Mem Brain: An Easy-to-Use Online Webserver for Transmembrane Protein Structure Prediction

Membrane proteins are an important kind of proteins embedded in the membranes of cells and play crucial roles in living organisms, such as ion channels,transporters, receptors. Because it is difficult to determinate the membrane protein's structure by wet-lab experiments,accurate and fast amino acid sequence-based computational methods are highly desired. In this paper, we report an online prediction tool called Mem Brain, whose input is the amino acid sequence. Mem Brain consists of specialized modules for predicting transmembrane helices, residue–residue contacts and relative accessible surface area of a-helical membrane proteins. Mem Brain achieves aprediction accuracy of 97.9% of ATMH, 87.1% of AP,3.2 ± 3.0 of N-score, 3.1 ± 2.8 of C-score. Mem BrainContact obtains 62%/64.1% prediction accuracy on training and independent dataset on top L/5 contact prediction,respectively. And Mem Brain-Rasa achieves Pearson correlation coefficient of 0.733 and its mean absolute error of13.593. These prediction results provide valuable hints for revealing the structure and function of membrane proteins.Mem Brain web server is free for academic use and available at www.csbio.sjtu.edu.cn/bioinf/Mem Brain/.

New crystal structure and physical properties of TcB from first-principles calculations

By combining first-principles calculations with the particle swarm optimization algorithm, we predicted a hexagonal Pˉ3m1 structure for Tc B, which is energetically more favorable than the previously reported WC-type and Cmcm structures.The new phase is mechanically and dynamically stable, as confirmed by its phonon and elastic constants calculations.The calculated mechanical properties show that it is an ultra-incompressible and hard material. Meanwhile, the elastic anisotropy is investigated by the shear anisotropic factors and ratio of the directional bulk modulus. Density of states analysis reveals that the strong covalent bonding between Tc and B atoms plays a leading role in forming a hard material. Additionally, the compressibility, bulk modulus, Debye temperature, Gruneisen parameter, specific heat, and thermal expansion coefficient of Tc B are also successfully obtained by using the quasi-harmonic Debye model.

Computational prediction of RNA tertiary structures using machine learning methods

RNAs play crucial and versatile roles in biological processes. Computational prediction approaches can help to understand RNA structures and their stabilizing factors, thus providing information on their functions, and facilitating the design of new RNAs. Machine learning (ML) techniques have made tremendous progress in many fields in the past few years. Although their usage in protein-related fields has a long history, the use of ML methods in predicting RNA tertiary structures is new and rare. Here, we review the recent advances of using ML methods on RNA structure predictions and discuss the advantages and limitation, the difficulties and potentials of these approaches when applied in the field.

RNA structure prediction:Progress and perspective

Many recent exciting discoveries have revealed the versatility of RNAs and their importance in a variety of cellular functions which are strongly coupled to RNA structures. To understand the functions of RNAs, some structure prediction models have been developed in recent years. In this review, the progress in computational models for RNA structure prediction is introduced and the distinguishing features of many outstanding algorithms are discussed, emphasizing three- dimensional （3D） structure prediction. A promising coarse-grained model for predicting RNA 3D structure, stability and salt effect is also introduced briefly. Finally, we discuss the major challenges in the RNA 3D structure modeling.

Protein Secondary Structure Prediction with Dynamic Self-Adaptation Combination Strategy Based on Entropy

The algorithm based on combination learning usually is superior to a singleclassification algorithm on the task of protein secondary structure prediction. However,the assignment of the weight of the base classifier usually lacks decision-makingevidence. In this paper, we propose a protein secondary structure prediction method withdynamic self-adaptation combination strategy based on entropy, where the weights areassigned according to the entropy of posterior probabilities outputted by base classifiers.The higher entropy value means a lower weight for the base classifier. The final structureprediction is decided by the weighted combination of posterior probabilities. Extensiveexperiments on CB513 dataset demonstrates that the proposed method outperforms theexisting methods, which can effectively improve the prediction performance.

