Molecular Graphics: Bridging Structural Biologists and Computer Scientists
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The interpretation of cryo-EM maps often includes the docking of known or predicted structures of the components, which is particularly useful when the map resolution is worse than 4 Å. Although it can be effective to search the entire map to find the best placement of a component, the process can be slow when the maps are large. However, frequently there is a well-founded hypothesis about where particular components are located. In such cases, a local search using a map subvolume will be much faster because the search volume is smaller, and more sensitive because optimizing the search volume for the rotation-search step enhances the signal to noise. A Fourier-space likelihood-based local search approach, based on the previously published em_placement software, has been implemented in the new emplace_local program. Tests confirm that the local search approach enhances the speed and sensitivity of the computations. An interactive graphical interface in the ChimeraX molecular-graphics program provides a convenient way to set up and evaluate docking calculations, particularly in defining the part of the map into which the components should be placed.
The structural sciences are undergoing a transformation driven by advancements in visualization technologies that aid researchers in understanding and communicating experimental data from complex molecular systems. New applications of integrative structural biological and biophysical approaches add a wide variety of complementary information from a broad range of scientific disciplines. These approaches extend structural biophysical methodologies to enable research by the incorporation of a variety of data streams and utilization of tools like molecular graphics, virtual reality, and machine learning. To redefine how structural data—particularly from cryo-electron microscopy and x-ray crystallography—are fed forward for scientific exploration and communication, the advances in tools for data visualization and interpretation have been critical. By bringing molecular systems into an interactive three-dimensional space, these novel technologies enhance research workflows, facilitate structure-based drug design, and create engaging educational experiences. Taken together, these visualization innovations are essential tools for advancing the field by making concepts more accessible and compelling.
The molecular graphics program, PyMol, was employed to produce all images in the training/validation/testing datasets. A PyMol script identified the clusters of interest from a list of all proteins containing 1) Fe (III) and Fe (II) ion and 2) FeS, respectively. A PyMol script and a Python webscraper were also used to classify [2Fe-2S] cofactors into three classes: standard, Rieske, and other and to identify iron atoms with four cysteine residues. We also used the pyMol script to rotate amino acids for training
The properties of molecules derive in part from their structures. Because of the importance of understanding molecular structures various methodologies, ranging from first principles to empirical technique, were developed for computing the structure of molecules. For large molecules such as polymer model compounds, the structural information is difficult to comprehend by examining tabulated data. Therefore, a molecular graphics display system, called MOLDS, was developed to help interpret the data. MOLDS is a menu-driven program developed to run on the LADC SNS computer systems. This program can read a data file generated by the modeling programs or data can be entered using the keyboard. MOLDS has the following capabilities: draws the 3-D representation of a molecule using stick, ball and ball, or space filled model from Cartesian coordinates, draws different perspective views of the molecule; rotates the molecule on the X, Y, Z axis or about some arbitrary line in space, zooms in on a small area of the molecule in order to obtain a better view of a specific region; and makes hard copy representation of molecules on a graphic printer. In addition, MOLDS can be easily updated and readily adapted to run on most computer systems.
The molecular electrostatic potential (MEP) generated by quantum chemistry methods and Gaussian functions is evaluated over graphics processing units (GPUs). This implementation is based on full-range Rys polynomials with nodes and weights obtained in each thread of a GPU. For high angular moments, the corresponding integral is solved using a one dimension vertical recurrence relation. Thus, we computed the MEP with minimal approximations. We show that this implementation is stable and very efficient since the time consumed over GPUs is quite small compared with similar implementations over CPUs. The implementation was done by using CUDA-C programming techniques within the Graphics Processing Units for Atoms and Molecules (GPUAM) project, which has been designed to analyze quantum chemistry fields over heterogeneous computational resources. With this new scalar field GPUAM is a useful application for the quantum chemistry community, in particular for people interested in chemical reactivity analysis.
Developed over the past decade, TeraChem is an electronic structure and ab initio molecular dynamics software package designed from the ground up to leverage graphics processing units (GPUs) to perform large-scale ground and excited state quantum chemistry calculations in the gas and the condensed phase. TeraChem’s speed stems from the reformulation of conventional electronic structure theories in terms of a set of individually optimized high-performance electronic structure operations (e.g., Coulomb and exchange matrix builds, one- and two-particle density matrix builds) and rank-reduction techniques (e.g., tensor hypercontraction). Recent efforts have encapsulated these core operations and provided language-agnostic interfaces. Finally, this greatly increases the accessibility and flexibility of TeraChem as a platform to develop new electronic structure methods on GPUs and provides clear optimization targets for emerging parallel computing architectures.
