Techniques For Structure Prediction Research Articles

Minimizing Polymorphic Risk through Cooperative Computational and Experimental Exploration.

We combine state-of-the-art computational crystal structure prediction (CSP) techniques with a wide range of experimental crystallization methods to understand and explore crystal structure in pharmaceuticals and minimize the risk of unanticipated late-appearing polymorphs. Initially, we demonstrate the power of CSP to rationalize the difficulty in obtaining polymorphs of the well-known pharmaceutical isoniazid and show that CSP provides the structure of the recently obtained, but unsolved, Form III of this drug despite there being only a single resolved form for almost 70 years. More dramatically, our blind CSP study predicts a significant risk of polymorphism for the related iproniazid. Employing a wide variety of experimental techniques, including high-pressure experiments, we experimentally obtained the first three known nonsolvated crystal forms of iproniazid, all of which were successfully predicted in the CSP procedure. We demonstrate the power of CSP methods and free energy calculations to rationalize the observed elusiveness of the third form of iproniazid, the success of high-pressure experiments in obtaining it, and the ability of our synergistic computational-experimental approach to “de-risk” solid form landscapes.

The ResiRole server to enable assessments of structure prediction techniques using functional site predictions

The ResiRole server to enable assessments of structure prediction techniques using functional site predictions

ResiRole: residue-level functional site predictions to gauge the accuracies of protein structure prediction techniques.

MotivationMethods to assess the quality of protein structure models are needed for user applications. To aid with the selection of structure models and further inform the development of structure prediction techniques, we describe the ResiRole method for the assessment of the quality of structure models.ResultsStructure prediction techniques are ranked according to the results of round-robin, head-to-head comparisons using difference scores. Each difference score was defined as the absolute value of the cumulative probability for a functional site prediction made with the FEATURE program for the reference structure minus that for the structure model. Overall, the difference scores correlate well with other model quality metrics; and based on benchmarking studies with NaïveBLAST, they are found to detect additional local structural similarities between the structure models and reference structures.Availabilityand implementationAutomated analyses of models addressed in CAMEO are available via the ResiRole server, URL http://protein.som.geisinger.edu/ResiRole/. Interactive analyses with user-provided models and reference structures are also enabled. Code is available at github.com/wamclaughlin/ResiRole.Supplementary information Supplementary data are available at Bioinformatics online.

Pauling’s rules guided Monte Carlo search (PAMCARS): A shortcut of predicting inorganic crystal structures

The rapid development of the high-throughput calculations and materials genome approaches in recent years has improved researchers’ ability to design advanced materials. Crystal structure prediction techniques play an important role in high-throughput calculations and materials genome approaches. However, the huge computational cost of the widely used crystal structure prediction techniques based on the global optimization algorithms hinders the researchers from predicting the large or medium-sized crystal structures. In recent years, some metastable materials with excellent electrical, magnetic, optical and catalytic properties have attracted a lot of attention. However, there is currently a lack of a structure prediction technique with high energy resolution near the ground-state structures to meet the growing demands for the prediction of low-energy metastable materials. In this article, a set of easy-operation guidelines for constructing crystal structures is extracted from Pauling’s rules. Based on this set of guidelines, we have designed an algorithm called Pauling’s rules guided Monte Carlo search (PAMCARS), which predict the ground-state and meta-stable inorganic crystal structures of a given composition by combining a classical Monte Carlo search in the configuration space constrained by Pauling’s rules with ab initio structural relaxations. In the actual tests, the crystal structures of carbon allotropes, CaSO4, Ba2TiSi2O8 and BaAlBO3F2 are successfully predicted by this crystal structure prediction algorithm. In addition, a number of unreported high-symmetric metastable crystal structures of carbon, CaSO4 and Ba2TiSi2O8 are also predicted. The successful prediction of graphite layers indicates that PAMCARS is able to predict some layered materials assembled by the Van der Waals forces. Two of the predicted low-energy metastable crystal structures of Ba2TiSi2O8 may explain the property anomaly of Ba2TiSi2O8 around 160 °C. Pauling’s rules guided Monte Carlo search exhibits high efficiency and energy resolution in predicting the ground-state and metastable crystal structures in the tests.

