Homology Detection Methods Research Articles

Firmware Vulnerabilities Homology Detection Based on Clonal Selection Algorithm for IoT Devices

With the wide application of Internet of Things (IoT) devices, security attacks against their firmware often occur, which has attracted more attention from the research community. Firmware is an important part of IoT devices, and attacks against them is one of the main means to destroy them. Therefore, firmware security is considered a core of the overall devices’ security. At present, most of the firmware vulnerabilities have a small number of related samples, so it is difficult to use machine learning methods to generate detectors for some of them. Therefore, based on the collected data of related firmware vulnerabilities, this article proposes a firmware vulnerability homology detection method based on the clonal selection algorithm. We design the numerical and structural characteristics of vulnerability functions, train a detector for each function separately, and improve the recall rate of vulnerability detection. Compared with existing machine learning methods, this method only depends on the affinity between the objective function and the detector, which avoids the requirement of a large number of sample data sets. Finally, relevant experiments are carried out to verify the effectiveness of the method.

Homology detection method for voltage sags based on space vector and maximum mean discrepancy

A single voltage sag source may trigger multiple power quality monitors to record waveforms resulting in the redundancy of voltage sag monitoring data. To address this problem, a homology detection method for voltage sags based on a space vector and maximum mean discrepancy was proposed, which is conducive for locating accurate voltage sag sources and inverting sag event propagation paths. First, a waveform transformation method based on a space vector was proposed to eliminate the influence of the transformer on voltage sag propagation. Second, a two-sample test method for voltage sags based on the maximum mean discrepancy was proposed to detect whether any two voltage sag waveforms are caused by the same voltage sag source. The connected graph method was used to identify the number of sag sources; this avoids the difficulty in accurately selecting the number of clusters. The measured and simulation data of the IEEE 30-bus system were used for verification, and the results verified the correctness and effectiveness of the proposed method. The proposed method can be implemented in a power quality monitoring system as a pre-processing step to support related research activities.

PHR-search: a search framework for protein remote homology detection based on the predicted protein hierarchical relationships.

Protein remote homology detection is one of the most fundamental research tool for protein structure and function prediction. Most search methods for protein remote homology detection are evaluated based on the Structural Classification of Proteins-extended (SCOPe) benchmark, but the diverse hierarchical structure relationships between the query protein and candidate proteins are ignored by these methods. In order to further improve the predictive performance for protein remote homology detection, a search framework based on the predicted protein hierarchical relationships (PHR-search) is proposed. In the PHR-search framework, the superfamily level prediction information is obtained by extracting the local and global features of the Hidden Markov Model (HMM) profile through a convolution neural network and it is converted to the fold level and class level prediction information according to the hierarchical relationships of SCOPe. Based on these predicted protein hierarchical relationships, filtering strategy and re-ranking strategy are used to construct the two-level search of PHR-search. Experimental results show that the PHR-search framework achieves the state-of-the-art performance by employing five basic search methods, including HHblits, JackHMMER, PSI-BLAST, DELTA-BLAST and PSI-BLASTexB. Furthermore, the web server of PHR-search is established, which can be accessed at http://bliulab.net/PHR-search.

Detecting phishing webpages via homology analysis of webpage structure.

Phishing webpages are often generated by phishing kits or evolved from existing kits. Therefore, the homology analysis of phishing webpages can help curb the proliferation of phishing webpages from the source. Based on the observation that phishing webpages belonging to the same family have similar page structures, a homology detection method based on webpage clustering according to structural similarity is proposed. The method consists of two stages. The first stage realizes model construction. Firstly, it extracts the structural features and style attributes of webpages through the document structure and vectorizes them, and then assigns different weights to different features, and measures the similarity of webpages and guides webpage clustering by webpage difference index. The second phase completes the detection of webpages to be tested. The fingerprint generation algorithm using double compressions generates fingerprints for the centres of the clusters and the webpages to be tested respectively and accelerates the detection process of the webpages to be tested through bitwise comparison. Experiments show that, compared with the existing methods, the proposed method can accurately locate the family of phishing webpages and can detect phishing webpages efficiently.

