Ludwig Krippahl

BiGGER: A new (soft) docking algorithm for predicting protein interactions

A new computationally efficient and automated “soft docking” algorithm is described to assist the prediction of the mode of binding between two proteins, using the three‐dimensional structures of the unbound molecules. The method is implemented in a software package called BiGGER (Bimolecular Complex Generation with Global Evaluation and Ranking) and works in two sequential steps: first, the complete 6‐dimensional binding spaces of both molecules is systematically searched. A population of candidate protein‐protein docked geometries is thus generated and selected on the basis of the geometric complementarity and amino acid pairwise affinities between the two molecular surfaces. Most of the conformational changes observed during protein association are treated in an implicit way and test results are equally satisfactory, regardless of starting from the bound or the unbound forms of known structures of the interacting proteins. In contrast to other methods, the entire molecular surfaces are searched during the simulation, using absolutely no additional information regarding the binding sites. In a second step, an interaction scoring function is used to rank the putative docked structures. The function incorporates interaction terms that are thought to be relevant to the stabilization of protein complexes. These include: geometric complementarity of the surfaces, explicit electrostatic interactions, desolvation energy, and pairwise propensities of the amino acid side chains to contact across the molecular interface. The relative functional contribution of each of these interaction terms to the global scoring function has been empirically adjusted through a neural network optimizer using a learning set of 25 protein‐protein complexes of known crystallographic structures. In 22 out of 25 protein‐protein complexes tested, near‐native docked geometries were found with Cα RMS deviations ≤ 4.0 Å from the experimental structures, of which 14 were found within the 20 top ranking solutions. The program works on widely available personal computers and takes 2 to 8 hours of CPU time to run any of the docking tests herein presented. Finally, the value and limitations of the method for the study of macromolecular interactions, not yet revealed by experimental techniques, are discussed. Proteins 2000;39:372–384.

PSICO: Solving Protein Structures with Constraint Programming and Optimization

In this paper we propose PSICO (Processing Structural Information with Constraint programming and Optimisation) as a constraint-based approach to determining protein structures compatible with distance constraints obtained from Nuclear Magnetic Resonance (NMR) data. We compare the performance of our proposed algorithm with DYANA (“Dynamics algorithm for NMR applications”) an existing commercial application based on simulated annealing. On a test case with experimental data on the dimeric protein Desulforedoxin, the method proposed here supplied similar results in less than 10 minutes compared to approximately 10 hours of computation time for DYANA. Although the quality of results can still be improved, this shows that CP technology can greatly reduce computation time, a major advantage because structural NMR technique generally demands multiple runs of structural computation.

Modeling protein complexes with BiGGER

This article describes the method and results of our participation in the Critical Assessment of PRediction of Interactions (CAPRI) experiment, using the protein docking program BiGGER (Bimolecular complex Generation with Global Evaluation and Ranking) (Palma et al., Proteins 2000;39:372–384). Of five target complexes (CAPRI targets 2, 4, 5, 6, and 7), only one was successfully predicted (target 6), but BiGGER generated reasonable models for targets 4, 5, and 7, which could have been identified if additional biochemical information had been available. Proteins 2003;52:19–23.

Electrochemical studies on small electron transfer proteins using membrane electrodes

Membrane electrodes (ME) were constructed using gold, glassy carbon and pyrolytic graphite supports and a dialysis membrane, and used to study the electrochemical behavior of small size electron transfer proteins: monohemic cytochrome c522 from Pseudomonas nautica and cytochrome c533 as well as rubredoxin from Desulfovibrio vulgaris . Different electrochemical techniques were used including cyclic voltammetry (CV), square wave voltammetry (SW) and differential pulse voltammetry (DP). A direct electrochemical response was obtained in all cases except with rubredoxin where a facilitator was added to the protein solution entrapped between the membrane and the electrode surface. Formal potentials and heterogeneous charge transfer rate constants were determined from the voltammetric data. The influence of the ionic strength and the pH of the medium on the electrochemical response at the ME were analyzed. The benefits from the use of the ME in protein electrochemistry and its role in modulating the redox behavior are analyzed. A critical comparison is presented with data obtained at non-MEs. Finally, the interactions that must be established between the proteins and the electrode surfaces are discussed, thereby modeling molecular interactions that occur in biological systems. # 2002 Elsevier Science B.V. All rights reserved.

Modulation of the proteolytic activity of matrix metalloproteinase-2 (gelatinase A) on fibrinogen.

The proteolytic processing of bovine fibrinogen by MMP-2 (gelatinase A), which brings about the formation of a product unable to form fibrin clots, has been studied at 37 degrees C. Catalytic parameters, although showing a somewhat lower catalytic efficiency with respect to thrombin and plasmin, indeed display values indicating a pathophysiological significance of this process. A parallel molecular modelling study predicts preferential binding of MMP-2 to the beta-chain of fibrinogen through its haemopexin-like domain, which has been directly demonstrated by the inhibitory effect in the presence of the exogenous haemopexin-like domain. However, the removal of this domain does not impair the interaction between MMP-2 and fibrinogen, but it dramatically alters the proteolytic mechanism, producing different fragmentation intermediates. The investigation at various pH values between 6.0 and 9.3 indicates a proton-linked behaviour, which is relevant for interpreting the influence on the process by environmental conditions occurring at the site of an injury. Furthermore, the action of MMP-2 on peroxynitrite-treated fibrinogen has been investigated, a situation possibly occurring under oxidative stress. The chemical alteration of fibrinogen, which has been shown to abolish its clotting activity, brings about only limited modifications of the catalytic parameters without altering the main enzymatic mechanism.

