Analysis of the steric strain in the polypeptide backbone of protein molecules.

The extent to which local strain is present in the polypeptide backbone of folded protein molecules has been examined. The occurrence of steric strain associated with nonproline cis peptide bonds and energetically unfavorable main chain dihedral angles can be identified reliably from the well ordered parts of high resolution, refined crystal structures. The analysis reveals that there are relatively few sterically strained features. Those that do occur are located overwhelmingly in regions concerned with function. We attribute this to the greater precision necessary for ligand binding and catalysis, compared with the requirements of satisfactory folding.     

Characterization of the backbone geometry of protein native state structures

We present a simple method for analyzing the geometry of noncovalent residue-residue interactions stabilizing the protein structure, which takes into account the constraints on the local backbone geometry. We find that the principal geometrical constraints are amino acid aspecific and are associated with hydrogen bond formation in helices and sheets. In contrast, amino acid residues in nonhelical and nonextended conformations, which make noncovalent interactions stabilizing the protein tertiary structure, display greater flexibility. We apply the method to an analysis of the packing of helices in helical bundle proteins requiring an efficient packing of amino acid side-chains of the interacting helices.     

Determination of three-dimensional protein structures from nuclear magnetic resonance data using fragments of known structures

A method to build a three-dimensional protein model from nuclear magnetic resonance (NMR) data using fragments from a data base of crystallographically determined protein structures is presented. The interproton distances derived from the nuclear Overhauser effect (NOE) data are compared to the precalculated distances in the known protein structures. An efficient search algorithm is used, which arranges the distances in matrices akin to a C alpha diagonal distance plot, and compares the NOE distance matrices for short sequential zones of the protein to the data base matrices. After cluster analysis of the fragments found in this way, the structure is built by aligning fragments in overlapping zones. The sequentially long-range NOEs cannot be used in the initial fragments search but are vital to discriminate between several possible combinations of different groups of fragments. The method has been tested on one simulated NOE data set derived from a crystal structure and one experimental NMR data set. The method produces models that have good local structure, but may contain larger global errors. These models can be used as the starting point for further refinement, e.g., by restrained molecular dynamics or interactive graphics.     

Modelling of peptide and protein structures

Summary The modelling of protein structures (whether isolated, in solution, or involved in recognition processes) is reviewed, free of any mathematical apparatus, to provide an overview of the concepts as well as leading references. A general feeling for this field of work is first established by a sampling of some impressions on its difficulties and chances of success. Then, the main body of this work examines the information available (databases and parameters), presents the theoretical foundations for the modelling procedures (with emphasis on the potential energy functions), surveys the existing simulation techniques and prediction methods, and discusses the problems still to be faced. For completeness, a representative list of existing software packages is presented in the Appendix.Presented at the Third International Congress on Amino Acids, Vienna, August 23–27, 1993.     

Secondary structures without backbone: an analysis of backbone mimicry by polar side chains in protein structures.

Backbone mimicry by the formation of closed-loop C7, C10 and C13 (mimics of gamma-, beta- and alpha-turns) conformations through side chain-main chain hydrogen bonds by polar groups is a frequent observation in protein structures. A data set of 250 non-homologous and high-resolution protein crystal structures was used to analyze these conformations for their characteristic features. Seven out of the nine polar residues (Ser, Thr, Asn, Asp, Gln, Glu and His) have hydrogen bonding groups in their side chains which can participate in such mimicry and as many as 15% of all these polar residues engage in such conformations. The distributions of dihedral angles of these mimics indicate that only certain combinations of the dihedral angles involved aid the formation of these mimics. The observed examples were categorized into various classes based on these combinations, resulting in well defined motifs. Asn and Asp residues show a very high capability to perform such backbone secondary structural mimicry. The most highly mimicked backbone structure is of the C10 conformation by the Asx residues. The mimics formed by His, Ser, Thr and Glx residues are also discussed. The role of such conformations in initiating the formation of regular secondary structures during the course of protein folding seems significant.     

