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15
Sali A: Statistical potentials for fold assessment
 Protein Sci 2002
"... A protein structure model generally needs to be evaluated to assess whether or not it has the correct fold. To improve fold assessment, four types of a residuelevel statistical potential were optimized, including distancedependent, contact, �/ � dihedral angle, and accessible surface statistical p ..."
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Cited by 56 (16 self)
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A protein structure model generally needs to be evaluated to assess whether or not it has the correct fold. To improve fold assessment, four types of a residuelevel statistical potential were optimized, including distancedependent, contact, �/ � dihedral angle, and accessible surface statistical potentials. Approximately 10,000 test models with the correct and incorrect folds were built by automated comparative modeling of protein sequences of known structure. The criterion used to discriminate between the correct and incorrect models was the Zscore of the model energy. The performance of a Zscore was determined as a function of many variables in the derivation and use of the corresponding statistical potential. The performance was measured by the fractions of the correctly and incorrectly assessed test models. The most discriminating combination of any one of the four tested potentials is the sum of the normalized distancedependent and accessible surface potentials. The distancedependent potential that is optimal for assessing models of all sizes uses both C � and C � atoms as interaction centers, distinguishes between all 20 standard residue types, has the distance range of 30 Å, and is derived and used by taking into account the sequence separation of the interacting atom pairs. The terms for the sequentially local interactions are significantly less informative than those for the sequentially nonlocal interactions. The accessible surface potential that
Y (2005) A knowledgebased energy function for proteinligand, proteinprotein, and proteindna complexes. J Med Chem 48:2325–35
"... We developed a knowledgebased statistical energy function for proteinligand, proteinprotein, and proteinDNA complexes by using 19 atom types and a distancescale finite idealgas reference (DFIRE) state. The correlation coefficients between experimentally measured proteinligand binding affinitie ..."
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Cited by 48 (9 self)
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We developed a knowledgebased statistical energy function for proteinligand, proteinprotein, and proteinDNA complexes by using 19 atom types and a distancescale finite idealgas reference (DFIRE) state. The correlation coefficients between experimentally measured proteinligand binding affinities and those predicted by the DFIRE energy function are around 0.63 for one training set and two testing sets. The energy function also makes highly accurate predictions of binding affinities of proteinprotein and proteinDNA complexes. Correlation coefficients between theoretical and experimental results are 0.73 for 82 proteinprotein (peptide) complexes and 0.83 for 45 proteinDNA complexes, despite the fact that the structures of proteinprotein (peptide) and proteinDNA complexes were not used in training the energy function. The results of the DFIRE energy function on proteinligand complexes are compared to the published results of 12 other scoring functions generated from either physicalbased, knowledgebased, or empirical methods. They include AutoDock, XScore, DrugScore, four scoring functions in Cerius 2 (LigScore, PLP, PMF, and LUDI), four scoring functions in SYBYL (FScore, GScore, DScore, and ChemScore), and BLEEP. While the DFIRE energy function is only moderately successful in ranking native or near native conformations, it yields the strongest correlation between theoretical and experimental binding affinities of the testing sets and between rmsd values and energy scores of docking decoys in a benchmark of 100 proteinligand complexes. The parameters and the program of the allatom DFIRE energy function are freely available for academic users at
The dependence of allatom statistical potentials on structural training database
 Biophys. J
, 2004
"... ABSTRACT An accurate statistical energy function that is suitable for the prediction of protein structures of all classes should be independent of the structural database used for energy extraction. Here, two highresolution, lowsequenceidentity structural databases of 333 aproteins and 271 bpro ..."
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Cited by 8 (1 self)
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ABSTRACT An accurate statistical energy function that is suitable for the prediction of protein structures of all classes should be independent of the structural database used for energy extraction. Here, two highresolution, lowsequenceidentity structural databases of 333 aproteins and 271 bproteins were built for examining the database dependence of three allatom statistical energy functions. They are RAPDF (residuespecific allatom conditional probability discriminatory function), atomic KBP (atomic knowledgebased potential), and DFIRE (statistical potential based on distancescaled finite idealgas reference state). These energy functions differ in the reference states used for energy derivation. The energy functions extracted from the different structural databases are used to select native structures from multiple decoys of 64 aproteins and 28 bproteins. The performance in native structure selections indicates that the DFIREbased energy function is mostly independent of the structural database whereas RAPDF and KBP have a significant dependence. The construction of two additional structural databases of a/b and a 1 bproteins further confirmed the weak dependence of DFIRE on the structural databases of various structural classes. The possible source for the difference between the three allatom statistical energy functions is that the physical reference state of ideal gas used in the DFIREbased energy function is least dependent on the structural database.
Geometric Filtering of Pairwise Atomic Interactions Applied to the Design of Efficient Statistical Potentials
"... Distancedependent, pairwise, statistical potentials are based on the concept that the packing observed in known protein structures can be used as a reference for comparing different models for a protein structure. Here, packing refers to the set of all pairs of atoms in the protein. Among all metho ..."