AllesTM: predicting multiple structural features of transmembrane proteins

BackgroundThis study is motivated by the following three considerations: a) the physico-chemical properties of transmembrane (TM) proteins are distinctly different from those of globular proteins, necessitating the development of specialized structure prediction techniques, b) for many structural features no specialized predictors for TM proteins are available at all, and c) deep learning algorithms allow to automate the feature engineering process and thus facilitate the development of multi-target methods for predicting several protein properties at once.ResultsWe present AllesTM, an integrated tool to predict almost all structural features of transmembrane proteins that can be extracted from atomic coordinate data. It blends several machine learning algorithms: random forests and gradient boosting machines, convolutional neural networks in their original form as well as those enhanced by dilated convolutions and residual connections, and, finally, long short-term memory architectures. AllesTM outperforms other available methods in predicting residue depth in the membrane, flexibility, topology, relative solvent accessibility in its bound state, while in torsion angles, secondary structure and monomer relative solvent accessibility prediction it lags only slightly behind the currently leading technique SPOT-1D. High accuracy on a multitude of prediction targets and easy installation make AllesTM a one-stop shop for many typical problems in the structural bioinformatics of transmembrane proteins.ConclusionsIn addition to presenting a highly accurate prediction method and eliminating the need to install and maintain many different software tools, we also provide a comprehensive overview of the impact of different machine learning algorithms and parameter choices on the prediction performance.AllesTM is freely available at https://github.com/phngs/allestm.

An efficient Pearson correlation based improved random forest classification for protein structure prediction techniques

In biochemistry, the protein structure prediction from the primary sequence is a significant issue. Few research works are intended for performing protein structure prediction with assist of diverse data mining techniques. However, the existing technique does not provide enhanced performance for protein structure prediction. To resolve this limitation, Weighted Pearson Correlation based Improved Random Forest Classification (WPC-IRFC) Technique is introduced. The WPC-IRFC Technique is developed for enhancing the protein structure prediction performance with higher accuracy and lesser time. The WPC-IRFC uses Weighted Pearson Correlation (WPC) to select relevant amino acid features based on weighted mean and weighted covariance. After selecting the relevant amino acid features, WPC-IRFC Technique designs an Improved Random Forest Classification (IRFC) for predicting the protein structure from a big protein dataset (DS). IRFC significantly lessens the error rate of classification with aid of iteratively reweighted least squares model to accurately identify protein structures.

Improved Consensus-Fragment Selection in Template-Assisted Prediction of Protein Structures with the UNRES Force Field in CASP13.

The method for protein-structure prediction, which combines the physics-based coarse-grained UNRES force field with knowledge-based modeling, has been developed further and tested in the 13th Community Wide Experiment on the Critical Assessment of Techniques for Protein Structure Prediction (CASP13). The method implements restraints from the consensus fragments common to server models. In this work, the server models to derive fragments have been chosen on the basis of quality assessment; a fully automatic fragment-selection procedure has been introduced, and Dynamic Fragment Assembly pseudopotentials have been fully implemented. The Global Distance Test Score (GDT_TS), averaged over our “Model 1” predictions, increased by over 10 units with respect to CASP12 for the free-modeling category to reach 40.82. Our “Model 1” predictions ranked 20 and 14 for all and free-modeling targets, respectively (upper 20.2% and 14.3% of all models submitted to CASP13 in these categories, respectively), compared to 27 (upper 21.1%) and 24 (upper 18.9%) in CASP12, respectively. For oligomeric targets, the Interface Patch Similarity (IPS) and Interface Contact Similarity (ICS) averaged over our best oligomer models increased from 0.28 to 0.36 and from 12.4 to 17.8, respectively, from CASP12 to CASP13, and top-ranking models of 2 targets (H0968 and T0997o) were obtained (none in CASP12). The improvement of our method in CASP13 over CASP12 was ascribed to the combined effect of the overall enhancement of server-model quality, our success in selecting server models and fragments to derive restraints, and improvements of the restraint and potential-energy functions.

