Paul Smith, Ph.D. - US grants

The bis(2,6-dioxopiperazine), ICRF-193, is potent non-DNA damaging inhibitor of the decantenation activity of topoisomerase II. The mechanism of inhibition is being interpreted in terms of an ATP-modulated protein-clamp model. ICRF-193 inhibits cell division but allows cell cycle traverse and progression to polyploidy with a delay at the G2 checkpoint. We will use conventional FCM methods to examine cell cycle regulation in conjunction with the various cyclins, including A and B1 and fluorescence lifetime analysis will be performed on cellular-bound ICRF-123 to analyze its interaction with chromatin.

It has been established that peptide and protein aggregation lies at the heart of many diseases. Hence, a detailed understanding of exactly when and how peptides or proteins tend to aggregate would be beneficial to a wide range of researchers studying a variety of health related issues. This proposal outlines a program for the systematic study of peptide aggregation using a unique combination of approaches including: currently available experimental thermodynamic data;the theory of preferential interactions as characterized by Kirkwood-Buff (KB) integrals;a simple model for peptide aggregation using preferential interactions between different functional groups in solution;and computer simulation. A theory and model is developed and demonstrated that can decompose and quantify the interactions between different amino acid side chains using existing experimental data on activity coefficients of small peptides, thus enabling predictions of the tendency for aggregation of any small peptide. Computer simulations are proposed to investigate the atomic level details of the aggregation process. A new peptide and protein force field (KBFF) that can reproduce the experimental KB integrals will be completed and used for the simulations. Aim 1. To quantify and characterize the interactions between functional groups observed in peptides. Analysis of existing experimental data will be performed in aqueous solution to determine preferential interaction (PI) parameters for different amino acid and small peptide systems. A simple model of the Pis will be developed and will then be used to isolate and quantify the Pis between different function groups on those peptides. Aim 2. To produce an accurate force field specifically designed for the study of preferential interactions in biomolecular systems. The KBFF approach will be extended to include all amino acid side chains. Aim 3. To understand the role of cosolvents in modifying intermolecular interactions. The addition of cosolvents to a solution of a solute and solvent changes the interactions between the solute molecules. This provides a tool for investigating the strength of intermolecular interactions common in biological systems and how they may be modified. We will focus on the effects of urea and NaCI during our initial studies.

? DESCRIPTION (provided by applicant): Studies of protein denaturation provide critical information concerning the forces that stabilize protein structure and other assemblies. This has a significant effect on our general understanding of the structure/function aspects of proteins that will directly impact our understanding of many diseases that are related to protein unfolding, misfolding, and aggregation. While the vast majority of studies have focused on the native state of proteins, the role of the denatured state is of equal importance. It is now generally accepted that the denatured state ensemble (DSE) is not random in nature but can possess residual native and non-native structure. While our knowledge of the properties of the native state has been advanced by a variety of experimental and simulation results, our understanding of the denatured state remains somewhat simplistic primarily due to the difficulties obtaining atomic level data from experiment, and our inability to determine the thermodynamic properties of the DSE from simulation. It is proposed that a combination of recent theoretical developments using Fluctuation Solution Theory, coupled with computer simulation approaches, can provide reliable data concerning the similarities and differences between denatured states generated by changes in pressure, temperature and composition at the residue level. There are three major aims to the proposed project. Aim 1: To Determine Residue Based Contributions to Protein Thermodynamics. Aim 2: To Determine the Thermodynamic Properties of Amylin and Mutant Amylin DSEs. Aim 3: To Determine the Effects of Environment on the Thermodynamic and Aggregation Properties of Small Peptide Sequences Derived from Amylin. The results of these studies will provide valuable information concerning the nature of the denatured state and the consequences for the role the DSE may play in a variety of diseases.

When genomes are sequenced for personalized medicine, each patient can have up to 10,000 varia- tions in their protein sequences. To identify which amino acid changes are medically relevant, many computer algorithms have been developed. In thinking about how to improve these algorithms, we considered that, among their input, many include evolutionary information about the affected proteins. Algorithms also include ?rules? devised from decades of mutation experiments: Similar amino acids allow function (toggle on); other amino acids abolish function or structure (toggle off); each mutation will have the same outcome in any homolog. However, experiments have been very heavily biased to conserved positions. In contrast, >50% of amino acid positions are not conserved during the evolution of most proteins. If nonconserved positions follow different rules, this may be one source for false positive and negative predictions in genome analyses. We are bridging this gap between experimental protein chemistry and computer predictions. In our first study, we used 10 homologs to assess the outcomes for >1000 mutations at nonconserved positions. Strikingly, these positions did not follow any of the substitution rules listed above. First, when multiple amino acids were substituted into one position, they caused a wide range of functional outcomes (?rheostat position?). Second, chemically similar amino acids did not always have similar outcomes. Third, when a given position was substituted in multiple homologs, the same amino acid had different outcomes. Thus, rheostatic nonconserved positions are likely to give false results in current predictions. Preliminary results show that other proteins have rheostat positions. The central hypothesis of this proposal is that rheostat positions have general properties that distinguish them from other nonconserved positions. In Aim 1, we will test the hypothe- sis that rheostat positions can be detected by a particular pattern of evolutionary change, using pyruvate kinase, aldolase, and an organic anion transmembrane transporter as model systems. If prediction is possible, amino acid variants at rheostat positions should be ? for now ? classified as having ?unknown significance? to reduce false predictions. Further, all experimental results can be used by the CAGI community to assess the development of new algorithms. In Aim 2, we will use molecular dynamics simulations and hydrogen ex- change experiments to determine how rheostat mutations affect protein motions. In Aim 3, we will use X-ray crystallography and structural predictions to determine how rheostat mutations affect side-chain packing. The results from Aims 2-3 (i) can be used to identify regions in other proteins that contain rheostat positions, and (ii) will provide the groundwork for formulating new rules for predicting the outcomes of rheostat mutations. The new rules are needed to reach our long-term goals of improving computer predictions and reducing the number of clinical variants with unknown significance.