Stability and fluctuations of amide hydrogen bonds in a bacterial cytochrome c: a molecular dynamics study

Triple-Action Therapy: Combining Machine Learning, Docking, and Dynamics to Combat BRCA1-Mutated Breast Cancer

Breast cancer dominates women's mortality, and among other factors, mutations in the BRCA1 gene are significant risk factors. Several approaches are followed to treat the BRCA1 affected cancer patients. However, specific BRCA1 inhibitors are not available till date due to its structural complexity. In addition, there are several limitations associated with the existing drugs used to treat BRCA1-related breast cancer and some side effects. The side effects include symptoms such as hot flashes, joint pain, nausea, fatigue, hair loss, diarrhea, chills, fever, and others. Therefore, advanced approaches needed that can overcome all the limitations and side effects of the current inhibitors. In this study, we adopted a multistep approach to identify potential inhibitors for BRCA1-mutated breast cancer. We used our developed machine learning models to screen potential inhibitors. Molecular docking approach was carried out for the screened hit compounds with BRCA1 and its mutated forms. Two ligands, β-amyrin and Narirutin, has shown significant performance in multiple scoring schemes such as molecular docking and RF score calculations. Molecular dynamics simulations demonstrated the stability of the complexes formed by β-amyrin and Narirutin with BRCA1, with lower RMSD values and less RMSF fluctuations at the binding site locations. Principal component analysis (PCA) and free energy landscape (FEL) further confirmed the compactness and favorable binding of β-Amyrin and Narirutin to BRCA1. These findings suggest that β-amyrin and Narirutin have potential as therapeutic agents against BRCA1-mutated breast cancer.

Computational Insights into Acrylamide Fragment Inhibition of SARS-CoV-2 Main Protease

The pathogen of COVID-19, SARS-CoV-2, has caused a severe global health crisis. So far, while COVID-19 has been suppressed, the continuous evolution of SARS-CoV-2 variants has reduced the effectiveness of vaccines such as mRNA-1273 and drugs such as Remdesivir. To uphold the effectiveness of vaccines and drugs prior to potential coronavirus outbreaks, it is necessary to explore the underlying mechanisms between biomolecules and nanodrugs. The experimental study reported that acrylamide fragments covalently attached to Cys145, the main protease enzyme (Mpro) of SARS-CoV-2, and occupied the substrate binding pocket, thereby disrupting protease dimerization. However, the potential mechanism linking them is unclear. The purpose of this work is to complement and validate experimental results, as well as to facilitate the study of novel antiviral drugs. Based on our experimental studies, we identified two acrylamide fragments and constructed corresponding protein-ligand complex models. Subsequently, we performed molecular dynamics (MD) simulations to unveil the crucial interaction mechanisms between these nanodrugs and SARS-CoV-2 Mpro. This approach allowed the capture of various binding conformations of the fragments on both monomeric and dimeric Mpro, revealing significant conformational dissociation between the catalytic and helix domains, which indicates the presence of allosteric targets. Notably, Compound 5 destabilizes Mpro dimerization and acts as an effective inhibitor by specifically targeting the active site, resulting in enhanced inhibitory effects. Consequently, these fragments can modulate Mpro's conformational equilibrium among extended monomeric, compact, and dimeric forms, shedding light on the potential of these small molecules as novel inhibitors against coronaviruses. Overall, this research contributes to a broader understanding of drug development and fragment-based approaches in antiviral covalent therapeutics.

Computational Modeling of Molecular Structures Guided by Hydrogen-Exchange Data

Data produced by hydrogen-exchange monitoring experiments have been used in structural studies of molecules for several decades. Despite uncertainties about the structural determinants of hydrogen exchange itself, such data have successfully helped guide the structural modeling of challenging molecular systems, such as membrane proteins or large macromolecular complexes. As hydrogen-exchange monitoring provides information on the dynamics of molecules in solution, it can complement other experimental techniques in so-called integrative modeling approaches. However, hydrogen-exchange data have often only been used to qualitatively assess molecular structures produced by computational modeling tools. In this paper, we look beyond qualitative approaches and survey the various paradigms under which hydrogen-exchange data have been used to quantitatively guide the computational modeling of molecular structures. Although numerous prediction models have been proposed to link molecular structure and hydrogen exchange, none of them has been widely accepted by the structural biology community. Here, we present as many hydrogen-exchange prediction models as we could find in the literature, with the aim of providing the first exhaustive list of its kind. From purely structure-based models to so-called fractional-population models or knowledge-based models, the field is quite vast. We aspire for this paper to become a resource for practitioners to gain a broader perspective on the field and guide research toward the definition of better prediction models. This will eventually improve synergies between hydrogen-exchange monitoring and molecular modeling.

