Initio Prediction Research Articles

Predicting the crystal structure of hbox {N}_5hbox {AsF}_6 high energy density material using ab initio evolutionary algorithms

hbox {N}_5hbox {AsF}_6 is the first successfully synthesized salt that has a polymeric nitrogen moeity (hbox {N}_5^+). Although 12 other hbox {N}_5^+ salts followed, with hbox {N}_5hbox {SbF}_6 and hbox {N}_5hbox {Sb}_2hbox {F}_{11} being the most stable, the crystal structure of hbox {N}_5hbox {AsF}_6 remains unknown. Currently, it is impossible to experimentally determine the structures of hbox {N}_5hbox {AsF}_6 due to its marginal stability and explosive nature. Here, following an ab initio evolutionary prediction and using only the stoichiometry of hbox {N}_5hbox {AsF}_6 as a starting point, we were able to reveal the crystal structure of this high energy density material (HEDM). The hbox {C}_{2V} symmetry of the hbox {N}_5^+ cation, as suggested from earlier investigations, is confirmed to be the symmetry adopted by this polymeric nitrogen within the crystal. This result gave full confidence in the validity of this crystal prediction approach. While stability of the hbox {N}_5^+ within the crystal is found to be driven by electronic considerations, the marginal stability of this HEDM is found to be related to a partial softening of its phonon modes.

Telomere length de novo assembly of all 7 chromosomes and mitogenome sequencing of the model entomopathogenic fungus, Metarhizium brunneum, by means of a novel assembly pipeline

BackgroundMore accurate and complete reference genomes have improved understanding of gene function, biology, and evolutionary mechanisms. Hybrid genome assembly approaches leverage benefits of both long, relatively error-prone reads from third-generation sequencing technologies and short, accurate reads from second-generation sequencing technologies, to produce more accurate and contiguous de novo genome assemblies in comparison to using either technology independently. In this study, we present a novel hybrid assembly pipeline that allowed for both mitogenome de novo assembly and telomere length de novo assembly of all 7 chromosomes of the model entomopathogenic fungus, Metarhizium brunneum.ResultsThe improved assembly allowed for better ab initio gene prediction and a more BUSCO complete proteome set has been generated in comparison to the eight current NCBI reference Metarhizium spp. genomes. Remarkably, we note that including the mitogenome in ab initio gene prediction training improved overall gene prediction. The assembly was further validated by comparing contig assembly agreement across various assemblers, assessing the assembly performance of each tool. Genomic synteny and orthologous protein clusters were compared between Metarhizium brunneum and three other Hypocreales species with complete genomes, identifying core proteins, and listing orthologous protein clusters shared uniquely between the two entomopathogenic fungal species, so as to further facilitate the understanding of molecular mechanisms underpinning fungal-insect pathogenesis.ConclusionsThe novel assembly pipeline may be used for other haploid fungal species, facilitating the need to produce high-quality reference fungal genomes, leading to better understanding of fungal genomic evolution, chromosome structuring and gene regulation.

DeepDist: real-value inter-residue distance prediction with deep residual convolutional network

BackgroundDriven by deep learning, inter-residue contact/distance prediction has been significantly improved and substantially enhanced ab initio protein structure prediction. Currently, most of the distance prediction methods classify inter-residue distances into multiple distance intervals instead of directly predicting real-value distances. The output of the former has to be converted into real-value distances to be used in tertiary structure prediction.ResultsTo explore the potentials of predicting real-value inter-residue distances, we develop a multi-task deep learning distance predictor (DeepDist) based on new residual convolutional network architectures to simultaneously predict real-value inter-residue distances and classify them into multiple distance intervals. Tested on 43 CASP13 hard domains, DeepDist achieves comparable performance in real-value distance prediction and multi-class distance prediction. The average mean square error (MSE) of DeepDist’s real-value distance prediction is 0.896 Å2 when filtering out the predicted distance ≥ 16 Å, which is lower than 1.003 Å2 of DeepDist’s multi-class distance prediction. When distance predictions are converted into contact predictions at 8 Å threshold (the standard threshold in the field), the precision of top L/5 and L/2 contact predictions of DeepDist’s multi-class distance prediction is 79.3% and 66.1%, respectively, higher than 78.6% and 64.5% of its real-value distance prediction and the best results in the CASP13 experiment.ConclusionsDeepDist can predict inter-residue distances well and improve binary contact prediction over the existing state-of-the-art methods. Moreover, the predicted real-value distances can be directly used to reconstruct protein tertiary structures better than multi-class distance predictions due to the lower MSE. Finally, we demonstrate that predicting the real-value distance map and multi-class distance map at the same time performs better than predicting real-value distances alone.

