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Institute
- Biochemie und Chemie (1110) (remove)
Background: The flavin in its FMN and FAD forms is a versatile cofactor that is involved in catalysis of most disparate types of biological reactions. These include redox reactions such as dehydrogenations, activation of dioxygen, electron transfer, bioluminescence, blue light reception, photobiochemistry (as in photolyases), redox signaling etc. Recently, hitherto unrecognized types of biological reactions have been uncovered that do not involve redox shuffles, and might involve the reduced form of the flavin as a catalyst. The present work addresses properties of reduced flavin relevant in this context. Results: N(5)-H exchange reactions of the flavin reduced form and its pH dependence were studied using the 15N-NMR-signals of 15N-enriched, reduced flavin in the pH range from 5 to 12. The chemical shifts of the N(3) and N(5) resonances are not affected to a relevant extent in this pH range. This contrasts with the multiplicity of the N(5)-resonance, which strongly depends on pH. It is a doublet between pH 8.45 and 10.25 that coalesces into a singlet at lower and higher pH values. From the line width of the 15N(5) signal the pH-dependent rate of hydrogen exchange was deduced. The multiplicity of the 15N(5) signal and the proton exchange rates are little dependent on the buffer system used. Conclusion: The exchange rates allow an estimation of the pKa value of N(5)-H deprotonation in reduced flavin to be ≥ 20. This value imposes specific constraints for mechanisms of flavoprotein catalysis based on this process. On the other hand the pK ≈ 4 for N(5)-H protonation (to form N(5)+-H2) would be consistent with a role of N(5)-H as a base.
Background: Particle Swarm Optimization (PSO) is an established method for parameter optimization. It represents a population-based adaptive optimization technique that is influenced by several "strategy parameters". Choosing reasonable parameter values for the PSO is crucial for its convergence behavior, and depends on the optimization task. We present a method for parameter meta-optimization based on PSO and its application to neural network training. The concept of the Optimized Particle Swarm Optimization (OPSO) is to optimize the free parameters of the PSO by having swarms within a swarm. We assessed the performance of the OPSO method on a set of five artificial fitness functions and compared it to the performance of two popular PSO implementations. Results: Our results indicate that PSO performance can be improved if meta-optimized parameter sets are applied. In addition, we could improve optimization speed and quality on the other PSO methods in the majority of our experiments. We applied the OPSO method to neural network training with the aim to build a quantitative model for predicting blood-brain barrier permeation of small organic molecules. On average, training time decreased by a factor of four and two in comparison to the other PSO methods, respectively. By applying the OPSO method, a prediction model showing good correlation with training-, test- and validation data was obtained. Conclusion: Optimizing the free parameters of the PSO method can result in performance gain. The OPSO approach yields parameter combinations improving overall optimization performance. Its conceptual simplicity makes implementing the method a straightforward task.
The 5'-terminal cloverleaf (CL)-like RNA structures are essential for the initiation of positive- and negative-strand RNA synthesis of entero- and rhinoviruses. SLD is the cognate RNA ligand of the viral proteinase 3C (3Cpro), which is an indispensable component of the viral replication initiation complex. The structure of an 18mer RNA representing the apical stem and the cGUUAg D-loop of SLD from the first 5'-CL of BEV1 was determined in solution to a root-mean-square deviation (r.m.s.d.) (all heavy atoms) of 0.59 A (PDB 1Z30). The first (antiG) and last (synA) nucleotide of the D-loop forms a novel ‘pseudo base pair’ without direct hydrogen bonds. The backbone conformation and the base-stacking pattern of the cGUUAg-loop, however, are highly similar to that of the coxsackieviral uCACGg D-loop (PDB 1RFR) and of the stable cUUCGg tetraloop (PDB 1F7Y) but surprisingly dissimilar to the structure of a cGUAAg stable tetraloop (PDB 1MSY), even though the cGUUAg BEV D-loop and the cGUAAg tetraloop differ by 1 nt only. Together with the presented binding data, these findings provide independent experimental evidence for our model [O. Ohlenschläger, J. Wöhnert, E. Bucci, S. Seitz, S. Häfner, R. Ramachandran, R. Zell and M. Görlach (2004) Structure, 12, 237–248] that the proteinase 3Cpro recognizes structure rather than sequence.
In order to further understand how DNA polymerases discriminate against incorrect dNTPs, we synthesized two sets of dNTP analogues and tested them as substrates for DNA polymerase a (pol alpha) and Klenow fragment (exo-) of DNA polymerase I (Escherichia coli ). One set of analogues was designed to test the importance of the electronic nature of the base. The bases consisted of a benzimidazole ring with one or two exocyclic substituent(s) that are either electron-donating (methyl and methoxy) or electronwithdrawing (trifluoromethyl and dinitro). Both pol a and Klenow fragment exhibit a remarkable inability to discriminate against these analogues as compared to their ability to discriminate against incorrect natural dNTPs. Neither polymerase shows any distinct electronic or steric preferences for analogue incorporation. The other set of analogues, designed to examine the importance of hydrophobicity in dNTP incorporation, consists of a set of four regioisomers of trifluoromethyl benzimidazole. Whereas pol a and Klenow fragment exhibited minimal discrimination against the 5- and 6-regioisomers, they discriminated much more effectively against the 4- and 7-regioisomers. Since all four of these analogues will have similar hydrophobicity and stacking ability, these data indicate that hydrophobicity and stacking ability alone cannot account for the inability of pol a and Klenow fragment to discriminate against unnatural bases. After incorporation, however, both sets of analogues were not efficiently elongated. These results suggest that factors other than hydrophobicity, sterics and electronics govern the incorporation of dNTPs into DNA by pol {alpha} and Klenow fragment.