RNAGCN:RNA tertiary structure assessment with a graph convolutional network

RNAs play crucial and versatile roles in cellular biochemical reactions.Since experimental approaches of determining their three-dimensional(3D)structures are costly and less efficient,it is greatly advantageous to develop computational methods to predict RNA 3D structures.For these methods,designing a model or scoring function for structure quality assessment is an essential step but this step poses challenges.In this study,we designed and trained a deep learning model to tackle this problem.The model was based on a graph convolutional network(GCN)and named RNAGCN.The model provided a natural way of representing RNA structures,avoided complex algorithms to preserve atomic rotational equivalence,and was capable of extracting features automatically out of structural patterns.Testing results on two datasets convincingly demonstrated that RNAGCN performs similarly to or better than four leading scoring functions.Our approach provides an alternative way of RNA tertiary structure assessment and may facilitate RNA structure predictions.RNAGCN can be downloaded from https://gitee.com/dcw-RNAGCN/rnagcn.

A Hybrid Ant Colony Optimization for the Prediction of Protein Secondary Structure

Based on the concept of ant colony optimization and the idea of population in genetic algorithm, a novel global optimization algorithm, called the hybrid ant colony optimization （HACO）, is proposed in this paper to tackle continuous-space optimization problems. It was compared with other well-known stochastic methods in the optimization of the benchmark functions and was also used to solve the problem of selecting appropriate dilation efficiently by optimizing the wavelet power spectrum of the hydrophobic sequence of protein, which is the key step on using continuous wavelet transform （CWT） to predict a-helices and connecting peptides.

A Deep Learning Approach for Prediction of Protein Secondary Structure

The secondary structure of a protein is critical for establishing a link between the protein primary and tertiary structures.For this reason,it is important to design methods for accurate protein secondary structure prediction.Most of the existing computational techniques for protein structural and functional prediction are based onmachine learning with shallowframeworks.Different deep learning architectures have already been applied to tackle protein secondary structure prediction problem.In this study,deep learning based models,i.e.,convolutional neural network and long short-term memory for protein secondary structure prediction were proposed.The input to proposed models is amino acid sequences which were derived from CulledPDB dataset.Hyperparameter tuning with cross validation was employed to attain best parameters for the proposed models.The proposed models enables effective processing of amino acids and attain approximately 87.05%and 87.47%Q3 accuracy of protein secondary structure prediction for convolutional neural network and long short-term memory models,respectively.

Functional structures and folding dynamics of two peptides

The folding dynamics and structural characteristics of peptides RTKAWNRQLYPEW （P1） and RTKQLYPEW （P2） are investigated by using all-atomic simulation procedure CHARMM in this work. The results show that P1, a segment of an antigen, has a folding motif of α-helix, whereas P2, which is derived by deleting four residues AWNR from peptide P1, prevents the formation of helix and presents a β-strand. And peptlde P1 experiences a more rugged energy landscape than peptide P2. From our results, it is inferred that the antibody CD8 cytolytic T lymphocyte prefers an antigen with a β-folding structure to that with an α-helical one.

Structure Prediction Based on Hydrophobic to Hydrophilic Volume Ratios in Small Molecule Amphiphilic Organic Crystals

The structure type for the crystal of 4,4'-bis-(2-hydroxy-ethoxyl)-biphenyl 1 has been predicted by using the previously developed interfacial model for small organic molecules. Based on the calculated hydrophobic to hydrophilic volume of 1, this model predicts the crystal structure to be of lamellar or bicontinuous type, which has been confirmed by the X-ray single-crystal structure analysis (C20H26O6, monoclinic, P21/C, a = 16.084(1), b = 6.0103(4), c = 9.6410(7) A, β9 = 103.014(2)°, V= 908.1(1) A3, Z = 2, Dc= 1.325 g/cm3, F(000)=388,μ = 0.097 mm-1, MoKα radiation, λ = 0.71073 A, R = 0.0382 and wR = 0.0882 with I > 2σ(I) for 7121 reflections collected, 1852 unique reflections and 170 parameters). As predicted, the hydrophobic and hydrophilic portions of 1 form in the lamellae. The same interfacial model is applied to other amphilphilic small molecule organic systems for structural type prediction.

