Protein folding is studied within the context of two coarse-grained lattice models that separate all amino acids into only a few types. The hydrophobic-polar (HP) model is a simplified lattice protein model for simulating protein folding and for understanding many biological problems of interest. In this work, an "improved" model, the semi-flexible H0P model, was proposed by introducing a new type of "neutral" monomer, "0", i.e., neither hydrophobic nor polar and also taking into consideration the stiffness of bonds connecting monomers. Even though both models are highly simplified protein models, finding the lowest energy conformations and determining the density of states are extremely difficult. We applied replica-exchange Wang-Landau sampling with appropriate trial moves for determining the density of states of multiple HP and H0P proteins, from which the thermodynamic properties such as specific heat can be calculated. Moreover, we developed a heuristic method for determining the ground state degeneracy of lattice proteins, based on multicanonical sampling. It is applied during comprehensive studies of single-site mutations in specific lattice proteins with different sequences. The effects in which we are interested include structural changes in ground states, changes of ground state energy, degeneracy, and thermodynamic properties of the system. With respect to mutations, both extremely sensitive and insensitive positions in the protein sequence have been found. That is, ground state energies and degeneracies, as well as other thermodynamic and structural quantities may be either largely unaffected or may change significantly due to mutation. Moreover, comparison between the HP model and the semi-flexible H0P model have been performed based on two real proteins: Crambin and Ribonuclease A. We found that, compared with the HP model, the semi-flexible H0P model possesses significantly reduced ground state degeneracy, and rich folding signals as the proteins rearranging into native states from very compact structures at low temperatures. We calculated the free energy vs end-to-end distance as a function of temperature. The HP model shows a relatively shallow folding funnel and flat free energy minimum, reflecting the high degeneracy of the ground state. In contrast, the semi-flexible H0P model has a well developed, rough free energy funnel with a low degeneracy ground state. In both cases, folding funnels are asymmetric with temperature dependent shape.

Designing and developing new drugs is an expensive and time-consuming process, and there is a need to discover new tools or approaches that can optimize this process. Applied Computer-Aided Drug Design: Models and Methods compiles information about the main advances in computational tools for discovering new drugs in a simple and accessible language for academic students to early career researchers. The book aims to help readers understand how to discover molecules with therapeutic potential by bringing essential information about the subject into one volume. Key Features · Presents the concepts and evolution of classical techniques, up to the use of modern methods based on computational chemistry in accessible format. · Gives a primer on structure- and ligand-based drug design and their predictive capacity to discover new drugs. · Explains theoretical fundamentals and applications of computer-aided drug design. · Focuses on a range of applications of the computations tools, such as molecular docking; molecular dynamics simulations; homology modeling, pharmacophore modeling, quantitative structure-activity relationships (QSAR), density functional theory (DFT), fragment-based drug design (FBDD), and free energy perturbation (FEP). · Includes scientific reference for advanced readers Readership Students, teachers and early career researchers.

Since the second half of the 20th century machine computations have played a critical role in science and engineering. Computer-based techniques have become especially important in molecular biology, since they often represent the only viable way to gain insights into the behavior of a biological system as a whole. The complexity of biological systems, which usually needs to be analyzed on different time- and size-scales and with different levels of accuracy, requires the application of different approaches, ranging from comparative analysis of sequences and structural databases, to the analysis of networks of interdependence between cell components and processes, through coarse-grained modeling to atomically detailed simulations, and finally to molecular quantum mechanics. This book provides a comprehensive overview of modern computer-based techniques for computing the structure, properties and dynamics of biomolecules and biomolecular processes. The twenty-two chapters, written by scientists from all over the world, address the theory and practice of computer simulation techniques in the study of biological phenomena. The chapters are grouped into four thematic sections dealing with the following topics: the methodology of molecular simulations; applications of molecular simulations; bioinformatics methods and use of experimental information in molecular simulations; and selected applications of molecular quantum mechanics. The book includes an introductory chapter written by Harold A. Scheraga, one of the true pioneers in simulation studies of biomacromolecules.

A guide to applying the power of modern simulation tools to better drug design Biomolecular Simulations in Structure-based Drug Discovery offers an up-to-date and comprehensive review of modern simulation tools and their applications in real-life drug discovery, for better and quicker results in structure-based drug design. The authors describe common tools used in the biomolecular simulation of drugs and their targets and offer an analysis of the accuracy of the predictions. They also show how to integrate modeling with other experimental data. Filled with numerous case studies from different therapeutic fields, the book helps professionals to quickly adopt these new methods for their current projects. Experts from the pharmaceutical industry and academic institutions present real-life examples for important target classes such as GPCRs, ion channels and amyloids as well as for common challenges in structure-based drug discovery. Biomolecular Simulations in Structure-based Drug Discovery is an important resource that: -Contains a review of the current generation of biomolecular simulation tools that have the robustness and speed that allows them to be used as routine tools by non-specialists -Includes information on the novel methods and strategies for the modeling of drug-target interactions within the framework of real-life drug discovery and development -Offers numerous illustrative case studies from a wide-range of therapeutic fields -Presents an application-oriented reference that is ideal for those working in the various fields Written for medicinal chemists, professionals in the pharmaceutical industry, and pharmaceutical chemists, Biomolecular Simulations in Structure-based Drug Discovery is a comprehensive resource to modern simulation tools that complement and have the potential to complement or replace laboratory assays for better results in drug design.

Protein folding is a fundamental problem in modern structural biology. The nature of the problem poses challenges to the understanding of the process via computer simulations. One of the challenges in the computer simulation of proteins at the atomic level is the efficiency of sampling conformational space. Replica exchange (RE) methods are widely employed to alleviate the difficulty. To study how to best employ RE to protein folding and binding problems, we constructed a kinetic network model for RE studies of protein folding and used this simplified model to carry out "simulations of simulations" to analyze how the underlying temperature dependence of the conformational kinetics and the basic parameters of RE all interact to affect the number of folding transitions observed. When protein folding follows anti-Arrhenius kinetics, we observe a speed limit for the number of folding transitions observed at the low temperature of interest, which depends on the maximum of the harmonic mean of the folding and unfolding transition rates at high temperature. The efficiency of temperature RE was also studied on a more complicated and realistic continuous two-dimensional potential. Comparison of the efficiencies obtained using the continuous and discrete models makes it possible to identify non-Markovian effects which slow down equilibration of the RE ensemble on the more complex continuous potential. In particular, the efficiency of RE is limited by the timescale of conformational relaxation within free energy basins. The other challenges we are facing in all-atom simulations is to obtain meaningful information on the slow kinetics and pathways of folding. We present a kinetic network model which recover the kinetics using RE-generated states as the nodes of a kinetic network. Choosing the appropriate neighbors and the microscopic rates between the neighbors, the correct kinetics of the system can be recovered by running a simulation on the network.