Three-dimensional Atomic Coordinates Research Articles

Conf-GEM: A geometric information-assisted direct conformation generation model

Molecular conformations generation (MCG) aims to efficiently obtain reasonable and stable three-dimensional (3D) atomic coordinates of the atoms in the molecule from scratch, providing a structural foundation for molecular representation learning models and advanced downstream molecular design tasks such as molecular property prediction, molecular generation, and molecular docking. Existing MCG methods mostly rely on indirect distance-based strategies, which which can result in geometrically unrealistic conformations, or direct coordinate-based methods, which have larger search spaces and are prone to overfitting. Therefore, this study introduces Conf-GEM, a novel geometric information-assisted direct conformation generation model based on E-GeoGNN, a geometrically augmented 3D graph neural network with multiple scales. Pre-training and divide-and-conquer strategies, are integrated into the proposed model. Conf-GEM outperforms RDKit and nine deep-learning-based MCG models on the GEOM-QM9 and GEOM-Drugs datasets, achieving conformational coverage of 96.69% and 96.07%, respectively, without force field optimization. It also excels on the X-ray diffraction crystal structure dataset with up to 97.04% conformational coverage. In conclusion, Conf-GEM provides a novel solution for stabilizing 3D conformations generation. We provide an online prediction service (https://confgem.cmdrg.com) with a user-friendly interface for researchers.

A method for multiple-sequence-alignment-free protein structure prediction using a protein language model

Protein structure prediction pipelines based on artificial intelligence, such as AlphaFold2, have achieved near-experimental accuracy. These advanced pipelines mainly rely on multiple sequence alignments (MSAs) as inputs to learn the co-evolution information from the homologous sequences. Nonetheless, searching MSAs from protein databases is time consuming, usually taking tens of minutes. Consequently, we attempt to explore the limits of fast protein structure prediction by using only primary structures of proteins. Our proposed method, HelixFold-Single, combines a large-scale protein language model with the superior geometric learning capability of AlphaFold2. HelixFold-Single first pre-trains a large-scale protein language model with thousands of millions of primary structures utilizing the self-supervised learning paradigm, which will be used as an alternative to MSAs for learning the co-evolution information. Then, by combining the pre-trained protein language model and the essential components of AlphaFold2, we obtain an end-to-end differentiable model to predict the three-dimensional coordinates of atoms from only the primary structure. HelixFold-Single is validated on datasets CASP14 and CAMEO, achieving competitive accuracy with the MSA-based methods on targets with large homologous families. Furthermore, HelixFold-Single consumes much less time than the mainstream pipelines for protein structure prediction, demonstrating its potential in tasks requiring many predictions.

Evaluation of Deep Learning Architectures for Aqueous Solubility Prediction.

Determining the aqueous solubility of molecules is a vital step in many pharmaceutical, environmental, and energy storage applications. Despite efforts made over decades, there are still challenges associated with developing a solubility prediction model with satisfactory accuracy for many of these applications. The goals of this study are to assess current deep learning methods for solubility prediction, develop a general model capable of predicting the solubility of a broad range of organic molecules, and to understand the impact of data properties, molecular representation, and modeling architecture on predictive performance. Using the largest currently available solubility data set, we implement deep learning-based models to predict solubility from the molecular structure and explore several different molecular representations including molecular descriptors, simplified molecular-input line-entry system strings, molecular graphs, and three-dimensional atomic coordinates using four different neural network architectures—fully connected neural networks, recurrent neural networks, graph neural networks (GNNs), and SchNet. We find that models using molecular descriptors achieve the best performance, with GNN models also achieving good performance. We perform extensive error analysis to understand the molecular properties that influence model performance, perform feature analysis to understand which information about the molecular structure is most valuable for prediction, and perform a transfer learning and data size study to understand the impact of data availability on model performance.

Protein Structure Prediction with Mass Spectrometry Data.

