of substitution at position 38 is dependent on the amino-acid introduced, suggesting that specific interactions control α -Syn aggregation.

Mechanisms of protein evolution

How do proteins evolve? How do changes in sequence mediate changes in protein structure, and in turn in function? This question has multiple angles, ranging from biochemistry and biophysics to evolutionary biology. This review provides a brief integrated view of some key mechanistic aspects of protein evolution. First, we explain how protein evolution is primarily driven by randomly acquired genetic mutations and selection for function, and how these mutations can even give rise to completely new folds. Then, we also comment on how phenotypic protein variability, including promiscuity, transcriptional and translational errors, may also accelerate this process, possibly via "plasticity-first" mechanisms. Finally, we highlight open questions in the field of protein evolution, with respect to the emergence of more sophisticated protein systems such as protein complexes, pathways, and the emergence of pre-LUCA enzymes.

Plasticity of Membrane Binding by the Central Region of α-Synuclein

Membrane binding by α-synuclein (αS), an intrinsically disordered protein whose aggregation is associated with Parkinson’s disease, is a key step in determining its biological properties under both physiological and pathological conditions. Upon membrane interaction, αS retains a partial level of structural disorder despite acquiring α-helical content. In the membrane-bound state, the equilibrium between the helical-bound and disordered-detached states of the central region of αS (residues 65–97) has been involved in a double-anchor mechanism that promotes the clustering of synaptic vesicles. Herein, we investigated the underlying molecular bases of this equilibrium using enhanced coarse-grained molecular dynamics simulations. The results enabled clarifying the conformational dependencies of the membrane affinity by this protein region that, in addition to playing a role in physiological membrane binding, has key relevance for the aggregation of αS and the mechanisms of the toxicity of the resulting assemblies.

Role of Ubiquitin-Proteasome and Autophagy-Lysosome Pathways in α-Synuclein Aggregate Clearance

Synuclein aggregation in neuronal cells is the primary underlying cause of synucleinopathies. Changes in gene expression patterns, structural modifications, and altered interactions with other cellular proteins often trigger aggregation of α-synuclein, which accumulates as oligomers or fibrils in Lewy bodies. Although fibrillar forms of α-synuclein are primarily considered pathological, recent studies have revealed that even the intermediate states of aggregates are neurotoxic, complicating the development of therapeutic interventions. Autophagy and ubiquitin-proteasome pathways play a significant role in maintaining the soluble levels of α-synuclein inside cells; however, the heterogeneous nature of the aggregates presents a significant bottleneck to its degradation by these cellular pathways. With studies focused on identifying the proteins that modulate synuclein aggregation and clearance, detailed mechanistic insights are emerging about the individual and synergistic effects of these degradation pathways in regulating soluble α-synuclein levels. In this article, we discuss the impact of α-synuclein aggregation on autophagy-lysosome and ubiquitin-proteasome pathways and the therapeutic strategies that target various aspects of synuclein aggregation or degradation via these pathways. Additionally, we also highlight the natural and synthetic compounds that have shown promise in alleviating the cellular damage caused due to synuclein aggregation.

From systems to structure - using genetic data to model protein structures

Understanding the effects of genetic variation is a fundamental problem in biology that requires methods to analyse both physical and functional consequences of sequence changes at systems-wide and mechanistic scales. To achieve a systems view, protein interaction networks map which proteins physically interact, while genetic interaction networks inform on the phenotypic consequences of perturbing these protein interactions. Until recently, understanding the molecular mechanisms that underlie these interactions often required biophysical methods to determine the structures of the proteins involved. The past decade has seen the emergence of new approaches based on coevolution, deep mutational scanning and genome-scale genetic or chemical-genetic interaction mapping that enable modelling of the structures of individual proteins or protein complexes. Here, we review the emerging use of large-scale genetic datasets and deep learning approaches to model protein structures and their interactions, and discuss the integration of structural data from different sources.