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.

Selecting Near-Native Protein Structures from Predicted Decoy Sets Using Ordered Graphlet Degree Similarity.

Effective prediction of protein tertiary structure from sequence is an important and challenging problem in computational structural biology. Ab initio protein structure prediction is based on amino acid sequence alone, thus, it has a wide application area. With the ab initio method, a large number of candidate protein structures called decoy set can be predicted, however, it is a difficult problem to select a good near-native structure from the predicted decoy set. In this work we propose a new method for selecting the near-native structure from the decoy set based on both contact map overlap (CMO) and graphlets. By generalizing graphlets to ordered graphs, and using a dynamic programming to select the optimal alignment with an introduced gap penalty, a GR_score is defined for calculating the similarity between the three-dimensional (3D) decoy structures. The proposed method was applied to all 54 single-domain targets in CASP11 and all 43 targets in CASP10, and ensemble clustering was used to cluster the protein decoy structures based on the computed CR_scores. The most popular centroid structure was selected as the near-native structure. The experiments showed that compared to the SPICKER method, which is used in I-TASSER, the proposed method can usually select better near-native structures in terms of the similarity between the selected structure and the true native structure.

Molecular basis of benzimidazole inhibitors to hepatitis C virus envelope glycoprotein

The molecular basis for the inhibitory action of a benzimidazole inhibitor compound to hepatitis C virus E1 envelope protein was examined computationally. Structures for the wild-type E1 protein and seven mutants were modelled using an ab initio protein structure prediction algorithm, and these models were docked with the benzimidazole inhibitor. Top-ranked conformers for each docked structure were examined in the context of the putative function of the inhibitor that blocks fusion of the envelope protein to the host cells. The results for the wild-type protein and that for a series of mutants containing reported single, double and triple resistance mutations demonstrate that the inhibitor binds in the vicinity of residue Phe99 (at position 291 in the encoded polyprotein) at the C-terminal end of a putative fusion domain. In so doing, the compound inhibits the virus from fusing to host cells and blocks viral replication in accord with the results from cell-based infection studies.

Assessing Predicted Contacts for Building Protein Three-Dimensional Models.

Recent successes of contact-guided protein structure prediction methods have revived interest in solving the long-standing problem of ab initio protein structure prediction. With homology modeling failing for many protein sequences that do not have templates, contact-guided structure prediction has shown promise, and consequently, contact prediction has gained a lot of interest recently. Although a few dozen contact prediction tools are already currently available as web servers and downloadables, not enough research has been done towards using existing measures like precision and recall to evaluate these contacts with the goal of building three-dimensional models. Moreover, when we do not have a native structure for a set of predicted contacts, the only analysis we can perform is a simple contact map visualization of the predicted contacts. A wider and more rigorous assessment of the predicted contacts is needed, in order to build tertiary structure models. This chapter discusses instructions and protocols for using tools and applying techniques in order to assess predicted contacts for building three-dimensional models.