Cross-Platform Binary Code Homology Analysis Based on GRU Graph Embedding

Binary code homology analysis refers to detecting whether two pieces of binary code are compiled from the same piece of source code, which is a fundamental technique for many security applications, such as vulnerability search, plagiarism detection, and malware detection. With the increase in critical vulnerabilities in IoT devices, homology analysis is increasingly needed to perform cross-platform vulnerability searches. Existing methods for cross-platform binary code homology detection usually convert binary code to instruction sequences and do semantic embedding of the sequences as if they were natural language. However, the gap between natural language and binary code is large, and the spatial features of the binary code are easily lost by directly comparing the semantics. In this paper, we propose a GRU-based graph embedding method to compare the homology of binary functions. First, the attribute control flow graph (ACFG) is built for the assembly function, then the GRU-based graph embedding neural network is used to generate the embedding vector for the ACFG, and finally the homology of the binary code is determined by calculating the distance between the embedding vectors. The experimental results show that our method greatly improves the detection accuracy of negative samples compared with Gemini, the latest method based on graph embedding binary code similarity detection.

CHLAMYDIA TRACHOMATIS EXPRESSES PHYLOGENETICALLY HOMOLOGOUS PROTEINS TO HUMAN PAPILLOMA VIRUS TYPE 16: INFERENCES FOR ORAL AND OROPHARYNGEAL SQUAMOUS CELL CARCINOMA

Background Oral and oropharyngeal squamous cell carcinoma have been linked to infection by human papilloma virus (HPV) types 16 and 18. They produce proteins E6 and E7 that interfere with p53 and pRb1 proteins and hence support oncogenesis. HPV infection is typically via oro-genital contact and other organisms cohabiting the genitalia may have had sufficient time for a genetic exchange with HPV oncoproteins and hence develop oncogenic potential also within the oral cavity. Objective Our aim was to search for organisms with homologous match for HPV 16 E6/E7 protein sequences. Methods HPV 16 sequences were obtained from the NCBI database. E6 and E7 homologous sequences with accession numbers >ACQ90217 and >ACQ90218 were retrieved through BLAST search. The SWISS-MODEL template library was searched with BLAST and HHBlits for evolutionary related structures matching the target sequence. Utilizing the MPI Bioinformatics remote homology detection method linked with the UniProt database, best matches of E6 and E7 protein sequences were retrieved. Multiple sequence alignment was done and ProMod3 was used to build a protein model for E7. Results HPV 16 E7 protein and Chlamydia trachomatis homologous protein sequences were the most closely related from our bioinformatic search. Conclusions This similarity between C. trachomatis proteins and HPV 16 E7 oncogenic proteins could be because both organisms inhabit similar mucosae and have had considerable time to exchange genes. We postulate that a possible co-infection of C. trachomatis may be implicated in the development of oral and oropharyngeal squamous cell carcinoma as documented with cervical cancers. Oral and oropharyngeal squamous cell carcinoma have been linked to infection by human papilloma virus (HPV) types 16 and 18. They produce proteins E6 and E7 that interfere with p53 and pRb1 proteins and hence support oncogenesis. HPV infection is typically via oro-genital contact and other organisms cohabiting the genitalia may have had sufficient time for a genetic exchange with HPV oncoproteins and hence develop oncogenic potential also within the oral cavity. Our aim was to search for organisms with homologous match for HPV 16 E6/E7 protein sequences. HPV 16 sequences were obtained from the NCBI database. E6 and E7 homologous sequences with accession numbers >ACQ90217 and >ACQ90218 were retrieved through BLAST search. The SWISS-MODEL template library was searched with BLAST and HHBlits for evolutionary related structures matching the target sequence. Utilizing the MPI Bioinformatics remote homology detection method linked with the UniProt database, best matches of E6 and E7 protein sequences were retrieved. Multiple sequence alignment was done and ProMod3 was used to build a protein model for E7. HPV 16 E7 protein and Chlamydia trachomatis homologous protein sequences were the most closely related from our bioinformatic search. This similarity between C. trachomatis proteins and HPV 16 E7 oncogenic proteins could be because both organisms inhabit similar mucosae and have had considerable time to exchange genes. We postulate that a possible co-infection of C. trachomatis may be implicated in the development of oral and oropharyngeal squamous cell carcinoma as documented with cervical cancers.

