Conformational Propensities Research Articles

Isolated auto-citrullinated regions of PADI4 associate to the intact protein without altering their disordered conformation

PADI4 is one of the human isoforms of a group of enzymes intervening in the conversion of arginine to citrulline. It is involved in the development of several types of tumors, as well as other immunological illnesses, such as psoriasis, multiple sclerosis, or rheumatoid arthritis. PADI4 auto-citrullinates in several regions of its sequence, namely in correspondence of residues Arg205, Arg212, Arg218, and Arg383. We wanted to study whether the citrullinated moiety affects the conformation of nearby regions and its binding to intact PADI4. We designed two series of synthetic peptides comprising either the wild-type or the relative citrullinated versions of such regions – i.e., a first series of peptides comprising the first three arginines, and a second series comprising Arg383. We studied their conformational properties in isolation by using fluorescence, far-ultraviolet (UV) circular dichroism (CD), and 2D1H NMR. Furthermore, we characterized the binding of the wild-type and citrullinated peptides in the two series to the intact PADI4, by using isothermal titration calorimetry (ITC), fluorescence, and biolayer interferometry (BLI), as well as by molecular docking simulations. We observed that citrullination did not alter the local conformational propensities of the isolated peptides. Nevertheless, for all the peptides in the two series, citrullination slowed down the kinetic koff rates of the binding reaction to PADI4, probably due to differences in electrostatic effects compared to the presence of arginine. The affinities of PADI4 for unmodified peptides were slightly larger than those of the corresponding citrullinated ones in the two series, but they were all within the same range, indicating that there were no relevant variations in the thermodynamics of binding due to sequence effects. These results highlight details of the self-citrullination of PADI4 and, more generally, of possible auto-catalytic mechanisms taking place in vivo for other citrullinating enzymes or, alternatively, in proteins undergoing citrullination passively.

Effects of Phosphorylation on Protein Backbone Dynamics and Conformational Preferences.

Phosphorylations are the most common and extensively studied post-translational modification (PTM) of proteins in eukaryotes. They constitute a major regulatory mechanism, modulating protein function, protein-protein interactions, as well as subcellular localization. Phosphorylation sites are preferably located in intrinsically disordered regions and have been shown to trigger structural rearrangements and order-to-disorder transitions. They can therefore have a significant effect on protein backbone dynamics or conformation, but only sparse experimental data are available. To obtain a more general description of how and when phosphorylations have a significant effect on protein behavior, molecular dynamics (MD) currently provides the only suitable framework to study these effects at a large scale in atomistic detail. This study develops a systematic MD simulation framework to explore the influence of phosphorylations on the local backbone dynamics and conformational propensities of proteins. Through a series of glycine-backbone peptides, we studied the effects of amino acid residues including the three most common phosphorylations (Ser, Thr, and Tyr), on local backbone dynamics and conformational propensities. We further extended our study to investigate the interactions of all such residues between position i to positions i + 1, i + 2, i + 3, and i + 4 in such peptides. The final data set comprises structural ensembles for 3393 sequences with more than 1 μs of sampling for each ensemble. To validate the relevance of the results, the structural and conformational properties extracted from the MD simulations are compared to NMR data from the Biological Magnetic Resonance Data Bank. The systematic nature of this study enables the projection of the gained knowledge onto any phosphorylation site in the proteome and provides a general framework for the study of further PTMs. The full data set is publicly available, as a training and reference set.

Insight into the Effect of Methylglyoxal on the Conformation, Function, and Aggregation Propensity of α-Synuclein.