Ab Initio Chemical Kinetics for the HCCO + H Reaction

ABSTRACTKetenyl radical (HCCO) is an important hydrocarbon combustion intermediate. The mechanisms and kinetics for the reaction of HCCO (X2A″) with H(2S) occurring on both singlet and triplet surfaces have been studied by a combination of ab initio calculations and rate constant predictions at the CCSD(T)/6-311++G(3df,2p)//CCSD/6-311++G(d,p) level of theory. The kinetics and product branching ratios have been investigated in the temperature range of 297–3000 K by variational transition state and Rice–Ramsperger–Kassel–Marcus (RRKM) theories for the production of CH2(a1A1) + CO(X1Σ+) and CH2(X3B1) + CO(X1Σ+). Our prediction for the primary product CH2(a1A1) + CO(X1Σ+) formation is in good agreement with earlier experimental results. The pressure independent rate constant for this channel can be expressed by k1(T) = 8.62 × 10–11T0.16exp(–20/T) cm3 molecule–1 s–1. For the production of CH2(X3B1) + CO(X1Σ+), the rate constant k2 can be represented as k2(T) = 7.63 × 10–16T1.56exp(–386/T) cm3 molecule–1 s–1. The predicted product branching ratios for the reaction are in close agreement with experimental data as well. We also predicted the heat of formation at 0 K for 2HCCO, 3CCO, and 1CCO by CCSD(T)/6-311++G(3df,2p), CBS-QB3, and G2M; the values are in good agreement among one another. Specifically, the CCSD(T) values are: ΔfH°(HCCO, X2A″) = 42.52 ± 0.70; ΔfH°(CCO, X3Σg) = 91.50 ± 0.54; and ΔfH°(CCO, a1Δ) = 110.22 ± 0.54 kcal/mol.

Accurate Ab Initio and Template-Based Prediction of Short Intrinsically-Disordered Regions by Bidirectional Recurrent Neural Networks Trained on Large-Scale Datasets.

Intrinsically-disordered regions lack a well-defined 3D structure, but play key roles in determining the function of many proteins. Although predictors of disorder have been shown to achieve relatively high rates of correct classification of these segments, improvements over the the years have been slow, and accurate methods are needed that are capable of accommodating the ever-increasing amount of structurally-determined protein sequences to try to boost predictive performances. In this paper, we propose a predictor for short disordered regions based on bidirectional recurrent neural networks and tested by rigorous five-fold cross-validation on a large, non-redundant dataset collected from MobiDB, a new comprehensive source of protein disorder annotations. The system exploits sequence and structural information in the forms of frequency profiles, predicted secondary structure and solvent accessibility and direct disorder annotations from homologous protein structures (templates) deposited in the Protein Data Bank. The contributions of sequence, structure and homology information result in large improvements in predictive accuracy. Additionally, the large scale of the training set leads to low false positive rates, making our systems a robust and efficient way to address high-throughput disorder prediction.

A Gibbs free energy formula for protein folding derived from quantum statistics

The fundamental law for protein folding is the thermodynamic principle. The amino acid sequence of a protein determines its native structure and the native structure has the minimum Gibbs free energy. Lacking of a Gibbs free energy formula is the reason that all ab initio protein structure prediction only empirical and various empirical energy surfaces or landscapes are introduced to fill the gap. We make a quantum mechanics derivation of the Gibbs free energy formula G(X) using quantum statistics for a single conformation X. For simplicity, only monomeric self folding globular proteins are considered.

A Parallel Framework for Multipoint Spiral Search in ab Initio Protein Structure Prediction.

Protein structure prediction is computationally a very challenging problem. A large number of existing search algorithms attempt to solve the problem by exploring possible structures and finding the one with the minimum free energy. However, these algorithms perform poorly on large sized proteins due to an astronomically wide search space. In this paper, we present a multipoint spiral search framework that uses parallel processing techniques to expedite exploration by starting from different points. In our approach, a set of random initial solutions are generated and distributed to different threads. We allow each thread to run for a predefined period of time. The improved solutions are stored threadwise. When the threads finish, the solutions are merged together and the duplicates are removed. A selected distinct set of solutions are then split to different threads again. In our ab initio protein structure prediction method, we use the three-dimensional face-centred-cubic lattice for structure-backbone mapping. We use both the low resolution hydrophobic-polar energy model and the high-resolution 20 × 20 energy model for search guiding. The experimental results show that our new parallel framework significantly improves the results obtained by the state-of-the-art single-point search approaches for both energy models on three-dimensional face-centred-cubic lattice. We also experimentally show the effectiveness of mixing energy models within parallel threads.