Here, this Letter introduces excited-state molecular dynamics in PYSEQM, a GPU-accelerated semiempirical quantum chemistry engine implemented in PyTorch. The new module enables Born–Oppenheimer molecular dynamics (BOMD) using configuration-interaction singles and random phase approximation for excited states, allowing long trajectories and large statistical ensembles to be simulated efficiently on a single GPU. We also implement an extended Lagrangian excited-state BOMD (XL-ESMD) scheme that propagates auxiliary electronic variables, enabling relaxed ground and excited-state convergence thresholds without compromising energy conservation. The excited-state BOMD implementation scales smoothly from small chromophores to a nearly 900-atom dendrimer (taking 6.5 s per MD step). PYSEQM also supports batched execution, allowing many geometries or trajectories to be evaluated in a single GPU launch, substantially increasing throughput and making ensemble-based protocols routine. As a demonstration, we compute absorption, emission, and infrared spectra from trajectories propagated on the ground and first excited states. The XL-ESMD scheme yields identical spectra at significantly lower computational cost, establishing the role of extended Lagrangian based dynamics for efficient excited-state BOMD simulations. Beyond raw performance, PYSEQM’s PyTorch foundation provides automatic differentiation for forces, efficient GPU batching, and seamless interfacing with machine learning models. These capabilities position PYSEQM as a practical platform for machine learning-augmented excited-state dynamics and lay the foundation for future data-driven nonadiabatic excited-state dynamics modeling of ultrafast spectroscopic probes.
A comprehensive understanding of the interfacial behaviors of biomolecules holds great significance in the development of biomaterials and biosensing technologies. In this work, we used discontinuous molecular dynamics (DMD) simulations and graphic contrastive learning analysis to study the adsorption of ubiquitin protein on a graphene surface. Our high-throughput DMD simulations can explore the whole protein adsorption process including the protein structural evolution with sufficient accuracy. Contrastive learning was employed to train a protein contact map feature extractor aiming at generating contact map feature vectors. Subsequently, these features were grouped using the k-means clustering algorithm to identify the protein structural transition stages throughout the adsorption process. The machine learning analysis can illustrate the dynamics of protein structural changes, including the pathway and the rate-limiting step. Our study indicated that the protein–graphene surface hydrophobic interactions and the π–π stacking were crucial to the seven-stage adsorption process. Upon adsorption, the secondary structure and tertiary structure of ubiquitin disintegrated. The unfolding stages obtained by contrastive learning-based algorithm were not only consistent with the detailed analyses of protein structures but also provided more hidden information about the transition states and pathway of protein adsorption process and structural dynamics. Our combination of efficient DMD simulations and machine learning analysis could be a valuable approach to studying the interfacial behaviors of biomolecules.
Background data was provided for the assessment of the environmental impact of a proposed oil refinery location. Climatic background, particulate data, digitized portrayal of site molecular and meteorological data, graphical portrayal of molecular data, hourly meteorological data, and streamflow charts and radiosonde data are given
Background data is provided for the photochemical oxident problem in the Richmond area. Particulate data, digitized portrayal of site molecular and meteorological data, and graphical portrayal of molecular data, hourly meteorological data, and streamflow charts and radiosonde data are also given.
We report the Research Collaboratory for Structural Bioinformatics Protein Data Bank (RCSB PDB), funded by the US National Science Foundation, National Institutes of Health, and Department of Energy, has served structural biologists and Protein Data Bank (PDB) data consumers worldwide since 1999. RCSB PDB, a founding member of the Worldwide Protein Data Bank (wwPDB) partnership, is the US data center for the global PDB archive housing biomolecular structure data. RCSB PDB is also responsible for the security of PDB data, as the wwPDB-designated Archive Keeper. Annually, RCSB PDB serves tens of thousands of three-dimensional (3D) macromolecular structure data depositors (using macromolecular crystallography, nuclear magnetic resonance spectroscopy, electron microscopy, and micro-electron diffraction) from all inhabited continents. RCSB PDB makes PDB data available from its research-focused RCSB.org web portal at no charge and without usage restrictions to millions of PDB data consumers working in every nation and territory worldwide. In addition, RCSB PDB operates an outreach and education PDB101.RCSB.org web portal that was used by more than 800,000 educators, students, and members of the public during calendar year 2020. This invited Tools Issue contribution describes (i) how the archive is growing and evolving as new experimental methods generate ever larger and more complex biomolecular structures; (ii) the importance of data standards and data remediation in effective management of the archive and facile integration with more than 50 external data resources; and (iii) new tools and features for 3D structure analysis and visualization made available during the past year via the RCSB.org web portal.
Molecular Dynamics (MD) simulations monitor time-resolved motions of macromolecules. While visualization of MD trajectories allows an instant and intuitive understanding of dynamics and function, so far mainly static representations are provided in published literature. Recent advances in browser technology may allow for the sharing of trajectories through interactive visualization on the web. We believe that by providing intuitive and interactive visualization along with related protocols and analysis data promotes understanding, reliability, and re-usability of MD simulations. Existing barriers for sharing MD simulations are discussed and emerging solutions are highlighted. We predict that interactive visualization of MD trajectories will quickly be adopted by researchers, research consortiums, journals, and funding agencies to gather and distribute results from MD simulations via the web.