Geometry motivated alternative view on local protein backbone structures

We present an alternative to the classical Ramachandran plot (R-plot) to display local protein backbone structure. Instead of the (ϕ, ψ)-backbone angles relating to the chemical architecture of polypeptides generic helical parameters are used. These are the rotation or twist angle ϑ and the helical rise parameter d. Plots with these parameters provide a different view on the nature of local protein backbone structures. It allows to display the local structures in polar (d, ϑ)-coordinates, which is not possible for an R-plot, where structural regimes connected by periodicity appear disconnected. But there are other advantages, like a clear discrimination of the handedness of a local structure, a larger spread of the different local structure domains—the latter can yield a better separation of different local secondary structure motives—and many more. Compared to the R-plot we are not aware of any major disadvantage to classify local polypeptide structures with the (d, ϑ)-plot, except that it requires some elementary computations. To facilitate usage of the new (d, ϑ)-plot for protein structures we provide a web application (http://agknapp.chemie.fu-berlin.de/secsass), which shows the (d, ϑ)-plot side-by-side with the R-plot.     

Characterization of novel proteins based on known protein structures

The genome sciences face the challenge to characterize structure and function of a vast number of novel genes. Sequence search techniques are used to infer functional and structural information from similarities to experimentally characterized genes or proteins. The persistent goal is to refine these techniques and to develop alternative and complementary methods to increase the range of reliable inference.Here, we focus on the structural and functional assignments that can be inferred from the known three-dimensional structures of proteins. The study uses all structures in the Protein Data Bank that were known by the end of 1997. The protein structures released in 1998 were then characterized in terms of functional and structural similarity to the previously known structures, yielding an estimate of the maximum amount of information on novel protein sequences that can be obtained from inference techniques.The 147 globular proteins corresponding to 196 domains released in 1998 have no clear sequence similarity to previously known structures. However, 75 % of the domains have extensive structure similarity to previously known folds, and most importantly, in two out of three cases similarity in structure coincides with related function. In view of this analysis, full utilization of existing structure data bases would provide information for many new targets even if the relationship is not accessible from sequence information alone. Currently, the most sophisticated techniques detect of the order of one-third of these relationships.     

Prediction and evaluation of side-chain conformations for protein backbone structures

A common approach to protein modeling is to propose a backbone structure based on homology or threading and then to attempt to build side chains onto this backbone. A fast algorithm using the simple criteria of atomic overlap and overall rotamer probability is proposed for this purpose. The method was first tested in the context of exhaustive searches of side chain configuration space in protein cores and was then applied to all side chains in 49 proteins of known structure, using simulated annealing to sample space. The latter procedure obtains the correct rotamer for 57% and the correct χ₁ value for 74% of the 6751 residues in the sample. When low-temperature Monte-Carlo simulations are initiated from the results of the simulated-annealing processes, consensus configurations are obtained which exhibit slightly more accurate predictions. The Monte-Carlo procedure also allows converged side chain entropies to be calculated for all residues. These prove to be accurate indicators of prediction reliability. For example, the correct rotamer is obtained for 79% and the correct χ₁ value is obtained for 84% of the half of the sample residues exhibiting the lowest entropies. Side chain entropy and predictability are nearly completely uncorrelated with solvent-accessible area. Some precedents for and implications of this observation are discussed. © 1996 Wiley-Liss, Inc.     

Comparison of protein secondary structures based on backbone dihedral angles

In this article, we propose a relatively similar measure to compare protein secondary structures. We first transform a protein secondary structure into a special sequence representation (angle sequence) based on a partition of the backbone φ,ψ-space. Then, pairwise sequence distance is evaluated on the basis of a symbolic sequence complexity. To illustrate our approach, we construct the similarity tree of 24 proteins from PDB.     

Conversion from a virtual-bond chain to a complete polypeptide backbone chain

A method for generating a complete polypeptide backbone structure from a set of C^α coordinates is presented. Initial trial values of ? and ψ for a selected residue are chosen (essentially from an identification of the conformational region of the virtual-bond backbone, e.g., and α-helical region), and values of ? and ψ for the remaining residues (both towards the N- and C-terminus) are then computed, subject to the constraint that the chain have the same virtual-bond angles and virtual-bond dihedral angles as the given set of C^α coordinates. The conversion from C^α coordinates to full backbone dihedral angles (?,ψ) involves the solution of a set of algebraic equations relating the virtual-bond angles and virtual-bond dihedral angles to standard peptide geometry and backbone dihedral angles. The procedure has been tested successfully on C^α coordinates taken from standard-geometry full-atom structures of bovine pancreatic trypsin inhibitor (BPTI). Some difficulty was encountered with error-sensitive residues, but on the whole the backbone generation was successful. Application of the method to C^α coordinates for BPTI derived from simplified model calculations (involving nonstandard geometry) showed that such coordinates may be inconsistent with the requirement that ?_Pro be near ?75°. In such a case, i.e., for residues for which the algebraic method failed, a leastsquares minimizer was then used in conjunction with the algebraic method; the mean-square deviation of the calculated C^α coordinates from the given ones was minimized by varying the backbone dihedral angles. Thus, these inconsistencies were circumvented and a full backbone structure whose C^α coordinates had an rms deviation of 0.26 Å from the given set of C^α coordinates was obtained.     

