A high-throughput approach to protein structure analysis.
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Biophysical Phenomena↗
Biomedical subjects
András Fiser
Publications and source records attributed to András Fiser.
MMM: a sequence-to-structure alignment protocol.
MOTIVATION: Accurate alignment of a target sequence to a template structure continues to be a bottleneck in producing good quality comparative protein structure models. RESULTS: Multiple Mapping Method (MMM) is a comparative protein structure modeling server with an emphasis on a novel alignment optimization protocol. MMM takes inputs from five profile-to-profile based alignment methods. The alternatively aligned regions from the input alignment set are combined according to their fit in the structural environment of the template structure. The resulting, optimally spliced MMM alignment is used as input to an automated comparative modeling module to produce a full atom model. AVAILABILITY: The MMM server is freely accessible at http://www.fiserlab.org/servers/mmm
Algorithms↗
Saturating representation of loop conformational fragments in structure databanks.
BACKGROUND: Short fragments of proteins are fundamental starting points in various structure prediction applications, such as in fragment based loop modeling methods but also in various full structure build-up procedures. The applicability and performance of these approaches depend on the availability of short fragments in structure databanks. RESULTS: We studied the representation of protein loop fragments up to 14 residues in length. All possible query fragments found in sequence databases (Sequence Space) were clustered and cross referenced with available structural fragments in Protein Data Bank (Structure Space). We found that the expansion of PDB in the last few years resulted in a dense coverage of loop conformational fragments. For each loops of length 8 in the current Sequence Space there is at least one loop in Structure Space with 50% or higher sequence identity. By correlating sequence and structure clusters of loops we found that a 50% sequence identity generally guarantees structural similarity. These percentages of coverage at 50% sequence cutoff drop to 96, 94, 68, 53, 33 and 13% for loops of length 9, 10, 11, 12, 13, and 14, respectively. There is not a single loop in the current Sequence Space at any length up to 14 residues that is not matched with a conformational segment that shares at least 20% sequence identity. This minimum observed identity is 40% for loops of 12 residues or shorter and is as high as 50% for 10 residue or shorter loops. We also assessed the impact of rapidly growing sequence databanks on the estimated number of new loop conformations and found that while the number of sequentially unique sequence segments increased about six folds during the last five years there are almost no unique conformational segments among these up to 12 residues long fragments. CONCLUSION: The results suggest that fragment based prediction approaches are not limited any more by the completeness of fragments in databanks but rather by the effective scoring and search algorithms to locate them. The current favorable coverage and trends observed will be further accentuated with the progress of Protein Structure Initiative that targets new protein folds and ultimately aims at providing an exhaustive coverage of the structure space.
Amino Acid Sequence↗
ArchPRED: a template based loop structure prediction server.
ArchPRED server (http://www.fiserlab.org/servers/archpred) implements a novel fragment-search based method for predicting loop conformations. The inputs to the server are the atomic coordinates of the query protein and the position of the loop. The algorithm selects candidate loop fragments from a regularly updated loop library (Search Space) by matching the length, the types of bracing secondary structures of the query and by satisfying the geometrical restraints imposed by the stem residues. Subsequently, candidate loops are inserted in the query protein framework where their side chains are rebuilt and their fit is assessed by the root mean square deviation (r.m.s.d.) of stem regions and by the number of rigid body clashes with the environment. In the final step remaining candidate loops are ranked by a Z-score that combines information on sequence similarity and fit of predicted and observed [/psi] main chain dihedral angle propensities. The final loop conformation is built in the protein structure and annealed in the environment using conjugate gradient minimization. The prediction method was benchmarked on artificially prepared search datasets where all trivial sequence similarities on the SCOP superfamily level were removed. Under these conditions it was possible to predict loops of length 4, 8 and 12 with coverage of 98, 78 and 28% with at least of 0.22, 1.38 and 2.47 A of r.m.s.d. accuracy, respectively. In a head to head comparison on loops extracted from freshly deposited new protein folds the current method outperformed in a approximately 5:1 ratio an earlier developed database search method.
Algorithms↗
Multiple mapping method: a novel approach to the sequence-to-structure alignment problem in comparative protein structure modeling.
