The ability to predict RNA secondary structure is fundamental for understanding and manipulating RNA function. The structural information obtained from selective 2'-hydroxyl acylation analyzed by primer extension (SHAPE) experiments greatly improves the accuracy of RNA secondary structure prediction. Recently, Das and colleagues [Kladwang et al., Biochemistry
50:8049 (2011)] proposed a “bootstrapping” approach to estimate the variance and helix-by-helix confidence levels of predicted secondary structures based on resampling (randomizing and summing) the measured SHAPE data. We show that the specific resampling approach described by Kladwang et al. introduces systematic errors and underestimates confidence in secondary structure prediction using SHAPE data. Instead, a leave-data-out jackknife approach better estimates the influence of a given experimental dataset on SHAPE-directed secondary structure modeling. Even when 35% of the data were left out in the jackknife approach, the confidence levels of SHAPE-directed secondary structure prediction were significantly higher than those calculated by Das and colleagues using bootstrapping. Helix confidence levels were thus significantly underestimated in the recent study, and resampling approach implemented by Kladwang et al. is not an appropriate metric for assigning confidences in SHAPE-directed secondary structure modeling.
Aggregation of Cu, Zn Superoxide Dismutase (SOD1) is often found in Amyotrophic Lateral Sclerosis (ALS) patients. The fibrillar aggregates formed by wildtype and various disease-associated mutants have recently been found to have distinct cores and morphologies. Previous computational and experimental studies of wildtype SOD1 suggest that the apo-monomer, highly aggregation-prone, displays substantial local unfolding dynamics. The residual folded structure of locally unfolded apoSOD1 corresponds to peptide segments forming the aggregation core as identified by a combination of proteolysis and mass spectroscopy. Therefore, we hypothesize that the destabilization of apoSOD1 caused by various mutations leads to distinct local unfolding dynamics. The partially unfolded structure, exposing the hydrophobic core and backbone hydrogen bond donors and acceptors, is prone to aggregate. The peptide segments in the residual folded structures form the “building block” for aggregation, which in turn determines the morphology of the aggregates. To test this hypothesis, we apply a multiscale simulation approach to study the aggregation of three typical SOD1 variants: wildtype, G37R, and I149T. Each of these SOD1 variants has distinct peptide segments forming the core structure and features different aggregate morphologies. We perform atomistic molecular dynamics simulations to study the conformational dynamics of apoSOD1 monomer, and coarse-grained molecular dynamics simulations to study the aggregation of partially unfolded SOD1 monomers. Our computational studies of monomer local unfolding and the aggregation of different SOD1 variants are consistent with experiments, supporting the hypothesis of the formation of aggregation “building blocks” via apo-monomer local unfolding as the mechanism of SOD1 fibrillar aggregation.
SOD1 misfolding and aggregation; fibrillar aggregate; aggregation building block; molecular dynamics; multiscale modeling
Limited proteolysis, accomplished by endopeptidases, is a ubiquitous phenomenon underlying the regulation and activation of many enzymes, receptors and other proteins synthesized as inactive precursors. Serine proteases are one of the largest and conserved families of endopeptidases involved in diverse cellular activities including wound healing, blood coagulation and immune responses. Heteromeric α,β,γ-epithelial sodium channels (ENaC) associated with diseases like cystic fibrosis and Liddle’s syndrome, are irreversibly stimulated by membrane-anchored proteases (MAPs) and furin-like convertases. Matriptase/Channel activating protease-3 (CAP3) is one of the several MAPs that potently activate ENaC. Despite identification of protease cleavage sites, the basis for enhanced susceptibility of α- and γ-ENaC to proteases remains elusive. Here, we elucidate the energetic and structural bases for activation of ENaC by CAP3. We find a region near the γ-ENaC furin site that is previously unidentified as a critical cleavage site for CAP3-mediated stimulation. We also report that CAP3 mediates cleavage of ENaC at basic residues downstream of the furin site. Our results indicate that surface proteases alone are sufficient to fully activate uncleaved ENaC, and explain how ENaC in epithelia expressing surface-active proteases can appear refractory to soluble proteases. Our results support a model in which proteases prime ENaC for activation by cleaving at the furin site, and cleavage at downstream sites is accomplished by membrane surface proteases or extracellular soluble proteases. Based on our results, we propose a dynamics-driven “anglerfish” mechanism that explains less stringent sequence requirements for substrate recognition and cleavage by matriptase compared to furin.