Improved protein structure prediction using predicted interresidue orientations

The prediction of interresidue contacts and distances from coevolutionary data using deep learning has considerably advanced protein structure prediction. Here, we build on these advances by developing a deep residual network for predicting interresidue orientations, in addition to distances, and a Rosetta-constrained energy-minimization protocol for rapidly and accurately generating structure models guided by these restraints. In benchmark tests on 13th Community-Wide Experiment on the Critical Assessment of Techniques for Protein Structure Prediction (CASP13)- and Continuous Automated Model Evaluation (CAMEO)-derived sets, the method outperforms all previously described structure-prediction methods. Although trained entirely on native proteins, the network consistently assigns higher probability to de novo-designed proteins, identifying the key fold-determining residues and providing an independent quantitative measure of the "ideality" of a protein structure. The method promises to be useful for a broad range of protein structure prediction and design problems.

First-principles study of Ca<sub>5</sub>N<sub>4</sub> at high pressure

Recent studies have shown that introducing metal elements into nitrogen matrix can induce more stable poly-nitrogen structures than the pure nitrogen phase due to the ionic interaction between metal elements and nitrogen matrix. Many types of poly-nitrogen structures have been reported by using the alkaline earth metal elements (<i>M</i> = Be, Mg, Ca, Sr, Ba) as the coordinate elements. For example, the one-dimensional (1D) infinite armchair poly-nitrogen chain (N<sub>∞</sub>) structure and N<sub>6</sub> ring structure are obtained for the <i>M</i>N<sub>4</sub> and <i>M</i>N<sub>3</sub> chemical stoichiometry, respectively. Interestingly, the stabilities of theses <i>M</i>N<sub><i>x</i></sub> structures are enhanced 2–3 times compared with that of the pure nitrogen. Therefore, exploring the novel and stable poly-nitrogen structure by introducing alkaline earth metal elements under high pressure is a great significant job. As an alkaline earth element, Ca is abundant in the earth. Its ionization energy (<i>I</i><sub>1</sub> = 590 kJ/mol) is far lower than that of Be (900 kJ/mol) and Mg (738 kJ/mol), which means that Ca can form calcium nitrides more easily. Zhu et al. (Zhu S, Peng F, Liu H, Majumdar A, Gao T, Yao Y 2016<i>Inorg. Chem.</i> <b>55</b> 7550) proposed that the Ca-N system can obtain poly-nitrogen structures under high pressure, such as CaN<sub>4</sub> structure with armchair nitrogen chain, CaN<sub>5</sub> and CaN<sub>3</sub> consisting of pentazolate “N<sub>5</sub>” and benzene-like “N<sub>6</sub>” anions. These poly-nitrogen structures have potential applications in the field of high energy density materials. Here, we report the prediction of Ca-N system at 100 GPa by using particle swarm optimization algorithm technique for crystal structure prediction. A new thermal stable phase with <i>P</i> 2<sub>1</sub>/<i>c</i>-Ca<sub>5</sub>N<sub>4</sub> space group is found at 100 GPa, which enriches the phase of Ca-N system under high pressure. The dynamic stability and mechanical stability of new phase are confirmed by phono dispersion spectrum and elastic constant calculations. The electron localization function analysis shows that the nitrogen atoms in <i>P</i> 2<sub>1</sub>/<i>c</i>-Ca<sub>5</sub>N<sub>4</sub> are bonded by N—N single bond and electron transfer from Ca atom to N atom enables Ca<sub>5</sub>N<sub>4</sub> to serve as an ionic-bonding interaction structure. Band structure calculation shows that the Ca<sub>5</sub>N<sub>4</sub> has a semiconductor structure with a direct band gap of 1.447 eV. The PDOS calculation shows the valence band near Fermi energy is mainly contributed by N_p electrons, while the conduction band is mainly contributed by Ca_d electrons, indicating that electrons are transferred from Ca atom to N atom. Bader calculation shows that each N atom obtains 1.26e from Ca atom in <i>P</i> 2<sub>1</sub>/<i>c</i>-Ca<sub>5</sub>N<sub>4</sub>. The Raman spectrum and X-ray diffraction spectrum are calculated and detailed Raman vibration modes are identified, which provides theoretical guidance for experimental synthesis.

