A new method for building protein conformations from sequence alignments with homologues of known structure

Automated method to differentiate between native and mirror protein models obtained from contact maps

Mirror protein structures are often considered as artifacts in modeling protein structures. However, they may soon become a new branch of biochemistry. Moreover, methods of protein structure reconstruction, based on their residue-residue contact maps, need methodology to differentiate between models of native and mirror orientation, especially regarding the reconstructed backbones. We analyzed 130 500 structural protein models obtained from contact maps of 1 305 SCOP domains belonging to all 7 structural classes. On average, the same numbers of native and mirror models were obtained among 100 models generated for each domain. Since their structural features are often not sufficient for differentiating between the two types of model orientations, we proposed to apply various energy terms (ETs) from PyRosetta to separate native and mirror models. To automate the procedure for differentiating these models, the k-means clustering algorithm was applied. Using total energy did not allow to obtain appropriate clusters–the accuracy of the clustering for class A (all helices) was no more than 0.52. Therefore, we tested a series of different k-means clusterings based on various combinations of ETs. Finally, applying two most differentiating ETs for each class allowed to obtain satisfying results. To unify the method for differentiating between native and mirror models, independent of their structural class, the two best ETs for each class were considered. Finally, the k-means clustering algorithm used three common ETs: probability of amino acid assuming certain values of dihedral angles Φ and Ψ, Ramachandran preferences and Coulomb interactions. The accuracies of clustering with these ETs were in the range between 0.68 and 0.76, with sensitivity and selectivity in the range between 0.68 and 0.87, depending on the structural class. The method can be applied to all fully-automated tools for protein structure reconstruction based on contact maps, especially those analyzing big sets of models.

Comparative Protein Structure Modeling Using MODELLER

Comparative protein structure modeling predicts the three-dimensional 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 how to use the ModBase database of such models, 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. © 2016 by John Wiley & Sons, Inc.

Comparative Protein Structure Modeling Using MODELLER

Comparative protein structure modeling predicts the three-dimensional 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 how to use the ModBase database of such models, 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. © 2016 by John Wiley & Sons, Inc.

Applying PyRosetta molecular energies to separate properly oriented protein models from mirror models, obtained from contact maps

Reconstructing protein structure based on contact maps leads to two types of models: properly oriented models and mirror models. This is due to the fact that contact maps do not include information on protein chirality. Therefore, both types of model orientations share the same contact map and are geometrically allowed. In this work, we verified the hypothesis that some of the energy terms calculated by PyRosetta could be useful to distinguish between properly oriented and mirror models. We studied 440 models of all-alpha protein domains reconstructed manually from their contact maps, where 50 % of the models were properly oriented and 50 % had mirror orientation. We showed that dihedral angles and energy terms, based on the probability of specific geometrical arrangement of the residues, differed significantly for properly oriented and mirror models.

Restraint-based three-dimensional modeling of genomes and genomic domains

Chromosomes are large polymer molecules composed of nucleotides. In some species, such as humans, this polymer can sum up to meters long and still be properly folded within the nuclear space of few microns in size. The exact mechanisms of how the meters long DNA is folded into the nucleus, as well as how the regulatory machinery can access it, is to a large extend still a mystery. However, and thanks to newly developed molecular, genomic and computational approaches based on the Chromosome Conformation Capture (3C) technology, we are now obtaining insight on how genomes are spatially organized. Here we review a new family of computational approaches that aim at using 3C-based data to obtain spatial restraints for modeling genomes and genomic domains.

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.

Protein structure modeling in the proteomics era

Structural proteomics aims to understand the structural basis of protein interactions and functions. A prerequisite for this is the availability of 3D protein structures that mediate the biochemical interactions. The explosion in the number of available gene sequences set the stage for the next step in genome-scale projects -- to obtain 3D structures for each protein. To achieve this ambitious goal, the slow and costly structure determination experiments are supplemented with theoretical approaches. The current state and recent advances in structure modeling approaches are reviewed here, with special emphasis on comparative protein structure modeling techniques.

Comparative Modeling of Drug Target Proteins☆

In this perspective, we begin by describing the comparative protein structure modeling technique and the accuracy of the corresponding models. We then discuss the significant role that comparative prediction plays in drug discovery. We focus on virtual ligand screening against comparative models and illustrate the state-of-the-art by a number of specific examples.

Homology modeling a fast tool for drug discovery: current perspectives

Major goal of structural biology involve formation of protein-ligand complexes; in which the protein molecules act energetically in the course of binding. Therefore, perceptive of protein-ligand interaction will be very important for structure based drug design. Lack of knowledge of 3D structures has hindered efforts to understand the binding specificities of ligands with protein. With increasing in modeling software and the growing number of known protein structures, homology modeling is rapidly becoming the method of choice for obtaining 3D coordinates of proteins. Homology modeling is a representation of the similarity of environmental residues at topologically corresponding positions in the reference proteins. In the absence of experimental data, model building on the basis of a known 3D structure of a homologous protein is at present the only reliable method to obtain the structural information. Knowledge of the 3D structures of proteins provides invaluable insights into the molecular basis of their functions. The recent advances in homology modeling, particularly in detecting and aligning sequences with template structures, distant homologues, modeling of loops and side chains as well as detecting errors in a model contributed to consistent prediction of protein structure, which was not possible even several years ago. This review focused on the features and a role of homology modeling in predicting protein structure and described current developments in this field with victorious applications at the different stages of the drug design and discovery.

