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Proteomics is a field dedicated to the analysis of proteins in cells, tissues, and organisms, aiming to gain insights into their structures, functions, and interactions. A crucial aspect within proteomics is protein family prediction, which involves identifying evolutionary relationships between proteins by examining similarities in their sequences or structures. This approach holds great potential for applications such as drug discovery and functional annotation of genomes. However, current methods for protein family prediction have certain limitations, including limited accuracy, high false positive rates, and challenges in handling large datasets. Some methods also rely on homologous sequences or protein structures, which introduce biases and restrict their applicability to specific protein families or structures. To overcome these limitations, researchers have turned to machine learning (ML) approaches that can identify connections between protein features and simplify complex high-dimensional datasets. This paper presents a comprehensive survey of articles that employ various ML techniques for predicting protein families. The primary objective is to explore and improve ML techniques specifically for protein family prediction, thus advancing future research in the field. Through qualitative and quantitative analyses of ML techniques, it is evident that multiple methods utilizing a range of classifiers have been applied for protein family prediction. However, there has been limited focus on developing novel classifiers for protein family classification, highlighting the urgent need for improved approaches in this area. By addressing these challenges, this research aims to enhance the accuracy and effectiveness of protein family prediction, ultimately facilitating advancements in proteomics and its diverse applications. [ABSTRACT FROM AUTHOR]

Background: The identification of essential proteins is of great significance in biology and pathology. However, protein–protein interaction (PPI) data obtained through high-throughput technology include a high number of false positives. To overcome this limitation, numerous computational algorithms based on biological characteristics and topological features have been proposed to identify essential proteins. Results: In this paper, we propose a novel method named SESN for identifying essential proteins. It is a seed expansion method based on PPI sub-networks and multiple biological characteristics. Firstly, SESN utilizes gene expression data to construct PPI sub-networks. Secondly, seed expansion is performed simultaneously in each sub-network, and the expansion process is based on the topological features of predicted essential proteins. Thirdly, the error correction mechanism is based on multiple biological characteristics and the entire PPI network. Finally, SESN analyzes the impact of each biological characteristic, including protein complex, gene expression data, GO annotations, and subcellular localization, and adopts the biological data with the best experimental results. The output of SESN is a set of predicted essential proteins. Conclusions: The analysis of each component of SESN indicates the effectiveness of all components. We conduct comparison experiments using three datasets from two species, and the experimental results demonstrate that SESN achieves superior performance compared to other methods. [ABSTRACT FROM AUTHOR]

Cancer is one of the major diseases that seriously threaten human life. Traditional anticancer therapies have achieved remarkable efficacy but have also some unavoidable side effects. Therefore, more and more research focuses on highly effective and less-toxic anticancer substances of natural origin. Amphibian skin is rich in active substances such as biogenic amines, alkaloids, alcohols, esters, peptides, and proteins, which play a role in various aspects such as anti-inflammatory, immunomodulatory, and anticancer functions, and are one of the critical sources of anticancer substances. Currently, a range of natural anticancer substances are known from various amphibians. This paper aims to review the physicochemical properties, anticancer mechanisms, and potential applications of these peptides and proteins to advance the identification and therapeutic use of natural anticancer agents. [ABSTRACT FROM AUTHOR]

Hypomagnetic conditions (HMCs) will be an integral part of the complex of factors affecting astronauts in long-term interplanetary missions outside Earth's magnetic field. The adaptation of the human body under these conditions will affect the regulatory processes in various physiological systems, the molecular pathways of which, with the participation of proteins, have not yet been studied. In order to fill this gap, dry blood spots collected on a special paper from healthy volunteers were studied by proteomic methods in two sessions of a model experiment: with HMCs and in a placebo session. The experiment was carried out by the method of double-blind control, with the participation of the same volunteers in both sessions. 1219 different proteins were semiquantitatively determined in the samples. In the HMC series, changes relative to individual values before the experiment series were detected in three proteins: tropomyosin alpha-3 chain (TPM3), abhydrolase domain-containing protein 14B (ABHD14B), and acetylcholinesterase-associated protein (CUTA). However, comparison of the data obtained in the placebo session smoothed out the effect of HMCs on changes in the above proteins. The results may mean either the absence of HMC influence with a short exposure and the absence of a cumulative effect, or they are unreliable due to the insufficient number of subjects in the sample. [ABSTRACT FROM AUTHOR]