It is well-known that people suffering from hyperglycemia have a higher propensity to develop Parkinson's disease (PD). One of the most plausible mechanisms linking these two pathologies is the glycation of neuronal proteins and the pathological consequences of it. α-Synuclein, a key component in PD, can be glycated at its fifteen lysine. In fact, the end products of this process have been detected on aggregated α-synuclein isolated from in vivo. However, the consequences of glycation are not entirely clear, which are of crucial importance to understand the mechanism underlying the connection between diabetes and PD. To better clarify this, we have here examined how methylglyoxal (the most important carbonyl compound found in the cytoplasm) affects the conformation and aggregation propensity of α-synuclein, as well as its ability to cluster and fuse synaptic-like vesicles. The obtained data prove that methylglyoxal induces the Lys-Lys crosslinking through the formation of MOLD. However, this does not have a remarkable effect on the averaged conformational ensemble of α-synuclein, although it completely depletes its native propensity to form soluble oligomers and insoluble amyloid fibrils. Moreover, methylglyoxal has a disrupting effect on the ability of α-synuclein to bind, cluster and fusion synaptic-like vesicles.

RNA-Seq Analysis of Mammalian Prion Disease.

A protein, which can attain a prion state, differs from standard proteins in terms of structural conformation and aggregation propensity. High-throughput sequencing technology provides an opportunity to gain insight into the prion disease condition when coupled with single-cell RNA-Seq analysis to reveal transcriptional changes during prion-based pathogenicity. In this chapter, we present a protocol for RNA-Seq analysis of mammalian prion disease using a single-cell RNA sequencing dataset procured from the NCBI GEO database. This protocol is a tool that can assist researchers in characterizing mammalian prion disease in a reproducible and reusable manner. Further, the resulting output has the potential to provide transcript biomarkers for mammalian prion diseases, which can be employed for diagnostic and prognostic purposes.

SHP2 clinical phenotype, cancer, or RASopathies, can be predicted by mutant conformational propensities.

SHP2 phosphatase promotes full activation of the RTK-dependent Ras/MAPK pathway. Its mutations can drive cancer and RASopathies, a group of neurodevelopmental disorders (NDDs). Here we ask how same residue mutations in SHP2 can lead to both cancer and NDD phenotypes, and whether we can predict what the outcome will be. We collected and analyzed mutation data from the literature and cancer databases and performed molecular dynamics simulations of SHP2 mutants. We show that both cancer and Noonan syndrome (NS, a RASopathy) mutations favor catalysis-prone conformations. As to cancer versus RASopathies, we demonstrate that cancer mutations are more likely to accelerate SHP2 activation than the NS mutations at the same genomic loci, in line with NMR data for K-Ras4B more aggressive mutations. The compiled experimental data and dynamic features of SHP2 mutants lead us to propose that different from strong oncogenic mutations, SHP2 activation by NS mutations is less likely to induce a transition of the ensemble from the SHP2 inactive state to the active state. Strong signaling promotes cell proliferation, a hallmark of cancer. Weak, or moderate signals are associated with differentiation. In embryonic neural cells, dysregulated differentiation is connected to NDDs. Our innovative work offers structural guidelines for identifying and correlating mutations with clinical outcomes, and an explanation for why bearers of RASopathy mutations may have a higher probability of cancer. Finally, we propose a drug strategy against SHP2 variants-promoting cancer and RASopathies.

Mechanisms and pathology of protein misfolding and aggregation.

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

Base pair compositional variability influences DNA structural stability and tunes hydration thermodynamics and dynamics.

DNA deformability and differential hydration are crucial determinants of biological processes ranging from genetic material packaging to gene expression; their associative details, however, remain inadequately understood. Herein, we report investigations of the dynamic and thermodynamic responses of the local hydration of a variety of base pair sequences. Leveraging in silico sampling and our in-house analyses, we first report the local conformational propensity of sequences that are either predisposed toward the canonical A- or B-conformations or are restrained to potential transitory pathways. It is observed that the transition from the unrestrained A-form to the B-form leads to lengthwise structural deformation. The insertion of intermittent -(CG)- base pairs in otherwise homogeneous -(AT)- sequences bears dynamical consequences for the vicinal hydration layer. Calculation of the excess (pair) entropy suggests substantially higher values of hydration water surrounding A conformations over the B- conformations. Applying the Rosenfeld approximation, we project that the diffusivity of water molecules proximal to canonical B conformation is least for the minor groove of the canonical B-conformation. We determine that structure, composition, and conformation specific groove dimension together influence the local hydration characteristics and, therefore, are expected to be important determinants of biological processes.

