Rosetta Algorithm Research Articles

Accounting for Neighboring Residue Hydrophobicity in Diethylpyrocarbonate Labeling Mass Spectrometry Improves Rosetta Protein Structure Prediction.

Covalent labeling mass spectrometry allows for protein structure elucidation via covalent modification and identification of exposed residues. Diethylpyrocarbonate (DEPC) is a commonly used covalent labeling reagent that provides insight into structure through the labeling of lysine, histidine, serine, threonine, and tyrosine residues. We recently implemented a Rosetta algorithm that used binary DEPC labeling data to improve protein structure prediction efforts. In this work, we improved on our modeling efforts by accounting for the level of hydrophobicity of neighboring residues in the microenvironment of serine, threonine, and tyrosine residues to obtain a more accurate estimate of the hydrophobic neighbor count. This was incorporated into Rosetta functionality, along with considerations for solvent-exposed histidine and lysine residues. Overall, our new Rosetta score term successfully identified best scoring models with less than 2 Å root-mean-squared deviations (RMSDs) for five of the seven benchmark proteins tested. We additionally developed a confidence metric to measure prediction success for situations in which a native structure is unavailable.

Utilization of Hydrophobic Microenvironment Sensitivity in Diethylpyrocarbonate Labeling for Protein Structure Prediction.

Diethylpyrocarbonate (DEPC) labeling analyzed with mass spectrometry can provide important insights into higher order protein structures. It has been previously shown that neighboring hydrophobic residues promote a local increase in DEPC concentration such that serine, threonine, and tyrosine residues are more likely to be labeled despite low solvent exposure. In this work, we developed a Rosetta algorithm that used the knowledge of labeled and unlabeled serine, threonine, and tyrosine residues and assessed their local hydrophobic environment to improve protein structure prediction. Additionally, DEPC-labeled histidine and lysine residues with higher relative solvent accessible surface area values (i.e., more exposed) were scored favorably. Application of our score term led to reductions of the root-mean-square deviations (RMSDs) of the lowest scoring models. Additionally, models that scored well tended to have lower RMSDs. A detailed tutorial describing our protocol and required command lines is included. Our work demonstrated the considerable potential of DEPC covalent labeling data to be used for accurate higher order structure determination.

PEA-15 C-Terminal Tail Allosterically Modulates Death-Effector Domain Conformation and Facilitates Protein-Protein Interactions.

Phosphoprotein enriched in astrocytes, 15 kDa (PEA-15) exerts its regulatory roles on several critical cellular pathways through protein–protein interactions depending on its phosphorylation states. It can either inhibit the extracellular signal-regulated kinase (ERK) activities when it is dephosphorylated or block the assembly of death-inducing signaling complex (DISC) and the subsequent activation of apoptotic initiator, caspase-8, when it is phosphorylated. Due to the important roles of PEA-15 in regulating these pathways that lead to opposite cellular outcomes (cell proliferation vs. cell death), we proposed a phosphostasis (phosphorylation homeostasis) model, in which the phosphorylation states of the protein are vigorously controlled and regulated to maintain a delicate balance. The phosphostasis gives rise to the protective cellular functions of PEA-15 to preserve optimum cellular conditions. In this article, using advanced multidimensional nuclear magnetic resonance (NMR) techniques combined with a novel chemical shift (CS)-Rosetta algorithm for de novo protein structural determination, we report a novel conformation of PEA-15 death-effector domain (DED) upon interacting with ERK2. This new conformation is modulated by the irregularly structured C-terminal tail when it first recognizes and binds to ERK2 at the d-peptide recruitment site (DRS) in an allosteric manner, and is facilitated by the rearrangement of the surface electrostatic and hydrogen-bonding interactions on the DED. In this ERK2-bound conformation, three of the six helices (α2, α3, and α4) comprising the DED reorient substantially in comparison to the free-form structure, exposing key residues on the other three helices that directly interact with ERK2 at the DEF-docking site (docking site for ERK, FxF) and the activation loop. Additionally, we provide evidence that the phosphorylation of the C-terminal tail leads to a distinct conformation of DED, allowing efficient interactions with Fas-associated death domain (FADD) protein at the DISC. Our results substantiate the allosteric regulatory roles of the C-terminal tail in modulating DED conformation and facilitating protein–protein interactions of PEA-15.

