Computational approaches to understanding protein aggregation in neurodegeneration

Advanced computational approaches to understand protein aggregation

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

Alpha-synuclein phosphorylation induces amyloid conversion via enhanced electrostatic bridging: Insights from molecular modeling of the full-length protein

Fibril formation from alpha-synuclein is a key point in Parkinson's disease, multiple system atrophy, and other synucleinopathies. The mechanism of the amyloid-like conversion followed by the formation of pre-fibrillar soluble oligomers and fibrils is not completely clear; furthermore, it is unclear how the Parkinson's disease-related point mutations located in the pre-NAC region enhance fibrillation. In the present paper, atomistic replica exchange molecular dynamics simulations of the full-length alpha-synuclein and its two mutants, A53T and E46K, elucidated amyloid conversion intermediates. Both mutants demonstrated an enhanced tendency for the conversion but in different manners; the main intermediate conformations populated in the WT alpha-synuclein conformational ensemble disappeared due to mutations, indicating a different conversion pathway. Analysis of the preferable beta-hairpin positions and intermediate conformations seems to reflect a tendency to form a particular amyloid fibril polymorph. A strong elevation of amyloid transformation level was shown also for Ser129-phosphorylated alpha-synuclein. Altered intermediate conformations, the most preferable beta-hairpin positions in the NAC region, and prevalent salt bridges propose the formation of so-called polymorph 2 or even a novel type of fibrils. A better understanding of the detailed mechanism of the amyloid conversion sheds light on the effect of Lewy body-related phosphorylation and might help in the development of new therapeutics for synucleinopathies.

Prediction of Kv11.1 potassium channel PAS-domain variants trafficking via machine learning

Congenital long QT syndrome (LQTS) is characterized by a prolonged QT-interval on an electrocardiogram (ECG). An abnormal prolongation in the QT-interval increases the risk for fatal arrhythmias. Genetic variants in several different cardiac ion channel genes, including KCNH2, are known to cause LQTS. Here, we evaluated whether structure-based molecular dynamics (MD) simulations and machine learning (ML) could improve the identification of missense variants in LQTS-linked genes. To do this, we investigated KCNH2 missense variants in the Kv11.1 channel protein shown to have wild type (WT) like or class II (trafficking-deficient) phenotypes in vitro. We focused on KCNH2 missense variants that disrupt normal Kv11.1 channel protein trafficking, as it is the most common phenotype for LQTS-associated variants. Specifically, we used computational techniques to correlate structural and dynamic changes in the Kv11.1 channel protein PAS domain (PASD) with Kv11.1 channel protein trafficking phenotypes. These simulations unveiled several molecular features, including the numbers of hydrating waters and hydrogen bonding pairs, as well as folding free energy scores, that are predictive of trafficking. We then used statistical and machine learning (ML) (Decision tree (DT), Random forest (RF), and Support vector machine (SVM)) techniques to classify variants using these simulation-derived features. Together with bioinformatics data, such as sequence conservation and folding energies, we were able to predict with reasonable accuracy (≈75%) which KCNH2 variants do not traffic normally. We conclude that structure-based simulations of KCNH2 variants localized to the Kv11.1 channel PASD led to an improvement in classification accuracy. Therefore, this approach should be considered to complement the classification of variant of unknown significance (VUS) in the Kv11.1 channel PASD.

Governing dynamics and preferential binding of the AXH domain influence the aggregation pathway of Ataxin-1