Dynamics and Conformational Changes in Human NEIL2 DNA Glycosylase Analyzed by Hydrogen/Deuterium Exchange Mass Spectrometry

Base excision DNA repair (BER) is necessary for removal of damaged nucleobases from the genome and their replacement with normal nucleobases. BER is initiated by DNA glycosylases, the enzymes that cleave the N-glycosidic bonds of damaged deoxynucleotides. Human endonuclease VIII-like protein 2 (hNEIL2), belonging to the helix-two-turn-helix structural superfamily of DNA glycosylases, is an enzyme uniquely specific for oxidized pyrimidines in non-canonical DNA substrates such as bubbles and loops. The structure of hNEIL2 has not been solved; its closest homologs with known structures are NEIL2 from opossum and from giant mimivirus. Here we analyze the conformational dynamics of free hNEIL2 using a combination of hydrogen/deuterium exchange mass spectrometry, homology modeling and molecular dynamics simulations. We show that a prominent feature of vertebrate NEIL2 - a large insert in its N-terminal domain absent from other DNA glycosylases - is unstructured in solution. It was suggested that helix-two-turn-helix DNA glycosylases undergo open-close transition upon DNA binding, with the large movement of their N- and C-terminal domains, but the open conformation has been elusive to capture. Our data point to the open conformation as favorable for free hNEIL2 in solution. Overall, our results are consistent with the view of hNEIL2 as a conformationally flexible protein, which may be due to its participation in the repair of non-canonical DNA structures and/or to the involvement in functional and regulatory protein-protein interactions.

Interpretation of HDX Data by Maximum-Entropy Reweighting of Simulated Structural Ensembles

Hydrogen-deuterium exchange combined with mass spectrometry (HDX-MS) is a widely applied biophysical technique that probes the structure and dynamics of biomolecules without the need for site-directed modifications or bio-orthogonal labels. The mechanistic interpretation of HDX data, however, is often qualitative and subjective, owing to a lack of quantitative methods to rigorously translate observed deuteration levels into atomistic structural information. To help address this problem, we have developed a methodology to generate structural ensembles that faithfully reproduce HDX-MS measurements. In this approach, an ensemble of protein conformations is first generated, typically using molecular dynamics simulations. A maximum-entropy bias is then applied post hoc to the resulting ensemble such that averaged peptide-deuteration levels, as predicted by an empirical model, agree with target values within a given level of uncertainty. We evaluate this approach, referred to as HDX ensemble reweighting (HDXer), for artificial target data reflecting the two major conformational states of a binding protein. We demonstrate that the information provided by HDX-MS experiments and by the model of exchange are sufficient to recover correctly weighted structural ensembles from simulations, even when the relevant conformations are rarely observed. Degrading the information content of the target data—e.g., by reducing sequence coverage, by averaging exchange levels over longer peptide segments, or by incorporating different sources of uncertainty—reduces the structural accuracy of the reweighted ensemble but still allows for useful insights into the distinctive structural features reflected by the target data. Finally, we describe a quantitative metric to rank candidate structural ensembles according to their correspondence with target data and illustrate the use of HDXer to describe changes in the conformational ensemble of the membrane protein LeuT. In summary, HDXer is designed to facilitate objective structural interpretations of HDX-MS data and to inform experimental approaches and further developments of theoretical exchange models.

