Prediction of NMR order parameters in proteins using weighted protein contact-number model

In the NMR experiment, the protein backbone motion can be described by the N–H order parameters. Though protein dynamics is determined by a complex network of atomic interactions, we show that the order parameter of residues can be determined using a very simple method, the weighted protein contact number model. We computed for each Cα atom the number of neighboring Cα atoms weighted by the inverse distance squared between them. We show that the weighted contact number of each residue is directly related to its order parameter. Despite the simplicity of this model, it performs better than the other method. Since we can compute the order parameters directly from the topological properties (such as protein contact number) of protein structures, our study underscores a very direct link between protein topological structure and its dynamics.     

Computational methods for identification of functional residues in protein structures

The recent accumulation of experimentally determined protein 3D structures combined with our ability to computationally model structure from amino acid sequence has resulted in an increased importance of structure-based methods for protein function prediction. Two types of methods for function prediction have been proposed: those that can accurately predict overall biochemical or biological roles of a protein and those that predict its functional residues. Here, we review approaches used for the computational identification of functional residues in protein structures and summarize their applications to a wide variety of problems in functional proteomics, such as the prediction of catalytic residues, post-translational modifications, or nucleic acid-binding sites. We examine four different problems in order to perform a comparison between several recently proposed methods and, finally, conclude by identifying limitations and future challenges in this field.     

Exploring the relationship between hub proteins and drug targets based on GO and intrinsic disorder

Protein–protein interactions (PPIs) play essential roles in many biological processes. In protein–protein interaction networks, hubs involve in numbers of PPIs and may constitute an important source of drug targets. The intrinsic disorder proteins (IDPs) with unstable structures can promote the promiscuity of hubs and also involve in many disease pathways, so they also could serve as potential drug targets. Moreover, proteins with similar functions measured by semantic similarity of gene ontology (GO) terms tend to interact with each other. Here, the relationship between hub proteins and drug targets based on GO terms and intrinsic disorder was explored. The semantic similarities of GO terms and genes between two proteins, and the rate of intrinsic disorder residues of each protein were extracted as features to characterize the functional similarity between two interacting proteins. Only using 8 feature variables, prediction models by support vector machine (SVM) were constructed to predict PPIs. The accuracy of the model on the PPI data from human hub proteins is as high as 83.72%, which is very promising compared with other PPI prediction models with hundreds or even thousands of features. Then, 118 of 142 PPIs between hubs are correctly predicted that the two interacting proteins are targets of the same drugs. The results indicate that only 8 functional features are fully efficient for representing PPIs. In order to identify new targets from IDP dataset, the PPIs between hubs and IDPs are predicted by the SVM model and the model yields a prediction accuracy of 75.84%. Further research proves that 3 of 5 PPIs between hubs and IDPs are correctly predicted that the two interacting proteins are targets of the same drugs. All results demonstrate that the model with only 8-dimensional features from GO terms and intrinsic disorder still gives a good performance in predicting PPIs and further identifying drug targets.     

原位冷冻电子断层扫描的发展及在生物研究方面的应用

对生物大分子复合物的研究和结构分析对于全面了解其功能和生物学意义至关重要. 冷冻电子显微镜在提供生物大分子结构及大分子分布等方面起到重要的作用. 近年来, 冷冻电子显微镜的硬件和软件的发展进一步提高了冷冻电子显微镜的有效性, 使其对各种生物结构、 蛋白质结构的解析更加准确快捷. 但是, 对于生物系统来说, 蛋白质和大分子复合物等均处于复杂的生理环境中, 因此原位检测生物分子的三维结构对于生物体系和结构生物学具有重要意义. 冷冻电子断层扫描作为一种功能强大的技术, 可以无需标记直接通过冷冻样品的固有衬度识别生物大分子的结构, 并且可在原位生理环境中对生物分子进行纳米级分辨率的三维成像. 本文综述了与冷冻电子断层扫描相关的样品制备和数据处理技术, 并总结了冷冻电子断层扫描技术在分离的大分子复合物和整个细胞或组织中的生物学应用.     