In silico analysis and molecular dynamics simulation of human superoxide dismutase 3 (SOD3) genetic variants.

Oxidative stress is a major factor in aging processes. Superoxide dismutase 3 (SOD3) plays a key role in the protection of extracellular oxidative stress. Missense mutations in SOD3 have been described to be associated with the occurrence of pulmonary, cardiovascular, and neoplastic diseases. This study aims to analyze the effects of missense mutations on the SOD3 structure and function by modeling a complete SOD3 structure as well as analyzing the differences between the wild-types and mutants using computational simulations. Here, ten algorithms were used to predict the structural and functional effects of missense mutations. A complete model of SOD3 protein was made by ab initio and comparative modeling using the Rosetta algorithm and validated by PROCHECK, Verify 3D, QMEAN, and ProSa. Molecular dynamics (MD) simulations were performed and analyzed using the GROMACS package. The deleterious potential of the A58T and R231G mutants was not predicted by the majority of the used algorithms. The analyzed mutations were predicted as destabilizing by at least one algorithm. The MD analyses indicated that protein flexibility may be increased by all of the analyzed mutations, while the protein-ligand stability may be decreased. They also suggested that the variants A91T and R231G increase the overall dimensions of SOD3 and decrease its accessible surface area. Our findings, therefore, indicated that the analyzed mutations could affect the protein structure and its ability to interact with other molecules, which may be related to the functional impairment of SOD3 upon A58T and R231G mutations, as well as their involvement in pathologies.

Big data technique predicts 12% of unknown protein structures

In a landmark achievement for computer-based prediction of protein structure, researchers have reported the three-dimensional conformations of 12% of the protein families that still have unknown structure. The cohort of 600 newly determined protein family structures includes 100 protein folds not found in the Protein Data Bank and 200 membrane proteins (Science 2017, DOI: 10.1126/science.aah4043). A team led by David Baker of the University of Washington modeled the structures of these proteins using its Rosetta algorithm on a distributed network of volunteer computers. Baker’s team initially deposited some of the predicted structures it found into public databases. In the months afterward, structural biologists serendipitously solved—and confirmed—the 3-D conformation of six of these unknown protein families. “The similarity is impressive,” comments Johannes Soding of the Max Planck Institute for Biophysical Chemistry, who was not involved in the work. “And these aren’t simple, small proteins—most have s...

Structural Analysis of Viral Infectivity Factor of HIV Type 1 and Its Interaction with A3G, EloC and EloB

BackgroundThe virion infectivity factor (Vif) is an accessory protein, which is essential for HIV replication in host cells. Vif neutralizes the antiviral host protein APOBEC3 through recruitment of the E3 ubiquitin ligase complex.MethodologyFifty thousand Vif models were generated using the ab initio relax protocol of the Rosetta algorithm from sets of three- and nine-residue fragments using the fragment Monte Carlo insertion-simulated annealing strategy, which favors protein-like features, followed by an all-atom refinement. In the protocol, a constraints archive was used to define the spatial relationship between the side chains from Cys/His residues and zinc ions that formed the zinc-finger motif that is essential for Vif function. We also performed centroids analysis and structural analysis with respect to the formation of the zinc-finger, and the residue disposal in the protein binding domains. Additionally, molecular docking was used to explore details of Vif-A3G and Vif-EloBC interactions. Furthermore, molecular dynamics simulation was used to evaluate the stability of the complexes Vif-EloBC-A3G and Vif-EloC.Principal FindingsThe zinc in the HCCH domain significantly alters the folding of Vif and changes the structural dynamics of the HCCH region. Ab initio modeling indicated that the Vif zinc-finger possibly displays tetrahedral geometry as suggested by Mehle et al. (2006). Our model also showed that the residues L146 and L149 of the BC-box motif bind to EloC by hydrophobic interactions, and the residue P162 of the PPLP motif is important to EloB binding.Conclusions/SignificanceThe model presented here is the first complete three-dimensional structure of the Vif. The interaction of Vif with the A3G protein and the EloBC complex is in agreement with empirical data that is currently available in the literature and could therefore provide valuable structural information for advances in rational drug design.

