Biomedical subjects

Jean-François Gibrat

Publications and source records attributed to Jean-François Gibrat.

4 recordsLinked to original sources

PubMed2006

Analysis of an optimal hidden Markov model for secondary structure prediction.

BACKGROUND: Secondary structure prediction is a useful first step toward 3D structure prediction. A number of successful secondary structure prediction methods use neural networks, but unfortunately, neural networks are not intuitively interpretable. On the contrary, hidden Markov models are graphical interpretable models. Moreover, they have been successfully used in many bioinformatic applications. Because they offer a strong statistical background and allow model interpretation, we propose a method based on hidden Markov models. RESULTS: Our HMM is designed without prior knowledge. It is chosen within a collection of models of increasing size, using statistical and accuracy criteria. The resulting model has 36 hidden states: 15 that model alpha-helices, 12 that model coil and 9 that model beta-strands. Connections between hidden states and state emission probabilities reflect the organization of protein structures into secondary structure segments. We start by analyzing the model features and see how it offers a new vision of local structures. We then use it for secondary structure prediction. Our model appears to be very efficient on single sequences, with a Q3 score of 68.8%, more than one point above PSIPRED prediction on single sequences. A straightforward extension of the method allows the use of multiple sequence alignments, rising the Q3 score to 75.5%. CONCLUSION: The hidden Markov model presented here achieves valuable prediction results using only a limited number of parameters. It provides an interpretable framework for protein secondary structure architecture. Furthermore, it can be used as a tool for generating protein sequences with a given secondary structure content.

Computational Biology↗

PubMed2005

Protein secondary structure assignment revisited: a detailed analysis of different assignment methods.

BACKGROUND: A number of methods are now available to perform automatic assignment of periodic secondary structures from atomic coordinates, based on different characteristics of the secondary structures. In general these methods exhibit a broad consensus as to the location of most helix and strand core segments in protein structures. However the termini of the segments are often ill-defined and it is difficult to decide unambiguously which residues at the edge of the segments have to be included. In addition, there is a "twilight zone" where secondary structure segments depart significantly from the idealized models of Pauling and Corey. For these segments, one has to decide whether the observed structural variations are merely distorsions or whether they constitute a break in the secondary structure. METHODS: To address these problems, we have developed a method for secondary structure assignment, called KAKSI. Assignments made by KAKSI are compared with assignments given by DSSP, STRIDE, XTLSSTR, PSEA and SECSTR, as well as secondary structures found in PDB files, on 4 datasets (X-ray structures with different resolution range, NMR structures). RESULTS: A detailed comparison of KAKSI assignments with those of STRIDE and PSEA reveals that KAKSI assigns slightly longer helices and strands than STRIDE in case of one-to-one correspondence between the segments. However, KAKSI tends also to favor the assignment of several short helices when STRIDE and PSEA assign longer, kinked, helices. Helices assigned by KAKSI have geometrical characteristics close to those described in the PDB. They are more linear than helices assigned by other methods. The same tendency to split long segments is observed for strands, although less systematically. We present a number of cases of secondary structure assignments that illustrate this behavior. CONCLUSION: Our method provides valuable assignments which favor the regularity of secondary structure segments.

Algorithms↗

PubMed2003

Homology modeling and molecular dynamics simulations of the N-terminal domain of wheat high molecular weight glutenin subunit 10.

High molecular weight glutenin subunits (HMW-GS) are of a particular interest because of their biomechanical properties, which are important in many food systems such as breadmaking. Using fold-recognition techniques, we identified a fold compatible with the N-terminal domain of HMW-GS Dy10. This fold corresponds to the one adopted by proteins belonging to the cereal inhibitor family. Starting from three known protein structures of this family as templates, we built three models for the N-terminal domain of HMW-GS Dy10. We analyzed these models, and we propose a number of hypotheses regarding the N-terminal domain properties that can be tested experimentally. In particular, we discuss two possible ways of interaction between the N-terminal domains of the y-type HMW glutenin subunits. The first way consists in the creation of interchain disulfide bridges. According to our models, we propose two plausible scenarios: (1) the existence of an intrachain disulfide bridge between cysteines 22 and 44, leaving the three other cysteines free of engaging in intermolecular bonds; and (2) the creation of two intrachain disulfide bridges (involving cysteines 22-44 and cysteines 10-55), leaving a single cysteine (45) for creating an intermolecular disulfide bridge. We discuss these scenarios in relation to contradictory experimental results. The second way, although less likely, is nevertheless worth considering. There might exist a possibility for the N-terminal domain of Dy10, Nt-Dy10, to create oligomers, because homologous cereal inhibitor proteins are known to exist as monomers, homodimers, and heterooligomers. We also discuss, in relation to the function of the cereal inhibitor proteins, the possibility that this N-terminal domain has retained similar inhibitory functions.

Amino Acid Sequence↗

PubMed2002

FROST: a filter-based fold recognition method.

To assess the reliability of fold assignments to protein sequences, we developed a fold recognition method called FROST (Fold Recognition-Oriented Search Tool) based on a series of filters and a database specifically designed as a benchmark for this new method under realistic conditions. This benchmark database consists of proteins for which there exists, at least, another protein with an extensively similar 3D structure in a database of representative 3D structures (i.e., more than 65% of the residues in both proteins can be structurally aligned). Because the testing of our method must be carried out under conditions similar to those of real fold recognition experiments, no protein pair with sequence similarity detectable using standard sequence comparison methods such as FASTA is included in the benchmark database. While using FROST, we achieved a coverage of 60% for a rate of error of 1%. To obtain a baseline for our method, we used PSI-BLAST and 3D-PSSM. Under the same conditions, for a 1% error rate, coverages for PSI-BLAST and 3D-PSSM were 33 and 56%, respectively.

Algorithms↗