Prediction of the structures of helical membrane proteins based on a minimum unfavorable contacts approach

An understanding of structure–function relationships of membrane proteins continues to be a challenging problem, owing to the difficulty in obtaining their structures experimentally. This study suggests a method for modeling membrane protein structures that can be used to generate a reliable initial conformation prior to the use of other approaches for sampling conformations. It involves optimizing the orientation of hydrophilic residues so as to minimize unfavorable contacts with the hydrophobic tails of the lipid bilayer. Starting with the optimized initial conformation for three different proteins modeled based on this method, two independent approaches have been used for sampling the conformational space of the proteins. Both approaches are able to predict structures reasonably close to experimental structures, indicating that the initial structure enables the sampling of conformations that are close to the native structure. Possible improvements in the method for making it broadly applicable to helical membrane proteins are discussed. © 2015 Wiley Periodicals, Inc.     

Soft energy function and generic evolutionary method for discriminating native from nonnative protein conformations

We have developed a soft energy function, termed GEMSCORE, for the protein structure prediction, which is one of emergent issues in the computational biology. The GEMSORE consists of the van der Waals, the hydrogen-bonding potential and the solvent potential with 12 parameters which are optimized by using a generic evolutionary method. The GEMSCORE is able to successfully identify 86 native proteins among 96 target proteins on six decoy sets from more 70,000 near-native structures. For these six benchmark datasets, the predictive performance of the GEMSCORE, based on native structure ranking and Z-scores, was superior to eight other energy functions. Our method is based solely on a simple and linear function and thus is considerably faster than other methods that rely on the additional complex calculations. In addition, the GEMSCORE recognized 17 and 2 native structures as the first and the second rank, respectively, among 21 targets in CASP6 (Critical Assessment of Techniques for Protein Structure Prediction). These results suggest that the GEMSCORE is fast and performs well to discriminate between native and nonnative structures from thousands of protein structure candidates. We believe that GEMSCORE is robust and should be a useful energy function for the protein structure prediction.     

RNA-binding residues in sequence space: Conservation and interaction patterns

RNA-binding proteins (RBPs) perform fundamental and diverse functions within the cell. Approximately 15% of proteins sequences are annotated as RNA-binding, but with a significant number of proteins without functional annotation, many RBPs are yet to be identified. A percentage of uncharacterised proteins can be annotated by transferring functional information from proteins sharing significant sequence homology. However, genomes contain a significant number of orphan open reading frames (ORFs) that do not share significant sequence similarity to other ORFs, but correspond to functional proteins. Hence methods for protein function annotation that go beyond sequence homology are essential. One method of annotation is the identification of ligands that bind to proteins, through the characterisation of binding site residues. In the current work RNA-binding residues (RBRs) are characterised in terms of their evolutionary conservation and the patterns they form in sequence space. The potential for such characteristics to be used to identify RBPs from sequence is then evaluated.In the current work the conservation of residues in 261 RBPs is compared for (a) RBRs vs. non-RBRs surface residues, and for (b) specific and non-specific RBRs. The analysis shows that RBRs are more conserved than other surface residues, and RBRs hydrogen-bonded to the RNA backbone are more conserved than those making hydrogen bonds to RNA bases. This observed conservation of RBRs was then used to inform the construction of RBR sequence patterns from known protein–RNA structures. A series of RBR patterns were generated for a case study protein aspartyl-tRNA synthetase bound to tRNA; and used to differentiate between RNA-binding and non-RNA-binding protein sequences. Six sequence patterns performed with high precision values of >80% and recall values 7 times that of an homology search. When the method was expanded to the complete dataset of 261 proteins, many patterns were of poor predictive value, as they had not been manipulated on a family-specific basis. However, two patterns with precision values ≥85% were used to make function predictions for a set of hypothetical proteins. This revealed a number of potential RBPs that require experimental verification.     

