Computer simulations aimed at structure prediction of supersecondary motifs in proteins

It is well established that protein structures are more conserved than protein sequences. One-third of all known protein structures can be classified into ten protein folds, which themselves are composed mainly of alpha-helical hairpin, beta hairpin, and betaalphabeta supersecondary structural elements. In this study, we explore the ability of a recent Monte Carlo-based procedure to generate the 3D structures of eight polypeptides that correspond to units of supersecondary structure and three-stranded antiparallel beta sheet. Starting from extended or misfolded compact conformations, all Monte Carlo simulations show significant success in predicting the native topology using a simplified chain representation and an energy model optimized on other structures. Preliminary results on model peptides from nucleotide binding proteins suggest that this simple protein folding model can help clarify the relation between sequence and topology.     

Analogous standard motifs in myelin basic protein and in MARCKS

Myelin basic protein (MBP) and myristoylated alanine-rich C-kinase substrate (MARCKS) are similar in terms of having extended conformations regulated by their environment (i.e., solubilised or lipid-associated), N-terminal modifications, a dual nature of interactions with lipids, binding to actin and Ca²⁺-calmodulin, and being substrates for different kinds of protein kinases. The further sequence similarities of segments of MBP with lipid effector regions of MARCKS, and numerous reports in the literature, support the thesis that some developmental isoform of MBP functions in signal transduction.     

Stereochemistry of Schellman motifs in peptides: Crystal structure of a hexapeptide with a C‐terminus 6 → 1 hydrogen bond

The Schellman motif is a widely observed helix terminating structural motif in proteins, which is generated when the C‐terminus residue adopts a left‐handed helical (α_L) conformation. The resulting hydrogen‐bonding pattern involves the formation of an intramolecular 6 → 1 interaction. This helix terminating motif is readily mimicked in synthetic helical peptides by placing an achiral residue at the penultimate position of the sequence. Thus far, the Schellman motif has been characterized crystallographically only in peptide helices of length 7 residues or greater. The structure of the hexapeptide Boc–Pro–Aib–Gly–Leu–Aib–Leu–OMe in crystals reveal a short helical stretch terminated by a Schellman motif, with the formation of 6 → 1 C‐terminus hydrogen bond. The crystals are in the space group P2₁2₁2₁ with a = 18.155(3) Å, b = 18.864(8) Å, c = 11.834(4) Å, and Z = 4 . The final R₁ and wR₂ values are 7.68 and 14.6%, respectively , for 1524 observed reflections [F_o ≥ 3ς(F_o)]. A 6 → 1 hydrogen bond between Pro(1)CO · · · Leu(6)NH and a 5 → 2 hydrogen bond between Aib(2)CO · · · Aib(5)NH are observed. An analysis of the available oligopeptides having an achiral Aib residue at the penultimate position suggests that chain length and sequence effects may be the other determining factors in formation of Schellman motifs. © 1999 John Wiley & Sons, Inc. Biopoly 50: 13–22, 1999     

Data mining the protein data bank: automatic detection and assignment of carbohydrate structures

Knowledge of the 3D structure of glycans is a prerequisite for a complete understanding of the biological processes glycoproteins are involved in. However, due to a lack of standardised nomenclature, carbohydrate compounds are difficult to locate within the Protein Data Bank (PDB). Using an algorithm that detects carbohydrate structures only requiring element types and atom coordinates, we were able to detect 1663 entries containing a total of 5647 carbohydrate chains. The majority of chains are found to be N-glycosidically bound. Noncovalently bound ligands are also frequent, while O-glycans form a minority. About 30% of all carbohydrate containing PDB entries comprise one or several errors. The automatic assignment of carbohydrate structures in PDB entries will improve the cross-linking of glycobiology resources with genomic and proteomic data collections, which will be an important issue of the upcoming glycomics projects. By aiding in detection of erroneous annotations and structures, the algorithm might also help to increase database quality.     