Unification and extensive diversification of M/Orf3-related ion channel proteins in coronaviruses and other nidoviruses

The coronavirus, Severe Acute Respiratory Syndrome (SARS)-CoV-2, responsible for the ongoing coronavirus disease 2019 (COVID-19) pandemic, has emphasized the need for a better understanding of the evolution of virus-host interactions. ORF3a in both SARS-CoV-1 and SARS-CoV-2 are ion channels (viroporins) implicated in virion assembly and membrane budding. Using sensitive profile-based homology detection methods, we unify the SARS-CoV ORF3a family with several families of viral proteins, including ORF5 from MERS-CoVs, proteins from beta-CoVs (ORF3c), alpha-CoVs (ORF3b), most importantly, the Matrix (M) proteins from CoVs, and more distant homologs from other nidoviruses. We present computational evidence that these viral families might utilize specific conserved polar residues to constitute an aqueous pore within the membrane-spanning region. We reconstruct an evolutionary history of these families and objectively establish the common origin of the M proteins of CoVs and Toroviruses. We also show that the divergent ORF3 clade (ORF3a/ORF3b/ORF3c/ORF5 families) represents a duplication stemming from the M protein in alpha- and beta-CoVs. By phyletic profiling of major structural components of primary nidoviruses, we present a hypothesis for their role in virion assembly of CoVs, ToroVs, and Arteriviruses. The unification of diverse M/ORF3 ion channel families in a wide range of nidoviruses, especially the typical M protein in CoVs, reveal a conserved, previously under-appreciated role of ion channels in virion assembly and membrane budding. We show that M and ORF3 are under different evolutionary pressures; in contrast to the slow evolution of M as core structural component, the ORF3 clade is under selection for diversification, which suggests it might act at the interface with host molecules and/or immune attack.

PL-search: a profile-link-based search method for protein remote homology detection

Protein remote homology detection is a fundamental and important task for protein structure and function analysis. Several search methods have been proposed to improve the detection performance of the remote homologues and the accuracy of ranking lists. The position-specific scoring matrix (PSSM) profile and hidden Markov model (HMM) profile can contribute to improving the performance of the state-of-the-art search methods. In this paper, we improved the profile-link (PL) information for constructing PSSM or HMM profiles, and proposed a PL-based search method (PL-search). In PL-search, more robust PLs are constructed through the double-link and iterative extending strategies, and an accurate similarity score of sequence pairs is calculated from the two-level Jaccard distance for remote homologues. We tested our method on two widely used benchmark datasets. Our results show that whether HHblits, JackHMMER or position-specific iterated-BLAST is used, PL-search obviously improves the search performance in terms of ranking quality as well as the number of detected remote homologues. For ease of use of PL-search, both its stand-alone tool and the web server are constructed, which can be accessed at http://bliulab.net/PL-search/.

Sequence Alignment Using Machine Learning for Accurate Template-based Protein Structure Prediction.

Template-based modeling, the process of predicting the tertiary structure of a protein by using homologous protein structures, is useful when good templates can be available. Indeed, modern homology detection methods can find remote homologs with high sensitivity. However, the accuracy of template-based models generated from the homology-detection-based alignments is often lower than that from ideal alignments. In this study, we propose a new method that generates pairwise sequence alignments for more accurate template-based modeling. Our method trains a machine learning model using the structural alignment of known homologs. When calculating sequence alignments, instead of a fixed substitution matrix, this method dynamically predicts a substitution score from the trained model.

Sequence alignment using machine learning for accurate template-based protein structure prediction.

