FACTS: Fast analytical continuum treatment of solvation

Peptide and Protein Structure Prediction with a Simplified Continuum Solvent Model

A continuum solvent model based on screened Coulomb potentials has been simplified and parametrized to sample native-like structures in replica-exchange simulations of each of six different peptides and miniproteins. Low-energy, native, and non-native structures were used to iteratively refine 11 parameter values. The centroid of the largest cluster of structures sampled in simulations initiated from an extended conformation represents the predicted structure. The main-chain rms deviation of this prediction from the experimental structure was 0.47 Å for the 12-residue Trp-zip2, 0.86 Å for the 14-residue MBH12, 2.53 Å for the 17-residue U(1-17)T9D, 2.03 Å for the 20-residue BS1, 1.08 Å for the 20-residue Trp-cage, and 3.64 Å for the 35-residue villin headpiece subdomain HP35. The centroid of the sixth largest cluster sampled for HP35 deviated by 0.91 Å. The CHARMM22/CMAP force field was used, with an additional ψ torsion term for residues other than glycine and proline. Six parameters govern the dielectric response of the continuum solvent, and four values of surface tension approximate nonpolar effects. An atom's self-energy and interaction energies are screened independently, each depending on whether the atom is part of a charged group, a neutral hydrogen-bonding main-chain group, or any other neutral group. The parameters inferred result in strong main-chain hydrogen bonds, consistent with the view that protein folding is dominated by the formation of these bonds. (1,2) Conformations of MBH12 and BS1 were excluded from the energy-function refinement, suggesting the parameters, referred to as SCP18, are transferable. An efficient estimate of solvent-accessible surface area is also described.

Compound Molecular Logic in Accessing the Active Site of Mycobacterium tuberculosis Protein Tyrosine Phosphatase B

Protein tyrosine phosphatase B (PtpB) from Mycobacterium tuberculosis (Mtb) extends the bacteria's survival in hosts and hence is a potential target for Mtb-specific drugs. To study how Mtb-specific sequence insertions in PtpB may regulate access to its active site through large-amplitude conformational changes, we performed free-energy calculations using an all-atom explicit solvent model. Corroborated by biochemical assays, the results show that PtpB's active site is controlled via an "either/or" compound conformational gating mechanism, an unexpected discovery that Mtb has evolved to bestow a single enzyme with such intricate logical operations. In addition to providing unprecedented insights for its active-site surroundings, the findings also suggest new ways of inactivating PtpB.

Targeting the Pentose Phosphate Pathway: Characterization of a New 6PGL Inhibitor

Human African trypanosomiasis, or sleeping sickness, is a lethal disease caused by the protozoan parasite Trypanosoma brucei. However, although many efforts have been made to understand the biochemistry of this parasite, drug development has led to treatments that are of limited efficiency and of great toxicity. To develop new drugs, new targets must be identified, and among the several metabolic processes of trypanosomes that have been proposed as drug targets, carbohydrate metabolism (glycolysis and the pentose phosphate pathway (PPP)) appears as a promising one. As far as the PPP is concerned, a limited number of studies are related to the glucose-6-phosphate dehydrogenase. In this work, we have focused on the activity of the second PPP enzyme (6-phospho-gluconolactonase (6PGL)) that transforms 6-phosphogluconolactone into 6-phosphogluconic acid. A lactam analog of the natural substrate has been synthesized, and binding of the ligand to 6PGL has been investigated by NMR titration. The ability of this ligand to inhibit 6PGL has also been demonstrated using ultraviolet experiments, and protein-inhibitor interactions have been investigated through docking calculations and molecular dynamics simulations. In addition, a marginal inhibition of the third enzyme of the PPP (6-phosphogluconate dehydrogenase) was also demonstrated. Our results thus open new prospects for targeting T. brucei.

Explicit ions/implicit water generalized Born model for nucleic acids

The ion atmosphere around highly charged nucleic acid molecules plays a significant role in their dynamics, structure, and interactions. Here we utilized the implicit solvent framework to develop a model for the explicit treatment of ions interacting with nucleic acid molecules. The proposed explicit ions/implicit water model is based on a significantly modified generalized Born (GB) model and utilizes a non-standard approach to define the solute/solvent dielectric boundary. Specifically, the model includes modifications to the GB interaction terms for the case of multiple interacting solutes-disconnected dielectric boundary around the solute-ion or ion-ion pairs. A fully analytical description of all energy components for charge-charge interactions is provided. The effectiveness of the approach is demonstrated by calculating the potential of mean force for Na⁺-Cl^- ion pair and by carrying out a set of Monte Carlo (MC) simulations of mono- and trivalent ions interacting with DNA and RNA duplexes. The monovalent (Na⁺) and trivalent (CoHex³⁺) counterion distributions predicted by the model are in close quantitative agreement with all-atom explicit water molecular dynamics simulations used as reference. Expressed in the units of energy, the maximum deviations of local ion concentrations from the reference are within k _B T. The proposed explicit ions/implicit water GB model is able to resolve subtle features and differences of CoHex distributions around DNA and RNA duplexes. These features include preferential CoHex binding inside the major groove of the RNA duplex, in contrast to CoHex biding at the "external" surface of the sugar-phosphate backbone of the DNA duplex; these differences in the counterion binding patters were earlier shown to be responsible for the observed drastic differences in condensation propensities between short DNA and RNA duplexes. MC simulations of CoHex ions interacting with the homopolymeric poly(dA·dT) DNA duplex with modified (de-methylated) and native thymine bases are used to explore the physics behind CoHex-thymine interactions. The simulations suggest that the ion desolvation penalty due to proximity to the low dielectric volume of the methyl group can contribute significantly to CoHex-thymine interactions. Compared to the steric repulsion between the ion and the methyl group, the desolvation penalty interaction has a longer range and may be important to consider in the context of methylation effects on DNA condensation.

Helical Polyampholyte Sequences Have Unique Thermodynamic Properties

Helices are the most common structural pattern observed in structured proteins. Polypeptide sequences that form helices in isolation have been identified and extensively studied. These are generally rich in alanine, the amino acid with strongest helical propensity. Insertion of charged or polar amino acids has been shown to be necessary to make alanine-rich peptides soluble and sometimes even increase the helicity of the peptides. More recently sequences that contain mostly charged residues (E-R/K rich) have been found in naturally occurring proteins that are highly helical, soluble, and extended regardless their length. Artificial sequences composed mostly or exclusively of charged amino acids have been designed that are also highly helical, depending on the specific pattern of oppositely charged residues. Here we explore the thermodynamic properties of a number of 16-residue long peptides with varying helical propensity by performing equilibrium simulations over a broad range of temperatures. We observe quantitative differences in the peptides' helical propensities that can be related to qualitative differences in the free energy landscape, depending on the ampholytic patterns in the sequence. The results provide hints on how the specific physical properties of naturally occurring long sequences with similar patterns of charged residues may relate to their biological function.

