An iterative method for extracting energy-like quantities from protein structures.

Despite its central role in the protein folding process, the specific mechanism(s) behind beta-sheet formation has yet to be determined. For example, whether the nucleation of beta-sheets, often containing strands separated in sequence by many residues, is local or not remains hotly debated. Here, we investigate the initial nucleation step of beta-sheet formation by performing an analysis of the smallest beta-sheets in a non-redundant dataset on the grounds that the smallest sheets, having undergone little growth after nucleation, will be enriched for nucleating characteristics. We find that the residue propensities are similar for small and large beta-sheets as are their interstrand pairing preferences, suggesting that nucleation is not primarily driven by specific residues or interacting pairs. Instead, an examination of the structural environments of the two-stranded sheets shows that virtually all of them are contained in single, compact structural modules, or when multiple modules are present, one or both of the chain termini are involved. We, therefore, find that beta-nucleation is a local phenomenon resulting either from sequential or topological proximity. We propose that beta-nucleation is a result of two opposite factors; that is, the relative rigidity of an associated folding module that holds two stretches of coil close together in topology coupled with sufficient chain flexibility that enables the stretches of coil to bring their backbones in close proximity. Our findings lend support to the hydrophobic zipper model of protein folding (Dill, K. A., Fiebig, K. M., and Chan, H. S. (1993) Proc. Natl. Acad. Sci. U.S.A. 90, 1942-1946). Implications for protein folding are discussed.

The ubiquitous thioredoxin fold proteins catalyze oxidation, reduction, or disulfide exchange reactions depending on their redox properties. They also play vital roles in protein folding, redox control, and disease. Here, we have shown that a single residue strongly modifies both the redox properties of thioredoxin fold proteins and their ability to interact with substrates. This residue is adjacent in three-dimensional space to the characteristic CXXC active site motif of thioredoxin fold proteins but distant in sequence. This residue is just N-terminal to the conservative cis-proline. It is isoleucine 75 in the case of thioredoxin. Our findings support the conclusion that a very small percentage of the amino acid residues of thioredoxin-related proteins are capable of dictating the functions of these proteins.

The abundance of different protein variants generated by alternative splicing (AS) is not fully represented in the Protein Structure Database (PDB). Among the already limited set of proteins with experimentally determined structures deposited in PDB, only a small fraction have structures ofmore than one splicing variant. In the absence of experimental data, computational methods can be used to predict the structure of protein isoforms. In this chapter, the established approaches to computational protein structure prediction will be briefly described, and their use for studying differences that result from AS will be detailed. In particular, it will be noted when a reliable structural model can be obtained for a given protein sequence. Topics of finding the best template structures for modeling individual protein domains, predicting the structure of long, multidomain proteins, and assessing the quality of theoretical models, together with error correction, will also be addressed. The suggested protocol will be illustrated by the structure prediction of isoform C of phosphotyrosine phosphatase (LMPTP-C). 54.1 Theoretical Background In 1961, based on studies with ribonuclease A, Christian Anfinsen hypothesized that the structure of a protein in its native environment would be determined by the protein’s amino acid sequence, and that this native conformation is the one in which the Gibbs free energy of the system is lowest [1]. Since then, the prediction of a protein’s structure from its amino acid sequence has become the “holy grail” of computational biology. However, despite great efforts having been made, a universal algorithm with which to infer protein structure has not yet been developed. Nonetheless, several methods have been developed that can provide useful models, depending on a variety of conditions. There are two major approaches for protein structure prediction. The first approach, termed “comparative modeling” or “template-based modeling,” is based on the experimental observation that evolutionarily related proteins usually retain similar structures, despite an accumulation of substitutions at the level of amino acid sequence, and that the structure changes very slowly compared to the sequence [2]. Thus, an experimentally determined structure of one protein can be used as a template to model the structure of another related protein (a modeling target) by simulating the process of evolution at the sequence level.Modification of the template to “transmute” it into the target requires only limited computation. Therefore, comparative modeling does not require special computer resources, and can be easily carried out on a personal workstation using computer programs that are simple to install and use. Template-based modeling requires that, for a given target sequence, a structurally similar template is identified, and a correct target–template sequence alignment is Alternative pre-mRNA Splicing: Theory and Protocols, First Edition. Edited by Stefan Stamm, Chris Smith, and Reinhard Luhrmann. 2012 Wiley-VCH Verlag GmbH & Co. KGaA. Published 2012 by Wiley-VCH Verlag GmbH & Co. KGaA. j 583