A Mutational and Computational Study of Water and Ion Movement through the S6 Bundle-Crossing of CaV1.2 Channel

How exactly voltage-gated calcium and sodium channels open their physical gate formed by the S6 TM segments is not clear yet. To study this, we modeled the pore-forming region of CaV1.2 channel using a combination of homology-based modeling and abinitio Rosetta algorithm. The obtained fold was stable during 100-200 ns long molecular dynamics runs with implicit membrane and solvent. In explicit membrane/water systems, it remained stable during multiple 40-50 ns runs. When tested with the ZDOCK protein-docking algorithm, the fold binds toxin Calcicludine at a site that incorporates residues, which previously were shown to be involved in binding of the toxin. Molecular dynamics with SWM4-NDP water and polarizable ions reveals the presence of novel water-filled cavities on the intracellular side of the channel. We mutated the principal residues that form them and show their critical role in channel gating.According to molecular dynamics calculations, water polarizability appears to be important for the filling of channel. We observe that water molecules form stable structures (an enthalpy stabilized ice-like phase) inside the channel. We mutated the principal residues that are predicted to alter water structures in the pore and confirm their role in channel gating. Overall, our findings further elaborate previously proposed involvement of water in channel gating and uncover a novel molecular view on the final steps of channel opening. Supported by NIH R01 MH079406.

Tridimensional model structure and patterns of molecular evolution of Pepino mosaic virus TGBp3 protein

BackgroundPepino mosaic virus (PepMV) is considered one of the most dangerous pathogens infecting tomatoes worldwide. The virus is highly diverse and four distinct genotypes, as well as inter-strain recombinants, have already been described. The isolates display a wide range on symptoms on infected plant species, ranging from mild mosaic to severe necrosis. However, little is known about the mechanisms and pattern of PepMV molecular evolution and about the role of individual proteins in host-pathogen interactions.MethodsThe nucleotide sequences of the triple gene block 3 (TGB3) from PepMV isolates varying in symptomatology and geographic origin have been analyzed. The modes and patterns of molecular evolution of the TGBp3 protein were investigated by evaluating the selective constraints to which particular amino acid residues have been subjected during the course of diversification. The tridimensional structure of TGBp3 protein has been modeled de novo using the Rosetta algorithm. The correlation between symptoms development and location of specific amino acids residues was analyzed.ResultsThe results have shown that TGBp3 has been evolving mainly under the action of purifying selection operating on several amino acid sites, thus highlighting its functional role during PepMV infection. Interestingly, amino acid 67, which has been previously shown to be a necrosis determinant, was found to be under positive selection.ConclusionsIdentification of diverse selection events in TGB3p3 will help unraveling its biological functions and is essential to an understanding of the evolutionary constraints exerted on the Potexvirus genome. The estimated tridimensional structure of TGBp3 will serve as a platform for further sequence, structural and function analysis and will stimulate new experimental advances.

Using the Rosetta algorithm and selected inter‐residue distances to predict protein structure

AbstractThe Rosetta algorithm has had much success in protein structure prediction as demonstrated in the recent Critical Assessment of Protein Structure Prediction (CASP) experiments. For many proteins, Rosetta generates several low root mean square deviation (RMSD) decoy structures but finding the best structure among the decoys can be difficult. Experimental data can be used to aid in the discrimination process. Our protein structure prediction method involves three steps: using the Rosetta algorithm to generate decoys, measuring inter‐residue distances, and comparing the measured distances with those calculated in each decoy. Decoys with similar three‐dimensional structure will also have several similar inter‐residue distances. To develop our search protocol, we determined the optimal number of decoys to generate as well as the minimum number of distance constraints needed to distinguish between the low and high RMSD structures. To test our method, we simulate experimental data by measuring α‐carbon distances from the experimentally determined structures of our target proteins. We have employed the Rosetta algorithm to generate decoy sets of different sizes for four target proteins. Our predicted structures ranged in Cα RMSD from 2.4 to 4.6 Å compared with the experimental structures. Using only twenty‐five distance constraints, reliable predictions were made. © 2008 Wiley Periodicals, Inc. Int J Quantum Chem, 2008

The sequence and model structure analysis of three Polish peanut stunt virus strains