Reconciling Simulated Ensembles of Apomyoglobin with Experimental Hydrogen/Deuterium Exchange Data Using Bayesian Inference and Multiensemble Markov State Models

Hydrogen/deuterium exchange (HDX) is a powerful technique to investigate protein conformational dynamics at amino acid resolution. Because HDX provides a measurement of solvent exposure of backbone hydrogens, ensemble-averaged over potentially slow kinetic processes, it has been challenging to use HDX protection factors to refine structural ensembles obtained from molecular dynamics simulations. This entails dual challenges: (1) identifying structural observables that best correlate with backbone amide protection from exchange and (2) restraining these observables in molecular simulations to model ensembles consistent with experimental measurements. Here, we make significant progress on both fronts. First, we describe an improved predictor of HDX protection factors from structural observables in simulated ensembles, parametrized from ultralong molecular dynamics simulation trajectory data, with a Bayesian inference approach used to retain the full posterior distribution of model parameters. We next present a new method for obtaining simulated ensembles in agreement with experimental HDX protection factors, in which molecular simulations are performed at various temperatures and restraint biases and used to construct multiensemble Markov State Models (MSMs). Finally, the BICePs (Bayesian Inference of Conformational Populations) algorithm is then used with our HDX protection factor predictor to infer which thermodynamic ensemble agrees best with the experiment and estimate populations of each conformational state in the MSM. To illustrate the approach, we use a combination of HDX protection factor restraints and chemical shift restraints to model the conformational ensemble of apomyoglobin at pH 6. The resulting ensemble agrees well with the experiment and gives insight into the all-atom structure of disordered helices F and H in the absence of heme.

POPPeT: a New Method to Predict the Protection Factor of Backbone Amide Hydrogens

Hydrogen exchange (HX) has become an important tool to monitor protein structure and dynamics. The interpretation of HX data with respect to protein structure requires understanding of the factors that influence exchange. Simulated protein structures can be validated by comparing experimental deuteration profiles with the profiles derived from the modeled protein structure. To do this, we propose here a new method, POPPeT, for protection factor prediction based on protein motions that enable HX. By comparing POPPeT with two existing methods, the phenomenological approximation and COREX, we show enhanced predictability measured at both protection factor and deuteration level. This method can be subsequently used by modeling strategies for protein structure prediction. Graphical Abstract ᅟ.

Revealing Unknown Protein Structures Using Computational Conformational Sampling Guided by Experimental Hydrogen-Exchange Data

Both experimental and computational methods are available to gather information about a protein's conformational space and interpret changes in protein structure. However, experimentally observing and computationally modeling large proteins remain critical challenges for structural biology. Our work aims at addressing these challenges by combining computational and experimental techniques relying on each other to overcome their respective limitations. Indeed, despite its advantages, an experimental technique such as hydrogen-exchange monitoring cannot produce structural models because of its low resolution. Additionally, the computational methods that can generate such models suffer from the curse of dimensionality when applied to large proteins. Adopting a common solution to this issue, we have recently proposed a framework in which our computational method for protein conformational sampling is biased by experimental hydrogen-exchange data. In this paper, we present our latest application of this computational framework: generating an atomic-resolution structural model for an unknown protein state. For that, starting from an available protein structure, we explore the conformational space of this protein, using hydrogen-exchange data on this unknown state as a guide. We have successfully used our computational framework to generate models for three proteins of increasing size, the biggest one undergoing large-scale conformational changes.

Interpreting Hydrogen-Deuterium Exchange Events in Proteins Using Atomistic Simulations: Case Studies on Regulators of G-Protein Signaling Proteins

Hydrogen-deuterium exchange (HDX) experiments are widely used in studies of protein dynamics. To predict the propensity of amide hydrogens for exchange with deuterium, several models have been reported in which computations of amide-hydrogen protection factors are carried out using molecular dynamics (MD) simulations. Given significant variation in the criteria used in different models, the robustness and broader applicability of these models to other proteins, especially homologous proteins showing distinct amide-exchange patterns, remains unknown. The sensitivity of the predictions when MD simulations are conducted with different force-fields is yet to tested and quantified. Using MD simulations and experimental HDX data on three homologous signaling proteins, we report detailed studies quantifying the performance of seven previously reported models (M1-M7) of two general types: empirical and fractional-population models. We find that the empirical models show inconsistent predictions but predictions of the fractional population models are robust. Contrary to previously reported work, we find that the solvent-accessible surface area of amide hydrogens is a useful metric when combined with a new metric defining the distances of amide hydrogens from the first polar atoms in proteins. On the basis of this, we report two new models, one empirical (M8) and one population-based (M9). We find strong protection of amide hydrogens from solvent exchange both within the stable helical motifs and also in the interhelical loops. We further observe that the exchange-competent states of amide hydrogens occur on the sub 100 ps time-scale via localized fluctuations, and such states among amides of a given protein do not appear to show any cooperativity or allosteric coupling.

