Reconstruction Of Protein Structures Research Articles

Features of the CD1 gene family in rodents and the uniqueness of the immune system of naked mole-rat

Cluster of Differentiation 1 (CD1) proteins are widely expressed throughout jawed vertebrates and present lipid antigens to specific CD1-restricted T lymphocytes. CD1 molecules play an important role in immune defense with the presence or absence of particular CD1 proteins frequently associated with the functional characteristics of the immune system. Here, we show the evolution of CD1 proteins in the Rodentia family and the diversity among its members. Based on the analysis of CD1 protein-coding regions in rodent genomes and the reconstruction of protein structures, we found that Heterocephalus glaber represents a unique member of the suborder Hystricomorpha with significant changes in protein sequences and structures of the CD1 family. Multiple lines of evidence point to the absence of CD1d and CD1e and probably a dysfunctional CD1b protein in Heterocephalus glaber. In addition, the impact of CD1d loss on the CD1d/Natural killer T (NKT) cell axis in the naked mole-rat and its potential implications for immune system function are discussed in detail.

Family characteristics, phylogenetic reconstruction, and potential applications of the plant BAHD acyltransferase family.

The BAHD acyltransferase family is a class of proteins in plants that can acylate a variety of primary and specialized secondary metabolites. The typically acylated products have greatly improved stability, lipid solubility, and bioavailability and thus show significant differences in their physicochemical properties and pharmacological activities. Here, we review the protein structure, catalytic mechanism, and phylogenetic reconstruction of plant BAHD acyltransferases to describe their family characteristics, acylation reactions, and the processes of potential functional differentiation. Moreover, the potential applications of the BAHD family in human activities are discussed from the perspectives of improving the quality of economic plants, enhancing the efficacy of medicinal plants, improving plant biomass for use in biofuel, and promoting stress resistance of land plants. This review provides a reference for the research and production of plant BAHD acyltransferases.

Inter-Residue Distance Prediction From Duet Deep Learning Models.

Residue distance prediction from the sequence is critical for many biological applications such as protein structure reconstruction, protein–protein interaction prediction, and protein design. However, prediction of fine-grained distances between residues with long sequence separations still remains challenging. In this study, we propose DuetDis, a method based on duet feature sets and deep residual network with squeeze-and-excitation (SE), for protein inter-residue distance prediction. DuetDis embraces the ability to learn and fuse features directly or indirectly extracted from the whole-genome/metagenomic databases and, therefore, minimize the information loss through ensembling models trained on different feature sets. We evaluate DuetDis and 11 widely used peer methods on a large-scale test set (610 proteins chains). The experimental results suggest that 1) prediction results from different feature sets show obvious differences; 2) ensembling different feature sets can improve the prediction performance; 3) high-quality multiple sequence alignment (MSA) used for both training and testing can greatly improve the prediction performance; and 4) DuetDis is more accurate than peer methods for the overall prediction, more reliable in terms of model prediction score, and more robust against shallow multiple sequence alignment (MSA).

MP-NeRF: A massively parallel method for accelerating protein structure reconstruction from internal coordinates.

The conversion of proteins between internal and cartesian coordinates is a limiting step in many pipelines, such as molecular dynamics simulations and machine learning models. This conversion is typically carried out by sequential or parallel applications of the Natural extension of Reference Frame (NeRF) algorithm. This work proposes a massively parallel NeRF implementation which, depending on the polymer length, achieves speedups between 400 and 1200× over the previous state-of-the-art. It accomplishes this by dividing the conversion into three main phases: parallel composition of the monomer backbone, assembly of backbone subunits, and parallel elongation of sidechains; and by batching these computations into a minimal number of efficient matrix operations. Special emphasis is placed on reusability and ease of use. We open source the code (available at https://github.com/EleutherAI/mp_nerf) and provide a corresponding python package.

Evaluation of residue-residue contact prediction methods: From retrospective to prospective.

