Comprehensive Analysis and Application Research of Advanced Computational Algorithms in Protein Folding Simulations

Protein folding: Web alert

Artificial intelligencemethods for protein folding and design.

This review focuses on recent advances in understanding protein folding kinetics in the context of nucleation theory. We present basic concepts such as nucleation, folding nucleus, and transition state ensemble and then discuss recent advances and challenges in theoretical understanding of several key aspects of protein folding kinetics. We cover recent topology-based approaches as well as evolutionary studies and molecular dynamics approaches to determine protein folding nucleus and analyze other aspects of folding kinetics. Finally, we briefly discuss successful all-atom Monte-Carlo simulations of protein folding and conclude with a brief outlook for the future.

During the last three decades, the research area of systems engineering has emerged as a domain of fundamental importance and major impact within chemical engineering, as well as a cornerstone area in interdisciplinary research initiatives with computer science, operations research, applied mathematics, materials and life sciences. This is attributed to the unique characteristics of systems engineering which are the combination of analysis and synthesis for the design, optimization, and operation of processes and products. The product and process discovery research efforts are founded on fundamental advances in mathematical modeling, optimization theory and algorithms, and insights derived either from existing operations and/or from biology, chemistry, and physics. The fundamental advances are epitomized through new theoretical, algorithmic, and modeling frameworks for (a) mixed-integer linear and nonlinear optimization, (b) deterministic global optimization, and (c) dynamic simulation and optimization. The proposed modeling and optimization approaches have multiscale applications ranging from macroscopic to mesoscopic to microscopic systems, and which provide a natural and fundamental link between systems engineering, computational chemistry, computational biology and systems biology. Approaches based on mixed-integer linear and nonlinear optimization which were typically identified with process synthesis, scheduling and planning applications, have entered the domains of gene regulatory networks, metabolic networks, signal transduction networks, beta-sheet topology prediction in proteins, de novo peptide and protein design, DNA recombination, phase problem in X-ray crystallography, side chain optimization in protein prediction, peptide identification via tandem mass spectroscopy. Approaches based on deterministic global optimization associated with process design, synthesis, scheduling, and pooling/blending applications, are now in the main stream of product design, structure prediction in protein folding, dynamics of protein folding, NMR protein structure refinement, and de novo protein design. Approaches based on dynamic models and large scale optimization which were identified with process models and their applications, are suited for metabolic and signal transduction networks. As a result, a synergism between systems engineering, computational biology, and systems biology has evolved gradually, opened new research avenues and has reached the stage where fascinating research contributions address important questions in computational biology with methods and tools from systems engineering which combine mathematical rigor with key biological insights. This article provides a perspective on the challenges and opportunities that emerge from the fundamental developments in the research fields of systems engineering and computational biology. The advances in the areas of deterministic global optimization and process scheduling are introduced first, followed by their respective research opportunities. The revolution of genomics is discussed next with a particular focus on the advances and challenges in the areas of structure prediction in protein folding, de novo peptide and protein design, and peptide and protein identification via tandem mass spectroscopy. This article is based on material presented as an invited talk at the session on “The Future of Chemical Engineering Research III” during the 2004 annual AIChE meeting.

Measuring “Unmeasurable” Folding Kinetics of Proteins by Single-Molecule Force Spectroscopy

The folding pathways of some proteins include the population of partially structured species en route to the native state. Identification and characterization of these folding intermediates are particularly difficult as they are often only transiently populated and play different mechanistic roles, being either on-pathway productive species or off-pathway kinetic traps. To define the role of folding intermediates, a quantitative analysis of the folding and unfolding rate constants over a wide range of denaturant concentration is often required. Such a task is further complicated by the reversible nature of the folding reaction, which implies the observed kinetics to be governed by a complex combination of different microscopic rate constants. Here, we tackled this problem by measuring directly the folding rate constant under highly denaturing conditions, namely by inducing the folding of a PDZ domain through a quasi-irreversible binding reaction with a specific peptide. In analogy with previous works based on hydrogen exchange experiments, we present evidence that the folding pathway of the PDZ domain involves the formation of an obligatory on-pathway intermediate. The results presented exemplify a novel type of kinetic test to detect an on-pathway folding intermediate.

Protein folding is a fascinating, not fully understood phenomenon in biology. Molecular dynamics (MD) simulations are an invaluable tool to study conformational changes in atomistic detail, including folding and unfolding processes of proteins. However, the accuracy of the conformational ensembles derived from MD simulations inevitably relies on the quality of the underlying force field in combination with the respective water model. Here, we investigate protein folding, unfolding, and misfolding of fast-folding proteins by examining different force fields with their recommended water models, i.e., ff14SB with the TIP3P model and ff19SB with the OPC model. To this end, we generated long conventional MD simulations highlighting the perks and pitfalls of these setups. Using Markov state models, we defined kinetically independent conformational substates and emphasized their distinct characteristics, as well as their corresponding state probabilities. Surprisingly, we found substantial differences in thermodynamics and kinetics of protein folding, depending on the combination of the protein force field and water model, originating primarily from the different water models. These results emphasize the importance of carefully choosing the force field and the respective water model as they determine the accuracy of the observed dynamics of folding events. Thus, the findings support the hypothesis that the water model is at least equally important as the force field and hence needs to be considered in future studies investigating protein dynamics and folding in all areas of biophysics.