Protein structure prediction: challenging targets for CASP10

Functional characterization of proteins being one of the major issues in molecular biology is still unsolved due to several resource and technical limitations of experimental structure determination methods. A suitable methodology for accurate prediction of protein confirmations simply from sequence is therefore emerging as the primary modeling goal of researchers today. Global blind protein structure prediction summit, entitled Critical Assessment of Structure Prediction (CASP), critically assesses the modeling methodologies, to track our algorithmic path development. But our success is still impeded by incompetent modeling methodologies and several key technical lacunas. There is still a great need to focus some key issues for bridging the major though considered trivial gaps, in the upcoming CASP to pave and demarcate our correct way of developing a consistently accurate prediction methodology in the near future. Major problems resulting in divergence of our predicted models from their actual native states are thus highlighted with suggested more stringent and reliable assessment considerations in the CASP test.

Conformational dynamics of insulin

We have exploited a prandial insulin analog to elucidate the underlying structure and dynamics of insulin as a monomer in solution. A model was provided by insulin lispro (the active component of Humalog(®); Eli Lilly and Co.). Whereas NMR-based modeling recapitulated structural relationships of insulin crystals (T-state protomers), dynamic anomalies were revealed by amide-proton exchange kinetics in D(2)O. Surprisingly, the majority of hydrogen bonds observed in crystal structures are only transiently maintained in solution, including key T-state-specific inter-chain contacts. Long-lived hydrogen bonds (as defined by global exchange kinetics) exist only at a subset of four α-helical sites (two per chain) flanking an internal disulfide bridge (cystine A20-B19); these sites map within the proposed folding nucleus of proinsulin. The anomalous flexibility of insulin otherwise spans its active surface and may facilitate receptor binding. Because conformational fluctuations promote the degradation of pharmaceutical formulations, we envisage that "dynamic re-engineering" of insulin may enable design of ultra-stable formulations for humanitarian use in the developing world.

Optimal contact definition for reconstruction of contact maps

Background

Contact maps have been extensively used as a simplified representation of protein structures. They capture most important features of a protein's fold, being preferred by a number of researchers for the description and study of protein structures. Inspired by the model's simplicity many groups have dedicated a considerable amount of effort towards contact prediction as a proxy for protein structure prediction. However a contact map's biological interest is subject to the availability of reliable methods for the 3-dimensional reconstruction of the structure.

Results

We use an implementation of the well-known distance geometry protocol to build realistic protein 3-dimensional models from contact maps, performing an extensive exploration of many of the parameters involved in the reconstruction process. We try to address the questions: a) to what accuracy does a contact map represent its corresponding 3D structure, b) what is the best contact map representation with regard to reconstructability and c) what is the effect of partial or inaccurate contact information on the 3D structure recovery. Our results suggest that contact maps derived from the application of a distance cutoff of 9 to 11Å around the C_β atoms constitute the most accurate representation of the 3D structure. The reconstruction process does not provide a single solution to the problem but rather an ensemble of conformations that are within 2Å RMSD of the crystal structure and with lower values for the pairwise average ensemble RMSD. Interestingly it is still possible to recover a structure with partial contact information, although wrong contacts can lead to dramatic loss in reconstruction fidelity.

Conclusions

Thus contact maps represent a valid approximation to the structures with an accuracy comparable to that of experimental methods. The optimal contact definitions constitute key guidelines for methods based on contact maps such as structure prediction through contacts and structural alignments based on maximum contact map overlap.

A self-organizing algorithm for modeling protein loops

Protein loops, the flexible short segments connecting two stable secondary structural units in proteins, play a critical role in protein structure and function. Constructing chemically sensible conformations of protein loops that seamlessly bridge the gap between the anchor points without introducing any steric collisions remains an open challenge. A variety of algorithms have been developed to tackle the loop closure problem, ranging from inverse kinematics to knowledge-based approaches that utilize pre-existing fragments extracted from known protein structures. However, many of these approaches focus on the generation of conformations that mainly satisfy the fixed end point condition, leaving the steric constraints to be resolved in subsequent post-processing steps. In the present work, we describe a simple solution that simultaneously satisfies not only the end point and steric conditions, but also chirality and planarity constraints. Starting from random initial atomic coordinates, each individual conformation is generated independently by using a simple alternating scheme of pairwise distance adjustments of randomly chosen atoms, followed by fast geometric matching of the conformationally rigid components of the constituent amino acids. The method is conceptually simple, numerically stable and computationally efficient. Very importantly, additional constraints, such as those derived from NMR experiments, hydrogen bonds or salt bridges, can be incorporated into the algorithm in a straightforward and inexpensive way, making the method ideal for solving more complex multi-loop problems. The remarkable performance and robustness of the algorithm are demonstrated on a set of protein loops of length 4, 8, and 12 that have been used in previous studies.