Peanut stunt virus (PSV) belongs to the Cucumovirus genus of the family Bromoviridae and is widely distributed worldwide, also in Poland. PSV is a common pathogen of a wide range of economically important plants. Its coat protein (CP), similarly as in other viruses, plays an important role in many processes during viral life cycle and has great impact on the infectivity. In this study, we present the results of sequence-structure analysis of CP derived from three Polish strains of PSV: PSV Ag, G, and P. Sequences were determined using RT-PCR amplification followed by sequencing and compared with each other and also with CP from other known PSV viruses. We analyzed their phylogenetic relationship, based on CP sequence, using bioinformatic tools as well as their spatial model using homology-modeling approach with combination of ROSETTA algorithm for de novo modeling. We compared our model with those recently obtained for other cucumoviruses including PSV-Er. Our results have shown that all Polish strains probably belong to the first subgroup of PSV viruses. Homology level between strains Ag and G proved very high. Using theoretical modeling approach we obtained a model very similar to the one resolved previously with the differences caused by slightly different amino acid sequence. We have also undertaken an attempt to analyze its distant regions; however, results are not unequivocal. Analysis of symptoms and their correlation with specific amino acid position was also performed on the basis of results published elsewhere. The definite interpretation is impeded by the presence of satellite RNAs in Ag and P strains modulating symptoms' severity, though.

Structure and Function of the Voltage Sensor of Sodium Channels Probed by a β-Scorpion Toxin

Voltage sensing by voltage-gated sodium channels determines the electrical excitability of cells, but the molecular mechanism is unknown. beta-Scorpion toxins bind specifically to neurotoxin receptor site 4 and induce a negative shift in the voltage dependence of activation through a voltage sensor-trapping mechanism. Kinetic analysis showed that beta-scorpion toxin binds to the resting state, and subsequently the bound toxin traps the voltage sensor in the activated state in a voltage-dependent but concentration-independent manner. The rate of voltage sensor trapping can be fit by a two-step model, in which the first step is voltage-dependent and correlates with the outward gating movement of the IIS4 segment, whereas the second step is voltage-independent and results in shifted voltage dependence of activation of the channel. Mutations of Glu(779) in extracellular loop IIS1-S2 and both Glu(837) and Leu(840) in extracellular loop IIS3-S4 reduce the binding affinity of beta-scorpion toxin. Mutations of positively charged and hydrophobic amino acid residues in the IIS4 segment do not affect beta-scorpion toxin binding but alter voltage dependence of activation and enhance beta-scorpion toxin action. Structural modeling with the Rosetta algorithm yielded a three-dimensional model of the toxin-receptor complex with the IIS4 voltage sensor at the extracellular surface. Our results provide mechanistic and structural insight into the voltage sensor-trapping mode of scorpion toxin action, define the position of the voltage sensor in the resting state of the sodium channel, and favor voltage-sensing models in which the S4 segment spans the membrane in both resting and activated states.

Protein Structure Prediction Using Rosetta

Publisher Summary This chapter elaborates protein structure prediction using Rosetta. Double-blind assessments of protein structure prediction methods have indicated that the Rosetta algorithm is perhaps the most successful current method for de novo protein structure prediction. In the Rosetta method, short fragments of known proteins are assembled by a Monte Carlo strategy to yield native-like protein conformations. Using only sequence information, successful Rosetta predictions yield models with typical accuracies of 3–6 A˚ Cα root mean square deviation (RMSD) from the experimentally determined structures for contiguous segments of 60 or more residues. For each structure prediction, many short simulations starting from different random seeds are carried out to generate an ensemble of decoy structures that have both favorable local interactions and protein-like global properties. This set is then clustered by structural similarity to identify the broadest free energy minima. The effectiveness of conformation modification operators for energy function optimization is also described in this chapter.

Distributions of beta sheets in proteins with application to structure prediction.

We recently developed the Rosetta algorithm for ab initio protein structure prediction, which generates protein structures from fragment libraries using simulated annealing. The scoring function in this algorithm favors the assembly of strands into sheets. However, it does not discriminate between different sheet motifs. After generating many structures using Rosetta, we found that the folding algorithm predominantly generates very local structures. We surveyed the distribution of beta-sheet motifs with two edge strands (open sheets) in a large set of non-homologous proteins. We investigated how much of that distribution can be accounted for by rules previously published in the literature, and developed a filter and a scoring method that enables us to improve protein structure prediction for beta-sheet proteins. Proteins 2002;48:85-97.