Knowledge of protein structure is crucial to our understanding of biological function and is routinely used in drug discovery. High-resolution techniques to determine the three-dimensional atomic coordinates of proteins are available. However, such methods are frequently limited by experimental challenges such as sample quantity, target size, and efficiency. Structural mass spectrometry (MS) is a technique in which structural features of proteins are elucidated quickly and relatively easily. Computational techniques that convert sparse MS data into protein models that demonstrate agreement with the data are needed. This review features cutting-edge computational methods that predict protein structure from MS data such as chemical cross-linking, hydrogen-deuterium exchange, hydroxyl radical protein footprinting, limited proteolysis, ion mobility, and surface-induced dissociation. Additionally, we address future directions for protein structure prediction with sparse MS data.

Correlating the three-dimensional atomic defects and electronic properties of two-dimensional transition metal dichalcogenides.

The electronic, optical and chemical properties of two-dimensional transition metal dichalcogenides strongly depend on their three-dimensional atomic structure and crystal defects. Using Re-doped MoS2 as a model system, here we present scanning atomic electron tomography as a method to determine three-dimensional atomic positions as well as positions of crystal defects such as dopants, vacancies and ripples with a precision down to 4 pm. We measure the three-dimensional bond distortion and local strain tensor induced by single dopants. By directly providing these experimental three-dimensional atomic coordinates to density functional theory, we obtain more accurate electronic band structures than derived from conventional density functional theory calculations that relies on relaxed three-dimensional atomic coordinates. We anticipate that scanning atomic electron tomography not only will be generally applicable to determine the three-dimensional atomic coordinates of two-dimensional materials, but also will enable ab initio calculations to better predict the physical, chemical and electronic properties of these materials.

A Quantum-Inspired Method for Three-Dimensional Ligand-Based Virtual Screening.

Measuring similarity between molecules is an important part of virtual screening (VS) experiments deployed during the early stages of drug discovery. Most widely used methods for evaluating the similarity of molecules use molecular fingerprints to encode structural information. While similarity methods using fingerprint encodings are efficient, they do not consider all the relevant aspects of molecular structure. In this paper, we describe a quantum-inspired graph-based molecular similarity (GMS) method for ligand-based VS. The GMS method is formulated as a quadratic unconstrained binary optimization problem that can be solved using a quantum annealer, providing the opportunity to take advantage of this nascent and potentially groundbreaking technology. In this study, we consider various features relevant to ligand-based VS, such as pharmacophore features and three-dimensional atomic coordinates, and include them in the GMS method. We evaluate this approach on various datasets from the DUD_LIB_VS_1.0 library. Our results show that using three-dimensional atomic coordinates as features for comparison yields higher early enrichment values. In addition, we evaluate the performance of the GMS method against conventional fingerprint approaches. The results demonstrate that the GMS method outperforms fingerprint methods for most of the datasets, presenting a new alternative in ligand-based VS with the potential for future enhancement.

B21-O-09Determination of Three-Dimensional Coordinates of Individual Atoms in Nano-Materials by Electron Tomography

Richard Feynman pointed out in his 1959 speech, “It would be very easy to make an analysis of any complicated chemical substance; all one would have to do would be to look at it and see where the atoms are… I put this out as a challenge: Is there no way to make the electron microscope more powerful?” Over more than 50 years, with significant achievements in electron microscope and the developments of aberration-corrected electron optics, scanning transmission electron microscopy (STEM) has reached spatial resolution of 0.5 Å. Recently, in combination of the state-of-the-art scanning transmission electron microscope (STEM) and a tomographic reconstruction technique known as equally sloped tomography (EST), we have achieved electron tomography at 2.4 angstrom resolution [ 1 ]. This general method has been applied to reveal almost all atoms inside a Pt nanoparticle with unprecedented details [ 2 ]. Most recently, Feynman's 1959 challenge has been tackled by successfully determining 3D coordinates of 3,769 individual atoms at the tip of a tungsten needle with a precision of ~19 picometers [ 3 ]. In this talk, I will present the achievements of this novel technique.

