Rotamer-specific Statistical Potentials for Protein Structure Modeling.

Knowledge-based (or statistical) potentials are widely used as essential tools in protein structure modeling and quality assessment. They are derived from experimentally determined protein structures aiming to extract relevant structural features that characterize the tightly folded structures. Since the surrounding circumstances are inhomogeneous and anisotropic, multibody contributions are important for accurate account of cooperative effects of molecular interactions. On the other hand, protein residues have great flexibility. It is energetically favorable for residues to adopt only a limited number of staggered conformations, known as rotamers. Depending on the rotameric state, the residue conformation and intra-residue interaction vary significantly within protein structures, resulting in different solvent accessibility and different electric polarization effect as well as different steric effect on residue elements. The major goal of this thesis is the design and development of statistical potentials that take into account the rotamer-dependence of interactions. We hypothesized that the rotameric state of residues is related to the specificity of interactions within protein structures. We first investigated how amino acid residues in PDB structures show different interaction patterns with the environment depending on their rotameric states. Observed rotamer-specific environmental features were incorporated to a scoring function, ProtGrid for protein designs. Our tests demonstrated that the ProtGrid is superior to widely used Rosetta energy function in prediction of the native amino acid types and rotameric states. Next, we formulated a rotamer-specific atomic statistical potential, named ROTAS that extends an existing orientation-dependent atomic potential (GOAP) by including the influence of rotameric states of residues on the specificity of interactions. The results showed that ROTAS performs better than other competing potentials not only in native structure recognition, but also in best model selection and correlation coefficients between energy and model quality. Finally, we applied the ROTAS potential to the problem of side-chain prediction. Compared with the existing side-chain modeling programs, ROTAS achieved comparable or even better prediction accuracy. We expect that the effectiveness of our energy functions would provide insightful information for the development of many applications which require accurate side-chain modeling such as homology modeling, protein design, mutation analysis, protein-protein docking and flexible ligand docking.PHDMechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/102342/1/jungkap_1.pd

Image-based Automated Chemical Database Annotation with Ensemble of Machine-Vision Classifiers

This paper presents an image-based annotation strategy for automated annotation of chemical databases. The proposed strategy is based on the use of a machine vision-based classifier for extracting a 2D chemical structure diagram in research articles and converting them into standard chemical file formats, a virtual Chemical Expert" system for screening the converted structures based on the level of estimated conversion accuracy, and a fragment-based measure for calculation intermolecular similarity. In particular, in order to overcome limited accuracies of individual machine-vision classifier, inspired by ensemble methods in machine learning, it is attempted to use of the ensemble of machine-vision classifiers. For annotation, calculated chemical similarity between the converted structures and entries in a virtual small molecule database is used to establish the links. Annotation test to link 121 journal articles to entries in PubChem database demonstrates that ensemble approach increases the coverage of annotation, while keeping the annotation quality (e.g., recall and precision rates) comparable to using a single machine-vision classifier.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/87266/4/Saitou55.pd

Automated extraction of chemical structure information from digital raster images

Background: To search for chemical structures in research articles, diagrams or text representing molecules need to be translated to a standard chemical file format compatible with cheminformatic search engines. Nevertheless, chemical information contained in research articles is often referenced as analog diagrams of chemical structures embedded in digital raster images. To automate analog-to-digital conversion of chemical structure diagrams in scientific research articles, several software systems have been developed. But their algorithmic performance and utility in cheminformatic research have not been investigated. Results: This paper aims to provide critical reviews for these systems and also report our recent development of ChemReader -- a fully automated tool for extracting chemical structure diagrams in research articles and converting them into standard, searchable chemical file formats. Basic algorithms for recognizing lines and letters representing bonds and atoms in chemical structure diagrams can be independently run in sequence from a graphical user interface-and the algorithm parameters can be readily changed-to facilitate additional development specifically tailored to a chemical database annotation scheme. Compared with existing software programs such as OSRA, Kekule, and CLiDE, our results indicate that ChemReader outperforms other software systems on several sets of sample images from diverse sources in terms of the rate of correct outputs and the accuracy on extracting molecular substructure patterns. Conclusion: The availability of ChemReader as a cheminformatic tool for extracting chemical structure information from digital raster images allows research and development groups to enrich their chemical structure databases by annotating the entries with published research articles. Based on its stable performance and high accuracy, ChemReader may be sufficiently accurate for annotating the chemical database with links to scientific research articles.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/90875/1/Saitou8.pd

ROTAS: a rotamer-dependent, atomic statistical potential for assessment and prediction of protein structures

Abstract Background Multibody potentials accounting for cooperative effects of molecular interactions have shown better accuracy than typical pairwise potentials. The main challenge in the development of such potentials is to find relevant structural features that characterize the tightly folded proteins. Also, the side-chains of residues adopt several specific, staggered conformations, known as rotamers within protein structures. Different molecular conformations result in different dipole moments and induce charge reorientations. However, until now modeling of the rotameric state of residues had not been incorporated into the development of multibody potentials for modeling non-bonded interactions in protein structures. Results In this study, we develop a new multibody statistical potential which can account for the influence of rotameric states on the specificity of atomic interactions. In this potential, named “rotamer-dependent atomic statistical potential” (ROTAS), the interaction between two atoms is specified by not only the distance and relative orientation but also by two state parameters concerning the rotameric state of the residues to which the interacting atoms belong. It was clearly found that the rotameric state is correlated to the specificity of atomic interactions. Such rotamer-dependencies are not limited to specific type or certain range of interactions. The performance of ROTAS was tested using 13 sets of decoys and was compared to those of existing atomic-level statistical potentials which incorporate orientation-dependent energy terms. The results show that ROTAS performs better than other competing potentials not only in native structure recognition, but also in best model selection and correlation coefficients between energy and model quality. Conclusions A new multibody statistical potential, ROTAS accounting for the influence of rotameric states on the specificity of atomic interactions was developed and tested on decoy sets. The results show that ROTAS has improved ability to recognize native structure from decoy models compared to other potentials. The effectiveness of ROTAS may provide insightful information for the development of many applications which require accurate side-chain modeling such as protein design, mutation analysis, and docking simulation.http://deepblue.lib.umich.edu/bitstream/2027.42/109687/1/12859_2014_Article_6637.pd

Design of Automotive Torsion Beam Suspension Using Lumped-Compliance Linkage Models

This paper presents a new method for efficiently and accurately modeling the elasto-kinematic behaviors of torsion beam suspension systems and of other similar classes of mechanical systems, and a design method utilizing the models. The torsion beam is represented as a linkage of lumped mass joined by nonlinear springs, bending and torsion, whose stiffness are identified via off-line computational experiments using nonlinear finite element simulations. A number of such computer experiments are conducted off-line for representative dimensions of torsion beams, and the results are stored in surrogate response models. During design iterations, these surrogate response models are utilized to automatically construct a lumped-compliance linkage model of a torsion beam and integrate it into a multi-body suspension system model that can be simulated using commercial software. Comparison with a nonlinear finite element analysis demonstrates much improved accuracy of the proposed model over commercial flexible multi-body simulation software, with comparable computational speed. Finally, an example is presented on the multi-objective optimization of the cross section of the torsion beam using the developed surrogate response models.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/87213/4/Saitou65.pd

PNNL-Comp-Mass-Spec/Informed-Proteomics: Version 1.0.6619

Release of internally used versions for proper referencing/validation of data used in publications. The zip file includes the PromexAlign application, which is not included in the installer