The present state of understanding the mechanism of Spinocerebellar Ataxia-1, a fatal neurodegenerative disease linked to the protein Ataxin-1 (ATXN1), is baffled by a set of self-contradictory, and hence, inconclusive observations. This fallacy poses a bottleneck to the effective designing of curable drugs as the field is currently missing the specific druggable site. To understand the fundamentals of pathogenesis, we tried to decipher the intricacies of the extremely complicated landscape by targeting the relevant species that supposedly dictate the structure-function paradigm. The atomic-level description and characterization of the dynamism of the systems reveal the existence of structural polymorphism in all the leading stakeholders of the overall system. The very existence of conformational heterogeneity in every species creates numerous possible combinations of favorable interactions because of the variability in segmental cross-talks and hence claims its role in the choice of routes between functional activity and dysfunctional disease-causing aggregation. Despite this emergent configurational diversity, there is a common mode of operative intermolecular forces that dictates the extent of stability of all the multimeric complexes due to the localized population of a specific type of residue. The present research proposes a dynamic switch mechanism between aggregability and functional activity, based on the logical interpretation of the estimated variables, which is practically dictated by the effective concentration of the interacting species involved in the cell.

Insights on Aggregation of Hen Egg-White Lysozyme from Raman Spectroscopy and MD Simulations

Protein misfolding and aggregation play a significant role in several neurodegenerative diseases. In the present work, the spontaneous aggregation of hen egg-white lysozyme (HEWL) in an alkaline pH 12.2 at an ambient temperature was studied to obtain molecular insights. The time-dependent changes in spectral peaks indicated the formation of β sheets and their effects on the backbone and amino acids during the aggregation process. Introducing iodoacetamide revealed the crucial role of intermolecular disulphide bonds amidst monomers in the aggregation process. These findings were corroborated by Molecular Dynamics (MD) simulations and protein-docking studies. MD simulations helped establish and visualize the unfolding of the proteins when exposed to an alkaline pH. Protein docking revealed a preferential dimer formation between the HEWL monomers at pH 12.2 compared with the neutral pH. The combination of Raman spectroscopy and MD simulations is a powerful tool to study protein aggregation mechanisms.

Monitoring early-stage β-amyloid dimer aggregation by histidine site-specific two-dimensional infrared spectroscopy in a simulation study

Monitoring early-stage β-amyloid (Aβ) dimerization is a formidable challenge for understanding neurological diseases. We compared β-sheet formation and histidine site-specific two-dimensional infrared (2D IR) spectroscopic signatures of Aβ dimers with different histidine states (δ; N^δ1-H, ε; N^ε2-H, or π; both protonated). Molecular dynamics (MD) simulations revealed that β-sheet formation is favored for the δδδ:δδδ and πππ:πππ tautomeric isomers showing strong couplings and frequent contacts between the central hydrophobic core and C-terminus compared with the εεε:εεε isomer. Characteristic blue-shifts in the 2D IR central bands were observed upon monomer-dimer transformation. The εεε:εεε dimer exhibited larger frequency shifts than δδδ:δδδ and πππ:πππ implying that the red-shift may have a correlation with N^δ1-H(δ) protonation. Our results support the tautomerization/protonation hypothesis that attributes Aβ misfolding to histidine tautomers as a possible primary initiator for Aβ aggregation and facilitates the application of histidine site-specific 2D IR spectroscopy for studying early-stage Aβ self-assembly.

A review on Huntington protein Insight into protein aggregation and therapeutic interventions

Huntington disease (HD) is a distressing, innate neurodegenerative disease that descends from CAG repeat expansion in the huntingtin gene causing behavioral changes, motor dysfunction, and dementia in children and adults. Mutation in huntingtin (HTT) protein has been suggested to cause neuron loss in the cortex and striatum through various mechanisms including abnormal regulation of transcription, proteasomal dysfunction, post-translational modification, and other events, regulating toxicity. Pathogenesis of HD involves cleavage of the huntingtin protein followed by the neuronal accumulation of its aggregated form. Several research groups made possible efforts to reduce huntingtin gene expression, protein accumulation, and protein aggregation using inhibitors and molecular chaperones as developing drugs against HD. Herein, we review the mechanism proposed towards the formation of HTT protein aggregation and the impact of therapeutic strategies for the treatment of HD.

