Proton flux across artificial vesicle bilayers

The study of proton conductance across artificial membranes has revealed a surprisingly high permeability for H+, (Pnet H+). A high Pnet H+ is difficult to reconcile with the biological requirement for the maintenance of pH gradients across the plasma membranes of cells, organellar study was undertaken to examine the role played by cholesterol and phospholipid fatty acid side chain composition in determining how well a membrane will function as a barrier to acid. The effects of counter-ion movement on acidification rates were examined in order to interpret the data obtained from variations in membrane composition. In phosphate buffered saline solutions, vesicle membranes composed of unsaturated fatty acid phosphatidylcholines proved to be poorer barriers to acid than membranes composed of saturated fatty acids. The barrier properties of these membranes could be ranked in the following order: DPL, (palmitic) $>$ Egg PC, (mixed chains) $>$ DLL, (linoleic), with DPL being the most effective in maintaining a one pH unit gradient near neutrality. Cholesterol decreased acidification rates of membranes made from the unsaturated phosphatidylcholines Egg PC and DLL, but enhanced acidification rates in vesicle membranes composed of the saturated phospholipid DPL. The cholesterol and fatty acid side chain effects were mediated by changes in membrane fluidity, with more rigid bilayers forming better barriers to acid. Experimental evidence was obtained which confirmed the Pnet H+ is very high relative to the permeabilities of other ions. Counter-ion controlled acidification rates depended on the size and charge of the ion which was moving in order to maintain electroneutrality. The biological relevance of a high intrinsic Pnet H+ and the possible role of counter-ion controlled acidification were discussed. ^

Peptide nucleic acids: Mechanism of tight binding and application in artificial nuclease

Peptide nucleic acids (PNA) are mimics of nucleic acids with a peptidic backbone. Duplexes and triplexes formed between PNA and DNA or RNA possess remarkable thermal stability, they are resistant to nuclease cleavage and can better discriminate mismatches. Understanding the mechanism for the tight binding between PNA and oligonucleotides is important for the design and development of better PNA-based drugs.^ We have performed molecular dynamics (MD) simulations of 8-mer PNA/DNA duplex and two analogous duplexes with chiral modification of PNA strand (D- or L-Alanine modification). MD simulations were performed with explicit water and Na$\sp{+}$ counter ions. The 1.5-ns simulations were carried out with AMBER using periodic boundary and particle mesh Ewald summation. The point charges for PNA monomers were derived from fitting electrostatic potentials, obtained from ab initio calculation, to atomic centers using RESP. Derived charges reveal significantly altered charge distribution on the PNA bases and predict the Watson-Crick H-bonds involving PNA to be stronger. Results from NMR studies investigating H-bond interactions between DNA-DNA and DNA-PNA base pairs in non-polar environment are consistent with this prediction. MD simulations demonstrated that the PNA strand is more flexible than the DNA strand in the same duplex. That this flexibility might be important for the duplex stability is tested by introducing modification into the PNA backbones. Results from MD simulation revealed dramatically altered structures for the modified PNA-DNA duplexes. Consistent with previous NMR results, we also found no intrachain hydrogen bonds between O7$\sp\prime$ and N1$\sp\prime$ of the neighboring residues in our MD study. Our study reveals that in addition to the lack of charge repulsion, stronger Watson-Crick hydrogen bonds together with flexible backbone are important factors for the enhanced stability of the PNA-DNA duplex.^ In a related study, we have developed an application of Gly-Gly-His-(Gly)$\sb3$-PNA conjugate as an artificial nuclease. We were able to demonstrate cleavage of single stranded DNA at a single site upon Ni(II) binding to Gly-Gly-His tripeptide and activation of nuclease with monoperoxyphthalic acid. ^

New algorithms for automated phylogenetic reconstruction using artificial intelligence and data mining techniques

Academic and industrial research in the late 90s have brought about an exponential explosion of DNA sequence data. Automated expert systems are being created to help biologists to extract patterns, trends and links from this ever-deepening ocean of information. Two such systems aimed on retrieving and subsequently utilizing phylogenetically relevant information have been developed in this dissertation, the major objective of which was to automate the often difficult and confusing phylogenetic reconstruction process. ^ Popular phylogenetic reconstruction methods, such as distance-based methods, attempt to find an optimal tree topology (that reflects the relationships among related sequences and their evolutionary history) by searching through the topology space. Various compromises between the fast (but incomplete) and exhaustive (but computationally prohibitive) search heuristics have been suggested. An intelligent compromise algorithm that relies on a flexible “beam” search principle from the Artificial Intelligence domain and uses the pre-computed local topology reliability information to adjust the beam search space continuously is described in the second chapter of this dissertation. ^ However, sometimes even a (virtually) complete distance-based method is inferior to the significantly more elaborate (and computationally expensive) maximum likelihood (ML) method. In fact, depending on the nature of the sequence data in question either method might prove to be superior. Therefore, it is difficult (even for an expert) to tell a priori which phylogenetic reconstruction method—distance-based, ML or maybe maximum parsimony (MP)—should be chosen for any particular data set. ^ A number of factors, often hidden, influence the performance of a method. For example, it is generally understood that for a phylogenetically “difficult” data set more sophisticated methods (e.g., ML) tend to be more effective and thus should be chosen. However, it is the interplay of many factors that one needs to consider in order to avoid choosing an inferior method (potentially a costly mistake, both in terms of computational expenses and in terms of reconstruction accuracy.) ^ Chapter III of this dissertation details a phylogenetic reconstruction expert system that selects a superior proper method automatically. It uses a classifier (a Decision Tree-inducing algorithm) to map a new data set to the proper phylogenetic reconstruction method. ^