Automated alignment-based curation of gene models in filamentous fungi

BackgroundAutomated gene-calling is still an error-prone process, particularly for the highly plastic genomes of fungal species. Improvement through quality control and manual curation of gene models is a time-consuming process that requires skilled biologists and is only marginally performed. The wealth of available fungal genomes has not yet been exploited by an automated method that applies quality control of gene models in order to obtain more accurate genome annotations.ResultsWe provide a novel method named alignment-based fungal gene prediction (ABFGP) that is particularly suitable for plastic genomes like those of fungi. It can assess gene models on a gene-by-gene basis making use of informant gene loci. Its performance was benchmarked on 6,965 gene models confirmed by full-length unigenes from ten different fungi. 79.4% of all gene models were correctly predicted by ABFGP. It improves the output of ab initio gene prediction software due to a higher sensitivity and precision for all gene model components. Applicability of the method was shown by revisiting the annotations of six different fungi, using gene loci from up to 29 fungal genomes as informants. Between 7,231 and 8,337 genes were assessed by ABFGP and for each genome between 1,724 and 3,505 gene model revisions were proposed. The reliability of the proposed gene models is assessed by an a posteriori introspection procedure of each intron and exon in the multiple gene model alignment. The total number and type of proposed gene model revisions in the six fungal genomes is correlated to the quality of the genome assembly, and to sequencing strategies used in the sequencing centre, highlighting different types of errors in different annotation pipelines. The ABFGP method is particularly successful in discovering sequence errors and/or disruptive mutations causing truncated and erroneous gene models.ConclusionsThe ABFGP method is an accurate and fully automated quality control method for fungal gene catalogues that can be easily implemented into existing annotation pipelines. With the exponential release of new genomes, the ABFGP method will help decreasing the number of gene models that require additional manual curation.

Functional hybrid co-crystals of humic substances: a growth forecast

Basing our reasoning on the only known and available crystalline structure of a hybrid co-crystal of caffeic acid, we extended our study to the analysis of the behaviour of another humic substance, the protocatechuic acid. The abilities and tendencies of both these compounds to give rise to stable hybrid co-crystals with the elements of group I were studied by predicting, via molecular dynamics simulations, their crystalline setups on the basis of a detailed energetic analysis, which allows the detection of the most stable possible structures and their layouts. Without claiming to have performed an ab initio crystal structure prediction, we tried instead to determine the tendencies of a specific set of humic substances to give rise to a particular type of hybrid co-crystals.

UnSplicer: mapping spliced RNA-seq reads in compact genomes and filtering noisy splicing

Accurate mapping of spliced RNA-Seq reads to genomic DNA has been known as a challenging problem. Despite significant efforts invested in developing efficient algorithms, with the human genome as a primary focus, the best solution is still not known. A recently introduced tool, TrueSight, has demonstrated better performance compared with earlier developed algorithms such as TopHat and MapSplice. To improve detection of splice junctions, TrueSight uses information on statistical patterns of nucleotide ordering in intronic and exonic DNA. This line of research led to yet another new algorithm, UnSplicer, designed for eukaryotic species with compact genomes where functional alternative splicing is likely to be dominated by splicing noise. Genome-specific parameters of the new algorithm are generated by GeneMark-ES, an ab initio gene prediction algorithm based on unsupervised training. UnSplicer shares several components with TrueSight; the difference lies in the training strategy and the classification algorithm. We tested UnSplicer on RNA-Seq data sets of Arabidopsis thaliana, Caenorhabditis elegans, Cryptococcus neoformans and Drosophila melanogaster. We have shown that splice junctions inferred by UnSplicer are in better agreement with knowledge accumulated on these well-studied genomes than predictions made by earlier developed tools.

Ab initio crystal structure and charge density distribution of a highly energetic 2,4-dinitrobenzoic acid molecule

The energetic parameters, such as density, bond strength, and sensitivity of explosives/ propellants decide their detonation power and safety. Experimentally, optimization of these parameters is found to be a difficult task; therefore, prior to synthesis, it makes sense to estimate these parameters by computational techniques, ab initio crystal structure prediction, and quantum chemical calculation coupled with the AIM analysis. Here, we predict the density of an energetic 2,4-dinitrobenzoic acid (DNBA) molecule from different ab initio crystal structure models and validate the results through comparisons with experimental data. The bond topological characterization reveals that the C NO2 bonds are the weakest bonds and are identified as sensitive bonds in the molecule. The bond sensitivity is estimated from Murray’s method. The impact sensitivity of this molecule is also calculated. Large negative electrostatic potential regions are found near the NO2 and carboxylic groups, which are the reactive sites of the molecule.

