Detection of unrealistic molecular environments in protein structures based on expected electron densities

Understanding the relationship between protein structure and biological function is a central theme in structural biology. Advances are severely hampered by errors in experimentally determined protein structures. Detection and correction of such errors is therefore of utmost importance. Electron densities in molecular structures obey certain rules which depend on the molecular environment. Here we present and discuss a new approach that relates electron densities computed from a structural model to densities expected from prior observations on identical or closely related molecular environments. Strong deviations of computed from expected densities reveal unrealistic molecular structures. Most importantly, structure analysis and error detection are independent of experimental data and hence may be applied to any structural model. The comparison to state-of-the-art methods reveals that our approach is able to identify errors that formerly remained undetected. The new technique, called RefDens, is accessible as a public web service at http://refdens.services.came.sbg.ac.at

Pyrido- and benzisothiazolones as inhibitors of histone acetyltransferases (HATs)

Histone acetyltransferases (HATs) are interesting targets for the treatment of cancer and HIV infections but reports on selective inhibitors are very limited. Here we report structure–activity studies of pyrido- and benzisothiazolones in the in vitro inhibition of histone acetyltransferases, namely PCAF, CBP, Gcn5 and p300 using a heterogeneous assay with antibody mediated quantitation of the acetylation of a peptidic substrate. Dependent on the chemical structure distinct subtype selectivity profiles can be obtained. While N-aryl derivatives usually are rather pan-HAT inhibitors, N-alkyl derivatives show mostly a preference for CBP/p300. Selected compounds were also shown to be inhibitors of MOF. The best inhibitors show submicromolar inhibition of CBP. Selected compounds affect growth of HL-60 leukemic cells and LNCaP prostate carcinoma cells with higher potency on the leukemic cells. Target engagement was shown with reduction of histone acetylation in LNCaP cells

Potentials of Mean Force for Protein Structure Prediction Vindicated, Formalized and Generalized

Understanding protein structure is of crucial importance in science, medicine and biotechnology. For about two decades, knowledge based potentials based on pairwise distances -- so-called "potentials of mean force" (PMFs) -- have been center stage in the prediction and design of protein structure and the simulation of protein folding. However, the validity, scope and limitations of these potentials are still vigorously debated and disputed, and the optimal choice of the reference state -- a necessary component of these potentials -- is an unsolved problem. PMFs are loosely justified by analogy to the reversible work theorem in statistical physics, or by a statistical argument based on a likelihood function. Both justifications are insightful but leave many questions unanswered. Here, we show for the first time that PMFs can be seen as approximations to quantities that do have a rigorous probabilistic justification: they naturally arise when probability distributions over different features of proteins need to be combined. We call these quantities reference ratio distributions deriving from the application of the reference ratio method. This new view is not only of theoretical relevance, but leads to many insights that are of direct practical use: the reference state is uniquely defined and does not require external physical insights; the approach can be generalized beyond pairwise distances to arbitrary features of protein structure; and it becomes clear for which purposes the use of these quantities is justified. We illustrate these insights with two applications, involving the radius of gyration and hydrogen bonding. In the latter case, we also show how the reference ratio method can be iteratively applied to sculpt an energy funnel. Our results considerably increase the understanding and scope of energy functions derived from known biomolecular structures

Deriving amino acid contact potentials from their frequencies of occurence in proteins: a lattice model study

The possibility of deriving the contact potentials between amino acids from their frequencies of occurence in proteins is discussed in evolutionary terms. This approach allows the use of traditional thermodynamics to describe such frequencies and, consequently, to develop a strategy to include in the calculations correlations due to the spatial proximity of the amino acids and to their overall tendency of being conserved in proteins. Making use of a lattice model to describe protein chains and defining a "true" potential, we test these strategies by selecting a database of folding model sequences, deriving the contact potentials from such sequences and comparing them with the "true" potential. Taking into account correlations allows for a markedly better prediction of the interaction potentials

Global Optimization by Energy Landscape Paving

We introduce a novel heuristic global optimization method, energy landscape paving (ELP), which combines core ideas from energy surface deformation and tabu search. In appropriate limits, ELP reduces to existing techniques. The approach is very general and flexible and is illustrated here on two protein folding problems. For these examples, the technique gives faster convergence to the global minimum than previous approaches.Comment: to appear in Phys. Rev. Lett. (2002

Numerical comparison of two approaches for the study of phase transitions in small systems

We compare two recently proposed methods for the characterization of phase transitions in small systems. The validity and usefulness of these approaches are studied for the case of the q=4 and q=5 Potts model, i.e. systems where a thermodynamic limit and exact results exist. Guided by this analysis we discuss then the helix-coil transition in polyalanine, an example of structural transitions in biological molecules.Comment: 16 pages and 7 figure

From Nonspecific DNA–Protein Encounter Complexes to the Prediction of DNA–Protein Interactions

©2009 Gao, Skolnick. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.doi:10.1371/journal.pcbi.1000341DNA–protein interactions are involved in many essential biological activities. Because there is no simple mapping code between DNA base pairs and protein amino acids, the prediction of DNA–protein interactions is a challenging problem. Here, we present a novel computational approach for predicting DNA-binding protein residues and DNA–protein interaction modes without knowing its specific DNA target sequence. Given the structure of a DNA-binding protein, the method first generates an ensemble of complex structures obtained by rigid-body docking with a nonspecific canonical B-DNA. Representative models are subsequently selected through clustering and ranking by their DNA–protein interfacial energy. Analysis of these encounter complex models suggests that the recognition sites for specific DNA binding are usually favorable interaction sites for the nonspecific DNA probe and that nonspecific DNA–protein interaction modes exhibit some similarity to specific DNA–protein binding modes. Although the method requires as input the knowledge that the protein binds DNA, in benchmark tests, it achieves better performance in identifying DNA-binding sites than three previously established methods, which are based on sophisticated machine-learning techniques. We further apply our method to protein structures predicted through modeling and demonstrate that our method performs satisfactorily on protein models whose root-mean-square Ca deviation from native is up to 5 Å from their native structures. This study provides valuable structural insights into how a specific DNA-binding protein interacts with a nonspecific DNA sequence. The similarity between the specific DNA–protein interaction mode and nonspecific interaction modes may reflect an important sampling step in search of its specific DNA targets by a DNA-binding protein