Protein loops play an important role in protein function, such as ligand binding, recognition, and allosteric regulation. However, due to their flexibility, it is notoriously difficult to determine their 3D structures using traditional experimental techniques. As a result, one can often find protein structures with missing loops in the Protein Data Bank. Their sequence variability also presents a particular challenge for homology modeling methods, which can only yield good overall structures given sufficient sequence identity and good experimental reference structures. Despite extensive research, the construction of protein loop 3D structures remains an open problem, since a sensible conformation should seamlessly bridge the anchor points without introducing steric clashes within the loop itself or between the loop and its surroundings environment. Here, we present a conceptually simple, mathematically straightforward, numerically robust and computationally efficient approach for building protein loop conformations that simultaneously satisfy end-point, steric, planar and chiral constraints. More importantly, additional constraints derived from experimental sources can be incorporated in a straightforward manner, allowing the processing of more complex structures involving multiple interlocking loops.

SCWRL and MolIDE: computer programs for side-chain conformation prediction and homology modeling

SCWRL and MolIDE are software applications for prediction of protein structures. SCWRL is designed specifically for the task of prediction of side-chain conformations given a fixed backbone usually obtained from an experimental structure determined by X-ray crystallography or NMR. SCWRL is a command-line program that typically runs in a few seconds. MolIDE provides a graphical interface for basic comparative (homology) modeling using SCWRL and other programs. MolIDE takes an input target sequence and uses PSI-BLAST to identify and align templates for comparative modeling of the target. The sequence alignment to any template can be manually modified within a graphical window of the target-template alignment and visualization of the alignment on the template structure. MolIDE builds the model of the target structure on the basis of the template backbone, predicted side-chain conformations with SCWRL and a loop-modeling program for insertion-deletion regions with user-selected sequence segments. SCWRL and MolIDE can be obtained at (http://dunbrack.fccc.edu/Software.php).

Comparative protein structure modeling using MODELLER

Functional characterization of a protein sequence is a common goal in biology, and is usually facilitated by having an 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.

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.

Comparative Modeling of Drug Target Proteins

In this perspective, we begin by describing the comparative protein structure modeling technique and the accuracy of the corresponding models. We then discuss the significant role that comparative prediction plays in drug discovery. We focus on virtual ligand screening against comparative models and illustrate the state of the art by a number of specific examples.

Scoring function for automated assessment of protein structure template quality

We have developed a new scoring function, the template modeling score (TM-score), to assess the quality of protein structure templates and predicted full-length models by extending the approaches used in Global Distance Test (GDT)1 and MaxSub.2 First, a protein size-dependent scale is exploited to eliminate the inherent protein size dependence of the previous scores and appropriately account for random protein structure pairs. Second, rather than setting specific distance cutoffs and calculating only the fractions with errors below the cutoff, all residue pairs in alignment/modeling are evaluated in the proposed score. For comparison of various scoring functions, we have constructed a large-scale benchmark set of structure templates for 1489 small to medium size proteins using the threading program PROSPECTOR_3 and built the full-length models using MODELLER and TASSER. The TM-score of the initial threading alignments, compared to the GDT and MaxSub scoring functions, shows a much stronger correlation to the quality of the final full-length models. The TM-score is further exploited as an assessment of all 'new fold' targets in the recent CASP5 experiment and shows a close coincidence with the results of human-expert visual assessment. These data suggest that the TM-score is a useful complement to the fully automated assessment of protein structure predictions. The executable program of TM-score is freely downloadable at http://bioinformatics.buffalo.edu/TM-score.

Comparative modelling of protein structure and its impact on microbial cell factories

Comparative modeling is becoming an increasingly helpful technique in microbial cell factories as the knowledge of the three-dimensional structure of a protein would be an invaluable aid to solve problems on protein production. For this reason, an introduction to comparative modeling is presented, with special emphasis on the basic concepts, opportunities and challenges of protein structure prediction. This review is intended to serve as a guide for the biologist who has no special expertise and who is not involved in the determination of protein structure. Selected applications of comparative modeling in microbial cell factories are outlined, and the role of microbial cell factories in the structural genomics initiative is discussed.

A restraint molecular dynamics and simulated annealing approach for protein homology modeling utilizing mean angles

BACKGROUND

We have developed the program PERMOL for semi-automated homology modeling of proteins. It is based on restrained molecular dynamics using a simulated annealing protocol in torsion angle space. As main restraints defining the optimal local geometry of the structure weighted mean dihedral angles and their standard deviations are used which are calculated with an algorithm described earlier by Doker et al. (1999, BBRC, 257, 348-350). The overall long-range contacts are established via a small number of distance restraints between atoms involved in hydrogen bonds and backbone atoms of conserved residues. Employing the restraints generated by PERMOL three-dimensional structures are obtained using standard molecular dynamics programs such as DYANA or CNS.

