Comparison of protein structures using 3D profile alignment

Osteocalcin and Sex Hormone Binding Globulin Compete on a Specific Binding Site of GPRC6A

The undercarboxylated form of osteocalcin (ucOC) regulates male fertility and energy metabolism, acting through the G protein-coupled receptor (GPRC)6A, thus forming a new pancreas-bone-testis axis. Recently, GPRC6A has also been suggested to mediate the nongenomic responses of free testosterone (T). However, these data did not consider the physiological scenario, where circulating T is mainly bound to sex hormone-binding globulin (SHBG) and only a small percentage circulates freely in the blood. Here, by the use of computational modelling, we document the existence of similar structural moieties between ucOC and SHBG that are predicted to bind to GPRC6A at docking analysis. This hypothesis of competition was assessed by binding experiments on human embryonic kidney-293 cells transfected with human GPRC6A gene. Unliganded SHBG specifically bound the membrane of human embryonic kidney-293 cells transfected with GPRC6A and was displaced by ucOC when coincubated at 100-fold molar excess. Furthermore, specific downstream Erk1/2 phosphorylation after stimulation of GPRC6A with ucOC was significantly blunted by 100-fold molar excess of unliganded SHBG. Intriguingly previous incubation with unliganded SHBG, followed by incubation with T, induced Erk1/2 phosphorylation in a dose-dependent manner. Neither binding nor stimulating activities were shown for SHBG saturated with T. Experiments on mutation constructs of GPRC6A strengthened the hypothesis of a common binding site of ucOC and SHBG. Given the role of GPRC6A on energy metabolism, these data agree with epidemiological association between SHBG levels and insulin sensitivity, suggest GPRC6A as a likely SHBG receptor, and add bases for the possible regulation of androgen activity in a nonsteroidal manner.

A novel antibody humanization method based on epitopes scanning and molecular dynamics simulation

1-17-2 is a rat anti-human DEC-205 monoclonal antibody that induces internalization and delivers antigen to dendritic cells (DCs). The potentially clinical application of this antibody is limited by its murine origin. Traditional humanization method such as complementarity determining regions (CDRs) graft often leads to a decreased or even lost affinity. Here we have developed a novel antibody humanization method based on computer modeling and bioinformatics analysis. First, we used homology modeling technology to build the precise model of Fab. A novel epitope scanning algorithm was designed to identify antigenic residues in the framework regions (FRs) that need to be mutated to human counterpart in the humanization process. Then virtual mutation and molecular dynamics (MD) simulation were used to assess the conformational impact imposed by all the mutations. By comparing the root-mean-square deviations (RMSDs) of CDRs, we found five key residues whose mutations would destroy the original conformation of CDRs. These residues need to be back-mutated to rescue the antibody binding affinity. Finally we constructed the antibodies in vitro and compared their binding affinity by flow cytometry and surface plasmon resonance (SPR) assay. The binding affinity of the refined humanized antibody was similar to that of the original rat antibody. Our results have established a novel method based on epitopes scanning and MD simulation for antibody humanization.

Serine protease identification (in vitro) and molecular structure predictions (in silico) from a phytopathogenic fungus, Alternaria solani

Serine proteases are involved in an enormous number of biological processes. The present study aims at characterizing three-dimensional (3D) molecular architecture of serine proteases from early blight pathogen, Alternaria solani that are hypothesized to be markers of phytopathogenicity. A serine protease was purified to homogeneity and MALDI-TOF-MS/MS analysis revealed that protease produced by A. solani belongs to alkaline serine proteases (AsP). AsP is made up of 403 amino acid residues with molecular weight of 42.1 kDa (Isoelectric point - 6.51) and its molecular formula was C1859 H2930 N516 O595 S4 . AsP structure model was built based on its comparative homology with serine protease using the program, MODELER. AsP had 16 β-sheets and 10 α-helices, with Ser(350) (G347-G357), Asp(158) (D158-H169), and His(193) (H193-G203) in separate turn/coil structures. Biological metal binding region situated near 6th-helix and His(193) residue is responsible for metal binding site. Also, calcium ion (Ca(2+)) is coordinated by the carboxyl groups of Lys(84), Ile(85), Lys(86), Asp(87), Phe(88), Ala(89), Ala(90) (K84-A90) for first Ca(2+) binding site and carbonyl oxygen atom of Lys(244), Gly(245), Arg(246), Thr(247), Lys(248), Lys(249), and Ala(250) (K244-A250), for second Ca(2+) binding site. Moreover, Ramachandran plot analysis of protein residues falling into most favored secondary structures were determined (83.3%). The predicted molecular 3D structural model was further verified using PROCHECK, ERRAT, and VADAR servers to confirm the geometry and stereo-chemical parameters of the molecular structural design. The functional analysis of AsP 3D molecular structure predictions familiar in the current study may provide a new perspective in the understanding and identification of antifungal protease inhibitor designing.