Tight‐Binding Parameterizations of Ti and Ba Oxides and Their Application for the Prediction of 2D Phases

Herein, a set of Slater–Koster parameters for the O–Ti–Ba system to be used within the self‐consistent‐charge, density‐functional tight‐binding method is developed. This parameterization follows the recently suggested procedure by Huran et al. in 2018, and is based on the fitting to density‐functional theory (DFT) results. This approach yields improved energies and forces compared with existing parameterizations, and is, therefore, useful for the applications in molecular dynamics or global structural prediction. The O–Ba subset of the parameters is then used to investigate the low‐dimensional phase diagram of Ba–O using global structural prediction techniques. The resulting convex hull of stability is validated using DFT, and the most interesting phases are subsequently analyzed and characterized. Among the stable systems, superoxides, peroxides, BaO, and oxygen‐deficient phases are found with different geometrical arrangements and electronic properties.

YS2 monolayer as a high-efficient anode material for rechargeable Li-ion and Na-ion batteries

Here, we propose a new two-dimensional (2D) monolayer YS2 and investigate its properties as electrode material for Li-ion and Na-ion batteries (LIBs and NIBs), by combining the density functional theory (DFT) and the crystal structure prediction techniques. The results of phonon dispersion and ab-initio molecular dynamics (AIMD) simulations confirm that it is dynamically and thermally stable. The adsorption energies of Li and Na on YS2 are −2.038 and −1.939 eV, respectively. Besides, the superior electrical conductivity is maintained during the whole lithiation/sodiumation process. The calculated diffusion energy barriers for Li and Na on this YS2 monolayer are 0.33 and 0.24 eV, respectively, demonstrating a fast ion transfer rate. The theoretical specific capacities for Li and Na adatoms on YS2 are both 350 mA h g−1, comparable to the graphite electrode. These features indicate that 2D YS2 monolayer could be considered as a promising electrode material for both LIBs and NIBs.

Isolation and Characterization of C-C Chemokine Ligand 7 (CCL7) in Cynomolgus Macaques

Cynomolgus macaques (Macaca fascicularis) are an established animal model of asthma, which exhibit different responses to allergen exposure that are clinically relevant. The chemokine ligand gene (CCL7) encodes Monocyte Chemotactic Protein-3, which has an important role in asthma pathogenesis. While CCL7 polymorphism in humans is associated with asthma phenotype, very little is known about CCL7 in nonhuman primate models of respiratory disease. The objective of this study was to isolate and characterize CCL7 gene in cynomolgus macaques of Indonesian origin. In this study, we used sequencing and bioinformatics technique for gene isolation, characterization, and protein 3D structure prediction. We isolated a 2253 base-pair (bp) sequence of CCL7 in cynomolgus macaques, which exhibited 95% similarity in coding sequence to human CCL7. The amino acid sequence was more closely clustered with human CCL7 than with that of rodents. Importantly, the predictive protein structure of CCL7 was similar to that in humans. These similarities in CCL7 suggests the potential of cynomolgus macaque as a translational model to study asthma, particularly in the context of genetics and role of chemokines such as CCL7.