ENaC; serine endopeptidase; Xenopus; voltage clamp; discrete molecular dynamics
Nature has evolved proteins to counter-act forces applied on living cells, and designed proteins that can sense forces. One can appreciate Nature’s ingenuity in evolving these proteins to be highly sensitive to force and to have a high dynamic force range at which they operate. To achieve this level of sensitivity, many of these proteins are comprised of multiple domains and linking peptides connecting these domain, each of them have their own force response regimes. Here, using a simple model of a protein, we address the question of how each individual domain responds to force. We also ask how multi-domain proteins respond to forces. We find that the end-to-end distance of individual domains under force scales linearly with force. In multi-domain proteins, we find that the force response has a rich range: at low force, extension is predominantly governed by “weaker” linking peptides or domain intermediates, while at higher force, the extension is governed by unfolding of individual domains. Overall, the force extension curve comprises multiple sigmoidal transition governed by unfolding of linking peptides and domains. Our study provides a basic framework for the understanding of protein response to force, and allows for interpretation experiments in which force is used to study the mechanical properties of multi-domain proteins.
force; mechano-sensing proteins; multi-domain proteins
Poor performance of scoring functions is a well-known bottleneck in structure-based virtual screening, which is most frequently manifested in the scoring functions’ inability to discriminate between true ligands versus known non-binders (therefore designated as binding decoys). This deficiency leads to a large number of false positive hits resulting from virtual screening. We have hypothesized that filtering out or penalizing docking poses recognized as non-native (i.e., pose decoys) should improve the performance of virtual screening in terms of improved identification of true binders. Using several concepts from the field of cheminformatics, we have developed a novel approach to identifying pose decoys from an ensemble of poses generated by computational docking procedures. We demonstrate that the use of target-specific pose (-scoring) filter in combination with a physical force field-based scoring function (MedusaScore) leads to significant improvement of hit rates in virtual screening studies for 12 of the 13 benchmark sets from the clustered version of the Database of Useful Decoys (DUD). This new hybrid scoring function outperforms several conventional structure-based scoring functions, including XSCORE∷HMSCORE, ChemScore, PLP, and Chemgauss3, in six out of 13 data sets at early stage of VS (up 1% decoys of the screening database). We compare our hybrid method with several novel VS methods that were recently reported to have good performances on the same DUD data sets. We find that the retrieved ligands using our method are chemically more diverse in comparison with two ligand-based methods (FieldScreen and FLAP∷LBX). We also compare our method with FLAP∷RBLB, a high-performance VS method that also utilizes both the receptor and the cognate ligand structures. Interestingly, we find that the top ligands retrieved using our method are highly complementary to those retrieved using FLAP∷RBLB, hinting effective directions for best VS applications. We suggest that this integrative virtual screening approach combining cheminformatics and molecular mechanics methodologies may be applied to a broad variety of protein targets to improve the outcome of structure-based drug discovery studies.
Protein-peptide interactions play important roles in many cellular processes, including signal transduction, trafficking, and immune recognition. Protein conformational changes upon binding, an ill-defined peptide binding surface, and the large number of peptide degrees of freedom make the prediction of protein-peptide interactions particularly challenging. To address these challenges, we perform rapid molecular dynamics simulations in order to examine the energetic and dynamic aspects of protein-peptide binding. We find that, in most cases, we recapitulate the native binding sites and native-like poses of protein-peptide complexes. Inclusion of electrostatic interactions in simulations significantly improves the prediction accuracy. Our results also highlight the importance of protein conformational flexibility, especially side-chain movement, which allows the peptide to optimize its conformation. Our findings not only demonstrate the importance of sufficient sampling of the protein and peptide conformations, but also reveal the possible effects of electrostatics and conformational flexibility on peptide recognition.