Fast alignment and comparison of RNA structures

MOTIVATION

To recognize remote relationships between RNA molecules, one must be able to align structures without regard to sequence similarity. We have implemented a method, which is swift [O(n(2))], sensitive and tolerant of large gaps and insertions. Molecules are broken into overlapping fragments, which are characterized by their memberships in a probabilistic classification based on local geometry and H-bonding descriptors. This leads to a probabilistic similarity measure that is used in a conventional dynamic programming method.

RESULTS

Examples are given of database searching, the detection of structural similarities, which would not be found using sequence based methods, and comparisons with a previously published approach.

AVAILABILITY AND IMPLEMENTATION

Source code (C and perl) and binaries for linux are freely available at www.zbh.uni-hamburg.de/fries.

Structure prediction and analysis of MxaF from obligate, facultative and restricted facultative methylobacterium

Methylobacteria are ubiquitous in the biosphere which are capable of growing on C1 compounds such as formate, formaldehyde, methanol and methylamine as well as on a wide range of multi-carbon growth substrates such as C2, C3 and C4 compounds due to the methylotrophic enzymes methanol dehydrogenase (MDH). MDH is performing these functions with the help of a key protein mxaF. Unfortunately, detailed structural analysis and homology modeling of mxaF is remains undefined. Hence, the objective of this research is the characterization and three dimensional modeling of mxaF protein from three different methylotrophs by using I-TASSER server. The predicted model were further optimize and validate by Profile 3D, Errat, Verifiy3-D and PROCHECK server. Predicted and best evaluated models have been successfully deposited to PMDB database with PMDB ID PM0077505, PM0077506 and PM0077507. Active site identification revealed 11, 13 and 14 putative functional site residues in respected models. It may play a major role during protein-protein, and protein-cofactor interactions. This study can provide us an ab-initio and detail information to understand the structure, mechanism of action and regulation of mxaF protein.

Qualitative and Quantitative analysis of 3D predicted arachidonate 15-lipoxygenase-B (15-LOX-2) from Homo sapiens

15-Lipoxygenase-2 protein has been reported to play an important role in normal development of prostate, lung, skin, and cornea tissues. It behaves as a suppressor of prostate cancer development by restricting cell cycle progression and implicating a possible protective role against tumor formation. On the basis of the above report, we selected 15-LOX-2 protein to study the structural classification and functional relationship with associated protein network at computational level. Sequence alignment and protein functional study shows that it contains a highly conserved LOX motif. PLAT domain with PF01477 and LH2 domain with PF00305 were successfully observed. Arachidonate 5-lipoxygenase (PDB ID: 3O8Y) was selected as a template with 42% identity. 3D structure was successfully predicted and verified. Qualitative analysis suggests that the predicted model was reliable and stable with best quality. Quantitative study shows that the model contained expected volume and area with best resolution. Predicted and best evaluated model has been successfully deposited to PMDB database with PMDB ID PM0078035. Active site identification revealed GLU(369), ALA(370), LEU(371), THR(372), HIS(373), LEU(374), HIS(376), SER(377), HIS(378), THR(385), LEU(389), HIS(394), PHE(399), LYS(400), LEU(401), ILE(403) and PRO(404) residues may play a major role during protein-protein, protein-drug and protein-cofactor interactions. STRING database result indicated that IL (4), GPX (2 and 4), PPARG, PTGS (1 and 2), CYP (2J2, 2C8, 4A11 and 2B6), PLA (2G2A, 2G4A, 2G1B and 2G6) and A LOX (5, 15, 12 and 12B) members from their respective gene families have network based functional association with 15-LOX-2.

The SALAMI protein structure search server

Protein structures often show similarities to another which would not be seen at the sequence level. Given the coordinates of a protein chain, the SALAMI server atwww.zbh.uni-hamburg.de/salami will search the protein data bank and return a set of similar structures without using sequence information. The results page lists the related proteins, details of the sequence and structure similarity and implied sequence alignments. Via a simple structure viewer, one can view superpositions of query and library structures and finally download superimposed coordinates. The alignment method is very tolerant of large gaps and insertions, and tends to produce slightly longer alignments than other similar programs.