Sequence-based residue contact prediction plays a crucial role in protein structure reconstruction. In recent years, the combination of evolutionary coupling analysis (ECA) and deep learning (DL) techniques has made tremendous progress for residue contact prediction, thus a comprehensive assessment of current methods based on a large-scale benchmark data set is very needed. In this study, we evaluate 18 contact predictors on 610 non-redundant proteins and 32 CASP13 targets according to a wide range of perspectives. The results show that different methods have different application scenarios: (1) DL methods based on multi-categories of inputs and large training sets are the best choices for low-contact-density proteins such as the intrinsically disordered ones and proteins with shallow multi-sequence alignments (MSAs). (2) With at least 5L (L is sequence length) effective sequences in the MSA, all the methods show the best performance, and methods that rely only on MSA as input can reach comparable achievements as methods that adopt multi-source inputs. (3) For top L/5 and L/2 predictions, DL methods can predict more hydrophobic interactions while ECA methods predict more salt bridges and disulfide bonds. (4) ECA methods can detect more secondary structure interactions, while DL methods can accurately excavate more contact patterns and prune isolated false positives. In general, multi-input DL methods with large training sets dominate current approaches with the best overall performance. Despite the great success of current DL methods must be stated the fact that there is still much room left for further improvement: (1) With shallow MSAs, the performance will be greatly affected. (2) Current methods show lower precisions for inter-domain compared with intra-domain contact predictions, as well as very high imbalances in precisions between intra-domains. (3) Strong prediction similarities between DL methods indicating more feature types and diversified models need to be developed. (4) The runtime of most methods can be further optimized.

Reconstruction of protein structures from single-molecule time series.

Single-molecule experimental techniques track the real-time dynamics of molecules by recording a small number of experimental observables. Following these observables provides a coarse-grained, low-dimensional representation of the conformational dynamics but does not furnish an atomistic representation of the instantaneous molecular structure. Takens's delay embedding theorem asserts that, under quite general conditions, these low-dimensional time series can contain sufficient information to reconstruct the full molecular configuration of the system up to an a priori unknown transformation. By combining Takens's theorem with tools from statistical thermodynamics, manifold learning, artificial neural networks, and rigid graph theory, we establish an approach, Single-molecule TAkens Reconstruction, to learn this transformation and reconstruct molecular configurations from time series in experimentally measurable observables such as intramolecular distances accessible to single molecule Förster resonance energy transfer. We demonstrate the approach in applications to molecular dynamics simulations of a C24H50 polymer chain and the artificial mini-protein chignolin. The trained models reconstruct molecular configurations from synthetic time series data in the head-to-tail molecular distances with atomistic root mean squared deviation accuracies better than 0.2 nm. This work demonstrates that it is possible to accurately reconstruct protein structures from time series in experimentally measurable observables and establishes the theoretical and algorithmic foundations to do so in applications to real experimental data.

Application of CS in weak signal quantum measurement and protein structure calculation

It has been more than 15 years since the compressed sensing (CS) theory was put forward in 2006. The CS theory has been successfully applied and developed in many classical fields such as the single-pixel camera, CT imaging and radar imaging system, but there is still much space for its applications in quantum and biological system. In this paper, the basic knowledge of CS theory and its application/development history are briefly introduced. Then a review is given on a series of achievements in the CS theory applied in quantum precision measurement and biological system, mainly obtained by the Center for Quantum Technology Research in Beijing Institute of Technology (BIT). The review covers several different topics including the measurement and imaging in extremely weak signals, the density matrix calculation for multi-body quantum state tomography and the protein structure reconstruction from matrix completion theory, which is developed based on the CS theory. With the above reviews, we show that the CS theory has great potential for further application, not only in weak signal detection, but also in quantum calculation and even the research on bio-macromolecules. In the end, the summary and outlook are given on the future developments and applications of the CS theory.

ERGO: Efficient Recurrent Graph Optimized Emitter Density Estimation in Single Molecule Localization Microscopy.

Single molecule localization microscopy (SMLM) allows unprecedented insight into the three-dimensional organization of proteins at the nanometer scale. The combination of minimal invasive cell imaging with high resolution positions SMLM at the forefront of scientific discovery in cancer, infectious, and degenerative diseases. By stochastic temporal and spatial separation of light emissions from fluorescent labelled proteins, SMLM is capable of nanometer scale reconstruction of cellular structures. Precise localization of proteins in 3D astigmatic SMLM is dependent on parameter sensitive preprocessing steps to select regions of interest. With SMLM acquisition highly variable over time, it is non-trivial to find an optimal static parameter configuration. The high emitter density required for reconstruction of complex protein structures can compromise accuracy and introduce artifacts. To address these problems, we introduce two modular auto-tuning pre-processing methods: adaptive signal detection and learned recurrent signal density estimation that can leverage the information stored in the sequence of frames that compose the SMLM acquisition process. We show empirically that our contributions improve accuracy, precision and recall with respect to the state of the art. Both modules auto-tune their hyper-parameters to reduce the parameter space for practitioners, improve robustness and reproducibility, and are validated on a reference in silico dataset. Adaptive signal detection and density prediction can offer a practitioner, in addition to informed localization, a tool to tune acquisition parameters ensuring improved reconstruction of the underlying protein complex. We illustrate the challenges faced by practitioners in applying SMLM algorithms on real world data markedly different from the data used in development and show how ERGO can be run on new datasets without retraining while motivating the need for robust transfer learning in SMLM.

A new algorithm for the reconstruction of protein molecular envelopes from X-ray solution scattering data

At sufficiently low resolution, the scattering density within the volume occupied by a well folded protein molecule appears relatively flat. By enforcing this condition, three-dimensional protein molecular envelopes may be reconstructed using information obtained from X-ray solution scattering profiles. A practical approach for solving the low-resolution structures of protein molecules from solution scattering data involves modelling the protein shape using a set of volume-filling points (`beads') and transforming the scattering data to a more convenient target, the pair distance distribution function, P(r). Using algorithms described here, the beads interact via a modified Lennard–Jones potential and their positions are adjusted and confined until they fit the expected protein volume and agreement with P(r) is obtained. This methodology allows the protein volume to be modelled by an arbitrary, user-defined number of beads, enabling the rapid reconstruction of protein structures of widely varying sizes. Tests carried out with a variety of synthetic and experimental data sets show that this approach gives efficient and reliable determinations of protein molecular envelopes.

Directly obtaining high-strength silk fiber from silkworm fed with commercial protein powder

This work has proven that the high-strength silk fiber, SF, can be directly obtained from silkworm by feeding the commercial protein powder. Experimentally, the mulberry leave, ML, was pretreated by the protein/water solution and then fed to the silkworm to produce SF. SEM image shows that the surface of protein-fed SF is roughly with some white dots as compared with the only ML-fed SF. This suggests that some fed protein components have been separated by the silkworm spinning. X-ray diffraction, XRD, patterns, Fourier transform infrared, FTIR, and FT-Raman spectra analyses indicated that the fed protein powders were mostly mixed with the original silk protein to cause the protein structure reconstruction to lead the enhancement of both the β-sheet structure and crystallinity for protein-fed SF. The protein-fed SF shown enhanced a tensile stress at about 1.2 GPA and an elongation at about 34% both greater as compared with the normal SF even the literature reported values for various SFs.

Automated method to differentiate between native and mirror protein models obtained from contact maps.