Electromagnetic bioeffects: a multiscale molecular simulation perspective

Electromagnetic bioeffects remain an enigma from both the experimental and theoretical perspectives despite the ubiquitous presence of related technologies in contemporary life. Multiscale computational modelling can provide valuable insights into biochemical systems and predict how they will be perturbed by external stimuli. At a microscopic level, it can be used to determine what (sub)molecular scale reactions various stimuli might induce; at a macroscopic level, it can be used to examine how these changes affect dynamic behaviour of essential molecules within the crowded biomolecular milieu in living tissues. In this review, we summarise and evaluate recent computational studies that examined the impact of externally applied electric and electromagnetic fields on biologically relevant molecular systems. First, we briefly outline the various methodological approaches that have been employed to study static and oscillating field effects across different time and length scales. The practical value of such modelling is then illustrated through representative case-studies that showcase the diverse effects of electric and electromagnetic field on the main physiological solvent - water, and the essential biomolecules - DNA, proteins, lipids, as well as some novel biomedically relevant nanomaterials. The implications and relevance of the theoretical multiscale modelling to practical applications in therapeutic medicine are also discussed. Finally, we summarise ongoing challenges and potential opportunities for theoretical modelling to advance the current understanding of electromagnetic bioeffects for their modulation and/or beneficial exploitation in biomedicine and industry.

Exploration of Protein Aggregations in Parkinson's Disease Through Computational Approaches and Big Data Analytics

Protein aggregation has been implicated in numerous neurodegenerative disorders whose etiologies are poorly understood, and for which there are no effective treatments. Here we show that the computational approaches may help us to better understand the basics of Parkinson's disease (PD). The high-resolution structural, dynamical, and mechanistic insights delivered by computational studies of protein aggregation have a unique potential to enable the rational manipulation of oligomer formation. Additionally, big data and machine learning methods may provide valuable insights to better understand the nature of proteins involved in PD and their aggregative behavior for the betterment of PD treatment.

A3D 2.0 Update for the Prediction and Optimization of Protein Solubility

Protein aggregation propensity is a property imprinted in protein sequences and structures, being associated with the onset of human diseases and limiting the implementation of protein-based biotherapies. Computational approaches stand as cost-effective alternatives for reducing protein aggregation and increasing protein solubility. AGGRESCAN 3D (A3D) is a structure-based predictor of aggregation that takes into account the conformational context of a protein, aiming to identify aggregation-prone regions exposed in protein surfaces. Here we inspect the updated 2.0 version of the algorithm, which extends the application of A3D to previously inaccessible proteins and incorporates new modules to assist protein redesign. Among these features, the new server includes stability calculations and the possibility to optimize protein solubility using an experimentally validated computational pipeline. Finally, we employ defined examples to navigate the A3D RESTful service, a routine to handle extensive protein collections. Altogether, this chapter is conceived to train and assist A3D non-experts in the study of aggregation-prone regions and protein solubility redesign.

Theoretical Insights into Mutation and Histidine Tautomerism Effects on Tau Proteins

Research on misfolding of tau proteins will help to better understand the formation process of neurofibrillary tangles, a hallmark of Alzheimer's disease. Mutation and histidine tautomeric effects have been considered the two most important inherent factors for tau protein misfolding. In current research, replica-exchange molecular dynamics (REMD) were performed to characterize the structural properties of the key fragment R3 of tau protein under the collective effects of P332L mutation and histidine tautomerism. Simulation results suggest that though the content β-sheet of P332L R3 εδ isomer is slightly lower than that of the WT P332L R3 fragment, the total stable secondary structures including β-sheet and helix of P332L R3 isomers are generally more prevalent than those of wild type R3, which may be the reason that P332L R3 has a higher aggregation tendency. Further analysis showed that the hydrogen bond networks are affected by the mutation and histidine tautomerism. Furthermore, the interactions between N-terminus and C-terminus play a crucial role in β-hairpin formation in all isomers. The current study will contribute to revealing the collective effects of P332L and histidine tautomerism on the misfolding of tau proteins.

