Dataset Information

Modeling complex biological macromolecules: reduction of multibead models.

ABSTRACT: The shape of simple and complex biological macromolecules can be approximated by bead modeling procedures. Such approaches are required, for example, for the analysis of the scattering and hydrodynamic behavior of the models under analysis and the prediction of their molecular properties. Using the atomic coordinates of proteins for modeling inevitably leads to models composed of a multitude of beads. In particular, for hydrodynamic modeling, a drastic reduction of the bead number may become unavoidable to enable computation. A systematic investigation of different approaches and computation modes shows that the 'running mean', 'cubic grid,' and 'hexagonal grid' approaches are successful, provided that the extent of reduction does not exceed a factor of 100 and the grid approaches use beads of unequal size and the beads are located at the centers of gravity. Further precautions to be taken include usage of appropriate interaction tensors for overlapping beads of unequal size and appropriate volume corrections when calculating intrinsic viscosities. The applied procedures were tested with the small protein lysozyme in a case study and were then applied to the huge capsid of the phage fr and its trimeric building block. The appearance of the models and the agreement of molecular properties and distance distribution functions of unreduced and reduced models can be used as evaluation criteria.

SUBMITTER: Zipper P

PROVIDER: S-EPMC2565767 | biostudies-other | 2007 Dec

REPOSITORIES: biostudies-other

ACCESS DATA

Publications

Modeling complex biological macromolecules: reduction of multibead models.

Zipper Peter P Durchschlag Helmut H

Journal of biological physics 20071201 5-6

The shape of simple and complex biological macromolecules can be approximated by bead modeling procedures. Such approaches are required, for example, for the analysis of the scattering and hydrodynamic behavior of the models under analysis and the prediction of their molecular properties. Using the atomic coordinates of proteins for modeling inevitably leads to models composed of a multitude of beads. In particular, for hydrodynamic modeling, a drastic reduction of the bead number may become una ...[more]

PMID: 19669537

Similar Datasets

Project description:Most small-molecule drug candidates fail before entering the market, frequently because of unexpected toxicity. Often, toxicity is detected only late in drug development, because many types of toxicities, especially idiosyncratic adverse drug reactions (IADRs), are particularly hard to predict and detect. Moreover, drug-induced liver injury (DILI) is the most frequent reason drugs are withdrawn from the market and causes 50% of acute liver failure cases in the United States. A common mechanism often underlies many types of drug toxicities, including both DILI and IADRs. Drugs are bioactivated by drug-metabolizing enzymes into reactive metabolites, which then conjugate to sites in proteins or DNA to form adducts. DNA adducts are often mutagenic and may alter the reading and copying of genes and their regulatory elements, causing gene dysregulation and even triggering cancer. Similarly, protein adducts can disrupt their normal biological functions and induce harmful immune responses. Unfortunately, reactive metabolites are not reliably detected by experiments, and it is also expensive to test drug candidates for potential to form DNA or protein adducts during the early stages of drug development. In contrast, computational methods have the potential to quickly screen for covalent binding potential, thereby flagging problematic molecules and reducing the total number of necessary experiments. Here, we train a deep convolution neural network-the XenoSite reactivity model-using literature data to accurately predict both sites and probability of reactivity for molecules with glutathione, cyanide, protein, and DNA. On the site level, cross-validated predictions had area under the curve (AUC) performances of 89.8% for DNA and 94.4% for protein. Furthermore, the model separated molecules electrophilically reactive with DNA and protein from nonreactive molecules with cross-validated AUC performances of 78.7% and 79.8%, respectively. On both the site- and molecule-level, the model's performances significantly outperformed reactivity indices derived from quantum simulations that are reported in the literature. Moreover, we developed and applied a selectivity score to assess preferential reactions with the macromolecules as opposed to the common screening traps. For the entire data set of 2803 molecules, this approach yielded totals of 257 (9.2%) and 227 (8.1%) molecules predicted to be reactive only with DNA and protein, respectively, and hence those that would be missed by standard reactivity screening experiments. Site of reactivity data is an underutilized resource that can be used to not only predict if molecules are reactive, but also show where they might be modified to reduce toxicity while retaining efficacy. The XenoSite reactivity model is available at http://swami.wustl.edu/xenosite/p/reactivity.

Dataset Information

Modeling complex biological macromolecules: reduction of multibead models.

Publications

Modeling complex biological macromolecules: reduction of multibead models.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets