Genome scale enzyme-metabolite and drug-target interaction predictions using the signature molecular descriptor.

Motivation: Identifying protein enzymatic or pharmacological activities are important areas of research in biology and chemistry. Biological and chemical databases are increasingly being populated with linkages between protein sequences and chemical structures. There is now sufficient information to apply machine-learning techniques to predict interactions between chemicals and proteins at a genome scale.

Explicit treatment of spin labels in modeling of distance constraints from dipolar EPR and DEER.

Current SDSL-EPR methods allow measurement of dipolar distances in the 8-70 A range; however, the use of extrinsic probes complicates the interpretation of these distances in modeling macromolecular structure and conformational changes. The data presented here show that interprobe distances correlate only weakly with Cbeta-Cbeta distances, especially for distances that are on the order of the spin label tether lengths. Explicitly incorporating the spin label into the modeling process increases the experiment/model correlation 4-fold and reduces the distance error from 6 A to 3 A.

A deterministic algorithm for constrained enumeration of transmembrane protein folds.

A deterministic algorithm for enumeration of transmembrane protein folds is presented. Using a set of sparse pairwise atomic distance constraints (such as those obtained from chemical cross-linking, FRET, or dipolar EPR experiments), the algorithm performs an exhaustive search of secondary structure element packing conformations distributed throughout the entire conformational space. The end result is a set of distinct protein conformations, which can be scored and refined as part of a process designed for computational elucidation of transmembrane protein structures.

Optimal bundling of transmembrane helices using sparse distance constraints.

We present a two-step approach to modeling the transmembrane spanning helical bundles of integral membrane proteins using only sparse distance constraints, such as those derived from chemical cross-linking, dipolar EPR and FRET experiments. In Step 1, using an algorithm, we developed, the conformational space of membrane protein folds matching a set of distance constraints is explored to provide initial structures for local conformational searches. In Step 2, these structures refined against a custom penalty function that incorporates both measures derived from statistical analysis of solved membrane protein structures and distance constraints obtained from experiments.

Myosin regulatory domain orientation in skeletal muscle fibers: application of novel electron paramagnetic resonance spectral decomposition and molecular modeling methods.

Reorientation of the regulatory domain of the myosin head is a feature of all current models of force generation in muscle. We have determined the orientation of the myosin regulatory light chain (RLC) using a spin-label bound rigidly and stereospecifically to the single Cys-154 of a mutant skeletal isoform. Labeled RLC was reconstituted into skeletal muscle fibers using a modified method that results in near-stoichiometric levels of RLC and fully functional muscle.

Exploring the conformational space of membrane protein folds matching distance constraints.

Herein we present a computational technique for generating helix-membrane protein folds matching a predefined set of distance constraints, such as those obtained from NMR NOE, chemical cross-linking, dipolar EPR, and FRET experiments. The purpose of the technique is to provide initial structures for local conformational searches based on either energetic considerations or ad-hoc scoring criteria. In order to properly screen the conformational space, the technique generates an exhaustive list of conformations within a specified root-mean-square deviation (RMSD) where the helices are positioned in order to match the provided distances.

Structure of the inhibitory region of troponin by site directed spin labeling electron paramagnetic resonance.

Site-directed spin labeling EPR (SDSL-EPR) was used to determine the structure of the inhibitory region of TnI in the intact cardiac troponin ternary complex. Maeda and collaborators have modeled the inhibitory region of TnI (skeletal 96-112: the structural motif that communicates the Ca(2+) signal to actin) as a kinked alpha-helix [Vassylyev, D., Takeda, S.

Structural determination of spin label immobilization and orientation: a Monte Carlo minimization approach.

Electron paramagnetic resonance (EPR) is often used in the study of the orientation and dynamics of proteins. However, there are two major obstacles in the interpretation of EPR signals: (a) most spin labels are not fully immobilized by the protein, hence it is difficult to distinguish the mobility of the label with respect to the protein from the reorientation of the protein itself; (b) even in cases where the label is fully immobilized its orientation with respect to the protein is not known, which prevents interpretation of probe reorientation in terms of protein reorientation. We have developed a computational strategy for determining whether or not a spin label is immobilized and, if immobilized, predicting its conformation within the protein.