Native State of Complement Protein C3d Analysed via Hydrogen Exchange and Conformational Sampling

Hydrogen/deuterium exchange detected by mass spectrometry (HDXMS) provides valuable information on protein structure and dynamics. Although HDX-MS data is often interpreted using crystal structures, it was suggested that conformational ensembles produced by molecular dynamics simulations yield more accurate interpretations. In this paper, we analyse the complement protein C3d by performing an HDX-MS experiment, and evaluate several interpretation methodologies using an existing prediction model to derive HDX-MS data from protein structure. To interpret and refine C3d's HDX-MS data, we look for a conformation (or conformational ensemble) of C3d that allows computationally replicating this data. We confirm that crystal structures are not a good choice and suggest that conformational ensembles produced by molecular dynamics simulations might not always be satisfactory either. Finally, we show that coarse-grained conformational sampling of C3d produces a conformation from which its HDX-MS data can be replicated and refined.

A General Method for Predicting Amino Acid Residues Experiencing Hydrogen Exchange

Information on protein hydrogen exchange can help delineate key regions involved in protein-protein interactions and provides important insight towards determining functional roles of genetic variants and their possible mechanisms in disease processes. Previous studies have shown that the degree of hydrogen exchange is affected by hydrogen bond formations, solvent accessibility, proximity to other residues, and experimental conditions. However, a general predictive method for identifying residues capable of hydrogen exchange transferable to a broad set of proteins is lacking. We have developed a machine learning method based on random forest that can predict whether a residue experiences hydrogen exchange. Using data from the Start2Fold database, which contains information on 13,306 residues (3,790 of which experience hydrogen exchange and 9,516 which do not exchange), our method achieves good performance. Specifically, we achieve an overall out-of-bag (OOB) error, an unbiased estimate of the test set error, of 20.3 percent. Using a randomly selected test data set consisting of 500 residues experiencing hydrogen exchange and 500 which do not, our method achieves an accuracy of 0.79, a recall of 0.74, a precision of 0.82, and an F1 score of 0.78.

Coarse-Grained Conformational Sampling of Protein Structure Improves the Fit to Experimental Hydrogen-Exchange Data

Monitoring hydrogen/deuterium exchange (HDX) undergone by a protein in solution produces experimental data that translates into valuable information about the protein's structure. Data produced by HDX experiments is often interpreted using a crystal structure of the protein, when available. However, it has been shown that the correspondence between experimental HDX data and crystal structures is often not satisfactory. This creates difficulties when trying to perform a structural analysis of the HDX data. In this paper, we evaluate several strategies to obtain a conformation providing a good fit to the experimental HDX data, which is a premise of an accurate structural analysis. We show that performing molecular dynamics simulations can be inadequate to obtain such conformations, and we propose a novel methodology involving a coarse-grained conformational sampling approach instead. By extensively exploring the intrinsic flexibility of a protein with this approach, we produce a conformational ensemble from which we extract a single conformation providing a good fit to the experimental HDX data. We successfully demonstrate the applicability of our method to four small and medium-sized proteins.