Three-dimensional coordinates of individual atoms in materials revealed by electron tomography

Crystallography, the primary method for determining the 3D atomic positions in crystals, has been fundamental to the development of many fields of science. However, the atomic positions obtained from crystallography represent a global average of many unit cells in a crystal. Here, we report, for the first time, the determination of the 3D coordinates of thousands of individual atoms and a point defect in a material by electron tomography with a precision of ∼19 pm, where the crystallinity of the material is not assumed. From the coordinates of these individual atoms, we measure the atomic displacement field and the full strain tensor with a 3D resolution of ∼1 nm(3) and a precision of ∼10(-3), which are further verified by density functional theory calculations and molecular dynamics simulations. The ability to precisely localize the 3D coordinates of individual atoms in materials without assuming crystallinity is expected to find important applications in materials science, nanoscience, physics, chemistry and biology.

Online_DPI: a web server to calculate the diffraction precision index for a protein structure

An online computing server, Online_DPI (where DPI denotes the diffraction precision index), has been created to calculate the `Cruickshank DPI' value for a given three-dimensional protein or macromolecular structure. It also estimates the atomic coordinate error for all the atoms available in the structure. It is an easy-to-use web server that enables users to visualize the computed values dynamically on the client machine. Users can provide the Protein Data Bank (PDB) identification code or upload the three-dimensional atomic coordinates from the client machine. The computed DPI value for the structure and the atomic coordinate errors for all the atoms are included in the revised PDB file. Further, users can graphically view the atomic coordinate error along with `temperature factors' (i.e. atomic displacement parameters). In addition, the computing engine is interfaced with an up-to-date local copy of the Protein Data Bank. New entries are updated every week, and thus users can access all the structures available in the Protein Data Bank. The computing engine is freely accessible online at http://cluster.physics.iisc.ernet.in/dpi/.

Reading PDB: Perception of Molecules from 3D Atomic Coordinates

The analysis of small molecule crystal structures is a common way to gather valuable information for drug development. The necessary structural data is usually provided in specific file formats containing only element identities and three-dimensional atomic coordinates as reliable chemical information. Consequently, the automated perception of molecular structures from atomic coordinates has become a standard task in cheminformatics. The molecules generated by such methods must be both chemically valid and reasonable to provide a reliable basis for subsequent calculations. This can be a difficult task since the provided coordinates may deviate from ideal molecular geometries due to experimental uncertainties or low resolution. Additionally, the quality of the input data often differs significantly thus making it difficult to distinguish between actual structural features and mere geometric distortions. We present a method for the generation of molecular structures from atomic coordinates based on the recently published NAOMI model. By making use of this consistent chemical description, our method is able to generate reliable results even with input data of low quality. Molecules from 363 Protein Data Bank (PDB) entries could be perceived with a success rate of 98%, a result which could not be achieved with previously described methods. The robustness of our approach has been assessed by processing all small molecules from the PDB and comparing them to reference structures. The complete data set can be processed in less than 3 min, thus showing that our approach is suitable for large scale applications.