Iodine excess induces hepatic, renal and pancreatic injury in female mice as determined by attenuated total reflection Fourier-transform infrared spectrometry

Limited knowledge of the long-term effects of excessive iodine (EI) intake on biomolecular signatures in the liver/pancreas/kidney prompted this study. Herein, following 6 months of exposure in mice to 300, 600, 1200 or 2400 μg/L iodine, the biochemical signature of alterations to the liver/pancreas/kidney was profiled using attenuated total reflection Fourier-transform infrared (ATR-FTIR) spectroscopy coupled with principal component analysis-linear discriminant analysis (PCA-LDA). Our research showed that serum alanine aminotransferase (ALT), aspartate aminotransferase (AST), blood urea nitrogen (BUN), serum creatinine (Scr), insulin, blood glucose levels and homeostasis model assessment for insulin resistance (HOMA-IR) index in the 1200 and 2400 μg/L iodine-treated groups were significantly increased compared with those in the control group. Moreover, histological analysis showed that the liver/kidney/pancreas tissues of mice exposed to EI treatment displayed substantial morphological abnormalities, such as a loss of hepatic architecture, glomerular cell vacuolation and pancreatic neutrophilic infiltration. Notably, EI treatment caused distinct biochemical signature segregation between EI-exposed versus the control liver/pancreas/kidney. The main biochemical alterations between EI-exposed and control groups were observed for protein phosphorylation, protein secondary structures and lipids. The ratios of amide I-to-amide II (1674 cm^-1 /1570 cm^-1 ), α-helix-to-β-sheet (1657 cm^-1 /1635 cm^-1 ), glycogen-to-phosphate (1030 cm^-1 /1086 cm^-1 ) and the peptide aggregation (1 630 cm^-1 /1650 cm^-1 ) level of EI-treated groups significantly differed from the control group. Our study demonstrated that EI induced hepatic, renal and pancreatic injury by disturbing the structure, metabolism and function of the cell membrane. This finding provides the new method and implication for human health assessment regarding long-term EI intake.

Histidine Tautomeric Effect on the Key Fragment R3 of Tau Protein from Atomistic Simulations

Self-assembly of hyperphosphorylated tau proteins into neurofibrillary tangles (NFT) is a hallmark of Alzheimer's disease. Previous studies suggest that the tau monomer may play an important role in NFTs formation in two general categories: inert (M_i) monomer and seed-competent (M_s) monomer. In the current study, replica-exchange molecular dynamics (REMD) were performed to investigate the effect of histidine tautomerism on the structures of a key fragment (R3) of tau protein and the transformation between different conformations. Based on the simulation results, we propose the histidine tautomerism hypothesis for tau protein misfolding. Histidine tautomerism greatly expands the conformational library, which triggers the emergence of conformations with higher aggregation tendency. Moreover, the conversions existing in both isomers and conformations may cause protein misfolding to occur more readily.

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.

SOD1 oligomers in amyotrophic lateral sclerosis

Identifying nonnative, trimeric forms of SOD1 trimers as the toxic species, rather than large aggregates revolutionizes our understanding of ALS pathophysiology. Large protein aggregates, what was previously thought as the central cause of neurodegeneration, play protective role and are not responsible for neuronal death. SOD1 trimers are implicated at the molecular, cellular, and organismal level. Understanding the formation of the nonnative trimer and its role in the cell, leading to cell death, holds the key to developing a new standard of therapeutics for ALS and for other neurodegenerative diseases. This review highlights recent advances of knowledge for the role of SOD1 oligomers in ALS.

Studying the mechanism of phase separation in aqueous solutions of globular proteins via molecular dynamics computer simulations

Proteins are the most abundant biomacromolecules in living cells, where they perform vital roles in virtually every biological process. To maintain their function, proteins need to remain in a stable (native) state. Inter- and intramolecular interactions in aqueous protein solutions govern the fate of proteins, as they can provoke their unfolding or association into aggregates. The initial steps of protein aggregation are difficult to capture experimentally, therefore we used molecular dynamics simulations in this study. We investigated the initial phase of aggregation of two different lysozymes, hen egg-white (HEWL) and T4 WT* lysozyme and also human lens γ-D crystallin by using atomistic simulations. We monitored the phase stability of their aqueous solutions by calculating time-dependent density fluctuations. We found that all proteins remained in their compact form despite aggregation. With an extensive analysis of intermolecular residue-residue interactions we discovered that arginine is of paramount importance in the initial stage of aggregation of HEWL and γ-D crystallin, meanwhile lysine was found to be the most involved amino acid in forming initial contacts between T4 WT* molecules.