Modeling of loops in protein structures

Comparative protein structure prediction is limited mostly by the errors in alignment and loop modeling. We describe here a new automated modeling technique that significantly improves the accuracy of loop predictions in protein structures. The positions of all nonhydrogen atoms of the loop are optimized in a fixed environment with respect to a pseudo energy function. The energy is a sum of many spatial restraints that include the bond length, bond angle, and improper dihedral angle terms from the CHARMM-22 force field, statistical preferences for the main-chain and side-chain dihedral angles, and statistical preferences for nonbonded atomic contacts that depend on the two atom types, their distance through space, and separation in sequence. The energy function is optimized with the method of conjugate gradients combined with molecular dynamics and simulated annealing. Typically, the predicted loop conformation corresponds to the lowest energy conformation among 500 independent optimizations. Predictions were made for 40 loops of known structure at each length from 1 to 14 residues. The accuracy of loop predictions is evaluated as a function of thoroughness of conformational sampling, loop length, and structural properties of native loops. When accuracy is measured by local superposition of the model on the native loop, 100, 90, and 30% of 4-, 8-, and 12-residue loop predictions, respectively, had <2 A RMSD error for the mainchain N, C(alpha), C, and O atoms; the average accuracies were 0.59 +/- 0.05, 1.16 +/- 0.10, and 2.61 +/- 0.16 A, respectively. To simulate real comparative modeling problems, the method was also evaluated by predicting loops of known structure in only approximately correct environments with errors typical of comparative modeling without misalignment. When the RMSD distortion of the main-chain stem atoms is 2.5 A, the average loop prediction error increased by 180, 25, and 3% for 4-, 8-, and 12-residue loops, respectively. The accuracy of the lowest energy prediction for a given loop can be estimated from the structural variability among a number of low energy predictions. The relative value of the present method is gauged by (1) comparing it with one of the most successful previously described methods, and (2) describing its accuracy in recent blind predictions of protein structure. Finally, it is shown that the average accuracy of prediction is limited primarily by the accuracy of the energy function rather than by the extent of conformational sampling.     

Interactions of myelin basic protein with palmitoyllysophosphatidylcholine: characterization of the complexes and conformations of the protein

The stoichiometry of palmitoyllysophosphatidylcholine/myelin basic protein (PLPC/MBP) complexes, the location of the protein in the lysolipid micelles, and the conformational changes occurring in the basic protein and peptides derived from it upon interaction with lysolecithin micelles were investigated by circular dichroic spectropolarimetry, ultracentrifugation, electron paramagnetic resonance (EPR) and ³¹P, ¹³C, and ¹H nuclear magnetic resonance spectroscopy (NMR), and electron microscopy. Ultracentrifugation measurements indicated that well-defined complexes were formed by the association of one protein molecule with approximately 141 lysolipid molecules. Small-angle X-ray scattering data indicated that the PLPC/MBP complexes form particles with a radius of gyration of 3.8 nm. EPR spectral parameters of the spin labels 5–, and 16-doxylstearate incorporated into lysolecithin/basic protein aggregates, and ¹³C- and ¹H-NMR relaxation times of PLPC indicated that the addition of the protein did not affect the environment and location of the labels and the organization of the lysolipid micelles. The data suggested that MBP lies primarily near the surface of the micelles, with segments penetrating beyond the interfacial region into the hydrophobic interior, but without any part of the protein being protected against rapid exchange of its amide groups with the aqueous environment. The basic protein acquired about 20% -helix when bound to lysolipid micelles. Circular dichroic spectra of sequential peptides derived by cleavage of the protein revealed the formation of -helical regions in the association with lysolecithin. Specific residues in myelin basic protein that participated in binding to the micelles were identified from magnetic resonance data on changes in the chemical shifts and intensities of assigned resonances, and line broadening of peaks by fatty acid spin-labels incorporated into the micelles. Correspondence to: G. L. Mendz     

Mechanisms of myelin basic protein and proteolipid protein targeting in oligodendrocytes (Review)

Summary

The segregation of proteins to specific cellular membranes is recognized as a common phenomenon. In oligodendrocytes of the central nervous system, localization of certain proteins to select regions of the plasma membrane gives rise to the myelin membrane. Whilst the fundamental structure and composition of myelin is well understood, less is known of the mechanisms by which the constituent proteins are specifically recruited to those regions of plasma membrane that are forming myelin. The two principal proteins of myelin, the myelin basic protein and proteolipid protein, differ greatly in character and sites of synthesis. The message for myelin basic protein is selectively translocated to the ends of the cell processes, where it is translated on free ribosomes and is incorporated directly into the membrane. Proteolipid protein synthesized at the rough endoplasmic reticulum, processed through the Golgi apparatus, and presumably transported via vesicles to the myelin membrane. This review examines the mechanisms by which these two proteins are targeted to the myelin membrane.     