The volume changes of lithium and sodium under pressure are discussed with respect to the packing density of the atoms and their valence. In densely packed Li I (bcc), Li II (fcc), and Li III (alpha-Hg ype), valence increases from 1 at ~ 5 GPa to ~ 2.5 at 40 GPa. The maximum valence 3 is attained in Li IV (body-centered cubic, 16 atoms per cell, packing density q = 0.965) at 47 GPa. In densely packed Na I (bcc) a linear increase of valence from 1 at ~ 10 GPa to 2.9 at 65 GPa is found which continues in Na II (fcc) up to 4.1 at 103 GPa.
The volume changes of solid iodine under pressure are discussed with respect to the packing density of the atoms and to valence. The packing density of solid iodine which is 0.805 under ambient pressure increases to 0.976 in monoatomic iodine-II, 0.993 in iodine-III, and 1 in fcc iodine-IV. Simultaneously, the valence increases from 1 in the free molecule to 1.78 in the crystal structure under ambient pressure, 2.72 – 2.81 in iodine-II, 2.86 – 2.96 in iodine-III, and 3 in fcc iodine-IV. The valence then remains constant up to about 180 GPa and rises moderately to 3.15 at the highest investigated pressure of 276 GPa. Parameters for calculating bond numbers, valences and atomic volumes of densely packed halogens, hydrogen, oxygen, and nitrogen are given.
The volume changes of cesium under pressure are discussed with respect to the packing density of the atoms and valence. The element is univalent in densely packed Cs I and Cs II. Valence increases in Cs III (packing density q = 0.973), in Cs IV (q = 0.943), in Cs V (q ~ 0.99), and in close packed Cs VI. The diminuition of volume beyond ~ 15 GPa is caused by this increase only which implies that electrons of the fifth shell act as valence electrons.
Relationships between bond lengths and bond numbers and also between atomic volumes and valencies are derived and parameters for their calculation are given for the s-block, p-block, and d-block metals. From the atomic volumes under pressure, the valencies of three solid lanthanoids have been confirmed or redetermined: La 3; Ce 2. 3. and 4; Yb 2 and 3.
Metallic radii rm are correlated with the ionic radii ri by linear relationships. For groups 1 up to 7 as well as for Al, Ga, In, Tl, Sn, and Pb the ionic radii refer to the maximum valences (oxidation states) as known from compounds according to rm ~ 1.16 x (ri + 0.64) [A° ]. For groups 8 up to 12, rm ~ 0.48 x (ri + 2.26) [°A] with valences W = 14 - G (G = group number). These valences are considered regular (Wr). For groups 1 up to 12, they obey the equation Wr = 7 - |G - 7|. According to this equation all outer s electrons and the unpaired d electrons should be involved in chemical bonding, i.e. in the cohesion of the element in the solid state. From the melting temperatures and the atomic volumes it is concluded, however, that only 19 out of the 30 d-block elements have regular valences, namely the elements of groups 3, 5, 6, 10, 11 as well as Os, Ir, Zn, Cd, and possibly Ru. All of the non-regular valences are lower than the regular ones. Four of them are integers: Mn 3; Fe, Co 4; Re 6.
DCD – a novel plant specific domain in proteins involved in development and programmed cell death
(2005)
Background: Recognition of microbial pathogens by plants triggers the hypersensitive reaction, a common form of programmed cell death in plants. These dying cells generate signals that activate the plant immune system and alarm the neighboring cells as well as the whole plant to activate defense responses to limit the spread of the pathogen. The molecular mechanisms behind the hypersensitive reaction are largely unknown except for the recognition process of pathogens. We delineate the NRP-gene in soybean, which is specifically induced during this programmed cell death and contains a novel protein domain, which is commonly found in different plant proteins.
Results: The sequence analysis of the protein, encoded by the NRP-gene from soybean, led to the identification of a novel domain, which we named DCD, because it is found in plant proteins involved in d evelopment and c ell d eath. The domain is shared by several proteins in the Arabidopsis and the rice genomes, which otherwise show a different protein architecture. Biological studies indicate a role of these proteins in phytohormone response, embryo development and programmed cell by pathogens or ozone.
Conclusion: It is tempting to speculate, that the DCD domain mediates signaling in plant development and programmed cell death and could thus be used to identify interacting proteins to gain further molecular insights into these processes.