Topology Prediction Methods Research Articles

Reputation-based Raft-Poa layered consensus protocol converging UAV network

Integration of blockchain technology offers a robust solution for ensuring reliable data transmission within unmanned aerial vehicle (UAV) networks. Nevertheless, it is crucial to address challenges associated with UAV networks, including their dynamic nature, high latency, limited scalability, and algorithmic inefficiencies. Hence, our proposal involves integrating UAV networks with blockchain technology to enhance their performance by optimizing the consensus algorithm based on network efficiency. We first propose a comprehensive framework for optimizing network performance based on network tomography, incorporating a systematic analysis of network performance and fault avoidance strategies. To improve analysis of the core network’s real-time performance, we also propose a topology prediction method based on an effective probability matrix. Additionally, we establish a calculation method for optimally placing network monitoring points in the core network for enhanced efficiency. We then introduce an adjustable reputation-based Raft-Poa layered consensus protocol that improves consensus protocol scalability and efficiency. To tackle data and node security concerns, we further propose a trust management mechanism based on group signatures. This mechanism verifies the authenticity of node data and identifies malicious nodes. Finally, we validate the protocol’s effectiveness and compare the algorithm’s effectiveness with MMP, Raft, Poa, etc. The simulation results of this protocol show remarkable improvements in network communications and consensus process efficiency, and reductions in communication latency and node utilization rates.

Optimal Cluster-Based Topology and Deep LSTM-Based Prediction Method for Data Reduction in IoT

In the Wireless Sensor Network (WSN), the data prediction approach is needed to attain data effectively by diminishing node energy consumption. Hence, in this research, Water cycle Fire Fly Optimization and Deep Long Short-Term Memory (WCFO+ Deep LSTM) approach is employed for aggregation and reduction of data. The processes involved in the developed method are node simulation, cluster-based topology construction, routing tree construction, and data aggregation. Initially, IoT nodes are simulated in the network environment. The cluster-based topology construction is made using the WCFO algorithm. The WCFO is developed by the integration of the Firefly Optimization Algorithm (FOA) and Water cycle algorithm (WCA). The cluster-based topology is constructed by considering the objective function that includes the parameters, including distance, delay, link quality, and energy. After that, the routing process is performed using developed WCFO approach for constructing a routing tree and estimating the optimal path. Finally, the Deep LSTM is trained by the proposed WCFO algorithm, which is utilized for executing data reduction and data aggregation process with minimum energy consumption. The devised WCFO+ Deep LSTM approach achieved better performance in terms of prediction error, delay, energy, and Packet delivery ratio (PDR) with values 0.029, 0.001[Formula: see text]s, 0.161[Formula: see text]J and 99.054%, respectively.

New insights into the pathogenicity of TMEM165 variants using structural modeling based on AlphaFold 2 predictions

TMEM165 is a Golgi protein playing a crucial role in Mn2+ transport, and whose mutations in patients are known to cause Congenital Disorders of Glycosylation. Some of those mutations affect the highly-conserved consensus motifs E-φ-G-D-[KR]-[TS] characterizing the CaCA2/UPF0016 family, presumably important for the transport of Mn2+ which is essential for the function of many Golgi glycosylation enzymes. Others, like the G>R304 mutation, are far away from these motifs in the sequence. Until recently, the classical membrane protein topology prediction methods were unable to provide a clear picture of the organization of TMEM165 inside the cell membrane, or to explain in a convincing manner the impact of patient and experimentally-generated mutations on the transporter function of TMEM165. In this study, AlphaFold 2 was used to build a TMEM165 model that was then refined by molecular dynamics simulation with membrane lipids and water. This model provides a realistic picture of the 3D protein scaffold formed from a two-fold repeat of three transmembrane helices/domains where the consensus motifs face each other to form a putative acidic cation-binding site at the cytosolic side of the protein. It sheds new light on the impact of mutations on the transporter function of TMEM165, found in patients and studied experimentally in vitro, formerly and within this study. More particularly and very interestingly, this model explains the impact of the G>R304 mutation on TMEM165’s function. These findings provide great confidence in the predicted TMEM165 model whose structural features are discussed in the study and compared to other structural and functional TMEM165 homologs from the CaCA2/UPF0016 family and the LysE superfamily.

Genome Sequence Resource of Sarocladium terricola TR, an Endophytic Fungus as a Potential Biocontrol Agent Against Meloidogyne incognita.

