Protein Geometry Database: a flexible engine to explore backbone conformations and their relationships to covalent geometry

Current Approaches in Supersecondary Structures Investigation

Proteins expressed during the cell cycle determine cell function, topology, and responses to environmental influences. The development and improvement of experimental methods in the field of structural biology provide valuable information about the structure and functions of individual proteins. This work is devoted to the study of supersecondary structures of proteins and determination of their structural motifs, description of experimental methods for their detection, databases, and repositories for storage, as well as methods of molecular dynamics research. The interest in the study of supersecondary structures in proteins is due to their autonomous stability outside the protein globule, which makes it possible to study folding processes, conformational changes in protein isoforms, and aberrant proteins with high productivity.

A complete Fourier-synthesis-based backbone-conformation-dependent library for proteins

While broadening the applicability of (φ/ψ)-dependent target values for the bond angles in the peptide backbone, sequence/conformation categories with too few residues to analyze via previous methods were encountered. Here, a method of describing a conformation-dependent library (CDL) using two-dimensional Fourier coefficients is reported where the number of coefficients for individual categories is determined via complete cross-validation. Sample sizes are increased further by selective blending of categories with similar patterns of conformational dependence. An additional advantage of the Fourier-synthesis-based CDL is that it uses continuous functions and has no artifactual steps near the edges of populated regions of φ/ψ space. A set of libraries for the seven main-chain bond angles, along with the ω and ζ angles, was created based on a set of Fourier analyses of 48 368 residues selected from high-resolution models in the wwPDB. This new library encompasses both trans- and cis-peptide bonds and outperforms currently used discrete CDLs.

Arginine off-kilter: guanidinium is not as planar as restraints denote

Crystallographic refinement of macromolecular structures relies on stereochemical restraints to mitigate the typically poor data-to-parameter ratio. For proteins, each amino acid has a unique set of geometry restraints which represent stereochemical information such as bond lengths, valence angles, torsion angles, dihedrals and planes. It has been shown that the geometry in refined structures can differ significantly from that present in libraries; for example, it was recently reported that the guanidinium moiety in arginine is not symmetric. In this work, the asymmetry of the N^ϵ-C^ζ-N^η1 and N^ϵ-C^ζ-N^η2 valence angles in the guanidinium moiety is confirmed. In addition, it was found that the C^δ atom can deviate significantly (more than 20°) from the guanidinium plane. This requires the relaxation of the planar restraint for the C^δ atom, as it otherwise causes the other atoms in the group to compensate by distorting the guanidinium core plane. A new set of restraints for the arginine side chain have therefore been formulated, and are available in the software package Phenix, that take into account the asymmetry of the group and the planar deviation of the C^δ atom. This is an example of the need to regularly revisit the geometric restraint libraries used in macromolecular refinement so that they reflect the best knowledge of the structural chemistry of their components available at the time.

Potential therapeutic target identification in the novel 2019 coronavirus: insight from homology modeling and blind docking study

Background

The 2019-nCoV which is regarded as a novel coronavirus is a positive-sense single-stranded RNA virus. It is infectious to humans and is the cause of the ongoing coronavirus outbreak which has elicited an emergency in public health and a call for immediate international concern has been linked to it. The coronavirus main proteinase which is also known as the 3C-like protease (3CLpro) is a very important protein in all coronaviruses for the role it plays in the replication of the virus and the proteolytic processing of the viral polyproteins. The resultant cytotoxic effect which is a product of consistent viral replication and proteolytic processing of polyproteins can be greatly reduced through the inhibition of the viral main proteinase activities. This makes the 3C-like protease of the coronavirus a potential and promising target for therapeutic agents against the viral infection.

Results

This study describes the detailed computational process by which the 2019-nCoV main proteinase coding sequence was mapped out from the viral full genome, translated and the resultant amino acid sequence used in modeling the protein 3D structure. Comparative physiochemical studies were carried out on the resultant target protein and its template while selected HIV protease inhibitors were docked against the protein binding sites which contained no co-crystallized ligand.

Conclusion

In line with results from this study which has shown great consistency with other scientific findings on coronaviruses, we recommend the administration of the selected HIV protease inhibitors as first-line therapeutic agents for the treatment of the current coronavirus epidemic.