The nanostructure problem

Diffraction techniques are making progress in tackling the difficult problem of solving the structures of nanoparticles and nanoscale materials. The great gift of x-ray crystallography has made us almost complacent in our ability to locate the three-dimensional coordinates of atoms in a crystal with a precision of around 10{sup -4} nm. However, the powerful methods of crystallography break down for structures in which order only extends over a few nanometers. In fact, as we near the one hundred year mark since the birth of crystallography, we face a resilient frontier in condensed matter physics: our inability to routinely and robustly determine the structure of complex nanostructured and amorphous materials. Knowing the structure and arrangement of atoms in a solid is so fundamental to understanding its properties that the topic routinely occupies the early chapters of every solid-state physics textbook. Yet what has become clear with the emergence of nanotechnology is that diffraction data alone may not be enough to uniquely solve the structure of nanomaterials. As part of a growing effort to incorporate the results of other techniques to constrain x-ray refinements - a method called 'complex modeling' which is a simple but elegant approach for combining information from spectroscopymore » with diffraction data to solve the structure of several amorphous and nanostructured materials. Crystallography just works, so we rarely question how and why this is so, yet understanding the physics of diffraction can be very helpful as we consider the nanostructure problem. The relationship between the electron density distribution in three dimensions (i.e., the crystal structure) and an x-ray diffraction pattern is well established: the measured intensity distribution in reciprocal space is the square of the Fourier transform of the autocorrelation function of the electron density distribution {rho}(r). The fact that we get the autocorrelation function (rather than just the density distribution) by Fourier transforming the measured intensity leaves us with a very tricky inverse problem: we have to extract the density from its autocorrelation function. The direct problem of predicting the diffraction intensity given a particular density distribution is trivial, but the inverse, unraveling from the intensity distribution the density that gives rise to it, is a highly nontrivial problem in global optimization. In crystallography, this challenging, nontrivial task is sometimes referred to as the 'phase problem.' The diffraction pattern is a wave-interference pattern, but we measure only the intensities (the squares of the waves) not the wave amplitudes. To get the amplitude, you take the square root of the intensity I, but in so doing you lose any knowledge of the phase of the wave {phi}, and half the information needed to reconstruct the density is lost. When solving such inverse problems, you hope you can start with a uniqueness theorem that reassures you that, under ideal conditions, there is only one solution: one density distribution that corresponds to the measured intensity. Then you have to establish that your data set contains sufficient information to constrain that unique solution. This is a problem from information theory that originated with Reverend Thomas Bayes work in the 18th century, and the work of Nyquist and Shannon in the 20 th century, and describes the fact that the degrees of freedom in the model must not exceed the number of pieces of independent information in the data. Finally, you need an efficient algorithm for doing the reconstruction. This is exactly how crystallography works. The information is in the form of Bragg peak intensities and the degrees of freedom are the atomic coordinates. Crystal symmetry lets us confine the model to the contents of a unit cell, rather than all of the atoms in the crystal, keeping the degrees of freedom admirably small in number. A measurement yields a multitude of Bragg peak intensities, providing ample redundant intensity information to make up for the lost phases. Finally, there are highly efficient algorithms, such as 'direct methods,' that make excellent use of the available information and constraints to find the solution quickly from a horrendously large search space. The problem is often so overconstrained that we can cavalierly throw away lots of directional information. In particular, even though Bragg peaks are orientationally averaged to a 1D function in a powder diffraction measurement, we still can get a 3D structural solution. Now it becomes easy to understand the enormous challenge of solving nanostructures: the information content in the data is degraded while the complexity of the model is much greater.« less

Spatiotemporal Kinetics in Solution Studied by Time‐Resolved X‐Ray Liquidography (Solution Scattering)

Information about temporally varying molecular structure during chemical processes is crucial for understanding the mechanism and function of a chemical reaction. Using ultrashort optical pulses to trigger a reaction in solution and using time-resolved X-ray diffraction (scattering) to interrogate the structural changes in the molecules, time-resolved X-ray liquidography (TRXL) is a direct tool for probing structural dynamics for chemical reactions in solution. TRXL can provide direct structural information that is difficult to extract from ultrafast optical spectroscopy, such as the time dependence of bond lengths and angles of all molecular species including short-lived intermediates over a wide range of times, from picoseconds to milliseconds. TRXL elegantly complements ultrafast optical spectroscopy because the diffraction signals are sensitive to all chemical species simultaneously and the diffraction signal from each chemical species can be quantitatively calculated from its three-dimensional atomic coordinates and compared with experimental TRXL data. Since X-rays scatter from all the atoms in the solution sample, solutes as well as the solvent, the analysis of TRXL data can provide the temporal behavior of the solvent as well as the structural progression of all the solute molecules in all the reaction pathways, thus providing a global picture of the reactions and accurate branching ratios between multiple reaction pathways. The arrangement of the solvent around the solute molecule can also be extracted. This review summarizes recent developments in TRXL, including technical innovations in synchrotron beamlines and theoretical analysis of TRXL data, as well as several examples from simple molecules to an organometallic complex, nanoparticles, and proteins in solution. Future potential applications of TRXL in femtosecond studies and biologically relevant molecules are also briefly mentioned.