RNA conformational propensities determine cellular activity.

Cellular processes are the product of interactions between biomolecules, which associate to form biologically active complexes1. These interactions are mediated by intermolecular contacts, which if disrupted, lead to alterations in cell physiology. Nevertheless, the formation of intermolecular contacts nearly universally requires changes in the conformations of the interacting biomolecules. As a result, binding affinity and cellular activity crucially depend both on the strength of the contacts and on the inherent propensities to form binding-competent conformational states2,3. Thus, conformational penalties are ubiquitous in biology and must be known in order to quantitatively model binding energetics for protein and nucleic acid interactions4,5. However, conceptual and technological limitations have hindered our ability to dissect and quantitatively measure how conformational propensities affect cellular activity. Here we systematically altered and determined the propensities for forming the protein-bound conformation of HIV-1 TAR RNA. These propensities quantitatively predicted the binding affinities of TAR to the RNA-binding region of the Tat protein and predicted the extent of HIV-1 Tat-dependent transactivation in cells. Our results establish the role of ensemble-based conformational propensities in cellular activity and reveal an example of a cellular process driven by an exceptionally rare and short-lived RNA conformational state.

Homocysteine thiolactone and H2 O2 induce amino acid modifications and alter the fibrillation propensity of the Aβ25-35 peptide.

Of the proteinaceous β-sheet-rich amyloid fibrillar structures, the Aβ25-35 peptide, a component of the full-length Aβ involved in Alzheimer's disease, has similar toxicity to the parent peptide. In this study, the effects of homocysteine thiolactone (HCTL) and hydrogen peroxide (H2 O2 ) on the conformation and fibrillation propensity of the Aβ25-35 peptide were investigated. Both HCTL and H2 O2 induced amino acid modifications along with alteration in aggregation propensity. Methionine (Met)-35 was oxidized by H2 O2 and aggregation was attenuated following the increased hydrophilicity of the peptide due to sulfoxide/sulfone formation. The HCTL-modified lysine (Lys-28) residue destabilizes the structure of the peptide, which leads to fibrillation. Our studies provide important information regarding the relationship between amino acid modifications and the amyloid fibrillation process.

Using tetrapeptide GSSS and β-catenin17-48 to study the effects of phosphoserine on polypeptide conformational ensembles.

Phosphorylation is one of the most common post-translational modifications and is known to have roles in protein transport, localization, function, and stability. Studying both the phosphorylated and nonphosphorylated states of a protein is important as they can be correlated to health and disease states, such as in Alzheimer's and Parkinson's disease. β-catenin is a protein that has two independent functions: cell-cell adhesion and transcriptional regulation, and when there is a change in the balance of these roles, β-catenin acts as an oncogene. It is thought that the phosphorylation state of the unstructured N-terminal region plays an important role in regulating the function and localization of β-catenin. Additionally, mutations at Ser33 and Ser37 phosphorylation sites have been associated with several types of cancer including liver, ovarian, and colorectal cancer. The addition of a large, negatively charged group on the serine sidechain is expected to strongly perturb local electric fields, thus influencing backbone conformational sampling and propensity for hydrogen bonding. As such, dipole-dipole interactions are expected to feature prominently in these systems and are best suited to be investigated with an explicitly polarizable force field. Parameters for phosphoserine were recently developed for the Drude polarizable force field. To validate these parameters and study a biologically relevant system, we performed both unbiased and enhanced-sampling molecular dynamics simulations of the phosphorylated and nonphosphorylated states of model tetrapeptide GSSS and a peptide derived from the N-terminal region of β-catenin, including residues 17-48 (β-catenin17-48). We calculated 3JHNHα-couplings to compare against experimental data and assessed the effect of phosphorylation on secondary structure content, peptide-bond dipole moments, and the electric fields acting around the phosphorylation sites. In doing so, we seek to relate electronic structure with polypeptide conformational dynamics.