Protein Folding Landscapes for Alpha- and Beta-Miniproteins Using All-Atom Simulations with an Optimized Force-Field

One of the central challenges in structural molecular biology today is the protein folding problem, i.e. the acquisition of the 3D structure of a protein from its linear sequence of amino-acids. Different computational approaches to study protein folding and protein unfolding have recently become common tools available to the researcher. However, due to the lack of appropriate infrastructures, it is very difficult to directly compare simulations performed by different groups, with different methods, in different experimental conditions or for different proteins. Thus, we set out to create a public data repository with the goal of addressing the problem of comparison, analysis and sharing of information and data on protein folding and protein unfolding simulations. The P-found system for protein folding and protein unfolding simulations is presented. At the moment, the data repository allows uploading of molecular dynamics (MD) protein folding and unfolding simulations, calculates and stores several time series with the variation over time of pre-defined molecular properties, and allows searching and downloading of these data. In the near future, simulations performed by other than MD methods may be uploaded, and data mining techniques for analysis and comparison of multiple simulations will be implemented. The home page for the P-found system is accessible at http://www.p-found.org

Introduction/purpose: Simulated annealing is a powerful technique widely used in optimization problems. One critical aspect of using simulated annealing effectively is a proper and optimal adjustment of its parameters. This paper presents a novel approach to efficiently adjust the parameters of simulated annealing to enhance its performance and convergence speed. Methods: Since the simulated algorithm is inspired by the cooling Metropolis process, the basic idea is to simulate and analyze this process using a mathematical model. The proposed work tends to properly imitate the Metropolis cooling process in the algorithmic field. By intelligently adjusting the temperature schedule, temperature reduction and cooling rate, the algorithm optimizes the balance between exploration and exploitation, leading to improved convergence and higher-quality solutions. Results: To evaluate the effectiveness of this approach, it was applied first on a chosen sample function to be minimized, and then on some usual known optimization functions. The results demonstrate that our approach, called Optimal Adjusting of Simulated Annealing parameters (OASA), achieves superior performance compared to traditional static parameter settings and other existing approaches, showing how to well adjust the parameters of the simulated annealing algorithm to improve its efficiency in terms of solution quality and processing time. Conclusion: Adjusting the algorithm parameters could have a significant contribution in the optimization field even for other metaheuristics.

Single-molecule fluorescence resonance energy transfer (smFRET) experiments are extremely useful in studying protein folding but are generally limited to time scales of greater than approximately 100 micros and distances greater than approximately 2 nm. We used single-molecule fluorescence quenching by photoinduced electron transfer, detecting short-range events, in combination with fluorescence correlation spectroscopy (PET-FCS) to investigate folding dynamics of the small binding domain BBL with nanosecond time resolution. The kinetics of folding appeared as a 10-micros decay in the autocorrelation function, resulting from stochastic fluctuations between denatured and native conformations of individual molecules. The observed rate constants were probe independent and in excellent agreement with values derived from conventional temperature-jump (T-jump) measurements. A submicrosecond relaxation was detected in PET-FCS data that reported on the kinetics of intrachain contact formation within the thermally denatured state. We engineered a mutant of BBL that was denatured under the reaction conditions that favored folding of the parent wild type ("D(phys)"). D(phys) had the same kinetic signature as the thermally denatured state and revealed segmental diffusion with a time constant of intrachain contact formation of 500 ns. This time constant was more than 10 times faster than folding and in the range estimated to be the "speed limit" of folding. D(phys) exhibited significant deviations from a random coil. The solvent viscosity and temperature dependence of intrachain diffusion showed that chain motions were slaved by the presence of intramolecular interactions. PET-FCS in combination with protein engineering is a powerful approach to study the early events and mechanism of ultrafast protein folding.