Opioids that stimulate the μ-opioid receptor (MOR1) are the most frequently prescribed and effective analgesics. Here we present a structural model of MOR1. Molecular dynamics simulations show a ligand-dependent increase in the conformational flexibility of the third intracellular loop that couples with the G-protein complex. These simulations likewise identified residues that form frequent contacts with ligands. We validated the binding residues using site-directed mutagenesis coupled with radioligand binding and functional assays. The model was used to blindly screen a library of ~1.2 million compounds. From the thirty-four compounds predicted to be strong binders, the top three candidates were examined using biochemical assays. One compound showed high efficacy and potency. Post hoc testing revealed this compound to be nalmefene, a potent clinically used antagonist, thus further validating the model. In summary, the MOR1 model provides a tool for elucidating the structural mechanism of ligand-initiated cell signaling and screening for novel analgesics.
The curated CSAR-NRC benchmark sets provide valuable opportunity for testing or comparing the performance of both existing and novel scoring functions. We apply two different scoring functions, both independently and in combination, to predict binding affinity of ligands in the CSAR-NRC datasets. One, reported here for the first time, employs multiple chemical-geometrical descriptors of the protein-ligand interface to develop Quantitative Structure – Binding Affinity Relationships (QSBAR) models; these models are then used to predict binding affinity of ligands in the external dataset. Second is a physical force field-based scoring function, MedusaScore. We show that both individual scoring functions achieve statistically significant prediction accuracies with the squared correlation coefficient (R2) between actual and predicted binding affinity of 0.44/0.53 (Set1/Set2) with QSBAR models and 0.34/0.47 (Set1/Set2) with MedusaScore. Importantly, we find that the combination of QSBAR models and MedusaScore into consensus scoring function affords higher prediction accuracy than any of the contributing methods achieving R2 of 0.45/0.58 (Set1/Set2). Furthermore, we identify several chemical features and non-covalent interactions that may be responsible for the inaccurate prediction of binding affinity for several ligands by the scoring functions employed in this study.
Aggregation of Cu, Zn superoxide dismutase (SOD1) is implicated in Amyotrophic Lateral Sclerosis (ALS). Glutathionylation and phosphorylation of SOD1 is omnipresent in the human body, even in healthy individuals, and has been shown to increase SOD1 dimer dissociation, which is the first step on the pathway toward SOD1 aggregation. We find that post-translational modification of SOD1, especially glutathionylation, promotes dimer dissociation. We discover an intermediate state in the pathway to dissociation, a conformational change that involves a “loosening” of the β-barrels and a loss or shift of dimer interface interactions. In modified SOD1, this intermediate state is stabilized as compared to unmodified SOD1. The presence of post-translational modifications could explain the environmental factors involved in the speed of disease progression. Because post-translational modifications such as glutathionylation are often induced by oxidative stress, post-translational modification of SOD1 could be a factor in the occurrence of sporadic cases of ALS, which make up 90% of all cases of the disease.
Catechol O-methyltransferase (COMT) metabolizes catechol moieties by methylating a single hydroxyl group at the meta- or para- hydroxyl position. Hydrophobic amino acids near the active site of COMT influence the regioselectivity of this reaction. Our sequence analysis highlights their importance by showing that these residues are highly conserved throughout evolution. Reaction barriers calculated in the gas phase reveal a lower barrier during methylation at the meta- position, suggesting that the observed meta-regioselectivity of COMT can be attributed to the substrate itself, and that COMT has evolved residues to orient the substrate in a manner that increases the rate of catalysis.
Molecular modeling of proteins including homology modeling, structure determination, and knowledge-based protein design requires tools to evaluate and refine three-dimensional protein structures. Steric clash is one of the artifacts prevalent in low-resolution structures and homology models. Steric clashes arise due to the unnatural overlap of any two non-bonding atoms in a protein structure. Usually, removal of severe steric clashes in some structures is challenging since many existing refinement programs do not accept structures with severe steric clashes. Here, we present a quantitative approach of identifying steric clashes in proteins by defining clashes based on the Van der Waals repulsion energy of the clashing atoms. We also define a metric for quantitative estimation of the severity of clashes in proteins by performing statistical analysis of clashes in high-resolution protein structures. We describe a rapid, automated and robust protocol, Chiron, which efficiently resolves severe clashes in low-resolution structures and homology models with minimal perturbation in the protein backbone. Benchmark studies highlight the efficiency and robustness of Chiron compared to other widely used methods. We provide Chiron as an automated web server to evaluate and resolve clashes in protein structures that can be further used for more accurate protein design.