Systematic Exploration of Protein Conformational Space Using a Distance Geometry Approach

The optimization approaches classically used during the determination of protein structure encounter various difficulties, especially when the size of the conformational space is large. Indeed, in such a case, algorithmic convergence criteria are more difficult to set up. Moreover, the size of the search space makes it difficult to achieve a complete exploration. The interval branch-and-prune (iBP) approach, based on the reformulation of the distance geometry problem (DGP) provides a theoretical frame for the generation of protein conformations, by systematically sampling the conformational space. When an appropriate subset of interatomic distances is known exactly, this worst-case exponential-time algorithm is provably complete and fixed-parameter tractable. These guarantees, however, immediately disappear as distance measurement errors are introduced. Here we propose an improvement of this approach: threading-augmented interval branch-and-prune (TAiBP), where the combinatorial explosion of the original iBP approach arising from its exponential complexity is alleviated by partitioning the input instances into consecutive peptide fragments and by using self-organizing maps (SOMs) to obtain clusters of similar solutions. A validation of the TAiBP approach is presented here on a set of proteins of various sizes and structures. The calculation inputs are a uniform covalent geometry extracted from force field covalent terms, the backbone dihedral angles with error intervals, and a few long-range distances. For most of the proteins smaller than 50 residues and interval widths of 20°, the TAiBP approach yielded solutions with RMSD values smaller than 3 Å with respect to the initial protein conformation. The efficiency of the TAiBP approach for proteins larger than 50 residues will require the use of nonuniform covalent geometry and may have benefits from the recent development of residue-specific force-fields.

Recent applications of deep learning and machine intelligence on in silico drug discovery: methods, tools and databases

The identification of interactions between drugs/compounds and their targets is crucial for the development of new drugs. In vitro screening experiments (i.e. bioassays) are frequently used for this purpose; however, experimental approaches are insufficient to explore novel drug-target interactions, mainly because of feasibility problems, as they are labour intensive, costly and time consuming. A computational field known as 'virtual screening' (VS) has emerged in the past decades to aid experimental drug discovery studies by statistically estimating unknown bio-interactions between compounds and biological targets. These methods use the physico-chemical and structural properties of compounds and/or target proteins along with the experimentally verified bio-interaction information to generate predictive models. Lately, sophisticated machine learning techniques are applied in VS to elevate the predictive performance. The objective of this study is to examine and discuss the recent applications of machine learning techniques in VS, including deep learning, which became highly popular after giving rise to epochal developments in the fields of computer vision and natural language processing. The past 3 years have witnessed an unprecedented amount of research studies considering the application of deep learning in biomedicine, including computational drug discovery. In this review, we first describe the main instruments of VS methods, including compound and protein features (i.e. representations and descriptors), frequently used libraries and toolkits for VS, bioactivity databases and gold-standard data sets for system training and benchmarking. We subsequently review recent VS studies with a strong emphasis on deep learning applications. Finally, we discuss the present state of the field, including the current challenges and suggest future directions. We believe that this survey will provide insight to the researchers working in the field of computational drug discovery in terms of comprehending and developing novel bio-prediction methods.

Dissection of Factors Affecting the Variability of the Peptide Bond Geometry and Planarity

Proteins frequently assume complex three-dimensional structures characterized by marginal thermodynamic stabilities. In this scenario, deciphering the folding code of these molecular giants with clay feet is a cumbersome task. Studies performed in last years have shown that the interplay between backbone geometry and local conformation has an important impact on protein structures. Although the variability of several geometrical parameters of protein backbone has been established, the role of the structural context in determining these effects has been hitherto limited to the valence bond angle τ (NC^αC). We here investigated the impact of different factors on the observed variability of backbone geometry and peptide bond planarity. These analyses corroborate the notion that the local conformation expressed in terms of (ϕ, ψ) dihedrals plays a predominant role in dictating the variability of these parameters. The impact of secondary structure is limited to bond angles which involve atoms that are usually engaged in H-bonds and, therefore, more susceptible to the structural context. Present data also show that the nature of the side chain has a significant impact on angles such as NC^αC^β and C^βC^αC. In conclusion, our analyses strongly support the use of variability of protein backbone geometry in structure refinement, validation, and prediction.