Furthermore, in terms of protein identification, Xu et al. concentrated on the study of antioxidant protein identification; they proposed a machine learning method, SeqSVM, to predict antioxidant proteins through extracted sequence features [[10]]. The identification of special protein or RNA molecules via computational methods is of great importance in understanding their biological functions and developing new treatments for diseases. Seven papers focus on describing protein function prediction or protein identification, which include the prediction of signal peptides in proteins, protein hydroxylation site prediction, protein-protein interaction (PPI) prediction, and protein identification. [Extracted from the article]

Background: Identifying protein complexes from protein-protein interaction network is one of significant tasks in the postgenome era. Protein complexes, none of which exceeds 10 in size play an irreplaceable role in life activities and are also a hotspot of scientific research, such as PSD-95, CD44, PKM2 and BRD4. And in MIPS, CYC2008, SGD, Aloy and TAP06 datasets, the proportion of small protein complexes is over 75%. But up to now, protein complex identification methods do not perform well in the field of small protein complexes. Results: In this paper, we propose a novel method, called BOPS. It is a three-step procedure. Firstly, it calculates the balanced weights to replace the original weights. Secondly, it divides the graphs larger than MAXP until the original PPIN is divided into small PPINs. Thirdly, it enumerates the connected subset of each small PPINs, identifies potential protein complexes based on cohesion and removes those that are similar. Conclusions: In four yeast PPINs, experimental results have shown that BOPS has an improvement of about 5% compared with the SOTA model. In addition, we constructed a weighted Homo sapiens PPIN based on STRINGdb and BioGRID, and BOPS gets the best result in it. These results give new insights into the identification of small protein complexes, and the weighted Homo sapiens PPIN provides more data for related research. [ABSTRACT FROM AUTHOR]

Background: Essential proteins are indispensable to the development and survival of cells. The identification of essential proteins not only is helpful for the understanding of the minimal requirements for cell survival, but also has practical significance in disease diagnosis, drug design and medical treatment. With the rapidly amassing of protein–protein interaction (PPI) data, computationally identifying essential proteins from protein–protein interaction networks (PINs) becomes more and more popular. Up to now, a number of various approaches for essential protein identification based on PINs have been developed. Results: In this paper, we propose a new and effective approach called iMEPP to identify essential proteins from PINs by fusing multiple types of biological data and applying the influence maximization mechanism to the PINs. Concretely, we first integrate PPI data, gene expression data and Gene Ontology to construct weighted PINs, to alleviate the impact of high false-positives in the raw PPI data. Then, we define the influence scores of nodes in PINs with both orthological data and PIN topological information. Finally, we develop an influence discount algorithm to identify essential proteins based on the influence maximization mechanism. Conclusions: We applied our method to identifying essential proteins from saccharomyces cerevisiae PIN. Experiments show that our iMEPP method outperforms the existing methods, which validates its effectiveness and advantage. [ABSTRACT FROM AUTHOR]

Background: Applying directed acyclic graph (DAG) models to proteogenomic data has been shown effective for detecting causal biomarkers of complex diseases. However, there remain unsolved challenges in DAG learning to jointly model binary clinical outcome variables and continuous biomarker measurements. Results: In this paper, we propose a new tool, DAGBagM, to learn DAGs with both continuous and binary nodes. By using appropriate models, DAGBagM allows for either continuous or binary nodes to be parent or child nodes. It employs a bootstrap aggregating strategy to reduce false positives in edge inference. At the same time, the aggregation procedure provides a flexible framework to robustly incorporate prior information on edges. Conclusions: Through extensive simulation experiments, we demonstrate that DAGBagM has superior performance compared to alternative strategies for modeling mixed types of nodes. In addition, DAGBagM is computationally more efficient than two competing methods. When applying DAGBagM to proteogenomic datasets from ovarian cancer studies, we identify potential protein biomarkers for platinum refractory/resistant response in ovarian cancer. DAGBagM is made available as a github repository at https://github.com/jie108/dagbagM. [ABSTRACT FROM AUTHOR]

