Biomedical subjects

M Rooman

Publications and source records attributed to M Rooman.

6 recordsLinked to original sources

PubMed1999

Expression of a novel factor in human breast cancer cells with metastatic potential.

Clinical and experimental evidence suggests that tumor cells shed into the circulation from solid cancers are ineffective in forming distant metastasis unless the cells are able to respond to growth conditions offered by the secondary organs. To identify the phenotypic properties that are specific for such growth response, we injected carcinoma cells, which had been recovered from bone marrow micrometastases in a breast cancer patient who was clinically devoid of overt metastatic disease and established in culture, into the systemic circulation of immunodeficient rats. The animals developed metastases in the central nervous system, and metastatic tumor cells were isolated with immunomagnetic beads coated with an antibody that was reactive with human cells. The segregated cell population was compared with the injected cells by means of differential display analysis, and two candidate fragments were identified as up-regulated in the fully metastatic cells. The first was an intracellular effector molecule involved in tyrosine kinase signaling, known to mediate nerve growth factor-dependent promotion of cell survival. The second was a novel gene product (termed candidate of metastasis-1), presumably encoding a DNA-binding protein of helix-turn-helix type. Constitutive expression of candidate of metastasis-1 seemed to distinguish breast cancer cells with metastatic potential from cells without metastatic potential. Hence, our experimental approach identified factors that may mediate the growth response of tumor cells upon establishment in a secondary organ and, thereby, contribute to the metastatic phenotype.

Amino Acid Sequence↗

PubMed1998

Different derivations of knowledge-based potentials and analysis of their robustness and context-dependent predictive power.

The possibility of defining effective potentials from known protein structures, which are sufficiently accurate to be used for protein-structure-prediction purposes, is investigated. Three types of distance potentials and three types of backbone torsion potentials are defined, based on propensities of amino acid pairs to be separated by a given spatial distance or to be associated to a backbone torsion angle domain. Their differences reside in the way the physical correlations between the amino acids and the conformational states are extracted from the bulk interactions due to the presence of many residues in a protein. For the distance potentials, a physical meaning can be associated to the different definitions, given that some of the potentials favor hydrophobic interactions and others favor interactions between oppositely charged residues. The performance of the different torsion and distance potentials in structure prediction procedures, in particular native-fold recognition and evaluation of protein stability changes upon point mutations, is analyzed. It appears to differ according to the specific proteins and protein environments. In particular, one of the distance potentials performs better than the others for membrane proteins and in protein regions involving charged residues, but less well in other protein regions. Furthermore, the dependence of the potentials on the characteristics of the proteins from which they are derived is analyzed. It is shown that the dependence of the potentials on the length, amino acid composition and secondary-structure content of the proteins from the dataset is either very limited or rather strong, according to the type of potential. The results obtained suggest that the main problem limiting the performance of database-derived potentials is their lack of universality: each potential describes with satisfactory accuracy only the interactions present in certain protein environments.

Algorithms↗

PubMed1998

Typical interaction patterns in alphabeta and betaalpha turn motifs.

A fully automatic classification procedure of short protein fragments is applied to identify connections between alpha-helices and beta-strands in a dataset of 141 protein chains. It yields 15 structural families of alphabeta turns and 15 families of betaalpha turns with at least five members. The sequence and structural features of these turn motifs are analysed with the focus on the local interactions located at alpha-helix and beta-strand ends. This analysis reveals specific interaction patterns that occur frequently among the members of many of the identified turn motifs. For the beta-strands, novel patterns are identified at the strands' entry and exit; they involve side chain/side chain contacts and beta-turns, generally of type I or II. For the alpha-helices, the interaction patterns consist of several backbone/backbone or backbone/side chain hydrogen bonds and of hydrophobic contacts; they generalize the well known N-terminal capping and C-terminal Schellman motifs. The interaction patterns at both ends of alpha-helices and beta-strands are found to constitute favourable structure motifs with low amino acid sequence specificity; their possible stabilizing role is discussed. Finally, the robustness of our classification procedure and of the description of N- and C-cap interaction patterns is validated by repeating our analysis on a larger dataset of 381 protein chains and showing that the results are maintained.

Amino Acid Sequence↗

PubMed1997

Predicting protein stability changes upon mutation using database-derived potentials: solvent accessibility determines the importance of local versus non-local interactions along the sequence.