The Structure of an Electron Transfer Complex Containing a Cytochrome c and a Peroxidase

Efficient biological electron transfer may require a fluid association of redox partners. Two noncrystallographic methods (a new molecular docking program and 1H NMR spectroscopy) have been used to study the electron transfer complex formed between the cytochrome c peroxidase (CCP) ofParacoccus denitrificans and cytochromes c. For the natural redox partner, cytochrome c 550, the results are consistent with a complex in which the heme of a single cytochrome lies above the exposed electron-transferring heme of the peroxidase. In contrast, two molecules of the nonphysiological but kinetically competent horse cytochrome bind between the two hemes of the peroxidase. These dramatically different patterns are consistent with a redox active surface on the peroxidase that may accommodate more than one cytochrome and allow lateral mobility.

Evidence for a purifying selection acting on the β-lactamase locus in epidemic clones of methicillin-resistant Staphylococcus aureus

BackgroundThe β-lactamase (bla) locus, which confers resistance to penicillins only, may control the transcription of mecA, the central element of methicillin resistance, which is embedded in a polymorphic heterelogous chromosomal cassette (the SCCmec element). In order to assess the eventual correlation between bla allotypes and genetic lineages, SCCmec types and/or β-lactam resistance phenotypes, the allelic variation on the bla locus was evaluated in a representative collection of 54 international epidemic methicillin-resistant Staphylococcus aureus (MRSA) clinical strains and, for comparative purposes, also in 24 diverse methicillin-susceptible S. aureus (MSSA) strains.ResultsInternal fragments of blaZ (the β-lactamase structural gene) were sequenced for all strains. A subset of strains, representative of blaZ allotypes, was further characterized by sequencing of internal fragments of the blaZ transcriptional regulators, blaI and blaR1. Thirteen allotypes for blaZ, nine for blaI and 12 for blaR1 were found. In a total of 121 unique single-nucleotide polymorphisms (SNP) detected, no frameshift mutations were identified and only one nonsense mutation within blaZ was found in a MRSA strain. On average, blaZ alleles were more polymorphic among MSSA than in MRSA (14.7 vs 11.4 SNP/allele). Overall, blaR1 was the most polymorphic gene with an average of 24.8 SNP/allele. No correlation could be established between bla allotypes and genetic lineages, SCCmec types and/or β-lactam resistance phenotypes. In order to estimate the selection pressure acting on the bla locus, the average dN/dS values were computed. In the three genes and in both collections dN/dS ratios were significantly below 1.ConclusionsThe data strongly suggests the existence of a purifying selection to maintain the bla locus fully functional even on MRSA strains. Although, this is in agreement with the notion that in most clinical MRSA strains mecA gene is under the control of the bla regulatory genes, these findings also suggest that the apparently redundant function of blaZ gene for the MRSA resistant phenotype is still important for these strains. In addition, the data shows that the sensor-inducer blaR1 is the primary target for the accumulation of mutations in the bla locus, presumably to modulate the response to the presence of β-lactam antibiotic.

Applying Constraint Programming to Protein Structure Determination

In this paper, we propose a constraint-based approach to determining protein structures compatible with distance constraints obtained from Nuclear Magnetic Resonance (NMR) data. We compare the performance of our proposed algorithm with DYANA (“Dynamics algorithm for NMR applications” [1]) an existing commercial application based on simulated annealing. For our test case, computation time for DYANA was more than six hours, whereas the method we propose produced similar results in 8 minutes, so we show that the application of Constraint Programming (CP) technology can greatly reduce computation time. This is a major advantage because this NMR technique generally demands multiple runs of structural computation.

Synechocystis ferredoxin/ferredoxin-NADP+-reductase/NADP+ complex: Structural model obtained by NMR-restrained docking

Ferredoxin (Fd) and ferredoxin‐NADP+‐reductase (FNR) are two terminal physiological partners of the photosynthetic electron transport chain. Based on a nuclear magnetic resonance (NMR)‐restrained‐docking approach, two alternative structural models of the Fd–FNR complex in the presence of NADP+ are proposed. The protein docking simulations were performed with the software BiGGER. NMR titration revealed a 1:1 stoichiometry for the complex and allowed the mapping of the interacting residues at the surface of Fd. The NMR chemical shifts were encoded into distance constraints and used with theoretically calculated electronic coupling between the redox cofactors to propose experimentally validated docked complexes.

Constraint Programming in Structural Bioinformatics

Bioinformatics aims at applying computer science methods to the wealth of data collected in a variety of experiments in life sciences (e.g. cell and molecular biology, biochemistry, medicine, etc.) in order to help analysing such data and eliciting new knowledge from it. In addition to string processing bioinformatics is often identified with machine learning used for mining the large banks of bio-data available in electronic format, namely in a number of web servers. Nevertheless, there are opportunities of applying other computational techniques in some bioinformatics applications. In this paper, we report the application of constraint programming to address two structural bioinformatics problems, protein structure prediction and protein interaction (docking). The efficient application of constraint programming requires innovative modelling of these problems, as well as the development of advanced propagation techniques (e.g. global reasoning and propagation), which were adopted in Chemera, a system that is currently used to support biochemists in their research.