Comparative protein structure modeling by combining multiple templates and optimizing sequence-to-structure alignments

Computational and experimental analysis of the transmembrane domain 4/5 dimerization interface of the serotonin 5-HT(1A) receptor

Experimental evidence suggests that most members of class A G-protein coupled receptors (GPCRs) can form homomers and heteromers in addition to functioning as single monomers. In particular, serotonin (5-HT) receptors were shown to homodimerize and heterodimerize with other GPCRs, although the details and the physiological role of the oligomerization has not yet been fully elucidated. Here we used computational modeling of the 5-HT(1A) receptor monomer and dimer to predict residues important for dimerization. Based on these results, we carried out rationally designed site-directed mutagenesis. The ability of the mutants to dimerize was evaluated using different FRET-based approaches. The reduced levels of acceptor photobleaching-Förster resonance energy transfer (FRET) and the lower number of monomers participating in oligomers, as assessed by lux-FRET, confirmed the decreased ability of the mutants to dimerize and the involvement of the predicted contacts (Trp175(4.64), Tyr198(5.41), Arg151(4.40), and Arg152(4.41)) at the interface. This information was reintroduced as constraints for computational protein-protein docking to obtain a high-quality dimer model. Analysis of the refined model as well as molecular dynamics simulations of wild-type (WT) and mutant dimers revealed compensating interactions in dimers composed of WT and W175A mutant. This provides an explanation for the requirement of mutations of Trp175(4.64) in both homomers for disrupting dimerization. Our iterative computational-experimental study demonstrates that transmembrane domains TM4/TM5 can form an interaction interface in 5-HT(1A) receptor dimers and indicates that specific amino acid interactions maintain this interface. The mutants and the optimized model of the dimer structure may be used in functional studies of serotonin dimers.

GalaxyTBM: template-based modeling by building a reliable core and refining unreliable local regions

Background

Protein structures can be reliably predicted by template-based modeling (TBM) when experimental structures of homologous proteins are available. However, it is challenging to obtain structures more accurate than the single best templates by either combining information from multiple templates or by modeling regions that vary among templates or are not covered by any templates.

Results

We introduce GalaxyTBM, a new TBM method in which the more reliable core region is modeled first from multiple templates and less reliable, variable local regions, such as loops or termini, are then detected and re-modeled by an ab initio method. This TBM method is based on “Seok-server,” which was tested in CASP9 and assessed to be amongst the top TBM servers. The accuracy of the initial core modeling is enhanced by focusing on more conserved regions in the multiple-template selection and multiple sequence alignment stages. Additional improvement is achieved by ab initio modeling of up to 3 unreliable local regions in the fixed framework of the core structure. Overall, GalaxyTBM reproduced the performance of Seok-server, with GalaxyTBM and Seok-server resulting in average GDT-TS of 68.1 and 68.4, respectively, when tested on 68 single-domain CASP9 TBM targets. For application to multi-domain proteins, GalaxyTBM must be combined with domain-splitting methods.

Conclusion

Application of GalaxyTBM to CASP9 targets demonstrates that accurate protein structure prediction is possible by use of a multiple-template-based approach, and ab initio modeling of variable regions can further enhance the model quality.

Structural modelling and dynamics of proteins for insights into drug interactions

Proteins are the workhorses of biomolecules and their function is affected by their structure and their structural rearrangements during ligand entry, ligand binding and protein-protein interactions. Hence, the knowledge of protein structure and, importantly, the dynamic behaviour of the structure are critical for understanding how the protein performs its function. The predictions of the structure and the dynamic behaviour can be performed by combinations of structure modelling and molecular dynamics simulations. The simulations also need to be sensitive to the constraints of the environment in which the protein resides. Standard computational methods now exist in this field to support the experimental effort of solving protein structures. This review presents a comprehensive overview of the basis of the calculations and the well-established computational methods used to generate and understand protein structure and function and the study of their dynamic behaviour with the reference to lung-related targets.