RESULTS

To test this modeling approach it has been used for predicting the structure of the histidine-containing phosphocarrier protein HPr from E. coli and the structure of the human peroxisome proliferator activated receptor gamma (Ppar gamma). The divergence between the modeled HPr and the previously determined X-ray structure was comparable to the divergence between the X-ray structure and the published NMR structure. The modeled structure of Ppar gamma was also very close to the previously solved X-ray structure with an RMSD of 0.262 nm for the backbone atoms.

CONCLUSION

In summary, we present a new method for homology modeling capable of producing high-quality structure models. An advantage of the method is that it can be used in combination with incomplete NMR data to obtain reasonable structure models in accordance with the experimental data.

Prediction of protein tertiary structure using PROFESY, a novel method based on fragment assembly and conformational space annealing

A novel method for ab initio prediction of protein tertiary structures, PROFESY (PROFile Enumerating SYstem), is proposed. This method utilizes the secondary structure prediction information of a query sequence and the fragment assembly procedure based on global optimization. Fifteen-residue-long fragment libraries are constructed using the secondary structure prediction method PREDICT, and fragments in these libraries are assembled to generate full-length chains of a query protein. Tertiary structures of 50 to 100 conformations are obtained by minimizing an energy function for proteins, using the conformational space annealing method that enables one to sample diverse low-lying local minima of the energy. We apply PROFESY for benchmark tests to proteins with known structures to demonstrate its feasibility. In addition, we participated in CASP5 and applied PROFESY to four new-fold targets for blind prediction. The results are quite promising, despite the fact that PROFESY was in its early stages of development. In particular, PROFESY successfully provided us the best model-one structure for the target T0161.

Cluster distance geometry of polypeptide chains

Distance geometry has been a broadly useful tool for dealing with conformational calculations. Customarily each atom is represented as a point, constraints on the distances between some atoms are obtained from experimental or theoretical sources, and then a random sampling of conformations can be calculated that are consistent with the constraints. Although these methods can be applied to small proteins having on the order of 1000 atoms, for some purposes it is advantageous to view the problem at lower resolution. Here distance geometry is generalized to deal with distances between sets of points. In the end, much of the same techniques produce a sampling of different configurations of these sets of points subject to distance constraints, but now the radii of gyration of the different sets play an important role. A simple example is given of how the packing constraints for polypeptide chains combine with loose distance constraints to give good calculated protein conformers at a very low resolution.

Conformational sampling by self-organization

A new stochastic algorithm for conformational sampling is described. The algorithm generates molecular conformations that are consistent with a set of geometric constraints, which include interatomic distance bounds and chiral volumes derived from the molecular connectivity table. The algorithm repeatedly selects individual geometric constraints at random and updates the respective atomic coordinates toward satisfying the chosen constraint. When compared to a conventional distance geometry algorithm based on the same set of geometric constraints, our method is faster and generates conformations that are more diverse and more energetically favorable.

Rebuilt 3D structure of the chloroplast f1 ATPase-tentoxin complex

The F1 part of the chloroplast H+ adenosine triphosphate (ATP)-synthase (CF1) strongly interacts with tentoxin, a natural fungous cyclic tetrapeptide known to inhibit the chloroplast enzyme and not the mammalian mitochondrial enzyme. Whereas the synthesis or the hydrolysis of ATP requires the stepwise rotation of the protein rotor gamma within the (alphabeta)3 crown, only one molecule of tentoxin is needed to fully inhibit the complex. With the help of an original homology modeling technique, based on robust distance geometry protocols, we built a tridimensional model of the alpha3beta3gamma CF1) subcomplex (3200 esidues), in which we introduced three different nucleotide occupancies to check their possible influence on the tentoxin binding site. Simultaneous comparison of three available high-resolution X-ray structures of F1, performed with a local structural alignment search tool, led to characterizing common structural blocks and the distorsions experienced by the complex during the catalytic turnover. The common structural blocks were used as a starting point of the spinach CF1 structure rebuilding. Finally, tentoxin was docked into its putative binding site of the reconstructed structure. The docking method was initially validated in the mitochondrial enzyme by its ability to relocate nucleotides into their original position in the crystal. Tentoxin binding was found possible to the two alpha/beta interfaces associated with the empty and adenosine diphosphate (ADP)-loaded catalytic sites, but not to the one associated with the ATP-loaded site. These results suggest a mechanism of CF1 inhibition by one molecule of tentoxin, by the impossibility of the alpha/beta interface bearing tentoxin to pass through the ATP-loaded state.