Structural Analysis of Variability and Interaction of the N-terminal of the Oncogenic Effector CagA of Helicobacter pylori with Phosphatidylserine

Helicobacter pylori cytotoxin-associated gene A protein (CagA) has been associated with the increase in virulence and risk of cancer. It has been demonstrated that CagA’s translocation is dependent on its interaction with phosphatidylserine. We evaluated the variability of the N-terminal CagA in 127 sequences reported in NCBI, by referring to molecular interaction forces with the phosphatidylserine and the docking of three mutations chosen from variations in specific positions. The major sites of conservation of the residues involved in CagA–Phosphatidylserine interaction were 617, 621 and 626 which had no amino acid variation. Position 636 had the lowest conservation score; mutations in this position were evaluated to observe the differences in intermolecular forces for the CagA–Phosphatidylserine complex. We evaluated the docking of three mutations: K636A, K636R and K636N. The crystal and mutation models presented a ΔG of −8.919907, −8.665261, −8.701923, −8.515097 Kcal/mol, respectively, while mutations K636A, K636R, K636N and the crystal structure presented 0, 3, 4 and 1 H-bonds, respectively. Likewise, the bulk effect of the ΔG and amount of H-bonds was estimated in all of the docking models. The type of mutation affected both the ΔG (χ2(1)=93.82, p-value <2.2×10−16) and the H-bonds (χ2(1)=91.93, p-value <2.2×10−16). Overall, 76.9% of the strains that exhibit the K636N mutation produced a severe pathology. The average H-bond count diminished when comparing the mutations with the crystal structure of all the docking models, which means that other molecular forces are involved in the CagA–Phosphatidylserine complex interaction.

The Zinc Linchpin Motif in the DNA Repair Glycosylase MUTYH: Identifying the Zn2+ Ligands and Roles in Damage Recognition and Repair

The DNA base excision repair (BER) glycosylase MUTYH prevents DNA mutations by catalyzing adenine (A) excision from inappropriately formed 8-oxoguanine (8-oxoG):A mismatches. The importance of this mutation suppression activity in tumor suppressor genes is underscored by the association of inherited variants of MUTYH with colorectal polyposis in a hereditary colorectal cancer syndrome known as MUTYH-associated polyposis, or MAP. Many of the MAP variants encompass amino acid changes that occur at positions surrounding the two-metal cofactor-binding sites of MUTYH. One of these cofactors, found in nearly all MUTYH orthologs, is a [4Fe-4S]2+ cluster coordinated by four Cys residues located in the N-terminal catalytic domain. We recently uncovered a second functionally relevant metal cofactor site present only in higher eukaryotic MUTYH orthologs: a Zn2+ ion coordinated by three Cys residues located within the extended interdomain connector (IDC) region of MUTYH that connects the N-terminal adenine excision and C-terminal 8-oxoG recognition domains. In this work, we identified a candidate for the fourth Zn2+ coordinating ligand using a combination of bioinformatics and computational modeling. In addition, using in vitro enzyme activity assays, fluorescence polarization DNA binding assays, circular dichroism spectroscopy, and cell-based rifampicin resistance assays, the functional impact of reduced Zn2+ chelation was evaluated. Taken together, these results illustrate the critical role that the "Zn2+ linchpin motif" plays in MUTYH repair activity by providing for proper engagement of the functional domains on the 8-oxoG:A mismatch required for base excision catalysis. The functional importance of the Zn2+ linchpin also suggests that adjacent MAP variants or exposure to environmental chemicals may compromise Zn2+ coordination, and ability of MUTYH to prevent disease.

Protein Conformational Transitions from All-Atom Adaptively Biased Path Optimization

Simulation methods are valuable for elucidating protein conformational transitions between functionally diverse states given that transition pathways are difficult to capture experimentally. Nonetheless, specific computational algorithms are required because of the high free energy barriers between these different protein conformational states. Adaptively biased path optimization (ABPO) is an unrestrained, transition-path optimization method that works in a reduced-variable space to construct an adaptive biasing potential to aid convergence. ABPO was previously applied using a coarse-grained Go̅-model to study conformational activation of Lyn, a Src family tyrosine kinase. How effectively ABPO can be applied at the higher resolution of an all-atom model to explore protein conformational transitions is not yet known. Here, we report the all-atom conformational transition paths of three protein systems constructed using the ABPO methodology. Two systems, triose phosphate isomerase and dihydrofolate reductase, undergo local flipping of a short loop that promotes ligand binding. The third system, estrogen receptor α ligand binding domain, has a helix that adopts different conformations when the protein is bound to an agonist or an antagonist. For each protein, distance-based or torsion-angle reduced variables were identified from unbiased trajectories. ABPO was computed in this reduced variable space to obtain the transition path between the two states. The all-atom ABPO is shown to successfully converge an optimal transition path for each of the three systems.

Elucidating the multi-targeted anti-amyloid activity and enhanced islet amyloid polypeptide binding of β-wrapins

β-wrapins are engineered binding proteins stabilizing the β-hairpin conformations of amyloidogenic proteins islet amyloid polypeptide (IAPP), amyloid-β, and α-synuclein, thus inhibiting their amyloid propensity. Here, we use computational and experimental methods to investigate the molecular recognition of IAPP by β-wrapins. We show that the multi-targeted, IAPP, amyloid-β, and α-synuclein, binding properties of β-wrapins originate mainly from optimized interactions between β-wrapin residues and sets of residues in the three amyloidogenic proteins with similar physicochemical properties. Our results suggest that IAPP is a comparatively promiscuous β-wrapin target, probably due to the low number of charged residues in the IAPP β-hairpin motif. The sub-micromolar affinity of β-wrapin HI18, specifically selected against IAPP, is achieved in part by salt-bridge formation between HI18 residue Glu10 and the IAPP N-terminal residue Lys1, both located in the flexible N-termini of the interacting proteins. Our findings provide insights towards developing novel protein-based single- or multi-targeted therapeutics.

In vitro screening, homology modeling and molecular docking studies of some pyrazole and imidazole derivatives

A series of synthesized compounds based on pyrazole and imidazole skeletons prepared by palladium catalysts via a one-pot reaction was screened to determine their inhibitory potency against the pathogen fungus Fusarium oxysporum f.sp. albedinis (F.o.a) and four bacteria strains namely Micrococcus luteus, Bacillus subtilis, Staphylococcus aureus and Escherichia coli. The obtained result showed that these compounds exhibit an efficiency antifungal action. Whereas, they showed a very weak antibacterial activity. The structure-activity relationship (SAR) Analysis and lipophilicity study demonstrates the presence of a strong relation between the structure of the ligands and the antifungal activity. On the other hand, a homology modeling and molecular docking study was carried out on the most active compounds against F.o.a fungus, in order to understand and determine the molecular interactions taking place between the ligand and the corresponding receptor of the studied target.