Visualizing Solution-Phase Reaction Dynamics with Time-Resolved X-ray Liquidography

Most chemical reactions occur in solution, and complex interactions between solute and solvent influence the rich chemistry of these processes. To track time-dependent processes in such reactions, researchers often use time-resolved spectroscopy. In these experiments, an optical pulse (pump) initiates a reaction, and another time-delayed optical pulse (probe) monitors the progress of the reaction. However, because of the wavelength range of the probe light used in these experiments, from infrared to ultraviolet, researchers cannot directly determine detailed structural information such as the bond lengths and bond angles of reaction intermediates. In addition, not all intermediates might be sensitive to the spectroscopic signal chosen for the experiment. This Account describes time-resolved X-ray liquidography (TRXL), a technique that overcomes these problems. In this technique, we replace the optical probe with the diffraction of hard X-ray pulses emitted from a synchrotron source. In TRXL, diffraction signals are sensitive to all chemical species simultaneously. In addition, each chemical species has a characteristic diffraction signal, a fingerprint, that we calculate from its three-dimensional atomic coordinates. Because, X-rays scatter from all atoms in the solution sample, including both the solute and the solvent, the analysis of TRXL data can track not only the reaction pathways of the solute molecules but also the solvent behavior and the solute-solvent arrangement, thus providing a global picture of the reactions. We have used TRXL to study structural dynamics and spatiotemporal kinetics of many molecular systems including diatomic molecules, haloalkanes, organometallic complexes, and protein molecules over timescales from picoseconds to milliseconds. We have observed that TRXL data adds to and, in some cases, contradicts results from time-resolved spectroscopy. For example, TRXL has shown that the reaction intermediates upon C-I bond dissociation in C(2)H(4)I(2) and C(2)F(4)I(2) have completely different structures and corresponding subsequent reaction pathways, underscoring the dramatic effect of the fluorine substitution. We have also used TRXL to identify a new reaction intermediate of the photolysis of Ru(3)(CO)(12) that has no bridging carbonyl groups. Though not detected by time-resolved infrared spectroscopy, this intermediate predominates based on the TRXL data. In looking at the quaternary conformational changes of hemoglobin, TRXL analysis suggests a faster transition than was suggested by optical spectroscopy. The time resolution of TRXL is currently limited by the X-ray pulse width available from synchrotron sources ( approximately 100 ps). The resolution should improve to 100 fs or better with X-ray free electron lasers. With this higher resolution, real time observation of ultrafast chemical events such as bond-breaking and bond-making will be possible.

PROSIGN: A method for protein secondary structure assignment based on three-dimensional coordinates of consecutive C α atoms

The automatic assignment of secondary structure from three-dimensional atomic coordinates of proteins is an essential step for the analysis and modeling of protein structures. So different methods based on different criteria have been designed to perform this task. We introduce a new method for protein secondary structure assignment based solely on C α coordinates. We introduce four certain relations between C α three-dimensional coordinates of consecutive residues, each of which applies to one of the four regular secondary structure categories: α-helix, 3 10-helix, π-helix and β-strand. In our approach, the deviation of the C α coordinates of each residue from each relation is calculated. Based on these deviation values, secondary structures are assigned to all residues of a protein. We show that our method agrees well with popular methods as DSSP, STRIDE and assignments in PDB files. It is shown that our method gives more information about helix geometry leading to more accurate secondary structure assignment.

PSAP: protein structure analysis package

A computing engine, theProtein Structure Analysis Package(PSAP), has been developed to calculate and display various hidden structural and functional features of three-dimensional protein structures. The proposed computing engine has several utilities to enable structural biologists to analyze three-dimensional protein molecules and provides an easy-to-use Web interface to compute and visualize the necessary features dynamically on the client machine. Users need to provide the Protein Data Bank (PDB) identification code or upload three-dimensional atomic coordinates from the client machine. For visualization, the free molecular graphics programsRasMolandJmolare deployed in the computing engine. Furthermore, the computing engine is interfaced with an up-to-date local copy of the PDB. The atomic coordinates are updated every week and hence users can access all the structures available in the PDB. The computing engine is free and is accessible online at http://iris.physics.iisc.ernet.in/psap/.