The Early Phase of β2-Microglobulin Aggregation: Perspectives From Molecular Simulations

Protein β2-microglobulin is the causing agent of two amyloidosis, dialysis related amyloidosis (DRA), affecting the bones and cartilages of individuals with chronic renal failure undergoing long-term hemodialysis, and a systemic amyloidosis, found in one French family, which impairs visceral organs. The protein’s small size and its biomedical significance attracted the attention of theoretical scientists, and there are now several studies addressing its aggregation mechanism in the context of molecular simulations. Here, we review the early phase of β2-microglobulin aggregation, by focusing on the identification and structural characterization of monomers with the ability to trigger aggregation, and initial small oligomers (dimers, tetramers, hexamers etc.) formed in the so-called nucleation phase. We focus our analysis on results from molecular simulations and integrate our views with those coming from in vitro experiments to provide a broader perspective of this interesting field of research. We also outline directions for future computer simulation studies.

Tautomeric Effect of Histidine on β-Sheet Formation of Amyloid Beta 1-40: 2D-IR Simulations

Histidine state (protonated or δ or ε tautomer) has been considered the origin of abnormal misfolding and aggregation of β-amyloid (Aβ). Our previous studies reported that the δδδ isomer of Aβ (1-40) has a greater propensity for β-sheet conformation compared to other isomers. However, direct proof of the tautomeric effect has not been reported. In this context, we calculated histidine site-specific two-dimensional infrared spectroscopy of the δδδ, εεε, and πππ (all protonated histidine) systems within the framework of classical molecular dynamics simulations aiming at connecting our previous results with the current experimental observations. Our results showed that β-sheet formation is favored for the δδδ and πππ tautomers compared with the εεε tautomer, consistent with our previous studies. This result was further supported by contact map analyses and the strength of dipole coupling between the amide-I bonds of each residue. The two-dimensional infrared diagonal trace for each tautomer included three distinctive spectrally resolvable peaks near 1680, 1686, and 1693 cm^-1, as was also observed for histidine dipeptides. However, the peak positions at His6, His13, and His14 did not show a consensus trend with the histidine or protonation state but were instead affected by the presence of surrounding hydrogen bonds. Our study provides a deeper insight into the influence of tautomerism and protonation of histidine residues in Aβ (1-40) on amyloid misfolding and provides a connection between our previous simulations and experimental observations.

Impact of A2V Mutation and Histidine Tautomerism on Aβ42 Monomer Structures from Atomistic Simulations

The self-assembly of amyloid-β (Aβ) peptides into senile plaques in the brain is a hallmark of Alzheimer's disease (AD) pathology. Mutation and histidine tautomerism are considered intrinsic origins in the accumulation of Aβ. As a first step toward understanding the impact of A2V mutation and histidine tautomerism on the Aβ42 isoform, we performed replica-exchange molecular dynamics (REMD) simulations to investigate the effects of histidine tautomerism on the structural properties of A2V Aβ42 peptides. There are generally more β-sheet and less α-helix secondary structures in A2V Aβ42 monomers than in WT Aβ42, implying a higher aggregation tendency in A2V Aβ42, which is consistent with previous studies. The current research will help develop the histidine tautomerism hypothesis of misfolded protein aggregation and eventually elucidate the pathogenesis of AD.