For 238 mutations of residues totally or partially buried in the protein core, we estimate the folding free energy changes upon mutation using database-derived potentials and correlate them with the experimentally measured ones. Several potentials are tested, representing different kinds of interactions. Local interactions along the chain are described by torsion potentials, based on propensities of amino acids to be associated with backbone torsion angle domains. Non-local interactions along the sequence are represented by distance potentials, derived from propensities of amino acid pairs or triplets to be at a given spatial distance. We find that for the set of totally buried residues, the best performing potential is a combination of a distance potential and a torsion potential weighted by a factor of 0.4; it yields a correlation coefficient between computed and measured changes in folding free energy of 0.80. For mutations of partially buried residues, the best potential is a combination of a torsion potential and a distance potential weighted by a factor of 0.7, and for the previously analysed mutations of solvent accessible residues, it is a torsion potential taken individually; the respective correlation coefficients reach 0.82 and 0.87. These results show that distance potentials, dominated by hydrophobic interactions, represent best the main interactions stabilizing the protein core, whereas torsion potentials, describing local interactions along the chain, represent best the interactions at the protein surface. The prediction accuracy reached by the distance potentials is, however, lower than that of the torsion potentials. A possible reason for this is that distance potentials would not describe correctly the effect on protein stability due to cavity formation upon mutating a large into a small amino acid. Last but not least, our results indicate that although local interactions, responsible for secondary structure formation, do not dominate in the protein core, they are not negligible for all that. They have a significant weight in the delicate balance between all the interactions that ensure protein stability.

Animals↗

PubMed1996

Structural classification of HTH DNA-binding domains and protein-DNA interaction modes.

This paper constitutes an attempt to rationalize the structural similarities and differences that are observed among the HTH DNA-binding domains, and the various modes of protein-DNA interactions. It consists of classifying all the domains of known structure into families on the basis of the spatial arrangement of their helices, irrespective of the type of loops and the presence of beta-strands, and examining the interaction patterns between amino acids and DNA within each family. It is found that the recognition helix and the preceding helix along the chain have always the same relative orientation. Structural differences arise when considering three helices, corresponding usually to the recognition helix and the two preceding ones, but sometimes to the recognition helix and the two flanking helices. Using an automatic classification procedure, seven main families are obtained, whose members have in common the spatial arrangement of their three key helices, but have sometimes different topology and belong to different species. The structural divergence among these families and the existence of structural intermediates are analyzed. Searching these families systematically for recurrent motifs, leads to identify two specific turns, besides the HTH turn. They both link the two helices preceding the recognition helix and are each characteristic of a given family. Furthermore, the conservation of protein-DNA interaction patterns is examined with respect to the structural alignments. These patterns are found to be relatively well conserved within each family and to be different between the different families. The agreement of the structural classification and the patterns of protein-DNA contacts justify our approach, and suggests its applicability, in particular for modelling protein-DNA interactions.

Amino Acid Sequence↗

PubMed1996

Stability changes upon mutation of solvent-accessible residues in proteins evaluated by database-derived potentials.

The stability changes in peptides and proteins caused by the substitution of a single amino acid, which can be measured experimentally by the change in folding free energy, are evaluated here using effective potentials derived from known protein structures. The analysis is focused on mutations of residues that are accessible to the solvent. These represent in total 106 mutations, introduced at different sites in barnase, bacteriophage T4 lysozyme and chymotrypsin inhibitor 2, and in a synthetic helical peptide. Assuming that the mutations do not modify the backbone structure, the changes in folding free energies are computed using various types of database-derived potentials and are compared with the measured ones. Distance-dependent residue-residue potentials are found to be inadequate for estimating the stability changes caused by these mutations, as they are dominated by hydrophobic interactions, which do not play an essential role at the protein surface. On the contrary, the potentials based on backbone torsion angle propensities yield quite good results. Indeed, for a subset of 96 out of the 106 mutations, the computed and measured changes in folding free energy correlate with a linear correlation coefficient of 0.87. Moreover, the ten mutations that are excluded from the correlation either seem to cause modifications of the backbone structure or to involve strong hydrophobic interactions, which are atypical for solvent-accessible residues. We find furthermore that raising the ionic strength of the solvent used for measuring the changes in folding free energies improves the correlation, as it tends to mask the electrostatic interactions. When adding to these 106 mutations 44 mutations performed in staphylococcal nuclease and chemotactic protein, which were first discarded because some of them were suspected to affect the backbone conformation or the denatured state, the correlation between measured and computed folding free energy changes remains quite good: the correlation coefficient is 0.86 for 135 out of the 150 mutations. The success of the backbone torsion potentials in predicting stability changes indicates that the approximations made for deriving these potentials are adequate. It suggests moreover that the local interactions along the chain dominate at the protein surface.

Algorithms↗