Monte Carlo refinement of rigid-body protein docking structures with backbone displacement and side-chain optimization

Structures of hitherto unknown protein complexes can be predicted by docking the solved protein monomers. Here, we present a method to refine initial docking estimates of protein complex structures by a Monte Carlo approach including rigid-body moves and side-chain optimization. The energy function used is comprised of van der Waals, Coulomb, and atomic contact energy terms. During the simulation, we gradually shift from a novel smoothed van der Waals potential, which prevents trapping in local energy minima, to the standard Lennard-Jones potential. Following the simulation, the conformations are clustered to obtain the final predictions. Using only the first 100 decoys generated by a fast Fourier transform (FFT)-based rigid-body docking method, our refinement procedure is able to generate near-native structures (interface RMSD <2.5 A) as first model in 14 of 59 cases in a benchmark set. In most cases, clear binding funnels around the native structure can be observed. The results show the potential of Monte Carlo refinement methods and emphasize their applicability for protein-protein docking.     

TALI: local alignment of protein structures using backbone torsion angles

Torsion angle alignment (TALI) is a novel approach to local structural motif alignment, based on backbone torsion angles (phi, psi) rather than the more traditional atomic distance matrices. Representation of a protein structure in the form of a sequence of torsion angles enables easy integration of sequence and structural information, and adopts mature techniques in sequence alignment to improve performance and alignment quality. We show that TALI is able to match local structural motifs as well as identify global structural similarity. TALI is also compared to other structure alignment methods such as DALI, CE, and SSM, as well as sequence alignment based on PSI-BLAST; TALI is shown to be equally successful as, or more successful than, these other methods when applied to challenging structural alignments. The inference of the evolutionary tree of class II aminoacyl-tRNA synthetase shows the potential for TALI in estimating protein structural evolution and in identifying structural divergence among homologous structures. Availability: http://redcat.cse.sc.edu/index.php/Project:TALI/.     

On contribution of known atomic partial charges of protein backbone in electrostatic potential density maps

Partial charges of atoms in a molecule and electrostatic potential (ESP) density for that molecule are known to bear a strong correlation. In order to generate a set of point‐field force field parameters for molecular dynamics, Kollman and coworkers have extracted atomic partial charges for each of all 20 amino acids using restrained partial charge‐fitting procedures from theoretical ESP density obtained from condensed‐state quantum mechanics. The magnitude of atomic partial charges for neutral peptide backbone they have obtained is similar to that of partial atomic charges for ionized carboxylate side chain atoms. In this study, the effect of these known atomic partial charges on ESP is examined using computer simulations and compared with the experimental ESP density recently obtained for proteins using electron microscopy. It is found that the observed ESP density maps are most consistent with the simulations that include atomic partial charges of protein backbone. Therefore, atomic partial charges are integral part of atomic properties in protein molecules and should be included in model refinement.     

Pyridoxamine protects protein backbone from oxidative fragmentation

Oxidative damage to proteins is one of the major pathogenic mechanisms in many chronic diseases. Therefore, inhibition of this oxidative damage can be an important part of therapeutic strategies. Pyridoxamine (PM), a prospective drug for treatment of diabetic nephropathy, has been previously shown to inhibit several oxidative and glycoxidative pathways, thus protecting amino acid side chains of the proteins from oxidative damage. Here, we demonstrated that PM can also protect protein backbone from fragmentation induced via different oxidative mechanisms including autoxidation of glucose. This protection was due to hydroxyl radical scavenging by PM and may contribute to PM therapeutic effects shown in clinical trials.     

A unified picture of protein hydration: prediction of hydrodynamic properties from known structures.

