Biomedical subjects

E Bornberg-Bauer

Publications and source records attributed to E Bornberg-Bauer.

8 recordsLinked to original sources

PubMed2000

Switching from simple to complex oscillations in calcium signaling.

We present a new model for calcium oscillations based on experiments in hepatocytes. The model considers feedback inhibition on the initial agonist receptor complex by calcium and activated phospholipase C, as well as receptor type-dependent self-enhanced behavior of the activated G(alpha) subunit. It is able to show simple periodic oscillations and periodic bursting, and it is the first model to display chaotic bursting in response to agonist stimulations. Moreover, our model offers a possible explanation for the differences in dynamic behavior observed in response to different agonists in hepatocytes.

Aequorin↗

PubMed1999

Modeling evolutionary landscapes: mutational stability, topology, and superfunnels in sequence space.

Random mutations under neutral or near-neutral conditions are studied by considering plausible evolutionary trajectories on "neutral nets"-i.e., collections of sequences (genotypes) interconnected via single-point mutations encoding for the same ground-state structure (phenotype). We use simple exact lattice models for the mapping between sequence and conformational spaces. Densities of states based on model intrachain interactions are determined by exhaustive conformational enumeration. We compare results from two very different interaction schemes to ascertain robustness of the conclusions. In both models, sequences in a majority of neutral nets center around a single "prototype sequence" of maximum mutational stability, tolerating the largest number of neutral mutations. General analytical considerations show that these topologies by themselves lead to higher steady-state evolutionary populations at prototype sequences. On average, native thermodynamic stability increases toward a maximum at the prototype sequence, resulting in funnel-like arrangements of native stabilities in sequence space. These observations offer a unified perspective on sequence design, native stability, and mutational stability of proteins. These principles are generalizable from native stability to any measure of fitness provided that its variation with respect to mutations is essentially smooth.

Algorithms↗

PubMed1999

Application of constraint programming techniques for structure prediction of lattice proteins with extended alphabets.

MOTIVATION: Predicting the ground state of biopolymers is a notoriously hard problem in biocomputing. Model systems, such as lattice proteins, are simple tools and valuable to test and improve new methods. Best known are models with sequences composed from a binary (hydrophobic and polar) alphabet. The major drawback is the degeneracy, i.e. the number of different ground state conformations. RESULTS: We show how recently developed constraint programming techniques can be used to solve the structure prediction problem efficiently for a higher order alphabet. To our knowledge it is the first report of an exact and computationally feasible solution to model proteins of length up to 36 and without resorting to maximally compact states. We further show that degeneracy is reduced by more than one order of magnitude and that ground state conformations are not necessarily compact. Therefore, more realistic protein simulations become feasible with our model.

Algorithms↗

PubMed1999

WWW access to the SYSTERS protein sequence cluster set.

SUMMARY: We present a Web server where the SYSTERS cluster set of the non-redundant protein database consisting of sequences from SWISS-PROT and PIR is being made available for querying and browsing. The cluster set can be searched with a new sequence using the SSMAL search tool. Additionally, a multiple alignment is generated for each cluster and annotated with domain information from the Pfam protein family database. AVAILABILITY: The server address is http://www.dkfz-heidelberg.de/tbi/services/cluster/ systersform

Algorithms↗

PubMed1998

Computational approaches to identify leucine zippers.

The leucine zipper is a dimerization domain occurring mostly in regulatory and thus in many oncogenic proteins. The leucine repeat in the sequence has been traditionally used for identification, however with poor reliability. The coiled coil structure of a leucine zipper is required for dimerization and can be predicted with reasonable accuracy by existing algorithms. We exploit this fact for identification of leucine zippers from sequence alone. We present a program, 2ZIP, which combines a standard coiled coil prediction algorithm with an approximate search for the characteristic leucine repeat. No further information from homologues is required for prediction. This approach improves significantly over existing methods, especially in that the coiled coil prediction turns out to be highly informative and avoids large numbers of false positives. Many problems in predicting zippers or assessing prediction results stem from wrong sequence annotations in the database.

Algorithms↗

PubMed1997

How are model protein structures distributed in sequence space?

The figure-to-structure maps for all uniquely folding sequences of short hydrophobic polar (HP) model proteins on a square lattice is analyzed to investigate aspects considered relevant to evolution. By ranking structures by their frequencies, few very frequent and many rare structures are found. The distribution can be empirically described by a generalized Zipf's law. All structures are relatively compact, yet the most compact ones are rare. Most sequences falling to the same structure belong to "neutral nets." These graphs in sequence space are connected by point mutations and centered around prototype sequences, which tolerate the largest number (up to 55%) of neutral mutations. Profiles have been derived from these homologous sequences. Frequent structures conserve hydrophobic cores only while rare ones are sensitive to surface mutations as well. Shape space covering, i.e., the ability to transform any structure into most others with few point mutations, is very unlikely. It is concluded that many characteristic features of the sequence-to-structure map of real proteins, such as the dominance of few folds, can be explained by the simple HP model. In analogy to protein families, nets are dense and well separated in sequence space. Potential implications in better understanding the evolution of proteins and applications to improving database searches are discussed.

Amino Acid Sequence↗

PubMed1997

Exploring the fitness landscapes of lattice proteins.

We present methods to investigate the sequence to structure relation for proteins. We use random structures of HP-type lattice models as a coarse grained model to study generic properties of biopolymers. To circumvent the computational limitations imposed by most lattice protein folding algorithms we apply a simple and fast deterministic approximation algorithm with a tunable accuracy. We investigate ensemble properties such as the conditional probability to find structures with a certain similarity at a given distance of the underlying sequence for various alphabets. Our results suggest that the structure landscapes for lattice proteins are generally very rugged, while larger alphabets fine tune the folding process and smoothen the map. This implies a simplification for evolutionary strategies. The applied methods appear to be helpful in the study of the complex interplay between folding strategies, energy functions and alphabets. Possible implications to the investigation of evolutionary strategies or the optimization of biopolymers are discussed.

Algorithms↗

PubMed1996

Structure formation of biopolymers is complex, their evolution may be simple.

Evolutionary strategies depend on the ability of evolving entities to conserve acquired features and to quickly adapt to new requirements as well. We use computer simulations of simplified exact biopolymers models to investigate the influence of mutations on structure formation. Our computations on large ensembles of random RNA secondary structures show that the sequence to structure mapping is ideally suited for evolutionary optimisation under point mutations: from any random structure it is not far to any target and yet most mutations will preserve the structure. The aim of this paper is to discuss the analogies as well as some recently developed methods to apply our approach to proteins: there we use Dill's HP - model of lattice proteins and apply a novel fast and efficient folding rule. There are remarkable similarities as both landscapes are rugged and structure formation largely depends on local interactions such that it is possible to accomplish a characterisation of the mapping similar to the RNA case.

Algorithms↗