Ab initio structure of human seminal plasma prostatic inhibin gives significant insight into its biological functions

Human seminal plasma prostatic inhibin (HSPI) is a protein isolated from the human prostate gland. Despite its profound biomedical and biotechnological importance, the 3D structure of this protein of 94 amino acids remains undeciphered. The difficulties in extracting it in pure form and crystallizing it have restrained the determination of its structure experimentally. The homology-based computational methods are also not applicable, as HSPI lacks sufficient sequence homology with known structures in the protein data banks. We have predicted the structure of HSPI by a knowledge-based method using nonparametric multivariate statistical techniques. Stereochemical and other standard validation tests confirm this to be a well-refined structure. The biophysical properties exhibited by this structure are in good agreement with the NMR experimental observations. Docking and other computational studies on this structure provide significant explanation and insight into its binding activities and related biological and immunogenic functions and offer new directions for its potential applications.     

A computational method for NMR-constrained protein threading.

Protein threading provides an effective method for fold recognition and backbone structure prediction. But its application is currently limited due to its level of prediction accuracy and scope of applicability. One way to significantly improve its usefulness is through the incorporation of underconstrained (or partial) NMR data. It is well known that the NMR method for protein structure determination applies only to small proteins and that its effectiveness decreases rapidly as the protein mass increases beyond about 30 kD. We present, in this paper, a computational framework for applying underconstrained NMR data (that alone are insufficient for structure determination) as constraints in protein threading and also in all-atom model construction. In this study, we consider both secondary structure assignments from chemical shifts and NOE distance restraints. Our results have shown that both secondary structure assignments and a small number of long-range NOEs can significantly improve the threading quality in both fold recognition and threading-alignment accuracy, and can possibly extend threading's scope of applicability from homologs to analogs. An accurate backbone structure generated by NMR-constrained threading can then provide a great amount of structural information, equivalent to that provided by many NMR data; and hence can help reduce the number of NMR data typically required for an accurate structure determination. This new technique can potentially accelerate current NMR structure determination processes and possibly expand NMR's capability to larger proteins.     

Backbone solution structures of proteins using residual dipolar couplings: application to a novel structural genomics target

Structural genomics (or proteomics) activities are critically dependent on the availability of high-throughput structure determination methodology. Development of such methodology has been a particular challenge for NMR based structure determination because of the demands for isotopic labeling of proteins and the requirements for very long data acquisition times. We present here a methodology that gains efficiency from a focus on determination of backbone structures of proteins as opposed to full structures with all sidechains in place. This focus is appropriate given the presumption that many protein structures in the future will be built using computational methods that start from representative fold family structures and replace as many as 70% of the sidechains in the course of structure determination. The methodology we present is based primarily on residual dipolar couplings (RDCs), readily accessible NMR observables that constrain the orientation of backbone fragments irrespective of separation in space. A new software tool is described for the assembly of backbone fragments under RDC constraints and an application to a structural genomics target is presented. The target is an 8.7 kDa protein from Pyrococcus furiosus, PF1061, that was previously not well annotated, and had a nearest structurally characterized neighbor with only 33% sequence identity. The structure produced shows structural similarity to this sequence homologue, but also shows similarity to other proteins, which suggests a functional role in sulfur transfer. Given the backbone structure and a possible functional link this should be an ideal target for development of modeling methods.     

Residual dipolar couplings: Synergy between NMR and structural genomics

Structural genomics is on a quest for the structure and function of a significant fraction of gene products. Current efforts are focusing on structure determination of single-domain proteins, which can readily be targeted by X-ray crystallography, NMR spectroscopy and computational homology modeling. However, comprehensive association of gene products with functions also requires systematic determination of more complex protein structures and other biomolecules participating in cellular processes such as nucleic acids, and characterization of biomolecular interactions and dynamics relevant to function. Such NMR investigations are becoming more feasible, not only due to recent advances in NMR methodology, but also because structural genomics is providing valuable structural information and new experimental and computational tools. The measurement of residual dipolar couplings in partially oriented systems and other new NMR methods will play an important role in this synergistic relationship between NMR and structural genomics. Both an expansion in the domain of NMR application, and important contributions to future structural genomics efforts can be anticipated.     