Phosphorylation in the catalytic cleft stabilizes and attracts domains of a phosphohexomutase

Phosphorylation can modulate the activities of enzymes. The phosphoryl donor in the catalytic cleft of α-D-phosphohexomutases is transiently dephosphorylated while the reaction intermediate completes a 180° reorientation within the cleft. The phosphorylated form of 52 kDa bacterial phosphomannomutase/phosphoglucomutase is less accessible to dye or protease, more stable to chemical denaturation, and widely stabilized against NMR-detected hydrogen exchange across the core of domain 3 to juxtaposed domain 4 (each by ≥ 1.3 kcal/mol) and parts of domains 1 and 2. However, phosphorylation accelerates hydrogen exchange in specific regions of domains 1 and 2, including a metal-binding residue in the active site. Electrostatic field lines reveal attraction across the catalytic cleft between phosphorylated Ser-108 and domain 4, but repulsion when Ser-108 is dephosphorylated. Molecular dynamics (MD) simulated the dephosphorylated form to be expanded due to enhanced rotational freedom of domain 4. The contacts and fluctuations of the MD trajectories enabled correct simulation of more than 80% of sites that undergo either protection or deprotection from hydrogen exchange due to phosphorylation. Electrostatic attraction in the phosphorylated enzyme accounts for 1) domain 4 drawing closer to domains 1 and 3; 2) decreased accessibility; and 3) increased stability within these domains. The electrostriction due to phosphorylation may help capture substrate, whereas the opening of the cleft upon transient dephosphorylation allows rotation of the intermediate. The long-range effects of phosphorylation on hydrogen exchange parallel reports on protein kinases, suggesting a conceptual link among these multidomain, phosphoryl transfer enzymes.

Estimation of Hydrogen-Exchange Protection Factors from MD Simulation Based on Amide Hydrogen Bonding Analysis

Hydrogen exchange (HX) studies have provided critical insight into our understanding of protein folding, structure, and dynamics. More recently, hydrogen exchange mass spectrometry (HX-MS) has become a widely applicable tool for HX studies. The interpretation of the wealth of data generated by HX-MS experiments as well as other HX methods would greatly benefit from the availability of exchange predictions derived from structures or models for comparison with experiment. Most reported computational HX modeling studies have employed solvent-accessible-surface-area based metrics in attempts to interpret HX data on the basis of structures or models. In this study, a computational HX-MS prediction method based on classification of the amide hydrogen bonding modes mimicking the local unfolding model is demonstrated. Analysis of the NH bonding configurations from molecular dynamics (MD) simulation snapshots is used to determine partitioning over bonded and nonbonded NH states and is directly mapped into a protection factor (PF) using a logistics growth function. Predicted PFs are then used for calculating deuteration values of peptides and compared with experimental data. Hydrogen exchange MS data for fatty acid synthase thioesterase (FAS-TE) collected for a range of pHs and temperatures was used for detailed evaluation of the approach. High correlation between prediction and experiment for observable fragment peptides is observed in the FAS-TE and additional benchmarking systems that included various apo/holo proteins for which literature data were available. In addition, it is shown that HX modeling can improve experimental resolution through decomposition of in-exchange curves into rate classes, which correlate with prediction from MD. Successful rate class decompositions provide further evidence that the presented approach captures the underlying physical processes correctly at the single residue level. This assessment is further strengthened in a comparison of residue resolved protection factor predictions for staphylococcal nuclease with NMR data, which was also used to compare prediction performance with other algorithms described in the literature. The demonstrated transferable and scalable MD based HX prediction approach adds significantly to the available tools for HX-MS data interpretation based on available structures and models.

Efficient and robust analysis of biomacromolecular flexibility using ensembles of network topologies based on fuzzy noncovalent constraints

We describe an approach (ENT(FNC)) for performing rigidity analyses of biomacromolecules on ensembles of network topologies (ENT) generated from a single input structure. The ENT is based on fuzzy noncovalent constraints, which considers thermal fluctuations of biomacromolecules without actually sampling conformations. Definitions for fuzzy noncovalent constraints were derived from persistency data from molecular dynamics (MD) simulations. A very good agreement between local flexibility and rigidity characteristics from ENT(FNC) and MD simulations-generated ensembles is found. Regarding global characteristics, convincing results were obtained when relative thermostabilities of citrate synthase and lipase A structures were computed. The ENT(FNC) approach significantly improves the robustness of rigidity analyses, is highly efficient, and does not require a protein-specific parameterization. Its low computational demand makes it especially valuable for the analysis of large data sets, e.g., for data-driven protein engineering.

