Dynameomics: a multi-dimensional analysis-optimized database for dynamic protein data

The Dynameomics project is our effort to characterize the native-state dynamics and folding/unfolding pathways of representatives of all known protein folds by way of molecular dynamics simulations, as described by Beck et al. (in Protein Eng. Des. Select., the first paper in this series). The data produced by these simulations are highly multidimensional in structure and multi-terabytes in size. Both of these features present significant challenges for storage, retrieval and analysis. For optimal data modeling and flexibility, we needed a platform that supported both multidimensional indices and hierarchical relationships between related types of data and that could be integrated within our data warehouse, as described in the accompanying paper directly preceding this one. For these reasons, we have chosen On-line Analytical Processing (OLAP), a multi-dimensional analysis optimized database, as an analytical platform for these data. OLAP is a mature technology in the financial sector, but it has not been used extensively for scientific analysis. Our project is further more unusual for its focus on the multidimensional and analytical capabilities of OLAP rather than its aggregation capacities. The dimensional data model and hierarchies are very flexible. The query language is concise for complex analysis and rapid data retrieval. OLAP shows great promise for the dynamic protein analysis for bioengineering and biomedical applications. In addition, OLAP may have similar potential for other scientific and engineering applications involving large and complex datasets.     

Dynameomics: mass annotation of protein dynamics and unfolding in water by high-throughput atomistic molecular dynamics simulations

The goal of Dynameomics is to perform atomistic molecular dynamics (MD) simulations of representative proteins from all known folds in explicit water in their native state and along their thermal unfolding pathways. Here we present 188-fold representatives and their native state simulations and analyses. These 188 targets represent 67% of all the structures in the Protein Data Bank. The behavior of several specific targets is highlighted to illustrate general properties in the full dataset and to demonstrate the role of MD in understanding protein function and stability. As an example of what can be learned from mining the Dynameomics database, we identified a protein fold with heightened localized dynamics. In one member of this fold family, the motion affects the exposure of its phosphorylation site and acts as an entropy sink to offset another portion of the protein that is relatively immobile in order to present a consistent interface for protein docking. In another member of this family, a polymorphism in the highly mobile region leads to a host of disease phenotypes. We have constructed a web site to provide access to a novel hybrid relational/multidimensional database (described in the succeeding two papers) to view and interrogate simulations of the top 30 targets: http://www.dynameomics.org. The Dynameomics database, currently the largest collection of protein simulations and protein structures in the world, should also be useful for determining the rules governing protein folding and kinetic stability, which should aid in deciphering genomic information and for protein engineering and design.     

Protein folds and protein folding

The classification of protein folds is necessarily based on the structural elements that distinguish domains. Classification of protein domains consists of two problems: the partition of structures into domains and the classification of domains into sets of similar structures (or folds). Although similar topologies may arise by convergent evolution, the similarity of their respective folding pathways is unknown. The discovery and the characterization of the majority of protein folds will be followed by a similar enumeration of available protein folding pathways. Consequently, understanding the intricacies of structural domains is necessary to understanding their collective folding pathways. We review the current state of the art in the field of protein domain classification and discuss methods for the systematic and comprehensive study of protein folding across protein fold space via atomistic molecular dynamics simulation. Finally, we discuss our large-scale Dynameomics project, which includes simulations of representatives of all autonomous protein folds.     

A Hamiltonian Replica Exchange Molecular Dynamics (MD) Method for the Study of Folding,Based on the Analysis of the Stabilization Determinants of Proteins

Herein, we present a novel Hamiltonian replica exchange protocol for classical molecular dynamics simulations of protein folding/unfolding. The scheme starts from the analysis of the energy-networks responsible for the stabilization of the folded conformation, by means of the energy-decomposition approach. In this framework, the compact energetic map of the native state is generated by a preliminary short molecular dynamics (MD) simulation of the protein in explicit solvent. This map is simplified by means of an eigenvalue decomposition. The highest components of the eigenvector associated with the lowest eigenvalue indicate which sites, named “hot spots”, are likely to be responsible for the stability and correct folding of the protein. In the Hamiltonian replica exchange protocol, we use modified force-field parameters to treat the interparticle non-bonded potentials of the hot spots within the protein and between protein and solvent atoms, leaving unperturbed those relative to all other residues, as well as solvent-solvent interactions. We show that it is possible to reversibly simulate the folding/unfolding behavior of two test proteins, namely Villin HeadPiece HP35 (35 residues) and Protein A (62 residues), using a limited number of replicas. We next discuss possible implications for the study of folding mechanisms via all atom simulations.     