A systematic study of helix-helix packing in a comprehensive database of protein structures revealed that the side chains inside helix-helix interfaces on average are shorter than those in the noninterface parts of the helices. The study follows our earlier study of this effect in transmembrane helices. The results obtained on the entire database of protein structures are consistent with those obtained on the transmembrane helices. The difference in the length of interface and noninterface side chains is small but statistically significant. It indicates that helices, if viewed along their main axis, statistically are not circular, but have a flattened interface. This effect brings the helices closer to each other and creates a tighter structural packing. The results provide an interesting insight into the aspects of protein structure and folding.

A relational database of protein structure has been developed to enable rapid and flexible enquiries about the occurrence of many aspects of protein architecture. The coordinates of 294 proteins from the Brookhaven Data Bank have been processed by standard computer programs to generate many additional terms that quantify aspects of protein structure. These terms include solvent accessibility, main-chain and side-chain dihedral angles, and secondary structure. In a relational database, the information is stored in tables with columns holding the different terms and rows holding the different entries for the terms. The different relational base tables store the information about the protein coordinate set, the different chains in the protein, the amino acid residues and ligands, the atomic coordinates, the salt bridges, the hydrogen bonds, the disulphide bridges and the close tertiary contacts. The database was established under ORACLE management system. Enquiries are constructed in ORACLE using SQL (structured query language) which is simple to use and alleviates the need for extensive computer programs. A single table can be searched for entries that meet various criteria, e.g. all protein solved to better than a given resolution. The power of the database occurs when several tables, or the entries in a single table, are cross-correlated. For example the dihedral angles of proline in the fourth position in an alpha-helix in high resolution structures can be rapidly obtained. The structural database provides a powerful tool to obtain empirical rules about protein conformation. This database of protein structures is part of a joint project between Birkbeck College and Leeds University to establish an integrated data resource of protein sequences and structures (ISIS) that encodes the complex patterns of residues and coordinates that define protein conformation. The entire data resource (ISIS) will provide a system to guide all areas of protein modelling including structure prediction, site-directed mutagenesis and de novo protein design. The availability of ISIS is described in the paper.

Membrane proteins, although constituting about one-third of all proteins encoded by the genomes of living organisms, are still strongly underrepresented in the database of 3D protein structures, which reflects the big challenge posed by this class of proteins. Novel and fundamental insights into the structure, function, assembly and interaction with lipids of membrane proteins are continuously revealed by structural biologists employing electron and x-ray approaches. To date, two structural motifs, α-helices and β-sheets, have been found in membrane proteins and interestingly these two structural motives correlate with the location: while α-helical bundles are most often found in the receptors and ion channels of plasma and endoplasmic reticulum membranes, β-barrels are restricted to the outer membrane of Gram-negative bacteria, the mitochondrial membrane and chloroplasts and represent the structural motif used by several microbial toxins to form cytotoxic transmembrane channels. The β-barrel, while being a rigid and stable motif is a versatile scaffold, having a wide variation in the size of the barrel, in the mechanism to open or close the gate and to impose selectivity on substrates. The difficulty in obtaining crystals suitable for high-resolution studies of outer membrane proteins has resulted in their under-representation in the Protein data Bank [1]. Even if the number of x-ray structures of integral membrane proteins has greatly increased in recent years, only a few of them provide information at a molecular level on how proteins interact with lipids that surround them in the membrane. The detailed mechanism of protein lipid interactions is of fundamental importance for understanding membrane protein folding, membrane adsorption, insertion and function in lipid bilayers. Both specific and aspecific interactions with lipids may participate in protein folding and assembly.

