A Small Molecule Stabilizes the Disordered Native State of the Alzheimer's Aβ Peptide

Estimation of Ligand Binding Free Energy Using Multi-eGO

The computational study of ligand binding to a target protein provides mechanistic insight into the molecular determinants of this process and can improve the success rate of in silico drug design. All-atom molecular dynamics (MD) simulations can be used to evaluate the binding free energy, typically by thermodynamic integration, and to probe binding mechanisms, including the description of protein conformational dynamics. The advantages of MD come at a high computational cost, which limits its use. Such cost could be reduced by using coarse-grained models, but their use is generally associated with an undesirable loss of resolution and accuracy. To address the trade-off between speed and accuracy of MD simulations, we describe the use of the recently introduced multi-eGO atomic model for the estimation of binding free energies. We illustrate this approach in the case of the binding of benzene to lysozyme by both thermodynamic integration and metadynamics, showing multiple binding/unbinding pathways of benzene. We then provide equally accurate results for the binding free energy of dasatinib and PP1 to Src kinase by thermodynamic integration. Finally, we show how we can describe the binding of the small molecule 10074-G5 to Aβ42 by single molecule simulations and by explicit titration of the ligand as a function of concentration. These results demonstrate that multi-eGO has the potential to significantly reduce the cost of accurate binding free energy calculations and can be used to develop and benchmark in silico ligand binding techniques.

An integrated machine learning approach delineates an entropic expansion mechanism for the binding of a small molecule to α-synuclein

The mis-folding and aggregation of intrinsically disordered proteins (IDPs) such as α-synuclein (αS) underlie the pathogenesis of various neurodegenerative disorders. However, targeting αS with small molecules faces challenges due to the lack of defined ligand-binding pockets in its disordered structure. Here, we implement a deep artificial neural network-based machine learning approach, which is able to statistically distinguish the fuzzy ensemble of conformational substates of αS in neat water from those in aqueous fasudil (small molecule of interest) solution. In particular, the presence of fasudil in the solvent either modulates pre-existing states of αS or gives rise to new conformational states of αS, akin to an ensemble-expansion mechanism. The ensembles display strong conformation-dependence in residue-wise interaction with the small molecule. A thermodynamic analysis indicates that small-molecule modulates the structural repertoire of αS by tuning protein backbone entropy, however entropy of the water remains unperturbed. Together, this study sheds light on the intricate interplay between small molecules and IDPs, offering insights into entropic modulation and ensemble expansion as key biophysical mechanisms driving potential therapeutics.

Determining accurate conformational ensembles of intrinsically disordered proteins at atomic resolution

Determining accurate atomic resolution conformational ensembles of intrinsically disordered proteins (IDPs) is extremely challenging. Molecular dynamics (MD) simulations provide atomistic conformational ensembles of IDPs, but their accuracy is highly dependent on the quality of physical models, or force fields, used. Here, we demonstrate how to determine accurate atomic resolution conformational ensembles of IDPs by integrating all-atom MD simulations with experimental data from nuclear magnetic resonance (NMR) spectroscopy and small-angle x-ray scattering (SAXS) with a simple, robust and fully automated maximum entropy reweighting procedure. We demonstrate that when this approach is applied with sufficient experimental data, IDP ensembles derived from different MD force fields converge to highly similar conformational distributions. The maximum entropy reweighting procedure presented here facilitates the integration of MD simulations with extensive experimental datasets and enables the calculation of accurate, force-field independent atomic resolution conformational ensembles of IDPs.