Improving RNA secondary structure prediction using direct coupling analysis

Secondary structures of RNAs are the basis of understanding their tertiary structures and functions and so their predictions are widely needed due to increasing discovery of noncoding RNAs.In the last decades,a lot of methods have been proposed to predict RNA secondary structures but their accuracies encountered bottleneck.Here we present a method for RNA secondary structure prediction using direct coupling analysis and a remove-and-expand algorithm that shows better performance than four existing popular multiple-sequence methods.We further show that the results can also be used to improve the prediction accuracy of the single-sequence methods.

The ground-state structure and physical properties of RuC: first-principles calculations

We have extensively explored the ground-state structure of RuC using the particle swarm optimization algorithm for crystal structural prediction. A hexagonal I：t-3m structure has been proposed ms the best candidate, which is energetically more favorable than the previously proposed zinc blend structure. The R-3m-RuC possesses alternative stacking of double hexagonal close-packed Ru atom layers and C atom layers, and it is dynamically stable evidenced by the calculation of phonon dispersion. The calculated large bulk modulus, shear modulus, and elastic constant C44 reveal that it is an ultra-incompressible and hard material. The evidence of strong covalent bonding of Ru C, which plays an important role to form a hard material, is manifested by the partial densities of states analysis.

IMPROVED METHOD FOR RNA SECONDARY STRUCTURE PREDICTION'

A simple stepwise folding process has been developed to simulate RNA secondary structure formation.Modifications for the energy parameters of various loops were included in the program.Five possible types of pseudoknots including the well known H-type pseudoknot were permitted to occur if reasonable.We have applied this approach to e number of RNA sequences.The prediction accuracies we obtained were higher than those in published papers.

The Evolutionary Computation Techniques for Protein Structure Prediction:A Survey

In this paper, the applications of evolutionary algorithm in prediction of protein secondary structure and tertiary structures are introduced, and recent studies on solving protein structure prediction problems using evolutionary algorithms are reviewed, and the challenges and prospects of EAs applied to protein structure modeling are analyzed and discussed.

Structure and Function Analysis of Canine ZP3 Protein

To further study the biological function and mechanism of ZP3 in domestic canine （ CaMs lupu familiaris） , the coding sequence （CDS） of ZP3 gene was searched from NCBI database using bioinformatics method, and further transformed into protein sequence via MAGE software. The primary, secondary and terti- ary structure of protein was predicted with ExPASy, BLAST and DNA Star bioinformatics online software and program; the evolution and selected sites of ZP3 pro- tein extracted from 11 species were analyzed using PAML software; the conservation of ZP3 protein gene was analyzed with Predictprotein and Weblogo program; the tertiary structure of protein was edited by Python and PyMOL. The results showed that canine ZP3 gene encoded 426 amino acids, and the encoding product was a hydrophilic transmembrane protein with signal peptide; the 1 -23ra amino acids were signal peptide areas, and transmembrane domain distributed in the （x-helix area of the 386th -408th amino acids; four loci were affected by phosphorylation, and these phosphorylation sites might be associated with signal transduction ; there were nine protein binding sites on ZP domain; a high variation region was found in 325 -385 section of ZP3, and most of phosphorylation and selected amino acid sites were distributed in this area. This indicated that the area had experienced rapid evolution, suggesting that ZP domain and the high variation area might be in- volved in interaction of sperm and egg.