Ensemblator v3: Robust atom-level comparative analyses and classification of protein structure ensembles

Ensembles of protein structures are increasingly used to represent the conformational variation of a protein as determined by experiment and/or by molecular simulations, as well as uncertainties that may be associated with structure determinations or predictions. Making the best use of such information requires the ability to quantitatively compare entire ensembles. For this reason, we recently introduced the Ensemblator (Clark et al., Protein Sci 2015; 24:1528), a novel approach to compare user-defined groups of models, in residue level detail. Here we describe Ensemblator v3, an open-source program that employs the same basic ensemble comparison strategy but includes major advances that make it more robust, powerful, and user-friendly. Ensemblator v3 carries out multiple sequence alignments to facilitate the generation of ensembles from non-identical input structures, automatically optimizes the key global overlay parameter, optionally performs "ensemble clustering" to classify the models into subgroups, and calculates a novel "discrimination index" that quantifies similarities and differences, at residue or atom level, between each pair of subgroups. The clustering and automatic options mean that no pre-knowledge about an ensemble is required for its analysis. After describing the novel features of Ensemblator v3, we demonstrate its utility using three case studies that illustrate the ease with which complex analyses are accomplished, and the kinds of insights derived from clustering into subgroups and from the detailed information that locates significant differences. The Ensemblator v3 enhances the structural biology toolbox by greatly expanding the kinds of problems to which this ensemble comparison strategy can be applied.

Mass & secondary structure propensity of amino acids explain their mutability and evolutionary replacements

Why is an amino acid replacement in a protein accepted during evolution? The answer given by bioinformatics relies on the frequency of change of each amino acid by another one and the propensity of each to remain unchanged. We propose that these replacement rules are recoverable from the secondary structural trends of amino acids. A distance measure between high-resolution Ramachandran distributions reveals that structurally similar residues coincide with those found in substitution matrices such as BLOSUM: Asn ↔ Asp, Phe ↔ Tyr, Lys ↔ Arg, Gln ↔ Glu, Ile ↔ Val, Met → Leu; with Ala, Cys, His, Gly, Ser, Pro, and Thr, as structurally idiosyncratic residues. We also found a high average correlation (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\overline{R}$$\end{document}R¯ = 0.85) between thirty amino acid mutability scales and the mutational inertia (I_X), which measures the energetic cost weighted by the number of observations at the most probable amino acid conformation. These results indicate that amino acid substitutions follow two optimally-efficient principles: (a) amino acids interchangeability privileges their secondary structural similarity, and (b) the amino acid mutability depends directly on its biosynthetic energy cost, and inversely with its frequency. These two principles are the underlying rules governing the observed amino acid substitutions.

Factors affecting the amplitude of the τ angle in proteins: a revisitation

The protein folded state is the result of the fine balance of a variety of different forces. Even minor structural perturbations may have a significant impact on the stability of these macromolecules. Studies carried out in recent decades have led to the convergent view that proteins are endowed with a flexible spine. One of the open issues related to protein local backbone geometry is the identification of the factors that influence the amplitude of the τ (N—Cα—C) angle. Here, statistical analyses performed on an updated ensemble of X-ray protein structures by dissecting the contribution of the major factors that can potentially influence the local backbone geometry of proteins are reported. The data clearly indicate that the local backbone conformation has a prominent impact on the modulation of the τ angle. Therefore, a proper assessment of the impact of the other potential factors can only be appropriately evaluated when small (φ, ψ) regions are considered. Here, it is shown that when the contribution of the backbone conformation is removed by considering small (φ, ψ) areas, an impact of secondary structure, as defined byDSSP, and/or the residue type on τ is still detectable, although to a limited extent. Indeed, distinct τ-value distributions are detected for Pro/Gly and β-branched (Ile/Val) residues. The key role of the local backbone conformation highlighted here supports the use of variable local backbone geometry in protein refinement protocols.

