Ab Initio Structure Prediction Research Articles

Reducing Ensembles of Protein Tertiary Structures Generated De Novo via Clustering.

Controlling the quality of tertiary structures computed for a protein molecule remains a central challenge in de-novo protein structure prediction. The rule of thumb is to generate as many structures as can be afforded, effectively acknowledging that having more structures increases the likelihood that some will reside near the sought biologically-active structure. A major drawback with this approach is that computing a large number of structures imposes time and space costs. In this paper, we propose a novel clustering-based approach which we demonstrate to significantly reduce an ensemble of generated structures without sacrificing quality. Evaluations are related on both benchmark and CASP target proteins. Structure ensembles subjected to the proposed approach and the source code of the proposed approach are publicly-available at the links provided in Section 1.

A benchmark study of ab initio gene prediction methods in diverse eukaryotic organisms

BackgroundThe draft genome assemblies produced by new sequencing technologies present important challenges for automatic gene prediction pipelines, leading to less accurate gene models. New benchmark methods are needed to evaluate the accuracy of gene prediction methods in the face of incomplete genome assemblies, low genome coverage and quality, complex gene structures, or a lack of suitable sequences for evidence-based annotations.ResultsWe describe the construction of a new benchmark, called G3PO (benchmark for Gene and Protein Prediction PrOgrams), designed to represent many of the typical challenges faced by current genome annotation projects. The benchmark is based on a carefully validated and curated set of real eukaryotic genes from 147 phylogenetically disperse organisms, and a number of test sets are defined to evaluate the effects of different features, including genome sequence quality, gene structure complexity, protein length, etc. We used the benchmark to perform an independent comparative analysis of the most widely used ab initio gene prediction programs and identified the main strengths and weaknesses of the programs. More importantly, we highlight a number of features that could be exploited in order to improve the accuracy of current prediction tools.ConclusionsThe experiments showed that ab initio gene structure prediction is a very challenging task, which should be further investigated. We believe that the baseline results associated with the complex gene test sets in G3PO provide useful guidelines for future studies.

Ab initio Structure Prediction Methods for Battery Materials

Portable electronic devices, electric vehicles and stationary energy storage applications, which encourage carbon-neutral energy alternatives, are driving demand for batteries that have concurrently higher energy densities, faster charging rates, safer operation and lower prices. These demands can no longer be met by incrementally improving existing technologies but require the discovery of new materials with exceptional properties. Experimental materials discovery is both expensive and time consuming: before the efficacy of a new battery material can be assessed, its synthesis and stability must be well-understood. Computational materials modelling can expedite this process by predicting novel materials, both in stand-alone theoretical calculations and in tandem with experiments. In this review, we describe a materials discovery framework based on density functional theory (DFT) to predict the properties of electrode and solid-electrolyte materials and validate these predictions experimentally. First, we discuss crystal structure prediction using the ab initio random structure searching (AIRSS) method. Next, we describe how DFT results allow us to predict which phases form during electrode cycling, as well as the electrode voltage profile and maximum theoretical capacity. We go on to explain how DFT can be used to simulate experimentally measurable properties such as nuclear magnetic resonance (NMR) spectra and ionic conductivities. We illustrate the described workflow with multiple experimentally validated examples: materials for lithium-ion and sodium-ion anodes and lithium-ion solid electrolytes. These examples highlight the power of combining computation with experiment to advance battery materials research.

A malaria parasite subtilisin propeptide-like protein is a potent inhibitor of the egress protease SUB1.

Subtilisin-like serine peptidases (subtilases) play important roles in the life cycle of many organisms, including the protozoan parasites that are the causative agent of malaria, Plasmodium spp. As with other peptidases, subtilase proteolytic activity has to be tightly regulated in order to prevent potentially deleterious uncontrolled protein degradation. Maturation of most subtilases requires the presence of an N-terminal propeptide that facilitates folding of the catalytic domain. Following its proteolytic cleavage, the propeptide acts as a transient, tightly bound inhibitor until its eventual complete removal to generate active protease. Here we report the identification of a stand-alone malaria parasite propeptide-like protein, called SUB1-ProM, encoded by a conserved gene that lies in a highly syntenic locus adjacent to three of the four subtilisin-like genes in the Plasmodium genome. Template-based modelling and ab initio structure prediction showed that the SUB1-ProM core structure is most similar to the X-ray crystal structure of the propeptide of SUB1, an essential parasite subtilase that is discharged into the parasitophorous vacuole (PV) to trigger parasite release (egress) from infected host cells. Recombinant Plasmodium falciparum SUB1-ProM was found to be a fast-binding, potent inhibitor of P. falciparum SUB1, but not of the only other essential blood-stage parasite subtilase, SUB2, or of other proteases examined. Mass-spectrometry and immunofluorescence showed that SUB1-ProM is expressed in the PV of blood stage P. falciparum, where it may act as an endogenous inhibitor to regulate SUB1 activity in the parasite.