Homology modeling; refinement; Chiron; Discrete Molecular Dynamics; Protein Design
Existing flexible docking approaches model the ligand and receptor flexibility either separately or in a loosely-coupled manner, which captures the conformational changes inefficiently. Here, we propose a flexible docking approach, MedusaDock, which models both ligand and receptor flexibility simultaneously with sets of discrete rotamers. We develop an algorithm to build the ligand rotamer library “on-the-fly” during docking simulations. MedusaDock benchmarks demonstrate a rapid sampling efficiency and high prediction accuracy in both self-docking (to the co-crystallized state) and cross-docking (to a state co-crystallized with a different ligand), the latter of which mimics the virtual-screening procedure in computational drug discovery. We also perform a virtual-screening test of four flexible kinase targets including cyclin-dependent kinase 2, vascular endothelial growth factor receptor 2, HIV reverse transcriptase, and HIV protease. We find significant improvements of virtual-screening enrichments when compared to rigid-receptor methods. The predictive power of MedusaDock in cross-docking and preliminary virtual-screening benchmarks highlights the importance to model both ligand and receptor flexibility simultaneously in computational docking.
Catechol-O-methyltransferase (COMT) is a major enzyme controlling catecholamine levels that plays a central role in cognition, affective mood and pain perception. There are three common COMT haplotypes in the human population reported to have functional effects, divergent in two synonymous and one nonsynonymous position. We demonstrate that one of the haplotypes, carrying the non-synonymous variation known to code for a less stable protein, exhibits increased protein expression in vitro. This increased protein expression, which would compensate for lower protein stability, is solely produced by a synonymous variation (C166T) situated within the haplotype and located in the 5′ region of the RNA transcript. Based on mRNA secondary structure predictions, we suggest that structural destabilization near the start codon caused by the T allele could be related to the observed increase in COMT expression. Our folding simulations of the tertiary mRNA structures demonstrate that destabilization by the T allele lowers the folding transition barrier, thus decreasing the probability of occupying its native state. These data suggest a novel structural mechanism whereby functional synonymous variations near the translation initiation codon affect the translation efficiency via entropy-driven changes in mRNA dynamics and present another example of stable compensatory genetic variations in the human population.
Conformational changes of filamin A under stress have been postulated to play crucial roles in signaling pathways of cell responses. Direct observation of conformational changes under stress is beyond the resolution of current experimental techniques. On the other hand, computational studies are mainly limited to either traditional molecular dynamics simulations of short durations and high forces or simulations of simplified models. Here we perform all-atom discrete molecular dynamics (DMD) simulations to study thermally and force-induced unfolding of filamin A. The high conformational sampling efficiency of DMD allows us to observe force-induced unfolding of filamin A Ig domains under physiological forces. The computationally identified critical unfolding forces agree well with experimental measurements. Despite a large heterogeneity in the population of force-induced intermediate states, we find a common initial unfolding intermediate in all the Ig domains of filamin, where the N-terminal strand unfolds. We also study the thermal unfolding of several filamin Ig-like domains. We find that thermally induced unfolding features an early-stage intermediate state similar to the one observed in force-induced unfolding and characterized by N-terminal strand being unfurled. We propose that the N-terminal strand may act as a conformational switch that unfolds under physiological forces leading to exposure of cryptic binding sites, removal of native binding sites, and modulating the quaternary structure of domains.
The differences in efficacy and molecular mechanisms of platinum based anti-cancer drugs cisplatin (CP) and oxaliplatin (OX) have been hypothesized to be in part due to the differential binding affinity of cellular and damage recognition proteins to CP and OX adducts formed on adjacent guanines in genomic DNA. HMGB1a in particular exhibits higher binding affinity to CP-GG adducts, and the extent of discrimination between CP- and OX-GG adducts is dependent on the bases flanking the adducts. However, the structural basis for this differential binding is not known. Here, we show that the conformational dynamics of CP- and OX-GG adducts are distinct and depend on the sequence context of the adduct. Molecular dynamics simulations of the Pt-GG adducts in the TGGA sequence context revealed that even though the major conformations of CP- and OX-GG adducts were similar, the minor conformations were distinct. Using the pattern of hydrogen bond formation between the Pt–ammines and the adjacent DNA bases, we identified the major and minor conformations sampled by Pt–DNA. We found that the minor conformations sampled exclusively by the CP-GG adduct exhibit structural properties that favor binding by HMGB1a, which may explain its higher binding affinity to CP-GG adducts, while these conformations are not sampled by OX-GG adducts because of the constraints imposed by its cyclohexane ring, which may explain the negligible binding affinity of HMGB1a for OX-GG adducts in the TGGA sequence context. Based on these results, we postulate that the constraints imposed by the cyclohexane ring of OX affect the DNA conformations explored by OX-GG adduct compared to those of CP-GG adduct, which may influence the binding affinities of HMG-domain proteins for Pt-GG adducts, and that these conformations are further influenced by the DNA sequence context of the Pt-GG adduct.