Insights into Thiol-Aromatic Interactions: A Stereoelectronic Basis for S-H/π Interactions

Thiols can engage favorably with aromatic rings in S-H/π interactions, within abiological systems and within proteins. However, the underlying bases for S-H/π interactions are not well understood. The crystal structure of Boc-l-4-thiolphenylalanine tert-butyl ester revealed crystal organization centered on the interaction of the thiol S-H with the aromatic ring of an adjacent molecule, with a through-space Hthiol···Caromatic distance of 2.71 Å, below the 2.90 Å sum of the van der Waals radii of H and C. The nature of this interaction was further examined by DFT calculations, IR spectroscopy, solid-state NMR spectroscopy, and analysis of the Cambridge Structural Database. The S-H/π interaction was found to be driven significantly by favorable molecular orbital interactions, between an aromatic π donor orbital and the S-H σ* acceptor orbital (a π → σ* interaction). For comparison, a structural analysis of O-H/π interactions and of cation/π interactions of alkali metal cations with aromatic rings was conducted. Na+ and K+ exhibit a significant preference for the centroid of the aromatic ring and distances near the sum of the van der Waals and ionic radii, as expected for predominantly electrostatic interactions. Li+ deviates substantially from Na+ and K+. The S-H/π interaction differs from classical cation/π interactions by the preferential alignment of the S-H σ* toward the ring carbons and an aromatic π orbital rather than toward the aromatic centroid. These results describe a potentially broadly applicable approach to understanding the interactions of weakly polar bonds with π systems.

Beyond basins: φ,ψ preferences of a residue depend heavily on the φ,ψ values of its neighbors

The Ramachandran plot distributions of nonglycine residues from experimentally determined structures are routinely described as grouping into one of six major basins: β, PII , α, αL , ξ and γ'. Recent work describing the most common conformations adopted by pairs of residues in folded proteins [i.e., (φ,ψ)2 -motifs] showed that commonly described major basins are not true single thermodynamic basins, but are composed of distinct subregions that are associated with various conformations of either the preceding or following neighbor residue. Here, as documentation of the extent to which the conformational preferences of a central residue are influenced by the conformations of its two neighbors, we present a set of φ,ψ-plots that are delimited simultaneously by the φ,ψ-angles of its neighboring residues on both sides. The level of influence seen here is typically greater than the influence associated with considering the identities of neighboring residues, implying that the use of this heretofore untapped information can improve the accuracy of structure prediction algorithms and low resolution protein structure refinement.

Native proteins trap high-energy transit conformations

During protein folding and as part of some conformational changes that regulate protein function, the polypeptide chain must traverse high-energy barriers that separate the commonly adopted low-energy conformations. How distortions in peptide geometry allow these barrier-crossing transitions is a fundamental open question. One such important transition involves the movement of a non-glycine residue between the left side of the Ramachandran plot (that is, ϕ < 0°) and the right side (that is, ϕ > 0°). We report that high-energy conformations with ϕ ~ 0°, normally expected to occur only as fleeting transition states, are stably trapped in certain highly resolved native protein structures and that an analysis of these residues provides a detailed, experimentally derived map of the bond angle distortions taking place along the transition path. This unanticipated information lays to rest any uncertainty about whether such transitions are possible and how they occur, and in doing so lays a firm foundation for theoretical studies to better understand the transitions between basins that have been little studied but are integrally involved in protein folding and function. Also, the context of one such residue shows that even a designed highly stable protein can harbor substantial unfavorable interactions.

Residue-level global and local ensemble-ensemble comparisons of protein domains

Many methods of protein structure generation such as NMR-based solution structure determination and template-based modeling do not produce a single model, but an ensemble of models consistent with the available information. Current strategies for comparing ensembles lose information because they use only a single representative structure. Here, we describe the ENSEMBLATOR and its novel strategy to directly compare two ensembles containing the same atoms to identify significant global and local backbone differences between them on per-atom and per-residue levels, respectively. The ENSEMBLATOR has four components: eePREP (ee for ensemble-ensemble), which selects atoms common to all models; eeCORE, which identifies atoms belonging to a cutoff-distance dependent common core; eeGLOBAL, which globally superimposes all models using the defined core atoms and calculates for each atom the two intraensemble variations, the interensemble variation, and the closest approach of members of the two ensembles; and eeLOCAL, which performs a local overlay of each dipeptide and, using a novel measure of local backbone similarity, reports the same four variations as eeGLOBAL. The combination of eeGLOBAL and eeLOCAL analyses identifies the most significant differences between ensembles. We illustrate the ENSEMBLATOR's capabilities by showing how using it to analyze NMR ensembles and to compare NMR ensembles with crystal structures provides novel insights compared to published studies. One of these studies leads us to suggest that a "consistency check" of NMR-derived ensembles may be a useful analysis step for NMR-based structure determinations in general. The ENSEMBLATOR 1.0 is available as a first generation tool to carry out ensemble-ensemble comparisons.