Molecular dynamics studies on the thermostability of family 11 xylanases

Twelve members of the family 11 xylanases, including both mesophilic and thermophilic proteins, were studied using molecular dynamics (MD). Simulations of xylanases were carried out in an explicit water environment at four different temperatures, 300, 400, 500 and 600 K. A difference in thermotolerance between mesophilic and thermophilic xylanases became clear: thermophilic xylanases endured heat in higher simulation temperatures better than mesophilic ones. The unfolding pathways seemed to be similar for all simulations regardless of the protein. The unfolding initiates at the N-terminal region or alternatively from the alpha-helix region and proceeds to the 'finger region'. Unfolding of these regions led to denaturated structures within the 4.5 ns simulation at 600 K. The results are in agreement with experimental mutant studies. The results show clearly that the stability of the protein is not evenly distributed over the whole structure. The MD analysis suggests regions in the protein structure which are more unstable and thus potential targets for mutation experiments to improve thermostability.     

Transition Pathway and Its Free-Energy Profile: A Protocol for Protein Folding Simulations

We propose a protocol that provides a systematic definition of reaction coordinate and related free-energy profile as the function of temperature for the protein-folding simulation. First, using action-derived molecular dynamics (ADMD), we investigate the dynamic folding pathway model of a protein between a fixed extended conformation and a compact conformation. We choose the pathway model to be the reaction coordinate, and the folding and unfolding processes are characterized by the ADMD step index, in contrast to the common a priori reaction coordinate as used in conventional studies. Second, we calculate free-energy profile as the function of temperature, by employing the replica-exchange molecular dynamics (REMD) method. The current method provides efficient exploration of conformational space and proper characterization of protein folding/unfolding dynamics from/to an arbitrary extended conformation. We demonstrate that combination of the two simulation methods, ADMD and REMD, provides understanding on molecular conformational changes in proteins. The protocol is tested on a small protein, penta-peptide of met-enkephalin. For the neuropeptide met-enkephalin system, folded, extended, and intermediate sates are well-defined through the free-energy profile over the reaction coordinate. Results are consistent with those in the literature.     

Molecular dynamics simulations of the protein unfolding/folding reaction

All-atom molecular dynamics simulations of proteins in solvent are now able to realistically map the protein-unfolding pathway. The agreement with experiments probing both folding and unfolding suggests that these simulated unfolding events also shed light on folding. The simulations have produced detailed models of protein folding transition, intermediate, and denatured states that are in both qualitative and quantitative agreement with experiment. The various studies presented here highlight how such simulations both complement and extend experiment.     

Molecular Dynamics Simulations of Bromodomains Reveal Binding-Site Flexibility and Multiple Binding Modes of the Natural Ligand Acetyl-Lysine

Experimental protein structures provide spatial information at the atomic level. A further dimension, time, is supplemented by molecular dynamics. Since the pioneering work on the 58-residue inhibitor of bovine pancreatic trypsin in the group of Martin Karplus in the seventies, molecular dynamics simulations have shown that the intrinsic flexibility of proteins is essential for their function. Here, we review simulation studies of bromodomains. These protein modules are involved in the recognition of acetylated lysine side chains, a post-translational modification frequently observed in histone tails. The molecular dynamics simulations have unmasked: (i) the large plasticity of the loops lining the acetyl-lysine binding site (coupled to its self-occlusion), and (ii) multiple binding modes of acetyl-lysine. These simulation results suggest that recognition of histone tails by bromodomains is modulated by their intrinsic flexibility, and further corroborate the utility of molecular dynamics in understanding (macro)molecular recognition.     

Folding and Unfolding of Two Mixed α/β Peptides

We present a molecular dynamics simulation study of two peptides containing α‐ and β‐amino acid residues. According to experiment, the two peptides differ in the dominant fold when solvated in methanol: one shows a helical fold, the other a β hairpin. The simulations at 300 and 340 K were done by starting from a NMR spectroscopic model structure and from an extended (denatured) structure. The typical structural features of the two peptides are reproduced and a folding/unfolding equilibrium is observed on the nanosecond timescale at 300 K. Analysis of proton–proton NOE distance bounds and backbone ³J coupling constants gives results consistent with the experimental data. We conclude that our simulations are complementary to the experiments by providing detailed information on the conformational distributions.     