Evolution and physics in comparative protein structure modeling

From a physical perspective, the native structure of a protein is a consequence of physical forces acting on the protein and solvent atoms during the folding process. From a biological perspective, the native structure of proteins is a result of evolution over millions of years. Correspondingly, there are two types of protein structure prediction methods, de novo prediction and comparative modeling. We review comparative protein structure modeling and discuss the incorporation of physical considerations into the modeling process. A good starting point for achieving this aim is provided by comparative modeling by satisfaction of spatial restraints. Incorporation of physical considerations is illustrated by an inclusion of solvation effects into the modeling of loops.

Molecular modelling in structural biology

Molecular modelling is a powerful methodology for analysing the three dimensional structure of biological macromolecules. There are many ways in which molecular modelling methods have been used to address problems in structural biology. It is not widely appreciated that modelling methods are often an integral component of structure determination by NMR spectroscopy and X-ray crystallography. In this review we consider some of the numerous ways in which modelling can be used to interpret and rationalise experimental data and in constructing hypotheses that can be tested by experiment. Genome sequencing projects are producing a vast wealth of data describing the protein coding regions of the genome under study. However, only a minority of the protein sequences thus identified will have a clear sequence homology to a known protein. In such cases valuable three-dimensional models of the protein coding sequence can be constructed by homology modelling methods. Threading methods, which used specialised schemes to relate protein sequences to a library of known structures, have been shown to be able to identify the likely protein fold even in cases where there is no clear sequence homology. The number of protein sequences that cannot be assigned to a structural class by homology or threading methods, simply because they belong to a previously unidentified protein folding class, will decrease in the future as collaborative efforts in systematic structure determination begin to develop. For this reason, modelling methods are likely to become increasingly useful in the near future. The role of the blind prediction contests, such as the Critical Assessment of techniques for protein Structure Prediction (CASP), will be briefly discussed. Methods for modelling protein-ligand and protein-protein complexes are also described and examples of their applications given.

Protein structure prediction and structural genomics

Genome sequencing projects are producing linear amino acid sequences, but full understanding of the biological role of these proteins will require knowledge of their structure and function. Although experimental structure determination methods are providing high-resolution structure information about a subset of the proteins, computational structure prediction methods will provide valuable information for the large fraction of sequences whose structures will not be determined experimentally. The first class of protein structure prediction methods, including threading and comparative modeling, rely on detectable similarity spanning most of the modeled sequence and at least one known structure. The second class of methods, de novo or ab initio methods, predict the structure from sequence alone, without relying on similarity at the fold level between the modeled sequence and any of the known structures. In this Viewpoint, we begin by describing the essential features of the methods, the accuracy of the models, and their application to the prediction and understanding of protein function, both for single proteins and on the scale of whole genomes. We then discuss the important role that protein structure prediction methods play in the growing worldwide effort in structural genomics.

The dimerization domain of HNF-1alpha: structure and plasticity of an intertwined four-helix bundle with application to diabetes mellitus

Maturity-onset diabetes mellitus of the young (MODY) is a human genetic syndrome most commonly due to mutations in hepatocyte nuclear factor-1alpha (HNF-1alpha). Here, we describe the crystal structure of the HNF-1alpha dimerization domain at 1.7 A resolution and assess its structural plasticity. The crystal's low solvent content (23%, v/v) leads to tight packing of peptides in the lattice. Two independent dimers, similar in structure, are formed in the unit cell by a 2-fold crystallographic symmetry axis. The dimers define a novel intertwined four-helix bundle (4HB). Each protomer contains two alpha-helices separated by a sharp non-canonical turn. Dimer-related alpha-helices form anti-parallel coiled-coils, including an N-terminal "mini-zipper" complementary in structure, symmetry and surface characteristics to transcriptional coactivator dimerization cofactor of HNF-1 (DCoH). A confluence of ten leucine side-chains (five per protomer) forms a hydrophobic core. Isotope-assisted NMR studies demonstrate that a similar intertwined dimer exists in solution. Comparison of structures obtained in multiple independent crystal forms indicates that the mini-zipper is a stable structural element, whereas the C-terminal alpha-helix can adopt a broad range of orientations. Segmental alignment of the mini-zipper (mean pairwise root-mean-square difference (rmsd) in C(alpha) coordinates of 0.29 A) is associated with a 2.1 A mean C(alpha) rmsd displacement of the C-terminal coiled-coil. The greatest C-terminal structural variation (4.1 A C(alpha) rmsd displacement) is observed in the DCoH-bound peptide. Diabetes-associated mutations perturb distinct structural features of the HNF-1alpha domain. One mutation (L12H) destabilizes the domain but preserves structural specificity. Adjoining H12 side-chains in a native-like dimer are predicted to alter the functional surface of the mini-zipper involved in DCoH recognition. The other mutation (G20R), by contrast, leads to a dimeric molten globule, as indicated by its 1H-NMR features and fluorescent binding of 1-anilino-8-naphthalene sulfonate. We propose that a glycine-specific turn configuration enables specific interactions between the mini-zipper and the C-terminal coiled-coil.