Molecular modeling of A1 and A2A adenosine receptors: comparison of rhodopsin- and beta2-adrenergic-based homology models through the docking studies

Adenosine receptors (ARs) are members of the superfamily of G protein-coupled receptors. The homology models of adenosine A1 and A2A receptors were constructed. The high-resolution X-ray structure of bovine rhodopsin and crystal structure of beta2-adrenergic receptor were used as templates. The binding sites of the A1 and A2A ARs were constructed by using data obtained from mutagenesis experiments as well as docking simulations of the respective AR antagonsists DPCPX and XAC. To compare rhodopsin- and beta2-adrenergic-based models, the binding mode of A1 (KW-3902, LUF-5437) and A2A (KW-6002, ZM-241385) ARs antagonists were also examined. The differences in the binding ability of both models were noted during the study. The beta2-adrenergic-based A2A AR model was much more capable to stabilize the ligand in the binding site cavity than the corresponding rhodopsin-based A2A AR model, however, such differences were not so clear in case of A1 AR models. It was suggested that for the A1 AR it is possible to use the crystal structure of rhodopsin as a template as well as beta2-adrenergic receptor, but for A2A AR, with the now available beta2-adrenergic receptor X-ray structure, docking studies should be avoided on the rhodopsin-based model. However, taking into account that the beta2AR shares about 31% of the residues with the AR in comparison to 21% in case of bRho, we suggest using beta2-adrenergic-based models for the A1 and A2A ARs for further in silico ligand screening also because of their generally better ability to stabilize ligands inside the binding pocket.

Protein sequence and structure alignments within one framework

Background

Protein structure alignments are usually based on very different techniques to sequence alignments. We propose a method which treats sequence, structure and even combined sequence + structure in a single framework. Using a probabilistic approach, we calculate a similarity measure which can be applied to fragments containing only protein sequence, structure or both simultaneously.

Results

Proof-of-concept results are given for the different problems. For sequence alignments, the methodology is no better than conventional methods. For structure alignments, the techniques are very fast, reliable and tolerant of a range of alignment parameters. Combined sequence and structure alignments may provide a more reliable alignment for pairs of proteins where pure structural alignments can be misled by repetitive elements or apparent symmetries.

Conclusion

The probabilistic framework has an elegance in principle, merging sequence and structure descriptors into a single framework. It has a practical use in fast structural alignments and a potential use in finding those examples where sequence and structural similarities apparently disagree.

Matt: local flexibility aids protein multiple structure alignment

Even when there is agreement on what measure a protein multiple structure alignment should be optimizing, finding the optimal alignment is computationally prohibitive. One approach used by many previous methods is aligned fragment pair chaining, where short structural fragments from all the proteins are aligned against each other optimally, and the final alignment chains these together in geometrically consistent ways. Ye and Godzik have recently suggested that adding geometric flexibility may help better model protein structures in a variety of contexts. We introduce the program Matt (Multiple Alignment with Translations and Twists), an aligned fragment pair chaining algorithm that, in intermediate steps, allows local flexibility between fragments: small translations and rotations are temporarily allowed to bring sets of aligned fragments closer, even if they are physically impossible under rigid body transformations. After a dynamic programming assembly guided by these "bent" alignments, geometric consistency is restored in the final step before the alignment is output. Matt is tested against other recent multiple protein structure alignment programs on the popular Homstrad and SABmark benchmark datasets. Matt's global performance is competitive with the other programs on Homstrad, but outperforms the other programs on SABmark, a benchmark of multiple structure alignments of proteins with more distant homology. On both datasets, Matt demonstrates an ability to better align the ends of alpha-helices and beta-strands, an important characteristic of any structure alignment program intended to help construct a structural template library for threading approaches to the inverse protein-folding problem. The related question of whether Matt alignments can be used to distinguish distantly homologous structure pairs from pairs of proteins that are not homologous is also considered. For this purpose, a p-value score based on the length of the common core and average root mean squared deviation (RMSD) of Matt alignments is shown to largely separate decoys from homologous protein structures in the SABmark benchmark dataset. We postulate that Matt's strong performance comes from its ability to model proteins in different conformational states and, perhaps even more important, its ability to model backbone distortions in more distantly related proteins.

Advances in homology protein structure modeling

Homology modeling plays a central role in determining protein structure in the structural genomics project. The importance of homology modeling has been steadily increasing because of the large gap that exists between the overwhelming number of available protein sequences and experimentally solved protein structures, and also, more importantly, because of the increasing reliability and accuracy of the method. In fact, a protein sequence with over 30% identity to a known structure can often be predicted with an accuracy equivalent to a low-resolution X-ray structure. The recent advances in homology modeling, especially in detecting distant homologues, aligning sequences with template structures, modeling of loops and side chains, as well as detecting errors in a model, have contributed to reliable prediction of protein structure, which was not possible even several years ago. The ongoing efforts in solving protein structures, which can be time-consuming and often difficult, will continue to spur the development of a host of new computational methods that can fill in the gap and further contribute to understanding the relationship between protein structure and function.