Mirror protein structures are often considered as artifacts in modeling protein structures. However, they may soon become a new branch of biochemistry. Moreover, methods of protein structure reconstruction, based on their residue-residue contact maps, need methodology to differentiate between models of native and mirror orientation, especially regarding the reconstructed backbones. We analyzed 130 500 structural protein models obtained from contact maps of 1 305 SCOP domains belonging to all 7 structural classes. On average, the same numbers of native and mirror models were obtained among 100 models generated for each domain. Since their structural features are often not sufficient for differentiating between the two types of model orientations, we proposed to apply various energy terms (ETs) from PyRosetta to separate native and mirror models. To automate the procedure for differentiating these models, the k-means clustering algorithm was applied. Using total energy did not allow to obtain appropriate clusters–the accuracy of the clustering for class A (all helices) was no more than 0.52. Therefore, we tested a series of different k-means clusterings based on various combinations of ETs. Finally, applying two most differentiating ETs for each class allowed to obtain satisfying results. To unify the method for differentiating between native and mirror models, independent of their structural class, the two best ETs for each class were considered. Finally, the k-means clustering algorithm used three common ETs: probability of amino acid assuming certain values of dihedral angles Φ and Ψ, Ramachandran preferences and Coulomb interactions. The accuracies of clustering with these ETs were in the range between 0.68 and 0.76, with sensitivity and selectivity in the range between 0.68 and 0.87, depending on the structural class. The method can be applied to all fully-automated tools for protein structure reconstruction based on contact maps, especially those analyzing big sets of models.

Structural and functional features of lysine acetylation of plant and animal tubulins.

The study of the genome and the proteome of different species and representatives of distinct kingdoms, especially detection of proteome via wide-scaled analyses has various challenges and pitfalls. Attempts to combine all available information together and isolate some common features for determination of the pathway and their mechanism of action generally have a highly complicated nature. However, microtubule (MT) monomers are highly conserved protein structures, and microtubules are structurally conserved from Homo sapiens to Arabidopsis thaliana. The interaction of MT elements with microtubule-associated proteins and post-translational modifiers is fully dependent on protein interfaces, and almost all MT modifications are well described except acetylation. Crystallography and interactome data using different approaches were combined to identify conserved proteins important in acetylation of microtubules. Application of computational methods and comparative analysis of binding modes generated a robust predictive model of acetylation of the ϵ-amino group of Lys40 in α-tubulins. In turn, the model discarded some probable mechanisms of interaction between elements of interest. Reconstruction of unresolved protein structures was carried out with modeling by homology to the existing crystal structure (PDBID: 1Z2B) from B. taurus using Swiss-model server, followed by a molecular dynamics simulation. Docking of the human tubulin fragment with Lys40 into the active site of α-tubulin acetyltransferase, reproduces the binding mode of peptidomimetic from X-ray structure (PDBID: 4PK3).

Improved protein structure reconstruction using secondary structures, contacts at higher distance thresholds, and non-contacts

BackgroundResidue-residue contacts are key features for accurate de novo protein structure prediction. For the optimal utilization of these predicted contacts in folding proteins accurately, it is important to study the challenges of reconstructing protein structures using true contacts. Because contact-guided protein modeling approach is valuable for predicting the folds of proteins that do not have structural templates, it is necessary for reconstruction studies to focus on hard-to-predict protein structures.ResultsUsing a data set consisting of 496 structural domains released in recent CASP experiments and a dataset of 150 representative protein structures, in this work, we discuss three techniques to improve the reconstruction accuracy using true contacts – adding secondary structures, increasing contact distance thresholds, and adding non-contacts. We find that reconstruction using secondary structures and contacts can deliver accuracy higher than using full contact maps. Similarly, we demonstrate that non-contacts can improve reconstruction accuracy not only when the used non-contacts are true but also when they are predicted. On the dataset consisting of 150 proteins, we find that by simply using low ranked predicted contacts as non-contacts and adding them as additional restraints, can increase the reconstruction accuracy by 5% when the reconstructed models are evaluated using TM-score.ConclusionsOur findings suggest that secondary structures are invaluable companions of contacts for accurate reconstruction. Confirming some earlier findings, we also find that larger distance thresholds are useful for folding many protein structures which cannot be folded using the standard definition of contacts. Our findings also suggest that for more accurate reconstruction using predicted contacts it is useful to predict contacts at higher distance thresholds (beyond 8 Å) and predict non-contacts.

A Template-Based Protein Structure Reconstruction Method Using Deep Autoencoder Learning.