Nitroxyl Hybrids with Curcumin and Stilbene Scaffolds Display Potent Antioxidant Activity, Remodel the Amyloid Beta Oligomer, and Reverse Amyloid Beta-Induced Cytotoxicity

The disorder and heterogeneity of low-molecular-weight amyloid-beta oligomers (AβOs) underlie their participation in multiple modes of cellular dysfunction associated with the etiology of Alzheimer's disease (AD). The lack of specified conformational states in these species complicates efforts to select or design small molecules to targeting discrete pathogenic states. Furthermore, targeting AβOs alone may be therapeutically insufficient, as AD progresses as a multifactorial, self-amplifying cascade. To address these challenges, we have screened the activity of seven new candidates that serve as Paramagnetic Amyloid Ligand (PAL) candidates. PALs are bifunctional small molecules that both remodel the AβO structure and localize a potent antioxidant that mimics the activity of SOD within live cells. The candidates are built from either a stilbene or curcumin scaffold with nitroxyl moiety to serve as catalytic antioxidants. Measurements of PAL AβO binding and remolding along with assessments of bioactivity allow for the extraction of useful SAR information from screening data. One candidate (HO-4450; PMT-307), with a six-membered nitroxyl ring attached to a stilbene ring, displays the highest potency in protecting against cell-derived Aβ. A preliminary low-dose evaluation in AD model mice provides evidence of modest treatment effects by HO-4450. The results for the curcumin PALs demonstrate that the retention of the native curcumin phenolic groups is advantageous to the design of the hybrid PAL candidates. Finally, the PAL remodeling of AβO secondary structures shows a reasonable correlation between a candidate's bioactivity and its ability to reduce the fraction of antiparallel β-strand.

Covalent adducts formed by the androgen receptor transactivation domain and small molecule drugs remain disordered

Intrinsically disordered proteins are implicated in many human diseases. Small molecules that target the disordered androgen receptor transactivation domain have entered human trials for the treatment of castration-resistant prostate cancer. These molecules have been shown to react with cysteine residues of the androgen receptor transactivation domain and form covalent adducts under physiological conditions. It is currently unclear how covalent attachment of these molecules alters the conformational ensemble of the androgen receptor. Here, we utilize all-atom molecular dynamics computer simulations to simulate covalent adducts of the small molecule ligands EPI-002 and EPI-7170 bound to the disordered androgen receptor transactivation domain. Our simulations reveal that the conformational ensembles of androgen receptor transactivation domain covalent adducts are heterogeneous and disordered. We find that covalent attachment of EPI-002 and EPI-7170 increases the population of collapsed helical transactivation domain conformations relative to the populations observed in non-covalent binding simulations and we identify networks of protein-ligand interactions that stabilize collapsed conformations in covalent adduct ensembles. We compare the populations of protein-ligand interactions observed in covalent adduct ensembles to those observed in non-covalent ligand-bound ensembles and find substantial differences. Our results provide atomically detailed descriptions of covalent adducts formed by small molecules and an intrinsically disordered protein and suggest strategies for developing more potent covalent inhibitors of intrinsically disordered proteins.

AlzDiscovery: A computational tool to identify Alzheimer's disease-causing missense mutations using protein structure information

Alzheimer's disease (AD) is one of the most common forms of dementia and neurodegenerative diseases, characterized by the formation of neuritic plaques and neurofibrillary tangles. Many different proteins participate in this complicated pathogenic mechanism, and missense mutations can alter the folding and functions of these proteins, significantly increasing the risk of AD. However, many methods to identify AD-causing variants did not consider the effect of mutations from the perspective of a protein three-dimensional environment. Here, we present a machine learning-based analysis to classify the AD-causing mutations from their benign counterparts in 21 AD-related proteins leveraging both sequence- and structure-based features. Using computational tools to estimate the effect of mutations on protein stability, we first observed a bias of the pathogenic mutations with significant destabilizing effects on family AD-related proteins. Combining this insight, we built a generic predictive model, and improved the performance by tuning the sample weights in the training process. Our final model achieved the performance on area under the receiver operating characteristic curve up to 0.95 in the blind test and 0.70 in an independent clinical validation, outperforming all the state-of-the-art methods. Feature interpretation indicated that the hydrophobic environment and polar interaction contacts were crucial to the decision on pathogenic phenotypes of missense mutations. Finally, we presented a user-friendly web server, AlzDiscovery, for researchers to browse the predicted phenotypes of all possible missense mutations on these 21 AD-related proteins. Our study will be a valuable resource for AD screening and the development of personalized treatment.

