Ramy S. Farid

Biological electron transfer

Many oxidoreductases are constructed from (a) local sites of strongly coupled substrate-redox cofactor partners participating in exchange of electron pairs, (b) electron pair/single electron transducing redox centers, and (c) nonadiabatic, long-distance, single-electron tunneling between weakly coupled redox centers. The latter is the subject of an expanding experimental program that seeks to manipulate, test, and apply the parameters of theory. New results from the photosynthetic reaction center protein confirm that the electronic-tunneling medium appears relatively homogeneous, with any variances evident having no impact on function, and that control of intraprotein rates and directional specificity rests on a combination of distance, free energy, and reorganization energy. Interprotein electron transfer between cytochromec and the reaction center and in lactate dehydrogenase, a typical oxidoreductase from yeast, are examined. Rates of interprotein electron transfer appear to follow intraprotein guidelines with the added essential provision of binding forces to bring the cofactors of the reacting proteins into proximity.

Electron transfer in proteins

The past year has seen significant advances in our understanding of electron transfer (ET) events in biological and synthetic protein systems. Experiment and theory have begun to merge towards a more complete picture of the role that protein plays in mediating ET reactions. This review highlights these developments and discusses the roles played by distance, protein medium, driving force, and reorganization energy in controlling ET rates in natural and artificial systems. These parameters, drawn from ET theory, are discussed in terms of their natural selection to govern biological ET. We find that distance, driving force, and reorganization energy are the main determinants of ET rates and directional specificity. Another parameter that can have significant effects on rates over physiological distances is the structure of the intervening medium between the electron donor and acceptor cofactors. There is nevertheless no clear evidence that the effect of distance on ET is modified by the intervening protein structure so as to favor productive forward ETs over energy-wasting reverse reactions in key biological ET systems.

Secondary structure in lung surfactant SP-B peptides: IR and CD studies of bulk and monolayer phases

Pulmonary surfactant protein SP-B is known to facilitate adsorption and spreading of surfactant components across the air/water interface. This property appears essential for in vivo function in the alveolar subphase and at the air/alveolar surface. Three peptides with amino acid sequences based on SP-B containing predicted alpha-helical regions (SP-B(1--20), SP-B(9--36A), SP-B(40--60A)) have been synthesized to probe structure-function relationships and protein-lipid interaction in bulk phase and monolayer environments. IR and CD studies are reported along with traditional surface pressure-molecular area (pi-A) isotherms and IR reflection-absorption spectroscopy (IRRAS) investigations conducted at the air/water interface. In bulk phase, helix-promoting environments (methanol and aqueous dispersions of lipid vesicles), SP-B(1--20) and SP-B(9--36A) contained significant amounts of alpha-helical structure, whereas varying degrees of alpha-helix, random coil, and beta-sheet were observed in aqueous solutions and monolayers. The most striking behavior was observed for SP-B(9--36A), which displayed reversible surface pressure-induced beta-sheet formation. Bulk phase lipid melting curves and monolayer experiments with peptide-lipid mixtures showed subtle differences in the degree of bulk phase interaction and substantial differences in peptide surface activity. The uniqueness of IRRAS is emphasized as the importance of evaluating secondary structure in both bulk phase and monolayer environments for lung surfactant peptide mimics is demonstrated.

WScore: A Flexible and Accurate Treatment of Explicit Water Molecules in Ligand-Receptor Docking

We have developed a new methodology for protein-ligand docking and scoring, WScore, incorporating a flexible description of explicit water molecules. The locations and thermodynamics of the waters are derived from a WaterMap molecular dynamics simulation. The water structure is employed to provide an atomic level description of ligand and protein desolvation. WScore also contains a detailed model for localized ligand and protein strain energy and integrates an MM-GBSA scoring component with these terms to assess delocalized strain of the complex. Ensemble docking is used to take into account induced fit effects on the receptor conformation, and protein reorganization free energies are assigned via fitting to experimental data. The performance of the method is evaluated for pose prediction, rank ordering of self-docked complexes, and enrichment in virtual screening, using a large data set of PDB complexes and compared with the Glide SP and Glide XP models; significant improvements are obtained.

Design, synthesis, and characterization of a novel hemoprotein

Here we describe a synthetic protein (6H7H) designed to bind four heme groups via bis–histidine axial ligation. The hemes are designed to bind perpendicular to another in an orientation that mimics the relative geometry of the two heme a groups in the active site of cytochrome c oxidase. Our newly developed protein‐design program, called CORE, was implemented in the design of this novel hemoprotein. Heme titration studies resolved four distinct KD values (KD1 = 80 nM, KD2 = 18 nM, KD3 ≥ 3 mM, KD4 ≤ 570 nM, with KD3 × K D4 = 1700); positive cooperativity in binding between the first and second heme, as well as substantial positive cooperativity between the third and forth heme, was observed. Chemical and thermal denaturation studies reveal a stable protein with native‐like properties. Visible circular dichroism spectroscopy of holo‐6H7H indicates excitonic coupling between heme groups. Further electrochemical and spectroscopic characterization of the holo‐protein support a structure that is consistent with the predefined target structure.

Electron-Transfer Mechanisms in Reaction Centers: Engineering Guidelines

This chapter discusses electron transfer mechanisms in reaction centers. Biological electron transfer has been made possible by the solution of the X-ray diffraction structure of two photosynthetic reaction centers and by extensive measurements on the free-energy dependence of photosynthetic electron transfer reactions. However, a relatively simple form of electron transfer theory is sufficient to define the rate of the majority of intraprotein electron transfer reactions within the uncertainty currently associated with measuring the fundamental parameters of distance, free energy, and reorganization energy. This chapter presents a first-order description of the important parameters of intraprotein electron transfer and presents a list of basic principles of electron transfer protein design. A simple equation derived from quantum mechanical perturbation theory, known as Fermis Golden Rule, provides a good first-order description of the rate of nonadiabatic electron transfer. The extensive free-energy dependence of the quinone electron transfer reactions in the bacterial photosynthetic reaction centers provides the best illustration of how the Franck–Condon factors and the optimal rates of intraprotein electron transfer are determined experimentally.

Modeling the value of predictive affinity scoring in preclinical drug discovery

Drug discovery is widely recognized to be a difficult and costly activity in large part due to the challenge of identifying chemical matter which simultaneously optimizes multiple properties, one of which is affinity for the primary biological target. Further, many of these properties are difficult to predict ahead of expensive and time-consuming compound synthesis and experimental testing. Here we highlight recent work to develop compound affinity prediction models, and extensively investigate the value such models may provide to preclinical drug discovery. We demonstrate that the ability of these models to improve the overall probability of success is crucially dependent on the shape of the error distribution, not just the root-mean-square error. In particular, while scoring more molecule ideas generally improves the probability of project success when the error distribution is Gaussian, fat-tail distributions such as a Cauchy distribution, can lead to a situation where scoring more ideas actually decreases the overall probability of success.