Hydration is essential for the structural and functional integrity of globular proteins. How much hydration water is required for that integrity? A number of techniques such as X-ray diffraction, nuclear magnetic resonance (NMR) spectroscopy, calorimetry, infrared spectroscopy, and molecular dynamics (MD) simulations indicate that the hydration level is 0.3-0.5 g of water per gram of protein for medium sized proteins. Hydrodynamic properties, when accounted for by modeling proteins as ellipsoids, appear to give a wide range of hydration levels. In this paper we describe an alternative numerical technique for hydrodynamic calculations that takes account of the detailed protein structures. This is made possible by relating hydrodynamic properties (translational and rotational diffusion constants and intrinsic viscosity) to electrostatic properties (capacitance and polarizability). We show that the use of detailed protein structures in predicting hydrodynamic properties leads to hydration levels in agreement with other techniques. A unified picture of protein hydration emerges. There are preferred hydration sites around a protein surface. These sites are occupied nearly all the time, but by different water molecules at different times. Thus, though a given water molecule may have a very short residence time (approximately 100-500 ps from NMR spectroscopy and MD simulations) in a particular site, the site appears fully occupied in experiments in which time-averaged properties are measured.     

Biodegradability of synthetic branched polypeptide with poly(L-lysine) backbone

A detailed investigation is reported about the biodegradation of poly[Lys(DL-Alam)], m approximately 3, (AK) the common inside area of a branched polypeptide model system developed by our group over the last decade. Enzymatic hydrolysis was carried out by the exopeptidase aminopeptidase M, or the endopeptidase trypsin, or their mixture. Ion-exchange column chromatography, paper electrophoresis and thin-layer chromatography were utilised to achieve separation of metabolites. Breakdown products were identified by the aid of synthetic oligopeptides representing the potential fragments (DL-Ala2, DL-Ala3, Lys(DL-Alam), m = 1-3). The kinetics and the degree of enzymatic degradation were determined. The ratio of peptide/amino acid amounts in the hydrolysate was found to be 1.07 after 24 h treatment with aminopeptidase M, 3.0 with trypsin and 1.3 with aminopeptidase - trypsin mixture. The overall results indicated that the proteolysis of AK by an aminopeptidase M and trypsin mixture proceeds stepwise at multiple sites on the polypeptide chain. The degradation is significantly retarded as compared to that of alpha- or epsilon-polylysine. A mechanism of degradation is suggested based on the experimental results.     

Automated real-space refinement of protein structures using a realistic backbone move set

Crystals of many important biological macromolecules diffract to limited resolution, rendering accurate model building and refinement difficult and time-consuming. We present a torsional optimization protocol that is applicable to many such situations and combines Protein Data Bank-based torsional optimization with real-space refinement against the electron density derived from crystallography or cryo-electron microscopy. Our method converts moderate- to low-resolution structures at initial (e.g., backbone trace only) or late stages of refinement to structures with increased numbers of hydrogen bonds, improved crystallographic R-factors, and superior backbone geometry. This automated method is applicable to DNA-binding and membrane proteins of any size and will aid studies of structural biology by improving model quality and saving considerable effort. The method can be extended to improve NMR and other structures. Our backbone score and its sequence profile provide an additional standard tool for evaluating structural quality.     

Bayesian probabilistic approach for predicting backbone structures in terms of protein blocks

By using an unsupervised cluster analyzer, we have identified a local structural alphabet composed of 16 folding patterns of five consecutive C(alpha) ("protein blocks"). The dependence that exists between successive blocks is explicitly taken into account. A Bayesian approach based on the relation protein block-amino acid propensity is used for prediction and leads to a success rate close to 35%. Sharing sequence windows associated with certain blocks into "sequence families" improves the prediction accuracy by 6%. This prediction accuracy exceeds 75% when keeping the first four predicted protein blocks at each site of the protein. In addition, two different strategies are proposed: the first one defines the number of protein blocks in each site needed for respecting a user-fixed prediction accuracy, and alternatively, the second one defines the different protein sites to be predicted with a user-fixed number of blocks and a chosen accuracy. This last strategy applied to the ubiquitin conjugating enzyme (alpha/beta protein) shows that 91% of the sites may be predicted with a prediction accuracy larger than 77% considering only three blocks per site. The prediction strategies proposed improve our knowledge about sequence-structure dependence and should be very useful in ab initio protein modelling.