scholarly journals FoldRate: A Web-Server for Predicting Protein Folding Rates from Primary Sequence

2009 ◽  
Vol 3 (1) ◽  
pp. 31-50 ◽  
Author(s):  
Chou Kuo-Chen ◽  
Shen Hong-Bin
Keyword(s):  
3D Structure ◽  
Three Dimensional ◽  
Web Server ◽  
Query Protein ◽  
Sequence Information ◽  
Folding Rate ◽  
Structure Information ◽  
The Public ◽  
Folding Rates ◽  
User Friendly

With the avalanche of gene products in the postgenomic age, the gap between newly found protein sequences and the knowledge of their 3D (three dimensional) structures is becoming increasingly wide. It is highly desired to develop a method by which one can predict the folding rates of proteins based on their amino acid sequence information alone. To address this problem, an ensemble predictor, called FoldRate, was developed by fusing the folding-correlated features that can be either directly obtained or easily derived from the sequences of proteins. It was demonstrated by the jackknife cross-validation on a benchmark dataset constructed recently that FoldRate is at least comparable with or even better than the existing methods that, however, need both the sequence and 3D structure information for predicting the folding rate. As a user-friendly web-server, FoldRate is freely accessible to the public at www.csbio.sjtu.edu.cn/bioinf/FoldRate/, by which one can get the desired result for a query protein sequence in around 30 seconds.

scholarly journals Cell Bioprinting: The 3D-Bioplotter™ Case

Materials ◽  
2019 ◽  
Vol 12 (23) ◽  
pp. 4005 ◽  
Author(s):  
Angelats Lobo ◽  
Ginestra
Keyword(s):  
3D Structure ◽  
Three Dimensional ◽  
Petri Dish ◽  
Two Dimensions ◽  
Point Of View ◽  
Lack Of Information ◽  
Synthetic Materials ◽  
Different Types ◽  
Available Information ◽  
User Friendly

The classic cell culture involves the use of support in two dimensions, such as a well plate or a Petri dish, that allows the culture of different types of cells. However, this technique does not mimic the natural microenvironment where the cells are exposed to. To solve that, three-dimensional bioprinting techniques were implemented, which involves the use of biopolymers and/or synthetic materials and cells. Because of a lack of information between data sources, the objective of this review paper is, to sum up, all the available information on the topic of bioprinting and to help researchers with the problematics with 3D bioprinters, such as the 3D-Bioplotter™. The 3D-Bioplotter™ has been used in the pre-clinical field since 2000 and could allow the printing of more than one material at the same time, and therefore to increase the complexity of the 3D structure manufactured. It is also very precise with maximum flexibility and a user-friendly and stable software that allows the optimization of the bioprinting process on the technological point of view. Different applications have resulted from the research on this field, mainly focused on regenerative medicine, but the lack of information and/or the possible misunderstandings between papers makes the reproducibility of the tests difficult. Nowadays, the 3D Bioprinting is evolving into another technology called 4D Bioprinting, which promises to be the next step in the bioprinting field and might promote great applications in the future.

scholarly journals Amalgamation of 3D structure and sequence information for protein–protein interaction prediction

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Kanchan Jha ◽  
Sriparna Saha
Keyword(s):  
Amino Acids ◽  
Deep Learning ◽  
Protein Interactions ◽  
3D Structure ◽  
Protein Sequences ◽  
Building Blocks ◽  
New Drugs ◽  
Sequence Information ◽  
Protein Protein Interactions ◽  
Structure Information

Abstract Protein is the primary building block of living organisms. It interacts with other proteins and is then involved in various biological processes. Protein–protein interactions (PPIs) help in predicting and hence help in understanding the functionality of the proteins, causes and growth of diseases, and designing new drugs. However, there is a vast gap between the available protein sequences and the identification of protein–protein interactions. To bridge this gap, researchers proposed several computational methods to reveal the interactions between proteins. These methods merely depend on sequence-based information of proteins. With the advancement of technology, different types of information related to proteins are available such as 3D structure information. Nowadays, deep learning techniques are adopted successfully in various domains, including bioinformatics. So, current work focuses on the utilization of different modalities, such as 3D structures and sequence-based information of proteins, and deep learning algorithms to predict PPIs. The proposed approach is divided into several phases. We first get several illustrations of proteins using their 3D coordinates information, and three attributes, such as hydropathy index, isoelectric point, and charge of amino acids. Amino acids are the building blocks of proteins. A pre-trained ResNet50 model, a subclass of a convolutional neural network, is utilized to extract features from these representations of proteins. Autocovariance and conjoint triad are two widely used sequence-based methods to encode proteins, which are used here as another modality of protein sequences. A stacked autoencoder is utilized to get the compact form of sequence-based information. Finally, the features obtained from different modalities are concatenated in pairs and fed into the classifier to predict labels for protein pairs. We have experimented on the human PPIs dataset and Saccharomyces cerevisiae PPIs dataset and compared our results with the state-of-the-art deep-learning-based classifiers. The results achieved by the proposed method are superior to those obtained by the existing methods. Extensive experimentations on different datasets indicate that our approach to learning and combining features from two different modalities is useful in PPI prediction.