Improved predictions of phase behaviour of intrinsically disordered proteins by tuning the interaction range.

The formation and viscoelastic properties of condensates of intrinsically disordered proteins (IDPs) is dictated by amino acid sequence and solution conditions. Because of the involvement of biomolecular condensates in cell physiology and disease, advancing our understanding of the relationship between protein sequence and phase separation (PS) may have important implications in the formulation of new therapeutic hypotheses. Here, we present CALVADOS 2, a coarse-grained model of IDPs that accurately predicts conformational properties and propensities to undergo PS for diverse sequences and solution conditions. In particular, we systematically study the effect of varying the range of the nonionic interactions and use our findings to improve the temperature scale of the model. We further optimize the residue-specific model parameters against experimental data on the conformational properties of 55 proteins, while also leveraging 70 hydrophobicity scales from the literature to avoid overfitting the training data. Extensive testing shows that the model accurately predicts chain compaction and PS propensity for sequences of diverse length and charge patterning, as well as at different temperatures and salt concentrations.

Conformational landscapes of artificial peptides predicted by various force fields: are we ready to simulate β-amino acids?

With the introduction of artificial peptides as antimicrobial agents and organic catalysts, numerous efforts have been made to design foldamers with desirable structures and functions. Computational tools are a helpful proxy for revealing the dynamic structures at atomic resolution and understanding foldamer's complex structure-function relationships. However, the performance of conventional force fields in predicting the structures of artificial peptides has not been systematically evaluated. In this study, we critically assessed three popular force fields, AMBER ff14SB, CHARMM36m, and OPLS-AA/L, in predicting conformational propensities of a β-peptide foldamer at monomer and hexamer levels. Simulation results were compared to those obtained from quantum chemistry calculations and experimental data. We also utilised replica exchange molecular dynamics simulations to investigate the energy landscape of each force field and assess the similarities and differences between force fields. We compared different solvent systems in the AMBER ff14SB and CHARMM36m frameworks and confirmed the unanimous role of hydrogen bonds in shaping energy landscapes. We anticipate that our data will pave the way for further improvements to force fields and for understanding the role of solvents in peptide folding, crystallisation, and engineering.

Influence of force field choice on the conformational landscape of rat and human islet amyloid polypeptide.

Human islet amyloid polypeptide (hIAPP) is a naturally occurring, intrinsically disordered protein (IDP) whose abnormal aggregation into toxic soluble oligomers and insoluble amyloid fibrils is a pathological feature in type-2 diabetes. Rat IAPP (rIAPP) differs from hIAPP by only six amino acids yet has a reduced tendency to aggregate or form fibrils. The structures of the monomeric forms of IAPP are difficult to characterize due to their intrinsically disordered nature. Molecular dynamics simulations can provide a detailed characterization of the monomeric forms of rIAPP and hIAPP in near-physiological conditions. In this work, the conformational landscapes of rIAPP and hIAPP as a function of secondary structure content were predicted using well-tempered bias exchange metadynamics simulations. Several combinations of commonly used biomolecular force fields and water models were tested. The predicted conformational preferences of both rIAPP and hIAPP are typical of IDPs, exhibiting dominant random coil structures but showing a low propensity for transient α-helical conformations. Predicted nuclear magnetic resonance Cα chemical shifts reveal different preferences with each force field towards certain conformations, with AMBERff99SBnmr2/TIP4Pd showing the best agreement with the experiment. Comparisons of secondary structure content demonstrate residue-specific differences between hIAPP and rIAPP that may reflect their different aggregation propensities.

Conformational Heterogeneity and Interchain Percolation Revealed in an Amorphous Conjugated Polymer.