Template-based modeling, the process of predicting the tertiary structure of a protein by using homologous protein structures, is useful if good templates can be found. Although modern homology detection methods can find remote homologs with high sensitivity, the accuracy of template-based models generated from homology-detection-based alignments is often lower than that from ideal alignments. In this study, we propose a new method that generates pairwise sequence alignments for more accurate template-based modeling. The proposed method trains a machine learning model using the structural alignment of known homologs. It is difficult to directly predict sequence alignments using machine learning. Thus, when calculating sequence alignments, instead of a fixed substitution matrix, this method dynamically predicts a substitution score from the trained model. We evaluate our method by carefully splitting the training and test datasets and comparing the predicted structure's accuracy with that of state-of-the-art methods. Our method generates more accurate tertiary structure models than those produced from alignments obtained by other methods. https://github.com/shuichiro-makigaki/exmachina. Supplementary data are available at Bioinformatics online.

ProtDec-LTR3.0: Protein Remote Homology Detection by Incorporating Profile-Based Features Into Learning to Rank

Protein remote homology detection is one of the most challenging problems in the field of protein sequence analysis, which is an important step for both theoretical research (such as the understanding of structures and functions of proteins) and drug design. Previous studies have shown that combining different ranking methods via learning to the rank algorithm is an effective strategy for remote protein homology detection, and the performance can be further improved by the protein similarity networks. In this paper, we improved the ProtDec-LTR1.0 and ProtDec-LTR2.0 predictors by incorporating three profile-based features (Top-1-gram, Top-2-gram, and ACC) into the framework of learning to rank via feature mapping strategies. The predictive performance was further refined by the pagerank (PR) algorithm and hyperlink-induced topic search (HITS) algorithm. Finally, a predictor called ProtDec-LTR3.0 was proposed. Rigorous tests on two widely used benchmark datasets showed that the ProtDec-LTR3.0 predictor outperformed both ProtDec-LTR1.0 and ProtDec-LTR2.0, and other nine existing state-of-the-art predictors, indicating that the ProtDec-LTR3.0 is an efficient method for protein remote homology detection, and will become a useful tool for protein sequence analysis. A user-friendly web server of the ProtDec-LTR3.0 predictor was established for the convenience of users, which can be accessed at http://bliulab.net/ProtDec-LTR3.0/.

Toward Reducing Phylostratigraphic Errors and Biases

Phylostratigraphy is a method for estimating gene age, usually applied to large numbers of genes in order to detect nonrandom age-distributions of gene properties that could shed light on mechanisms of gene origination and evolution. However, phylostratigraphy underestimates gene age with a nonnegligible probability. The underestimation is severer for genes with certain properties, creating spurious age distributions of these properties and those correlated with these properties. Here we explore three strategies to reduce phylostratigraphic error/bias. First, we test several alternative homology detection methods (PSIBLAST, HMMER, PHMMER, OMA, and GLAM2Scan) in phylostratigraphy, but fail to find any that noticeably outperforms the commonly used BLASTP. Second, using machine learning, we look for predictors of error-prone genes to exclude from phylostratigraphy, but cannot identify reliable predictors. Finally, we remove from phylostratigraphic analysis genes exhibiting errors in simulation, which by definition minimizes error/bias if the simulation is sufficiently realistic. Using this last approach, we show that some previously reported phylostratigraphic trends (e.g., younger proteins tend to evolve more rapidly and be shorter) disappear or even reverse, reconfirming the necessity of controlling phylostratigraphic error/bias. Taken together, our analyses demonstrate that phylostratigraphic errors/biases are refractory to several potential solutions but can be controlled at least partially by the exclusion of error-prone genes identified via realistic simulations. These results are expected to stimulate the judicious use of error-aware phylostratigraphy and reevaluation of previous phylostratigraphic findings.