N-terminal domain of androgen receptor is a major therapeutic barrier and potential pharmacological target for treating castration resistant prostate cancer: a comprehensive review

The incidence rate of prostate cancer (PCa) has risen by 3% per year from 2014 through 2019 in the United States. An estimated 34,700 people will die from PCa in 2023, corresponding to 95 deaths per day. Castration resistant prostate cancer (CRPC) is the leading cause of deaths among men with PCa. Androgen receptor (AR) plays a critical role in the development of CRPC. N-terminal domain (NTD) is the essential functional domain for AR transcriptional activation, in which modular activation function-1 (AF-1) is important for gene regulation and protein interactions. Over last 2 decades drug discovery against NTD has attracted interest for CRPC treatment. However, NTD is an intrinsically disordered domain without stable three-dimensional structure, which has so far hampered the development of drugs targeting this highly dynamic structure. Employing high throughput cell-based assays, small-molecule NTD inhibitors exhibit a variety of unexpected properties, ranging from specific binding to NTD, blocking AR transactivation, and suppressing oncogenic proliferation, which prompts its evaluation in clinical trials. Furthermore, molecular dynamics simulations reveal that compounds can induce the formation of collapsed helical states. Nevertheless, our knowledge of NTD structure has been limited to the primary sequence of amino acid chain and a few secondary structure motif, acting as a barrier for computational and pharmaceutical analysis to decipher dynamic conformation and drug-target interaction. In this review, we provide an overview on the sequence-structure-function relationships of NTD, including the polymorphism of mono-amino acid repeats, functional elements for transcription regulation, and modeled tertiary structure of NTD. Moreover, we summarize the activities and therapeutic potential of current NTD-targeting inhibitors and outline different experimental methods contributing to screening novel compounds. Finally, we discuss current directions for structure-based drug design and potential breakthroughs for exploring pharmacological motifs and pockets in NTD, which could contribute to the discovery of new NTD inhibitors.

Context-dependent, fuzzy protein interactions: Towards sequence-based insights

Predicting protein interactions in the cellular environment still remains a challenge in the AlphaFold era. Protein interactions, similarly to their structures, sample a continuum from ordered to disordered states, with specific partners in many bound configurations. A multiplicity of binding modes (MBM) enables transition between these states under different cellular conditions. This review focuses on how the cellular environment affects protein interactions, highlighting the molecular mechanisms, biophysical origin, and sequence-based principles of context-dependent, fuzzy interactions. It summarises experimental and computational approaches to address the challenge of interaction heterogeneity and its contribution to a wide range of biological functions. These insights will help in understanding complex cellular processes, involving conversions between protein assembly states, such as from liquid-like droplet state to the amyloid state.

CoVAMPnet: Comparative Markov State Analysis for Studying Effects of Drug Candidates on Disordered Biomolecules

Computational study of the effect of drug candidates on intrinsically disordered biomolecules is challenging due to their vast and complex conformational space. Here, we developed a comparative Markov state analysis (CoVAMPnet) framework to quantify changes in the conformational distribution and dynamics of a disordered biomolecule in the presence and absence of small organic drug candidate molecules. First, molecular dynamics trajectories are generated using enhanced sampling, in the presence and absence of small molecule drug candidates, and ensembles of soft Markov state models (MSMs) are learned for each system using unsupervised machine learning. Second, these ensembles of learned MSMs are aligned across different systems based on a solution to an optimal transport problem. Third, the directional importance of inter-residue distances for the assignment to different conformational states is assessed by a discriminative analysis of aggregated neural network gradients. This final step provides interpretability and biophysical context to the learned MSMs. We applied this novel computational framework to assess the effects of ongoing phase 3 therapeutics tramiprosate (TMP) and its metabolite 3-sulfopropanoic acid (SPA) on the disordered Aβ42 peptide involved in Alzheimer's disease. Based on adaptive sampling molecular dynamics and CoVAMPnet analysis, we observed that both TMP and SPA preserved more structured conformations of Aβ42 by interacting nonspecifically with charged residues. SPA impacted Aβ42 more than TMP, protecting α-helices and suppressing the formation of aggregation-prone β-strands. Experimental biophysical analyses showed only mild effects of TMP/SPA on Aβ42 and activity enhancement by the endogenous metabolization of TMP into SPA. Our data suggest that TMP/SPA may also target biomolecules other than Aβ peptides. The CoVAMPnet method is broadly applicable to study the effects of drug candidates on the conformational behavior of intrinsically disordered biomolecules.