Structure of the water-splitting photocatalyst oxysulfide α-LaOInS2 and ab initio prediction of new polymorphs.

We unveil the structure and investigate the visible light water-splitting of the photocatalyst α-LaOInS2, the second polymorph in this composition. This remarkable oxysulfide exhibits rare mixed anion InS5O octahedra leading to both O-2p and S-3p hybridized with indium states in the vicinity of the Fermi level. Ab initio structure prediction shows the stability of such heteroleptic environments and points to other hypothetical polymorphs.

Molecular replacement using structure predictions from databases.

Molecular replacement (MR) is the predominant route to solution of the phase problem in macromolecular crystallography. Where the lack of a suitable homologue precludes conventional MR, one option is to predict the target structure using bioinformatics. Such modelling, in the absence of homologous templates, is called ab initio or de novo modelling. Recently, the accuracy of such models has improved significantly as a result of the availability, in many cases, of residue-contact predictions derived from evolutionary covariance analysis. Covariance-assisted ab initio models representing structurally uncharacterized Pfam families are now available on a large scale in databases, potentially representing a valuable and easily accessible supplement to the PDB as a source of search models. Here, the unconventional MR pipeline AMPLE is employed to explore the value of structure predictions in the GREMLIN and PconsFam databases. It was tested whether these deposited predictions, processed in various ways, could solve the structures of PDB entries that were subsequently deposited. The results were encouraging: nine of 27 GREMLIN cases were solved, covering target lengths of 109-355 residues and a resolution range of 1.4-2.9 Å, and with target-model shared sequence identity as low as 20%. The cluster-and-truncate approach in AMPLE proved to be essential for most successes. For the overall lower quality structure predictions in the PconsFam database, remodelling with Rosetta within the AMPLE pipeline proved to be the best approach, generating ensemble search models from single-structure deposits. Finally, it is shown that the AMPLE-obtained search models deriving from GREMLIN deposits are of sufficiently high quality to be selected by the sequence-independent MR pipeline SIMBAD. Overall, the results help to point the way towards the optimal use of the expanding databases of ab initio structure predictions.

Fueling ab initio folding with marine metagenomics enables structure and function predictions of new protein families

IntroductionThe ocean microbiome represents one of the largest microbiomes and produces nearly half of the primary energy on the planet through photosynthesis or chemosynthesis. Using recent advances in marine genomics, we explore new applications of oceanic metagenomes for protein structure and function prediction.ResultsBy processing 1.3 TB of high-quality reads from the Tara Oceans data, we obtain 97 million non-redundant genes. Of the 5721 Pfam families that lack experimental structures, 2801 have at least one member associated with the oceanic metagenomics dataset. We apply C-QUARK, a deep-learning contact-guided ab initio structure prediction pipeline, to model 27 families, where 20 are predicted to have a reliable fold with estimated template modeling score (TM-score) at least 0.5. Detailed analyses reveal that the abundance of microbial genera in the ocean is highly correlated to the frequency of occurrence in the modeled Pfam families, suggesting the significant role of the Tara Oceans genomes in the contact-map prediction and subsequent ab initio folding simulations. Of interesting note, PF15461, which has a majority of members coming from ocean-related bacteria, is identified as an important photosynthetic protein by structure-based function annotations. The pipeline is extended to a set of 417 Pfam families, built on the combination of Tara with other metagenomics datasets, which results in 235 families with an estimated TM-score over 0.5.ConclusionsThese results demonstrate a new avenue to improve the capacity of protein structure and function modeling through marine metagenomics, especially for difficult proteins with few homologous sequences.