Improved performance in CAPRI round 37 using LZerD docking and template-based modeling with combined scoring functions

We report our group's performance for protein-protein complex structure prediction and scoring in Round 37 of the Critical Assessment of PRediction of Interactions (CAPRI), an objective assessment of protein-protein complex modeling. We demonstrated noticeable improvement in both prediction and scoring compared to previous rounds of CAPRI, with our human predictor group near the top of the rankings and our server scorer group at the top. This is the first time in CAPRI that a server has been the top scorer group. To predict protein-protein complex structures, we used both multi-chain template-based modeling (TBM) and our protein-protein docking program, LZerD. LZerD represents protein surfaces using 3D Zernike descriptors (3DZD), which are based on a mathematical series expansion of a 3D function. Because 3DZD are a soft representation of the protein surface, LZerD is tolerant to small conformational changes, making it well suited to docking unbound and TBM structures. The key to our improved performance in CAPRI Round 37 was to combine multi-chain TBM and docking. As opposed to our previous strategy of performing docking for all target complexes, we used TBM when multi-chain templates were available and docking otherwise. We also describe the combination of multiple scoring functions used by our server scorer group, which achieved the top rank for the scorer phase.

Accuracy Comparison of Generalized Born Models in the Calculation of Electrostatic Binding Free Energies

The need for accurate yet efficient representation of the aqueous environment in biomolecular modeling has led to the development of a variety of generalized Born (GB) implicit solvent models. While many studies have focused on the accuracy of available GB models in predicting solvation free energies, a systematic assessment of the quality of these models in binding free energy calculations, crucial for rational drug design, has not been undertaken. Here, we evaluate the accuracies of eight common GB flavors (GB-HCT, GB-OBC, GB-neck2, GBNSR6, GBSW, GBMV1, GBMV2, and GBMV3), available in major molecular dynamics packages, in predicting the electrostatic binding free energies ( ΔΔ G_el) for a diverse set of 60 biomolecular complexes belonging to four main classes: protein-protein, protein-drug, RNA-peptide, and small complexes. The GB flavors are examined in terms of their ability to reproduce the results from the Poisson-Boltzmann (PB) model, commonly used as accuracy reference in this context. We show that the agreement with the PB of ΔΔ G_el estimates varies widely between different GB models and also across different types of biomolecular complexes, with R² correlations ranging from 0.3772 to 0.9986. A surface-based "R6" GB model recently implemented in AMBER shows the closest overall agreement with reference PB ( R² = 0.9949, RMSD = 8.75 kcal/mol). The RNA-peptide and protein-drug complex sets appear to be most challenging for all but one model, as indicated by the large deviations from the PB in ΔΔ G_el. Small neutral complexes present the least challenge for most of the GB models tested. The quantitative demonstration of the strengths and weaknesses of the GB models across the diverse complex types provided here can be used as a guide for practical computations and future development efforts.

Electrostatic Interactions in Protein Structure, Folding, Binding, and Condensation

Charged and polar groups, through forming ion pairs, hydrogen bonds, and other less specific electrostatic interactions, impart important properties to proteins. Modulation of the charges on the amino acids, e.g., by pH and by phosphorylation and dephosphorylation, have significant effects such as protein denaturation and switch-like response of signal transduction networks. This review aims to present a unifying theme among the various effects of protein charges and polar groups. Simple models will be used to illustrate basic ideas about electrostatic interactions in proteins, and these ideas in turn will be used to elucidate the roles of electrostatic interactions in protein structure, folding, binding, condensation, and related biological functions. In particular, we will examine how charged side chains are spatially distributed in various types of proteins and how electrostatic interactions affect thermodynamic and kinetic properties of proteins. Our hope is to capture both important historical developments and recent experimental and theoretical advances in quantifying electrostatic contributions of proteins.

Rotamer Libraries for the High-Resolution Design of β-Amino Acid Foldamers

β-Amino acids offer attractive opportunities to develop biologically active peptidomimetics, either employed alone or in conjunction with natural α-amino acids. Owing to their potential for unique conformational preferences that deviate considerably from α-peptide geometries, β-amino acids greatly expand the possible chemistries and physical properties available to polyamide foldamers. Complete in silico support for designing new molecules incorporating non-natural amino acids typically requires representing their side-chain conformations as sets of discrete rotamers for model refinement and sequence optimization. Such rotamer libraries are key components of several state-of-the-art design frameworks. Here we report the development, incorporation in to the Rosetta macromolecular modeling suite, and validation of rotamer libraries for β3-amino acids.

Force field development and simulations of intrinsically disordered proteins

Intrinsically disordered proteins (IDPs) play important roles in many physiological processes such as signal transduction and transcriptional regulation. Computer simulations that are based on empirical force fields have been increasingly used to understand the biophysics of disordered proteins. In this review, we focus on recent improvement of protein force fields, including polarizable force fields, concerning their accuracy in modeling intrinsically disordered proteins. Some recent benchmarks and applications of these force fields are also overviewed.

Simultaneous refinement of inaccurate local regions and overall structure in the CASP12 protein model refinement experiment

Advances in protein model refinement techniques are required as diverse sources of protein structure information are available from low-resolution experiments or informatics-based computations such as cryo-EM, NMR, homology models, or predicted residue contacts. Given semi-reliable or incomplete structural information, structure quality of a protein model has to be improved by ab initio methods such as energy-based simulation. In this study, we describe a new automatic refinement server method designed to improve locally inaccurate regions and overall structure simultaneously. Locally inaccurate regions may occur in protein structures due to non-convergent or missing information in template structures used in homology modeling or due to intrinsic structural flexibilities not resolved by experimental techniques. However, such variable or dynamic regions often play important functional roles by participating in interactions with other biomolecules or in transitions between different functional states. The new refinement method introduced here utilizes diverse types of geometric operators which drive both local and global changes, and the effect of structure changes and relaxations are accumulated. This resulted in consistent refinement of both local and global structural features. Performance of this method in CASP12 is discussed.

