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. Current machine-learning techniques use as input either protein sequences and structures or chemical information. We propose here a method to infer protein-chemical interactions using heterogeneous input consisting of both protein sequence and chemical information. RESULTS: Our method relies on expressing proteins and chemicals with a common cheminformatics representation. We demonstrate our approach by predicting whether proteins can catalyze reactions not present in training sets. We also predict whether a given drug can bind a target, in the absence of prior binding information for that drug and target. Such predictions cannot be made with current machine-learning techniques requiring binding information for individual reactions or individual targets.

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. We begin by describing the statistical analysis of the solved membrane protein structures from which the theoretical portion of the penalty function was derived. We then describe the penalty function, and, using a set of six test cases, demonstrate that it is capable of distinguishing helical bundles that are close to the native bundle from those that are far from the native bundle. Finally, using a set of only 27 distance constraints extracted from the literature, we show that our method successfully recovers the structure of dark-adapted rhodopsin to within 3.2 A of the crystal structure.

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. Complex electron paramagnetic resonance spectra obtained in rigor necessitated the development of a novel decomposition technique. The strength of this method is that no specific model for a complex orientational distribution was presumed. The global analysis of a series of spectra, from fibers tilted with respect to the magnetic field, revealed two populations: one well-ordered (+/-15 degrees ) with the spin-label z axis parallel to actin, and a second population with a large distribution (+/-60 degrees ). A lack of order in relaxed or nonoverlap fibers demonstrated that regulatory domain ordering was defined by interaction with actin rather than the thick filament surface. No order was observed in the regulatory domain during isometric contraction, consistent with the substantial reorientation that occurs during force generation. For the first time, spin-label orientation has been interpreted in terms of the orientation of a labeled domain. A Monte Carlo conformational search technique was used to determine the orientation of the spin-label with respect to the protein. This in turn allows determination of the absolute orientation of the regulatory domain with respect to the actin axis. The comparison with the electron microscopy reconstructions verified the accuracy of the method; the electron paramagnetic resonance determined that axial orientation was within 10 degrees of the electron microscopy model.

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. Our results indicate that the number of structures decreases exponentially as the number of distances increases, and increases exponentially as the errors associated with the distances increases. We also found the number of solutions to be smaller when all the distances share one helix in common, compared to the case where the distances connect helices in a daisy-chain manner. We found that for 7 helices, at least 15 distances with errors up to 8 A are needed to produce a number of solutions that is not too large to be processed by local search refinement procedures. Finally, without energetic considerations, our enumeration technique retrieved the transmembrane domains of Bacteriorhodopsin (PDB entry1c3w), Halorhodopsin (1e12), Rhodopsin (1f88), Aquaporin-1 (1fqy), Glycerol uptake facilitator protein (1fx8), Sensory Rhodopsin (1jgj), and a subunit of Fumarate reductase flavoprotein (1qlaC) with Calpha level RMSDs of 3.0 A, 2.3 A, 3.2 A, 4.6 A, 6.0 A, 3.7 A, and 4.4 A, respectively.

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., Wakatsuki, S., Maeda, K. & Maeda, Y. (1998) Proc. Natl. Acad. Sci. USA 95, 4847-4852), whereas Trewhella and collaborators have proposed the same region to be a flexible beta-hairpin [Tung, C. S., Wall, M. E., Gallagher, S. C. & Trewhella, J. (2000) Protein Sci. 9, 1312-1326]. To distinguish between the two models, residues 129-145 of cardiac TnI were mutated sequentially to cysteines and labeled with the extrinsic spin probe, MTSSL. Sequence-dependent solvent accessibility was measured as a change in power saturation of the spin probe in the presence of the relaxation agent. In the ternary complex, the 129-137 region followed a pattern characteristic of a regular 3.6 residues/turn alpha-helix. The following region, residues 138-145, showed no regular pattern in solvent accessibility. Measurements of 4 intradomain distances within the inhibitory sequence, using dipolar EPR, were consistent with an alpha-helical structure. The difference in side-chain mobility between the ternary (C.I.T) and binary (C.I) complexes revealed a region of interaction of TnT located at the N-terminal end of the inhibitory sequence, residues 130-135. The above findings for the troponin complex in solution do not support either of the computational models of the binary complex; however, they are in very good agreement with a preliminary report of the x-ray structure of the cardiac ternary complex [Takeda, S. Yamashita, A., Maeda, K. & Maeda, Y. (2002) Biophys. J. 82, 832].

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. The method uses a Monte Carlo minimization algorithm to search the conformational space of labels within known atomic level structures of proteins. To validate the method a series of spin labels of varying size and geometry were docked to sites on the myosin head catalytic and regulatory domains. The predicted immobilization and conformation compared well with the experimentally determined mobility and orientation of the label. Thus, probes can now be targeted to report on various modes of molecular dynamics: immobilized probes to report on protein backbone and domain dynamics or floppy probes to report on the extent of steric restriction experienced by the side chain.