Annual Reports in Computational Chemistry

<p> <p>Contributors </p> <p> <p>Section 1: Bioinformatics</p>Preface <p>Potential Landscape and Flux Framework of Nonequilibrium Biological Networks <p>1. Introduction </p> <p>2. Biochemical Oscillation </p> <p>3. Stem-Cell Differentiation and Development: Arrows of Time </p> <p>Acknowledgments </p> <p> <p>Section 2: Simulation Methodologies</p>Predicting Structural and Functional Properties of Membrane Proteins from Protein Sequence <p>1. Topologies and 3D Structures of Integral Membrane Proteins </p> <p>2. Predicting TM Helices from Sequence </p> <p>3. Predicting Structural Features of Helical TM Proteins </p> <p>4. Predicting the Exposure Status of TM Residues </p> <p>5. Topology and Exposure Status Prediction of TMB </p> <p>6. Functional Classification of GPCRs and Membrane Transporters </p> <p>7. Outlook </p> <p>A Review of Coarse-Grained Molecular Dynamics Techniques to Access Extended Spatial and Temporal Scales in Biomolecular Simulations <p>1. Introduction </p> <p>2. Energy-Based Approach to Coarse-Graining </p> <p>3. Force-Matching Approach to Coarse-Graining </p> <p>4. Mixed Resolution Dynamics </p> <p>5. Prospective Utilization </p> <p>6. Outlook and Summary </p> <p>An Overview of String-Based Path Sampling Methods <p>1. Introduction </p> <p>2. Elastic Band Derived Methods </p> <p>3. Applications </p> <p>4. Conclusions and Outlook </p> <p>Constructing and Evaluating Predictive Models for Protein Biophysical Characteristics <p>1. Introduction </p> <p>2. Characterizing the Error Distribution </p> <p>3. Outliers </p> <p>4. Accurate Model Parameters </p> <p>5. Conclusion </p> <p>Supplementary Data </p> <p> <p>Section 3: Biological Modeling</p>Extracting Experimental Measurables from Molecular Dynamics Simulations of Membranes <p>1. Introduction </p> <p>2. Bilayer Structure </p> <p>3. Bilayer Dynamics </p> <p>4. Future Direction: Escaping the Timescale Limits of All-Atom MD </p> <p>Acknowledgment </p> <p>Advances in Scalable Computational Chemistry <p>1. Introduction </p> <p>2. Software Design </p> <p>3. Hartree–Fock and Density Functional Theory </p> <p>4. Gaussian Basis Set HF and DFT </p> <p>5. Plane-Wave Basis Set DFT </p> <p>6. CC Methods </p> <p>7. Perturbation Methods </p> <p>8. Electron Transfer Methods </p> <p>9. Relativistic Methods </p> <p>10. Classical MD Simulation </p> <p>11. Combined QM/MM </p> <p>12. Conclusions </p> <p>Acknowledgments </p> <p> <p>Section 4: Quantum Chemistry</p>The Super Instruction Architecture <p>1. Introduction </p> <p>2. Productivity for Electronic Structure Science and Engineering </p> <p>3. Productivity for Method Developers </p> <p>4. Outlook </p> <p>Acknowledgments </p> <p> <p>Section 5: Chemical Education</p>Electronically Excited States in Interstellar Chemistry <p>1. Introduction </p> <p>2. Theoretical Details of Coupled Cluster Excited States </p> <p>3. Excited States in the ISM: Radicals, Cations, and Anions, Oh My! </p> <p>4. Conclusions </p> <p>Acknowledgments </p> <p>Computational Chemistry of Vision in Vertebrates and Invertebrates <p>1. Introduction </p> <p>2. Retinal Proteins </p> <p>3. Theoretical Framework </p> <p>4. Spectral Tuning </p> <p>5. Conclusion </p> <p>Acknowledgments </p> <p>A Class Project Combining Organic Chemistry, Quantum Chemistry, and Statistics <p>1. Background </p> <p>2. Results and Discussion </p> <p>3. Conclusions </p> <p>Notes and Acknowledgment </p>