Next generation sequencing identifies mutations in Atonal homolog 7 (ATOH7) in families with global eye developmental defects

The atonal homolog 7 (ATOH7) gene encodes a transcription factor involved in determining the fate of retinal progenitor cells and is particularly required for optic nerve and ganglion cell development. Using a combination of autozygosity mapping and next generation sequencing, we have identified homozygous mutations in this gene, p.E49V and p.P18RfsX69, in two consanguineous families diagnosed with multiple ocular developmental defects, including severe vitreoretinal dysplasia, optic nerve hypoplasia, persistent fetal vasculature, microphthalmia, congenital cataracts, microcornea, corneal opacity and nystagmus. Most of these clinical features overlap with defects in the Norrin/β-catenin signalling pathway that is characterized by dysgenesis of the retinal and hyaloid vasculature. Our findings document Mendelian mutations within ATOH7 and imply a role for this molecule in the development of structures at the front as well as the back of the eye. This work also provides further insights into the function of ATOH7, especially its importance in retinal vascular development and hyaloid regression.

Homozygous mutations in PXDN cause congenital cataract, corneal opacity, and developmental glaucoma

Anterior segment dysgenesis describes a group of heterogeneous developmental disorders that affect the anterior chamber of the eye and are associated with an increased risk of glaucoma. Here, we report homozygous mutations in peroxidasin (PXDN) in two consanguineous Pakistani families with congenital cataract-microcornea with mild to moderate corneal opacity and in a consanguineous Cambodian family with developmental glaucoma and severe corneal opacification. These results highlight the diverse ocular phenotypes caused by PXDN mutations, which are likely due to differences in genetic background and environmental factors. Peroxidasin is an extracellular matrix-associated protein with peroxidase catalytic activity, and we confirmed localization of the protein to the cornea and lens epithelial layers. Our findings imply that peroxidasin is essential for normal development of the anterior chamber of the eye, where it may have a structural role in supporting cornea and lens architecture as well as an enzymatic role as an antioxidant enzyme in protecting the lens, trabecular meshwork, and cornea against oxidative damage.

A new clustering of antibody CDR loop conformations

Previous analyses of the complementarity-determining regions (CDRs) of antibodies have focused on a small number of "canonical" conformations for each loop. This is primarily the result of the work of Chothia and coworkers, most recently in 1997. Because of the widespread utility of antibodies, we have revisited the clustering of conformations of the six CDR loops with the much larger amount of structural information currently available. In this work, we were careful to use a high-quality data set by eliminating low-resolution structures and CDRs with high B-factors or high conformational energies. We used a distance function based on directional statistics and an effective clustering algorithm with affinity propagation. With this data set of over 300 nonredundant antibody structures, we were able to cover 28 CDR-length combinations (e.g., L1 length 11, or "L1-11" in our CDR-length nomenclature) for L1, L2, L3, H1, and H2. The Chothia analysis covered only 20 CDR-lengths. Only four of these had more than one conformational cluster, of which two could easily be distinguished by gene source (mouse/human; κ/λ) and one could easily be distinguished purely by the presence and the positions of Pro residues (L3-9). Thus, using the Chothia analysis does not require the complicated set of "structure-determining residues" that is often assumed. Of our 28 CDR-lengths, 15 have multiple conformational clusters, including 10 for which the Chothia analysis had only one canonical class. We have a total of 72 clusters for non-H3 CDRs; approximately 85% of the non-H3 sequences can be assigned to a conformational cluster based on gene source and/or sequence. We found that earlier predictions of "bulged" versus "nonbulged" conformations based on the presence or the absence of anchor residues Arg/Lys94 and Asp101 of H3 have not held up, since all four combinations lead to a majority of conformations that are bulged. Thus, the earlier analyses have been significantly enhanced by the increased data. We believe that the new classification will lead to improved methods for antibody structure prediction and design.

Incorporation of local structural preference potential improves fold recognition

Fold recognition, or threading, is a popular protein structure modeling approach that uses known structure templates to build structures for those of unknown. The key to the success of fold recognition methods lies in the proper integration of sequence, physiochemical and structural information. Here we introduce another type of information, local structural preference potentials of 3-residue and 9-residue fragments, for fold recognition. By combining the two local structural preference potentials with the widely used sequence profile, secondary structure information and hydrophobic score, we have developed a new threading method called FR-t5 (fold recognition by use of 5 terms). In benchmark testings, we have found the consideration of local structural preference potentials in FR-t5 not only greatly enhances the alignment accuracy and recognition sensitivity, but also significantly improves the quality of prediction models.

A biochemical mechanism for the oncogenic potential of the p110beta catalytic subunit of phosphoinositide 3-kinase

Class I PI3-kinases signal downstream of receptor tyrosine kinases and G protein-coupled receptors and have been implicated in tumorigenesis. Although the oncogenic potential of the PI3-kinase subunit p110α requires its mutational activation, other p110 isoforms can induce transformation when overexpressed in the wild-type state. In wild-type p110α, N345 in the C2 domain forms hydrogen bonds with D560 and N564 in the inter-SH2 (iSH2) domain of p85, and mutations of p110α or p85 that disrupt this interface lead to increased basal activity and transformation. Sequence analysis reveals that N345 in p110α aligns with K342 in p110β. This difference makes wild-type p110β analogous to a previously described oncogenic mutant, p110α-N345K. We now show that p110β is inhibited by p85 to a lesser extent than p110α and is not differentially inhibited by wild-type p85 versus p85 mutants that disrupt the C2-iSH2 domain interface. Similar results were seen in soft agar and focus-formation assays, where p110β was similar to p110α-N345K in transforming potential. Inhibition of p110β by p85 was enhanced by a K342N mutation in p110β, which led to decreased activity in vitro, decreased basal Akt and ribosomal protein S6 kinase (S6K1) activation, and decreased transformation in NIH 3T3 cells. Moreover, unlike wild-type p110β, p110β-K342N was differentially regulated by wild-type and mutant p85, suggesting that the inhibitory C2-iSH2 interface is functional in this mutant. This study shows that the enhanced transforming potential of p110β is the result of its decreased inhibition by p85, due to the disruption of an inhibitory C2-iSH2 domain interface.

PCRPi: Presaging Critical Residues in Protein interfaces, a new computational tool to chart hot spots in protein interfaces

Protein-protein interactions (PPIs) are ubiquitous in Biology, and thus offer an enormous potential for the discovery of novel therapeutics. Although protein interfaces are large and lack defining physiochemical traits, is well established that only a small portion of interface residues, the so-called hot spot residues, contribute the most to the binding energy of the protein complex. Moreover, recent successes in development of novel drugs aimed at disrupting PPIs rely on targeting such residues. Experimental methods for describing critical residues are lengthy and costly; therefore, there is a need for computational tools that can complement experimental efforts. Here, we describe a new computational approach to predict hot spot residues in protein interfaces. The method, called Presaging Critical Residues in Protein interfaces (PCRPi), depends on the integration of diverse metrics into a unique probabilistic measure by using Bayesian Networks. We have benchmarked our method using a large set of experimentally verified hot spot residues and on a blind prediction on the protein complex formed by HRAS protein and a single domain antibody. Under both scenarios, PCRPi delivered consistent and accurate predictions. Finally, PCRPi is able to handle cases where some of the input data is either missing or not reliable (e.g. evolutionary information).

Template-based protein structure modeling

Functional characterization of a protein is often facilitated by its 3D structure. However, the fraction of experimentally known 3D models is currently less than 1% due to the inherently time-consuming and complicated nature of structure determination techniques. Computational approaches are employed to bridge the gap between the number of known sequences and that of 3D models. Template-based protein structure modeling techniques rely on the study of principles that dictate the 3D structure of natural proteins from the theory of evolution viewpoint. Strategies for template-based structure modeling will be discussed with a focus on comparative modeling, by reviewing techniques available for all the major steps involved in the comparative modeling pipeline.

Protein structure prediction based on sequence similarity

The observation that similar protein sequences fold into similar three-dimensional structures provides a basis for the methods which predict structural features of a novel protein based on the similarity between its sequence and sequences of known protein structures. Similarity over entire sequence or large sequence fragment(s) enables prediction and modeling of entire structural domains while statistics derived from distributions of local features of known protein structures make it possible to predict such features in proteins with unknown structures. The accuracy of models of protein structures is sufficient for many practical purposes such as analysis of point mutation effects, enzymatic reactions, interaction interfaces of protein complexes, and active sites. Protein models are also used for phasing of crystallographic data and, in some cases, for drug design. By using models one can avoid the costly and time-consuming process of experimental structure determination. The purpose of this chapter is to give a practical review of the most popular protein structure prediction methods based on sequence similarity and to outline a practical approach to protein structure prediction. While the main focus of this chapter is on template-based protein structure prediction, it also provides references to other methods and programs which play an important role in protein structure prediction.

2,3-Dihydro-1-benzofuran derivatives as a series of potent selective cannabinoid receptor 2 agonists: design, synthesis, and binding mode prediction through ligand-steered modeling

We recently discovered and reported a series of N-alkyl-isatin acylhydrazone derivatives that are potent cannabinoid receptor 2 (CB(2)) agonists. In an effort to improve the druglike properties of these compounds and to better understand and improve the treatment of neuropathic pain, we designed and synthesized a new series of 2,3-dihydro-1-benzofuran derivatives bearing an asymmetric carbon atom that behave as potent selective CB(2) agonists. We used a multidisciplinary medicinal chemistry approach with binding mode prediction through ligand-steered modeling. Enantiomer separation and configuration assignment were carried out for the racemic mixture for the most selective compound, MDA7 (compound 18). It appeared that the S enantiomer, compound MDA104 (compound 33), was the active enantiomer. Compounds MDA42 (compound 19) and MDA39 (compound 30) were the most potent at CB(2). MDA42 was tested in a model of neuropathic pain and exhibited activity in the same range as that of MDA7. Preliminary ADMET studies for MDA7 were performed and did not reveal any problems.

Pathogenic NAP57 mutations decrease ribonucleoprotein assembly in dyskeratosis congenita

X-linked dyskeratosis congenita (DC) is a rare bone marrow failure syndrome caused by mostly missense mutations in the pseudouridine synthase NAP57 (dyskerin/Cbf5). As part of H/ACA ribonucleoproteins (RNPs), NAP57 is important for the biogenesis of ribosomes, spliceosomal small nuclear RNPs, microRNAs and the telomerase RNP. DC mutations concentrate in the N- and C-termini of NAP57 but not in its central catalytic domain raising questions as to their impact. We demonstrate that the N- and C-termini together form the binding surface for the H/ACA RNP assembly factor SHQ1 and that DC mutations modulate the interaction between the two proteins. Pinpointing impaired interaction between NAP57 and SHQ1 as a potential molecular basis for X-linked DC has implications for therapeutic approaches, e.g. by targeting the NAP57-SHQ1 interface with small molecules.

Alignment of multiple protein structures based on sequence and structure features

Comparing the structures of proteins is crucial to gaining insight into protein evolution and function. Here, we align the sequences of multiple protein structures by a dynamic programming optimization of a scoring function that is a sum of an affine gap penalty and terms dependent on various sequence and structure features (SALIGN). The features include amino acid residue type, residue position, residue accessible surface area, residue secondary structure state and the conformation of a short segment centered on the residue. The multiple alignment is built by following the 'guide' tree constructed from the matrix of all pairwise protein alignment scores. Importantly, the method does not depend on the exact values of various parameters, such as feature weights and gap penalties, because the optimal alignment across a range of parameter values is found. Using multiple structure alignments in the HOMSTRAD database, SALIGN was benchmarked against MUSTANG for multiple alignments as well as against TM-align and CE for pairwise alignments. On the average, SALIGN produces a 15% improvement in structural overlap over HOMSTRAD and 14% over MUSTANG, and yields more equivalent structural positions than TM-align and CE in 90% and 95% of cases, respectively. The utility of accurate multiple structure alignment is illustrated by its application to comparative protein structure modeling.

A comprehensive guide to the ROMK potassium channel: form and function in health and disease

The discovery of the renal outer medullary K+ channel (ROMK, K(ir)1.1), the founding member of the inward-rectifying K+ channel (K(ir)) family, by Ho and Hebert in 1993 revolutionized our understanding of potassium channel biology and renal potassium handling. Because of the central role that ROMK plays in the regulation of salt and potassium homeostasis, considerable efforts have been invested in understanding the underlying molecular mechanisms. Here we provide a comprehensive guide to ROMK, spanning from the physiology in the kidney to the organization and regulation by intracellular factors to the structural basis of its function at the atomic level.

Protein structure prediction and model quality assessment

Protein structures have proven to be a crucial piece of information for biomedical research. Of the millions of currently sequenced proteins only a small fraction is experimentally solved for structure and the only feasible way to bridge the gap between sequence and structure data is computational modeling. Half a century has passed since it was shown that the amino acid sequence of a protein determines its shape, but a method to translate the sequence code reliably into the 3D structure still remains to be developed. This review summarizes modern protein structure prediction techniques with the emphasis on comparative modeling, and describes the recent advances in methods for theoretical model quality assessment.