Bond distances in polypeptide backbones depend on the local conformation

By combining quantum-mechanical analysis of small model peptides and statistical surveys of high-resolution protein structures, a systematic conformational dependence of bond lengths in polypeptide backbones has been unveiled which involves both the peptide bond (C—O and C—N) and those bonds centred on the Cα atom. All of these bond lengths indeed display a systematic variability in the ψ angle according to both calculations and surveys of protein structures. The overall agreement between the computed and the statistical data suggests that these trends are essentially driven by local effects. The dependence of Cα distances on ψ is governed by interactions between the σ system of the Cα moiety and the C—O π system of the peptide bond. Maximum and minimum values for each bond distance are found for conformations with the specific bond perpendicular and parallel to the adjacent CONH peptide plane, respectively. On the other hand, the variability of the C—O and C—N distances is related to the strength of the interactions between the lone pair of the N atom and the C—O π* system, which is modulated by the ψ angle. The C—O and C—N distances are related but their trends are not strictly connected to peptide-bond planarity, although a correlation amongst all of these parameters is expected on the basis of the classical resonance model.

Crystal and molecular structure of the analgesic tetrapeptide, L-Phe-L-Leu-L-Pro-L-Ser

The tetrapeptide, L-Phe-L-Leu-L-Pro-L-Ser (FLPS), alleviates pain in a rat model of post-surgery pain. The crystal structure of the tetrapeptide is solved at high resolution (0.54 Å). The asymmetric unit contains two FLPS molecules, one Zn ion, and four molecules of water with a formula of [Zn(C23H33N4O6)2(H2O)4]. Each Zn ion is octahedrally coordinated with Phe and Ser residues from four peptide molecules [2N+4O]. The linking of Phe and Ser residues of one FLPS molecule to three other FLPS molecules by Zn ion forms a complex consisting of chains of metal ions and FLPS molecules oriented along the b axis. Analysis of molecular packing reveals the coexistence of two FLPS conformers in the same crystal. The crystallographic parameters for [Zn(C23H33N4O6)2(H2O)4] are as follows: space group P21 21 21 , a = 9.8698(2) Å, α = 90°, b = 20.1844(4) Å, β = 90°, c = 25.9302(6) Å, γ = 90°. Volume = 5165.71(19) Å(3), Z = 4, density (calc) = 1.364 Mg/cm(3), and agreement factor R1 = 4.13%.

Conformation-dependent backbone geometry restraints set a new standard for protein crystallographic refinement

Ideal values of bond angles and lengths used as external restraints are crucial for the successful refinement of protein crystal structures at all but the highest of resolutions. The restraints in common use today have been designed on the assumption that each type of bond or angle has a single ideal value that is independent of context. However, recent work has shown that the ideal values are, in fact, sensitive to local conformation, and, as a first step towards using such information to build more accurate models, ultra-high-resolution protein crystal structures have been used to derive a conformation-dependent library (CDL) of restraints for the protein backbone [Berkholz et al. (2009) Structure 17, 1316-1325]. Here, we report the introduction of this CDL into the phenix package and the results of test refinements of thousands of structures across a wide range of resolutions. These tests show that use of the CDL yields models that have substantially better agreement with ideal main-chain bond angles and lengths and, on average, a slightly enhanced fit to the X-ray data. No disadvantages of using the backbone CDL are apparent. In phenix, use of the CDL can be selected by simply specifying the cdl = True option. This successful implementation paves the way for further aspects of the context dependence of ideal geometry to be characterized and applied to improve experimental and predictive modeling accuracy.