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Cited by 5 (0 self)
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Distancedependent, pairwise, statistical potentials are based on the concept that the packing observed in known protein structures can be used as a reference for comparing different models for a protein structure. Here, packing refers to the set of all pairs of atoms in the protein. Among all methods developed to assess threedimensional models, statistical potentials are subject both to praise for their power of discrimination, and to criticism for the weaknesses of their theoretical foundations. Classical derivations of pairwise potentials assume statistical independence of all pairs of atoms. This assumption, however, is not valid in general. We show that we can filter the list of all interactions in a protein to generate a much smaller subset of pairs that retains most of the structural information contained in proteins. The filter is based on a geometric method called alpha shapes that captures the packing in a conformation. Statistical scoring functions derived from such subsets perform as well as scoring functions derived from the set of all pairwise interactions.
G: Four distances between pairs of amino acids provide a precise description of their interaction
 PLoS Comput Biol
"... The threedimensional structures of proteins are stabilized by the interactions between amino acid residues. Here we report a method where four distances are calculated between any two side chains to provide an exact spatial definition of their bonds. The data were binned into a fourdimensional gri ..."
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The threedimensional structures of proteins are stabilized by the interactions between amino acid residues. Here we report a method where four distances are calculated between any two side chains to provide an exact spatial definition of their bonds. The data were binned into a fourdimensional grid and compared to a random model, from which the preference for specific fourdistances was calculated. A clear relation between the quality of the experimental data and the tightness of the distance distribution was observed, with crystal structure data providing far tighter distance distributions than NMR data. Since the fourdistance data have higher information content than classical bond descriptions, we were able to identify many unique interresidue features not found previously in proteins. For example, we found that the side chains of Arg, Glu, Val and Leu are not symmetrical in respect to the interactions of their head groups. The described method may be developed into a function, which computationally models accurately protein structures.
Gutachter:
, 2012
"... Network analysis of protein structures has provided valuable insight into protein folding and function. However, the lack of a unifying view in network modelling and analysis of protein structures and the unexploited advances in network theory prompted me to address three important challenges: 1. Ra ..."
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Network analysis of protein structures has provided valuable insight into protein folding and function. However, the lack of a unifying view in network modelling and analysis of protein structures and the unexploited advances in network theory prompted me to address three important challenges: 1. Rationalise the choice of network representation of protein structures. 2. Propose a well fitting null model for protein structure networks. 3. Develop a novel graphbased wholeresidue empirical potential. Graphlets, a recently introduced and powerful concept in graph theory, are a fundamental aspect of this thesis. The topological similarity between protein structure networks or individual residues was assessed using graphletbased methods in order to propose an optimised null model and develop a novel potential. Chapter 2 unifies the view of network representations by means of a controlled vocabulary and outlines the motivation behind the details of constructing such networks, and the popularity and optimality of the representations. In Chapter 3, an exhaustive
METHODOLOGY ARTICLE Open Access
"... Experimental and computational validation of models of fluorescent and luminescent reporter genes in bacteria Hidde de Jong 2, Caroline Ranquet 1,2, Delphine Ropers 2, Corinne Pinel 1,2 and Johannes Geiselmann * 1,2 Background: Fluorescent and luminescent reporter genes have become popular tools for ..."
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Experimental and computational validation of models of fluorescent and luminescent reporter genes in bacteria Hidde de Jong 2, Caroline Ranquet 1,2, Delphine Ropers 2, Corinne Pinel 1,2 and Johannes Geiselmann * 1,2 Background: Fluorescent and luminescent reporter genes have become popular tools for the realtime monitoring of gene expression in living cells. However, mathematical models are necessary for extracting biologically meaningful quantities from the primary data. Results: We present a rigorous method for deriving relative protein synthesis rates (mRNA concentrations) and protein concentrations by means of kinetic models of gene expression. We experimentally and computationally validate this approach in the case of the protein Fis, a global regulator of transcription in Escherichia coli. We show that the mRNA and protein concentration profiles predicted from the models agree quite well with direct measurements obtained by Northern and Western blots, respectively. Moreover, we present computational procedures for taking into account systematic biases like the folding time of the fluorescent reporter protein and differences in the halflives of reporter and host gene products. The results show that large differences in protein halflives, more than mRNA halflives, may be
Mathematik der Universit"at Wien
"... Magister rerum naturalium an der Fakult"at f"ur Naturwissenschaften und ..."
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Magister rerum naturalium an der Fakult&quot;at f&quot;ur Naturwissenschaften und
Geometric Filtering of Pairwise Atomic Interactions Applied to the Design of Efficient Statistical Potentials
"... Distancedependent, pairwise, statistical potentials are based on the concept that the packing observed in known protein structures can be used as a reference for comparing different 3D models for a protein. Here, packing refers to the set of all pairs of atoms in the molecule. Among all methods dev ..."
Abstract
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Distancedependent, pairwise, statistical potentials are based on the concept that the packing observed in known protein structures can be used as a reference for comparing different 3D models for a protein. Here, packing refers to the set of all pairs of atoms in the molecule. Among all methods developed to assess threedimensional models, statistical potentials are subject both to praise for their power of discrimination, and to criticism for the weaknesses of their theoretical foundations. Classical derivations of pairwise potentials assume statistical independence of all pairs of atoms. This assumption, however, is not valid in general. We show that we can filter the list of all interactions in a protein to generate a much smaller subset of pairs that retains most of the structural information contained in proteins. The filter is based on a geometric method called alpha shapes that captures the packing in a conformation. Statistical scoring functions derived from such subsets perform as well as scoring functions derived from the set of all pairwise interactions.