Misfolded protein oligomers: mechanisms of formation, cytotoxic effects, and pharmacological approaches against protein misfolding diseases

The conversion of native peptides and proteins into amyloid aggregates is a hallmark of over 50 human disorders, including Alzheimer's and Parkinson's diseases. Increasing evidence implicates misfolded protein oligomers produced during the amyloid formation process as the primary cytotoxic agents in many of these devastating conditions. In this review, we analyze the processes by which oligomers are formed, their structures, physicochemical properties, population dynamics, and the mechanisms of their cytotoxicity. We then focus on drug discovery strategies that target the formation of oligomers and their ability to disrupt cell physiology and trigger degenerative processes.

Folding-upon-binding pathways of an intrinsically disordered protein from a deep Markov state model

A central challenge in the study of intrinsically disordered proteins is the characterization of the mechanisms by which they bind their physiological interaction partners. Here, we utilize a deep learning-based Markov state modeling approach to characterize the folding-upon-binding pathways observed in a long timescale molecular dynamics simulation of a disordered region of the measles virus nucleoprotein NTAIL reversibly binding the X domain of the measles virus phosphoprotein complex. We find that folding-upon-binding predominantly occurs via two distinct encounter complexes that are differentiated by the binding orientation, helical content, and conformational heterogeneity of NTAIL. We observe that folding-upon-binding predominantly proceeds through a multi-step induced fit mechanism with several intermediates and do not find evidence for the existence of canonical conformational selection pathways. We observe four kinetically separated native-like bound states that interconvert on timescales of eighty to five hundred nanoseconds. These bound states share a core set of native intermolecular contacts and stable NTAIL helices and are differentiated by a sequential formation of native and non-native contacts and additional helical turns. Our analyses provide an atomic resolution structural description of intermediate states in a folding-upon-binding pathway and elucidate the nature of the kinetic barriers between metastable states in a dynamic and heterogenous, or "fuzzy", protein complex.

Picosecond Dynamics of a Small Molecule in Its Bound State with an Intrinsically Disordered Protein

Intrinsically disordered proteins (IDPs) are highly dynamic biomolecules that rapidly interconvert among many structural conformations. These dynamic biomolecules are involved in cancers, neurodegeneration, cardiovascular illnesses, and viral infections. Despite their enormous therapeutic potential, IDPs have generally been considered undruggable because of their lack of classical long-lived binding pockets for small molecules. Currently, only a few instances are known where small molecules have been observed to interact with IDPs, and this situation is further exacerbated by the limited sensitivity of experimental techniques to detect such binding events. Here, using experimental nuclear magnetic resonance (NMR) spectroscopy 19F transverse spin-relaxation measurements, we discovered that a small molecule, 5-fluoroindole, interacts with the disordered domains of non-structural protein 5A from hepatitis C virus with a Kd of 260 ± 110 μM. Our analysis also allowed us to determine the rotational correlation times (τc) for the free and bound states of 5-fluoroindole. In the free state, we observed a rotational correlation time of 27.0 ± 1.3 ps, whereas in the bound state, τc only increased to 46 ± 10 ps. Our findings imply that it is possible for small molecules to engage with IDPs in exceptionally dynamic ways, in sharp contrast to the rigid binding modes typically exhibited when small molecules bind to well-defined binding pockets within structured proteins.