RNA 3D structure prediction guided by independent folding of homologous sequences

BackgroundThe understanding of the importance of RNA has dramatically changed over recent years. As in the case of proteins, the function of an RNA molecule is encoded in its tertiary structure, which in turn is determined by the molecule’s sequence. The prediction of tertiary structures of complex RNAs is still a challenging task.ResultsUsing the observation that RNA sequences from the same RNA family fold into conserved structure, we test herein whether parallel modeling of RNA homologs can improve ab initio RNA structure prediction. EvoClustRNA is a multi-step modeling process, in which homologous sequences for the target sequence are selected using the Rfam database. Subsequently, independent folding simulations using Rosetta FARFAR and SimRNA are carried out. The model of the target sequence is selected based on the most common structural arrangement of the common helical fragments. As a test, on two blind RNA-Puzzles challenges, EvoClustRNA predictions ranked as the first of all submissions for the L-glutamine riboswitch and as the second for the ZMP riboswitch. Moreover, through a benchmark of known structures, we discovered several cases in which particular homologs were unusually amenable to structure recovery in folding simulations compared to the single original target sequence.ConclusionThis work, for the first time to our knowledge, demonstrates the importance of the selection of the target sequence from an alignment of an RNA family for the success of RNA 3D structure prediction. These observations prompt investigations into a new direction of research for checking 3D structure “foldability” or “predictability” of related RNA sequences to obtain accurate predictions. To support new research in this area, we provide all relevant scripts in a documented and ready-to-use form. By exploring new ideas and identifying limitations of the current RNA 3D structure prediction methods, this work is bringing us closer to the near-native computational RNA 3D models.

Structural Model of the ETR1 Ethylene Receptor Transmembrane Sensor Domain

The structure, mechanism of action and copper stoichiometry of the transmembrane sensor domain of the plant ethylene receptor ETR1 and homologs have remained elusive, hampering the understanding on how the perception of the plant hormone ethylene is transformed into a downstream signal. We generated the first structural model of the transmembrane sensor domain of ETR1 by integrating ab initio structure prediction and coevolutionary information. To refine and independently validate the model, we determined protein-related copper stoichiometries on purified receptor preparations and explored the helix arrangement by tryptophan scanning mutagenesis. All-atom molecular dynamics simulations of the dimeric model reveal how ethylene can bind proximal to the copper ions in the receptor, illustrating the initial stages of the ethylene perception process.

Validation of protein backbone structures calculated from NMR angular restraints using Rosetta.

Multidimensional solid-state NMR spectra of oriented membrane proteins can be used to infer the backbone torsion angles and hence the overall protein fold by measuring dipolar couplings and chemical shift anisotropies, which depend on the orientation of each peptide plane with respect to the external magnetic field. However, multiple peptide plane orientations can be consistent with a given set of angular restraints. This ambiguity is further exacerbated by experimental uncertainty in obtaining and interpretingsuch restraints. The previously developed algorithms for structure calculations using angular restraints typically involve a sequential walkthrough along the backbone to find the torsion angles between the consecutive peptide plane orientations that are consistent with the experimental data. This method is sensitive to experimental uncertainty in interpreting the peak positions of as low as ± 10Hz, often yielding high structural RMSDs for the calculated structures. Here we present a significantly improved version of the algorithm which includes the fitting of several peptide planes at once in order to prevent propagation of error along the backbone. In addition, a protocol has been devised for filtering the structural solutions using Rosetta scoring functions in order to find the structures that both fit the spectrum and satisfy bioinformatics restraints. The robustness of the new algorithm has been tested using synthetic angular restraints generated from the known structures for two proteins: a soluble protein 2gb1 (56 residues), chosen for its diverse secondary structure elements, i.e. an alpha-helix and two beta-sheets, and a membrane protein 4a2n, from which the first two transmembrane helices (having a total of 64 residues) have been used. Extensive simulations have been performed by varying the number of fitted planes, experimental error, and the number of NMR dimensions. It has been found that simultaneously fitting two peptide planes always shifted the distribution of the calculated structures toward lower structural RMSD values as compared to fitting a single torsion-angle pair. For each protein, irrespective of the simulation parameters, Rosetta was able to distinguish the most plausible structures, often having structural RMSDs lower than 2Å with respect to the original structure. This study establishes a framework for de-novo protein structure prediction using a combination of solid-state NMR angular restraints and bioinformatics.