scholarly journals Zebra2: advanced and easy-to-use web-server for bioinformatic analysis of subfamily-specific and conserved positions in diverse protein superfamilies

2020 ◽  
Vol 48 (W1) ◽  
pp. W65-W71 ◽  
Author(s):  
Dmitry Suplatov ◽  
Yana Sharapova ◽  
Elizaveta Geraseva ◽  
Vytas Švedas
Keyword(s):  
Rational Design ◽  
Sequence Similarity ◽  
3D Structure ◽  
Web Server ◽  
Bioinformatic Analysis ◽  
Query Protein ◽  
Systematic Analysis ◽  
External Resources ◽  
Protein Superfamilies ◽  
Network Interfaces

Abstract Zebra2 is a highly automated web-tool to search for subfamily-specific and conserved positions (i.e. the determinants of functional diversity as well as the key catalytic and structural residues) in protein superfamilies. The bioinformatic analysis is facilitated by Mustguseal—a companion web-server to automatically collect and superimpose a large representative set of functionally diverse homologs with high structure similarity but low sequence identity to the selected query protein. The results are automatically prioritized and provided at four information levels to facilitate the knowledge-driven expert selection of the most promising positions on-line: as a sequence similarity network; interfaces to sequence-based and 3D-structure-based analysis of conservation and variability; and accompanied by the detailed annotation of proteins accumulated from the integrated databases with links to the external resources. The integration of Zebra2 and Mustguseal web-tools provides the first of its kind out-of-the-box open-access solution to conduct a systematic analysis of evolutionarily related proteins implementing different functions within a shared 3D-structure of the superfamily, determine common and specific patterns of function-associated local structural elements, assist to select hot-spots for rational design and to prepare focused libraries for directed evolution. The web-servers are free and open to all users at https://biokinet.belozersky.msu.ru/zebra2, no login required.

scholarly journals Prediction of Anticancer Peptides with High Efficacy and Low Toxicity by Hybrid Model Based on 3D Structure of Peptides

2021 ◽  
Vol 22 (11) ◽  
pp. 5630
Author(s):  
Yuhong Zhao ◽  
Shijing Wang ◽  
Wenyi Fei ◽  
Yuqi Feng ◽  
Le Shen ◽  
...  
Keyword(s):  
Hybrid Model ◽  
Computational Models ◽  
3D Structure ◽  
Three Dimensional ◽  
Peptide Sequence ◽  
Sequence Information ◽  
Anticancer Peptides ◽  
Multiple Datasets ◽  
Toxic Peptides

Recently, anticancer peptides (ACPs) have emerged as unique and promising therapeutic agents for cancer treatment compared with antibody and small molecule drugs. In addition to experimental methods of ACPs discovery, it is also necessary to develop accurate machine learning models for ACP prediction. In this study, features were extracted from the three-dimensional (3D) structure of peptides to develop the model, compared to most of the previous computational models, which are based on sequence information. In order to develop ACPs with more potency, more selectivity and less toxicity, the model for predicting ACPs, hemolytic peptides and toxic peptides were established by peptides 3D structure separately. Multiple datasets were collected according to whether the peptide sequence was chemically modified. After feature extraction and screening, diverse algorithms were used to build the model. Twelve models with excellent performance (Acc > 90%) in the ACPs mixed datasets were used to form a hybrid model to predict the candidate ACPs, and then the optimal model of hemolytic peptides (Acc = 73.68%) and toxic peptides (Acc = 85.5%) was used for safety prediction. Novel ACPs were found by using those models, and five peptides were randomly selected to determine their anticancer activity and toxic side effects in vitro experiments.