Integrative structure determination of histones H3 and H4 using genetic interactions

Integrative structure modeling is increasingly used for determining the architectures of biological assemblies, especially those that are structurally heterogeneous. Recently, we reported on how to convert in vivo genetic interaction measurements into spatial restraints for structural modeling: first, phenotypic profiles are generated for each point mutation and thousands of gene deletions or environmental perturbations. Following, the phenotypic profile similarities are converted into distance restraints on the pairs of mutated residues. We illustrate the approach by determining the structure of the histone H3-H4 complex. The method is implemented in our open-source IMP program, expanding the structural biology toolbox by allowing structural characterization based on in vivo data without the need to purify the target system. We compare genetic interaction measurements to other sources of structural information, such as residue coevolution and deep-learning structure prediction of complex subunits. We also suggest that determining genetic interactions could benefit from new technologies, such as CRISPR-Cas9 approaches to gene editing, especially for mammalian cells. Finally, we highlight the opportunity for using genetic interactions to determine recalcitrant biomolecular structures, such as those of disordered proteins, transient protein assemblies, and host-pathogen protein complexes.

Spiers Memorial Lecture: Analysis and de novo design of membrane-interactive peptides

Membrane-peptide interactions play critical roles in many cellular and organismic functions, including protection from infection, remodeling of membranes, signaling, and ion transport. Peptides interact with membranes in a variety of ways: some associate with membrane surfaces in either intrinsically disordered conformations or well-defined secondary structures. Peptides with sufficient hydrophobicity can also insert vertically as transmembrane monomers, and many associate further into membrane-spanning helical bundles. Indeed, some peptides progress through each of these stages in the process of forming oligomeric bundles. In each case, the structure of the peptide and the membrane represent a delicate balance between peptide-membrane and peptide-peptide interactions. We will review this literature from the perspective of several biologically important systems, including antimicrobial peptides and their mimics, α-synuclein, receptor tyrosine kinases, and ion channels. We also discuss the use of de novo design to construct models to test our understanding of the underlying principles and to provide useful leads for pharmaceutical intervention of diseases.

Concluding remarks: peptide-membrane interactions

This article is based on the concluding remarks lecture given at the Faraday Discussion meeting on peptide-membrane interactions, held online, 8-10th September 2021.

Rapid Identification of Secondary Structure and Binding Site Residues in an Intrinsically Disordered Protein Segment

Mycobacterium tuberculosis harbours nine toxin-antitoxin (TA) systems of the MazEF family. MazEF TA modules are of immense importance due to the perceived role of the MazF toxin in M. tuberculosis persistence and disease. The MazE antitoxin has a disordered C-terminal domain that binds the toxin, MazF and neutralizes its endoribonuclease activity. However, the structure of most MazEF TA complexes remains unsolved till date, obscuring structural and functional information about the antitoxins. We present a facile method to identify toxin binding residues on the disordered antitoxin. Charged residue scanning mutagenesis was used to screen a yeast surface displayed MazE6 antitoxin library against its purified cognate partner, the MazF6 toxin. Binding residues were deciphered by probing the relative reduction in binding to the ligand by flow cytometry. We have used this to identify putative antitoxin interface residues and local structure attained by the antitoxin upon interaction in the MazEF6 TA system and the same methodology is readily applicable to other intrinsically disordered protein regions.

A Platform for Deep Sequence-Activity Mapping and Engineering Antimicrobial Peptides