Purpose: This study will use Monte Carlo to simulate the Cyberknife system, and intend to develop the third-party tool to evaluate the dose verification of specific patient plans in TPS. Methods: By simulating the treatment head using the BEAMnrc and DOSXYZnrc software, the comparison between the calculated and measured data will be done to determine the beam parameters. The dose distribution calculated in the Raytracing, Monte Carlo algorithms of TPS (Multiplan Ver4.0.2) and in-house Monte Carlo simulation method for 30 patient plans, which included 10 head, lung and liver cases in each, were analyzed. The γ analysis with the combined 3mm/3% criteria would be introduced to quantitatively evaluate the difference of the accuracy between three algorithms. Results: More than 90% of the global error points were less than 2% for the comparison of the PDD and OAR curves after determining the mean energy and FWHM.The relative ideal Monte Carlo beam model had been established. Based on the quantitative evaluation of dose accuracy for three algorithms, the results of γ analysis shows that the passing rates (84.88±9.67% for head,98.83±1.05% for liver,98.26±1.87% for lung) of PTV in 30 plans between Monte Carlo simulation and TPS Monte Carlo algorithms were good. And the passing rates (95.93±3.12%,99.84±0.33% in each) of PTV in head and liver plans between Monte Carlo simulation and TPS Ray-tracing algorithms were also good. But the difference of DVHs in lung plans between Monte Carlo simulation and Ray-tracing algorithms was obvious, and the passing rate (51.263±38.964%) of γ criteria was not good. It is feasible that Monte Carlo simulation was used for verifying the dose distribution of patient plans. Conclusion: Monte Carlo simulation algorithm developed in the CyberKnife system of this study can be used as a reference tool for the third-party tool, which plays an important role in dose verification of patient plans. This work was supported in part by the grant from Chinese Natural Science Foundation (Grant No. 11275105). Thanks for the support from Accuray Corp.

Protein folding is an inherently complex process involving coordination of the intricate networks of weak interactions that stabilize native three-dimensional structures. In the conventional paradigm, simple protein structures are assumed to fold in an all-or-none process that is inaccessible to experiment. Existing experimental methods therefore probe folding mechanisms indirectly. A widely used approach interprets changes in protein stability and/or folding kinetics, induced by engineered mutations, in terms of the structure of the native protein. In addition to limitations in connecting energetics with structure, mutational methods have significant experimental uncertainties and are unable to map complex networks of interactions. In contrast, analytical theory predicts small barriers to folding and the possibility of downhill folding. These theoretical predictions have been confirmed experimentally in recent years, including the observation of global downhill folding. However, a key remaining question is whether downhill folding can indeed lead to the high-resolution analysis of protein folding processes. Here we show, with the use of nuclear magnetic resonance (NMR), that the downhill protein BBL from Escherichia coli unfolds atom by atom starting from a defined three-dimensional structure. Thermal unfolding data on 158 backbone and side-chain protons out of a total of 204 provide a detailed view of the structural events during folding. This view confirms the statistical nature of folding, and exposes the interplay between hydrogen bonding, hydrophobic forces, backbone conformation and side-chain entropy. From the data we also obtain a map of the interaction network in this protein, which reveals the source of folding cooperativity. Our approach can be extended to other proteins with marginal barriers (less than 3RT), providing a new tool for the study of protein folding.

With progress toward inexpensive, large-scale DNA assembly, the demand for simulation tools that allow the rapid construction of synthetic biological devices with predictable behaviors continues to increase. By combining engineered transcript components, such as ribosome binding sites, transcriptional terminators, ligand-binding aptamers, catalytic ribozymes, and aptamer-controlled ribozymes (aptazymes), gene expression in bacteria can be fine-tuned, with many corollaries and applications in yeast and mammalian cells. The successful design of genetic constructs that implement these kinds of RNA-based control mechanisms requires modeling and analyzing kinetically determined co-transcriptional folding pathways. Transcript design methods using stochastic kinetic folding simulations to search spacer sequence libraries for motifs enabling the assembly of RNA component parts into static ribozyme- and dynamic aptazyme-regulated expression devices with quantitatively predictable functions (rREDs and aREDs, respectively) have been described (Carothers et al., Science 334:1716-1719, 2011). Here, we provide a detailed practical procedure for computational transcript design by illustrating a high throughput, multiprocessor approach for evaluating spacer sequences and generating functional rREDs. This chapter is written as a tutorial, complete with pseudo-code and step-by-step instructions for setting up a computational cluster with an Amazon, Inc. web server and performing the large numbers of kinefold-based stochastic kinetic co-transcriptional folding simulations needed to design functional rREDs and aREDs. The method described here should be broadly applicable for designing and analyzing a variety of synthetic RNA parts, devices and transcripts.

Simulations of simplified protein folding models have provided much insight into solving the protein folding problem. We propose here a new off-lattice bead model, capable of simulating several different fold classes of small proteins. We present the sequence for an alpha/beta protein resembling the IgG-binding proteins L and G. The thermodynamics of the folding process for this model are characterized using the multiple multihistogram method combined with constant-temperature Langevin simulations. The folding is shown to be highly cooperative, with chain collapse nearly accompanying folding. Two parallel folding pathways are shown to exist on the folding free energy landscape. One pathway contains an intermediate--similar to experiments on protein G, and one pathway contains no intermediates-similar to experiments on protein L. The folding kinetics are characterized by tabulating mean-first passage times, and we show that the onset of glasslike kinetics occurs at much lower temperatures than the folding temperature. This model is expected to be useful in many future contexts: investigating questions of the role of local versus nonlocal interactions in various fold classes, addressing the effect of sequence mutations affecting secondary structure propensities, and providing a computationally feasible model for studying the role of solvation forces in protein folding.