Interplay between peptide bond geometrical parameters in nonglobular structural contexts

Several investigations performed in the last two decades have unveiled that geometrical parameters of protein backbone show a remarkable variability. Although these studies have provided interesting insights into one of the basic aspects of protein structure, they have been conducted on globular and water-soluble proteins. We report here a detailed analysis of backbone geometrical parameters in nonglobular proteins/peptides. We considered membrane proteins and two distinct fibrous systems (amyloid-forming and collagen-like peptides). Present data show that in these systems the local conformation plays a major role in dictating the amplitude of the bond angle N-C(α)-C and the propensity of the peptide bond to adopt planar/nonplanar states. Since the trends detected here are in line with the concept of the mutual influence of local geometry and conformation previously established for globular and water-soluble proteins, our analysis demonstrates that the interplay of backbone geometrical parameters is an intrinsic and general property of protein/peptide structures that is preserved also in nonglobular contexts. For amyloid-forming peptides significant distortions of the N-C(α)-C bond angle, indicative of sterical hidden strain, may occur in correspondence with side chain interdigitation. The correlation between the dihedral angles Δω/ψ in collagen-like models may have interesting implications for triple helix stability.

Nonplanar peptide bonds in proteins are common and conserved but not biased toward active sites

The planarity of peptide bonds is an assumption that underlies decades of theoretical modeling of proteins. Peptide bonds strongly deviating from planarity are considered very rare features of protein structure that occur for functional reasons. Here, empirical analyses of atomic-resolution protein structures reveal that trans peptide groups can vary by more than 25° from planarity and that the true extent of nonplanarity is underestimated even in 1.2 Å resolution structures. Analyses as a function of the ϕ,ψ-backbone dihedral angles show that the expected value deviates by ± 8° from planar as a systematic function of conformation, but that the large majority of variation in planarity depends on tertiary effects. Furthermore, we show that those peptide bonds in proteins that are most nonplanar, deviating by over 20° from planarity, are not strongly associated with active sites. Instead, highly nonplanar peptides are simply integral components of protein structure related to local and tertiary structural features that tend to be conserved among homologs. To account for the systematic ϕ,ψ-dependent component of nonplanarity, we present a conformation-dependent library that can be used in crystallographic refinement and predictive protein modeling.

(φ,ψ)₂ motifs: a purely conformation-based fine-grained enumeration of protein parts at the two-residue level

A deep understanding of protein structure benefits from the use of a variety of classification strategies that enhance our ability to effectively describe local patterns of conformation. Here, we use a clustering algorithm to analyze 76,533 all-trans segments from protein structures solved at 1.2 Å resolution or better to create a purely φ,ψ-based comprehensive empirical categorization of common conformations adopted by two adjacent φ,ψ pairs (i.e., (φ,ψ)(2) motifs). The clustering algorithm works in an origin-shifted four-dimensional space based on the two φ,ψ pairs to yield a parameter-dependent list of (φ,ψ)(2) motifs, in order of their prominence. The results are remarkably distinct from and complementary to the standard hydrogen-bond-centered view of secondary structure. New insights include an unprecedented level of precision in describing the φ,ψ angles of both previously known and novel motifs, ordering of these motifs by their population density, a data-driven recommendation that the standard C(α(i))…C(α(i+3))<7 Å criteria for defining turns be changed to 6.5 Å, identification of β-strand and turn capping motifs, and identification of conformational capping by residues in polypeptide II conformation. We further document that the conformational preferences of a residue are substantially influenced by the conformation of its neighbors, and we suggest that accounting for these dependencies will improve protein modeling accuracy. Although the CUEVAS-4D(r(10)є(14)) 'parts list' presented here is only an initial exploration of the complex (φ,ψ)(2) landscape of proteins, it shows that there is value to be had from this approach, and it opens the door to more in-depth characterizations at the (φ,ψ)(2) level and at higher dimensions.

