Zhanwen Li

FFAS03: a server for profile–profile sequence alignments

The FFAS03 server provides a web interface to the third generation of the profile–profile alignment and fold-recognition algorithm of fold and function assignment system (FFAS) [L. Rychlewski, L. Jaroszewski, W. Li and A. Godzik (2000), Protein Sci., 9, 232–241]. Profile–profile algorithms use information present in sequences of homologous proteins to amplify the patterns defining the family. As a result, they enable detection of remote homologies beyond the reach of other methods. FFAS, initially developed in 2000, is consistently one of the best ranked fold prediction methods in the CAFASP and LiveBench competitions. It is also used by several fold-recognition consensus methods and meta-servers. The FFAS03 server accepts a user supplied protein sequence and automatically generates a profile, which is then compared with several sets of sequence profiles of proteins from PDB, COG, PFAM and SCOP. The profile databases used by the server are automatically updated with the latest structural and sequence information. The server provides access to the alignment analysis, multiple alignment, and comparative modeling tools. Access to the server is open for both academic and commercial researchers. The FFAS03 server is available at .

Three-Dimensional Structural View of the Central Metabolic Network of Thermotoga maritima

Now Shown in 3D With the advent of systems-wide technologies and the development of analytical methods, data produced by analyzing individual or small groups of molecular components can be integrated to reassemble whole biological systems. Zhang et al. (p. 1544) have undertaken a major technological challenge: to integrate biochemical data with experimentally determined or predicted three-dimensional structures of all proteins involved in the central metabolism of a bacterial cell. This integration of large-scale data sets provides evolutionary and functional insights and furthers our understanding of the molecular assembly of complex biological networks. Protein structure and biochemical data generate a three-dimensional view of the metabolic network of a bacterial cell. Metabolic pathways have traditionally been described in terms of biochemical reactions and metabolites. With the use of structural genomics and systems biology, we generated a three-dimensional reconstruction of the central metabolic network of the bacterium Thermotoga maritima. The network encompassed 478 proteins, of which 120 were determined by experiment and 358 were modeled. Structural analysis revealed that proteins forming the network are dominated by a small number (only 182) of basic shapes (folds) performing diverse but mostly related functions. Most of these folds are already present in the essential core (~30%) of the network, and its expansion by nonessential proteins is achieved with relatively few additional folds. Thus, integration of structural data with networks analysis generates insight into the function, mechanism, and evolution of biological networks.

FFAS server: novel features and applications.

The Fold and Function Assignment System (FFAS) server [Jaroszewski et al. (2005) FFAS03: a server for profile–profile sequence alignments. Nucleic Acids Research, 33, W284–W288] implements the algorithm for protein profile–profile alignment introduced originally in [Rychlewski et al. (2000) Comparison of sequence profiles. Strategies for structural predictions using sequence information. Protein Science: a Publication of the Protein Society, 9, 232–241]. Here, we present updates, changes and novel functionality added to the server since 2005 and discuss its new applications. The sequence database used to calculate sequence profiles was enriched by adding sets of publicly available metagenomic sequences. The profile of a user’s protein can now be compared with ∼20 additional profile databases, including several complete proteomes, human proteins involved in genetic diseases and a database of microbial virulence factors. A newly developed interface uses a system of tabs, allowing the user to navigate multiple results pages, and also includes novel functionality, such as a dotplot graph viewer, modeling tools, an improved 3D alignment viewer and links to the database of structural similarities. The FFAS server was also optimized for speed: running times were reduced by an order of magnitude. The FFAS server, http://ffas.godziklab.org, has no log-in requirement, albeit there is an option to register and store results in individual, password-protected directories. Source code and Linux executables for the FFAS program are available for download from the FFAS server.

Exploration of Uncharted Regions of the Protein Universe

Determination of first protein structures, from hundreds of families of unknown function, have shown that divergence, rather than novelty, is the dominant force that shapes the evolution of the protein universe.

Using an alignment of fragment strings for comparing protein structures

MOTIVATION Most methods that are used to compare protein structures use three-dimensional (3D) structural information. At the same time, it has been shown that a 1D string representation of local protein structure retains a degree of structural information. This type of representation can be a powerful tool for protein structure comparison and classification, given the arsenal of sequence comparison tools developed by computational biology. However, in order to do so, there is a need to first understand how much information is contained in various possible 1D representations of protein structure. RESULTS Here we describe the use of a particular structure fragment library, denoted here as KL-strings, for the 1D representation of protein structure. Using KL-strings, we develop an infrastructure for comparing protein structures with a 1D representation. This study focuses on the added value gained from such a description. We show the new local structure language adds resolution to the traditional three-state (helix, strand and coil) secondary structure description, and provides a high degree of accuracy in recognizing structural similarities when used with a pairwise alignment benchmark. The results of this study have immediate applications towards fast structure recognition, and for fold prediction and classification.

Structural determinants of limited proteolysis.