Developing potent antimicrobials, and platforms for their study and engineering, is critical as antibiotic resistance grows. A high-throughput method to quantify antimicrobial peptide and protein (AMP) activity across a broad continuum would be powerful to elucidate sequence-activity landscapes and identify potent mutants. Yet the complexity of antimicrobial activity has largely constrained the scope and mechanistic bandwidth of AMP variant analysis. We developed a platform to efficiently perform sequence-activity mapping of AMPs via depletion (SAMP-Dep): a bacterial host culture is transformed with an AMP mutant library, induced to intracellularly express AMPs, grown under selective pressure, and deep sequenced to quantify mutant depletion. The slope of mutant growth rate versus induction level indicates potency. Using SAMP-Dep, we mapped the sequence-activity landscape of 170 000 mutants of oncocin, a proline-rich AMP, for intracellular activity against Escherichia coli. Clonal validation supported the platform's sensitivity and accuracy. The mapped landscape revealed an extended oncocin pharmacophore contrary to earlier structural studies, clarified the C-terminus role in internalization, identified functional epistasis, and guided focused, successful synthetic peptide library design, yielding a mutant with 2-fold enhancement in both intracellular and extracellular activity. The efficiency of SAMP-Dep poises the platform to transform AMP engineering, characterization, and discovery.

On the potential of machine learning to examine the relationship between sequence, structure, dynamics and function of intrinsically disordered proteins

Intrinsically disordered proteins (IDPs) constitute a broad set of proteins with few uniting and many diverging properties. IDPs-and intrinsically disordered regions (IDRs) interspersed between folded domains-are generally characterized as having no persistent tertiary structure; instead they interconvert between a large number of different and often expanded structures. IDPs and IDRs are involved in an enormously wide range of biological functions and reveal novel mechanisms of interactions, and while they defy the common structure-function paradigm of folded proteins, their structural preferences and dynamics are important for their function. We here discuss open questions in the field of IDPs and IDRs, focusing on areas where machine learning and other computational methods play a role. We discuss computational methods aimed to predict transiently formed local and long-range structure, including methods for integrative structural biology. We discuss the many different ways in which IDPs and IDRs can bind to other molecules, both via short linear motifs, as well as in the formation of larger dynamic complexes such as biomolecular condensates. We discuss how experiments are providing insight into such complexes and may enable more accurate predictions. Finally, we discuss the role of IDPs in disease and how new methods are needed to interpret the mechanistic effects of genomic variants in IDPs.

Excess membrane binding of monomeric alpha-, beta-, and gamma-synuclein is invariably associated with inclusion formation and toxicity

α-Synuclein (αS) has been well-documented to play a role in human synucleinopathies such as Parkinson’s disease (PD) and dementia with Lewy bodies (DLB). First, the lesions found in PD/DLB brains—Lewy bodies and Lewy neurites—are rich in aggregated αS. Second, genetic evidence links missense mutations and increased αS expression to familial forms of PD/DLB. Third, toxicity and cellular stress can be caused by αS under certain experimental conditions. In contrast, the homologs β-synuclein (βS) and γ-synuclein (γS) are not typically found in Lewy bodies/neurites, have not been clearly linked to brain diseases and have been largely non-toxic in experimental settings. In αS, the so-called non-amyloid-β component of plaques (NAC) domain, constituting amino acids 61–95, has been identified to be critical for aggregation in vitro. This domain is partially absent in βS and only incompletely conserved in γS, which could explain why both homologs do not cause disease. However, αS in vitro aggregation and cellular toxicity have not been firmly linked experimentally, and it has been proposed that excess αS membrane binding is sufficient to induce neurotoxicity. Indeed, recent characterizations of Lewy bodies have highlighted the accumulation of lipids and membranous organelles, raising the possibility that βS and γS could also become neurotoxic if they were more prone to membrane/lipid binding. Here, we increased βS and γS membrane affinity by strategic point mutations and demonstrate that these proteins behave like membrane-associated monomers, are cytotoxic and form round cytoplasmic inclusions that can be prevented by inhibiting stearoyl-CoA desaturase.