Over the past three decades the protein folding field has undergone monumental changes. Originally a purely academic question, how a protein folds has now become vital in understanding diseases and our abilities to rationally manipulate cellular life by engineering protein folding pathways. We review and contrast past and recent developments in the protein folding field. Specifically, we discuss the progress in our understanding of protein folding thermodynamics and kinetics, the properties of evasive intermediates, and unfolded states. We also discuss how some abnormalities in protein folding lead to protein aggregation and human diseases.
Understanding the role of biomolecular dynamics in cellular processes leading to human diseases and the ability to rationally manipulate these processes is of fundamental importance in scientific research. The last decade has witnessed significant progress in probing biophysical behavior of proteins. However, we are still limited in understanding how changes in protein dynamics and inter-protein interactions occurring in short length- and time-scales lead to aberrations in their biological function. Bridging this gap in biology probed using computer simulations marks a challenging frontier in computational biology. Here we examine hypothesis-driven simplified protein models in conjunction with discrete molecular dynamics in the study of protein aggregation, implicated in series of neurodegenerative diseases, such as Alzheimer's and Huntington's diseases. Discrete molecular dynamics simulations of simplified protein models have emerged as a powerful methodology with its ability to bridge the gap in time and length scales from protein dynamics to aggregation, and provide an indispensable tool for probing protein aggregation.
Protein Aggregation; Protein Misfolding; Simplified Modeling; Aggregation Kinetics; Folding Thermodynamics; Discrete Molecular Dynamics; Molecular Dynamics; Computational Biology; Biophysics; MD; DMD; Misfolding; Molecular Dynamics; Review
Cystic Fibrosis (CF) is caused by mutations in the CF transmembrane conductance regulator (CFTR). Newly developed “correctors” such as lumacaftor (VX-809) that improve CFTR maturation and trafficking and “potentiators” such as ivacaftor (VX-770) that enhance channel activity may provide important advances in CF therapy. Although VX-770 has demonstrated substantial clinical efficacy in the small subset of patients with a mutation (G551D) that affects only channel activity, a single compound is not sufficient to treat patients with the more common CFTR mutation, ΔF508. Thus, patients with ΔF508 will likely require treatment with both correctors and potentiators to achieve clinical benefit. However, whereas the effectiveness of acute treatment with this drug combination has been demonstrated in vitro, the impact of chronic therapy has not been established. In studies of human primary airway epithelial cells, we found that both acute and chronic treatment with VX-770 improved CFTR function in cells with the G551D mutation, consistent with clinical studies. In contrast, chronic VX-770 administration caused a dose-dependent reversal of VX-809-mediated CFTR correction in ΔF508 homozygous cultures. This result reflected the destabilization of corrected ΔF508 CFTR by VX-770, dramatically increasing its turnover rate. Chronic VX-770 treatment also reduced mature wild-type CFTR levels and function. These findings demonstrate that chronic treatment with CFTR potentiators and correctors may have unexpected effects that cannot be predicted from short-term studies. Combining of these drugs to maximize rescue of ΔF508 CFTR may require changes in dosing and/or development of new potentiator compounds that do not interfere with CFTR stability.