Using Grey Model to Predict Protein Remote Homologous Family

Protein remote homology detection is the most basic and core problems of protein structure and function research. The purpose of protein remote homology detection to detect the remote evolutionary relationship between proteins by computation methods. At present, there are many methods for remote homology detection of proteins, but some methods are not very effective. In bioinformatics, it is urgent to further improve the performance of protein remote homology detection. In this study, we propose a new model called PHom-GRA to detect protein remote homology, which is the integration of various ranking methods via using grey relational analysis. Our experiment constructs benchmark dataset with lower homology, in which any two proteins have not more than 40% identity or homology. We achieve an ROC1 score of 0.7372 and an ROC50 score of 0.7968 in jack-knife test.

Ultra-fast global homology detection with Discrete Cosine Transform and Dynamic Time Warping.

Evolutionary information is crucial for the annotation of proteins in bioinformatics. The amount of retrieved homologs often correlates with the quality of predicted protein annotations related to structure or function. With a growing amount of sequences available, fast and reliable methods for homology detection are essential, as they have a direct impact on predicted protein annotations. We developed a discriminative, alignment-free algorithm for homology detection with quasi-linear complexity, enabling theoretically much faster homology searches. To reach this goal, we convert the protein sequence into numeric biophysical representations. These are shrunk to a fixed length using a novel vector quantization method which uses a Discrete Cosine Transform compression. We then compute, for each compressed representation, similarity scores between proteins with the Dynamic Time Warping algorithm and we feed them into a Random Forest. The WARP performances are comparable with state of the art methods. The method is available at http://ibsquare.be/warp. Supplementary data are available at Bioinformatics online.

In Silico Discovery of a Substituted 6-Methoxy-quinalidine with Leishmanicidal Activity in Leishmania infantum.

There is an urgent need for the discovery of new antileishmanial drugs with a new mechanism of action. Type 2 NADH dehydrogenase from Leishmania infantum (LiNDH2) is an enzyme of the parasite’s respiratory system, which catalyzes the electron transfer from NADH to ubiquinone without coupled proton pumping. In previous studies of the related NADH: ubiquinone oxidoreductase crystal structure from Saccharomyces cerevisiae, two ubiquinone-binding sites (UQI and UQII) were identified and shown to play an important role in the NDH-2-catalyzed oxidoreduction reaction. Based on the available structural data, we developed a three-dimensional structural model of LiNDH2 using homology detection methods and performed an in silico virtual screening campaign to search for potential inhibitors targeting the LiNDH2 ubiquinone-binding site 1–UQI. Selected compounds displaying favorable properties in the computational screening experiments were assayed for inhibitory activity in the structurally similar recombinant NDH-2 from S. aureus and leishmanicidal activity was determined in the wild-type axenic amastigotes and promastigotes of L. infantum. The identified compound, a substituted 6-methoxy-quinalidine, showed promising nanomolar leishmanicidal activity on wild-type axenic promastigotes and amastigotes of L. infantum and the potential for further development.

Systematic classification of the His-Me finger superfamily

The His-Me finger endonucleases, also known as HNH or ββα-metal endonucleases, form a large and diverse protein superfamily. The His-Me finger domain can be found in proteins that play an essential role in cells, including genome maintenance, intron homing, host defense and target offense. Its overall structural compactness and non-specificity make it a perfectly-tailored pathogenic module that participates on both sides of inter- and intra-organismal competition. An extremely low sequence similarity across the superfamily makes it difficult to identify and classify new His-Me fingers. Using state-of-the-art distant homology detection methods, we provide an updated and systematic classification of His-Me finger proteins. In this work, we identified over 100 000 proteins and clustered them into 38 groups, of which three groups are new and cannot be found in any existing public domain database of protein families. Based on an analysis of sequences, structures, domain architectures, and genomic contexts, we provide a careful functional annotation of the poorly characterized members of this superfamily. Our results may inspire further experimental investigations that should address the predicted activity and clarify the potential substrates, to provide more detailed insights into the fundamental biological roles of these proteins.