Conformational inhibitors of protein aggregation

Amyloidoses are fatal conditions associated with the aggregation of proteins into amyloid fibrils that deposit systemically and/or locally. Possibly because the causal mechanism of protein aggregation and deposition is not fully understood, this group of diseases remains uncurable. Advances in structural biology, such as the use of nuclear magnetic resonance and cryo-electron microscopy, have enabled the study of the structures and the conformational nature of the proteins whose aggregation is associated with the underlying pathogenesis of amyloidosis. As a result, the last years of research have translated into the development of directed therapeutic strategies that target the specific conformations of precursors, fibrils, and intermediary species. Current efforts include the use of small molecules, peptides, and antibodies. This review summarizes the recent progress in developing strategies that target specific protein conformations for the treatment of amyloidoses.

Computationally Designed Small Molecules Disassemble Both Soluble Oligomers and Protofibrils of Amyloid β-Protein Responsible for Alzheimer's Disease

Alzheimer's disease (AD) is one of the world's most pressing health crises. AD is an incurable disease affecting more than 6.5 million Americans, predominantly the elderly, and in its later stages, leads to memory loss, dementia, and death. Amyloid β (Aβ) protein aggregates have been one of the pathological hallmarks of AD since its initial characterization. The early stages of Aβ accumulation and aggregation involve the formation of oligomers, which are considered neurotoxic and play a key role in further aggregation into fibrils that eventually appear in the brain as amyloid plaques. We have recently shown by combining ion mobility mass spectrometry (IM-MS) and atomic force microscopy (AFM) that Aβ42 rapidly forms dodecamers (12-mers) as the terminal oligomeric state, and these dodecamers seed the early formation of Aβ42 protofibrils. The link between soluble oligomers and fibril formation is one of the essential aspects for understanding the root cause of the disease state and is critical to developing therapeutic interventions. Utilizing a joint pharmacophore space (JPS) method, potential drugs have been designed specifically for amyloid-related diseases. These small molecules were generated based on crucial chemical features necessary for target selectivity. In this paper, we utilize our combined IM-MS and AFM methods to investigate the impact of three second-generation JPS small-molecule inhibitors, AC0201, AC0202, and AC0203, on dodecamer as well as fibril formation in Aβ42. Our results indicate that AC0201 works well as an inhibitor and remodeler of both dodecamers and fibril formation, AC0203 behaves less efficiently, and AC0202 is ineffective.

Protein aggregation and neurodegenerative disease: Structural outlook for the novel therapeutics

Before the controversial approval of humanized monoclonal antibody lecanemab, which binds to the soluble amyloid-β protofibrils, all the treatments available earlier, for Alzheimer's disease (AD) were symptomatic. The researchers are still struggling to find a breakthrough in AD therapeutic medicine, which is partially attributable to lack in understanding of the structural information associated with the intrinsically disordered proteins and amyloids. One of the major challenges in this area of research is to understand the structural diversity of intrinsically disordered proteins under in vitro conditions. Therefore, in this review, we have summarized the in vitro applications of biophysical methods, which are aimed to shed some light on the heterogeneity, pathogenicity, structures and mechanisms of the intrinsically disordered protein aggregates associated with proteinopathies including AD. This review will also rationalize some of the strategies in modulating disease-relevant pathogenic protein entities by small molecules using structural biology approaches and biophysical characterization. We have also highlighted tools and techniques to simulate the in vivo conditions for native and cytotoxic tau/amyloids assemblies, urge new chemical approaches to replicate tau/amyloids assemblies similar to those in vivo conditions, in addition to designing novel potential drugs.

Folding-upon-binding pathways of an intrinsically disordered protein from a deep Markov state model

A central challenge in the study of intrinsically disordered proteins is the characterization of the mechanisms by which they bind their physiological interaction partners. Here, we utilize a deep learning based Markov state modeling approach to characterize the folding-upon-binding pathways observed in a long-time scale molecular dynamics simulation of a disordered region of the measles virus nucleoprotein NTAIL reversibly binding the X domain of the measles virus phosphoprotein complex. We find that folding-upon-binding predominantly occurs via two distinct encounter complexes that are differentiated by the binding orientation, helical content, and conformational heterogeneity of NTAIL. We do not, however, find evidence for the existence of canonical conformational selection or induced fit binding pathways. We observe four kinetically separated native-like bound states that interconvert on time scales of eighty to five hundred nanoseconds. These bound states share a core set of native intermolecular contacts and stable NTAIL helices and are differentiated by a sequential formation of native and non-native contacts and additional helical turns. Our analyses provide an atomic resolution structural description of intermediate states in a folding-upon-binding pathway and elucidate the nature of the kinetic barriers between metastable states in a dynamic and heterogenous, or "fuzzy", protein complex.