The short cytoplasmic tails of the α and β chains of integrin adhesion receptors regulate integrin activation and cell signaling. Significantly less is known about proteins that bind to α-integrin cytoplasmic tails (CTs) than β-CTs to regulate integrins. CIB1 was previously identified as an αIIb binding partner that inhibits agonist-induced activation of the platelet-specific integrin, αIIbβ3. A sequence alignment of all α-integrin CTs revealed that key residues in the CIB1 binding site on αIIb are well-conserved, and was used to delineate a consensus binding site (I/L-x-x-x-L/M-W/Y-K-x-G-F-F). Because the CIB1 binding site on αIIb is conserved in all α-integrins, and CIB1 expression is ubiquitous, we asked if CIB1 could interact with other α-integrin CTs. We predicted that multiple α-integrin CTs were capable of binding to the same hydrophobic binding pocket on CIB1 with docking models generated by all-atom replica exchange discrete molecular dynamics. After demonstrating novel in vivo interactions between CIB1 and other whole integrin complexes with co-immunopreceipitations, we validated the modeled predictions with solid-phase competitive binding assays showing that other α-integrin CTs compete with the αIIb CT for binding to CIB1 in vitro. Isothermal titration calorimetry measurements indicated that this binding is driven by hydrophobic interactions and depends on residues in the CIB1 consensus binding site. These new mechanistic details of CIB1-integrin binding imply that CIB1 could bind to all integrin complexes and act as a broad regulator of integrin function.
Investigation of protein activation in living cells is fundamental to understand
how proteins are influenced by the full complement of upstream regulators they experience.
We describe here the generation of a biosensor based on the Designed Ankyrin Repeat
Protein (DARPin) binding scaffold suited for intracellular applications. Combining
selection and evolution from libraries, knowledge-based design and efficient and rapid
testing of conjugate candidates, we created an ERK activity biosensor by derivatizing a
DARPin specific for phosphorylated ERK (pERK) with a solvatochromic merocyanine dye
(mero87), whose fluorescence increases upon pERK binding. The biosensor specifically
responded to pERK2, recognized by its conformation, but not to non-phosphorylated ERK2 or
other closely related mitogen-activated kinases tested. Activated endogenous ERK was
visualized in mouse embryo fibroblasts incubated in 2% serum, revealing greater
activation in the nucleus, perinuclear regions, and especially the nucleoli. Activity was
greatly reduced by the MEK1/2 inhibitor U0126. The DARPin-based biosensor will serve as
useful tool for studying biological functions of ERK in vitro and
We report on the prediction accuracy of ligand-based (2D QSAR) and structure-based (MedusaDock) methods used both independently and in consensus for ranking the congeneric series of ligands binding to three protein targets (UK, ERK2, and CHK1) from the CSAR 2011 benchmark exercise. An ensemble of predictive QSAR models was developed using known binders of these three targets extracted from the publicly-available ChEMBL database. Selected models were used to predict the binding affinity of CSAR compounds towards the corresponding targets and rank them accordingly; the overall ranking accuracy evaluated by Spearman correlation was as high as 0.78 for UK, 0.60 for ERK2, and 0.56 for CHK1, placing our predictions in top-10% among all the participants. In parallel, MedusaDock designed to predict reliable docking poses was also used for ranking the CSAR ligands according to their docking scores; the resulting accuracy (Spearman correlation) for UK, ERK2, and CHK1 were 0.76, 0.31, and 0.26, respectively. In addition, performance of several consensus approaches combining MedusaDock and QSAR predicted ranks altogether has been explored; the best approach yielded Spearman correlation coefficients for UK, ERK2, and CHK1 of 0.82, 0.50, and 0.45, respectively. This study shows that (i) externally validated 2D QSAR models were capable of ranking CSAR ligands at least as accurately as more computationally intensive structure-based approaches used both by us and by other groups and (ii) ligand-based QSAR models can complement structure-based approaches by boosting the prediction performances when used in consensus.
Intrinsically disordered proteins (IDPs) lack a stable tertiary structure, but their short binding regions termed Pre-Structured Motifs (PreSMo) can form transient secondary structure elements in solution. Although disordered proteins are crucial in many biological processes and designing strategies to modulate their function is highly important, both experimental and computational tools to describe their conformational ensembles and the initial steps of folding are sparse. Here we report that discrete molecular dynamics (DMD) simulations combined with replica exchange (RX) method efficiently samples the conformational space and detects regions populating α-helical conformational states in disordered protein regions. While the available computational methods predict secondary structural propensities in IDPs based on the observation of protein-protein interactions, our ab initio method rests on physical principles of protein folding and dynamics. We show that RX-DMD predicts α-PreSMos with high confidence confirmed by comparison to experimental NMR data. Moreover, the method also can dissect α-PreSMos in close vicinity to each other and indicate helix stability. Importantly, simulations with disordered regions forming helices in X-ray structures of complexes indicate that a preformed helix is frequently the binding element itself, while in other cases it may have a role in initiating the binding process. Our results indicate that RX-DMD provides a breakthrough in the structural and dynamical characterization of disordered proteins by generating the structural ensembles of IDPs even when experimental data are not available.