Isolation and Characterization of C-C Chemokine Ligand 7 (CCL7) in Cynomolgus Macaques

Cynomolgus macaques (Macaca fascicularis) are an established animal model of asthma, which exhibit different responses to allergen exposure that are clinically relevant. The chemokine ligand gene (CCL7) encodes Monocyte Chemotactic Protein-3, which has an important role in asthma pathogenesis. While CCL7 polymorphism in humans is associated with asthma phenotype, very little is known about CCL7 in nonhuman primate models of respiratory disease. The objective of this study was to isolate and characterize CCL7 gene in cynomolgus macaques of Indonesian origin. In this study, we used sequencing and bioinformatics technique for gene isolation, characterization, and protein 3D structure prediction. We isolated a 2253 base-pair (bp) sequence of CCL7 in cynomolgus macaques, which exhibited 95% similarity in coding sequence to human CCL7. The amino acid sequence was more closely clustered with human CCL7 than with that of rodents. Importantly, the predictive protein structure of CCL7 was similar to that in humans. These similarities in CCL7 suggests the potential of cynomolgus macaque as a translational model to study asthma, particularly in the context of genetics and role of chemokines such as CCL7.

Introducing "best single template" models as reference baseline for the Continuous Automated Model Evaluation (CAMEO).

Critical blind assessment of structure prediction techniques is crucial for the scientific community to establish the state of the art, identify bottlenecks, and guide future developments. In Critical Assessment of Techniques in Structure Prediction (CASP), human experts assess the performance of participating methods in relation to the difficulty of the prediction task in a biennial experiment on approximately 100 targets. Yet, the development of automated computational modeling methods requires more frequent evaluation cycles and larger sets of data. The "Continuous Automated Model EvaluatiOn (CAMEO)" platform complements CASP by conducting fully automated blind prediction evaluations based on the weekly pre-release of sequences of those structures, which are going to be published in the next release of the Protein Data Bank (PDB). Each week, CAMEO publishes benchmarking results for predictions corresponding to a set of about 20 targets collected during a 4-day prediction window. CAMEO benchmarking data are generated consistently for all methods at the same point in time, enabling developers to cross-validate their method's performance, and referring to their results in publications. Many successful participants of CASP have used CAMEO-either by directly benchmarking their methods within the system or by comparing their own performance to CAMEO reference data. CAMEO offers a variety of scores reflecting different aspects of structure modeling, for example, binding site accuracy, homo-oligomer interface quality, or accuracy of local model confidence estimates. By introducing the "bestSingleTemplate" method based on structure superpositions as a reference for the accuracy of 3D modeling predictions, CAMEO facilitates objective comparison of techniques and fosters the development of advanced methods.

Assessment of protein model structure accuracy estimation in CASP13: Challenges in the era of deep learning.

Scoring model structure is an essential component of protein structure prediction that can affect the prediction accuracy tremendously. Users of protein structure prediction results also need to score models to select the best models for their application studies. In Critical Assessment of techniques for protein Structure Prediction (CASP), model accuracy estimation methods have been tested in a blind fashion by providing models submitted by the tertiary structure prediction servers for scoring. In CASP13, model accuracy estimation results were evaluated in terms of both global and local structure accuracy. Global structure accuracy estimation was evaluated by the quality of the models selected by the global structure scores and by the absolute estimates of the global scores. Residue-wise, local structure accuracy estimations were evaluated by three different measures. A new measure introduced in CASP13 evaluates the ability to predict inaccurately modeled regions that may be improved by refinement. An intensive comparative analysis on CASP13 and the previous CASPs revealed that the tertiary structure models generated by the CASP13 servers show very distinct features. Higher consensus toward models of higher global accuracy appeared even for free modeling targets, and many models of high global accuracy were not well optimized at the atomic level. This is related to the new technology in CASP13, deep learning for tertiary contact prediction. The tertiary model structures generated by deep learning pose a new challenge for EMA (estimation of model accuracy) method developers. Model accuracy estimation itself is also an area where deep learning can potentially have an impact, although current EMA methods have not fully explored that direction.

High-accuracy refinement using Rosetta in CASP13.