Protein structure model refinement in CASP12 using short and long molecular dynamics simulations in implicit solvent

Protein structure prediction has matured over years, particularly those which use structure templates for building a model. It can build a model with correct overall conformation in cases where appropriate templates are available. Models with the correct topology can be practically useful for limited purposes that need residue-level accuracy, but further improvement of the models can allow the models to be used in tasks that need detailed structures, such as molecular replacement in X-ray crystallography or structure-based drug screening. Thus, model refinement is an important final step in protein structure prediction to bridge predictions to real-life applications. Model refinement is one of the categories in recent rounds of critical assessment of techniques in protein structure prediction (CASP) and has recently been drawing more attention due to its realized importance. Here we report our group's performance in the refinement category in CASP12. Our method is based on inexpensive short molecular dynamics (MD) simulations in implicit solvent. Our performance in CASP12 was among the top, which was consistent with the previous round, CASP11. Our method with short MD runs achieved comparable performance with other methods that used longer simulations. Detailed analyses found that improvements typically occurred in entire regions of a structure rather than only in flexible loop regions. The remaining challenge in the structure refinement includes large conformational refinement which involves substantial motions of secondary structure elements or domains.

Evaluation of Selected CYP51A1 Polymorphisms in View of Interactions with Substrate and Redox Partner

Cholesterol is essential for development, growth, and maintenance of organisms. Mutations in cholesterol biosynthetic genes are embryonic lethal and few polymorphisms have been so far associated with pathologies in humans. Previous analyses show that lanosterol 14α-demethylase (CYP51A1) from the late part of cholesterol biosynthesis has only a few missense mutations with low minor allele frequencies and low association with pathologies in humans. The aim of this study is to evaluate the role of amino acid changes in the natural missense mutations of the hCYP51A1 protein. We searched SNP databases for existing polymorphisms of CYP51A1 and evaluated their effect on protein function. We found rare variants causing detrimental missense mutations of CYP51A1. Some missense variants were also associated with a phenotype in humans. Two missense variants have been prepared for testing enzymatic activity in vitro but failed to produce a P450 spectrum. We performed molecular modeling of three selected missense variants to evaluate the effect of the amino acid substitution on potential interaction with its substrate and the obligatory redox partner POR. We show that two of the variants, R277L and especially D152G, have possibly lower binding potential toward obligatory redox partner POR. D152G and R431H have also potentially lower affinity toward the substrate lanosterol. We evaluated the potential effect of damaging variants also using data from other in vitro CYP51A1 mutants. In conclusion, we propose to include damaging CYP51A1 variants into personalized diagnostics to improve genetic counseling for certain rare disease phenotypes.

Finding multiple reaction pathways via global optimization of action

Global searching for reaction pathways is a long-standing challenge in computational chemistry and biology. Most existing approaches perform only local searches due to computational complexity. Here we present a computational approach, Action-CSA, to find multiple diverse reaction pathways connecting fixed initial and final states through global optimization of the Onsager-Machlup action using the conformational space annealing (CSA) method. Action-CSA successfully overcomes large energy barriers via crossovers and mutations of pathways and finds all possible pathways of small systems without initial guesses on pathways. The rank order and the transition time distribution of multiple pathways are in good agreement with those of long Langevin dynamics simulations. The lowest action folding pathway of FSD-1 is consistent with recent experiments. The results show that Action-CSA is an efficient and robust computational approach to study the multiple pathways of complex reactions and large-scale conformational changes.

Modeling disordered protein interactions from biophysical principles

Disordered protein-protein interactions (PPIs), those involving a folded protein and an intrinsically disordered protein (IDP), are prevalent in the cell, including important signaling and regulatory pathways. IDPs do not adopt a single dominant structure in isolation but often become ordered upon binding. To aid understanding of the molecular mechanisms of disordered PPIs, it is crucial to obtain the tertiary structure of the PPIs. However, experimental methods have difficulty in solving disordered PPIs and existing protein-protein and protein-peptide docking methods are not able to model them. Here we present a novel computational method, IDP-LZerD, which models the conformation of a disordered PPI by considering the biophysical binding mechanism of an IDP to a structured protein, whereby a local segment of the IDP initiates the interaction and subsequently the remaining IDP regions explore and coalesce around the initial binding site. On a dataset of 22 disordered PPIs with IDPs up to 69 amino acids, successful predictions were made for 21 bound and 18 unbound receptors. The successful modeling provides additional support for biophysical principles. Moreover, the new technique significantly expands the capability of protein structure modeling and provides crucial insights into the molecular mechanisms of disordered PPIs.

Princeton_TIGRESS 2.0: High refinement consistency and net gains through support vector machines and molecular dynamics in double-blind predictions during the CASP11 experiment

Protein structure refinement is the challenging problem of operating on any protein structure prediction to improve its accuracy with respect to the native structure in a blind fashion. Although many approaches have been developed and tested during the last four CASP experiments, a majority of the methods continue to degrade models rather than improve them. Princeton_TIGRESS (Khoury et al., Proteins 2014;82:794-814) was developed previously and utilizes separate sampling and selection stages involving Monte Carlo and molecular dynamics simulations and classification using an SVM predictor. The initial implementation was shown to consistently refine protein structures 76% of the time in our own internal benchmarking on CASP 7-10 targets. In this work, we improved the sampling and selection stages and tested the method in blind predictions during CASP11. We added a decomposition of physics-based and hybrid energy functions, as well as a coordinate-free representation of the protein structure through distance-binning Cα-Cα distances to capture fine-grained movements. We performed parameter estimation to optimize the adjustable SVM parameters to maximize precision while balancing sensitivity and specificity across all cross-validated data sets, finding enrichment in our ability to select models from the populations of similar decoys generated for targets in CASPs 7-10. The MD stage was enhanced such that larger structures could be further refined. Among refinement methods that are currently implemented as web-servers, Princeton_TIGRESS 2.0 demonstrated the most consistent and most substantial net refinement in blind predictions during CASP11. The enhanced refinement protocol Princeton_TIGRESS 2.0 is freely available as a web server at http://atlas.engr.tamu.edu/refinement/. Proteins 2017; 85:1078-1098. © 2017 Wiley Periodicals, Inc.

On-the-Fly QM/MM Docking with Attracting Cavities

We developed a hybrid quantum mechanical/molecular mechanical (QM/MM) on-the-fly docking algorithm to address the challenges of treating polarization and selected metal interactions in docking. The algorithm is based on our classical docking algorithm Attracting Cavities and relies on the semiempirical self-consistent charge density functional tight-binding (SCC-DFTB) method and the CHARMM force field. We benchmarked the performance of this approach on three very diverse data sets: (1) the Astex Diverse set of 85 common noncovalent drug/target complexes formed both by hydrophobic and electrostatic interactions; (2) a zinc metalloprotein data set of 281 complexes, where polarization is strong and ligand/protein interactions are dominated by electrostatic interactions; and (3) a heme protein data set of 72 complexes, where ligand/protein interactions are dominated by covalent ligand/iron binding. Redocking performance of the on-the-fly QM/MM docking algorithm was compared to the performance of classical Attracting Cavities, AutoDock, AutoDock Vina, and GOLD. The results demonstrate that the QM/MM code preserves the high accuracy of most classical scores on the Astex Diverse set, while it yields significant improvements on both sets of metalloproteins at moderate computational cost.