Thermodynamic and kinetic approaches for drug discovery to target protein misfolding and aggregation

INTRODUCTION

Protein misfolding diseases, including Alzheimer's and Parkinson's diseases, are characterized by the aberrant aggregation of proteins. These conditions are still largely untreatable, despite having a major impact on our healthcare systems and societies.

AREAS COVERED

We describe drug discovery strategies to target protein misfolding and aggregation. We compare thermodynamic approaches, which are based on the stabilization of the native states of proteins, with kinetic approaches, which are based on the slowing down of the aggregation process. This comparison is carried out in terms of the current knowledge of the process of protein misfolding and aggregation, the mechanisms of disease and the therapeutic targets.

EXPERT OPINION

There is an unmet need for disease-modifying treatments that target protein misfolding and aggregation for the over 50 human disorders known to be associated with this phenomenon. With the approval of the first drugs that can prevent misfolding or inhibit aggregation, future efforts will be focused on the discovery of effective compounds with these mechanisms of action for a wide range of conditions.

Identification of Catechins' Binding Sites in Monomeric Aβ42 through Ensemble Docking and MD Simulations

The assembly of the amyloid-β peptide (Aβ) into toxic oligomers and fibrils is associated with Alzheimer's disease and dementia. Therefore, disrupting amyloid assembly by direct targeting of the Aβ monomeric form with small molecules or antibodies is a promising therapeutic strategy. However, given the dynamic nature of Aβ, standard computational tools cannot be easily applied for high-throughput structure-based virtual screening in drug discovery projects. In the current study, we propose a computational pipeline-in the framework of the ensemble docking strategy-to identify catechins' binding sites in monomeric Aβ₄₂. It is shown that both hydrophobic aromatic interactions and hydrogen bonding are crucial for the binding of catechins to Aβ₄₂. Additionally, it has been found that all the studied ligands, especially EGCG, can act as potent inhibitors against amyloid aggregation by blocking the central hydrophobic region of Aβ. Our findings are evaluated and confirmed with multi-microsecond MD simulations. Finally, it is suggested that our proposed pipeline, with low computational cost in comparison with MD simulations, is a suitable approach for the virtual screening of ligand libraries against Aβ.

Molecular Integrative Analysis of the Inhibitory Effects of Dipeptides on Amyloid β Peptide 1-42 Polymerization

The major pathological feature of Alzheimer's disease (AD) is the aggregation of amyloid β peptide (Aβ) in the brain. Inhibition of Aβ42 aggregation may prevent the advancement of AD. This study employed molecular dynamics, molecular docking, electron microscopy, circular dichroism, staining of aggregated Aβ with ThT, cell viability, and flow cytometry for the detection of reactive oxygen species (ROS) and apoptosis. Aβ42 polymerizes into fibrils due to hydrophobic interactions to minimize free energy, adopting a β-strand structure and forming three hydrophobic areas. Eight dipeptides were screened by molecular docking from a structural database of 20 L-α-amino acids, and the docking was validated by molecular dynamics (MD) analysis of binding stability and interaction potential energy. Among the dipeptides, arginine dipeptide (RR) inhibited Aβ42 aggregation the most. The ThT assay and EM revealed that RR reduced Aβ42 aggregation, whereas the circular dichroism spectroscopy analysis showed a 62.8% decrease in β-sheet conformation and a 39.3% increase in random coiling of Aβ42 in the presence of RR. RR also significantly reduced the toxicity of Aβ42 secreted by SH-SY5Y cells, including cell death, ROS production, and apoptosis. The formation of three hydrophobic regions and polymerization of Aβ42 reduced the Gibbs free energy, and RR was the most effective dipeptide at interfering with polymerization.

