The Activation of Mucosal-Associated Invariant T (MAIT) Cells Is Affected by Microbial Diversity and Riboflavin Utilization in vitro

Recent research has demonstrated that MAIT cells are activated by individual bacterial or yeasts species that possess the riboflavin biosynthesis pathway. However, little is known about the MAIT cell activating potential of microbial communities and the contribution of individual community members. Here, we analyze the MAIT cell activating potential of a human intestinal model community (SIHUMIx) as well as intestinal microbiota after bioreactor cultivation. We determined the contribution of individual SIHUMIx community members to the MAIT cell activating potential and investigated whether microbial stress can influence their MAIT cell activating potential. The MAIT cell activating potential of SIHUMIx was directly related to the relative species abundances in the community. We therefore suggest an additive relationship between the species abundances and their MAIT cell activating potential. In diverse microbial communities, we found that a low MAIT cell activating potential was associated with high microbial diversity and a high level of riboflavin demand and vice versa. We suggest that microbial diversity might affect MAIT cell activation via riboflavin utilization within the community. Microbial acid stress significantly reduced the MAIT cell activating potential of SIHUMIx by impairing riboflavin availability through increasing the riboflavin demand.We show that MAIT cells can perceive microbial stress due to changes in riboflavin utilization and that riboflavin availability might also play a central role for the MAIT cell activating potential of diverse microbiota

Estimation and filtering of potential protein-protein docking positions

Ackermann F, Herrmann G, Posch S, Sagerer G. Estimation and filtering of potential protein-protein docking positions. Bioinformatics. 1998;14(2):196-205.Motivation: Software systems predicting automatically whether and how two proteins may interact are highly desirable, both for under-standing biological processes and for the rational design of new proteins. As a part of a future complete solution to this problem, a bundle of programs is presented designed (i) to estimate initial docking positions for a given pail of docking candidates, (ii) to adjust them, and (iii) to filter, them, thus preparing mole detailed computations of free energies. Results: The system is evaluated on a test set of 51 co-crystallized complexes aiming at redocking the subunits. It works completely automatically and the evaluation is performed using one single set of parameters for all complexes in the rest set. The number of solutions is fixed to 50 positions with a median CPU time of 26 min. For 30 complexes, these contain a near-correct Solution with root mean square deviation (RMSD) less than or equal to 5.0 Angstrom, which is ranked first in five cases. For all complexes, the best solution is scored on rank 16 as the wet-st case, and has a median RMSD of 4.3 Angstrom.. Alter-natively to this initial estimation of docking positions, a global sampling of rotations was tested. Whereas this yields top-ranked solutions with RMSD less than or equal to 3.0 Angstrom for all 51 complexes, the median CPU time increases to 11 h. This shows that this blind sampling is not feasible for most applications

3D-Segmentierungstechniken und vektorwertige Bewertungsfunktionen für symbolisches Protein-Protein-Docking

The growing number of known 3D protein structures asks for computing systems predicting whether and where two molecules interact with each other. This requires search for possible docking sites of proteins. Based on results of preprocessing techniques like computation of molecular surfaces and segmentation, a knowledge based control algorithm implemented with the semantic network ERNEST searches for geometrical and chemical complementarity on molecular surfaces, computes coarse docking positions considering steric clash and simple geometric judgement functions. Additionally, ERNEST guides a more detailed analysis of finer calcultations including correlation of geometry and hydrophobicity. The proposed hierarchical system allows to predict completely automatically and in reasonable short computing times possible docking sites for two given proteins. A set of 18 representative examples is discussed

Segmentation of molecular surfaces based on their convex hull

Meier R, Ackermann F, Herrmann G, Posch S, Sagerer G. Segmentation of molecular surfaces based on their convex hull. In: Proceedings International Conference on Image Processing. Washington, D.C: IEEE Computer Society Press; 1995: 552-555

Statistical classification and segmentation of biomolecular surfaces

Schillo C, Herrmann G, Ackermann F, Posch S, Sagerer G. Statistical classification and segmentation of biomolecular surfaces. In: Proceedings International Conference on Image Processing. Washinton, D.C.: IEEE Computer Society Press; 1995: 560-563

3D-Segmentierungstechniken und vektorwertige Bewertungsfunktionen für symbolisches Protein-Protein-Docking

Ackermann F, Herrmann G, Posch S, Sagerer G. 3D-Segmentation and Vectorvalued Scoring Functions for Symbolic Docking of Proteins. In: Schomburg D, Lessel U, eds. Bioinformatics : from nucleic acids and proteins to cell metabolism ; contributions to the Conference on "Bioinformatics", October 9 to 11, 1995, Braunschweig, Germany. GBF Monographs. Vol 18. Weinheim: VCH Publishers; 1995: 105-124

Evaluierung eines Protein-Dockingsystems durch Leave-One-Out-Test

Ackermann F, Herrmann G, Posch S, Sagerer G. Evaluierung eines Protein-Dockingsystems durch Leave-One-Out-Test. Informatik Aktuell. 1996:130-137.Beschrieben wird die Realisierung und Evaluierung eines wissensbasierten Ansatzes zur Lösung des Protein-Protein-Dockingproblems, der eine Anwendung des semantischen Netzwerksystems ERNEST darstellt. Aufbauend auf den Ergebnissen einer Segmentierung von dreidimensionalen Oberflächen strukturaufgelöster Proteine werden vom System unter Einbeziehung von Funktionen, die geometrische Merkmale berechnen und bewerten, mögliche Dockingpositionen für zwei betrachtete Proteine vorgeschlagen. Berechnet werden unter anderem der Steric Clash und die Volumendifferenz zu paarender Dockingregionen. Das Dockingsystem wurde für 17 bekannte Proteinkomplexe, bei denen die korrekte relative Position beider Proteine experimentell bestimmt wurde, trainiert und mit der Leave-One-Out-Methode getestet. In der überwiegenden Mehrzahl der Fälle werden vollautomatisch in kurzer Rechenzeit vom System die korrekten Dockingpositionen mit einer Genauigkeit von wenigen Angstroem DRMS vorhergesagt

Protein Docking Combining Symbolic Descriptions of Molecular Surfaces and Grid-Based Scoring Functions

Ackermann F, Herrmann G, Kummert F, Posch S, Sagerer G, Schomburg D. Protein Docking Combining Symbolic Descriptions of Molecular Surfaces and Grid-Based Scoring Functions. In: Rawlings C, Clark D, Altman R, Hunter L, Lengauer T, Wodak S, eds. Proceedings Third International Conference on Intelligent Systems for Molecular Biology. Vol 3. Menlo Park: AAAI Press; 1995: 3-11.With the growing number of known 3D protein structures, computing systems, that can predict where two protein molecules interact with each other is becoming of increasing interest. A system is presented, integrating preprocessing like the computation of molecular surfaces, segmentation, and searching for complementarity in the general framework of a pattern analyzing semantic network (ERNEST). The score of coarse symbolic computations is used by the problem independent control strategy of ERNEST to guide a more detailed analysis considering steric clash and judgements based on gridâbased surface representations. Successfull examples of the docking system are discussed that compare well with other approaches