Dependence of prevalence of contiguous pathways in proteins on structural complexity

Variable Regions of p53 Isoforms Allosterically Hard Code DNA Interaction

Allosteric regulation of protein activity pervades biology as the "second secret of life." We have been examining the allosteric regulation and mutant reactivation of the tumor suppressor protein p53. We have found that generalizing the definition of allosteric effector to include entire proteins and expanding the meaning of binding site to include the interface of a transcription factor with its DNA to be useful in understanding the modulation of protein activity. Here, we cast the variable regions of p53 isoforms as allosteric regulators of p53 interactions with its consensus DNA. We implemented molecular dynamics simulations and our lab's new techniques of molecular dynamics (MD) sectors and MD-Markov state models to investigate the effects of nine naturally occurring splice variant isoforms of p53. We find that all of the isoforms differ from wild type in their dynamic properties and how they interact with the DNA. We consider the implications of these findings on allostery and cancer treatment.

Mutagenic Activation of Glutathione Peroxidase-4: Approaches toward Rational Design of Allosteric Drugs

Glutathione peroxidase 4 (GPX4) reduces lipid hydroperoxides in lipid membranes, effectively inhibiting iron-dependent cell death or ferroptosis. The upregulation of the enzyme by the mutations at residues D21 and D23 has been suggested to be associated with higher protein activity, which confers more protection against neurodegenerative diseases such as Alzheimer's, Parkinson's, and Huntington's diseases. Therefore, it has become an attractive target for treating and preventing neurodegenerative diseases. However, identifying means of mimicking the beneficial effects of these mutations distant from the active site constitutes a formidable challenge in moving toward therapeutics. In this study, we explore using molecular dynamics simulations to computationally map the conformational and energetic landscape of the wild-type GPX4 protein and three mutant variants to identify the allosteric networks of the enzyme. We present the conformational dynamic profile providing the desired signature behavior of the enzyme. We also discuss the implications of these findings for drug design efforts.

Homologs of the Tumor Suppressor Protein p53: A Bioinformatics Study for Drug Design

Sequence and structure of proteins related to the tumor suppressor protein p53 were studied from the perspective of gaining insight for the development of therapeutic drugs. Our study addresses two major issues encumber bringing novel drugs to market: side effects and artifacts from animal models. In the first phase of our study, we performed a genome-wide search to identify potentially similar proteins to p53 that may be susceptible to off target effects. In the second phase, we chose a selection of common model organisms that could potentially be available to undergraduate researchers in the university setting to assess which ones utilize p53 most similar to humans on the basis of sequence homology and structural similarity from predicted structures. Our results confirm the proteins in the humans significantly similar to p53 are known paralogs within the p53 family. In considering model organisms, murine p53 bore great similarity to human p53 in terms of both sequence and structure, but others performed similarly well. We discuss the findings against the background of other structural benchmarks and point out potential benefits and drawbacks of various alternatives for use in future drug design pilot studies.

Spectral analysis of molecular dynamics simulations on PDZ: MD sectors

The idea of protein "sectors" posits that sparse subsets of amino acid residues form cooperative networks that are key elements of protein stability, ligand binding, and allosterism. To date, protein sectors have been calculated by the statistical coupling analysis (SCA) method of Ranganathan and co-workers via the spectral analysis of conservation-weighted evolutionary covariance matrices obtained from a multiple sequence alignments of homologous families of proteins. SCA sectors, a knowledge-based protocol, have been indentified with functional properties and allosterism for a number of systems. In this study, we investigate the utility of the sector idea for the analysis of physics-based molecular dynamics (MD) trajectories of proteins. Our test case for this procedure is PSD95- PDZ3, one of the smallest proteins for which allosterism has been observed. It has served previously as a model system for a number of prediction algorithms, and is well characterized by X-ray crystallography, NMR spectroscopy and site specific mutagenisis. All-atom MD simulations were performed for a total of 500 nanoseconds using AMBER, and MD-calculated covariance matrices for the fluctuations of residue displacements and non-bonded interaction energies were subjected to spectral analysis in a manner analogous to that of SCA. The composition of MD sectors was compared with results from SCA, site specific mutagenesis, and allosterism. The concordance indicates that MD sectors are a viable protocol for analyzing MD trajectories and provide insight into the physical origin of the phenomenon.Communicated by Ramaswamy H. Sarma.

Advances in the Computational Identification of Allosteric Sites and Pathways in Proteins

With the increasing difficulty to develop new drugs and the emergence of resistance to traditional orthosteric-site inhibitors, the search for alternatives is finally approaching the focus on allosteric sites. Allosteric sites offer opportunities to regulate many pharmacologically targeted pathways by inhibition or activation. In addition, allosteric sites tend to be less conserved than the functional site, which may facilitate the design of specific effectors in the protein families for which specific orthosteric inhibitors have proved difficult to design. Furthermore, recent evidence suggests that all proteins might be susceptible of allosteric regulation, increasing the space of druggable targets. Computational identification of allosteric sites has therefore become an active field of research. The problem can be approached from two sides: (1) the identification of allosteric-communication pathways between the functional site and potential allosteric sites and (2) the functional-site-independent identification of allosteric sites. While the first approach tends to be more laborious and thus restricted to a single protein, the second tends to be more amenable to larger-scale analysis, thus providing tools for the two drug discovery scenarios: the analysis of known targets and the screening for new potential targets. Here, I show some basic concepts and methods useful to the identification of allosteric sites and pathways, in line with these two approaches. I describe them in some detail to build a clear framework, at the risk of losing the interest of experts. Examples of recent studies involving these methods are also illustrated, focusing on the techniques rather than on their findings on allosterism.