Implicit Solvent Model for Million-Atom Atomistic Simulations: Insights into the Organization of 30-nm Chromatin Fiber

Molecular dynamics (MD) simulations based on the implicit solvent generalized Born (GB) models can provide significant computational advantages over the traditional explicit solvent simulations. However, the standard GB becomes prohibitively expensive for all-atom simulations of large structures; the model scales poorly, ∼n2, with the number of solute atoms. Here we combine our recently developed optimal point charge approximation (OPCA) with the hierarchical charge partitioning (HCP) approximation to present an ∼n log n multiscale, yet fully atomistic, GB model (GB-HCPO). The HCP approximation exploits the natural organization of biomolecules (atoms, groups, chains, and complexes) to partition the structure into multiple hierarchical levels of components. OPCA approximates the charge distribution for each of these components by a small number of point charges so that the low order multipole moments of these components are optimally reproduced. The approximate charges are then used for computing electrostatic interactions with distant components, while the full set of atomic charges are used for nearby components. We show that GB-HCPO can deliver up to 2 orders of magnitude speedup compared to the standard GB, with minimal impact on its accuracy. For large structures, GB-HCPO can approach the same nominal speed, as in nanoseconds per day, as the highly optimized explicit-solvent simulation based on particle mesh Ewald (PME). The increase in the nominal simulation speed, relative to the standard GB, coupled with substantially faster sampling of conformational space, relative to the explicit solvent, makes GB-HCPO a suitable candidate for MD simulation of large atomistic systems in implicit solvent. As a practical demonstration, we use GB-HCPO simulation to refine a ∼1.16 million atom structure of 30 nm chromatin fiber (40 nucleosomes). The refined structure suggests important details about spatial organization of the linker DNA and the histone tails in the fiber: (1) the linker DNA fills the core region, allowing the H3 histone tails to interact with the linker DNA, which is consistent with experiment; (2) H3 and H4 tails are found mostly in the core of the structure, closer to the helical axis of the fiber, while H2A and H2B are mostly solvent exposed. Potential functional consequences of these findings are discussed. GB-HCPO is implemented in the open source MD software NAB in Amber 2016.

Computational Discovery of Putative Leads for Drug Repositioning through Drug-Target Interaction Prediction

De novo experimental drug discovery is an expensive and time-consuming task. It requires the identification of drug-target interactions (DTIs) towards targets of biological interest, either to inhibit or enhance a specific molecular function. Dedicated computational models for protein simulation and DTI prediction are crucial for speed and to reduce the costs associated with DTI identification. In this paper we present a computational pipeline that enables the discovery of putative leads for drug repositioning that can be applied to any microbial proteome, as long as the interactome of interest is at least partially known. Network metrics calculated for the interactome of the bacterial organism of interest were used to identify putative drug-targets. Then, a random forest classification model for DTI prediction was constructed using known DTI data from publicly available databases, resulting in an area under the ROC curve of 0.91 for classification of out-of-sampling data. A drug-target network was created by combining 3,081 unique ligands and the expected ten best drug targets. This network was used to predict new DTIs and to calculate the probability of the positive class, allowing the scoring of the predicted instances. Molecular docking experiments were performed on the best scoring DTI pairs and the results were compared with those of the same ligands with their original targets. The results obtained suggest that the proposed pipeline can be used in the identification of new leads for drug repositioning. The proposed classification model is available at http://bioinformatics.ua.pt/software/dtipred/.

The emergence of multi-resistant bacterial strains and the existing void in the discovery and development of new classes of antibiotics is a growing concern. Indeed, some bacterial strains are now resistant to last-line antibiotics and considered untreatable. Drug repositioning has been suggested as a strategy to minimize time and cost expenses until the drug reaches the market, compared to traditional drug design. Drug-target interactions (DTIs) are the basis of rational drug design and thus, we proposed a computational approach to predict DTIs solely based on the primary sequence of the protein and the simplified molecular-input line-entry system of the ligand. In addition, network metrics are used to identify vital putative drug-targets in bacteria. Molecular docking experiments were performed to compare the binding affinities between a given ligand and a putative drug-target, as well as with their original targets. According to the docking results, the predicted DTIs have better or similar binding activities than the ligand and their real target, indicating the validity of the proposed model.

Template-based modeling and ab initio refinement of protein oligomer structures using GALAXY in CAPRI round 30

Many proteins function as homo- or hetero-oligomers; therefore, attempts to understand and regulate protein functions require knowledge of protein oligomer structures. The number of available experimental protein structures is increasing, and oligomer structures can be predicted using the experimental structures of related proteins as templates. However, template-based models may have errors due to sequence differences between the target and template proteins, which can lead to functional differences. Such structural differences may be predicted by loop modeling of local regions or refinement of the overall structure. In CAPRI (Critical Assessment of PRotein Interactions) round 30, we used recently developed features of the GALAXY protein modeling package, including template-based structure prediction, loop modeling, model refinement, and protein-protein docking to predict protein complex structures from amino acid sequences. Out of the 25 CAPRI targets, medium and acceptable quality models were obtained for 14 and 1 target(s), respectively, for which proper oligomer or monomer templates could be detected. Symmetric interface loop modeling on oligomer model structures successfully improved model quality, while loop modeling on monomer model structures failed. Overall refinement of the predicted oligomer structures consistently improved the model quality, in particular in interface contacts. Proteins 2017; 85:399-407. © 2016 Wiley Periodicals, Inc.

The myosin X motor is optimized for movement on actin bundles

Myosin X has features not found in other myosins. Its structure must underlie its unique ability to generate filopodia, which are essential for neuritogenesis, wound healing, cancer metastasis and some pathogenic infections. By determining high-resolution structures of key components of this motor, and characterizing the in vitro behaviour of the native dimer, we identify the features that explain the myosin X dimer behaviour. Single-molecule studies demonstrate that a native myosin X dimer moves on actin bundles with higher velocities and takes larger steps than on single actin filaments. The largest steps on actin bundles are larger than previously reported for artificially dimerized myosin X constructs or any other myosin. Our model and kinetic data explain why these large steps and high velocities can only occur on bundled filaments. Thus, myosin X functions as an antiparallel dimer in cells with a unique geometry optimized for movement on actin bundles.