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.

X-ray study and computational model of the solid solvate of [1,2,5]oxadiazolo[3,4-е][1,2,3,4]tetrazine 4,6-dioxide (FTDO) with benzene and ab initio crystal structure prediction of pure FTDO

X-ray study and computational modeling of [1,2,5]oxadiazole [3,4-e][1,2,3,4]tetrazine 4,6-dioxide (FTDO)–benzene (1 : 1) solvate structure have been carried out. Using the Atom-Atom Potentials Method, the original methodology and elaborated program packings the crystal structure of pure FTDO was predicted.

Identification and analysis of structurally critical fragments in HopS2

BackgroundAmong the diverse roles of the Type III secretion-system (T3SS), one of the notable functions is that it serves as unique nano machineries in gram-negative bacteria that facilitate the translocation of effector proteins from bacteria into their host. These effector proteins serve as potential targets to control the pathogenicity conferred to the bacteria. Despite being ideal choices to disrupt bacterial systems, it has been quite an ordeal in the recent times to experimentally reveal and establish a concrete sequence-structure-function relationship for these effector proteins. This work focuses on the disease-causing spectrum of an effector protein, HopS2 secreted by the phytopathogen Pseudomonas syringae pv. tomato DC3000.ResultsThe study addresses the structural attributes of HopS2 via a bioinformatics approach to by-pass some of the experimental shortcomings resulting in mining some critical regions in the effector protein. We have elucidated the functionally important regions of HopS2 with the assistance of sequence and structural analyses. The sequence based data supports the presence of important regions in HopS2 that are present in the other functional parts of Hop family proteins. Furthermore, these regions have been validated by an ab-initio structure prediction of the protein followed by 100 ns long molecular dynamics (MD) simulation. The assessment of these secondary structural regions has revealed the stability and importance of these regions in the protein structure.ConclusionsThe analysis has provided insights on important functional regions that may be vital to the effector functioning. In dearth of ample experimental evidence, such a bioinformatics approach has helped in the revelation of a few structural regions which will aid in future experiments to attain and evaluate the structural and functional aspects of this protein family.

First-principles investigation of Sc-III/IV under high pressure

Using an ab initio evolutionary structure prediction method in conjunction with density functional theory, we performed a systematic investigation of the structural transition of elemental scandium under pressure up to 250 GPa. Our prediction successfully reproduced several allotropes which have been reported in the literature, including the Sc-I, Sc-II, and Sc-V. Moreover, we observed a series of energetically degenerate and geometrically similar structures at 110--195 GPa, which can partly explain the experimental observations regarding the unsolved phases III and IV reported by Y. Akahama et al. [Phys. Rev. Lett. 94, 195503 (2005)]. A detailed comparison of the powder x-ray diffraction pattern suggested that the $Ccca\text{\ensuremath{-}}20$ phase is a likely candidate for the observed Sc-III, while Sc-IV may be explained by a structure with random stacking of two different structural units. We also used the candidate Sc-III structure as the model system to explore its superconducting behavior under pressures between 80 and 130 GPa. The predicted superconducting transition temperature ${T}_{\mathrm{c}}$ values are in satisfactory agreement with previous experimental results.

Synthetic protein alignments by CCMgen quantify noise in residue-residue contact prediction

