A method for protein accessibility prediction based on residue types and conformational states

Prediction of protein accessibility from sequence, as prediction of protein secondary structure is an intermediate step for predicting structures and consequently functions of proteins. Most of the currently used methods are based on single residue prediction, either by statistical means or evolutionary information, and accessibility state of central residue in a window predicted. By expansion of databases of proteins with known 3D structures, we extracted information of pairwise residue types and conformational states of pairs simultaneously. For solving the problem of ambiguity in state prediction by one residue window sliding, we used dynamic programming algorithm to find the path with maximum score. The three state overall per-residue accuracy, Q₃, of this method in a Jackknife test with dataset of known proteins is more than 65% which is an improvement on results of methods based on evolutionary information.     

Variable predictive model based classification algorithm for effective separation of protein structural classes

Variable predictive model based class discrimination (VPMCD) algorithm is proposed as an effective protein secondary structure classification tool. The algorithm mathematically represents the characteristics amino acid interactions specific to each protein structure and exploits them further to distinguish different structures. The new concept and the VPMCD classifier are established using well-studied datasets containing four protein classes as benchmark. The protein samples selected from SCOP and PDB databases with varying homology (25-100%) and non-uniform distribution of class samples provide challenging classification problem. The performance of the new method is compared with advanced classification algorithms like component coupled, SVM and neural networks. VPMCD provides superior performance for high homology datasets. 100% classification is achieved for self-consistency test and an improvement of 5% prediction accuracy is obtained during Jackknife test. The sensitivity of the new algorithm is investigated by varying model structures/types and sequence homology. Simpler to implement VPMCD algorithm is observed to be a robust classification technique and shows potential for effective extensions to other clinical diagnosis and data mining applications in biological systems.     

Protein family classification and functional annotation

With the accelerated accumulation of genomic sequence data, there is a pressing need to develop computational methods and advanced bioinformatics infrastructure for reliable and large-scale protein annotation and biological knowledge discovery. The Protein Information Resource (PIR) provides an integrated public resource of protein informatics to support genomic and proteomic research. PIR produces the Protein Sequence Database of functionally annotated protein sequences. The annotation problems are addressed by a classification-driven and rule-based method with evidence attribution, coupled with an integrated knowledge base system being developed. The approach allows sensitive identification, consistent and rich annotation, and systematic detection of annotation errors, as well as distinction of experimentally verified and computationally predicted features. The knowledge base consists of two new databases, sequence analysis tools, and graphical interfaces. PIR-NREF, a non-redundant reference database, provides a timely and comprehensive collection of all protein sequences, totaling more than 1,000,000 entries. iProClass, an integrated database of protein family, function, and structure information, provides extensive value-added features for about 830,000 proteins with rich links to over 50 molecular databases. This paper describes our approach to protein functional annotation with case studies and examines common identification errors. It also illustrates that data integration in PIR supports exploration of protein relationships and may reveal protein functional associations beyond sequence homology.     

Fast and accurate methods for predicting short-range constraints in protein models

Protein modeling tools utilize many kinds of structural information that may be predicted from amino acid sequence of a target protein or obtained from experiments. Such data provide geometrical constraints in a modeling process. The main aim is to generate the best possible consensus structure. The quality of models strictly depends on the imposed conditions. In this work we present an algorithm, which predicts short-range distances between Cα atoms as well as a set of short structural fragments that possibly share structural similarity with a query sequence. The only input of the method is a query sequence profile. The algorithm searches for short protein fragments with high sequence similarity. As a result a statistics of distances observed in the similar fragments is returned. The method can be used also as a scoring function or a short-range knowledge-based potential based on the computed statistics.     

Force mode atomic force microscopy as a tool for protein folding studies

The advent of a new class of force microscopes designed specifically to “pull” biomolecules has allowed non-specialists to use force microscopy as a tool to study single-molecule protein unfolding. This powerful new technique has the potential to explore regions of the protein energy landscape that are not accessible in conventional bulk studies. It has the added advantage of allowing direct comparison with single-molecule simulation experiments. However, as with any new technique, there is currently no well described consensus for carrying out these experiments. Adoption of standard schemes of data selection and analysis will facilitate comparison of data from different laboratories and on different proteins. In this review, some guidelines and principles, which have been adopted by our laboratories, are suggested. The issues associated with collecting sufficient high quality data and the analysis of those data are discussed. In single-molecule studies, there is an added complication since an element of judgement has to be applied in selecting data to analyse; we propose criteria to make this process more objective. The principal sources of error are identified and standardised methods of selecting and analysing the data are proposed. The errors associated with the kinetic parameters obtained from such experiments are evaluated. The information that can be obtained from dynamic force experiments is compared, both quantitatively and qualitatively to that derived from conventional protein folding studies.     