Background: Applying directed acyclic graph (DAG) models to proteogenomic data has been shown effective for detecting causal biomarkers of complex diseases. However, there remain unsolved challenges in DAG learning to jointly model binary clinical outcome variables and continuous biomarker measurements. Results: In this paper, we propose a new tool, DAGBagM, to learn DAGs with both continuous and binary nodes. By using appropriate models, DAGBagM allows for either continuous or binary nodes to be parent or child nodes. It employs a bootstrap aggregating strategy to reduce false positives in edge inference. At the same time, the aggregation procedure provides a flexible framework to robustly incorporate prior information on edges. Conclusions: Through extensive simulation experiments, we demonstrate that DAGBagM has superior performance compared to alternative strategies for modeling mixed types of nodes. In addition, DAGBagM is computationally more efficient than two competing methods. When applying DAGBagM to proteogenomic datasets from ovarian cancer studies, we identify potential protein biomarkers for platinum refractory/resistant response in ovarian cancer. DAGBagM is made available as a github repository at https://github.com/jie108/dagbagM. [ABSTRACT FROM AUTHOR]

Cerebrospinal fluid (CSF) exists in the surrounding spaces of mammalian central nervous systems (CNS); therefore, there are numerous potential protein biomarkers associated with CNS disease in CSF. Currently, approximately 4300 proteins have been identified in CSF by protein profiling. However, due to the diverse modifications, as well as the existing technical limits, large-scale protein identification in CSF is still considered a challenge. Inspired by computational methods, this paper proposes a deep learning framework, named DenSec, for secreted protein prediction in CSF. In the first phase of DenSec, all input proteins are encoded as a matrix with a fixed size of 1000 × 20 by calculating a position-specific score matrix (PSSM) of protein sequences. In the second phase, a dense convolutional network (DenseNet) is adopted to extract the feature from these PSSMs automatically. After that, Transformer with a fully connected dense layer acts as classifier to perform a binary classification in terms of secretion into CSF or not. According to the experiment results, DenSec achieves a mean accuracy of 86.00% in the test dataset and outperforms the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Toxoplasma gondii is a pathogenic protozoan parasite belonging to the apicomplexan phylum that infects the nucleated cells of warm-blooded hosts leading to an infectious disease known as toxoplasmosis. Apicomplexan parasites such as T. gondii can display different mechanisms to control or manipulate host cells signaling at different levels altering the host subcellular genome and proteome. Indeed, Toxoplasma is able to modulate host cell responses (especially immune responses) during infection to its advantage through both structural and functional changes in the proteome of different infected cells. Consequently, parasites can transform the invaded cells into a suitable environment for its own replication and the induction of infection. Proteomics as an applicable tool can identify such critical proteins involved in pathogen (Toxoplasma)-host cell interactions and consequently clarify the cellular mechanisms that facilitate the entry of pathogens into host cells, and their replication and transmission, as well as the central mechanisms of host defense against pathogens. Accordingly, the current paper reviews several proteins (identified using proteomic approaches) differentially expressed in the proteome of Toxoplasma-infected host cells (macrophages and human foreskin fibroblasts) and tissues (brain and liver) and highlights their plausible functions in the cellular biology of the infected cells. The identification of such modulated proteins and their related cell impact (cell responses/signaling) can provide further information regarding parasite pathogenesis and biology that might lead to a better understanding of therapeutic strategies and novel drug targets. [ABSTRACT FROM AUTHOR]

Background: The essential proteins in protein networks play an important role in complex cellular functions and in protein evolution. Therefore, the identification of essential proteins in a network can help to explain the structure, function, and dynamics of basic cellular networks. The existing dynamic protein networks regard the protein components as the same at all time points; however, the role of proteins can vary over time.Methods: To improve the accuracy of identifying essential proteins, an improved h-index algorithm based on the attenuation coefficient method is proposed in this paper. This method incorporates previously neglected node information to improve the accuracy of the essential protein search. Based on choosing the appropriate attenuation coefficient, the values, such as monotonicity, SN, SP, PPV and NPV of different essential protein search algorithms are tested.Results: The experimental results show that, the algorithm proposed in this paper can ensure the accuracy of the found proteins while identifying more essential proteins.Conclusions: The described experiments show that this method is more effective than other similar methods in identifying essential proteins in dynamic protein networks. This study can better explain the mechanism of life activities and provide theoretical basis for the research and development of targeted drugs. [ABSTRACT FROM AUTHOR]