1-Benzyl-2-methyl-3-indolylmethylene barbituric acid derivatives: Anti-cancer agents that target nucleophosmin 1 (NPM1)

In the present study, we have designed and synthesized a series of 1-benzyl-2-methyl-3-indolylmethylene barbituric acid analogs (7a-7h) and 1-benzyl-2-methyl-3-indolylmethylene thiobarbituric acid analogs (7 i-7 l) as nucleophosmin 1 (NPM1) inhibitors and have evaluated them for their anti-cancer activity against a panel of 60 different human cancer cell lines. Among these analogs 7 i, 7 j, and 7 k demonstrated potent growth inhibitory effects in various cancer cell types with GI50 values <2 μM. Compound 7 k exhibited growth inhibitory effects on a sub-panel of six leukemia cell lines with GI50 values in the range 0.22-0.35 μM. Analog 7 i also exhibited GI50 values <0.35 μM against three of the leukemia cell lines in the sub-panel. Analogs 7 i, 7 j, 7 k and 7 l were also evaluated against the mutant NPM1 expressing OCI-AML3 cell line and compounds 7 k and 7 l were found to cause dose-dependent apoptosis (AP50 = 1.75 μM and 3.3 μM, respectively). Compound 7k also exhibited potent growth inhibition against a wide variety of solid tumor cell lines: that is, A498 renal cancer (GI50 = 0.19 μM), HOP-92 and NCI-H522 lung cancer (GI50 = 0.25 μM), COLO 205 and HCT-116 colon cancer (GI50 = 0.20 and 0.26 μM, respectively), CNS cancer SF-539 (GI50 = 0.22 μM), melanoma MDA-MB-435 (GI50 = 0.22 μM), and breast cancer HS 578T (GI50 = 0.22 μM) cell lines. Molecular docking studies suggest that compounds 7 k and 7 l exert their anti-leukemic activity by binding to a pocket in the central channel of the NPM1 pentameric structure. These results indicate that the small molecule inhibitors 7 i, 7 j, 7 k, and 7 l could be potentially developed into anti-NPM1 drugs for the treatment of a variety of hematologic malignancies and solid tumors.

GalaxyRefineComplex: Refinement of protein-protein complex model structures driven by interface repacking

Protein-protein docking methods have been widely used to gain an atomic-level understanding of protein interactions. However, docking methods that employ low-resolution energy functions are popular because of computational efficiency. Low-resolution docking tends to generate protein complex structures that are not fully optimized. GalaxyRefineComplex takes such low-resolution docking structures and refines them to improve model accuracy in terms of both interface contact and inter-protein orientation. This refinement method allows flexibility at the protein interface and in the overall docking structure to capture conformational changes that occur upon binding. Symmetric refinement is also provided for symmetric homo-complexes. This method was validated by refining models produced by available docking programs, including ZDOCK and M-ZDOCK, and was successfully applied to CAPRI targets in a blind fashion. An example of using the refinement method with an existing docking method for ligand binding mode prediction of a drug target is also presented. A web server that implements the method is freely available at http://galaxy.seoklab.org/refinecomplex.

Differential Effects of Hydrophobic Core Packing Residues for Thermodynamic and Mechanical Stability of a Hyperthermophilic Protein

Proteins from organisms that have adapted to environmental extremes provide attractive systems to explore and determine the origins of protein stability. Improved hydrophobic core packing and decreased loop-length flexibility can increase the thermodynamic stability of proteins from hyperthermophilic organisms. However, their impact on protein mechanical stability is not known. Here, we use protein engineering, biophysical characterization, single-molecule force spectroscopy (SMFS), and molecular dynamics (MD) simulations to measure the effect of altering hydrophobic core packing on the stability of the cold shock protein TmCSP from the hyperthermophilic bacterium Thermotoga maritima. We make two variants of TmCSP in which a mutation is made to reduce the size of aliphatic groups from buried hydrophobic side chains. In the first, a mutation is introduced in a long loop (TmCSP L40A); in the other, the mutation is introduced on the C-terminal β-strand (TmCSP V62A). We use MD simulations to confirm that the mutant TmCSP L40A shows the most significant increase in loop flexibility, and mutant TmCSP V62A shows greater disruption to the core packing. We measure the thermodynamic stability (ΔGD-N) of the mutated proteins and show that there is a more significant reduction for TmCSP L40A (ΔΔG = 63%) than TmCSP V62A (ΔΔG = 47%), as might be expected on the basis of the relative reduction in the size of the side chain. By contrast, SMFS measures the mechanical stability (ΔG*) and shows a greater reduction for TmCSP V62A (ΔΔG* = 8.4%) than TmCSP L40A (ΔΔG* = 2.5%). While the impact on the mechanical stability is subtle, the results demonstrate the power of tuning noncovalent interactions to modulate both the thermodynamic and mechanical stability of a protein. Such understanding and control provide the opportunity to design proteins with optimized thermodynamic and mechanical properties.

Investigation of Structural Dynamics of Enzymes and Protonation States of Substrates Using Computational Tools

This review discusses the use of molecular modeling tools, together with existing experimental findings, to provide a complete atomic-level description of enzyme dynamics and function. We focus on functionally relevant conformational dynamics of enzymes and the protonation states of substrates. The conformational fluctuations of enzymes usually play a crucial role in substrate recognition and catalysis. Protein dynamics can be altered by a tiny change in a molecular system such as different protonation states of various intermediates or by a significant perturbation such as a ligand association. Here we review recent advances in applying atomistic molecular dynamics (MD) simulations to investigate allosteric and network regulation of tryptophan synthase (TRPS) and protonation states of its intermediates and catalysis. In addition, we review studies using quantum mechanics/molecular mechanics (QM/MM) methods to investigate the protonation states of catalytic residues of β-Ketoacyl ACP synthase I (KasA). We also discuss modeling of large-scale protein motions for HIV-1 protease with coarse-grained Brownian dynamics (BD) simulations.

A Simple and Accurate Method To Calculate Free Energy Profiles and Reaction Rates from Restrained Molecular Simulations of Diffusive Processes

A method is developed to obtain simultaneously free energy profiles and diffusion constants from restrained molecular simulations in diffusive systems. The method is based on low-order expansions of the free energy and diffusivity as functions of the reaction coordinate. These expansions lead to simple analytical relationships between simulation statistics and model parameters. The method is tested on 1D and 2D model systems; its accuracy is found to be comparable to or better than that of the existing alternatives, which are briefly discussed. An important aspect of the method is that the free energy is constructed by integrating its derivatives, which can be computed without need for overlapping sampling windows. The implementation of the method in any molecular simulation program that supports external umbrella potentials (e.g., CHARMM) requires modification of only a few lines of code. As a demonstration of its applicability to realistic biomolecular systems, the method is applied to model the α-helix ↔ β-sheet transition in a 16-residue peptide in implicit solvent, with the reaction coordinate provided by the string method. Possible modifications of the method are briefly discussed; they include generalization to multidimensional reaction coordinates [in the spirit of the model of Ermak and McCammon (Ermak, D. L.; McCammon, J. A. J. Chem. Phys. 1978, 69, 1352-1360)], a higher-order expansion of the free energy surface, applicability in nonequilibrium systems, and a simple test for Markovianity. In view of the small overhead of the method relative to standard umbrella sampling, we suggest its routine application in the cases where umbrella potential simulations are appropriate.