Limited or regulatory proteolysis plays a critical role in many important biological pathways like blood coagulation, cell proliferation, and apoptosis. A better understanding of mechanisms that control this process is required for discovering new proteolytic events and for developing inhibitors with potential therapeutic value. Two features that determine the susceptibility of peptide bonds to proteolysis are the sequence in the vicinity of the scissile bond and the structural context in which the bond is displayed. In this study, we assessed statistical significance and predictive power of individual structural descriptors and combination thereof for the identification of cleavage sites. The analysis was performed on a data set of >200 proteolytic events documented in CutDB for a variety of mammalian regulatory proteases and their physiological substrates with known 3D structures. The results confirmed the significance and provided a ranking within three main categories of structural features: exposure > flexibility > local interactions. Among secondary structure elements, the largest frequency of proteolytic cleavage was confirmed for loops and lower but significant frequency for helices. Limited proteolysis has lower albeit appreciable frequency of occurrence in certain types of β-strands, which is in contrast with some previous reports. Descriptors deduced directly from the amino acid sequence displayed only marginal predictive capabilities. Homology-based structural models showed a predictive performance comparable to protein substrates with experimentally established structures. Overall, this study provided a foundation for accurate automated prediction of segments of protein structure susceptible to proteolytic processing and, potentially, other post-translational modifications.

Modeling and analyzing three-dimensional structures of human disease proteins.

Three-dimensional structures of proteins, experimental or predicted, show us how these molecular machines actually work. With the help of information on disease-related mutations, they can also show us how they malfunction in diseases. Such understanding, currently lacking for most human diseases, is an important first step before designing drugs or therapies to cure specific diseases. Here we used homology modeling to model human disease-related proteins, and studied structural characteristics of disease related mutations and compared them with non synonymous SNPs. 1484 domains from 874 proteins were modeled, and together with experimentally determined structures of 369 domains they provided the structural coverage of 48% of total residues in 1237 human disease proteins. We found that disease-related mutations have statistically significantly preference to form clusters on protein surfaces. In contrast, the non-synonymous SNPs appear to be randomly distributed on the surface. We interpret these results as an indication that disease mutations affect protein-protein interaction interfaces. This interpretation is supported by the analysis of 8 experimentally determined complexes between disease proteins, where disease-related mutations are clearly located in the binding interface of proteins, while SNPs are not. The non-uniform distribution of disease mutations indicates that we can use this feature as guidance in modeling and evaluating human disease proteins and their complexes. We set up a resource for Disease Protein Models (DPM at http://ffas.burnham.org/DPM), which can be used for studying the relation between disease and mutation/polymorphism sites in the context of protein 3D structures and complexes.

Flexible Structural Neighborhood—a database of protein structural similarities and alignments

Protein structures are flexible, changing their shapes not only upon substrate binding, but also during evolution as a collective effect of mutations, deletions and insertions. A new generation of protein structure comparison algorithms allows for such flexibility; they go beyond identifying the largest common part between two proteins and find hinge regions and patterns of flexibility in protein families. Here we present a Flexible Structural Neighborhood (FSN), a database of structural neighbors of proteins deposited in PDB as seen by a flexible protein structure alignment program FATCAT, developed previously in our group. The database, searchable by a protein PDB code, provides lists of proteins with statistically significant structural similarity and on lower menu levels provides detailed alignments, interactive superposition of structures and positions of hinges that were identified in the comparison. While superficially similar to other structural protein alignment resources, FSN provides a unique resource to study not only protein structural similarity, but also how protein structures change. FSN is available from a server and by direct links from the PDB database.

AIDA: ab initio domain assembly server

AIDA: ab initio domain assembly server, available at http://ffas.burnham.org/AIDA/ is a tool that can identify domains in multi-domain proteins and then predict their 3D structures and relative spatial arrangements. The server is free and open to all users, and there is an option for a user to provide an e-mail to get the link to result page. Domains are evolutionary conserved and often functionally independent units in proteins. Most proteins, especially eukaryotic ones, consist of multiple domains while at the same time, most experimentally determined protein structures contain only one or two domains. As a result, often structures of individual domains in multi-domain proteins can be accurately predicted, but the mutual arrangement of different domains remains unknown. To address this issue we have developed AIDA program, which combines steps of identifying individual domains, predicting (separately) their structures and assembling them into multiple domain complexes using an ab initio folding potential to describe domain–domain interactions. AIDA server not only supports the assembly of a large number of continuous domains, but also allows the assembly of domains inserted into other domains. Users can also provide distance restraints to guide the AIDA energy minimization.

POSA: a user-driven, interactive multiple protein structure alignment server

POSA (Partial Order Structure Alignment), available at http://posa.godziklab.org, is a server for multiple protein structure alignment introduced in 2005 (Ye,Y. and Godzik,A. (2005) Multiple flexible structure alignment using partial order graphs. Bioinformatics, 21, 2362–2369). It is free and open to all users, and there is no login requirement, albeit there is an option to register and store results in individual, password-protected directories. In the updated POSA server described here, we introduce two significant improvements. First is an interface allowing the user to provide additional information by defining segments that anchor the alignment in one or more input structures. This interface allows users to take advantage of their intuition and biological insights to improve the alignment and guide it toward a biologically relevant solution. The second improvement is an interactive visualization with options that allow the user to view all superposed structures in one window (a typical solution for visualizing results of multiple structure alignments) or view them individually in a series of synchronized windows with extensive, user-controlled visualization options. The user can rotate structure(s) in any of the windows and study similarities or differences between structures clearly visible in individual windows.