Compensatory mutations between protein residues in physical contact can manifest themselves as statistical couplings between the corresponding columns in a multiple sequence alignment (MSA) of the protein family. Conversely, large coupling coefficients predict residue contacts. Methods for de-novo protein structure prediction based on this approach are becoming increasingly reliable. Their main limitation is the strong systematic and statistical noise in the estimation of coupling coefficients, which has so far limited their application to very large protein families. While most research has focused on improving predictions by adding external information, little progress has been made to improve the statistical procedure at the core, because our lack of understanding of the sources of noise poses a major obstacle. First, we show theoretically that the expectation value of the coupling score assuming no coupling is proportional to the product of the square roots of the column entropies, and we propose a simple entropy bias correction (EntC) that subtracts out this expectation value. Second, we show that the average product correction (APC) includes the correction of the entropy bias, partly explaining its success. Third, we have developed CCMgen, the first method for simulating protein evolution and generating realistic synthetic MSAs with pairwise statistical residue couplings. Fourth, to learn exact statistical models that reliably reproduce observed alignment statistics, we developed CCMpredPy, an implementation of the persistent contrastive divergence (PCD) method for exact inference. Fifth, we demonstrate how CCMgen and CCMpredPy can facilitate the development of contact prediction methods by analysing the systematic noise contributions from phylogeny and entropy. Using the entropy bias correction, we can disentangle both sources of noise and find that entropy contributes roughly twice as much noise as phylogeny.

Uranium polyhydrides at moderate pressures: Prediction, synthesis, and expected superconductivity.

Hydrogen-rich hydrides attract great attention due to recent theoretical (1) and then experimental discovery of record high-temperature superconductivity in H3S [T c = 203 K at 155 GPa (2)]. Here we search for stable uranium hydrides at pressures up to 500 GPa using ab initio evolutionary crystal structure prediction. Chemistry of the U-H system turned out to be extremely rich, with 14 new compounds, including hydrogen-rich UH5, UH6, U2H13, UH7, UH8, U2H17, and UH9. Their crystal structures are based on either common face-centered cubic or hexagonal close-packed uranium sublattice and unusual H8 cubic clusters. Our high-pressure experiments at 1 to 103 GPa confirm the predicted UH7, UH8, and three different phases of UH5, raising confidence about predictions of the other phases. Many of the newly predicted phases are expected to be high-temperature superconductors. The highest-T c superconductor is UH7, predicted to be thermodynamically stable at pressures above 22 GPa (with T c = 44 to 54 K), and this phase remains dynamically stable upon decompression to zero pressure (where it has T c = 57 to 66 K).

Secondary Structure and Contact Guided Differential Evolution for Protein Structure Prediction

Ab initio protein tertiary structure prediction is one of the long-standing problems in structural bioinformatics. With the help of residue-residue contact and secondary structure prediction information, the accuracy of ab initio structure prediction can be enhanced. In this study, an improved differential evolution with secondary structure and residue-residue contact information referred to as SCDE is proposed for protein structure prediction. In SCDE, two score models based on secondary structure and contact information are proposed, and two selection strategies, namely, secondary structure-based selection strategy and contact-based selection strategy, are designed to guide conformation space search. A probability distribution function is designed to balance these two selection strategies. Experimental results on a benchmark dataset with 28 proteins and four free model targets in CASP12 demonstrate that the proposed SCDE is effective and efficient.

From ab initio structure predictions to reaction calculations via EFT

We study how EFT can improve the description of reactions used to study the structure of exotic nuclei. Using Halo EFT helps constraining the potential that simulates the interaction between the projectile constituents. It enables us to include ab initio results within the reaction model and to clearly identify the nuclear properties probed by the reaction. New local chiral EFT nucleon-nucleon interactions can also be used to derive nucleus-nucleus optical potentials from first principles, which can provide reliable inputs in reaction modelling.

Boron monosulfide: Equation of state and pressure-induced phase transition

Quasi-hydrostatic compression of rhombohedral boron monosulfide (r-BS) has been studied up to 50 GPa at room temperature using diamond-anvil cells and angle-dispersive synchrotron X-ray diffraction. A fit of the experimental P–V data to the Vinet equation of state yields the bulk modulus B0 of 42.2(1.4) GPa and its first pressure derivative B0′ of 7.6(2) that are in excellent agreement with our ab initio calculations. Formation of a new high-pressure phase of boron monosulfide (hp-BS) has been observed above 35 GPa. According to ab initio evolutionary crystal structure predictions combined with Rietveld refinement of high-pressure X-ray diffraction data, the structure of hp-BS has trigonal symmetry and belongs to the space group P-3m1. As it follows from the electron density of state calculations, the phase transformation is accompanied by an insulator-metal transition.

De Novo Protein Structure Prediction by Combining Replica Exchange Simulations with Coevolutionary Data

De Novo Protein Structure Prediction by Combining Replica Exchange Simulations with Coevolutionary Data