蛋白质结构的“限域下最低能量结构片段”假说与蛋白质进化的“石器时代”

蛋白质折叠问题被称为第二遗传密码,至今未破译;蛋白质序列的天书仍然是"句读之不知,惑之不解"。在最近工作的基础上,我们提出了蛋白质结构的"限域下最低能量结构片段"假说。这一假说指出,蛋白质中存在一些关键的长程强相互作用位点,这些位点相当于标点符号,将蛋白质序列的天书变成可读的句子(多肽片段)。这些片段的天然结构是在这些强长程相互作用位点限域下的能量最低状态。完整的蛋白质结构由这些"限域下最低能量结构片段"拼合而成,而蛋白质整体结构并不一定是全局性的能量最低状态。在蛋白质折叠过程中,局部片段的天然结构倾向性为强长程相互作用的形成提供主要基于焓效应的驱动力,而天然强长程相互作用的形成为局部片段的天然结构提供主要基于熵效应的稳定性。在蛋白质进化早期,可能存在一个"石器时代",即依附不同界面(比如岩石)的限域作用而稳定的多肽片段先进化出来,后由这些片段逐步进化(包括拼合)而成蛋白质。     

Stabilization centers and protein stability

The well-balanced stability of protein structures allows large-scale fluctuations, which are indispensable in many biochemical functions, ensures the long-term persistence of the equilibrium structure and it regulates the degradation of proteins to provide amino acids for biosynthesis. This balance is studied in the present work with two sets of proteins by analyzing stabilization centers, defined as certain clusters of residues involved in cooperative long-range interactions. One data set contains 56 proteins, which belong to 16 families of homologous proteins, derived from organisms of various physiological temperatures. The other set is composed of 31 major histocompatibility complex (MHC)–peptide complexes, which represent peptide transporters complexed with peptide ligands that apparently contribute to the stabilization of the MHC proteins themselves. We show here that stabilization centers, which had been identified as special clusters of residues that protect the protein structure, evolved to serve also as regulators of function – related degradation of useless protein as part of protein housekeeping. Received: 25 August 2000 / Accepted: 6 September 2000 / Published online: 21 December 2000     

Structure prediction of an S-layer protein by the mean force method

S-layer proteins have a wide range of application potential due to their characteristic features concerning self-assembling, assembling on various surfaces, and forming of isoporous structures with functional groups located on the surface in an identical position and orientation. Although considerable knowledge has been experimentally accumulated on the structure, biochemistry, assemble characteristics, and genetics of S-layer proteins, no structural model at atomic resolution has been available so far. Therefore, neither the overall folding of the S-layer proteins-their tertiary structure-nor the exact amino acid or domain allocations in the lattices are known. In this paper, we describe the tertiary structure prediction for the S-layer protein SbsB from Geobacillus stearothermophilus PV72/p2. This calculation was based on its amino acid sequence using the mean force method (MF method) achieved by performing molecular dynamic simulations. This method includes mainly the thermodynamic aspects of protein folding as well as steric constraints of the amino acids and is therefore independent of experimental structure analysis problems resulting from biochemical properties of the S-layer proteins. Molecular dynamic simulations were performed in vacuum using the simulation software NAMD. The obtained tertiary structure of SbsB was systematically analyzed by using the mean force method, whereas the verification of the structure is based on calculating the global free energy minimum of the whole system. This corresponds to the potential of mean force, which is the thermodynamically most favorable conformation of the protein. Finally, an S-layer lattice was modeled graphically using CINEMA4D and compared with scanning force microscopy data down to a resolution of 1 nm. The results show that this approach leads to a thermodynamically favorable atomic model of the tertiary structure of the protein, which could be verified by both the MF Method and the lattice model.     

Effect of protein solution components in the adsorption of Herbaspirillum seropedicae GlnB protein on mica

The adsorption of proteins and its buffer solution on mica surfaces was investigated by atomic force microscopy (AFM). Different salt concentration of the Herbaspirillum seropedicae GlnB protein (GlnB-Hs) solution deposited on mica was investigated. This protein is a globular, soluble homotrimer (36 kDa), member of PII-like proteins family involved in signal transducing in prokaryote. Supramolecular structures were formed when this protein was deposited onto bare mica surface. The topographic AFM images of the GlnB-Hs films showed that at high salt concentration the supramolecular structures are spherical-like, instead of the typical doughnut-like shape for low salt concentration. AFM images of NaCl and Tris from the buffer solution showed structures with the same pattern as those observed for high salt protein solution, misleading the image interpretation. XPS experiments showed that GlnB protein film covers the mica surface without chemical reaction.     