How human IgGs against myelin basic protein (MBP) recognize oligopeptides and MBP

Myelin basic protein (MBP) is a major protein of myelin‐proteolipid shell of axons, and it plays an important role in pathogenesis of multiple sclerosis. In the literature, there are no data on how antibodies recognize different protein antigens including MBP. A stepwise increase in ligand complexity was used to estimate the relative contributions of virtually every amino acid residue (AA) of a specific 12‐mer LSRFSWGAEGQK oligopeptide corresponding to immunodominant sequence of MBP to the light chains and to intact anti‐MBP IgGs from sera of patients with multiple sclerosis. It was shown that the minimal ligands of the light chains of IgGs are many different free AAs (K _d = 0.51–0.016 M), and each free AA interacts with the specific subsite of the light chain intended for recognition of this AA in specific LSRFSW oligopeptide. A gradual transition from Leu to LSRFSWGAEGQK leads to an increase in the affinity from 10⁻¹ to 2.3 × 10⁻⁴ M because of additive interactions of the light chain with 6 AAs of this oligopeptide and then the affinity reaches plateau. The contributions of 6 various AAs to the affinity of the oligopeptide are different (K _d, M): 0.71 (S), 0.44 (R), 0.14 (F), 0.17 (S), and 0.62 (W). Affinity of nonspecific oligopeptides to the light chains of IgGs is significantly lower. Intact MBP interacts with both light and heavy chains of IgGs demonstrating 192‐fold higher affinity than the specific oligopeptide. It is a first quantitative analysis of the mechanism of proteins recognition by antibodies. The thermodynamic model was constructed to describe the interactions of IgGs with MBP. The data obtained can be very useful for understanding how antibodies against many different proteins can recognize these proteins.     

Structural motifs in protein cores and at protein–protein interfaces are different

Structures of proteins and protein–protein complexes are determined by the same physical principles and thus share a number of similarities. At the same time, there could be differences because in order to function, proteins interact with other molecules, undergo conformations changes, and so forth, which might impose different restraints on the tertiary versus quaternary structures. This study focuses on structural properties of protein–protein interfaces in comparison with the protein core, based on the wealth of currently available structural data and new structure‐based approaches. The results showed that physicochemical characteristics, such as amino acid composition, residue–residue contact preferences, and hydrophilicity/hydrophobicity distributions, are similar in protein core and protein–protein interfaces. On the other hand, characteristics that reflect the evolutionary pressure, such as structural composition and packing, are largely different. The results provide important insight into fundamental properties of protein structure and function. At the same time, the results contribute to better understanding of the ways to dock proteins. Recent progress in predicting structures of individual proteins follows the advancement of deep learning techniques and new approaches to residue coevolution data. Protein core could potentially provide large amounts of data for application of the deep learning to docking. However, our results showed that the core motifs are significantly different from those at protein–protein interfaces, and thus may not be directly useful for docking. At the same time, such difference may help to overcome a major obstacle in application of the coevolutionary data to docking—discrimination of the intramolecular information not directly relevant to docking.     

Comparative analysis of thermal unfolding simulations of RNA recognition motifs (RRMs) of TAR DNA-binding protein 43 (TDP-43)

TAR DNA-binding protein 43 (TDP-43) inclusions have been found in Amyotrophic lateral sclerosis (ALS) and several other neurodegenerative diseases. Many studies suggest the involvement of RNA recognition motifs (RRMs) in TDP-43 proteinopathy. To elucidate the structural stability and the unfolding dynamics of RRMs, we have carried out atomistic molecular dynamics simulations at two different temperatures (300 and 500 K). The simulations results indicate that there are distinct structural differences in the unfolding pathway between the two domains and RRM1 unfolds faster than RRM2 in accordance with the lower thermal stability found experimentally. The unfolding behaviors of secondary structures showed that the α-helix was more stable than β-sheet and structural rearrangements of β-sheets results in formation of additional α-helices. At higher temperature, RRM1 exhibit increased overall flexibility and unfolding than RRM2. The temperature-dependent free energy landscapes consist of multiple metastable states stabilized by non-native contacts and hydrogen bonds in RRM2, thus rendering the RRM2 more prone to misfolding. The structural rearrangements of RRM2 could lead to aberrant protein–protein interactions that may account for enhanced aggregation and toxicity of TDP-43. Our analysis, thus identify the structural and thermodynamic characteristics of the RRMs of TDP-43, which will serve to uncover molecular mechanisms and driving forces in TDP-43 misfolding and aggregation.     