Comparative protein structure modeling of genes and genomes

Comparative modeling predicts the three-dimensional 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. The number of protein sequences that can be modeled and the accuracy of the predictions are increasing steadily because of the growth in the number of known protein structures and because of the improvements in the modeling software. Further advances are necessary in recognizing weak sequence-structure similarities, aligning sequences with structures, modeling of rigid body shifts, distortions, loops and side chains, as well as detecting errors in a model. Despite these problems, it is currently possible to model with useful accuracy significant parts of approximately one third of all known protein sequences. The use of individual comparative models in biology is already rewarding and increasingly widespread. A major new challenge for comparative modeling is the integration of it with the torrents of data from genome sequencing projects as well as from functional and structural genomics. In particular, there is a need to develop an automated, rapid, robust, sensitive, and accurate comparative modeling pipeline applicable to whole genomes. Such large-scale modeling is likely to encourage new kinds of applications for the many resulting models, based on their large number and completeness at the level of the family, organism, or functional network.

The active site of drosomycin, a small insect antifungal protein, delineated by comparison with the modeled structure of Rs-AFP2, a plant antifungal protein

Drosomycin is the first strictly antifungal protein isolated from an insect (Drosophila melanogaster). The solution structure of this 44-residue protein has been reported previously. It involves a three-stranded beta-sheet and an alpha-helix, the protein global fold being maintained by four disulfide bridges. Rs-AFP2 is a plant antifungal protein exhibiting 41% sequence similarity with drosomycin. Mutational analysis of Rs-AFP2 showed the importance of some residues in the antifungal activity of the protein against the fungus target. In order to determine the structural features responsible for antifungal activity in both drosomycin and Rs-AFP2, we modeled the three-dimensional structure of Rs-AFP2, and of other antifungal proteins, using the solution structure of drosomycin as a template. Structure analysis of drosomycin and Rs-AFP2, and comparisons with the other modeled antifungal structures, revealed that the two proteins shared a hydrophobic cluster located at the protein surface in which a lysine residue is embedded. Based on these close structural similarities and the experimental data available for Rs-AFP2 mutants, an antifungal active site of the insect protein is proposed.

Refinement of modelled structures by knowledge-based energy profiles and secondary structure prediction: application to the human procarboxypeptidase A2

Knowledge-based energy profiles combined with secondary structure prediction have been applied to molecular modelling refinement. To check the procedure, three different models of human procarboxypeptidase A2 (hPCPA2) have been built using the 3D structures of procarboxypeptidase A1 (pPCPA1) and bovine procarboxypeptidase A (bPCPA) as templates. The results of the refinement can be tested against the X-ray structure of hPCPA2 which has been recently determined. Regions miss-modelled in the activation segment of hPCPA2 were detected by means of pseudo-energies using Prosa II and modified afterwards according to the secondary structure prediction. Moreover, models obtained by automated methods as COMPOSER, MODELLER and distance restraints have also been compared, where it was found possible to find out the best model by means of pseudo-energies. Two general conclusions can be elicited from this work: (1) on a given set of putative models it is possible to distinguish among them the one closest to the crystallographic structure, and (2) within a given structure it is possible to find by means of pseudo-energies those regions that have been defectively modelled.

Analysis and prediction of inter-strand packing distances between beta-sheets of globular proteins

Any two beta-strands belonging to two different beta-sheets in a protein structure are considered to pack interactively if each beta-strand has at least one residue that undergoes a loss of one tenth or more of its solvent contact surface area upon packing. A data set of protein 3-D structures (determined at 2.5 A resolution or better), corresponding to 428 protein chains, contains 1986 non-identical pairs of beta-strands involved in interactive packing. The inter-axial distance between these is significantly correlated to the weighted sum of the volumes of the interacting residues at the packing interface. This correlation can be used to predict the changes in the inter-sheet distances in equivalent beta-sheets in homologous proteins and, therefore, is of value in comparative modelling of proteins.

Determinants for differential effects on D-Ala-D-lactate vs D-Ala-D-Ala formation by the VanA ligase from vancomycin-resistant enterococci