Genome Sequence Resource of Sarocladium terricola TR, an Endophytic Fungus as a Potential Biocontrol Agent Against Meloidogyne incognita.

An Improved Topology Prediction of Alpha-Helical Transmembrane Protein Based on Deep Multi-Scale Convolutional Neural Network.

Alpha-helical proteins ( αTMPs) are essential in various biological processes. Despite their tertiary structures are crucial for revealing complex functions, experimental structure determination remains challenging and costly. In the past decades, various sequence-based topology prediction methods have been developed to bridge the gap between the sequences and structures by characterizing the structural features, but significant improvements are still required. Deep learning brings a great opportunity for its powerful representation learning capability from limited original data. In this work, we improved our αTMP topology prediction method DMCTOP using deep learning, which composed of two deep convolutional blocks to simultaneously extract local and global contextual features. Consequently, the inputs were simplified to reflect the original features of the sequence, including a protein sequence feature and an evolutionary conservation feature. DMCTOP can efficiently and accurately identify all topological types and the N-terminal orientation for an αTMP sequence. To validate the effectiveness of our method, we benchmarked DMCTOP against 13 peer methods according to the whole sequence, the transmembrane segment and the traditional criterion in testing experiments. All the results reveal that our method achieved the highest prediction accuracy and outperformed all the previous methods. The method is available at https://icdtools.nenu.edu.cn/dmctop.

The membrane topology of immunity proteins for the two-peptide bacteriocins carnobacteriocin XY, lactococcin G, and lactococcin MN shows structural diversity.

The two‐peptide bacteriocins produced by Gram‐positive bacteria require two different peptides, present in equimolar amounts, to elicit optimal antimicrobial activity. Producer organisms are protected from their bacteriocin by a dedicated immunity protein. The immunity proteins for two‐peptide bacteriocins contain putative transmembrane domains (TMDs) and might therefore be associated with the membrane. The immunity protein CbnZ for the two‐peptide bacteriocin carnobacteriocin XY (CbnXY) was identified by heterologously expressing the cbnZ gene in sensitive host strains. Using protein topology prediction methods and the dual pho‐lac reporter system, we mapped out the membrane topology of CbnZ, along with those of the immunity proteins LagC and LciM for the two‐peptide bacteriocins lactococcin G and lactococcin MN, respectively. Our results reveal wide structural variety between these immunity proteins that can contain as little as one TMD or as many as four TMDs.

Inclusion of dyad-repeat pattern improves topology prediction of transmembrane β-barrel proteins

: Accurate topology prediction of transmembrane β-barrels is still an open question. Here, we present BOCTOPUS2, an improved topology prediction method for transmembrane β-barrels that can also identify the barrel domain, predict the topology and identify the orientation of residues in transmembrane β-strands. The major novelty of BOCTOPUS2 is the use of the dyad-repeat pattern of lipid and pore facing residues observed in transmembrane β-barrels. In a cross-validation test on a benchmark set of 42 proteins, BOCTOPUS2 predicts the correct topology in 69% of the proteins, an improvement of more than 10% over the best earlier method (BOCTOPUS) and in addition, it produces significantly fewer erroneous predictions on non-transmembrane β-barrel proteins. BOCTOPUS2 webserver along with full dataset and source code is available at http://boctopus.bioinfo.se/ : arne@bioinfo.se Supplementary data are available at Bioinformatics online.

The human transmembrane proteome.