Sampling of near-native protein conformations during protein structure refinement using a coarse-grained model, normal modes, and molecular dynamics simulations

Protein structure refinement from comparative models with the goal of predicting structures at near-experimental accuracy remains an unsolved problem. Structure refinement might be achieved with an iterative protocol where the most native-like structure from a set of decoys generated from an initial model in one cycle is used as the starting structure for the next cycle. Conformational sampling based on the coarse-grained SICHO model, atomic level of detail molecular dynamics simulations, and normal-mode analysis is compared in the context of such a protocol. All of the sampling methods can achieve significant refinement close to experimental structures, although the distribution of structures and the ability to reach native-like structures differs greatly. Implications for the practical application of such sampling methods and the requirements for scoring functions in an iterative refinement protocol are analyzed in the context of theoretical predictions for the distribution of protein-like conformations with a random sampling protocol.     

Conformation dynamics of cyclic disulfides and selenosulfides in CXXC(U) (X = Gly,Ala) tetrapeptide redox motifs

Thioredoxin fold proteins often contain a Cys‐(Xxx)_n‐Cys(Sec) or CX_nC(U) motif, where the active cysteine (C) or selenocysteine (U) is bridged by X residues, which vary with protein function. The effect of the X residues on the conformation space of the oxidized disulfide and selenosulfide forms of the CXXC(U) motif has been investigated using molecular dynamics (MD) and density functional theory. Multi‐microsecond‐length MD simulations of the CGGC, CGAC, and CAGC cyclic peptides show that CGGC rings readily exchange between several conformations over the course of the simulation, but steric interactions with the methyl group of Ala limit the conformation space available to the cyclic peptide, especially for CGAC. The potential for the motif to be reduced, as measured by the energy of the lowest unoccupied molecular orbitals, is dependent upon the ring conformation. These results suggest that control of available conformations by the bridging residues and the protein tertiary structure may be important for defining the function of the CXXC motif. Theoretical ⁷⁷Se chemical shifts of the selenosulfide moiety are dependent upon the conformation and/or intramolecular Se···O interactions with the backbone carbonyl group of the C‐terminal U residue.     

In silico predictions of 3D structures of linear and cyclic peptides with natural and non‐proteinogenic residues

We extended the use of Peplook, an in silico procedure for the prediction of three‐dimensional (3D) models of linear peptides to the prediction of 3D models of cyclic peptides and thanks to the ab initio calculation procedure, to the calculation of peptides with non‐proteinogenic amino acids. Indeed, such peptides cannot be predicted by homology or threading. We compare the calculated models with NMR and X‐ray models and for the cyclic peptides, with models predicted by other in silico procedures (Pep‐Fold and I‐Tasser). For cyclic peptides, on a set of 38 peptides, average root mean square deviation of backbone atoms (BB‐RMSD) was 3.8 and 4.1 Å for Peplook and Pep‐Fold, respectively. The best results are obtained with I‐Tasser (2.5 Å) although evaluations were biased by the fact that the resolved Protein Data Bank models could be used as template by the server. Peplook and Pep‐Fold give similar results, better for short (up to 20 residues) than for longer peptides. For peptides with non‐proteinogenic residues, performances of Peplook are sound with an average BB‐RMSD of 3.6 Å for ‘non‐natural peptides’ and 3.4 Å for peptides combining non‐proteinogenic residues and cyclic structure. These results open interesting possibilities for the design of peptidic drugs. Copyright © 2011 European Peptide Society and John Wiley & Sons, Ltd.     