Data requirements for protein identification using chemically-assisted fragmentation and tandem mass spectrometry

Many laboratories identify proteins by searching tandem mass spectrometry data against genomic or protein sequence databases. These database searches typically use the measured peptide masses or the derived peptide sequence and, in this paper, we focus on the latter. We study the minimum peptide sequence data requirements for definitive protein identification from protein sequence databases. Accurate mass measurements are not needed for definitive protein identification, even when a limited amount of sequence data is available for searching. This information has implications for the mass spectrometry performance (and cost), data base search strategies and proteomics research.     

SSA: Subset sum approach to protein β-sheet structure prediction

The three-dimensional structures of proteins provide their functions and incorrect folding of its β-strands can be the cause of many diseases. There are two major approaches for determining protein structures: computational prediction and experimental methods that employ technologies such as Cryo-electron microscopy. Due to experimental methods’s high costs, extended wait times for its lengthy processes, and incompleteness of results, computational prediction is an attractive alternative. As the focus of the present paper, β-sheet structure prediction is a major portion of overall protein structure prediction. Prediction of other substructures, such as α-helices, is simpler with lower computational time complexities. Brute force methods are the most common approach and dynamic programming is also utilized to generate all possible conformations. The current study introduces the Subset Sum Approach (SSA) for the direct search space generation method, which is shown to outperform the dynamic programming approach in terms of both time and space. For the first time, the present work has calculated both the state space cardinality of the dynamic programming approach and the search space cardinality of the general brute force approaches. In regard to a set of pruning rules, SSA has demonstrated higher efficiency with respect to both time and accuracy in comparison to state-of-the-art methods.     

Supervised machine learning algorithms for protein structure classification

We explore automation of protein structural classification using supervised machine learning methods on a set of 11,360 pairs of protein domains (up to 35% sequence identity) consisting of three secondary structure elements. Fifteen algorithms from five categories of supervised algorithms are evaluated for their ability to learn for a pair of protein domains, the deepest common structural level within the SCOP hierarchy, given a one-dimensional representation of the domain structures. This representation encapsulates evolutionary information in terms of sequence identity and structural information characterising the secondary structure elements and lengths of the respective domains. The evaluation is performed in two steps, first selecting the best performing base learners and subsequently evaluating boosted and bagged meta learners. The boosted random forest, a collection of decision trees, is found to be the most accurate, with a cross-validated accuracy of 97.0% and F-measures of 0.97, 0.85, 0.93 and 0.98 for classification of proteins to the Class, Fold, Super-Family and Family levels in the SCOP hierarchy. The meta learning regime, especially boosting, improved performance by more accurately classifying the instances from less populated classes.     

A protein fold classifier formed by fusing different modes of pseudo amino acid composition via PSSM

Protein function is related to its chemical reaction to the surrounding environment including other proteins. On the other hand, this depends on the spatial shape and tertiary structure of protein and folding of its constituent components in space. The correct identification of protein domain fold solely using extracted information from protein sequence is a complicated and controversial task in the current computational biology. In this article a combined classifier based on the information content of extracted features from the primary structure of protein has been introduced to face this challenging problem. In the first stage of our proposed two-tier architecture, there are several classifiers each of which is trained with a different sequence based feature vector. Apart from the application of the predicted secondary structure, hydrophobicity, van der Waals volume, polarity, polarizability, and different dimensions of pseudo-amino acid composition vectors in similar studies, the position specific scoring matrix (PSSM) has also been used to improve the correct classification rate (CCR) in this study. Using K-fold cross validation on training dataset related to 27 famous folds of SCOP, the 28 dimensional probability output vector from each evidence theoretic K-NN classifier is used to determine the information content or expertness of corresponding feature for discrimination in each fold class. In the second stage, the outputs of classifiers for test dataset are fused using Sugeno fuzzy integral operator to make better decision for target fold class. The expertness factor of each classifier in each fold class has been used to calculate the fuzzy integral operator weights. Results make it possible to provide deeper interpretation about the effectiveness of each feature for discrimination in target classes for query proteins.     

A graph theory data base for storage of chemical structures organized by the block-cutpoint tree technique

The method presented for organizing a data base according to graph theory, is based on representation of chemical structures in terms of BCT (block-cutpoint tree). It is useful for quick substructure searches and is convenient for structure generation. The data base consists of four files: a master file, a bit sequence file of fixed length records which gives block components of compounds, a BCT file which gives the BCT structures of compounds, and a block file which specifies the blocks. These files are organized recursively and hierarchally, which simplifies the processing of structural information on compounds.     