Accurate RNA structure modeling is an important, incompletely solved, challenge. Single-nucleotide resolution SHAPE (selective 2'-hydroxyl acylation analyzed by primer extension) yields an experimental measurement of local nucleotide flexibility that can be incorporated as pseudo-free energy change constraints to direct secondary structure predictions. Prior work from our laboratory has emphasized both the overall accuracy of this approach and the need for nuanced interpretation of some apparent discrepancies between modeled and accepted structures. Recent studies by Das and colleagues [Kladwang et al., Biochemistry 50:8049 (2011) and Nat. Chem. 3:954 (2011)], focused on analyzing six small RNAs, yielded poorer RNA secondary structure predictions than expected based on prior benchmarking efforts. To understand the features that led to these divergent results, we re-examined four RNAs yielding the poorest results in this recent work – tRNAPhe, the adenine and cyclic-di-GMP riboswitches, and 5S rRNA. Most of the errors reported by Das and colleagues reflected non-standard experiment and data processing choices, and selective scoring rules. For two RNAs, tRNAPhe and the adenine riboswitch, secondary structure predictions are nearly perfect if no experimental information is included but were rendered inaccurate by the Das and colleagues SHAPE data. When best practices were used, single-sequence SHAPE-directed secondary structure modeling recovered ~93% of individual base pairs and greater than 90% of helices in the four RNAs, essentially indistinguishable from the mutate-and-map approach with the exception of a single helix in the 5S rRNA. The field of experimentally-directed RNA secondary structure prediction is entering a phase focused on the most difficult prediction challenges. We outline five constructive principles for guiding this field forward.
Most cystic fibrosis is caused by a deletion of a single residue (F508) in CFTR that disrupts the folding and biosynthetic maturation of the ion channel protein. Progress towards understanding the underlying mechanisms and overcoming the defect remain incomplete. Here we show that the thermal instability of human ΔF508 CFTR channel activity evident in both cell-attached membrane patches and planar phospholipid bilayers is not observed in corresponding mutant CFTRs of several non-mammalian species. These more stable orthologs are distinguished from their mammalian counterparts by the substitution of proline residues at several key dynamic locations in the first nucleotide domain (NBD1), including the structurally diverse region (SDR), the gamma phosphate switch loop and the Regulatory Insertion (RI). Molecular Dynamic analyses revealed that addition of the prolines could reduce flexibility at these locations and increase the temperatures of unfolding transitions of ΔF508 NBD1 to that of the wild-type. Introduction of these prolines experimentally into full-length human ΔF508 CFTR together with the already recognized I539T suppressor mutation, also in the SDR, restored channel function and thermodynamic stability as well as its trafficking to and lifetime at the cell surface. Thus, while cellular manipulations that circumvent its culling by quality control systems leave ΔF508 CFTR dysfunctional at physiological temperature, restoration of the delicate balance between the dynamic protein’s inherent stability and channel activity returns a near-normal state.
ABC transporters; CFTR; protein thermal stability; ion channel; DMD simulations
Until now it has been impractical to observe protein folding in silico for proteins larger than 50 residues. Limitations of both force field accuracy and computational efficiency make the folding problem very challenging. Here we employ discrete molecular dynamics (DMD) simulations with an all-atom force field to fold fast-folding proteins. We extend the DMD force field by introducing long-range electrostatic interactions to model salt-bridges and a sequence-dependent semi-empirical potential accounting for natural tendencies of certain amino acid sequences to form specific secondary structures. We enhance the computational performance by parallelizing the DMD algorithm. Using a small number of commodity computers, we achieve sampling quality and folding accuracy comparable to the explicit-solvent simulations performed on high-end hardware. We demonstrate that DMD can be used to observe equilibrium folding of villin headpiece and WW domain, study two-state folding kinetics and sample near-native states in ab initio folding of proteins of ~100 residues.
Conformational dynamics; structure prediction; implicit solvent; parallel event-driven simulation