scholarly journals iEzy-Drug: A Web Server for Identifying the Interaction between Enzymes and Drugs in Cellular Networking

2013 ◽  
Vol 2013 ◽  
pp. 1-13 ◽  
Author(s):  
Jian-Liang Min ◽  
Xuan Xiao ◽  
Kuo-Chen Chou
Keyword(s):  
Nearest Neighbor ◽  
Three Dimensional ◽  
Web Server ◽  
Dimensional Structure ◽  
Molecular Fingerprint ◽  
Physicochemical Features ◽  
Drug Compound ◽  
Almost All ◽  
User Friendly ◽  
In Cells

With the features of extremely high selectivity and efficiency in catalyzing almost all the chemical reactions in cells, enzymes play vitally important roles for the life of an organism and hence have become frequent targets for drug design. An essential step in developing drugs by targeting enzymes is to identify drug-enzyme interactions in cells. It is both time-consuming and costly to do this purely by means of experimental techniques alone. Although some computational methods were developed in this regard based on the knowledge of the three-dimensional structure of enzyme, unfortunately their usage is quite limited because three-dimensional structures of many enzymes are still unknown. Here, we reported a sequence-based predictor, called “iEzy-Drug,” in which each drug compound was formulated by a molecular fingerprint with 258 feature components, each enzyme by the Chou’s pseudo amino acid composition generated via incorporating sequential evolution information and physicochemical features derived from its sequence, and the prediction engine was operated by the fuzzyK-nearest neighbor algorithm. The overall success rate achieved by iEzy-Drug via rigorous cross-validations was about 91%. Moreover, to maximize the convenience for the majority of experimental scientists, a user-friendly web server was established, by which users can easily obtain their desired results.

scholarly journals ProDCoNN-server: a web server for protein sequence prediction and design from a three-dimensional structure

2021 ◽  
Author(s):  
Yuan Zhang ◽  
Arunima Mandal ◽  
Kevin Cui ◽  
Xiuwen Liu ◽  
Jinfeng Zhang
Keyword(s):  
Protein Design ◽  
Protein Sequence ◽  
Three Dimensional ◽  
Web Server ◽  
Query Protein ◽  
Dimensional Structure ◽  
Multiple Sequence ◽  
Sequence Prediction ◽  
Large Numbers ◽  
Benchmark Datasets

We present ProDCoNN-server, a web server for protein sequence design and prediction from a given protein structure. The server is based on a previously developed deep learning model for protein design, ProDCoNN, which achieved state-of-the-art performance when tested on large numbers of test proteins and benchmark datasets. The prediction is very fast compared with other protein sequence prediction servers - it takes only a few minutes for a query protein on average. Two models could be selected for different purposes: BBO for full sequence prediction, extendable for multiple sequence generation, and BBS for single position prediction with the type of other residues known. ProDCoNN-server outputs the predicted sequence and the probability matrix for each amino acid at each predicted residue. The probability matrix can also be visualized as a sequence logos figure (BBO) or probability distribution plot (BBS). The server is available at: https://prodconn.stat.fsu.edu/.

Optical-Tracker-Based 3D Reconstruction for Endoscopic Environment

2012 ◽  
Vol 249-250 ◽  
pp. 1277-1282
Author(s):  
Bo Yang ◽  
Ya Zhou ◽  
Xiao Ming Hu ◽  
Jun Qin Lin ◽  
Qiao Na Xing
Keyword(s):  
Minimally Invasive ◽  
Tracking System ◽  
3D Structure ◽  
Three Dimensional ◽  
Optical Tracking ◽  
Two Dimensional ◽  
Invasive Treatment ◽  
Optical Tracking System ◽  
Structure Information ◽  
3D Information

Endoscopic surgery is increasing for minimally invasive treatment in recent years.But the conventional two-dimensional endoscope technique cannot provide three-dimensional information for surgeon. Lacking depth and other 3D information often made doctorshave to carry out the surgery depending heavily clinical experience.In response to the above described issues,this paper proposed a method based on improved SIFT algorithmto recover 3D structure information of the endoscopic environment, with the help of an optical tracking system which can provide the orientation of the camera in real time. The proposed approach is evaluated on sequence digital images gotten from an 1394 camera and the experimental results show that the proposed approach iseffective.