A novel approach to structural alignment using realistic structural and environmental information

In the era of structural genomics, it is necessary to generate accurate structural alignments in order to build good templates for homology modeling. Although a great number of structural alignment algorithms have been developed, most of them ignore intermolecular interactions during the alignment procedure. Therefore, structures in different oligomeric states are barely distinguishable, and it is very challenging to find correct alignment in coil regions. Here we present a novel approach to structural alignment using a clique finding algorithm and environmental information (SAUCE). In this approach, we build the alignment based on not only structural coordinate information but also realistic environmental information extracted from biological unit files provided by the Protein Data Bank (PDB). At first, we eliminate all environmentally unfavorable pairings of residues. Then we identify alignments in core regions via a maximal clique finding algorithm. Two extreme value distribution (EVD) form statistics have been developed to evaluate core region alignments. With an optional extension step, global alignment can be derived based on environment-based dynamic programming linking. We show that our method is able to differentiate three-dimensional structures in different oligomeric states, and is able to find flexible alignments between multidomain structures without predetermined hinge regions. The overall performance is also evaluated on a large scale by comparisons to current structural classification databases as well as to other alignment methods.

Consistency matrices: quantified structure alignments for sets of related proteins

Comparing two remotely similar structures is a difficult problem: more often than not, resulting structure alignments will show ambiguities and a unique answer usually does not even exist. In addition, alignments in general have a limited information content because every aligned residue is considered equally important. To solve these issues to a certain extent, one can take the perspective of a whole group of similar structures and then evaluate common structural features. Here, we describe a consistency approach that, although not actually performing a multiple structure alignment, does produce the information that one would conceivably want from such an experiment: the key structural features of the group, e.g., a fold, which in this case are projected onto either a pair of proteins or a single protein. Both representations are useful for a number of applications, ranging from the detection of (partially) wrong structure alignments to protein structure classification and fold recognition. To demonstrate some of these applications, the procedure was applied to 195 SCOP folds containing a total of 1802 domains sharing very low sequence similarity.

Studies in the assessment of folding quality for protein modeling and structure prediction

A diagnostic for assessing the quality of a fold has been developed to which further criteria can be progressively added. The goal is to create a measure that can follow the status of a protein structure in a simulation or modeling process, when the answer (the experimental structure) is not known in advance, rather than simply reject deliberate misfolds. This places greater emphasis on the need to study, and calibrate against, marginal cases, i.e., unusual native structures, incomplete structures, partially erroneous X-ray structures, good models, poor models, and the effect of cofactors. The first three terms introduced in the diagnostic are appropriate core-forming properties or noncore properties of residues in relation to tertiary structure, appropriate neighboring structure density for each residue in relation to tertiary structure, and secondary structure consistency. While the method emerges as a useful simulation analysis tool, we find a need for further fine-tuning to diminish sensitivity to minor conformational changes that retain essential features of the fold, balanced against the need to obtain a more sensitive response when a conformational change involves less physically meaningful interatomic interactions. This dual utility is difficult to obtain: the investigation highlights some of the issues. Initial attempts to obtain it have led to terms in the diagnostic that are admittedly complex: simplifications must also be explored.

Protein structure similarities

Comparison of protein structures can reveal distant evolutionary relationships that would not be detected by sequence information alone. This helps to infer functional properties. In recent years, many methods for pairwise protein structure alignment have been proposed and are now available on the World Wide Web. Although these methods have made it possible to compare all available protein structures, they also highlight the remaining difficulties in defining a reliable score for protein structure similarities.

Protein structure alignment using environmental profiles

A new protein structure alignment procedure is described. An initial alignment is made by comparing a one-dimensional list of primary, secondary and tertiary structural features (profiles) of two proteins, without explicitly considering the three-dimensional geometry of the structures. The alignment is then iteratively refined in the second step, in which new alignments are found by three-dimensional superposition of the structures based on the current alignment. This new procedure is fast enough to do all-against-all structural comparisons routinely. The procedure sometimes finds an alignment that suggests an evolutionary relationship and which is not normally obtained if only geometry is considered. All pair-wise comparisons were made among 3539 protein structural domains that represent all known protein structures. The resulting 3539 z-scores were used to cluster the proteins. The number of main clusters increased continuously as the z-cutoff was raised, but the number of multiple-member clusters showed a maximum at z-cutoff values of 5.0 and 5.5. When a z-cutoff value of 5.0 was used, the total number of main clusters was 2043, of which only 336 clusters had more than one member.