Backbone solution structures of proteins using residual dipolar couplings: Application to a novel structural genomics target

Structural genomics (or proteomics) activities are critically dependent on the availability of high-throughput structure determination methodology. Development of such methodology has been a particular challenge for NMR based structure determination because of the demands for isotopic labeling of proteins and the requirements for very long data acquisition times. We present here a methodology that gains efficiency from a focus on determination of backbone structures of proteins as opposed to full structures with all sidechains in place. This focus is appropriate given the presumption that many protein structures in the future will be built using computational methods that start from representative fold family structures and replace as many as 70% of the sidechains in the course of structure determination. The methodology we present is based primarily on residual dipolar couplings (RDCs), readily accessible NMR observables that constrain the orientation of backbone fragments irrespective of separation in space. A new software tool is described for the assembly of backbone fragments under RDC constraints and an application to a structural genomics target is presented. The target is an 8.7 kDa protein from Pyrococcus furiosus, PF1061, that was previously not well annotated, and had a nearest structurally characterized neighbor with only 33% sequence identity. The structure produced shows structural similarity to this sequence homologue, but also shows similarity to other proteins, which suggests a functional role in sulfur transfer. Given the backbone structure and a possible functional link this should be an ideal target for development of modeling methods. This revised version was published online in March 2005 with corrections to the references.     

Recent developments in structural proteomics for protein structure determination

The major challenges in structural proteomics include identifying all the proteins on the genome-wide scale, determining their structure-function relationships, and outlining the precise three-dimensional structures of the proteins. Protein structures are typically determined by experimental approaches such as X-ray crystallography or nuclear magnetic resonance (NMR) spectroscopy. However, the knowledge of three-dimensional space by these techniques is still limited. Thus, computational methods such as comparative and de novo approaches and molecular dynamic simulations are intensively used as alternative tools to predict the three-dimensional structures and dynamic behavior of proteins. This review summarizes recent developments in structural proteomics for protein structure determination; including instrumental methods such as X-ray crystallography and NMR spectroscopy, and computational methods such as comparative and de novo structure prediction and molecular dynamics simulations.     

Automated structure determination from NMR spectra

Automated methods for protein structure determination by NMR have increasingly gained acceptance and are now widely used for the automated assignment of distance restraints and the calculation of three-dimensional structures. This review gives an overview of the techniques for automated protein structure analysis by NMR, including both NOE-based approaches and methods relying on other experimental data such as residual dipolar couplings and chemical shifts, and presents the FLYA algorithm for the fully automated NMR structure determination of proteins that is suitable to substitute all manual spectra analysis and thus overcomes a major efficiency limitation of the NMR method for protein structure determination.     

Bayesian Peak Picking for NMR Spectra

Protein structure determination is a very important topic in structural genomics,which helps people to understand varieties of biological functions such as protein-protein interactions,protein–DNA interactions and so on.Nowadays,nuclear magnetic resonance(NMR) has often been used to determine the three-dimensional structures of protein in vivo.This study aims to automate the peak picking step,the most important and tricky step in NMR structure determination.We propose to model the NMR spectrum by a mixture of bivariate Gaussian densities and use the stochastic approximation Monte Carlo algorithm as the computational tool to solve the problem.Under the Bayesian framework,the peak picking problem is casted as a variable selection problem.The proposed method can automatically distinguish true peaks from false ones without preprocessing the data.To the best of our knowledge,this is the first effort in the literature that tackles the peak picking problem for NMR spectrum data using Bayesian method.     

High-resolution structural validation of the computational redesign of human U1A protein

Achieving atomic-level resolution in the computational design of a protein structure remains a challenging problem despite recent progress. Rigorous experimental tests are needed to improve protein design algorithms, yet studies of the structure and dynamics of computationally designed proteins are very few. The NMR structure and backbone dynamics of a redesigned protein of 96 amino acids are compared here with the design target, human U1A protein. We demonstrate that the redesigned protein reproduces the target structure to within the uncertainty of the NMR coordinates, even as 65 out of 96 amino acids were simultaneously changed by purely computational methods. The dynamics of the backbone of the redesigned protein also mirror those of human U1A, suggesting that the protein design algorithm captures the shape of the potential energy landscape in addition to the local energy minimum.     

HMG-D complexed to a bulge DNA: an NMR model

An NMR model is presented for the structure of HMG-D, one of the DROSOPHILA: counterparts of mammalian HMG1/2 proteins, bound to a particular distorted DNA structure, a dA(2) DNA bulge. The complex is in fast to intermediate exchange on the NMR chemical shift time scale and suffers substantial linebroadening for the majority of interfacial resonances. This essentially precludes determination of a high-resolution structure for the interface based on NMR data alone. However, by introducing a small number of additional constraints based on chemical shift and linewidth footprinting combined with analogies to known structures, an ensemble of model structures was generated using a computational strategy equivalent to that for a conventional NMR structure determination. We find that the base pair adjacent to the dA(2) bulge is not formed and that the protein recognizes this feature in forming the complex; intermolecular NOE enhancements are observed from the sidechain of Thr 33 to all four nucleotides of the DNA sequence step adjacent to the bulge. Our results form the first experimental demonstration that when binding to deformed DNA, non-sequence-specific HMG proteins recognize the junction between duplex and nonduplex DNA. Similarities and differences of the present structural model relative to other HMG-DNA complex structures are discussed.     