Potent inhibitors of toxic alpha-synuclein identified via cellular time-resolved FRET biosensors

We have developed a high-throughput drug discovery platform, measuring fluorescence resonance energy transfer (FRET) with fluorescent alpha-synuclein (αSN) biosensors, to detect spontaneous pre-fibrillar oligomers in living cells. Our two αSN FRET biosensors provide complementary insight into αSN oligomerization and conformation in order to improve the success of drug discovery campaigns for the treatment of Parkinson's disease. We measure FRET by fluorescence lifetime, rather than traditional fluorescence intensity, providing a structural readout with greater resolution and precision. This facilitates identification of compounds that cause subtle but significant conformational changes in the ensemble of oligomeric states that are easily missed using intensity-based FRET. We screened a 1280-compound small-molecule library and identified 21 compounds that changed the lifetime by >5 SD. Two of these compounds have nanomolar potency in protecting SH-SY5Y cells from αSN-induced death, providing a nearly tenfold improvement over known inhibitors. We tested the efficacy of several compounds in a primary mouse neuron assay of αSN pathology (phosphorylation of mouse αSN pre-formed fibrils) and show rescue of pathology for two of them. These hits were further characterized with biophysical and biochemical assays to explore potential mechanisms of action. In vitro αSN oligomerization, single-molecule FRET, and protein-observed fluorine NMR experiments demonstrate that these compounds modulate αSN oligomers but not monomers. Subsequent aggregation assays further show that these compounds also deter or block αSN fibril assembly.

Applications of Deep Mutational Scanning in Virology

Several recently developed high-throughput techniques have changed the field of molecular virology. For example, proteomics studies reveal complete interactomes of a viral protein, genome-wide CRISPR knockout and activation screens probe the importance of every single human gene in aiding or fighting a virus, and ChIP-seq experiments reveal genome-wide epigenetic changes in response to infection. Deep mutational scanning is a relatively novel form of protein science which allows the in-depth functional analysis of every nucleotide within a viral gene or genome, revealing regions of importance, flexibility, and mutational potential. In this review, we discuss the application of this technique to RNA viruses including members of the Flaviviridae family, Influenza A Virus and Severe Acute Respiratory Syndrome Coronavirus 2. We also briefly discuss the reverse genetics systems which allow for analysis of viral replication cycles, next-generation sequencing technologies and the bioinformatics tools that facilitate this research.

Insights into the molecular mechanism of amyloid filament formation: Segmental folding of α-synuclein on lipid membranes

Recent advances in the structural biology of disease-relevant α-synuclein fibrils have revealed a variety of structures, yet little is known about the process of fibril aggregate formation. Characterization of intermediate species that form during aggregation is crucial; however, this has proven very challenging because of their transient nature, heterogeneity, and low population. Here, we investigate the aggregation of α-synuclein bound to negatively charged phospholipid small unilamellar vesicles. Through a combination of kinetic and structural studies, we identify key time points in the aggregation process that enable targeted isolation of prefibrillar and early fibrillar intermediates. By using solid-state nuclear magnetic resonance, we show the gradual buildup of structural features in an α-synuclein fibril filament, revealing a segmental folding process. We identify distinct membrane-binding domains in α-synuclein aggregates, and the combined data are used to present a comprehensive mechanism of the folding of α-synuclein on lipid membranes.

The Membrane Interactions of Synuclein: Physiology and Pathology

Specific proteins accumulate in neurodegenerative disease, and human genetics has indicated a causative role for many. In most cases, however, the mechanisms remain poorly understood. Degeneration is thought to involve a gain of abnormal function, although we do not know the normal function of many proteins implicated. The protein α-synuclein accumulates in the Lewy pathology of Parkinson's disease and related disorders, and mutations in α-synuclein cause degeneration, but we have not known its normal function or how it triggers disease. α-Synuclein localizes to presynaptic boutons and interacts with membranes in vitro. Overexpression slows synaptic vesicle exocytosis, and recent data suggest a normal role for the endogenous synucleins in dilation of the exocytic fusion pore. Disrupted membranes also appear surprisingly prominent in Lewy pathology. Synuclein thus interacts with membranes under both physiological and pathological conditions, suggesting that the normal function of synuclein may illuminate its role in degeneration.