A united-residue force field for off-lattice protein-structure simulations. I. Functional forms and parameters of long-range side-chain interaction potentials from protein crystal data

A two-stage procedure for the determination of a united-residue potential designed for protein simulations is outlined. In the first stage, the long-range and local-interaction energy terms of the total energy of a polypeptide chain are determined by analyzing protein-crystal data and averaging the all-atom energy surfaces. In the second stage (described in the accompanying article), the relative weights of the energy terms are optimized so as to locate the native structures of selected test proteins as the lowest energy structures. The goal of the work in the present study is to parameterize physically reasonable functional forms of the potentials of mean force for side-chain interactions. The potentials are of both radial and anisotropic type. Radial potentials include the Lennard-Jones and the shifted Lennard-Jones potential (with the shift parameter independent of orientation). To treat the angular dependence of side-chain interactions, three functional forms of the potential that were designed previously to describe anisotropic systems are evaluated: Berne-Pechukas (dilated Lennard-Jones); Gay-Berne (shifted Lennard-Jones with orientation-dependent shift parameters); and Gay-Berne-Vorobjev (the same as the preceding one, but with one more set of variable parameters). These functional forms were used to parameterize, within a short-distance range, the potentials of mean force for side-chain pair interactions that are related by the Boltzmann principle to the pair correlation functions determined from protein-crystal data. Parameter determination was formulated as a generalized nonlinear least-squares problem with the target function being the weighted sum of squares of the differences between calculated and “experimental” (i.e., estimated from protein-crystal data) angular, radial-angular, and radial pair correlation functions, as well as contact free energies. A set of 195 high-resolution nonhomologous structures from the Protein Data Bank was used to calculate the “experimental” values. The contact free energies were scaled by the slope of the correlation line between side-chain hydrophobicities, calculated from the contact free energies, and those determined by Fauchere and Pliška from the partition coefficients of amino acids between water and n-octanol. The methylene group served to define the reference contact free energy corresponding to that between the glycine methylene groups of backbone residues. Statistical analysis of the goodness of fit revealed that the Gay-Berne-Vorobjev anisotropic potential fits best to the experimental radial and angular correlation functions and contact free energies and therefore represents the free-energy surface of side-chain-side-chain interactions most accurately. Thus, its choice for simulations of protein structure is probably the most appropriate. However, the use of simpler functional forms is recommended, if the speed of computations is an issue. © 1997 by John Wiley & Sons, Inc. J Comput Chem 18: 849–873, 1997     

Aromatic-aromatic interactions in proteins: beyond the dimer

Aromatic residues are key widespread elements of protein structures and have been shown to be important for structure stability, folding, protein-protein recognition, and ligand binding. The interactions of pairs of aromatic residues (aromatic dimers) have been extensively studied in protein structures. Isolated aromatic molecules tend to form higher order clusters, like trimers, tetramers, and pentamers, that adopt particular well-defined structures. Taking this into account, we have surveyed protein structures deposited in the Protein Data Bank in order to find clusters of aromatic residues in proteins larger than dimers and characterized them. Our results show that larger clusters are found in one of every two unique proteins crystallized so far, that the clusters are built adopting the same trimer motifs found for benzene clusters in vacuum, and that they are clearly nonlocal brining primary structure distant sites together. We extensively analyze the trimers and tetramers conformations and found two main cluster types: a symmetric cluster and an extended ladder. Finally, using calmodulin as a test case, we show aromatic clsuters possible role in folding and protein-protein interactions. All together, our study highlights the relevance of aromatic clusters beyond the dimer in protein function, stability, and ligand recognition.     

SPOT-Fold: Fragment-Free Protein Structure Prediction Guided by Predicted Backbone Structure and Contact Map

Protein structure determination has long been one of the most challenging problems in molecular biology for the past 60 years. Here we present an ab initio protein tertiary-structure prediction method assisted by predicted contact maps from SPOT-Contact and predicted dihedral angles from SPIDER 3. These predicted properties were then fed to the crystallography and NMR system (CNS) for restrained structure modeling. The resulted structures are first evaluated by the potential energy calculated by CNS, followed by dDFIRE energy function for model selections. The method called SPOT-Fold has been tested on 241 CASP targets between 67 and 670 amino acid residues, 60 randomly selected globular proteins under 100 amino acids. The method has a comparable accuracy to other contact-map-based modeling techniques. © 2019 Wiley Periodicals, Inc.     