蛋白质结构确定领域中的几种矩阵填充算法的对比评估

目的 目前,如何从核磁共振(nuclear magnetic resonance,NMR)光谱实验中准确地确定蛋白质的三维结构是生物物理学中的一个热门课题,因为蛋白质是生物体的重要组成成分,了解蛋白质的空间结构对研究其功能至关重要,然而由于实验数据的严重缺乏使其成为一个很大的挑战。方法 在本文中,通过恢复距离矩阵的矩阵填充(matrix completion,MC)算法来解决蛋白质结构确定问题。首先,初始距离矩阵模型被建立,由于实验数据的缺乏,此时的初始距离矩阵为不完整矩阵,随后通过MC算法恢复初始距离矩阵的缺失数据,从而获得整个蛋白质三维结构。为了进一步测试算法的性能,本文选取了4种不同拓扑结构的蛋白质和6种现有的MC算法进行了测试,探究了算法在不同的采样率以及不同程度噪声的情况下算法的恢复效果。结果 通过分析均方根偏差(root-mean-square deviation,RMSD)和计算时间这两个重要指标的平均值及标准差评估了算法的性能,结果显示当采样率和噪声因子控制在一定范围内时,RMSD值和标准差都能达到很小的值。另外本文更加具体地比较了不同算法的特点和优势,在精确采样情况下...     

Armadillo: domain boundary prediction by amino acid composition

The identification and annotation of protein domains provides a critical step in the accurate determination of molecular function. Both computational and experimental methods of protein structure determination may be deterred by large multi-domain proteins or flexible linker regions. Knowledge of domains and their boundaries may reduce the experimental cost of protein structure determination by allowing researchers to work on a set of smaller and possibly more successful alternatives. Current domain prediction methods often rely on sequence similarity to conserved domains and as such are poorly suited to detect domain structure in poorly conserved or orphan proteins. We present here a simple computational method to identify protein domain linkers and their boundaries from sequence information alone. Our domain predictor, Armadillo (http://armadillo.blueprint.org), uses any amino acid index to convert a protein sequence to a smoothed numeric profile from which domains and domain boundaries may be predicted. We derived an amino acid index called the domain linker propensity index (DLI) from the amino acid composition of domain linkers using a non-redundant structure dataset. The index indicates that Pro and Gly show a propensity for linker residues while small hydrophobic residues do not. Armadillo predicts domain linker boundaries from Z-score distributions and obtains 35% sensitivity with DLI in a two-domain, single-linker dataset (within +/-20 residues from linker). The combination of DLI and an entropy-based amino acid index increases the overall Armadillo sensitivity to 56% for two domain proteins. Moreover, Armadillo achieves 37% sensitivity for multi-domain proteins, surpassing most other prediction methods. Armadillo provides a simple, but effective method by which prediction of domain boundaries can be obtained with reasonable sensitivity. Armadillo should prove to be a valuable tool for rapidly delineating protein domains in poorly conserved proteins or those with no sequence neighbors. As a first-line predictor, domain meta-predictors could yield improved results with Armadillo predictions.     

Structural NMR of protein oligomers using hybrid methods

Solving structures of native oligomeric protein complexes using traditional high-resolution NMR techniques remains challenging. However, increased utilization of computational platforms, and integration of information from less traditional NMR techniques with data from other complementary biophysical methods, promises to extend the boundary of NMR-applicable targets. This article reviews several of the techniques capable of providing less traditional and complementary structural information. In particular, the use of orientational constraints coming from residual dipolar couplings and residual chemical shift anisotropy offsets are shown to simplify the construction of models for oligomeric complexes, especially in cases of weak homo-dimers. Combining this orientational information with interaction site information supplied by computation, chemical shift perturbation, paramagnetic surface perturbation, cross-saturation and mass spectrometry allows high resolution models of the complexes to be constructed with relative ease. Non-NMR techniques, such as mass spectrometry, EPR and small angle X-ray scattering, are also expected to play increasingly important roles by offering alternative methods of probing the overall shape of the complex. Computational platforms capable of integrating information from multiple sources in the modeling process are also discussed in the article. And finally a new, detailed example on the determination of a chemokine tetramer structure will be used to illustrate how a non-traditional approach to oligomeric structure determination works in practice.     