Because proteins generally fold to their lowest free energy states, energy-guided refinement in principle should be able to systematically improve the quality of protein structure models generated using homologous structure or co-evolution derived information. However, because of the high dimensionality of the search space, there are far more ways to degrade the quality of a near native model than to improve it, and hence, refinement methods are very sensitive to energy function errors. In the 13th Critial Assessment of techniques for protein Structure Prediction (CASP13), we sought to carry out a thorough search for low energy states in the neighborhood of a starting model using restraints to avoid straying too far. The approach was reasonably successful in improving both regions largely incorrect in the starting models as well as core regions that started out closer to the correct structure. Models with GDT-HA over 70 were obtained for five targets and for one of those, an accuracy of 0.5 å backbone root-mean-square deviation (RMSD) was achieved. An important current challenge is to improve performance in refining oligomers and larger proteins, for which the search problem remains extremely difficult.

Robust double Weyl semimetal phase in a nonmagnetic hexagonal lattice system

We propose a simple strategy to find possible linear double Weyl semimetals (WSMs) among nonmagnetic materials. This strategy can reproduce several known robust WSM candidates, covering orthorhombic, tetragonal, and cubic lattice systems. This kind of WSM hosts Weyl points (WPs) carrying double chiral charges when spin degeneracy is considered while spin-orbit coupling (SOC) is switched off, and dispersions along all three directions are linear, distinct from the parabolic double WPs proposed in $\mathrm{HgC}{\mathrm{r}}_{2}\mathrm{S}{\mathrm{e}}_{4}$. When SOC is included, these double-charge WPs split into two single spin-1/2 WPs with equal chirality. Following this strategy and combining with ab initio structural prediction techniques, we propose a candidate for such kind of WSMs, rooted in a high-pressure phase of $\mathrm{Ti}{\mathrm{S}}_{2}$ with space group $P\text{\ensuremath{-}}62m$. In this system, all the WPs are pinned at the same energy due to crystal symmetry. In addition, the minimum spacing between opposite-chirality WPs is $\ensuremath{\sim}0.23\phantom{\rule{0.16em}{0ex}}\AA{}{}^{\ensuremath{-}1}$, which makes it a promising robust WSM candidate.

Monolayer Zr2B2: A promising two-dimensional anode material for Li-ion batteries

Aiming to find an excellent anode material for Li-ion batteries, we report the two-dimensional Zr2B2 monolayer combining the density functional theory and crystal structure prediction techniques. We find that the monolayer Zr2B2 has initial metallicity and good stability, and keeps excellent electrical conductivity during the whole process of lithiation. The adsorption energy of lithium is −0.628 eV, which is enough to make sure the stability of the processing of lithiation. Furthermore, the ultralow diffusion energy barrier of Li ion on the surface of the monolayer Zr2B2 (17 meV) indicates an excellent charge-discharge rate. The theoretical specific capacity of 526 mA h g−1 is larger than that of the commercial graphite electrode. All these results propose the hexagonal monolayer Zr2B2 as an excellent anode material for Li-ion batteries.

High-temperature superconductivity in alkaline and rare earth polyhydrides at high pressure: A theoretical perspective.

The theoretical exploration of the phase diagrams of binary hydrides under pressure using ab initio crystal structure prediction techniques coupled with first principles calculations has led to the in silico discovery of numerous novel superconducting materials. This Perspective focuses on the alkaline earth and rare earth polyhydrides whose superconducting critical temperature, Tc, was predicted to be above the boiling point of liquid nitrogen. After providing a brief overview of the computational protocol used to predict the structures of stable and metastable hydrides under pressure, we outline the equations that can be employed to estimate Tc. The systems with a high Tc can be classified according to the motifs found in their hydrogenic lattices. The highest Tcs are found for cages that are reminiscent of clathrates and the lowest for systems that contain atomic and molecular hydrogen. A wide variety of hydrogenic motifs including 1- and 2-dimensional lattices, as well as H10 δ- molecular units comprising fused H5 δ- pentagons, are present in phases with intermediate Tcs. Some of these phases are predicted to be superconducting at room temperature. Some may have recently been synthesized in diamond anvil cells.

Comparing parallel algorithms for van der waals energy with cell-list technique for protein structure prediction

Comparing parallel algorithms for van der waals energy with cell-list technique for protein structure prediction