Brian W. Beck

Structural origins of redox potentials in Fe-S proteins: electrostatic potentials of crystal structures

Redox potentials often differ dramatically for homologous proteins that have identical redox centers. For two types of iron-sulfur proteins, the rubredoxins and the high-potential iron-sulfur proteins (HiPIPs), no structural explanations for these differences have been found. We calculated the classical electrostatic potential at the redox site using static crystal structures of four rubredoxins and four HiPIPs to identify important structural determinants of their redox potentials. The contributions from just the backbone and polar side chains are shown to explain major features of the experimental redox potentials. For instance, in the rubredoxins, the presence of Val 44 versus Ala 44 causes a backbone shift that explains a approximately 50 mV lower redox potential in one of the four rubredoxins. This result is consistent with experimental redox potentials of five additional rubredoxins with known sequence. Also, we attribute the unusually lower redox potentials of two of the HiPIPs studied to a less positive electrostatic environment around their redox sites. Finally, molecular dynamics simulations of solvent around static rubredoxin crystal structures indicate that water alone is a major factor in dampening the contribution of charged side chains, in accord with experiments showing that mutations of surface charges produce relatively little effect on redox potentials.

Protein-protein interface detection using the energy centrality relationship (ECR) characteristic of proteins.

Specific protein interactions are responsible for most biological functions. Distinguishing Functionally Linked Interfaces of Proteins (FLIPs), from Functionally uncorrelated Contacts (FunCs), is therefore important to characterizing these interactions. To achieve this goal, we have created a database of protein structures called FLIPdb, containing proteins belonging to various functional sub-categories. Here, we use geometric features coupled with Kortemme and Bakers computational alanine scanning method to calculate the energetic sensitivity of each amino acid at the interface to substitution, identify hotspots, and identify other factors that may contribute towards an interface being FLIP or FunC. Using Principal Component Analysis and K-means clustering on a training set of 160 interfaces, we could distinguish FLIPs from FunCs with an accuracy of 76%. When these methods were applied to two test sets of 18 and 170 interfaces, we achieved similar accuracies of 78% and 80%. We have identified that FLIP interfaces have a stronger central organizing tendency than FunCs, due, we suggest, to greater specificity. We also observe that certain functional sub-categories, such as enzymes, antibody-heavy-light, antibody-antigen, and enzyme-inhibitors form distinct sub-clusters. The antibody-antigen and enzyme-inhibitors interfaces have patterns of physical characteristics similar to those of FunCs, which is in agreement with the fact that the selection pressures of these interfaces is differently evolutionarily driven. As such, our ECR model also successfully describes the impact of evolution and natural selection on protein-protein interfaces. Finally, we indicate how our ECR method may be of use in reducing the false positive rate of docking calculations.

Prediction of Functionally Linked Interface (FLIP) Regions in Residue Interaction Network (RIN) Models of Protein Structures

Various cellular processes involve participation of proteins as monomers or oligomers. The function of the oligomers is often related to their structural stability and interaction capabilities. Here, we continue our work on the characterization of the physico-chemical patterns of amino acid residues involved in quaternary interactions that form Functionally Linked Interfaces of Proteins (FLIPs). Proteins are represented as a network with residues as nodes and proximity between residues (both bonded and non-bonded interactions) being the edges (a residue interaction network or RIN). Our previous studies of RINs have shown that FLIPS can be distinguished from Functionally uncorrelated Contacts (FunCs) with ∼74% accuracy, indicating residues show organizational differences in the interfaces of FLIPS and FunCS. In the current work, we identify threshold values of network centrality features that predictively distinguish FLIP, FunC, and non-interface regions of RINs.

Systematic Perturbation of Protein:Protein Interfaces may Aid in Functional Classification

Protein:protein interactions play vital roles in many biological reactions. In a previous study we constructed a database of protein:protein interfaces (FLIPdb) and have shown that calculations of the computational alanine scanning (CAS) energy of residues along the interface can distinguish functional categories of proteins.Here, to further understand the underlying principles of protein interactions, we examine the effects of systematically translating one interfacial subunit over a grid in relation to the other stationary subunit for the structures in FLIPdb. A three-dimensional potential energy surface was generated from the change in spatial coordinates and energy of alanine substitution that accompanies each shift in conformation. Specific characteristics of the potential energy surfaces, including the volume within the surface, maximum depth, width at half depth, and energy per residue were found to discriminate between different functional classes of protein interfaces with an accuracy of approximately seventy-six percent. These results may suggest that Functionally-Linked Interfaces of Proteins (FLIPs) are more sensitive to perturbation and are thus more specific than Functionally uncorrelated Contacts (FunCs).