Structural genomics is the largest contributor of novel structural leverage

The Protein Structural Initiative (PSI) at the US National Institutes of Health (NIH) is funding four large-scale centers for structural genomics (SG). These centers systematically target many large families without structural coverage, as well as very large families with inadequate structural coverage. Here, we report a few simple metrics that demonstrate how successfully these efforts optimize structural coverage: while the PSI-2 (2005-now) contributed more than 8% of all structures deposited into the PDB, it contributed over 20% of all novel structures (i.e. structures for protein sequences with no structural representative in the PDB on the date of deposition). The structural coverage of the protein universe represented by today’s UniProt (v12.8) has increased linearly from 1992 to 2008; structural genomics has contributed significantly to the maintenance of this growth rate. Success in increasing novel leverage (defined in Liu et al. in Nat Biotechnol 25:849–851, 2007) has resulted from systematic targeting of large families. PSI’s per structure contribution to novel leverage was over 4-fold higher than that for non-PSI structural biology efforts during the past 8 years. If the success of the PSI continues, it may just take another ~15 years to cover most sequences in the current UniProt database.

Genome pool strategy for structural coverage of protein families

Even closely homologous proteins often have different crystallization properties and propensities. This observation can be used to introduce an additional dimension into crystallization trials by simultaneous targeting multiple homologs in what we call a "genome pool" strategy. We show that this strategy works because protein physicochemical properties correlated with crystallization success have a surprisingly broad distribution within most protein families. There are also "easy" and "difficult" families where this distribution is tilted in one direction. This leads to uneven structural coverage of protein families, with more "easy" ones solved. Increasing the size of the "genome pool" can improve chances of solving the "difficult" ones. In contrast, our analysis does not indicate that any specific genomes are "easy" or "difficult". Finally, we show that the group of proteins with known 3D structures is systematically different from the general pool of known proteins and we assess the structural consequences of these differences.

Improved scoring function for comparative modeling using the M4T method

Improvements in comparative protein structure modeling for the remote target-template sequence similarity cases are possible through the optimal combination of multiple template structures and by improving the quality of target-template alignment. Recently developed MMM and M4T methods were designed to address these problems. Here we describe new developments in both the alignment generation and the template selection parts of the modeling algorithms. We set up a new scoring function in MMM to deliver more accurate target-template alignments. This was achieved by developing and incorporating into the composite scoring function a novel statistical pairwise potential that combines local and non-local terms. The non-local term of the statistical potential utilizes a shuffled reference state definition that helped to eliminate most of the false positive signal from the background distribution of pairwise contacts. The accuracy of the scoring function was further increased by using BLOSUM mutation table scores.

Quantifying protein interface footprinting by hydroxyl radical oxidation and molecular dynamics simulation: application to galectin-1

Biomolecular surface mapping methods offer an important alternative method for characterizing protein-protein and protein-ligand interactions in cases in which it is not possible to determine high-resolution three-dimensional (3D) structures of complexes. Hydroxyl radical footprinting offers a significant advance in footprint resolution compared with traditional chemical derivatization. Here we present results of footprinting performed with hydroxyl radicals generated on the nanosecond time scale by laser-induced photodissociation of hydrogen peroxide. We applied this emerging method to a carbohydrate-binding protein, galectin-1. Since galectin-1 occurs as a homodimer, footprinting was employed to characterize the interface of the monomeric subunits. Efficient analysis of the mass spectrometry data for the oxidized protein was achieved with the recently developed ByOnic (Palo Alto, CA) software that was altered to handle the large number of modifications arising from side-chain oxidation. Quantification of the level of oxidation has been achieved by employing spectral intensities for all of the observed oxidation states on a per-residue basis. The level of accuracy achievable from spectral intensities was determined by examination of mixtures of synthetic peptides related to those present after oxidation and tryptic digestion of galectin-1. A direct relationship between side-chain solvent accessibility and level of oxidation emerged, which enabled the prediction of the level of oxidation given the 3D structure of the protein. The precision of this relationship was enhanced through the use of average solvent accessibilities computed from 10 ns molecular dynamics simulations of the protein.

Comparative protein structure modeling using Modeller

Functional characterization of a protein sequence is one of the most frequent problems in biology. This task is usually facilitated by accurate three-dimensional (3-D) structure of the studied protein. In the absence of an experimentally determined structure, comparative or homology modeling can sometimes provide a useful 3-D model for a protein that is related to at least one known protein structure. Comparative modeling predicts the 3-D structure of a given protein sequence (target) based primarily on its alignment to one or more proteins of known structure (templates). The prediction process consists of fold assignment, target-template alignment, model building, and model evaluation. This unit describes how to calculate comparative models using the program MODELLER and discusses all four steps of comparative modeling, frequently observed errors, and some applications. Modeling lactate dehydrogenase from Trichomonas vaginalis (TvLDH) is described as an example. The download and installation of the MODELLER software is also described.