Strategies for Conditional Regulation of Proteins

Design of the next-generation of therapeutics, biosensors, and molecular tools for basic research requires that we bring protein activity under control. Each protein has unique properties, and therefore, it is critical to tailor the current techniques to develop new regulatory methods and regulate new proteins of interest (POIs). This perspective gives an overview of the widely used stimuli and synthetic and natural methods for conditional regulation of proteins.

Extracellular protein homeostasis in neurodegenerative diseases

The protein homeostasis (proteostasis) system encompasses the cellular processes that regulate protein synthesis, folding, concentration, trafficking and degradation. In the case of intracellular proteostasis, the identity and nature of these processes have been extensively studied and are relatively well known. By contrast, the mechanisms of extracellular proteostasis are yet to be fully elucidated, although evidence is accumulating that their age-related progressive impairment might contribute to neuronal death in neurodegenerative diseases. Constitutively secreted extracellular chaperones are emerging as key players in processes that operate to protect neurons and other brain cells by neutralizing the toxicity of extracellular protein aggregates and promoting their safe clearance and disposal. Growing evidence indicates that these extracellular chaperones exert multiple effects to promote cell viability and protect neurons against pathologies arising from the misfolding and aggregation of proteins in the synaptic space and interstitial fluid. In this Review, we outline the current knowledge of the mechanisms of extracellular proteostasis linked to neurodegenerative diseases, and we examine the latest understanding of key molecules and processes that protect the brain from the pathological consequences of extracellular protein aggregation and proteotoxicity. Finally, we contemplate possible therapeutic opportunities for neurodegenerative diseases on the basis of this emerging knowledge.

Familial Alzheimer's Disease-Related Mutations Differentially Alter Stability of Amyloid-Beta Aggregates

Amyloid-beta (Aβ) deposition as senile plaques is a pathological hallmark of Alzheimer's disease (AD). AD is characterized by a large level of heterogeneity in amyloid pathology, whose molecular origin is poorly understood. Here, we employ NMR spectroscopy and MD simulation at ambient and high pressures and investigate how AD-related mutations in Aβ peptide influence the stability of Aβ aggregates. The pressure-induced monomer dissociation from Aβ aggregates monitored by NMR demonstrated that the Iowa (D23N), Arctic (E22G), and Osaka (ΔE22) mutations altered the pressure stability of Aβ40 aggregates in distinct manners. While the NMR data of monomeric Aβ40 showed only small localized effects of mutations, the MD simulation of mutated Aβ fibrils revealed their distinct susceptibility to elevated pressure. Our data propose a structural basis for the distinct stability of various Aβ fibrils and highlights "stability" as a molecular property potentially contributing to the large heterogeneity of amyloid pathology in AD.

PolyQ-Expanded Mutant Huntingtin Forms Inclusion Body Following Transient Cold Shock in a Two-Step Aggregation Mechanism

Age-dependent formation of insoluble protein aggregates is a hallmark of many neurodegenerative diseases. We are interested in the cell chemistry that drives the aggregation of polyQ-expanded mutant Huntingtin (mHtt) protein into insoluble inclusion bodies (IBs). Using an inducible cell model of Huntington's disease, we show that a transient cold shock (CS) at 4 °C followed by recovery incubation at temperatures of 25-37 °C strongly and rapidly induces the compaction of diffuse polyQ-expanded HuntingtinExon1-enhanced green fluorescent protein chimera protein (mHtt) into round, micron size, cytosolic IBs. This transient CS-induced mHtt IB formation is independent of microtubule integrity or de novo protein synthesis. The addition of millimolar concentrations of sodium chloride accelerates, whereas urea suppresses this transient CS-induced mHtt IB formation. These results suggest that the low temperature of CS constrains the conformation dynamics of the intrinsically disordered mHtt into labile intermediate structures to facilitate de-solvation and hydrophobic interaction for IB formation at the higher recovery temperature. This work, along with our previous observation of the effects of heat shock protein chaperones and osmolytes in driving mHtt IB formation, underscores the primacy of mHtt structuring and rigidification for H-bond-mediated cross-linking in a two-step mechanism of mHtt IB formation in living cells.

