Biomedical subjects

Narcis Fernandez-Fuentes

Publications and source records attributed to Narcis Fernandez-Fuentes.

8 recordsLinked to original sources

PubMed2006

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↗

PubMed2006

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↗

PubMed2006

Insights into the mechanism of microtubule stabilization by Taxol.

The antitumor drug Taxol stabilizes microtubules and reduces their dynamicity, promoting mitotic arrest and cell death. Upon assembly of the alpha/beta-tubulin heterodimer, GTP bound to beta-tubulin is hydrolyzed to GDP reaching a steady-state equilibrium between free tubulin dimers and microtubules. The binding of Taxol to beta-tubulin in the polymer results in cold-stable microtubules at the expense of tubulin dimers, even in the absence of exogenous GTP. However, there is little biochemical insight into the mechanism(s) by which Taxol stabilizes microtubules. Here, we analyze the structural changes occurring in both beta- and alpha-tubulin upon microtubule stabilization by Taxol. Hydrogen/deuterium exchange (HDX) coupled to liquid chromatography-electrospray ionization MS demonstrated a marked reduction in deuterium incorporation in both beta-and alpha-tubulin when Taxol was present. Decreased local HDX in peptic peptides was mapped on the tubulin structure and revealed both expected and new dimer-dimer interactions. The increased rigidity in Taxol microtubules was distinct from and complementary to that due to GTP-induced polymerization. The Taxol-induced changes in tubulin conformation act against microtubule depolymerization in a precise directional way. These results demonstrate that HDX coupled to liquid chromatography-electrospray ionization MS can be effectively used to study conformational effects induced by small ligands on microtubules. The present study also opens avenues for locating drug and protein binding sites and for deciphering the mechanisms by which their interactions alter the conformation of microtubules and tubulin dimers.

Animals↗

PubMed2006

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↗

PubMed2005

Prediction of the conformation and geometry of loops in globular proteins: testing ArchDB, a structural classification of loops.

In protein structure prediction, a central problem is defining the structure of a loop connecting 2 secondary structures. This problem frequently occurs in homology modeling, fold recognition, and in several strategies in ab initio structure prediction. In our previous work, we developed a classification database of structural motifs, ArchDB. The database contains 12,665 clustered loops in 451 structural classes with information about phi-psi angles in the loops and 1492 structural subclasses with the relative locations of the bracing secondary structures. Here we evaluate the extent to which sequence information in the loop database can be used to predict loop structure. Two sequence profiles were used, a HMM profile and a PSSM derived from PSI-BLAST. A jack-knife test was made removing homologous loops using SCOP superfamily definition and predicting afterwards against recalculated profiles that only take into account the sequence information. Two scenarios were considered: (1) prediction of structural class with application in comparative modeling and (2) prediction of structural subclass with application in fold recognition and ab initio. For the first scenario, structural class prediction was made directly over loops with X-ray secondary structure assignment, and if we consider the top 20 classes out of 451 possible classes, the best accuracy of prediction is 78.5%. In the second scenario, structural subclass prediction was made over loops using PSI-PRED (Jones, J Mol Biol 1999;292:195-202) secondary structure prediction to define loop boundaries, and if we take into account the top 20 subclasses out of 1492, the best accuracy is 46.7%. Accuracy of loop prediction was also evaluated by means of RMSD calculations.

Models, Molecular↗

PubMed2004

Classification of common functional loops of kinase super-families.

A structural classification of loops has been obtained from a set of 141 protein structures classified as kinases. A total of 1813 loops was classified into 133 subclasses (9 betabeta(links), 15 betabeta(hairpins), 31 alpha-alpha, 46 alpha-beta and 32 beta-alpha). Functional information and specific features relating subclasses and function were included in the classification. Functional loops such as the P-loop (shared by different folds) or the Gly-rich-loop, among others, were classified into structural motifs. As a result, a common mechanism of catalysis and substrate binding was proved for most kinases. Additionally, the multiple-alignment of loop sequences made within each subclass was shown to be useful for comparative modeling of kinase loops. The classification is summarized in a kinase loop database located at http://sbi.imim.es/archki.

Amino Acid Motifs↗

PubMed2004

ArchDB: automated protein loop classification as a tool for structural genomics.

The annotation of protein function has become a crucial problem with the advent of sequence and structural genomics initiatives. A large body of evidence suggests that protein structural information is frequently encoded in local sequences, and that folds are mainly made up of a number of simple local units of super-secondary structural motifs, consisting of a few secondary structures and their connecting loops. Moreover, protein loops play an important role in protein function. Here we present ArchDB, a classification database of structural motifs, consisting of one loop plus its bracing secondary structures. ArchDB currently contains 12,665 super-secondary elements classified into 1496 motif subclasses. The database provides an easy way to retrieve functional information from protein structures sharing a common motif, to search motifs found in a given SCOP family, superfamily or fold, or to search by keywords on proteins with classified loops. The ArchDB database of loops is located at http://sbi.imim.es/archdb.

Amino Acid Motifs↗

PubMed2002

Automated detection of remote homology.

The classification of a newly identified protein as a member of a superfamily is important for focusing experiments on its most likely functions. Such classification, often performed by hand, has now been fully automated. This sophisticated new approach takes into account not only alignment scores but also a number of other computable attributes, such as functional sites deduced from sequence conservation patterns.

Automation↗