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

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

Mechanistic insights into the deleterious roles of Nasu-Hakola disease associated TREM2 variants

Recently, the critical roles played by genetic variants of TREM2 (Triggering Receptor Expressed on Myeloid cells 2) in Alzheimer's disease have been aggressively highlighted. However, few studies have focused on the deleterious roles of Nasu-Hakola disease (NHD) associated TREM2 variants. In order to get insights into the contributions made by these variants to neurodegeneration, we investigated the influences of four NHD associated TREM2 mutations (Y38C, W50C, T66M, and V126G) on loss-of-function, and followed this with in silico prediction and conventional molecular dynamics simulation. NHD mutations were predicted to be highly deleterious by eight different in silico bioinformatics tools and found to induce conformational changes by molecular dynamics simulation. As compared with the wild-type, the four variants produced substantial differences in the collective motions of loop regions, which not only promoted structural remodeling in the CDR2 (complementarity-determining region 2) loop but also in the CDR1 loop, by changing inter- and intra-loop hydrogen bonding networks. In addition, structural studies in a free energy landscape analysis showed that Y38, T66, and V126 are crucial for maintaining the structural features of CDR1 and CDR2 loops, and that mutations in these positions produced steric clashes and loss of ligand binding. These results showed the presence of mutations in the TREM2 ectodomain induced flexibility and caused structural alterations. Dynamical scenarios, as provided by the present study, may be critical to our understanding of the roles of these TREM2 mutations in neurodegenerative diseases.

Intrinsic Origin of Amyloid Aggregation: Collective Effects of the Mutation and Tautomerism of Histidine

Mutation is considered an important factor in the accumulation of amyloid-β (Aβ), which is a hallmark of Alzheimer's disease (AD). A2V Aβ40 shows a higher aggregation tendency; however, the existing knowledge is not sufficient to explain the mechanism. We performed replica-exchange molecular dynamics simulations (REMD) to investigate the structural properties of A2V Aβ40 monomers and consider the tautomerism of histidine. The collective effects of the mutation and tautomerism leads A2V Aβ40 to much higher β-sheet and lower α-helix contents than WT Aβ40, which may explain the enhanced aggregation kinetics of A2V Aβ40 with respect to WT Aβ40. The current research provides new insights on understanding the pathology of AD.

A computational approach to get insights into multiple faces of additives in modulation of protein aggregation pathways

An enormous population worldwide is presently confronted with debilitating neurodegenerative diseases. The etiology of the disease is connected to protein aggregation and the events involved therein. Thus, a complete understanding of an inhibitor at different stages in the process is imperative for the formulation of a drug molecule. This review presents a detailed summary of the current status of different cosolvents. It further develops how the complex aggregation pathway can be simplified into three steps common to all proteins and the way computer simulations can be exploited to gain insights into the ways by which known inhibitors can affect all these stages. Computation of theoretical parameters in this regard and their correlation with experimental techniques is accentuated. In addition to providing an outline of the scope of different additives, this review showcases the way by which the problem of analyzing an effect of an additive can be addressed effectively via MD simulations.

Molecular Model for the Self-Assembly of the Cyclic Lipodepsipeptide Pseudodesmin A

Self-assembly of peptides into supramolecular structures represents an active field of research with potential applications ranging from material science to medicine. Their study typically involves the application of a large toolbox of spectroscopic and imaging techniques. However, quite often, the structural aspects remain underexposed. Besides, molecular modeling of the self-assembly process is usually difficult to handle, since a vast conformational space has to be sampled. Here, we have used an approach that combines short molecular dynamics simulations for peptide dimerization and NMR restraints to build a model of the supramolecular structure from the dimeric units. Experimental NMR data notably provide crucial information about the conformation of the monomeric units, the supramolecular assembly dimensions, and the orientation of the individual peptides within the assembly. This in silico/in vitro mixed approach enables us to define accurate atomistic models of supramolecular structures of the bacterial cyclic lipodepsipeptide pseudodesmin A.