PROSPECT: A web server for predicting protein histidine phosphorylation sites

2020 ◽  
Vol 18 (04) ◽  
pp. 2050018 ◽  
Author(s):  
Zhen Chen ◽  
Pei Zhao ◽  
Fuyi Li ◽  
André Leier ◽  
Tatiana T. Marquez-Lago ◽  
...  
Keyword(s):  
Mammalian Cells ◽  
Cell Metabolism ◽  
Web Server ◽  
Acid Group ◽  
Sequence Information ◽  
Phosphorylation Sites ◽  
Amino Acid Group ◽  
The One ◽  
Histidine Phosphorylation ◽  
User Friendly

Background: Phosphorylation of histidine residues plays crucial roles in signaling pathways and cell metabolism in prokaryotes such as bacteria. While evidence has emerged that protein histidine phosphorylation also occurs in more complex organisms, its role in mammalian cells has remained largely uncharted. Thus, it is highly desirable to develop computational tools that are able to identify histidine phosphorylation sites. Result: Here, we introduce PROSPECT that enables fast and accurate prediction of proteome-wide histidine phosphorylation substrates and sites. Our tool is based on a hybrid method that integrates the outputs of two convolutional neural network (CNN)-based classifiers and a random forest-based classifier. Three features, including the one-of-K coding, enhanced grouped amino acids content (EGAAC) and composition of k-spaced amino acid group pairs (CKSAAGP) encoding, were taken as the input to three classifiers, respectively. Our results show that it is able to accurately predict histidine phosphorylation sites from sequence information. Our PROSPECT web server is user-friendly and publicly available at http://PROSPECT.erc.monash.edu/ . Conclusions: PROSPECT is superior than other pHis predictors in both the running speed and prediction accuracy and we anticipate that the PROSPECT webserver will become a popular tool for identifying the pHis sites in bacteria.

Electron crystallographic determination of the 3-D structure of staurolite at atomic resolution

1991 ◽  
Vol 49 ◽  
pp. 540-541
Author(s):  
Kenneth H. Downing ◽  
Hu Meisheng ◽  
Hans-Rudolf Went ◽  
Michael A. O'Keefe
Keyword(s):  
High Resolution ◽  
Three Dimensional ◽  
High Resolution Electron Microscopy ◽  
Atomic Resolution ◽  
Dimensional Structure ◽  
Structure Information ◽  
Resolution Electron ◽  
Scattering Effects ◽  
High Resolution Images ◽  
Effects Of Radiation

With current advances in electron microscope design, high resolution electron microscopy has become routine, and point resolutions of better than 2Å have been obtained in images of many inorganic crystals. Although this resolution is sufficient to resolve interatomic spacings, interpretation generally requires comparison of experimental images with calculations. Since the images are two-dimensional representations of projections of the full three-dimensional structure, information is invariably lost in the overlapping images of atoms at various heights. The technique of electron crystallography, in which information from several views of a crystal is combined, has been developed to obtain three-dimensional information on proteins. The resolution in images of proteins is severely limited by effects of radiation damage. In principle, atomic-resolution, 3D reconstructions should be obtainable from specimens that are resistant to damage. The most serious problem would appear to be in obtaining high-resolution images from areas that are thin enough that dynamical scattering effects can be ignored.

Methods for three-dimensional reconstruction of cellular components within a thick section

1986 ◽  
Vol 44 ◽  
pp. 18-21
Author(s):  
J. Frank ◽  
B. F. McEwen ◽  
M. Radermacher ◽  
C. L. Rieder
Keyword(s):  
Tilt Angle ◽  
Tomographic Reconstruction ◽  
3D Structure ◽  
Three Dimensional ◽  
Thick Section ◽  
Three Dimensional Reconstruction ◽  
Axis Ratio ◽  
Dimensional Reconstruction ◽  
Cellular Components

The tomographic reconstruction from multiple projections of cellular components, within a thick section, offers a way of visualizing and quantifying their three-dimensional (3D) structure. However, asymmetric objects require as many views from the widest tilt range as possible; otherwise the reconstruction may be uninterpretable. Even if not for geometric obstructions, the increasing pathway of electrons, as the tilt angle is increased, poses the ultimate upper limitation to the projection range. With the maximum tilt angle being fixed, the only way to improve the faithfulness of the reconstruction is by changing the mode of the tilting from single-axis to conical; a point within the object projected with a tilt angle of 60° and a full 360° azimuthal range is then reconstructed as a slightly elliptic (axis ratio 1.2 : 1) sphere.

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