LySDB - Lysozyme Structural DataBase.

LySDB (Lysozyme Structural DataBase) is an integrated database containing 740 three-dimensional structures of lysozyme available in the Protein Data Bank. The database can be used to visualize the three-dimensional structure of the entire protein model or the substructures in which the user is interested (for example, insertions and deletions of amino acids) using the three-dimensional atomic coordinates. The database is provided with a search engine with several useful built-in facilities. The public domain graphics program RASMOL has been deployed for visualization. The three-dimensional structures used to create the database are updated at regular intervals and hence the users are provided with the current information available in the literature. The database LySDB is available over the World Wide Web and can be accessed at the URL http://iris.physics.iisc.ernet.in/lysdb/ or http://144.16.71.2/lysdb/.

SEM (Symmetry Equivalent Molecules): A web-based GUI to generate and visualize the macromolecules.

SEM, Symmetry Equivalent Molecules, is a web-based graphical user interface to generate and visualize the symmetry equivalent molecules (proteins and nucleic acids). In addition, the program allows the users to save the three-dimensional atomic coordinates of the symmetry equivalent molecules in the local machine. The widely recognized graphics program RasMol has been deployed to visualize the reference (input atomic coordinates) and the symmetry equivalent molecules. This program is written using CGI/Perl scripts and has been interfaced with all the three-dimensional structures (solved using X-ray crystallography) available in the Protein Data Bank. The program, SEM, can be accessed over the World Wide Web interface at http://dicsoft2.physics.iisc.ernet.in/sem/ or http://144.16.71.11/sem/.

PDB Goodies--a web-based GUI to manipulate the Protein Data Bank file.

PDB Goodies is a web-based graphical user interface (GUI) to manipulate the Protein Data Bank file containing the three-dimensional atomic coordinates of protein structures. The program also allows users to save the manipulated three-dimensional atomic coordinate file on their local client system. These fragments are used in various stages of structure elucidation and analysis. This software is incorporated with all the three-dimensional protein structures available in the Protein Data Bank, which presently holds approximately 18 000 structures. In addition, this program works on a three-dimensional atomic coordinate file (Protein Data Bank format) uploaded from the client machine. The program is written using CGI/PERL scripts and is platform independent. The program PDB Goodies can be accessed over the World Wide Web at http://144.16.71.11/pdbgoodies/.

Inversion of low-energy electron diffraction data with no pre-knowledge factor beyond optical holography

In this review, I describe how low-energy (typically below 500eV) electron diffraction spectra can be inverted to produce three-dimensional coordinates of atoms neighbouring a reference atom with no prior knowledge of what type or types of atom are present. The reference atom may be one of many equivalent near-surface atoms from which a core-level photoelectron is excited or, in the case of diffuse low-energy electron diffraction, one of many equivalent adsorbate atoms (lacking in long-range order) on the surface of a crystalline substrate. Other variants apply to low-energy electron diffraction, Kikuchi electron diffraction and time-reversed versions in which the wavenumber (energy) and direction of the incident beam are varied. I show that, for such low-energy electron diffraction spectra, the relative phases between the reference wave and scattered waves have a known geometric form if the spectra are taken from within a small angular cone in the near-back-scattering directions. By using the back-scattering small cone at each direction of interest, a simple algorithm is developed to invert the spectra and extract object atomic positions with no input of calculated dynamical factors. The article also reviews key ideas and works which led to the current understanding of this field.

Ge(113) reconstruction stabilized by subsurface interstitials: An x-ray diffraction structure analysis

The three-dimensional atomic coordinates of the Ge(113)-(3×1) surface have been determined by analyzing in-plane and out-of-plane x-ray intensity data. Besides dimer and adatom motifs, which reduce the number of dangling bonds, a random distribution of subsurface interstitials has been identified. Subsurface interstitials relieve elastic stress and lower the energy of the electronic system. Together with the delicate balance between the energy gain due to reduction of dangling bonds and the energy costs due to induced strain this determines the nature of the (3×1) reconstruction of Ge(113).