BackgroundTransmembrane proteins have important roles in cells, as they are involved in energy production, signal transduction, cell-cell interaction, cell-cell communication and more. In human cells, they are frequently targets for pharmaceuticals; therefore, knowledge about their properties and structure is crucial. Topology of transmembrane proteins provide a low resolution structural information, which can be a starting point for either laboratory experiments or modelling their 3D structures.ResultsHere, we present a database of the human α-helical transmembrane proteome, including the predicted and/or experimentally established topology of each transmembrane protein, together with the reliability of the prediction. In order to distinguish transmembrane proteins in the proteome as well as for topology prediction, we used a newly developed consensus method (CCTOP) that incorporates recent state of the art methods, with tested accuracies on a novel human benchmark protein set. CCTOP utilizes all available structure and topology data as well as bioinformatical evidences for topology prediction in a probabilistic framework provided by the hidden Markov model. This method shows the highest accuracy (98.5 % for discrinimating between transmembrane and non-transmembrane proteins and 84 % for per protein topology prediction) among the dozen tested topology prediction methods. Analysis of the human proteome with the CCTOP indicates that it contains 4998 (26 %) transmembrane proteins. Besides predicting topology, reliability of the predictions is estimated as well, and it is demonstrated that the per protein prediction accuracies of more than 60 % of the predictions are over 98 % on the benchmark sets and most probably on the predicted human transmembrane proteome too.ConclusionsHere, we present the most accurate prediction of the human transmembrane proteome together with the experimental topology data. These data, as well as various statistics about the human transmembrane proteins and their topologies can be downloaded from and can be visualized at the website of the human transmembrane proteome (http://htp.enzim.hu).ReviewersThis article was reviewed by Dr. Sandor Pongor, Dr. Michael Galperin and Dr. Pascale Gaudet (nominated by Dr Michael Galperin).Electronic supplementary materialThe online version of this article (doi:10.1186/s13062-015-0061-x) contains supplementary material, which is available to authorized users.

CCTOP: a Consensus Constrained TOPology prediction web server.

The Consensus Constrained TOPology prediction (CCTOP; http://cctop.enzim.ttk.mta.hu) server is a web-based application providing transmembrane topology prediction. In addition to utilizing 10 different state-of-the-art topology prediction methods, the CCTOP server incorporates topology information from existing experimental and computational sources available in the PDBTM, TOPDB and TOPDOM databases using the probabilistic framework of hidden Markov model. The server provides the option to precede the topology prediction with signal peptide prediction and transmembrane-globular protein discrimination. The initial result can be recalculated by (de)selecting any of the prediction methods or mapped experiments or by adding user specified constraints. CCTOP showed superior performance to existing approaches. The reliability of each prediction is also calculated, which correlates with the accuracy of the per protein topology prediction. The prediction results and the collected experimental information are visualized on the CCTOP home page and can be downloaded in XML format. Programmable access of the CCTOP server is also available, and an example of client-side script is provided.

TOPPER: topology prediction of transmembrane protein based on evidential reasoning.

The topology prediction of transmembrane protein is a hot research field in bioinformatics and molecular biology. It is a typical pattern recognition problem. Various prediction algorithms are developed to predict the transmembrane protein topology since the experimental techniques have been restricted by many stringent conditions. Usually, these individual prediction algorithms depend on various principles such as the hydrophobicity or charges of residues. In this paper, an evidential topology prediction method for transmembrane protein is proposed based on evidential reasoning, which is called TOPPER (topology prediction of transmembrane protein based on evidential reasoning). In the proposed method, the prediction results of multiple individual prediction algorithms can be transformed into BPAs (basic probability assignments) according to the confusion matrix. Then, the final prediction result can be obtained by the combination of each individual prediction base on Dempster's rule of combination. The experimental results show that the proposed method is superior to the individual prediction algorithms, which illustrates the effectiveness of the proposed method.

Improving transmembrane protein consensus topology prediction using inter-helical interaction

Alpha helix transmembrane proteins (αTMPs) represent roughly 30% of all open reading frames (ORFs) in a typical genome and are involved in many critical biological processes. Due to the special physicochemical properties, it is hard to crystallize and obtain high resolution structures experimentally, thus, sequence-based topology prediction is highly desirable for the study of transmembrane proteins (TMPs), both in structure prediction and function prediction. Various model-based topology prediction methods have been developed, but the accuracy of those individual predictors remain poor due to the limitation of the methods or the features they used. Thus, the consensus topology prediction method becomes practical for high accuracy applications by combining the advances of the individual predictors. Here, based on the observation that inter-helical interactions are commonly found within the transmembrane helixes (TMHs) and strongly indicate the existence of them, we present a novel consensus topology prediction method for αTMPs, CNTOP, which incorporates four top leading individual topology predictors, and further improves the prediction accuracy by using the predicted inter-helical interactions. The method achieved 87% prediction accuracy based on a benchmark dataset and 78% accuracy based on a non-redundant dataset which is composed of polytopic αTMPs. Our method derives the highest topology accuracy than any other individual predictors and consensus predictors, at the same time, the TMHs are more accurately predicted in their length and locations, where both the false positives (FPs) and the false negatives (FNs) decreased dramatically. The CNTOP is available at: http://ccst.jlu.edu.cn/JCSB/cntop/CNTOP.html.