An ab initio protein structure prediction system called ABLE is described. It is based on the fragment assembly method, which consists of two steps: dividing a target sequence into overlapping subsequences (fragments) of short length and assigning a local structure to each fragment; and generating models by assembling the local structures and selecting the models with low potential energy. One of the most important problems in conventional fragment assembly methods is the difficulty of selecting native-like structures by energy minimization only. ABLE thus employs a structural clustering method to select the native-like models from among the generated models. By applying the unit-vector root mean square distance (URMS) as a measure of structure similarity, we achieve more robust, effective structural clustering. When no enough clusters of good quality are obtained, ABLE runs the energy minimization procedure again by incorporating structural restraint conditions obtained from the consensus substructures in the previously generated models. This approach is based on our observation that there is a high probability that the consensus substructures of the generated models have native-like structures. Another feature of ABLE is that in assigning local structures to fragments, it assigns mainchain dihedral angles (phi, psi) to the central residue of each fragment according to a probability distribution map built from candidate sequences similar to each fragment. This enables the system to generate appropriate local structures that may not already exist in a protein structure database. We applied our system to 25 small proteins and obtain near-native folds for more than half of them. We also demonstrate the performance of our structural clustering method, which can be applied to other protein structure prediction systems.

The available protein structure data are rapidly increasing. Within these structures, numerous local structural sites depict the details characterizing structure and function. However, searching and analyzing these sites extensively and at scale poses a challenge. We present a new method to search local sites in protein structure databases using residue-defined local 3D micro-environments. We implemented the method in a new tool called MicroMiner and demonstrate the capabilities of residue micro-environment search on the example of structural mutation analysis. Usually, experimental structures for both the wild-type and the mutant are unavailable for comparison. With MicroMiner, we extracted $>255 \times 10^{6}$ amino acid pairs in protein structures from the PDB, exemplifying single mutations' local structural changes for single chains and $>45 \times 10^{6}$ pairs for protein-protein interfaces. We further annotate existing data sets of experimentally measured mutation effects, like $\Delta \Delta G$ measurements, with the extracted structure pairs to combine the mutation effect measurement with the structural change upon mutation. In addition, we show how MicroMiner can bridge the gap between mutation analysis and structure-based drug design tools. MicroMiner is available as a command line tool and interactively on the https://proteins.plus/ webserver.

This overview provides an illustrated, comprehensive survey of some commonly observed protein‐fold families and structural motifs, chosen for their functional significance. It opens with descriptions and definitions of the various elements of protein structure and associated terminology. Following is an introduction into web‐based structural bioinformatics that includes surveys of interactive web servers for protein fold or domain annotation, protein‐structure databases, protein‐structure‐classification databases, structural alignments of proteins, and molecular graphics programs available for personal computers. The rest of the overview describes selected families of protein folds in terms of their secondary, tertiary, and quaternary structural arrangements, including ribbon‐diagram examples, tables of representative structures with references, and brief explanations pointing out their respective biological and functional significance.

Modeling loops is a necessary step in protein structure determination, even with experimental nuclear magnetic resonance (NMR) data, it is widely known to be difficult. Database techniques have the advantage of producing a higher proportion of predictions with subangstrom accuracy when compared with ab initio techniques, but the disadvantage of also producing a higher proportion of clashing or highly inaccurate predictions. We introduce LoopWeaver, a database method that uses multidimensional scaling to achieve better, clash-free placement of loops obtained from a database of protein structures. This allows us to maintain the above-mentioned advantage while avoiding the disadvantage. Test results show that we achieve significantly better results than all other methods, including Modeler, Loopy, SuperLooper, and Rapper, before refinement. With refinement, our results (LoopWeaver and Loopy consensus) are better than ROSETTA, with 0.42 Å RMSD on average for 206 length 6 loops, 0.64 Å local RMSD for 168 length 7 loops, 0.81Å RMSD for 117 length 8 loops, and 0.98 Å RMSD for length 9 loops, while ROSETTA has 0.55, 0.79, 1.16, 1.42, respectively, at the same average time limit (3 hours). When we allow ROSETTA to run for over a week, it approaches, but does not surpass, our accuracy.

