Approaches of Computational Biophysics and Chemistry in Molecular Biology

This book covers a broad range of computational biophysics and chemistry methods and their applications to study various phenomena in molecular biology. Highlighting recent advances, it emphasizes enhanced modeling accuracy, longer timescales, and the ability to simulate large biological macromolecules. From molecular dynamics simulations to quantum mechanical methods, the book discusses innovations like polarizable force fields and the integration of machine learning (ML) and artificial intelligence (AI) for improved predictive accuracy. It examines how these techniques predict the pKa of ionizable groups in biological macromolecules such as proteins, DNAs, and RNAs. It is demonstrated that the abovementioned computational techniques can be used to infer the pathogenicity of DNA variants and to reveal the molecular mechanism of diseases. By providing extensive coverage of various methods and diverse applications, this book is a valuable resource that links computational approaches to understanding molecular effects in human diseases, ultimately advancing the field of personalized medicine. Contents: Polarizable Force Fields for Biomolecular Modeling: High-Order Ab Initio Valence Force Field with Chemical Pattern-Based Parameter Assignment (X Yang, C Liu and P Ren) Testing and Optimizing the Drude Polarizable Force Field for Blocked Amino Acids Based on High-Level Quantum-Mechanical Energy Surfaces (J Chen and G König) Accurate Modeling of RNA Hairpins Through the Explicit Treatment of Electronic Polarizability with the Classical Drude Oscillator Force Field (M Y Sengul and A D MacKerell Jr) Novel Methods in Computational Biophysics and Chemistry and their Applications to Biological Problems: Ab-initio Binding of Barnase–Barstar with DelPhiForce Steered Molecular Dynamics (DFMD) Approach (M Koirala and E Alexov) The Accuracy of Force Fields on the Simulation of Intrinsically Disordered Proteins: A Benchmark Test on the Human p53 Tumor Suppressor (S Ning, J Liu, N Liu and D Yan) Changes in Structure and Flexibility of p53 TAD2 Upon Binding to p300 Taz2 (T Li, A O Stevens, L I Gil Pineda, S Song, C A Ameyaw Baah and Y He) Computational Biophysics and Chemistry Methods to Predict pKa of Ionizable Groups in Proteins, RNAs, DNAs, and Small Molecules: Computing Protein pKas Using the TABI Poisson–Boltzmann Solver (J Chen, J Hu, Y Xu, R Krasny and W Geng) Characterizing the Water Wire in the Gramicidin Channel Found by Monte Carlo Sampling Using Continuum Electrostatics and in Molecular Dynamics Trajectories with Conventional or Polarizable Force Fields (Y Zhang, K Haider, D Kaur, V A Ngo, X Cai, J Mao, U Khaniya, X Zhu, S Noskov, T Lazaridis and M R Gunner) pH-Dependent Interactions of Apolipophorin-III with a Lipid Disk (Y Peng, R Kelle, C Little, E Michonova, K G Kornev and E Alexov) Artificial Intelligence in Biophysics and Chemistry: MLBKFD: Probabilistic Model Methods to infer Pseudo Trajectories from Single-cell Data (C Han, W Cao, C Li, Y Guo, Y Wang, Y-Z Shi and B-G Zhang) Inferring Transcriptional Bursting Kinetics Using Gene Expression Model with Memory and Crosstalk from scRNA-seq Data (M Wang, W Cao, Y Guo, G Wang, J Jiang, H Qiu and B-G Zhang) Investigating Enzyme Biochemistry by Deep Learning: A Computational Tool for a New Era (M Rayka, M Mirzaei, G Farnoosh and A M Latifi) Computational Biophysics and Chemistry and Diseases: Computational Analysis of Hereditary Spastic Paraplegia Mutations in the Kinesin Motor Domains of KIF1A and KIF5A (V Mahase, A Sobitan, C Johnson, F Cooper II, Y Xie, L Li and S Teng) In-Silico Analysis to Identify the Role of MEN1 Missense Mutations in Breast Cancer (S R Ganakammal, M Koirala, B Wu and E Alexov) Computational and Structural Studies of MeCP2 and Associated Mutants (T G Kucukkal and R U Amin) Readership: Advanced undergraduate and graduate students, researchers and practitioners in the fields of computational biophysics and chemistry, personalized medicine and drug design.