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.

Covering experiment and theory, bioinformatics approaches, and state-of-the-art simulation protocols for better sampling of the conformational space, this volume describes a broad range of techniques to study, predict, and analyze the protein folding process. Protein Folding Protocols also provides sample approaches toward the prediction of protein structure starting from the amino acid sequence, in the absence of overall homologous sequences.

Scientific understanding as well as the way of studying science has been greatly changed since the advent of computer modeling. Computer simulation has played a central role in bridging theoretical and experimental studies. In this work, computer simulations were applied to explore biological systems on both protein folding and protein structure prediction studies. In the first study, the folding mechanisms of two alanine based helical peptides (Fs-21 peptide and MABA bonded Fs-21 peptide) were investigated by all atom molecular dynamics simulations and compared with experimental results. Multi-phase folding processes were observed for both peptides. Temperature change affected the relative stability of different ensembles. Helix-turn-helix conformation was found to be the most populated state at 300K while the full helix became more stable at low temperature (273K). The turn structure was found to be stabilized mainly by hydrophobic interactions. In the second study, helix-coil transition theory was elaborately tested by both statistical and energetic methods based on simulations of alanine based peptides. A weighted Ising model was proposed, and the model-derived propagation constant agreed very well with the experimental results. Solvation effect and electrostatic interactions were found to be the two main contributors to helix-coil transition. The results challenged the classic helix-coil transition theory by proving that the single sequence assumption was not appropriate for helix-coil transition. Conformational sampling has been a long-standing issue in computational sciences. In the third study, we systematically tested the convergence of the Replica Exchange Molecular Dynamics method (REMD), which is a recently developed method for conformational sampling enhancement. The results suggested that REMD can significantly enhance the sampling efficiency and accurately reproduce the long-time MD results with high efficiency. However, fluctuations at low temperatures (300 K) indicated that REMD simulations did not converge within our simulation time (14 ns). Much longer REMD simulation time might be needed for the system to reach thermodynamic equilibrium than expected. Finding the optimal side chain packing is a common issue in structure prediction, protein design and protein docking. In the fourth study, a new method was presented. The method overcame the rough energy landscape problem and enabled all-atom MD simulation to be applied directly to protein structure refinement. The method showed very successful results on buried side-chain assignments, nearly 100% accuracy on all 6 randomly picked proteins was reached; the results also clearly demonstrated that the proposed method can significantly enhance conformational sampling. These encouraging results suggested prospective applications on many other protein related systems.

Although molecular modeling has been around for a while, the groundbreaking advancement of massively parallel supercomputers and novel algorithms for parallelization is shaping this field into an exciting new area. Developments in molecular modeling from experimental and computational techniques have enabled a wide range of biological applications. Responding to this renaissance, Molecular Modeling at the Atomic Scale: Methods and Applications in Quantitative Biology includes discussions of advanced techniques of molecular modeling and the latest research advancements in biomolecular applications from leading experts. The book begins with a brief introduction of major methods and applications, then covers the development of cutting-edge methods/algorithms, new polarizable force fields, and massively parallel computing techniques, followed by descriptions of how these novel techniques can be applied in various research areas in molecular biology. It also examines the self-assembly of biomacromolecules, including protein folding, RNA folding, amyloid peptide aggregation, and membrane lipid bilayer formation. Additional topics highlight biomolecular interactions, including protein interactions with DNA/RNA, membrane, ligands, and nanoparticles. Discussion of emerging topics in biomolecular modeling such as DNA sequencing with solid-state nanopores and biological water under nanoconfinement round out the coverage. This timely summary contains the perspectives of leading experts on this transformation in molecular biology and includes state-of-the-art examples of how molecular modeling approaches are being applied to critical questions in modern quantitative biology. It pulls together the latest research and applications of molecular modeling and real-world expertise that can boost your research and development of applications in this rapidly changing field.

Computer simulations are increasingly being used to predict thermodynamic observables for folding small proteins. Key to continued progress in this area is the development of algorithms that accelerate conformational sampling. Temperature-based replica exchange (ReX) is a commonly used protocol whereby simulations at several temperatures are simultaneously performed and temperatures are exchanged between simulations via a Metropolis criterion. Another method, self-guided Langevin dynamics (SGLD), expedites conformational sampling by accelerating low-frequency large-scale motions through the addition of an ad hoc momentum memory term. In this work, we combined these two complementary techniques and compared the results against conventional ReX formulations of molecular dynamics (MD) and Langevin dynamics (LD) simulations for the prediction of thermodynamic folding observables of the Trp-cage mini-protein. All simulations were performed with CHARMM using the PARAM22+CMAP force field and the generalized Born molecular volume implicit solvent model. While SGLD-ReX does not fold up the protein significantly faster than the two conventional ReX approaches, there is some evidence that the method improves sampling convergence by reducing topological folding barriers between energetically similar nearnative states. Unlike MD-ReX and LD-ReX, SGLD-ReX predicts melting temperatures, heat capacity curves, and folding free energies that are closer in agreement to the experimental observations. However, this favorable result may be due to distortions of the relative free energies of the folded and unfolded conformational basins caused by the ad hoc force term in the SGLD model.