Ensemble Machine Learning to Enhance Q8 Protein Secondary Structure Prediction

Protein structure prediction is one of the most essential objectives practiced by theoretical chemistry and bioinformatics as it is of a vital importance in medicine,biotechnology and more.Protein secondary structure prediction(PSSP)has a significant role in the prediction of protein tertiary structure,as it bridges the gap between the protein primary sequences and tertiary structure prediction.Protein secondary structures are classified into two categories:3-state category and 8-state category.Predicting the 3 states and the 8 states of secondary structures from protein sequences are called the Q3 prediction and the Q8 prediction problems,respectively.The 8 classes of secondary structures reveal more precise structural information for a variety of applications than the 3 classes of secondary structures,however,Q8 prediction has been found to be very challenging,that is why all previous work done in PSSP have focused on Q3 prediction.In this paper,we develop an ensemble Machine Learning(ML)approach for Q8 PSSP to explore the performance of ensemble learning algorithms compared to that of individual ML algorithms in Q8 PSSP.The ensemble members considered for constructing the ensemble models are well known classifiers,namely SVM(Support Vector Machines),KNN(K-Nearest Neighbor),DT(Decision Tree),RF(Random Forest),and NB(Naïve Bayes),with two feature extraction techniques,namely LDA(Linear Discriminate Analysis)and PCA(Principal Component Analysis).Experiments have been conducted for evaluating the performance of single models and ensemble models,with PCA and LDA,in Q8 PSSP.The novelty of this paper lies in the introduction of ensemble learning in Q8 PSSP problem.The experimental results confirmed that ensemble ML models are more accurate than individual ML models.They also indicated that features extracted by LDA are more effective than those extracted by PCA.

High-pressure structures of InBi predicted by particle swarm optimization algorithm

We extensively explore the high-pressure structures of InBi by using a newly developed particle swarm optimization algorithm. An orthorhombic Imma structure is discovered to be stable from 43.7 GPa to 107.9 GPa, ruling out the previously speculated cubic structure. Further increasing the pressure, we find a tetragonal P4/nmm structure which is energetically more favourable from 107.9 CPa to 200 GPa. Especially, the tetragonal P4/nmm structure is known to occur at high pressure in the structures of ZnO and MgTe. We also predict this structure to be a high-pressure structure of ZnTe. Thus the tetragonal P4/nmm structure may be a universal high-pressure structure of the Ⅱ-Ⅵ and the Ⅲ-Ⅴ compounds.

Heuristic Quasi-physical Algorithm for Protein Structure Prediction

A three-dimensional off-lattice protein model with two species of monomers, hydrophobic and hydrophilic, is studied. Enligh- tened by the law of reciprocity among things in the physical world, a heuristic quasi-physical algorithm for protein structure prediction problem is put forward. First, by elaborately simulating the movement of the smooth elastic balls in the physical world, the algorithm finds low energy configurations for a given monomer chain. An ＂off-trap＂ strategy is then proposed to get out of local minima. Experimental results show promising performance. For all chains with lengths 13≤n ≤55, the proposed algorithm finds states with lower energy than the putative ground states reported in literatures. Furthermore, for chain lengths n = 21, 34, and 55, the algorithm finds new low energy configurations different from those given in literatures.

A study on the numerical prediction method for the vertical thermal structure in the Bohai Sea and the Huanghai Sea-I.One-dimensional numerical prediction model

In this paper, on the basis of the heat conduction equation without consideration of the advection and turbulence effects, one-dimensional model for describing surface sea temperature ( T1), bottom sea temperature ( Tt ) and the thickness of the upper homogeneous layer ( h ) is developed in terms of the dimensionless temperature θT and depth η and self-simulation function θT - f(η) of vertical temperature profile by means of historical temperature data.The results of trial prediction with our one-dimensional model on T, Th, h , the thickness and gradient of thermocline are satisfactory to some extent.

Ground-State Structure and Physical Properties of NB_2 Predicted from First Principles

Using the newly developed particle swarm optimization algorithm on crystal structural prediction, we predict a new class of boron nitride with stoicMometry of NB2 at ambient pressure, which belongs to the tetragonal 14m2 space group. Then, its structure, elastic properties, electronic structure, and chemical bonding are investigated by first-principles calculations with the density functional theory. The phonon calculation and elastic constants confirm that the predicted NB2 is dynamically and mechanically stable, respectively. The large bulk modulus, large shear modulus, large Young＇s modulus, and small Poisson＇s ratio show that the 14m2 NB2 should be a new superhard material with a calculated theoretical Vickers hardness value of 66 GPa. Further analysis on density of states and electron localization function demonstrate that the strong B B and 13 N covalent bonds are the main reason for its high hardness in 14m2 NB2.