Proton‐Detected Solid‐State NMR Spectroscopy of a Zinc Diffusion Facilitator Protein in Native Nanodiscs

The structure, dynamics, and function of membrane proteins are intimately linked to the properties of the membrane environment in which the proteins are embedded. For structural and biophysical characterization, membrane proteins generally need to be extracted from the membrane and reconstituted in a suitable membrane‐mimicking environment. Ensuring functional and structural integrity in these environments is often a major concern. The styrene/maleic acid co‐polymer has recently been shown to be able to extract lipid/membrane protein patches directly from native membranes to form nanosize discoidal proteolipid particles, also referred to as native nanodiscs. In this work, we show that high‐resolution solid‐state NMR spectra can be obtained from an integral membrane protein in native nanodiscs, as exemplified by the 2×34 kDa bacterial cation diffusion facilitator CzcD.     

RATE OF INTRAMOLECULAR CONTACT FORMATION IN PROTEINS

In this paper, we estimate the rate of contact formation between two residues in the interior of the proteins using the Szabo, Schulten, and Schulten formula with the probability distribution P(r) based on 375 proteins from PDB (Protein Data Bank). The probability distribution for residue pair in proteins is different from the Gaussian distribution, especially for short distance between two residues in proteins. The rate of contact formation in the interior of protein is discussed as a function of distance n (=|j-i|) between two residues, and it decreases monotonically with n and follows the scaling relationship of k∞n-γwithγ= 1.43 for the contact radius a= 0.40 nm andγ= 1.05 for a = 0.50 nm. The diffusion coefficient for the relative diffusion of two residues in the interior of proteins is estimated as D = 6.4×10-6 cm2/s, which is close to the result that is found for monomer diffusion.     

The Construction of New Proteins and Enzymes-a Prospect for the Future?

Only a vanishingly small proportion of the almost infinite number of possible proteins occur in nature. Can this remaining potential of structural and functional diversity be used in the construction of new proteins? Is a “second evolution” of proteins and enzymes about to occur? These questions have suddenly become of interest because the recombinant DNA technique allows the synthesis of any given amino acid sequence. Examples of enzyme models demonstrate clearly that the unusual catalytic properties of enzymes are associated with the presence of a specifically folded polypeptide chain which has a complex three-dimensional form. The critical hurdle in the path of artificial proteins is thus the design of amino acid sequences which are able to fold into tertiary structures. — Recent studies on the topology and the mechanism of folding have provided considerable insight into the occurrence of, and the rules governing the three-dimensional architecture of proteins. Secondary structures apparently play a key role in the folding process; helices and “β-structures” act as nucleation centers directing folding and account for the surprisingly small number of different folding topologies. The problem of secondary structure formation can be investigated directly by means of conformational studies on model peptides. Oligopeptides with tailormade physicochemical, structural and conformational properties can already be designed. The theoretical and experimental basis for the construction of polypeptides with stable tertiary structures is therefore established. The path to macromolecules with an immense variety of novel properties lays before us.     

Influence of the Helical Backbone in the Behavior of a Simple Model for Dimeric Coiled‐Coil Proteins

Using computer simulations as a tool for thought experiments, we investigate the influence of the helical backbone geometry in the association process and the final structures of a simple model which mimics parallel, two‐stranded coiled‐coil proteins. We define three types of helices: two of them have straight helical axes and 3.5 or 3.6 residues per helical turn; the third type presents a coiled helical axis, according to the canonical scheme defined by Crick. By using a Monte Carlo simulation algorithm, we observe that the three models exhibit different transition temperatures for the formation of the dimeric structure from two independent peptides, and a different behavior concerning the appearance of out‐of‐register structures. The energy minimized dimer structures present strong deviations from the correct association for straight helices with 3.6 residues/turn, especially for long peptides, deviations which are absent for the other two types when only the burial of hydrophobic residues is considered. A careful analysis of the energies for the out‐of‐register configurations and the contact maps reveals also differences between dimers resulting from the model with Crick parameterization and with 3.5 residues/turn. The results presented in this paper may be relevant for the design of simple models which use rigid α‐helices built from predicted elements of secondary structure.

Top views of the helical models used in this work.     