Remote-3DD: un nuevo método para la detección de homólogos remotos que usa propiedades fisicoquímicas

En este artículo se presenta un nuevo método para la detección de homólogos remotos, llamado remote-3DD, que combina mapas de contacto predichos y una distribución de los valores en las matrices de interacción. Los mapas de contacto predichos son una aproximación de la forma 3D de proteína que se puede obtener a partir de su estructura primaria. Por su parte, una matriz de interacción permite representar una proteína a partir de las propiedades fisicoquímicas de los aminoácidos que la conforman. Remote-3DD se propone como una estrategia para mejorar la exactitud del método remote-C3D en el cual se utilizan solamente mapas de contacto. La hipótesis que se plantea en este artículo es que se puede mejorar la exactitud del método remote-C3D al incorporar las distribuciones de la matriz de interacción. Los resultados de las pruebas muestran que el método remote-3DD alcanza una exactitud mayor que los métodos basados en composición y en algunos casos una exactitud comparable con los métodos basados en perfiles. Además, las pruebas permiten demostrar que el método remote-3DD, en general, presenta exactitudes mayores que el método remote-C3D cuando se utiliza la misma cantidad de modelos y tamaños de submatrices.

ProtDec-LTR2.0: an improved method for protein remote homology detection by combining pseudo protein and supervised Learning to Rank.

As one of the most important tasks in protein sequence analysis, protein remote homology detection is critical for both basic research and practical applications. Here, we present an effective web server for protein remote homology detection called ProtDec-LTR2.0 by combining ProtDec-Learning to Rank (LTR) and pseudo protein representation. Experimental results showed that the detection performance is obviously improved. The web server provides a user-friendly interface to explore the sequence and structure information of candidate proteins and find their conserved domains by launching a multiple sequence alignment tool. The web server is free and open to all users with no login requirement at http://bioinformatics.hitsz.edu.cn/ProtDec-LTR2.0/. bliu@hit.edu.cn.

Comprehensive classification of the PIN domain-like superfamily.

PIN-like domains constitute a widespread superfamily of nucleases, diverse in terms of the reaction mechanism, substrate specificity, biological function and taxonomic distribution. Proteins with PIN-like domains are involved in central cellular processes, such as DNA replication and repair, mRNA degradation, transcription regulation and ncRNA maturation. In this work, we identify and classify the most complete set of PIN-like domains to provide the first comprehensive analysis of sequence–structure–function relationships within the whole PIN domain-like superfamily. Transitive sequence searches using highly sensitive methods for remote homology detection led to the identification of several new families, including representatives of Pfam (DUF1308, DUF4935) and CDD (COG2454), and 23 other families not classified in the public domain databases. Further sequence clustering revealed relationships between individual sequence clusters and showed heterogeneity within some families, suggesting a possible functional divergence. With five structural groups, 70 defined clusters, over 100,000 proteins, and broad biological functions, the PIN domain-like superfamily constitutes one of the largest and most diverse nuclease superfamilies. Detailed analyses of sequences and structures, domain architectures, and genomic contexts allowed us to predict biological function of several new families, including new toxin-antitoxin components, proteins involved in tRNA/rRNA maturation and transcription/translation regulation.

Protein Remote Homology Detection by Combining Pseudo Dimer Composition with an Ensemble Learning Method

Background: With the development of the next generation sequencing technique in biology, more and more protein sequence data is generated exponentially. However, the protein structure data grows slowly. The gap between them is growing large. The protein remote homology detection becomes an important and intense research problem. Objective: Although several methods have been reported to tackle this problem, their performance is still too low to be used for real world application. Therefore, it is necessary and urgent to characterize protein sequences from a new perspective so as to improve the predictive performance of protein remote homology detection. Method: In this study, we proposed a new feature of proteins called Pseudo Dimer Composition (PDC). A new computational method for protein remote homology detection called PDC-Ensemble was constructed by combining PDC via an ensemble learning approach. Result: Experimental results on a public benchmark dataset showed that the performance of PDC-Ensemble outperformed other sequence-based methods, and is highly comparable with some state-of-the-art predictors in the field of protein remote homology detection. Conclusion: PDC can extract more dipeptide information. PDC-Ensemble is a useful tool for the studies of protein remote homology detection. Keywords: Protein remote homology detection; Pseudo Dimer Composition; ensemble learning.