Rapid membrane protein topology prediction

Summary: State-of-the-art methods for topology of α-helical membrane proteins are based on the use of time-consuming multiple sequence alignments obtained from PSI-BLAST or other sources. Here, we examine if it is possible to use the consensus of topology prediction methods that are based on single sequences to obtain a similar accuracy as the more accurate multiple sequence-based methods. Here, we show that TOPCONS-single performs better than any of the other topology prediction methods tested here, but ∼6% worse than the best method that is utilizing multiple sequence alignments.Availability and Implementation: TOPCONS-single is available as a web server from http://single.topcons.net/ and is also included for local installation from the web site. In addition, consensus-based topology predictions for the entire international protein index (IPI) is available from the web server and will be updated at regular intervals.Contact: arne@bioinfo.seSupplementary information: Supplementary data are avaliable at Bioinformatics online.

Topology Prediction of Helical Transmembrane Proteins: How Far Have We Reached?

Transmembrane protein topology prediction methods play important roles in structural biology, because the structure determination of these types of proteins is extremely difficult by the common biophysical, biochemical and molecular biological methods. The need for accurate prediction methods is high, as the number of known membrane protein structures fall far behind the estimated number of these proteins in various genomes. The accuracy of these prediction methods appears to be higher than most prediction methods applied on globular proteins, however it decreases slightly with the increasing number of structures. Unfortunately, most prediction algorithms use common machine learning techniques, and they do not reveal why topologies are predicted with such a high success rate and which biophysical or biochemical properties are important to achieve this level of accuracy. Incorporating topology data determined so far into the prediction methods as constraints helps us to reach even higher prediction accuracy, therefore collection of such topology data is also an important issue.

Prediction of the human membrane proteome

Membrane proteins are key molecules in the cell, and are important targets for pharmaceutical drugs. Few three-dimensional structures of membrane proteins have been obtained, which makes computational prediction of membrane proteins crucial for studies of these key molecules. Here, seven membrane protein topology prediction methods based on different underlying algorithms, such as hidden Markov models, neural networks and support vector machines, have been used for analysis of the protein sequences from the 21,416 annotated genes in the human genome. The number of genes coding for a protein with predicted alpha-helical transmembrane region(s) ranged from 5508 to 7651, depending on the method used. Based on a majority decision method, we estimate 5539 human genes to code for membrane proteins, corresponding to approximately 26% of the human protein-coding genes. The largest fraction of these proteins has only one predicted transmembrane region, but there are also many proteins with seven predicted transmembrane regions, including the G-protein coupled receptors. A visualization tool displaying the topologies suggested by the eight prediction methods, for all predicted membrane proteins, is available on the public Human Protein Atlas portal (www.proteinatlas.org).

TOPTMH: TOPOLOGY PREDICTOR FOR TRANSMEMBRANE α-HELICES

Alpha-helical transmembrane proteins mediate many key biological processes and represent 20%-30% of all genes in many organisms. Due to the difficulties in experimentally determining their high-resolution 3D structure, computational methods to predict the location and orientation of transmembrane helix segments using sequence information are essential. We present TOPTMH, a new transmembrane helix topology prediction method that combines support vector machines, hidden Markov models, and a widely used rule-based scheme. The contribution of this work is the development of a prediction approach that first uses a binary SVM classifier to predict the helix residues and then it employs a pair of HMM models that incorporate the SVM predictions and hydropathy-based features to identify the entire transmembrane helix segments by capturing the structural characteristics of these proteins. TOPTMH outperforms state-of-the-art prediction methods and achieves the best performance on an independent static benchmark.

New Structural Arrangement of the Extracellular Regions of the Phosphate Transporter SLC20A1, the Receptor for Gibbon Ape Leukemia Virus

Infection of a host cell by a retrovirus requires an initial interaction with a cellular receptor. For numerous gammaretroviruses, such as the gibbon ape leukemia virus, woolly monkey virus, feline leukemia virus subgroup B, feline leukemia virus subgroup T, and 10A1 murine leukemia virus, this receptor is the human type III sodium-dependent inorganic phosphate transporter, SLC20A1, formerly known as PiT1. Understanding the critical receptor functionalities and interactions with the virus that lead to successful infection requires that we first know the surface structure of the cellular receptor. Previous molecular modeling from the protein sequence, and limited empirical data, predicted a protein with 10 transmembrane helices. Here we undertake the biochemical approach of substituted cysteine accessibility mutagenesis to resolve the topology of this receptor in live cells. We discover that there are segments of the protein that are unexpectedly exposed to the outside milieu. By using information determined by substituted cysteine accessibility mutagenesis to set constraints in HMMTOP, a hidden Markov model-based transmembrane topology prediction method, we now propose a comprehensive topological model for SLC20A1, a transmembrane protein with 12 transmembrane helices and 7 extracellular regions, that varies from previous models and should permit approaches that define both virus interaction and transport function.