Bacteria with either intrinsic or inducible resistance to vancomycin make peptidoglycan (PG) precursors of lowered affinity for the antibiotic by switching the PG-D-Ala-D-Ala termini that are the antibiotic-binding target to either PG-D-Ala-D-lactate or PG-D-Ala-D-Ser as a consequence of altered specificity of the D-Ala-D-X ligases in the cell wall biosynthetic pathway. The VanA ligase of vancomycin-resistant enterococci, a D-Ala-D-lactate depsipeptide ligase, has the ability to recognize and activate the weak nucleophile D-lactate selectively over D-Ala(2) to capture the D-Ala(1)-OPO(3)(2)(-) intermediate in the ligase active site. To ensure this selectivity in catalysis, VanA largely rejects the protonated (NH(3)(+)) form of D-Ala at subsite 2 (K(M2) of 210 mM at pH 7.5) but not at subsite 1. In contrast, the deprotonated (NH(2)) form of D-Ala (K(M2) of 0.66 mM, k(cat) of 550 min(-)(1)) is a 17-fold better substrate compared to D-lactate (K(M) of 0.69 mM, k(cat) of 32 min(-)(1)). The low concentration of the free amine form of D-Ala at physiological conditions (i.e., 0.1% at pH 7.0) explains the inefficiency of VanA in dipeptide synthesis. Mutational analysis revealed a residue in the putative omega-loop region, Arg242, which is partially responsible for electrostatically repelling the protonated form of D-Ala(2). The VanA enzyme represents a subfamily of D-Ala-D-X ligases in which two key active-site residues (Lys215 and Tyr216) in the active-site omega-loop of the Escherichia coli D-Ala-D-Ala ligase are absent. To look for functional complements in VanA, we have mutated 20 residues and evaluated effects on catalytic efficiency for both D-Ala-D-Ala dipeptide and D-Ala-D-lactate depsipeptide ligation. Mutation of Asp232 caused substantial defects in both dipeptide and depsipeptide ligase activity, suggesting a role in maintaining the loop position. In contrast, the H244A mutation caused an increase in K(M2) for D-lactate but not D-Ala, indicating a differential role for His244 in the recognition of the weaker nucleophile D-lactate. Replacement of the VanA omega-loop by that of VanC2, a D-Ala-D-Ser ligase, eliminated D-Ala-D-lactate activity while improving by 3-fold the catalytic efficacy of D-Ala-D-Ala and D-Ala-D-Ser activity.

Analysis of interactive packing of secondary structural elements in alpha/beta units in proteins

An alpha-helix and a beta-strand are said to be interactively packed if at least one residue in each of the secondary structural elements loses 10% of its solvent accessible contact area on association with the other secondary structural element. An analysis of all such 5,975 nonidentical alpha/beta units in protein structures, defined at < or = 2.5 A resolution, shows that the interaxial distance between the alpha-helix and the beta-strand is linearly correlated with the residue-dependent function, log[(V/nda)/n-int], where V is the volume of amino acid residues in the packing interface, nda is the normalized difference in solvent accessible contact area of the residues in packed and unpacked secondary structural elements, and n-int is the number of residues in the packing interface. The beta-sheet unit (beta u), defined as a pair of adjacent parallel or antiparallel hydrogen-bonded beta-strands, packing with an alpha-helix shows a better correlation between the interaxial distance and log(V/nda) for the residues in the packing interface. This packing relationship is shown to be useful in the prediction of interaxial distances in alpha/beta units using the interacting residue information of equivalent alpha/beta units of homologous proteins. It is, therefore, of value in comparative modeling of protein structures.

Structure-based drug design

Most of the techniques used in structure-based drug design have experienced significant improvements in the past few years, resulting in a marked enhancement of the speed and the efficacy of this approach. At the same time, it was thought that the future of drug design lay in strategies involving solely combinatorial chemistry. It is becoming evident, however, that the development of future drugs will use a combination of methods that will contain a major component of structure-based design.

Thermodynamic and structural analysis of phosphotyrosine polypeptide binding to Grb2-SH2

A thermodynamic analysis using isothermal titration calorimetry (ITC) has been performed to examine the binding interaction between the SH2 (Src homology 2) domain of growth factor receptor binding protein 2 (Grb2-SH2) and one of its phosphotyrosine (pY) polypeptide ligands. Interaction of the Shc-derived phosphotyrosine hexapeptide Ac-SpYVNVQ-NH2 with Grb2-SH2 was both enthalpically and entropically favorable (DeltaH = -7.55 kcal mol-1, -TDeltaS = -1.46 kcal mol-1 , DeltaG = -9.01 kcal mol-1, T = 20 degrees C). ITC experiments using five alanine-substituted peptides were performed to examine the role of each side chain in binding. The results were consistent with homology models of the Grb2-SH2-Shc hexapeptide complex which identified several possible hydrogen bonds between Grb2-SH2 and the phosphotyrosine and conserved asparagine(+2) side chains of the Shc hexapeptide. These studies also demonstrated that the hydrophobic valine(+1) side chain contributes significantly to the favorable entropic component of binding. The thermodynamic and structural data are consistent with a Grb2-SH2 recognition motif of pY-hydrophobic-N-X (where X is any amino acid residue). The measured heat capacity of binding (DeltaCp = -146 cal mol-1 K-1) was very similar to computed values using semiempirical estimates (DeltaCp = -106 to -193 cal mol-1 K-1) derived from apolar and polar accessible surface area values calculated from several homology models of the Grb2-SH2-Shc hexapeptide complex. The homology model which most closely reproduced the measured DeltaCp value is also the model which had the lowest RMS deviation from the subsequently determined crystal structure. Calculations based on the thermodynamic data and these semiempirical estimates indicated that the binding event involves burial of nearly comparable apolar (677 A2) and polar (609 A2) surface areas.