Machine Learning Enables Selection of Epistatic Enzyme Mutants for Stability Against Unfolding and Detrimental Aggregation

Machine learning (ML) has pervaded most areas of protein engineering, including stability and stereoselectivity. Using limonene epoxide hydrolase as the model enzyme and innov'SAR as the ML platform, comprising a digital signal process, we achieved high protein robustness that can resist unfolding with concomitant detrimental aggregation. Fourier transform (FT) allows us to take into account the order of the protein sequence and the nonlinear interactions between positions, and thus to grasp epistatic phenomena. The innov'SAR approach is interpolative, extrapolative and makes outside-the-box, predictions not found in other state-of-the-art ML or deep learning approaches. Equally significant is the finding that our approach to ML in the present context, flanked by advanced molecular dynamics simulations, uncovers the connection between epistatic mutational interactions and protein robustness.     

蛋白质色谱界面行为的分子模拟

蛋白质色谱界面行为解析对实现以高吸附容量、高活性收率和高传质速率为特征的高效色谱具有重要意义。利用分子模拟技术在微观过程展示方面的独特优势解析色谱界面过程已经广泛开展。本文综述色谱界面上蛋白质的取向、构象转换以及传质过程的分子模拟研究工作。首先,概述色谱界面上蛋白质取向研究,总结取向的成因和调控因素,探讨通过蛋白质取向调控实现高吸附容量。其次,概述色谱界面上蛋白质构象转换,尤其是变性现象的分子模拟研究,以通过色谱条件优化获得高活性收率。最后,阐述色谱介质表面传质过程研究及现存问题。本文以高吸附容量、高活性收率和高传质速率为目标,总结其对应微观过程的分子模拟解析,服务于色谱表面优化设计以实现高效色谱。     

Mechanism to Trigger Unfolding in O6‐Alkylguanine‐DNA Alkyltransferase

O⁶‐alkylguanine‐DNA alkyltransferase (AGT) adopts a non‐enzymatic suicide mechanism for the repair of methylated guanine bases by transferring the methyl adduct to itself, thereby initiating unfolding and fast degradation. Classical molecular dynamics simulations provide quantitative evidence that two conserved glycine residues at the centre of an α‐helix make the structure susceptible to structural perturbations. The stability of this helix, designated the “recognition helix”, is an important factor during the early onset of unfolding of human AGT (hAGT). By combining theory and experiment, we found that helical stability is controlled by key factors in the surrounding protein structure. By using a “double‐clip” mechanism, nearby residues hydrogen bond to both the base and centre of the helix. This double clip stabilises this site in the protein in the absence of substrate, but the helix is destabilised upon alkylation. The present investigation aimed to establish why alkylation of hAGT leads to conformational changes and how the protein environment functions as a switch, thus turning the stability of the protein “on” or “off” to tune degradability.     

Protein engineering to improve the thermostability of glucoamylase from Aspergillus awamori based on molecular dynamics simulations

Twelve mutations were constructed to improve the thermostabilityof glucoamylase from Aspergillus awamori based on the resultsof molecular dynamics simulations. The thermal unfolding ofthe catalytic domain followed a putative hierarchical behavior.In addition, the unfolding of the 13     

Effect of ligand chain length on hydrophobic charge induction chromatography revealed by molecular dynamics simulations

Hydrophobic charge induction chromatography (HCIC) is a mixed-mode chromatography which is advantageous for high adsorption capacity and facile elution. The effect of the ligand chain length on protein behavior in HCIC was studied. A coarse-grain adsorbent pore model established in an earlier work was modified to construct adsorbents with different chain lengths, including one with shorter ligands (CL2) and one with longer ligands (CL4). The adsorption, desorption, and conformational transition of the proteins with CL2 and CL4 were examined using molecular dynamics simulations. The ligand chain length has a significant effect on both the probability and the irreversibility of the adsorption/desorption. Longer ligands reduced the energy barrier of adsorption, leading to stronger and more irreversible adsorption, as well as a little more unfolding of the protein. The simulation results elucidated the effect of the ligand chain length, which is beneficial for the rational design of adsorbents and parameter optimization for high-performance HCIC.     

Combining Coarse-Grained Protein Models with Replica-Exchange All-Atom Molecular Dynamics

We describe a combination of all-atom simulations with CABS, a well-established coarse-grained protein modeling tool, into a single multiscale protocol. The simulation method has been tested on the C-terminal beta hairpin of protein G, a model system of protein folding. After reconstructing atomistic details, conformations derived from the CABS simulation were subjected to replica-exchange molecular dynamics simulations with OPLS-AA and AMBER99sb force fields in explicit solvent. Such a combination accelerates system convergence several times in comparison with all-atom simulations starting from the extended chain conformation, demonstrated by the analysis of melting curves, the number of native-like conformations as a function of time and secondary structure propagation. The results strongly suggest that the proposed multiscale method could be an efficient and accurate tool for high-resolution studies of protein folding dynamics in larger systems.     