CyBase was originally developed as a database for backbone-cyclized proteins, providing search and display capabilities for sequence, structure and function data. Cyclic proteins are interesting because, compared to conventional proteins, they have increased stability and enhanced binding affinity and therefore can potentially be developed as protein drugs. The new CyBase release features a redesigned interface and internal architecture to improve user-interactivity, collates double the amount of data compared to the initial release, and hosts a novel suite of tools that are useful for the visualization, characterization and engineering of cyclic proteins. These tools comprise sequence/structure 2D representations, a summary of grafting and mutation studies of synthetic analogues, a study of N- to C-terminal distances in known protein structures and a structural modelling tool to predict the best linker length to cyclize a protein. These updates are useful because they have the potential to help accelerate the discovery of naturally occurring cyclic proteins and the engineering of cyclic protein drugs. The new release of CyBase is available at http://research1t.imb.uq.edu.au/cybase

More than sixty years have passed since it has disclosed that protein can be developed from amino acid sequence. Structures of protein have proven to be a key piece of information for designing a drug, synthetic biology, and diagnosis of diseases. By knowing the protein structure, one can predict its function, find out how molecules or drugs skillfully bind with proteins and structural behavior of protein can help us to diagnose diseases and to invent new drugs. Due to the fast growth of protein sequence from the amino acid, it finds to be much more difficult with experimental techniques to obtain three-dimensional protein structures. Hence protein structure prediction (PSP) is still seen to be more important. Here in this paper, we are presenting a review about the formation of protein, different protein structure, factors affecting the tertiary structure, the importance of protein structure prediction, structural database, experimental and recent computational approaches for PSP, and similarity measures.

New protein structures are continuously being determined with the hope of deriving insights into the function and mechanisms of proteins, and consequently, protein structure repositories are growing by leaps and bounds. However, we are still far from having the right methods for sensitive and effective use of the available structural data. The fact that current structural analysis tools are impractical for large-scale applications have given rise to several approaches that try to quickly identify candidate proteins worthy of further analysis. Nonetheless, these approaches do not provide the desired sensitivity of identifying important structural similarities. In this study, we propose a new protein structure retrieval method (RCIndex: Residue-Contacts Index) that is based on accurate and efficient identification of similar residue contacts from a database of available protein structures. By defining a metric distance function for biologically meaningful comparison of residue contacts, distance-based indexing is made applicable for quick retrieval of similar residue contact seeds. These seeds are extended into high scoring segment pairs, which induce structural superpositions. The results show that RCIndex is effective in not only identifying related proteins, but also producing remarkably high quality structural alignments that are comparable to or better than those produced by popular pairwise alignment tools. To the best of our knowledge, this is the first time the protein structure retrieval and alignment tasks are successfully handled together.

Homology models of proteins are of great interest for planning and analysing biological experiments when no experimental three-dimensional structures are available. Building homology models requires specialized programs and up-to-date sequence and structural databases. Integrating all required tools, programs and databases into a single web-based workspace facilitates access to homology modelling from a computer with web connection without the need of downloading and installing large program packages and databases. SWISS-MODEL workspace is a web-based integrated service dedicated to protein structure homology modelling. It assists and guides the user in building protein homology models at different levels of complexity. A personal working environment is provided for each user where several modelling projects can be carried out in parallel. Protein sequence and structure databases necessary for modelling are accessible from the workspace and are updated in regular intervals. Tools for template selection, model building and structure quality evaluation can be invoked from within the workspace. Workflow and usage of the workspace are illustrated by modelling human Cyclin A1 and human Transmembrane Protease 3. The SWISS-MODEL workspace can be accessed freely at http://swissmodel.expasy.org/workspace/

Numerous methods for predicting γ-turns in proteins have been developed. However, the results they generally provided are not very good, with a Matthews correlation coefficient (MCC)≤0.18. Here, an attempt has been made to develop a method to improve the accuracy of γ-turn prediction. First, we employ the geometric mean metric as optimal criterion to evaluate the performance of support vector machine for the highly imbalanced γ-turn dataset. This metric tries to maximize both the sensitivity and the specificity while keeping them balanced. Second, a predictor to generate protein shape string by structure alignment against the protein structure database has been designed and the predicted shape string is introduced as new variable for γ-turn prediction. Based on this perception, we have developed a new method for γ-turn prediction. After training and testing the benchmark dataset of 320 non-homologous protein chains using a fivefold cross-validation technique, the present method achieves excellent performance. The overall prediction accuracy Qtotal can achieve 92.2% and the MCC is 0.38, which outperform the existing γ-turn prediction methods. Our results indicate that the protein shape string is useful for predicting protein tight turns and it is reasonable to use the dihedral angle information as a variable for machine learning to predict protein folding. The dataset used in this work and the software to generate predicted shape string from structure database can be obtained from anonymous ftp site ftp://cheminfo.tongji.edu.cn/GammaTurnPrediction/ freely.