Galaxy7TM: flexible GPCR-ligand docking by structure refinement

G-protein-coupled receptors (GPCRs) play important physiological roles related to signal transduction and form a major group of drug targets. Prediction of GPCR-ligand complex structures has therefore important implications to drug discovery. With previously available servers, it was only possible to first predict GPCR structures by homology modeling and then perform ligand docking on the model structures. However, model structures generated without explicit consideration of specific ligands of interest can be inaccurate because GPCR structures can be affected by ligand binding. The Galaxy7TM server, freely accessible at http://galaxy.seoklab.org/7TM, improves an input GPCR structure by simultaneous ligand docking and flexible structure refinement using GALAXY methods. The server shows better performance in both ligand docking and GPCR structure refinement than commonly used programs AutoDock Vina and Rosetta MPrelax, respectively.

Attracting cavities for docking. Replacing the rough energy landscape of the protein by a smooth attracting landscape

Molecular docking is a computational approach for predicting the most probable position of ligands in the binding sites of macromolecules and constitutes the cornerstone of structure-based computer-aided drug design. Here, we present a new algorithm called Attracting Cavities that allows molecular docking to be performed by simple energy minimizations only. The approach consists in transiently replacing the rough potential energy hypersurface of the protein by a smooth attracting potential driving the ligands into protein cavities. The actual protein energy landscape is reintroduced in a second step to refine the ligand position. The scoring function of Attracting Cavities is based on the CHARMM force field and the FACTS solvation model. The approach was tested on the 85 experimental ligand-protein structures included in the Astex diverse set and achieved a success rate of 80% in reproducing the experimental binding mode starting from a completely randomized ligand conformer. The algorithm thus compares favorably with current state-of-the-art docking programs.

In silico characterization of the interaction between LSKL peptide, a LAP-TGF-beta derived peptide, and ADAMTS1

Metalloproteases involved in extracellular matrix remodeling play a pivotal role in cell response by regulating the bioavailability of cytokines and growth factors. Recently, the disintegrin and metalloprotease, ADAMTS1 has been demonstrated to be able to activate the transforming growth factor TGF-β, a major factor in fibrosis and cancer. The KTFR sequence from ADAMTS1 is responsible for the interaction with the LSKL peptide from the latent form of TGF-β, leading to its activation. While the atomic details of the interaction site can be the basis of the rational design of efficient inhibitory molecules, the binding mode of interaction is totally unknown. In this study, we show that recombinant fragments of human ADAMTS1 containing KTFR sequence keep the ability to bind the latent form of TGF-β. The recombinant fragment with the best affinity is modeled to investigate the binding mode of LSKL peptide with ADAMTS1 at the atomic level. Using a combined approach with molecular docking and multiple independent molecular dynamics (MD) simulations, we provide the binding mode of LSKL peptide with ADAMTS1. The MD simulations starting with the two lowest energy model predicted by molecular docking shows stable interactions characterized by 3 salt bridges (K3-NH3(+) with E626-COO(-); L4-COO(-) with K619-NH3(+); L1-NH3(+) with E624-COO(-)) and 2 hydrogen bonds (S2-OH with E623-COO(-); L4-NH with E623-COO(-)). The knowledge of this interaction mechanism paves the way to the design of more potent and more specific inhibitors against the inappropriate activation of TGF-β by ADAMTS1 in liver diseases.

Parallelization and improvements of the generalized born model with a simple sWitching function for modern graphics processors

Two fundamental challenges of simulating biologically relevant systems are the rapid calculation of the energy of solvation and the trajectory length of a given simulation. The Generalized Born model with a Simple sWitching function (GBSW) addresses these issues by using an efficient approximation of Poisson-Boltzmann (PB) theory to calculate each solute atom's free energy of solvation, the gradient of this potential, and the subsequent forces of solvation without the need for explicit solvent molecules. This study presents a parallel refactoring of the original GBSW algorithm and its implementation on newly available, low cost graphics chips with thousands of processing cores. Depending on the system size and nonbonded force cutoffs, the new GBSW algorithm offers speed increases of between one and two orders of magnitude over previous implementations while maintaining similar levels of accuracy. We find that much of the algorithm scales linearly with an increase of system size, which makes this water model cost effective for solvating large systems. Additionally, we utilize our GPU-accelerated GBSW model to fold the model system chignolin, and in doing so we demonstrate that these speed enhancements now make accessible folding studies of peptides and potentially small proteins.

Speed of conformational change: comparing explicit and implicit solvent molecular dynamics simulations

Adequate sampling of conformation space remains challenging in atomistic simulations, especially if the solvent is treated explicitly. Implicit-solvent simulations can speed up conformational sampling significantly. We compare the speed of conformational sampling between two commonly used methods of each class: the explicit-solvent particle mesh Ewald (PME) with TIP3P water model and a popular generalized Born (GB) implicit-solvent model, as implemented in the AMBER package. We systematically investigate small (dihedral angle flips in a protein), large (nucleosome tail collapse and DNA unwrapping), and mixed (folding of a miniprotein) conformational changes, with nominal simulation times ranging from nanoseconds to microseconds depending on system size. The speedups in conformational sampling for GB relative to PME simulations, are highly system- and problem-dependent. Where the simulation temperatures for PME and GB are the same, the corresponding speedups are approximately onefold (small conformational changes), between ∼1- and ∼100-fold (large changes), and approximately sevenfold (mixed case). The effects of temperature on speedup and free-energy landscapes, which may differ substantially between the solvent models, are discussed in detail for the case of miniprotein folding. In addition to speeding up conformational sampling, due to algorithmic differences, the implicit solvent model can be computationally faster for small systems or slower for large systems, depending on the number of solute and solvent atoms. For the conformational changes considered here, the combined speedups are approximately twofold, ∼1- to 60-fold, and ∼50-fold, respectively, in the low solvent viscosity regime afforded by the implicit solvent. For all the systems studied, 1) conformational sampling speedup increases as Langevin collision frequency (effective viscosity) decreases; and 2) conformational sampling speedup is mainly due to reduction in solvent viscosity rather than possible differences in free-energy landscapes between the solvent models.