Homology modeling using simulated annealing of restrained molecular dynamics and conformational search calculations with CONGEN: application in predicting the three-dimensional structure of murine homeodomain Msx-1

We have developed an automatic approach for homology modeling using restrained molecular dynamics and simulated annealing procedures, together with conformational search algorithms available in the molecular mechanics program CONGEN (Bruccoleri RE, Karplus M, 1987, Biopolymers 26:137-168). The accuracy of the method is validated by "predicting" structures of two homeodomain proteins with known three-dimensional structures, and then applied to predict the three-dimensional structure of the homeodomain of the murine Msx-1 transcription factor. Regions of the unknown protein structure that are highly homologous to the known template structure are constrained by "homology distance constraints," whereas the conformations of nonhomologous regions of the unknown protein are defined only by the potential energy function. A full energy function (excluding explicit solvent) is employed to ensure that the calculated structures have good conformational energies and are physically reasonable. As in NMR structure determinations, information on the consistency of the structure prediction is obtained by superposition of the resulting family of protein structures. In this paper, our homology modeling algorithm is described and compared with related homology modeling methods using spatial constraints derived from the structures of homologous proteins. The software is then used to predict the DNA-bound structures of three homeodomain proteins from the X-ray crystal structure of the engrailed homeodomain protein (Kissinger CR et al., 1990, Cell 63:579-590). The resulting backbone and side-chain conformations of the modeled yeast Mat alpha 2 and D. melanogaster Antennapedia homeodomains are excellent matches to the corresponding published X-ray crystal (Wolberger C et al., 1991, Cell 67:517-528) and NMR (Billeter M et al., 1993, J Mol Biol 234:1084-1097) structures, respectively. Examination of these structures of Msx-1 reveals a network of highly conserved surface salt bridges that are proposed to play a role in regulating protein-protein interactions of homeodomains in transcription complexes.

Advances in comparative protein-structure modelling

Comparative modelling of protein 3D structure can now be applied with reasonable accuracy to ten times more protein sequences than the number of experimentally determined protein structures. A protein sequence that has at least 40% identity to a known structure can be modelled automatically with an accuracy approaching that of a low resolution X-ray structure or a medium resolution NMR structure. Currently, the errors in comparative models include mistakes in the packing of sidechains, in the conformation and shifts of the core segments and loops, and, most importantly, in an incorrect alignment of the modelled sequence with related known structures. Nevertheless, the number of applications in which comparative modelling has been proven to be useful has grown rapidly.

Predicting the conformational class of short and medium size loops connecting regular secondary structures: application to comparative modelling

Loops are regions of non-repetitive conformation connecting regular secondary structures. They are both the most difficult and error prone regions of a protein to solve by X-ray crystallography and the hardest regions to model using comparative procedures. Although a loop can sometimes be modelled from a homologue, very often it must be selected from outside the family. The loop prediction procedure, SLoop, attempts to identify the conformational class of the loop rather than to select a specific loop from a set of fragments extracted from known structures or generated ab initio. Templates are constructed for each of the 161 loop conformational classes that have been identified from the clustering of the structures of some 2024 loops of one to eight residues in length. A class template describes both sequence preferences and relative disposition of bounding secondary structures. During comparative modelling, the conformation of a loop can be predicted by identifying a loop class with which its sequence and disposition of bounding secondary structures are compatible. The procedure is tested on an unrelated non-redundant set of 1785 loops under stringent and lax evaluation schemes. Optimal sequence score cut-offs are identified such that the prediction rate is equal to the percentage of loops assigned to acceptable classes. Under the stringent evaluation, at the optimal sequence score cut-off, a conformation is predicted for 50% of loops of which 47% are correct, while under the lax evaluation a conformation is predicted for 63% of loops of which 54% are correct. Sequence score is shown to be a good indicator of the probability of a prediction being correct. Loop length also has a strong affect on prediction outcomes. Considering only loops of two to five residues in length, under the stringent evaluation 62% of loops are predicted with 52% of these predictions being correct while under the lax evaluation predictions are provided for 75% of loops of which 57% are correct.

Conformational analysis using distance geometry methods

Distance geometry methods have been used extensively to build models of molecules of various sizes, including small molecules, peptides, and proteins. These methods are often overlooked as tools for conformational analysis, even though they often perform as well as other conformational sampling methods. We have implemented two new distance geometry approaches in the DGEOM95 package. In the first new method, the traditional embedding algorithm is replaced with a procedure that generates random 4D coordinates for each atom, followed by refinement of these coordinates into 3D using the distance geometry error function. The conformational sampling produced by this method is comparable to that obtained with partial metrization, and superior to that obtained with the original embedding procedure. In the second method, a molecular dynamics step is included in the refinement stage. Although this method can be applied to any embedding algorithm, substantial improvements in sampling are seen primarily with the original embedding algorithm.