A major bottleneck in comparative protein structure modeling is the quality of input alignment between the target sequence and the template structure. A number of alignment methods are available, but none of these techniques produce consistently good solutions for all cases. Alignments produced by alternative methods may be superior in certain segments but inferior in others when compared to each other; therefore, an accurate solution often requires an optimal combination of them. To address this problem, we have developed a new approach, Multiple Mapping Method (MMM). The algorithm first identifies the alternatively aligned regions from a set of input alignments. These alternatively aligned segments are scored using a composite scoring function, which determines their fitness within the structural environment of the template. The best scoring regions from a set of alternative segments are combined with the core part of the alignments to produce the final MMM alignment. The algorithm was tested on a dataset of 1400 protein pairs using 11 combinations of two to four alignment methods. In all cases MMM showed statistically significant improvement by reducing alignment errors in the range of 3 to 17%. MMM also compared favorably over two alignment meta-servers. The algorithm is computationally efficient; therefore, it is a suitable tool for genome scale modeling studies.
Algorithms↗
A supersecondary structure library and search algorithm for modeling loops in protein structures.
We present a fragment-search based method for predicting loop conformations in protein models. A hierarchical and multidimensional database has been set up that currently classifies 105,950 loop fragments and loop flanking secondary structures. Besides the length of the loops and types of bracing secondary structures the database is organized along four internal coordinates, a distance and three types of angles characterizing the geometry of stem regions. Candidate fragments are selected from this library by matching the length, the types of bracing secondary structures of the query and satisfying the geometrical restraints of the stems and subsequently inserted in the query protein framework where their fit is assessed by the root mean square deviation (r.m.s.d.) of stem regions and by the number of rigid body clashes with the environment. In the final step remaining candidate loops are ranked by a Z-score that combines information on sequence similarity and fit of predicted and observed phi/psi main chain dihedral angle propensities. Confidence Z-score cut-offs were determined for each loop length that identify those predicted fragments that outperform a competitive ab initio method. A web server implements the method, regularly updates the fragment library and performs prediction. Predicted segments are returned, or optionally, these can be completed with side chain reconstruction and subsequently annealed in the environment of the query protein by conjugate gradient minimization. The prediction method was tested on artificially prepared search datasets where all trivial sequence similarities on the SCOP superfamily level were removed. Under these conditions it is possible to predict loops of length 4, 8 and 12 with coverage of 98, 78 and 28% with at least of 0.22, 1.38 and 2.47 A of r.m.s.d. accuracy, respectively. In a head-to-head comparison on loops extracted from freshly deposited new protein folds the current method outperformed in a approximately 5:1 ratio an earlier developed database search method.
Algorithms↗
Protein structure modeling in the proteomics era.
Structural proteomics aims to understand the structural basis of protein interactions and functions. A prerequisite for this is the availability of 3D protein structures that mediate the biochemical interactions. The explosion in the number of available gene sequences set the stage for the next step in genome-scale projects -- to obtain 3D structures for each protein. To achieve this ambitious goal, the slow and costly structure determination experiments are supplemented with theoretical approaches. The current state and recent advances in structure modeling approaches are reviewed here, with special emphasis on comparative protein structure modeling techniques.
Animals↗
ModLoop: automated modeling of loops in protein structures.
SUMMARY: ModLoop is a web server for automated modeling of loops in protein structures. The input is the atomic coordinates of the protein structure in the Protein Data Bank format, and the specification of the starting and ending residues of one or more segments to be modeled, containing no more than 20 residues in total. The output is the coordinates of the non-hydrogen atoms in the modeled segments. A user provides the input to the server via a simple web interface, and receives the output by e-mail. The server relies on the loop modeling routine in MODELLER that predicts the loop conformations by satisfaction of spatial restraints, without relying on a database of known protein structures. For a rapid response, ModLoop runs on a cluster of Linux PC computers. AVAILABILITY: The server is freely accessible to academic users at http://salilab.org/modloop
Amino Acid Sequence↗
Bimoclomol, a heat shock protein co-inducer, acts by the prolonged activation of heat shock factor-1.
The novel hydroxylamine derivative, bimoclomol, has been shown previously to act as a co-inducer of several heat shock proteins (Hsp-s), enhancing the amount of these proteins produced following a heat shock compared to heat shock alone. Here we show that the co-inducing effect of bimoclomol on Hsp expression is mediated via the prolonged activation of the heat shock transcription factor (HSF-1). Bimoclomol effects are abolished in cells from mice lacking HSF-1. Moreover, bimoclomol binds to HSF-1 and induces a prolonged binding of HSF-1 to the respective DNA elements. Since HSF-1 does not bind to DNA in the absence of stress, the bimoclomol-induced extension of HSF-1/DNA interaction may contribute to the chaperone co-induction of bimoclomol observed previously. These findings indicate that bimoclomol may be of value in targeting HSF-1 so as to induce up-regulation of protective Hsp-s in a non-stressful manner and for therapeutic benefit.
Animals↗
Servers for sequence-structure relationship analysis and prediction.