Gene Identification Programs in Bread Wheat: A Comparison Study

Seven ab initio web-based gene prediction programs (i.e., AUGUSTUS, BGF, Fgenesh, Fgenesh+, GeneID, Genemark.hmm, and HMMgene) were assessed to compare their prediction accuracy using protein-coding sequences of bread wheat. At both nucleotide and exon levels, Fgenesh+ was deduced as the superior program and BGF followed by Fgenesh were resided in the next positions, respectively. Conversely, at gene level, Fgenesh with the value of predicting more than 75% of all the genes precisely, concluded as the best ones. It was also found out that programs such as Fgenesh+, BGF, and Fgenesh, because of harboring the highest percentage of correct predictive exons appear to be much more applicable in achieving more trustworthy results, while using both GeneID and HMMgene the percentage of false negatives would be expected to enhance. Regarding initial exon, overall, the frequency of accurate recognition of 3′ boundary was significantly higher than that of 5′ and the reverse was true if terminal exon is taken into account. Lastly, HMMgene and Genemark.hmm, overall, presented independent tendency against GC content, while the others appear to be slightly more sensitive if GC-poor sequences are employed. Our results, overall, exhibited that to make adequate opportunity in acquiring remarkable results, gene finders still need additional improvements.

Prediction of Protein Secondary Structure Using Feature Selection and Analysis Approach

The prediction of the secondary structure of a protein from its amino acid sequence is an important step towards the prediction of its three-dimensional structure. However, the accuracy of ab initio secondary structure prediction from sequence is about 80% currently, which is still far from satisfactory. In this study, we proposed a novel method that uses binomial distribution to optimize tetrapeptide structural words and increment of diversity with quadratic discriminant to perform prediction for protein three-state secondary structure. A benchmark dataset including 2,640 proteins with sequence identity of less than 25% was used to train and test the proposed method. The results indicate that overall accuracy of 87.8% was achieved in secondary structure prediction by using ten-fold cross-validation. Moreover, the accuracy of predicted secondary structures ranges from 84 to 89% at the level of residue. These results suggest that the feature selection technique can detect the optimized tetrapeptide structural words which affect the accuracy of predicted secondary structures.

Genomic law guided gene prediction in fungi and metazoans

Protein coding gene prediction by computational approaches is a fundamental step for genome annotation. However, it is a challenge to accurately predict eukaryotic genes in silico. By surveying the model genomes, we found that the Spearman's rank correlation coefficient between the number of experimental-verified genes and the size of genomes was 0.96 for all eukaryotes except plants, indicating the relationship between genome size and the number of coding genes can be expressed with a monotonic function. Regression analysis found that the relationship of total protein coding genes over genome size followed a logarithmic equation. We integrated the equation into ab initio gene prediction software to guide the gene prediction by constraining the total number of predicted genes. We evaluated the software in three eukaryotic genomes. Results showed that >90% of false positive predictions were removed while >80% of true positives were retained, resulting in much higher specificity.

ChemInform Abstract: The Microwave Spectrum and the Molecular Structure of Bromochlorodifluoromethane (BCF).

Abstract The microwave spectrum of BCF has been assigned in the frequency region 48–72 GHz, and the rotational constants, some quartic distortion constants, and the bromine quadrupole coupling tensor have been obtained for the four isotopic species. Using the obtained 12 rotational constants, and r 0 structure has been fitted and compared to ab initio and semi-empirical theoretical structural predictions, along with other halogenated methane-type molecules. The present study opens the way for its possible spectroscopic atmospheric detection.

The dissociation energy of N2

The requirements for very accurate ab initio quantum chemical prediction of dissociation energies are examined using a detailed investigation of the nitrogen molecule. Although agreement with experiment to within 1 kcal/mol is not achieved even with the most elaborate multireference CI (configuration interaction) wave functions and largest basis sets currently feasible, it is possible to obtain agreement to within about 2 kcal/mol, or 1 percent of the dissociation energy. At this level it is necessary to account for core-valence correlation effects and to include up to h-type functions in the basis. The effect of i-type functions, the use of different reference configuration spaces, and basis set superposition error were also investigated. After discussing these results, the remaining sources of error in our best calculations are examined.