Conjugated polymers are employed in a variety of application areas due to their bright fluorescence and strong biocompatibility. However, understanding the structure of amorphous conjugated polymers on the nanoscale is extremely challenging compared to their related crystalline phases. Using a bespoke classical force field, we study amorphous poly(9,9-di-n-octylfluorene-alt-benzothiadiazole) (F8BT) with molecular dynamics simulations to investigate the role that its nanoscale structure plays in controlling its emergent (and all-important) optical properties. Notably, we show that a giant percolating cluster exists within amorphous F8BT, which has ramifications in understanding the nature of interchain species that drive the quantum yield reduction and bathochromic shift observed in conjugated polymer-based devices and nanostructures. We also show that distinct conformations can be unravelled from within the disordered structure of amorphous F8BT using a two-stage machine learning protocol, highlighting a link between molecular conformation and ring stacking propensity. This work provides predictive understanding by which to enhance the optical properties of next-generation conjugated polymer-based devices and materials by rational, simulation-led design principles.

Improved predictions of phase behaviour of intrinsically disordered proteins by tuning the interaction range

The formation and viscoelastic properties of condensates of intrinsically disordered proteins (IDPs) is dictated by amino acid sequence and solution conditions. Because of the involvement of biomolecular condensates in cell physiology and disease, advancing our understanding of the relationship between protein sequence and phase separation (PS) may have important implications in the formulation of new therapeutic hypotheses. Here, we present CALVADOS 2, a coarse-grained model of IDPs that accurately predicts conformational properties and propensities to undergo PS for diverse sequences and solution conditions. In particular, we systematically study the effect of varying the range of the nonionic interactions and use our findings to improve the temperature scale of the model. We further optimize the residue-specific model parameters against experimental data on the conformational properties of 55 proteins, while also leveraging 70 hydrophobicity scales from the literature to avoid overfitting the training data. Extensive testing shows that the model accurately predicts chain compaction and PS propensity for sequences of diverse length and charge patterning, as well as at different temperatures and salt concentrations.

Randomizing of Oligopeptide Conformations by Nearest Neighbor Interactions between Amino Acid Residues.

Flory’s random coil model assumes that conformational fluctuations of amino acid residues in unfolded poly(oligo)peptides and proteins are uncorrelated (isolated pair hypothesis, IPH). This implies that conformational energies, entropies and solvation free energies are all additive. Nearly 25 years ago, analyses of coil libraries cast some doubt on this notion, in that they revealed that aromatic, but also β-branched side chains, could change the 3J(HNHCα) coupling of their neighbors. Since then, multiple bioinformatical, computational and experimental studies have revealed that conformational propensities of amino acids in unfolded peptides and proteins depend on their nearest neighbors. We used recently reported and newly obtained Ramachandran plots of tetra- and pentapeptides with non-terminal homo- and heterosequences of amino acid residues to quantitatively determine nearest neighbor coupling between them with a Ising type model. Results reveal that, depending on the choice of amino acid residue pairs, nearest neighbor interactions either stabilize or destabilize pairs of polyproline II and β-strand conformations. This leads to a redistribution of population between these conformations and a reduction in conformational entropy. Interactions between residues in polyproline II and turn(helix)-forming conformations seem to be cooperative in most cases, but the respective interaction parameters are subject to large statistical errors.

Influence of the Nonprotein Amino Acid Mimosine in Peptide Conformational Propensities from Novel Amber Force Field Parameters.

Mimosine is a nonprotein amino acid derived from plants known for its ability to bind to divalent and trivalent metal cations such as Zn2+, Ni2+, Fe2+, or Al3+. This results in interesting antimicrobial and anticancer properties, which make mimosine a promising candidate for therapeutic applications. One possibility is to incorporate mimosine into synthetic short peptide drugs. However, how this amino acid affects the peptide structure is not well understood, reducing our ability to design effective therapeutic compounds. In this work, we used computer simulations to understand this question. We first built parameters for the mimosine residue to be used in combination with two classical force fields of the Amber family. Then, we used atomistic molecular dynamics simulations with the resulting parameter sets to evaluate the influence of mimosine in the structural propensities for this amino acid. We compared the results of these simulations with homologous peptides, where mimosine is replaced by either phenylalanine or tyrosine. We found that the strong dipole in mimosine induces a preference for conformations where the amino acid rings are stacked over more extended conformations. We validated our results using quantum mechanical calculations, which provide a robust foundation for the outcome of our classical simulations.