Protein structure prediction is an important problem in computational biology, and is widely applied to various biomedical problems such as protein function study, protein design, and drug design. In this work, we developed a novel deep learning approach based on a deeply stacked denoising autoencoder for protein structure reconstruction. We applied our approach to a template-based protein structure prediction using only the 3D structural coordinates of homologous template proteins as input. The templates were identified for a target protein by a PSI-BLAST search. 3DRobot (a program that automatically generates diverse and well-packed protein structure decoys) was used to generate initial decoy models for the target from the templates. A stacked denoising autoencoder was trained on the decoys to obtain a deep learning model for the target protein. The trained deep model was then used to reconstruct the final structural model for the target sequence. With target proteins that have highly similar template proteins as benchmarks, the GDT-TS score of the predicted structures is greater than 0.7, suggesting that the deep autoencoder is a promising method for protein structure reconstruction.

Automated Procedure for Contact-Map-Based Protein Structure Reconstruction

Knowledge of the three-dimensional structures of ion channels allows for modeling their conductivity characteristics using biophysical models and can lead to discovering their cellular functionality. Recent studies show that quality of structure predictions can be significantly improved using protein contact site information. Therefore, a number of procedures for protein structure prediction based on their contact-map have been proposed. Their comparison is difficult due to different methodologies used for validation. In this work, a Contact Map-to-Structure pipeline (C2S_pipeline) for contact-based protein structure reconstruction is designed and validated. The C2S_pipeline can be used to reconstruct monomeric and multimeric proteins. The median RMSD of structures obtained during validation on a representative set of protein structures, equaled 5.27 Å, and the best structure was reconstructed with RMSD of 1.59 Å. The validation is followed by a detailed case study on the KcsA ion channel. Models of KcsA are reconstructed based on different portions of contact site information. Structural feature analysis of acquired KcsA models is supported by a thorough analysis of electrostatic potential distributions inside the channels. The study shows that electrostatic parameters are correlated with structural quality of models. Therefore, they can be used to discriminate between high and low quality structures. We show that 30 % of contact information is needed to obtain accurate structures of KcsA, if contacts are selected randomly. This number increases to 70 % in case of erroneous maps in which the remaining contacts or non-contacts are changed to the opposite. Furthermore, the study reveals that local reconstruction accuracy is correlated with the number of contacts in which amino acid are involved. This results in higher reconstruction accuracy in the structure core than peripheral regions.

Differential gene expression in the brain of the African lungfish, Protopterus annectens, after six days or six months of aestivation in air.

The African lungfish, Protopterus annectens, can undergo aestivation during drought. Aestivation has three phases: induction, maintenance and arousal. The objective of this study was to examine the differential gene expression in the brain of P. annectens during the induction (6 days) and maintenance (6 months) phases of aestivation as compared with the freshwater control using suppression subtractive hybridization. During the induction phase of aestivation, the mRNA expression of prolactin (prl) and growth hormone were up-regulated in the brain of P. annectens, which indicate for the first time the possible induction role of these two hormones in aestivation. Also, the up-regulation of mRNA expression of tyrosine 3-monooxygenase/tryptophan 5-monooxygenase activation protein γ polypeptide and the down-regulation of phosphatidylethanolamine binding protein, suggest that there could be a reduction in biological and neuronal activities in the brain. The mRNA expression of cold inducible RNA-binding protein and glucose regulated protein 58 were also up-regulated in the brain, probably to enhance their cytoprotective effects. Furthermore, the down-regulation of prothymosin α expression suggests that there could be a suppression of transcription and cell proliferation in preparation for the maintenance phase. In general, the induction phase appeared to be characterized by reduction in glycolytic capacity and metabolic activity, suppression of protein synthesis and degradation, and an increase in defense against ammonia toxicity. In contrast, there was a down-regulation in the mRNA expression of prl in the brain of P. annectens during the maintenance phase of aestivation. In addition, there could be an increase in oxidative defense capacity, and up-regulation of transcription, translation, and glycolytic capacities in preparation for arousal. Overall, our results signify the importance of reconstruction of protein structures and regulation of energy expenditure during the induction phase, and the needs to suppress protein degradation and conserve metabolic fuel stores during the maintenance phase of aestivation.