Application of residue distribution along the sequence for discriminating outer membrane proteins

Discriminating outer membrane proteins from other folding types of globular and membrane proteins is an important problem both for detecting outer membrane proteins from genomic sequences and for the successful prediction of their secondary and tertiary structures. In this work, we have systematically analyzed the distribution of amino acid residues in the sequences of globular and outer membrane proteins. We observed that the occurrence of two neighboring aliphatic and polar residues is significantly higher in outer membrane proteins than in globular proteins. From the information about the dipeptide composition we have devised a statistical method for discriminating outer membrane proteins from other globular and membrane proteins. Our approach correctly picked up the outer membrane proteins with an accuracy of 95% for the training set of 337 proteins. On the other hand, our method has correctly excluded the globular proteins at an accuracy of 79% in a non-redundant dataset of 674 proteins. Furthermore, the present method is able to correctly exclude alpha-helical membrane proteins up to an accuracy of 87%. These accuracy levels are comparable to other methods in the literature. The influence of protein size and structural class for discrimination is discussed.     

基于序列特征筛选与支持向量回归预测蛋白质折叠速率

折叠速率预测对阐明蛋白质折叠机理意义重大.本文收集了115条目前已知折叠速率的蛋白质样本(包括二态、多态和混态蛋白),为了较全面地表征蛋白质分子的一级结构信息,提取序列长度、氨基酸残基多尺度组分、成对残基k-space特征与基于残基物理化学性质的地统计学关联总共9357维特征.经改进的二元矩阵重排过滤器和多轮末尾淘汰非线性筛选,获得23个物理化学意义明确的保留特征,建立的非线性支持向量回归模型Jackknife交叉验证的相关系数R=0.95,优于文献报道及其他参比特征选择方法.支持向量回归解释体系表明折叠速率与保留描述符的非线性回归极显著,分析了各保留描述符对折叠速率的影响,结果表明蛋白质折叠速率与序列长度、中短程关联特征、三联体残基组份特征等密切相关.     

Probing general base catalysis in the hammerhead ribozyme

Recent structural and computational studies have shed new light on the catalytic mechanism and active site structure of the RNA cleaving hammerhead ribozyme. Consequently, specific ribozyme functional groups have been hypothesized to be directly involved in general/acid base catalysis. In order to test this hypothesis, we have developed an affinity label to identify the functional general base in the S. mansoni hammerhead ribozyme. The ribozyme was reacted with a substrate analogue bearing a 2'-bromoacetamide group in place of the nucleophilic 2'-hydroxyl group which would normally be deprotonated by a general base. The electrophilic 2'-bromoacetamide group is poised to alkylate the general base, which is subsequently identified by footprinting analysis. Herein, we demonstrate alkylation of N1 of G12 in the hammerhead ribozyme in a pH and [Mg(2+)] dependent manner that is consistent with the native cleavage reaction. These results provide substantial evidence that deprotonated N1 of G12 functions directly as a general base in the hammerhead ribozyme; moreover, our experiments provide evidence that the pKa of G12 is perturbed downward in the context of the active site structure. We also observed other pH-independent alkylations, which do not appear to reflect the catalytic mechanism, but offer further insight into ribozyme conformation and structure.     

Understanding pre-structured motifs (PreSMos) in intrinsically unfolded proteins

Intrinsically unfolded proteins (IUPs) do not obey the golden rule of structural biology, 3D structure = function, as they manifest their inherent functions without resorting to three-dimensional structures. Absence of a compact globular topology in these proteins strongly implies that their ligand recognition processes should involve factors other than spatially well-defined binding pockets. Heteronuclear multidimensional (HetMulD) NMR spectroscopy assisted with a stable isotope labeling technology is a powerful tool for quantitatively investigating detailed structural features in IUPs. In particular, it allows us to delineate the presence and locations of pre-structured motifs (PreSMos) on a per-residue basis. PreSMos are the transient local structural elements that presage target-bound conformations and act as specificity determinants for IUP recognition by target proteins. Here, we present a brief chronicle of HetMulD NMR studies on IUPs carried out over the past two decades along with a discussion on the functional significance of PreSMos in IUPs.