TOPCONS: consensus prediction of membrane protein topology

TOPCONS (http://topcons.net/) is a web server for consensus prediction of membrane protein topology. The underlying algorithm combines an arbitrary number of topology predictions into one consensus prediction and quantifies the reliability of the prediction based on the level of agreement between the underlying methods, both on the protein level and on the level of individual TM regions. Benchmarking the method shows that overall performance levels match the best available topology prediction methods, and for sequences with high reliability scores, performance is increased by ∼10 percentage points. The web interface allows for constraining parts of the sequence to a known inside/outside location, and detailed results are displayed both graphically and in text format.

Estimating the length of transmembrane helices using Z‐coordinate predictions

Zpred2 is an improved version of ZPRED, a predictor for the Z-coordinates of alpha-helical membrane proteins, that is, the distance of the residues from the center of the membrane. Using principal component analysis and a set of neural networks, Zpred2 analyzes data extracted from the amino acid sequence, the predicted topology, and evolutionary profiles. Zpred2 achieves an average accuracy error of 2.18 A (2.17 A when an independent test set is used), an improvement by 15% compared to the previous version. We show that this accuracy is sufficient to enable the predictions of helix lengths with a correlation coefficient of 0.41. As a comparison, two state-of-the-art HMM-based topology prediction methods manage to predict the helix lengths with a correlation coefficient of less than 0.1. In addition, we applied Zpred2 to two other problems, the re-entrant region identification and model validation. Re-entrants were able to be detected with a certain consistency, but not better than with previous approaches, while incorrect models as well as mispredicted helices of transmembrane proteins could be distinguished based on the Z-coordinate predictions.

MCAM: A Database to Accelerate the Identification of Functional Cell Adhesion Molecules

In the post-genomic era, computational identification of cell adhesion molecules (CAMs) becomes important in defining new targets for diagnosis and treatment of various diseases including cancer. Lack of a comprehensive CAM-specific database restricts our ability to identify and characterize novel CAMs. Therefore, we developed a comprehensive mammalian cell adhesion molecule (MCAM) database. The current version is an interactive Web-based database, which provides the resources needed to search mouse, human and rat-specific CAMs and their sequence information and characteristics such as gene functions and virtual gene expression patterns in normal and tumor tissues as well as cell lines. Moreover, the MCAM database can be used for various bioinformatics and biological analyses including identifying CAMs involved in cell-cell interactions and homing of lymphocytes, hematopoietic stem cells and malignant cells to specific organs using data from high-throughput experiments. Furthermore, the database can also be used for training and testing existing transmembrane (TM) topology prediction methods specifically for CAM sequences. The database is freely available online at http://app1.unmc.edu/mcam.

A combinatorial pattern discovery approach for the prediction of membrane dipping (re-entrant) loops

Membrane dipping loops are sections of membrane proteins that reside in the membrane but do not traverse from one side to the other, rather they enter and leave the same side of the membrane. We applied a combinatorial pattern discovery approach to sets of sequences containing at least one characterised structure described as possessing a membrane dipping loop. Discovered patterns were found to be composed of residues whose biochemical role is known to be essential for function of the protein, thus validating our approach. TMLOOP (http://membraneproteins.swan.ac.uk/TMLOOP) was implemented to predict membrane dipping loops in polytopic membrane proteins. TMLOOP applies discovered patterns as weighted predictive rules in a collective motif method (a variation of the single motif method), to avoid inherent limitations of single motif methods in detecting distantly related proteins. The collective motif method applies several, partially overlapping patterns, which pertain to the same sequence region, allowing proteins containing small variations to be detected. The approach achieved 92.4% accuracy in sensitivity and 100% reliability in specificity. TMLOOP was applied to the Swiss-Prot database, identifying 1392 confirmed membrane dipping loops, 75 plausible membrane dipping loops hitherto uncharacterised by topology prediction methods or experimental approaches and 128 false positives (8.0%).