Probabilistic Interaction Network of Evidence Algorithm and its Application to Complete Labeling of Peak Lists from Protein NMR Spectroscopy

The process of assigning a finite set of tags or labels to a collection of observations, subject to side conditions, is notable for its computational complexity. This labeling paradigm is of theoretical and practical relevance to a wide range of biological applications, including the analysis of data from DNA microarrays, metabolomics experiments, and biomolecular nuclear magnetic resonance (NMR) spectroscopy. We present a novel algorithm, called Probabilistic Interaction Network of Evidence (PINE), that achieves robust, unsupervised probabilistic labeling of data. The computational core of PINE uses estimates of evidence derived from empirical distributions of previously observed data, along with consistency measures, to drive a fictitious system M with Hamiltonian H to a quasi-stationary state that produces probabilistic label assignments for relevant subsets of the data. We demonstrate the successful application of PINE to a key task in protein NMR spectroscopy: that of converting peak lists extracted from various NMR experiments into assignments associated with probabilities for their correctness. This application, called PINE-NMR, is available from a freely accessible computer server (http://pine.nmrfam.wisc.edu). The PINE-NMR server accepts as input the sequence of the protein plus user-specified combinations of data corresponding to an extensive list of NMR experiments; it provides as output a probabilistic assignment of NMR signals (chemical shifts) to sequence-specific backbone and aliphatic side chain atoms plus a probabilistic determination of the protein secondary structure. PINE-NMR can accommodate prior information about assignments or stable isotope labeling schemes. As part of the analysis, PINE-NMR identifies, verifies, and rectifies problems related to chemical shift referencing or erroneous input data. PINE-NMR achieves robust and consistent results that have been shown to be effective in subsequent steps of NMR structure determination.     

Addressing the intrinsic disorder bottleneck in structural proteomics

The Center for Eukaryotic Structural Genomics (CESG), as part of the Protein Structure Initiative (PSI), has established a high-throughput structure determination pipeline focused on eukaryotic proteins. NMR spectroscopy is an integral part of this pipeline, both as a method for structure determinations and as a means for screening proteins for stable structure. Because computational approaches have estimated that many eukaryotic proteins are highly disordered, about 1 year into the project, CESG began to use an algorithm (the Predictor of Naturally Disordered Regions, PONDR to avoid proteins that were likely to be disordered. We report a retrospective analysis of the effect of this filtering on the yield of viable structure determination candidates. In addition, we have used our current database of results on 70 protein targets from Arabidopsis thaliana and 1 from Caenorhabditis elegans, which were labeled uniformly with nitrogen-15 and screened for disorder by NMR spectroscopy, to compare the original algorithm with 13 other approaches for predicting disorder from sequence. Our study indicates that the efficiency of structural proteomics of eukaryotes can be improved significantly by removing targets predicted to be disordered by an algorithm chosen to provide optimal performance.     

NMR methodology for the study of nucleic acids.

During the past few years, NMR methodology for the study of nucleic acids has benefited from new developments that greatly improved state-of-the-art technology for the precise determination of three-dimensional structures. Substantial progress has been made in designing experimental protocols for the measurement of residual dipolar couplings, in sensitivity optimization of triple-resonance experiments and in detection of hydrogen bonds and in developing computational methods for structure refinement using NMR restraints.     

NMR of membrane proteins in micelles and bilayers: the FXYD family proteins

Determining the atomic resolution structures of membrane proteins is of particular interest in contemporary structural biology. Helical membrane proteins constitute one-third of the expressed proteins encoded in a genome, many drugs have membrane-bound proteins as their receptors, and mutations in membrane proteins result in human diseases. Although integral membrane proteins provide daunting technical challenges for all methods of protein structure determination, nuclear magnetic resonance (NMR) spectroscopy can be an extremely versatile and powerful method for determining their structures and characterizing their dynamics, in lipid environments that closely mimic the cell membranes. Once milligram amounts of isotopically labeled protein are expressed and purified, micelle samples can be prepared for solution NMR analysis, and lipid bilayer samples can be prepared for solid-state NMR analysis. The two approaches are complementary and can provide detailed structural and dynamic information. This paper describes the steps for membrane protein structure determination using solution and solid-state NMR. The methods for protein expression and purification, sample preparation and NMR experiments are described and illustrated with examples from the FXYD proteins, a family of regulatory subunits of the Na,K-ATPase.     

Strategies for dealing with conformational sampling in structural calculations of flexible or kinked transmembrane peptides.

Peptides corresponding to transmembrane (TM) segments from membrane proteins provide a potential route for the determination of membrane protein structure. We have determined that 2 functionally critical TM segments from the mammalian Na+/H+ exchanger display well converged structure in regions separated by break points. The flexibility of these break points results in conformational sampling in solution. A brief review of available NMR structures of helical membrane proteins demonstrates that there are a number of published structures showing similar properties. Such flexibility is likely indicative of kinks in the full-length protein. This minireview focuses on methods and protocols for NMR structure calculation and analysis of peptide structures under conditions of conformational sampling. The methods outlined allow the identification and analysis of structured peptides containing break points owing to conformational sampling and the differentiation between oligomerization and ensemble-averaged observation of multiple peptide conformations.