Insights to Human γD-Crystallin Unfolding by NMR Spectroscopy and Molecular Dynamics Simulations

Human γD-crystallin (HGDC) is an abundant lens protein residing in the nucleus of the human lens. Aggregation of this and other structural proteins within the lens leads to the development of cataract. Much has been explored on the stability and aggregation of HGDC and where detailed investigation at the atomic resolution was needed, the X-ray structure was used as an initial starting conformer for molecular modeling. In this study, we implemented NMR-solution HGDC structures as starting conformers for molecular dynamics simulations to provide the missing pieces of the puzzle on the very early stages of HGDC unfolding leading up to the domain swap theories proposed by past studies. The high-resolution details of the conformational dynamics also revealed additional insights to possible early intervention for cataractogenesis.     

Liquid li structure and dynamics: A comparison between OFDFT and second nearest‐neighbor embedded‐atom method

The structure and dynamics of liquid lithium are studied using two simulation methods: orbital‐free (OF) first‐principles molecular dynamics (MD), which employs OF density functional theory (DFT), and classical MD utilizing a second nearest‐neighbor embedded‐atom method potential. The properties studied include the dynamic structure factor, the self‐diffusion coefficient, the dispersion relation, the viscosity, and the bond angle distribution function. Simulation results were compared to available experimental data when possible. Each method has distinct advantages and disadvantages. For example, OFDFT gives better agreement with experimental dynamic structure factors, yet is more computationally demanding than classical simulations. Classical simulations can access a broader temperature range and longer time scales. The combination of first‐principles and classical simulations is a powerful tool for studying properties of liquid lithium. © 2015 American Institute of Chemical Engineers AIChE J, 61: 2841–2853, 2015     

Covalent Complex of DNA and Bacterial Topoisomerase: Implications in Antibacterial Drug Development

A topoisomerase-DNA transient covalent complex can be a druggable target for novel topoisomerase poison inhibitors that represent a new class of antibacterial or anticancer drugs. Herein, we have investigated molecular features of the functionally important Escherichia coli topoisomerase I (EctopoI)-DNA covalent complex (EctopoIcc) for molecular simulations, which is very useful in the development of new antibacterial drugs. To demonstrate the usefulness of our approach, we used a model small molecule (SM), NSC76027, obtained from virtual screening. We examined the direct binding of NSC76027 to EctopoI as well as inhibition of EctopoI relaxation activity of this SM via experimental techniques. We then performed molecular dynamics (MD) simulations to investigate the dynamics and stability of EctopoIcc and EctopoI-NSC76027-DNA ternary complex. Our simulation results show that NSC76027 forms a stable ternary complex with EctopoIcc. EctopoI investigated here also serves as a model system for investigating a complex of topoisomerase and DNA in which DNA is covalently attached to the protein.     

蛋白质-表面活性剂组装结构的分子模拟

采用Langevin分子动力学方法模拟β模型蛋白与表面活性剂在溶液中形成的各种组装结构,考察了表面活性剂疏水性强度与浓度对蛋白质分子构象的影响.结果显示：弱疏水性表面活性剂可以在蛋白质表面自组装形成限制性空间,使被包覆的蛋白质的立体结构更为稳定;强疏水性的表面活性剂则可以与蛋白质疏水核心区的疏水基团形成复合物,而使蛋白质的立体结构被破坏,即蛋白质发生去折叠.上述模拟可再现相关实验结果,其展现的蛋白质结构转换微观图景对于表面活性剂的分子设计及其应用于生物加工过程具有指导作用.     

Coarse-Grained and Atomistic MD Simulations of RNA and DNA Folding

Although the main features of the protein folding problem are coming into clearer focus, the microscopic viewpoint of nucleic acid folding mechanisms is only just beginning to be addressed. Experiments, theory, and simulations are pointing to complex thermodynamic and kinetic mechanisms. As is the case for proteins, molecular dynamics (MD) simulations continue to be indispensable tools for providing a molecular basis for nucleic acid folding mechanisms. In this review, we provide an overview of biomolecular folding mechanisms focusing on nucleic acids. We outline the important interactions that are likely to be the main determinants of nucleic acid folding energy landscapes. We discuss recent MD simulation studies of empirical force field and Go-type MD simulations of RNA and DNA folding mechanisms to outline recent successes and the theoretical and computational challenges that lie ahead.