Protein Interaction with Charged Macromolecules: From Model Polymers to Unfolded Proteins and Post-Translational Modifications

Interaction of proteins with charged macromolecules is involved in many processes in cells. Firstly, there are many naturally occurred charged polymers such as DNA and RNA, polyphosphates, sulfated glycosaminoglycans, etc., as well as pronouncedly charged proteins such as histones or actin. Electrostatic interactions are also important for "generic" proteins, which are not generally considered as polyanions or polycations. Finally, protein behavior can be altered due to post-translational modifications such as phosphorylation, sulfation, and glycation, which change a local charge of the protein region. Herein we review molecular modeling for the investigation of such interactions, from model polyanions and polycations to unfolded proteins. We will show that electrostatic interactions are ubiquitous, and molecular dynamics simulations provide an outstanding opportunity to look inside binding and reveal the contribution of electrostatic interactions. Since a molecular dynamics simulation is only a model, we will comprehensively consider its relationship with the experimental data.

Quantum chemical and molecular mechanics studies on the assessment of interactions between resveratrol and mutant SOD1 (G93A) protein

Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disease that has been associated with mutations in metalloenzyme superoxide dismutase (SOD1) causing protein structural destabilization and aggregation. However, the mechanistic action and the cure for the disease still remain obscure. Herein, we initially studied the conformational preferences of SOD1 protein structures upon substitution of Ala at Gly93 in comparison with that of wild type. Our results corroborated with the previous experimental studies on the aggregation and the destabilizing activity of mutant SOD1 protein G93A. On the therapeutic point of view, we computationally analyzed the influence of resveratrol, a natural polyphenol widely found in red wine on mutant SOD1 relative to wild type, using molecular docking studies. Further, FMO calculations were performed, using GAMESS to study the pair residual interaction on the wild type and mutant complex systems. Consequently, the resveratrol showed greater interaction with mutant than the wild type. Subsequently, we evaluated the conformational preferences of wild type and mutant complex systems, where the protein conformational structures of mutant that were earlier found to lose their conformational stability was regained, upon binding with resveratrol. Similar trend of results were found on the 2-D free energy landscapes of both the wild type and mutant systems. Hence, the combined biophysical and quantum chemical studies in our study supported the results of previous experimental studies, thereby stipulating an action of resveratrol on mutant SOD1 and paving a way for the design of highly potent effective inhibitors against fALS affecting the mankind.

The attachment of α-synuclein to a fiber: A coarse-grain approach

We present simulations of the amyloidogenic core of α-synuclein, the protein causing Parkinson's disease, as a short chain of coarse-grain patchy particles. Each particle represents a sequence of about a dozen amino acids. The fluctuating secondary structure of this intrinsically disordered protein is modelled by dynamic variations of the shape and interaction characteristics of the patchy particles, ranging from spherical with weak isotropic attractions for the disordered state to spherocylindrical with strong directional interactions for a β-sheet. Flexible linkers between the particles enable sampling of the tertiary structure. This novel model is applied here to study the growth of an amyloid fibril, by calculating the free energy profile of a protein attaching to the end of a fibril. The simulation results suggest that the attaching protein readily becomes trapped in a mis-folded state, thereby inhibiting further growth of the fibril until the protein has readjusted to conform to the fibril structure, in line with experimental findings and previous simulations on small fragments of other proteins.

Molecular simulations of cellular processes

It is, nowadays, possible to simulate biological processes in conditions that mimic the different cellular compartments. Several groups have performed these calculations using molecular models that vary in performance and accuracy. In many cases, the atomistic degrees of freedom have been eliminated, sacrificing both structural complexity and chemical specificity to be able to explore slow processes. In this review, we will discuss the insights gained from computer simulations on macromolecule diffusion, nuclear body formation, and processes involving the genetic material inside cell-mimicking spaces. We will also discuss the challenges to generate new models suitable for the simulations of biological processes on a cell scale and for cell-cycle-long times, including non-equilibrium events such as the co-translational folding, misfolding, and aggregation of proteins. A prominent role will be played by the wise choice of the structural simplifications and, simultaneously, of a relatively complex energetic description. These challenging tasks will rely on the integration of experimental and computational methods, achieved through the application of efficient algorithms. Graphical abstract.