Temperature influence on the structure and interfacial properties of surfactin micelle: a molecular dynamics simulation study

Surfactin is an efficient biosurfactant excreted by different strains of Bacillus subtilis. Our study provides a molecular view of the temperature dependence of the structure and the interfacial properties of un-ionized surfactin micelles. The overall size and shape, the surface area, the radial density distribution of the micelles, the conformation of the hydrocarbon chain, and the intramolecular/intermolecular hydrogen bonds formed in surfactin molecules were investigated. The micelles were mostly in sphere shapes, and the radii of surfactin micelle were estimated to be around 2.2 nm. The peptide rings occupied most of the surface of the micelles. Small amounts of β-turn and γ-turn structures were found in the conformations of the peptide rings. When the temperature increased, the shape of the peptide rings became planar; the solvent accessible surface area decreased as temperature dehydration occurred. At 343 K some hydrocarbon chains reversed their orientation (flip-flopped). In addition, the stability of the hydrogen bond interactions in the micelles decreases with the increasing temperature.

Interrogation of the protein-protein interactions between human BRCA2 BRC repeats and RAD51 reveals atomistic determinants of affinity

The breast cancer suppressor BRCA2 controls the recombinase RAD51 in the reactions that mediate homologous DNA recombination, an essential cellular process required for the error-free repair of DNA double-stranded breaks. The primary mode of interaction between BRCA2 and RAD51 is through the BRC repeats, which are ∼35 residue peptide motifs that interact directly with RAD51 in vitro. Human BRCA2, like its mammalian orthologues, contains 8 BRC repeats whose sequence and spacing are evolutionarily conserved. Despite their sequence conservation, there is evidence that the different human BRC repeats have distinct capacities to bind RAD51. A previously published crystal structure reports the structural basis of the interaction between human BRC4 and the catalytic core domain of RAD51. However, no structural information is available regarding the binding of the remaining seven BRC repeats to RAD51, nor is it known why the BRC repeats show marked variation in binding affinity to RAD51 despite only subtle sequence variation. To address these issues, we have performed fluorescence polarisation assays to indirectly measure relative binding affinity, and applied computational simulations to interrogate the behaviour of the eight human BRC-RAD51 complexes, as well as a suite of BRC cancer-associated mutations. Our computational approaches encompass a range of techniques designed to link sequence variation with binding free energy. They include MM-PBSA and thermodynamic integration, which are based on classical force fields, and a recently developed approach to computing binding free energies from large-scale quantum mechanical first principles calculations with the linear-scaling density functional code onetep. Our findings not only reveal how sequence variation in the BRC repeats directly affects affinity with RAD51 and provide significant new insights into the control of RAD51 by human BRCA2, but also exemplify a palette of computational and experimental tools for the analysis of protein-protein interactions for chemical biology and molecular therapeutics.

The atomic scale interactions that occur at the interfaces between proteins are fundamental to all biological processes. One such critical interface is formed between the proteins, human BRCA2 and RAD51. BRCA2 binds to and delivers RAD51 to sites of DNA damage, where RAD51 mediates the error-free repair of double-stranded DNA breaks. Mutations in BRCA2 have been linked to breast cancer predisposition. Therefore, an accurate picture of the interactions between these two proteins is of great importance. BRCA2 interacts with RAD51 via eight “BRC repeats” that are similar, but not identical, in sequence. Due to lack of experimental structural information regarding the binding of seven of the eight BRC repeats to RAD51, it is unknown how subtle sequence variations in the repeats translate to measurable variations in their binding affinity. We have used a range of computational methods, firstly based on classical force fields, and secondly based on first principles quantum mechanical techniques whose computational cost scales linearly with the number of atoms, allowing us to perform calculations on the entire protein complex. This is the first study comparing all eight BRC repeats at the atomic scale and our results provide critical insights into the control of RAD51 by human BRCA2.