Evaluating the dynamics and electrostatic interactions of folded proteins in implicit solvents

Three implicit solvent models, namely GBMVII, FACTS, and SCPISM, were evaluated for their abilities to emulate an explicit solvent environment by comparing the simulated conformational ensembles, dynamics, and electrostatic interactions of the Src SH2 domain and the Lyn kinase domain. This assessment in terms of structural features in folded proteins expands upon the use of hydration energy as a metric for comparison. All-against-all rms coordinate deviation, average positional fluctuations, and ion-pair distance distribution were used to compare the implicit solvent models with the TIP3P explicit solvent model. Our study shows that the Src SH2 domains solvated with TIP3P, GBMVII, and FACTS sample similar global conformations. Additionally, the Src SH2 ion-pair distance distributions of solvent-exposed side chains corresponding to TIP3P, GBMVII, and FACTS do not differ substantially, indicating that GBMVII and FACTS are capable of modeling these electrostatic interactions. The ion-pair distance distributions of SCPISM are distinct from others, demonstrating that these electrostatic interactions are not adequately reproduced with the SCPISM model. On the other hand, for the Lyn kinase domain, a non-globular protein with bilobal structure and a large concavity on the surface, implicit solvent does not accurately model solvation to faithfully reproduce partially buried electrostatic interactions and lobe-lobe conformations. Our work reveals that local structure and dynamics of small, globular proteins are modeled well using FACTS and GBMVII. Nonetheless, global conformations and electrostatic interactions in concavities of multi-lobal proteins resulting from simulations with implicit solvent models do not match those obtained from explicit water simulations.

Effective Approximation of Molecular Volume Using Atom-Centered Dielectric Functions in Generalized Born Models

The generalized Born (GB) theory is a prime choice for implicit treatment of solvent that provides a favorable balance between efficiency and accuracy for reliable simulation of protein conformational equilibria. In GB, the dielectric boundary is a key physical property that needs to be properly described. While it is widely accepted that the molecular surface (MS) should provide the most physical description, most existing GB models are based on van der Waals (vdW)-like surfaces for computational simplicity and efficiency. A simple and effective approximation to molecular volume is explored here using atom-centered dielectric functions within the context of a generalized Born model with simple switching (GBSW). The new model, termed GBSW/MS2, is as efficient as the original vdW-like-surface-based GBSW model, but is able to reproduce the Born radii calculated from the "exact" Poisson-Boltzmann theory with a correlation of 0.95. More importantly, examination of the potentials of mean force of hydrogen-bonding and charge-charge interactions demonstrates that GBSW/MS2 correctly captures the first desolvation peaks, a key signature of true MS. Physical parameters including atomic input radii and peptide backbone torsion were subsequently optimized on the basis of solvation free energies of model compounds, potentials of mean force of their interactions, and conformational equilibria of a set of helical and β-hairpin model peptides. The resulting GBSW/MS2 protein force field reasonably recapitulates the structures and stabilities of these model peptides. Several remaining limitations and possible future developments are also discussed.

Molecular dynamics simulations: advances and applications

Molecular dynamics simulations have evolved into a mature technique that can be used effectively to understand macromolecular structure-to-function relationships. Present simulation times are close to biologically relevant ones. Information gathered about the dynamic properties of macromolecules is rich enough to shift the usual paradigm of structural bioinformatics from studying single structures to analyze conformational ensembles. Here, we describe the foundations of molecular dynamics and the improvements made in the direction of getting such ensemble. Specific application of the technique to three main issues (allosteric regulation, docking, and structure refinement) is discussed.

Sampling of conformational ensemble for virtual screening using molecular dynamics simulations and normal mode analysis

Aim: Molecular dynamics simulations and normal mode analysis are well-established approaches to generate receptor conformational ensembles (RCEs) for ligand docking and virtual screening. Here, we report new fast molecular dynamics-based and normal mode analysis-based protocols combined with conformational pocket classifications to efficiently generate RCEs. Materials & Methods: We assessed our protocols on two well-characterized protein targets showing local active site flexibility, dihydrofolate reductase and large collective movements, CDK2. The performance of the RCEs was validated by distinguishing known ligands of dihydrofolate reductase and CDK2 among a dataset of diverse chemical decoys. Results & discussion: Our results show that different simulation protocols can be efficient for generation of RCEs depending on different kind of protein flexibility.[Formula: see text]

Determination of Protein Folding Intermediate Structures Consistent with Data from Oxidative Footprinting Mass Spectrometry

The mapping of folding landscapes remains an important challenge in protein chemistry. Pulsed oxidative labeling of exposed residues and their detection via mass spectrometry provide new means of taking time-resolved "snapshots" of the structural changes that occur during protein folding. However, such experiments have been so far only interpreted qualitatively. Here, we report the detailed structural interpretation of mass spectrometry data from fast photochemical oxidation of proteins (FPOP) experiments at atomic resolution in a biased molecular dynamics approach. We are able to calculate structures of the early folding intermediate of the model system barstar that are fully consistent with FPOP data and Φ values. Furthermore, structures calculated with both FPOP data and Φ values are significantly less compact and have fewer helical residues than intermediate structures calculated with Φ values only. This improves the agreement with the experimental β-Tanford value and CD measurements. The restraints that we introduce facilitate the structural interpretation of FPOP data and provide new means for refined structure calculations of transiently sampled states on protein folding landscapes.

Probing the folded state and mechanical unfolding pathways of T4 lysozyme using all-atom and coarse-grained molecular simulation

The Bacteriophage T4 Lysozyme (T4L) is a prototype modular protein comprised of an N-terminal and a C-domain domain, which was extensively studied to understand the folding/unfolding mechanism of modular proteins. To offer detailed structural and dynamic insights to the folded-state stability and the mechanical unfolding behaviors of T4L, we have performed extensive equilibrium and steered molecular dynamics simulations of both the wild-type (WT) and a circular permutation (CP) variant of T4L using all-atom and coarse-grained force fields. Our all-atom and coarse-grained simulations of the folded state have consistently found greater stability of the C-domain than the N-domain in isolation, which is in agreement with past thermostatic studies of T4L. While the all-atom simulation cannot fully explain the mechanical unfolding behaviors of the WT and the CP variant observed in an optical tweezers study, the coarse-grained simulations based on the Go model or a modified elastic network model (mENM) are in qualitative agreement with the experimental finding of greater unfolding cooperativity in the WT than the CP variant. Interestingly, the two coarse-grained models predict different structural mechanisms for the observed change in cooperativity between the WT and the CP variant--while the Go model predicts minor modification of the unfolding pathways by circular permutation (i.e., preserving the general order that the N-domain unfolds before the C-domain), the mENM predicts a dramatic change in unfolding pathways (e.g., different order of N/C-domain unfolding in the WT and the CP variant). Based on our simulations, we have analyzed the limitations of and the key differences between these models and offered testable predictions for future experiments to resolve the structural mechanism for cooperative folding/unfolding of T4L.