From the Evolution of Protein Sequences Able to Resist Self-Assembly to the Prediction of Aggregation Propensity

Folding of polypeptide chains into biologically active entities is an astonishingly complex process, determined by the nature and the sequence of residues emerging from ribosomes. While it has been long believed that evolution has pressed genomes so that specific sequences could adopt unique, functional three-dimensional folds, it is now clear that complex protein machineries act as quality control system and supervise folding. Notwithstanding that, events such as erroneous folding, partial folding, or misfolding are frequent during the life of a cell or a whole organism, and they can escape controls. One of the possible outcomes of this misbehavior is cross-β aggregation, a super secondary structure which represents the hallmark of self-assembled, well organized, and extremely ordered structures termed amyloid fibrils. What if evolution would have not taken into account such possibilities? Twenty years of research point toward the idea that, in fact, evolution has constantly supervised the risk of errors and minimized their impact. In this review we tried to survey the major findings in the amyloid field, trying to describe what the real pitfalls of protein folding are-from an evolutionary perspective-and how sequence and structural features have evolved to balance the need for perfect, dynamic, functionally efficient structures, and the detrimental effects implicit in the dangerous process of folding. We will discuss how the knowledge obtained from these studies has been employed to produce computational methods able to assess, predict, and discriminate the aggregation properties of protein sequences.

An Account of Amyloid Oligomers: Facts and Figures Obtained from Experiments and Simulations

The deposition of amyloid in brain tissue in the context of neurodegenerative diseases involves the formation of intermediate species-termed oligomers-of lower molecular mass and with structures that deviate from those of mature amyloid fibrils. Because these oligomers are thought to be primarily responsible for the subsequent disease pathogenesis, the elucidation of their structure is of enormous interest. Nevertheless, because of the high aggregation propensity and the polydispersity of oligomeric species formed by the proteins or peptides in question, the preparation of appropriate samples for high-resolution structural methods has proven to be rather difficult. This is why theoretical approaches have been of particular importance in gaining insights into possible oligomeric structures for some time. Only recently has it been possible to achieve some progress with regard to the experimentally based structural characterization of defined oligomeric species. Here we discuss how theory and experiment are used to determine oligomer structures and what can be done to improve the integration of the two disciplines.

Interplay between the hydrophobic effect and dipole interactions in peptide aggregation at interfaces

Amphipathic octapeptide aggregation at hydrophobic–hydrophilic interfaces is largely driven by backbone dipole interactions in peptide aggregation at interfaces.

Aggregation and network formation in self-assembly of protein (H3.1) by a coarse-grained Monte Carlo simulation

Multi-scale aggregation to network formation of interacting proteins (H3.1) are examined by a knowledge-based coarse-grained Monte Carlo simulation as a function of temperature and the number of protein chains, i.e., the concentration of the protein. Self-assembly of corresponding homo-polymers of constitutive residues (Cys, Thr, and Glu) with extreme residue-residue interactions, i.e., attractive (Cys-Cys), neutral (Thr-Thr), and repulsive (Glu-Glu), are also studied for comparison with the native protein. Visual inspections show contrast and similarity in morphological evolutions of protein assembly, aggregation of small aggregates to a ramified network from low to high temperature with the aggregation of a Cys-polymer, and an entangled network of Glu and Thr polymers. Variations in mobility profiles of residues with the concentration of the protein suggest that the segmental characteristic of proteins is altered considerably by the self-assembly from that in its isolated state. The global motion of proteins and Cys polymer chains is enhanced by their interacting network at the low temperature where isolated chains remain quasi-static. Transition from globular to random coil transition, evidenced by the sharp variation in the radius of gyration, of an isolated protein is smeared due to self-assembly of interacting networks of many proteins. Scaling of the structure factor S(q) with the wave vector q provides estimates of effective dimension D of the mass distribution at multiple length scales in self-assembly. Crossover from solid aggregates (D ∼ 3) at low temperature to a ramified fibrous network (D ∼ 2) at high temperature is observed for the protein H3.1 and Cys polymers in contrast to little changes in mass distribution (D ∼ 1.6) of fibrous Glu- and Thr-chain configurations.