A molecular dynamics approach for the generation of complete protein structures from limited coordinate data

Generation of full protein coordinates from limited information, e.g., the Cα coordinates, is an important step in protein homology modeling and structure determination, and molecular dynamics (MD) simulations may prove to be important in this task. We describe a new method, in which the protein backbone is built quickly in a rather crude way and then refined by minimization techniques. Subsequently, the side chains are positioned using extensive MD calculations. The method is tested on two proteins, and results compared to proteins constructed using two other MD-based methods. In the first method, we supplemented an existing backbone building method with a new procedure to add side chains. The second one largely consists of available methodology. The constructed proteins are compared to the corresponding X-ray structures, which became available during this study, and they are in good agreement (backbone RMS values of 0.5–0.7 Å, and all-atom RMS values of 1.5–1.9 Å). This comparative study indicates that extensive MD simulations are able, to some extent, to generate details of the native protein structure, and may contribute to the development of a standardized methodology to predict reliably (parts of) protein structures when only partial coordinate data are available. © 1994 John Wiley & Sons, Inc.     

A disintegrin and metalloproteinase with thrombospondin motifs 4 (ADAMTS-4) cleaves Reelin in an isoform-dependent manner

Reelin is a glycoprotein essential for brain development and functions. Reelin is subject to specific proteolysis at two distinct (N-t and C-t) sites, and these cleavages significantly diminish Reelin activity. The decrease of Reelin activity is detrimental for brain function, but the protease that catalyzes specific cleavage of Reelin remains elusive. Here we found that a disintegrin and metalloproteinase with thrombospondin motifs 4 (ADAMTS-4) cleaves Reelin in an isoform-specific manner. Among ADAMTS-4 isoforms, p50 cleaves the N-t site only, while p75 cleaves both sites. This is the first report identifying a protease that can specifically cleave Reelin.     

Multiple,short protein binding motifs in ORC1 and CDC6 control the initiation of DNA replication

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Common features of the conformations of antigen-binding loops in immunoglobulins and application to modeling loop conformations.

Using database screening techniques we have examined the relationship between antigen-binding loops in immunoglobulins, and regions of similar conformation in other protein families. The conformations of most antigen-binding loops are not unique to immunoglobulins. But in many cases, the geometrical relationship between the loop and the peptides flanking it differs between the immunoglobulins and other structures with the same loop. We assess model building by data base screening, compared with that based on canonical structures.     

ExSer: A standalone tool to mine protein data bank (PDB) for secondary structural elements

Detailed structural analysis of protein necessitates investigation at primary, secondary and tertiary levels, respectively. Insight into protein secondary structures pave way for understanding the type of secondary structural elements involved (α-helices, β-strands etc.), the amino acid sequence that encode the secondary structural elements, number of residues, length and, percentage composition of the respective elements in the protein. Here we present a standalone tool entitled "ExSer" which facilitate an automated extraction of the amino acid sequence that encode for the secondary structural regions of a protein from the protein data bank (PDB) file. AVAILABILITY: ExSer is freely downloadable from http://code.google.com/p/tool-exser/     

Refinement of protein structures by iterative comparative modeling and CryoEM density fitting

We developed a method for structure characterization of assembly components by iterative comparative protein structure modeling and fitting into cryo-electron microscopy (cryoEM) density maps. Specifically, we calculate a comparative model of a given component by considering many alternative alignments between the target sequence and a related template structure while optimizing the fit of a model into the corresponding density map. The method relies on the previously developed Moulder protocol that iterates over alignment, model building, and model assessment. The protocol was benchmarked using 20 varied target-template pairs of known structures with less than 30% sequence identity and corresponding simulated density maps at resolutions from 5A to 25A. Relative to the models based on the best existing sequence profile alignment methods, the percentage of C(alpha) atoms that are within 5A of the corresponding C(alpha) atoms in the superposed native structure increases on average from 52% to 66%, which is half-way between the starting models and the models from the best possible alignments (82%). The test also reveals that despite the improvements in the accuracy of the fitness function, this function is still the bottleneck in reducing the remaining errors. To demonstrate the usefulness of the protocol, we applied it to the upper domain of the P8 capsid protein of rice dwarf virus that has been studied by cryoEM at 6.8A. The C(alpha) root-mean-square deviation of the model based on the remotely related template, bluetongue virus VP7, improved from 8.7A to 6.0A, while the best possible model has a C(alpha) RMSD value of 5.3A. Moreover, the resulting model fits better into the cryoEM density map than the initial template structure. The method is being implemented in our program MODELLER for protein structure modeling by satisfaction of spatial restraints and will be applicable to the rapidly increasing number of cryoEM density maps of macromolecular assemblies.