Neural network pairwise interaction fields for protein model quality assessment and ab initio protein folding

In order to use a predicted protein structure one needs to know how good it is, as the utility of a model depends on its quality. To this aim, many Model Quality Assessment Programs (MQAP) have been developed over the last decade, with MQAP also being assessed at the CASP competition. We present a new knowledge-based MQAP which evaluates single protein structure models. We use a tree representation of the Cα trace to train a novel Neural Network Pairwise Interaction Field (NN-PIF) to predict the global quality of a model. NN-PIF allows fast evaluation of multiple structure models for a single sequence. In our tests on a large set of structures, our networks outperform most other methods based on different and more complex protein structure representations in global model quality prediction. Moreover, given NN-PIF can evaluate protein conformations very fast, we train a separate version of the model to gauge its ability to fold protein structures ab initio. We show that the resulting system, which relies only on basic information about the sequence and the Cα trace of a conformation, generally improves the quality of the structures it is presented with and may yield promising predictions in the absence of structural templates, although more research is required to harness the full potential of the model.     

Correlations Between Amino Acids at Different Sites in Local Sequences of Protein Fragments with Given Structural Patterns

Ample evidence suggests that the local structures of peptide fragments in native proteins are to some extent encoded by their local sequences. Detecting such local correlations is important but it is still an open question what would be the most appropriate method. This is partly because conventional sequence analyses treat amino acid preferences at each site of a protein sequence independently, while it is often the inter-site interactions that bring about local sequence-structure correlations. Here a new scheme is introduced to capture the correlation between amino acid preferences at different sites for different local structure types. A library of nine-residue fragments is constructed, and the fragments are divided into clusters based on their local structures. For each local structure cluster or type, chi-square tests are used to identify correlated preferences of amino acid combinations at pairs of sites. A score function is constructed including both the single site amino acid preferences and the dual-site amino acid combination preferences, which can be used to identify whether a sequence fragment would have a strong tendency to form a particular local structure in native proteins. The results show that, given a local structure pattern, dual-site amino acid combinations contain different information from single site amino acid preferences. Representative examples show that many of the statistically identified correlations agree with previously-proposed heuristic rules about local sequence-structure correlations, or are consistent with physical-chemical interactions required to stabilize particular local structures. Results also show that such dual-site correlations in the score function significantly improves the Z-score matching a sequence fragment to its native local structure relative to nonnative local structures, and certain local structure types are highly predictable from the local sequence alone if inter-site correlations are considered.     

Structure-based identification and clustering of protein families and superfamilies

Summary We describe an approach to protein structure comparison designed to detect distantly related proteins of similar fold, where the procedure must be sufficiently flexible to take into account the elasticity of protein folds without losing specificity. Protein structures are represented as a series of secondary structure elements, where for each element a local environment describes its relations with the elements that surround it. Secondary structures are then aligned by comparing their features and local environments. The procedure is illustrated with searches of a database of 468 protein structures in order to identify proteins of similar topology to porcine pepsin, porphobilinogen deaminase and serum amyloid P-component. In all cases the searches correctly identify protein structures of similar fold as the search proteins. Multiple cross-comparisons of protein structures allow the clustering of proteins of similar fold. This is exemplified with a clustering of /- and -class protein structures. We discuss applications of the comparison and clustering of three-dimensional protein structures to comparative modelling and structure-based protein design.     

FPDock: Protein–protein docking using flower pollination algorithm

Proteins play their vital role in biological systems through interaction and complex formation with other biological molecules. Indeed, abnormalities in the interaction patterns affect the proteins’ structure and have detrimental effects on living organisms. Research in structure prediction gains its gravity as the functions of proteins depend on their structures. Protein–protein docking is one of the computational methods devised to understand the interaction between proteins. Metaheuristic algorithms are promising to use owing to the hardness of the structure prediction problem. In this paper, a variant of the Flower Pollination Algorithm (FPA) is applied to get an accurate protein–protein complex structure. The algorithm begins execution from a randomly generated initial population, which gets flourished in different isolated islands, trying to find their local optimum. The abiotic and biotic pollination applied in different generations brings diversity and intensity to the solutions. Each round of pollination applies an energy-based scoring function whose value influences the choice to accept a new solution. Analysis of final predictions based on CAPRI quality criteria shows that the proposed method has a success rate of 58% in top10 ranks, which in comparison with other methods like SwarmDock, pyDock, ZDOCK is better. Source code of the work is available at: https://github.com/Sharon1989Sunny/_FPDock_.