Peptide bond distortions from planarity: new insights from quantum mechanical calculations and peptide/protein crystal structures

By combining quantum-mechanical analysis and statistical survey of peptide/protein structure databases we here report a thorough investigation of the conformational dependence of the geometry of peptide bond, the basic element of protein structures. Different peptide model systems have been studied by an integrated quantum mechanical approach, employing DFT, MP2 and CCSD(T) calculations, both in aqueous solution and in the gas phase. Also in absence of inter-residue interactions, small distortions from the planarity are more a rule than an exception, and they are mainly determined by the backbone ψ dihedral angle. These indications are fully corroborated by a statistical survey of accurate protein/peptide structures. Orbital analysis shows that orbital interactions between the σ system of C(α) substituents and the π system of the amide bond are crucial for the modulation of peptide bond distortions. Our study thus indicates that, although long-range inter-molecular interactions can obviously affect the peptide planarity, their influence is statistically averaged. Therefore, the variability of peptide bond geometry in proteins is remarkably reproduced by extremely simplified systems since local factors are the main driving force of these observed trends. The implications of the present findings for protein structure determination, validation and prediction are also discussed.

Protein design with fragment databases

Structure-based computational methods are popular tools for designing proteins and interactions between proteins because they provide the necessary insight and details required for rational engineering. Here, we first argue that large-scale databases of fragments contain a discrete but complete set of building blocks that can be used to design structures. We show that these structural alphabets can be saturated to provide conformational ensembles that sample the native structure space around energetic minima. Second, we show that catalogs of interaction patterns hold the key to overcome the lack of scaffolds when computationally designing protein interactions. Finally, we illustrate the power of database-driven computational protein design methods by recent successful applications and discuss what challenges remain to push this field forward.

The nucleotide addition cycle of RNA polymerase is controlled by two molecular hinges in the Bridge Helix domain

Background

Cellular RNA polymerases (RNAPs) are complex molecular machines that combine catalysis with concerted conformational changes in the active center. Previous work showed that kinking of a hinge region near the C-terminus of the Bridge Helix (BH-H_C) plays a critical role in controlling the catalytic rate.

Results

Here, new evidence for the existence of an additional hinge region in the amino-terminal portion of the Bridge Helix domain (BH-H_N) is presented. The nanomechanical properties of BH-H_N emerge as a direct consequence of the highly conserved primary amino acid sequence. Mutations that are predicted to influence its flexibility cause corresponding changes in the rate of the nucleotide addition cycle (NAC). BH-H_N displays functional properties that are distinct from BH-H_C, suggesting that conformational changes in the Bridge Helix control the NAC via two independent mechanisms.

Conclusions

The properties of two distinct molecular hinges in the Bridge Helix of RNAP determine the functional contribution of this domain to key stages of the NAC by coordinating conformational changes in surrounding domains.

BriX: a database of protein building blocks for structural analysis, modeling and design

High-resolution structures of proteins remain the most valuable source for understanding their function in the cell and provide leads for drug design. Since the availability of sufficient protein structures to tackle complex problems such as modeling backbone moves or docking remains a problem, alternative approaches using small, recurrent protein fragments have been employed. Here we present two databases that provide a vast resource for implementing such fragment-based strategies. The BriX database contains fragments from over 7000 non-homologous proteins from the Astral collection, segmented in lengths from 4 to 14 residues and clustered according to structural similarity, summing up to a content of 2 million fragments per length. To overcome the lack of loops classified in BriX, we constructed the Loop BriX database of non-regular structure elements, clustered according to end-to-end distance between the regular residues flanking the loop. Both databases are available online (http://brix.crg.es) and can be accessed through a user-friendly web-interface. For high-throughput queries a web-based API is provided, as well as full database downloads. In addition, two exciting applications are provided as online services: (i) user-submitted structures can be covered on the fly with BriX classes, representing putative structural variation throughout the protein and (ii) gaps or low-confidence regions in these structures can be bridged with matching fragments.

A fresh look at the Ramachandran plot and the occurrence of standard structures in proteins

The Ramachandran plot is among the most central concepts in structural biology, seen in publications and textbooks alike. However, with the increasing numbers of known protein-structures and greater accuracy of ultra-high resolution protein structures, we are still learning more about the basic principles of protein structure. Here we use high fidelity conformational information to explore novel ways, such a geo-style and wrapped Ramachandran plots, to convey some of the basic aspects of the Ramachandran plot and of protein conformation. We point out the pressing need for a standard nomenclature for peptide conformation and propose such a nomenclature. Finally, we summarize some recent conceptual advances related to the building blocks of protein structure. The results for linear groups imply the need for substantive revisions in how the basics of protein structure are handled.