Development and Validation of Fluorinated, Aromatic Amino Acid Parameters for Use with the AMBER ff15ipq Protein Force Field.

We developed force field parameters for fluorinated, aromatic amino acids enabling molecular dynamics (MD) simulations of fluorinated proteins. These parameters are tailored to the AMBER ff15ipq protein force field and enable the modeling of 4, 5, 6, and 7F-tryptophan, 3F- and 3,5F-tyrosine, and 4F- or 4-CF3-phenylalanine. The parameters include 181 unique atomic charges derived using the implicitly polarized charge (IPolQ) scheme in the presence of SPC/Eb explicit water molecules and 9 unique bond, angle, or torsion terms. Our simulations of benchmark peptides and proteins maintain expected conformational propensities on the μs time scale. In addition, we have developed an open-source Python program to calculate fluorine relaxation rates from MD simulations. The extracted relaxation rates from protein simulations are in good agreement with experimental values determined by 19F NMR. Collectively, our results illustrate the power and robustness of the IPolQ lineage of force fields for modeling the structure and dynamics of fluorine-containing proteins at the atomic level.

Data-informed reparameterization of modified RNA and the effect of explicit water models: application to pseudouridine and derivatives

Pseudouridine is one of the most abundant post-transcriptional modifications in RNA. We have previously shown that the FF99-derived parameters for pseudouridine and some of its naturally occurring derivatives in the AMBER distribution either alone or in combination with the revised γ torsion parameters (parmbsc0) failed to reproduce their conformational characteristics observed experimentally (Deb et al. in J Chem Inf Model 54:1129–1142, 2014; Deb et al. in J Comput Chem 37:1576–1588, 2016; Dutta et al. in J Chem Inf Model 60:4995–5002, 2020). However, the application of the recommended bsc0 correction did lead to an improvement in the description not only of the distribution in the γ torsional space but also of the sugar pucker distributions. In an earlier study, we examined the transferability of the revised glycosidic torsion parameters (χIDRP) for Ψ to its derivatives. We noticed that although these parameters in combination with the AMBER FF99-derived parameters and the revised γ torsional parameters resulted in conformational properties of these residues that were in better agreement with experimental observations, the sugar pucker distributions were still not reproduced accurately. Here we report a new set of partial atomic charges for pseudouridine, 1-methylpseudouridine, 3-methylpseudouridine and 2′-O-methylpseudouridine and a new set of glycosidic torsional parameters (χND) based on chosen glycosidic torsional profiles that most closely corresponded to the NMR data for conformational propensities and studied their effect on the conformational distributions using REMD simulations at the individual nucleoside level. We have also studied the effect of the choice of water model on the conformational characteristics of these modified nucleosides. Our observations suggest that the current revised set of parameters and partial atomic charges describe the sugar pucker distributions for these residues more accurately and that the choice of a suitable water model is important for the accurate description of their conformational properties. We have further validated the revised sets of parameters by studying the effect of substitution of uridine with pseudouridine within single stranded RNA oligonucleotides on their conformational and hydration characteristics.Supplementary InformationThe online version contains supplementary material available at 10.1007/s10822-022-00447-4.

Analyses of experiment based ramachandran plots of tri-, tetra- and pentapeptides reveal residue dependent interactions between neighbors

Flory's canonical random coil model is based on the assumption that conformational fluctuations of amino acid residues in unfolded poly(oligo)peptides and proteins are uncorrelated (isolated pair hypothesis, IPH). It implies that conformational energies, entropies, and solvation free energies are all additive. Nearly 25 years ago, analyses of coil libraries casted some doubt on this notion in that they revealed that aromatic residues could change the 3J(HNHCα) coupling of their neighbors. Since then multiple lines of bioinformatical, computational and lately also experimental evidence indicate that the conformational propensities of amino acids in unfolded peptides and proteins depend on their nearest neighbors and possibly even on the nature of the second neighbor.