Biomedical subjects

Michael Andrec

Publications and source records attributed to Michael Andrec.

6 recordsLinked to original sources

PubMed2005

Temperature weighted histogram analysis method, replica exchange, and transition paths.

We analyzed the data from a replica exchange molecular dynamics simulation using the weighted histogram analysis method to combine data from all of the temperature replicas (T-WHAM) to obtain the room-temperature potential of mean force of the G-peptide (the C-terminal beta-hairpin of the B1 domain of protein G) in regions of conformational space not sampled at room temperature. We were able to determine the potential of mean force in the transition region between a minor alpha-helical population and the major beta-hairpin population and identify a possible transition path between them along which the peptide retains a significant amount of secondary structure. This observation provides new insights into a possible mechanism of formation of beta-sheet secondary structures in proteins. We developed a novel Bayesian statistical uncertainty estimation method for any quantity derived from WHAM and used it to validate the calculated potential of mean force. The feasibility of estimating regions of the potential of mean force with unfavorable free energy at room temperature by T-WHAM analysis of replica exchange simulations was further tested on a system that can be solved analytically and presented some of the same challenges found in more complex chemical systems.

Algorithms↗

PubMed2005

Protein folding pathways from replica exchange simulations and a kinetic network model.

We present an approach to the study of protein folding that uses the combined power of replica exchange simulations and a network model for the kinetics. We carry out replica exchange simulations to generate a large ( approximately 10(6)) set of states with an all-atom effective potential function and construct a kinetic model for folding, using an ansatz that allows kinetic transitions between states based on structural similarity. We use this network to perform random walks in the state space and examine the overall network structure. Results are presented for the C-terminal peptide from the B1 domain of protein G. The kinetics is two-state after small temperature perturbations. However, the coil-to-hairpin folding is dominated by pathways that visit metastable helical conformations. We propose possible mechanisms for the alpha-helix/beta-hairpin interconversion.

Biophysics↗

PubMed2005

Inference of signaling and gene regulatory networks by steady-state perturbation experiments: structure and accuracy.

One of the fundamental problems of cell biology is the understanding of complex regulatory networks. Such networks are ubiquitous in cells and knowledge of their properties is essential for the understanding of cellular behavior. In earlier work (Kholodenko et al. (PNAS 99: 12841), it was shown how the structure of biological networks can be quantified from experimental measurements of steady-state concentrations of key intermediates as a result of perturbations using a simple algorithm called "unravelling". Here, we study the effect of experimental uncertainty on the accuracy of the inferred structure (i.e. whether interactions are excitatory or inhibitory) of the networks determined using the unravelling algorithm. We show that the accuracy of the network structure depends not only on the noise level but on the strength of the interactions within the network. In particular, both very small and very large values of the connection strengths lead to large uncertainty in the inferred network. We describe a powerful geometric tool for the intuitive understanding of the effect of experimental error on the qualitative accuracy of the inferred network. In addition, we show that the use of additional data beyond that needed to minimally constrain the network not only improves the accuracy of the inferred network, but also may allow the detection of situations in which the initial assumptions of unravelling with respect to the network and the perturbations have been violated. Our ideas are illustrated using the mitogen-activated protein kinase (MAPK) signaling network as an example.

Algorithms↗

PubMed2003

Have we seen all structures corresponding to short protein fragments in the Protein Data Bank? An update.

Assembling short fragments from known structures has been a widely used approach to construct novel protein structures. To what extent there exist structurally similar fragments in the database of known structures for short fragments of a novel protein is a question that is fundamental to this approach. This work addresses that question for seven-, nine- and 15-residue fragments. For each fragment size, two databases, a query database and a template database of fragments from high-quality protein structures in SCOP20 and SCOP90, respectively, were constructed. For each fragment in the query database, the template database was scanned to find the lowest r.m.s.d. fragment among non-homologous structures. For seven-residue fragments, there is a 99% probability that there exists such a fragment within 0.7 A r.m.s.d. for each loop fragment. For nine-residue fragments there is a 96% probability of a fragment within 1 A r.m.s.d., while for 15-residue fragments there is a 91% probability of a fragment within 2 A r.m.s.d. These results, which update previous studies, show that there exists sufficient coverage to model even a novel fold using fragments from the Protein Data Bank, as the current database of known structures has increased enormously in the last few years. We have also explored the use of a grid search method for loop homology modeling and make some observations about the use of a grid search compared with a database search for the loop modeling problem.

Databases, Factual↗

PubMed2002

Protein sequential resonance assignments by combinatorial enumeration using 13C alpha chemical shifts and their (i, i-1) sequential connectivities.

The need for the structural characterization of proteins on a genomic scale has brought with it demands for new technology to speed the structure determination process. In NMR, one bottleneck is the sequential assignment of backbone resonances. In this paper, we explore the computational complexity of the sequential assignment problem using only 13C alpha chemical shift data and C alpha (i, i - 1) sequential connectivity information, all of which can potentially be obtained from a single three-dimensional NMR spectrum. Although it is generally believed that there is too much ambiguity in such data to provide sufficient information for sequential assignment, we show that a straightforward combinatorial search algorithm can be used to find correct and unambiguous sequential assignments in a reasonable amount of CPU time for small proteins (approximately 80 residues or smaller) when there is little missing data. The deleterious effect of missing or spurious peaks and the dependence on match tolerances is also explored. This simple algorithm could be used as part of a semi-automated, interactive assignment procedure, e.g., to test partial manually determined solutions for uniqueness and to extend these solutions.

Algorithms↗

PubMed2002

Complete protein structure determination using backbone residual dipolar couplings and sidechain rotamer prediction.

Residual dipolar couplings provide significant structural information for proteins in the solution state, which makes them attractive for the rapid determination of protein structures. While dipolar couplings contain inherent structural ambiguities, these can be reduced via an overlap similarity measure that insists that protein fragments assigned to overlapping regions of the sequence must have self-consistent structures. This allows us to determine a backbone fold (including the correct Calpha-Cbeta bond orientations) using only residual dipolar coupling data from one ordering medium. The resulting backbone structures are of sufficient quality to allow for modeling of sidechain rotamer states using a rotamer prediction algorithm and a force field employing the Surface Generalized Born continuum solvation model. We demonstrate the applicability of the method using experimental data for ubiquitin. These results illustrate the synergies that are possible between protein structural database and molecular modeling methods and NMR spectroscopy, and we expect that the further development of these methods will lead to the extraction of high resolution structural information from minimal NMR data.

Computer Simulation↗