Perphenazine-Macrocycle Conjugates Rapidly Sequester the Aβ42 Monomer and Prevent Formation of Toxic Oligomers and Amyloid

Alzheimer's disease is imposing a growing social and economic burden worldwide, and effective therapies are urgently required. One possible approach to modulation of the disease outcome is to use small molecules to limit the conversion of monomeric amyloid (Aβ42) to cytotoxic amyloid oligomers and fibrils. We have synthesized modulators of amyloid assembly that are unlike others studied to date: these compounds act primarily by sequestering the Aβ42 monomer. We provide kinetic and nuclear magnetic resonance data showing that these perphenazine conjugates divert the Aβ42 monomer into amorphous aggregates that are not cytotoxic. Rapid monomer sequestration by the compounds reduces fibril assembly, even in the presence of pre-formed fibrillar seeds. The compounds are therefore also able to disrupt monomer-dependent secondary nucleation, the autocatalytic process that generates the majority of toxic oligomers. The inhibitors have a modular design that is easily varied, aiding future exploration and use of these tools to probe the impact of distinct Aβ42 species populated during amyloid assembly.

Self-Assembly of Amyloid-Beta (Aβ) Peptides from Solution to Near In Vivo Conditions

Understanding the atomistic resolution changes during the self-assembly of amyloid peptides or proteins is important to develop compounds or conditions to alter the aggregation pathways and suppress the toxicity and potentially aid in the development of drugs. However, the complexity of protein aggregation and the transient order/disorder of oligomers along the pathways to fibril are very challenging. In this Perspective, we discuss computational studies of amyloid polypeptides carried out under various conditions, including conditions closely mimicking in vivo and point out the challenges in obtaining physiologically relevant results, focusing mainly on the amyloid-beta Aβ peptides.

Small molecules targeting the disordered transactivation domain of the androgen receptor induce the formation of collapsed helical states

Intrinsically disordered proteins, which do not adopt well-defined structures under physiological conditions, are implicated in many human diseases. Small molecules that target the disordered transactivation domain of the androgen receptor have entered human trials for the treatment of castration-resistant prostate cancer (CRPC), but no structural or mechanistic rationale exists to explain their inhibition mechanisms or relative potencies. Here, we utilize all-atom molecular dynamics computer simulations to elucidate atomically detailed binding mechanisms of the compounds EPI-002 and EPI-7170 to the androgen receptor. Our simulations reveal that both compounds bind at the interface of two transiently helical regions and induce the formation of partially folded collapsed helical states. We find that EPI-7170 binds androgen receptor more tightly than EPI-002 and we identify a network of intermolecular interactions that drives higher affinity binding. Our results suggest strategies for developing more potent androgen receptor inhibitors and general strategies for disordered protein drug design.

Big dynorphin is a neuroprotector scaffold against amyloid β-peptide aggregation and cell toxicity

Amyloid β-peptide (Aβ) misfolding into β-sheet structures triggers neurotoxicity inducing Alzheimer’s disease (AD). Molecules able to reduce or to impair Aβ aggregation are highly relevant as possible AD treatments since they should protect against Aβ neurotoxicity. We have studied the effects of the interaction of dynorphins, a family of opioid neuropeptides, with Aβ₄₀ the most abundant species of Aβ. Biophysical measurements indicate that Aβ₄₀ interacts with Big Dynorphin (BigDyn), lowering the amount of hydrophobic aggregates, and slowing down the aggregation kinetics. As expected, we found that BigDyn protects against Aβ₄₀ aggregates when studied in human neuroblastoma cells by cell survival assays. The cross-interaction between BigDyn and Aβ₄₀ provides insight into the mechanism of amyloid pathophysiology and may open up new therapy possibilities.