Glocal: Reconstructing Protein 3D Structure from 2D Contact Map by Combining Global and Local Optimization Schemes

Prediction of protein 3D structure from solely its amino acid sequence is one of the most challenging problems in structural bioinformatics, where the 3D structure reconstruction from observed constraints is the key step. In this paper, we propose a novel protocol called Glocal to recover a protein’s 3D coordinates based on a given 2D contact map by combining both global and local optimization schemes achieved by the swarm intelligence of Particle Swarm Optimization (PSO) and the Simulated Annealing (SA) techniques respectively. Our results demonstrate that Glocal can recover the 3D structures with the average RMSD less than 2 Å from the native contact map. Further analysis also shows that Glocal is powerful for handling with noisy contact map with the proposed combination optimization approaches. Keywords: Glocal, particle swarm optimization, protein contact map, protein structure reconstruction, simulated annealing, Simulated Annealing, protein, Threshold, resolutions

Differential gene expression in the liver of the African lungfish, Protopterus annectens, after 6 days of estivation in air

This study aimed to identify estivation-specific gene clusters through the determination of differential gene expressions in the liver of Protopterus annectens after 6 days of estivation in a mucus cocoon in air (normoxia) using suppression subtractive hybridization polymerase chain reaction. Our results demonstrated that 6 days of estivation in normoxia led to up-regulation of mRNA expressions of several genes related to urea synthesis, including carbamoyl phosphate synthetase (Cps), argininosuccinate synthetase and glutamine synthetase. They indicate that increased urea synthesis, despite being energy-intensive, is an important adaptive response of estivation. They also offer indirect support to the proposition that urea synthesis in this lungfish involved a Cps that uses glutamine as a substrate. In addition, up- or down-regulation of several gene clusters occurred in the liver of P. annectens after 6 days of estivation in normoxia. These estivation-specific genes were involved in the prevention of clot formation, activation of the lectin pathway for complement activation, conservation of minerals (e.g. iron and copper) and increased production of hemoglobin beta. Since there were up- and down-regulation of mRNA expressions of genes related to ribosomal proteins and translational elongation factors, there could be simultaneous increases in protein degradation and protein synthesis during the first 6 days (the induction phase) of estivation, confirming the importance of reconstruction of protein structures in preparation for the maintenance phase of estivation.

On the Reconstruction of Three-dimensional Protein Structures from Contact Maps

The problem of protein structure prediction is one of the long-standing goals of Computational Biology. Although we are still not able to provide first principle solutions, several shortcuts have been discovered to compute the protein three-dimensional structure when similar protein sequences are available (by means of comparative modeling and remote homology detection). Nonetheless, these approaches can assign structures only to a fraction of proteins in genomes and ab-initio methods are still needed. One relevant step of ab-initio prediction methods is the reconstruction of the protein structures starting from inter-protein residue contacts. In this paper we review the methods developed so far to accomplish the reconstruction task in order to highlight their differences and similarities. The different approaches are fully described and their reported performances, together with their computational complexity, are also discussed.

Stochastic reconstruction of protein structures from effective connectivity profiles

We discuss a stochastic approach for reconstructing the native structures of proteins from the knowledge of the "effective connectivity", which is a one-dimensional structural profile constructed as a linear combination of the eigenvectors of the contact map of the target structure. The structural profile is used to bias a search of the conformational space towards the target structure in a Monte Carlo scheme operating on a Cα-chain of uniform, finite thickness. Structure information thus enters the folding dynamics via the effective connectivity, but the interaction is not restricted to pairs of amino acids that form native contacts, resulting in a free energy landscape which does not rely on the assumption of minimal frustration. Moreover, effective connectivity vectors can be predicted more readily from the amino acid sequence of proteins than the corresponding contact maps, thus suggesting that the stochastic protocol presented here could be effectively combined with other current methods for predicting native structures. PACS codes: 87.14.Ee.