A visualization of molecular models can lead to a clearer understanding of the models. Sophisticated graphics devices supported by minicomputers make it possible for the chemist to interact with the display of a very large model, altering its structure. In addition to user interaction, the need arises also for other ways of displaying information. These include the production of viewgraphs, film presentation, as well as publication quality prints of various models. To satisfy these needs, the display capability of the Ames Interactive Modeling System (AIMS) has been enhanced to provide a wide range of graphics and plotting capabilities. Attention is given to an overview of the AIMS system, graphics hardware used by the AIMS display subsystem, a comparison of graphics hardware, the representation of molecular models, graphics software used by the AIMS display subsystem, the display of a model obtained from data stored in molecule data base, a graphics feature for obtaining single frame permanent copy displays, and a feature for producing multiple frame displays.
Bio-based graphene-reinforced thermoplastic elastomers (TPE) based on natural rubber (NR), and poly lactic acid (PLA) were successfully prepared via melt blending. The effect of graphene nanosheets (GNS) content and coupling agent were investigated on morphological, thermal and rheological properties of the PLA/NR/GNS bio-nanocomposites. More stable morphology of uniformly dispersed NR phase in the continuous PLA matrix with a relatively narrower diameter-size distribution was achieved in the presence of a compatibilizer. Moreover, transmission electron micrographs showed more improved dispersion of GNS with predominantly exfoliated morphology in the case of ENR-compatibilized PLA/NR/GNS bio-nanocomposites. Crystallization/melting studies revealed that, as the GNS content increased, the cold-crystallization peak, melting peak and glass-to-rubber transition temperature values shifted to higher temperatures and the crystallinities of blends slightly decreased (about 7%) which might be attributed to the reduction of polymeric segments mobility by the restricting effect of GNS. Dynamical mechanical investigation showed that the storage modulus (E′) increased about 25% by the introduction of GNS due to the inherent stiffness of GNS and the occurrence of compatibility in the PLA/NR blend in the presence of ENR. The presence of ENR shifted the T{sub g} of the NR phase and the PLA matrix towards each other which is a characteristic of higher miscibility and compatibility. The thermogravimetry (TGA) and derivative thermogravimetry (DTG) curves revealed higher thermal stability of the compatibilized-PLA/NR blends due to the enhanced interfacial adhesion and the homogenous dispersion of GNSs as direct effects of the presence of ENR. Rheological studies indicated that the formation of the effective GNS-polymer networks by the presence of ENR increased the storage modulus (G′) and complex viscosity (η*) value due to the effectiveness of the GNS in taking loads and restricted molecular motion, respectively. Graphical Abstract: .
Abstract Understanding the interactions of secondary electrons generated by ionizing radiation provides a fundamental basis for developing strategies in cancer therapy. In this study, we investigated the interactions of 25 eV low-energy electrons (LEEs) with calf thymus DNA and its constituents, four types of nucleosides and nucleobases, using X-ray photoelectron spectroscopy (XPS). Based on the acquisition and analysis of core-level spectra (O 1s, C 1s, N 1s, and P 2p) in DNA, structural changes induced by 25 eV electrons suggest potential site- and base-specific selectivity. These changes may involve hydroxyl (C–OH) group release from the sugar moiety, cleavage of C–N bonds (likely corresponding to N-glycosidic linkages), and phosphate backbone damage in calf thymus DNA. Among the four nucleosides, thymidine and guanosine showed more evident structural modifications, while cytidine and adenosine were relatively stable. In addition, nucleosides displayed greater susceptibility to LEE-induced structural changes than their corresponding nucleobases. This study reveals the selective damage mechanisms of LEEs on various DNA constituents, which may provide mechanistic insights for future developments in precision cancer therapy based on molecular-level damage. Graphical abstract
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TeraChem was born in 2008 with the goal of providing fast on-the-fly electronic structure calculations to facilitate ab initio molecular dynamics studies of large biochemical systems such as photoswitchable proteins and multichromophoric antenna complexes. Originally developed for videogaming applications, graphics processing units (GPUs) offered a low-cost parallel computer architecture that became more accessible for general-purpose GPU computing with the release of CUDA in 2007. The evaluation of the electron repulsion integrals (ERIs) is a major bottleneck in electronic structure codes and provides an attractive target for acceleration on GPUs. Thus, highly efficient routines for evaluation of and contractions between the ERIs and density matrices were implemented in TeraChem. Here, electronic structure methods were developed and implemented to leverage these integral contraction routines, resulting in the first quantum chemistry package designed from the ground up for GPUs. This GPU acceleration makes TeraChem capable of performing large-scale ground and excited state calculations in the gas and condensed phase. Today, TeraChem's speed forms the basis for a suite of quantum chemistry applications, including optimization and dynamics of proteins, automated and interactive chemical discovery tools, and large-scale nonadiabatic dynamics simulations.