We describe several algorithms and public servers that were developed to analyze and predict various features of protein structures. These servers provide information about the covalent state of cysteine (CYSREDOX), as well as about residues involved in non-covalent cross links that play an important role in the structural stability of proteins (SCIDE and SCPRED). We also discuss methods and servers developed to identify helical transmembrane proteins from large databases and rough genomic data, including two of the most popular transmembrane prediction methods, DAS and HMMTOP. Several biologically interesting applications of these servers are also presented. The servers are available through http://www.enzim.hu/servers.html.
Algorithms↗
Cell wall branches, penicillin resistance and the secrets of the MurM protein.
Production of low-affinity forms of penicillin-binding proteins (PBPs), although essential, is not sufficient to protect pneumococci against the inhibitory action of penicillin. Resistance also requires the newly identified protein MurM which, together with MurN, is involved with the synthesis of short peptide branches in the pneumococcal cell wall. Cells in which murM was inactivated produced cell walls without branches and also completely lost penicillin resistance. To understand these surprising observations a 3D-model of MurM was constructed, which helped to put into structural context several of the biochemical and genetic observations made about this protein.
Bacteria↗
Modeller: generation and refinement of homology-based protein structure models.
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Amino Acid Sequence↗
Probing the specificity of a trypanosomal aromatic alpha-hydroxy acid dehydrogenase by site-directed mutagenesis.
The aromatic l-alpha-hydroxy acid dehydrogenase (AHDAH) from Trypanosoma cruzi has over 50% sequence identity with cytosolic malate dehydrogenases (cMDHs), yet it is unable to reduce oxaloacetate. Molecular modeling of the three-dimensional structure of AHADH using the pig cMDH as template directed the construction of several mutants. AHADH shares with MDHs the essential catalytic residues H195 and R171 (using Eventoff's numbering). The AHADH A102R mutant became able to reduce oxaloacetate, while remaining fully active towards aromatic alpha-oxoacids. The Y237G mutant diminished its affinity for all of the natural substrates, whereas the double mutant A102R/Y237G was more active than Y237G and had similar activity with oxaloacetate and with aromatic substrates. The present results reinforce our proposal that AHADH arose by a moderate number of point mutations from a cMDH no longer present in the parasite.
Alcohol Oxidoreductases↗
Predicting redox state of cysteines in proteins.
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Computational Biology↗
Reliability of assessment of protein structure prediction methods.
The reliability of ranking of protein structure modeling methods is assessed. The assessment is based on the parametric Student's t test and the nonparametric Wilcox signed rank test of statistical significance of the difference between paired samples. The approach is applied to the ranking of the comparative modeling methods tested at the fourth meeting on Critical Assessment of Techniques for Protein Structure Prediction (CASP). It is shown that the 14 CASP4 test sequences may not be sufficient to reliably distinguish between the top eight methods, given the model quality differences and their standard deviations. We suggest that CASP needs to be supplemented by an assessment of protein structure prediction methods that is automated, continuous in time, based on several criteria applied to a large number of models, and with quantitative statistical reliability assigned to each characterization.
Computer Simulation↗
Evolution and physics in comparative protein structure modeling.
From a physical perspective, the native structure of a protein is a consequence of physical forces acting on the protein and solvent atoms during the folding process. From a biological perspective, the native structure of proteins is a result of evolution over millions of years. Correspondingly, there are two types of protein structure prediction methods, de novo prediction and comparative modeling. We review comparative protein structure modeling and discuss the incorporation of physical considerations into the modeling process. A good starting point for achieving this aim is provided by comparative modeling by satisfaction of spatial restraints. Incorporation of physical considerations is illustrated by an inclusion of solvation effects into the modeling of loops.
Biophysical Phenomena↗
Noncovalent cross-links in context with other structural and functional elements of proteins.
Proteins are heteropolymers with evolutionary selected native sequences of residues. These native sequences code for unique and stable 3D structures indispensable for biochemical activity and for proteolysis resistance, the latter which guarantees an appropriate lifetime for the protein in the protease rich cellular environment. Cross-links between residues close in space but far in the primary structure are required to maintain the folded structure of proteins. Some of these cross-links are covalent, most frequently disulfide bonds, but the majority of the cross-links are sets of cooperative noncovalent long-range interactions. In this paper we focus on special clusters of noncovalent long-range interactions: the Stabilization Centers (SCs). The relation between the SCs and secondary structural elements as well as the relation between SCs and functionally important regions of proteins are presented to show a detailed picture of these clusters, which are believed to be primarily responsible for major aspects of protein stability.
Cross-Linking Reagents↗