Membrane Interactions of α-Synuclein Revealed by Multiscale Molecular Dynamics Simulations, Markov State Models, and NMR

α-Synuclein (αS) is a presynaptic protein that binds to cell membranes and is linked to Parkinson's disease (PD). Binding of αS to membranes is a likely first step in the molecular pathophysiology of PD. The αS molecule can adopt multiple conformations, being largely disordered in water, adopting a β-sheet conformation when present in amyloid fibrils, and forming a dynamic multiplicity of α-helical conformations when bound to lipid bilayers and related membrane-mimetic surfaces. Multiscale molecular dynamics simulations in conjunction with nuclear magnetic resonance (NMR) and cross-linking mass spectrometry (XLMS) measurements are used to explore the interactions of αS with an anionic lipid bilayer. The simulations and NMR measurements together reveal a break in the helical structure of the central non-amyloid-β component (NAC) region of αS in the vicinity of residues 65-70, which may facilitate subsequent oligomer formation. Coarse-grained simulations of αS starting from the structure of αS when bound to a detergent micelle reveal the overall pattern of protein contacts to anionic lipid bilayers, while subsequent all-atom simulations provide details of conformational changes upon membrane binding. In particular, simulations and NMR data for liposome-bound αS indicate incipient β-strand formation in the NAC region, which is supported by intramolecular contacts seen via XLMS and simulations. Markov state models based on the all-atom simulations suggest a mechanism of conformational change of membrane-bound αS via a dynamic helix break in the region of residue 65 in the NAC region. The emergent dynamic model of membrane-interacting αS advances our understanding of the mechanism of PD, potentially aiding the design of novel therapeutic approaches.

Amyloid Oligomers: A Joint Experimental/Computational Perspective on Alzheimer's Disease, Parkinson's Disease, Type II Diabetes, and Amyotrophic Lateral Sclerosis

Protein misfolding and aggregation is observed in many amyloidogenic diseases affecting either the central nervous system or a variety of peripheral tissues. Structural and dynamic characterization of all species along the pathways from monomers to fibrils is challenging by experimental and computational means because they involve intrinsically disordered proteins in most diseases. Yet understanding how amyloid species become toxic is the challenge in developing a treatment for these diseases. Here we review what computer, in vitro, in vivo, and pharmacological experiments tell us about the accumulation and deposition of the oligomers of the (Aβ, tau), α-synuclein, IAPP, and superoxide dismutase 1 proteins, which have been the mainstream concept underlying Alzheimer's disease (AD), Parkinson's disease (PD), type II diabetes (T2D), and amyotrophic lateral sclerosis (ALS) research, respectively, for many years.

The docking of synaptic vesicles on the presynaptic membrane induced by α-synuclein is modulated by lipid composition

α-Synuclein (αS) is a presynaptic disordered protein whose aberrant aggregation is associated with Parkinson’s disease. The functional role of αS is still debated, although it has been involved in the regulation of neurotransmitter release via the interaction with synaptic vesicles (SVs). We report here a detailed characterisation of the conformational properties of αS bound to the inner and outer leaflets of the presynaptic plasma membrane (PM), using small unilamellar vesicles. Our results suggest that αS preferentially binds the inner PM leaflet. On the basis of these studies we characterise in vitro a mechanism by which αS stabilises, in a concentration-dependent manner, the docking of SVs on the PM by establishing a dynamic link between the two membranes. The study then provides evidence that changes in the lipid composition of the PM, typically associated with neurodegenerative diseases, alter the modes of binding of αS, specifically in a segment of the sequence overlapping with the non-amyloid component region. Taken together, these results reveal how lipid composition modulates the interaction of αS with the PM and underlie its functional and pathological behaviours in vitro.

α-Synuclein is a presynaptic protein whose aberrant aggregation is associated with Parkinson’s disease. Here, the authors show how αSynuclein-induced docking of synaptic vesicles is modulated by the lipid composition changes typically observed in neurodegeneration using an in vitro system.