Molecular dynamic simulation of wild type and mutants of the polymorphic amyloid NNQNTF segments of elk prion: structural stability and thermodynamic of association

A hexapeptide with amino acid sequence NNQNTF from the elk prion protein forms amyloid fibrils. Here we use molecular dynamic simulations of the oligomers and their single point glycine mutants to study their stability. In an effort to probe the structural stability and association thermodynamic in a realistic environment, all wildtype of NNQNTF polymorphic forms with different size and their corresponding double layer 5 strands single point glycine mutants were subjected to a total of 500 ns of explicit-solvent molecular dynamics (MD) simulation. Our results show that the structural stability of the NNQNTF oligomers increases with increasing the number of β-strands for double layers. Our results also demonstrated that hydrophobic interaction is the principle driving force to stabilize the adjacent β-strands while the steric zipper is responsible for holding the neighboring β-sheet layers together. We used MM-PBSA approach free energy calculations to determine the role of nonpolar effects, electrostatics and entropy in binding. Nonpolar effects remained consistently more favorable in wild type and mutants reinforcing the importance of hydrophobic effects in protein-protein binding. While entropy systematically opposed binding in all cases, there was no observed trend in the entropy difference between wildtype and glycine mutant. Free energy decomposition shows residues situated at the interface were found to make favorable contributions to the peptide-peptide association. The study of the wild type and mutants in an explicit solvent may provide valuable insight for amyloid aggregation inhibitor design efforts.

Predicting protein folding cores by empirical potential functions

Theoretical and in vitro experiments suggest that protein folding cores form early in the process of folding, and that proteins may have evolved to optimize both folding speed and native-state stability. In our previous work (Chen et al., Structure, 14 (2006) 1401), we developed a set of empirical potential functions and used them to analyze interaction energies among secondary-structure elements in two beta-sandwich proteins. Our work on this group of proteins demonstrated that the predicted folding core also harbors residues that form native-like interactions early in the folding reaction. In the current work, we have tested our empirical potential functions on structurally-different proteins for which the folding cores have been revealed by protein hydrogen-deuterium exchange experiments. Using a set of 29 unrelated proteins, which have been extensively studied in the literature, we demonstrate that the average prediction result from our method is significantly better than predictions based on other computational methods. Our study is an important step towards the ultimate goal of understanding the correlation between folding cores and native structures.

Molecular dynamics simulations of Na+/Cl(-)-dependent neurotransmitter transporters in a membrane-aqueous system

We have performed molecular dynamics simulations of a homology model of the human serotonin transporter (hSERT) in a membrane environment and in complex with either the natural substrate 5-HT or the selective serotonin reuptake inhibitor escitalopram. We have also included a transporter homologue, the Aquifex aeolicus leucine transporter (LeuT), in our study to evaluate the applicability of a simple and computationally attractive membrane system. Fluctuations in LeuT extracted from simulations are in good agreement with crystallographic B factors. Furthermore, key interactions identified in the X-ray structure of LeuT are maintained throughout the simulations indicating that our simple membrane system is suitable for studying the transmembrane protein hSERT in complex with 5-HT or escitalopram. For these transporter complexes, only relatively small fluctuations are observed in the ligand-binding cleft. Specific interactions responsible for ligand recognition, are identified in the hSERT-5HT and hSERT-escitalopram complexes. Our findings are in good agreement with predictions from mutagenesis studies.

Molecular statistics of cytochrome c: structural plasticity and molecular environment

Nuclear magnetic resonance experiments performed on yeast mitochondrial cytochrome c (Cytc), a paradigmatic electron transfer protein, reveal that the two oxidation states have similar structures, but different mobility: despite the few structural differences compared with the reduced form, the oxidized form displays a larger unfolding propensity. Molecular dynamics simulations performed on both NMR reduced and NMR oxidized forms show that the reduced form has a larger solvent-accessible surface area (SASA). Starting from this observation, a molecular statistical approach was then applied in order to correlate the molecular surface to molecular mobility. Simulations started from biased initial conditions corresponding to different molecular sizes were combined with the maximal constrained entropy method. The NMR structure of oxidized Cytc is more suited to expose a smaller SASA than the NMR structure of the reduced form, but the accessible conformational landscape at 300 K around the NMR oxidized structure is flatter than for the NMR reduced structure. Protein configurations of smaller SASA and size display larger plasticity when they resemble the NMR oxidized structure, whereas they are more rigid when they resemble the NMR reduced structure. Implications of the results for the protein properties during its functional process are discussed.