Background: The study of protein complexes and protein functional modules has become an important method to further understand the mechanism and organization of life activities. The clustering algorithms used to analyze the information contained in protein-protein interaction network are effective ways to explore the characteristics of protein functional modules. Results: This paper conducts an intensive study on the problems of low recognition efficiency and noise in the overlapping structure of protein functional modules, based on topological characteristics of PPI network. Developing a protein function module recognition method ECTG based on Topological Features and Gene expression data for Protein Complex Identification. Conclusions: The algorithm can effectively remove the noise data reflected by calculating the topological structure characteristic values in the PPI network through the similarity of gene expression patterns, and also properly use the information hidden in the gene expression data. The experimental results show that the ECTG algorithm can detect protein functional modules better. [ABSTRACT FROM AUTHOR]

Bioluminescent proteins (BLPs) are a class of proteins that widely distributed in many living organisms with various mechanisms of light emission including bioluminescence and chemiluminescence from luminous organisms. Bioluminescence has been commonly used in various analytical research methods of cellular processes, such as gene expression analysis, drug discovery, cellular imaging, and toxicity determination. However, the identification of bioluminescent proteins is challenging as they share poor sequence similarities among them. In this paper, we briefly reviewed the development of the computational identification of BLPs and subsequently proposed a novel predicting framework for identifying BLPs based on eXtreme gradient boosting algorithm (XGBoost) and using sequence-derived features. To train the models, we collected BLP data from bacteria, eukaryote, and archaea. Then, for getting more effective prediction models, we examined the performances of different feature extraction methods and their combinations as well as classification algorithms. Finally, based on the optimal model, a novel predictor named iBLP was constructed to identify BLPs. The robustness of iBLP has been proved by experiments on training and independent datasets. Comparison with other published method further demonstrated that the proposed method is powerful and could provide good performance for BLP identification. The webserver and software package for BLP identification are freely available at http://lin-group.cn/server/iBLP. [ABSTRACT FROM AUTHOR]

Introduction: Dental pulp with special structure has become a good reference sample in paleomicrobiology‐related blood‐borne diseases, many pathogens were detected by different methods based on the diagnosis of nucleic acids and proteins. Objectives: This review aims to propose the preparation process from ancient teeth collection to organic molecule extraction of dental pulp and summary, analyze the methods that have been applied to detect septicemic pathogens through ancient dental pulps during the past 20 years following the first detection of an ancient microbe. Methods: The papers used in this review with two main objectives were obtained from PubMed and Google scholar with combining keywords: "ancient," "dental pulp," "teeth," "anatomy," "structure," "collection," "preservation," "selection," "photography," "radiography," "contamination," "decontamination," "DNA," "protein," "extraction," "bone," "paleomicrobiology," "bacteria," "virus," "pathogen," "molecular biology," "proteomics," "PCR," "MALDI‐TOF," "LC/MS," "ELISA," "immunology," "immunochromatography," "genome," "microbiome," "metagenomics." Results: The analysis of ancient dental pulp should have a careful preparation process with many different steps to give highly accurate results, each step complies with the rules in archaeology and paleomicrobiology. After the collection of organic molecules from dental pulp, they were investigated for pathogen identification based on the analysis of DNA and protein. Actually, DNA approach takes a principal role in diagnosis while the protein approach is more and more used. A total of seven techniques was used and ten bacteria (Yersinia pestis, Bartonella quintana, Salmonella enterica serovar Typhi, Salmonella enterica serovar Paratyphi C, Mycobacterium leprae, Mycobacterium tuberculosis, Rickettsia prowazeki, Staphylococcus aureus, Borrelia recurrentis, Bartonella henselae) and one virus (Anelloviridae) were identified. Y. pestis had the most published in quantity and all methods were investigated for this pathogen, S. aureus and B. recurrentis were identified by three different methods and others only by one. The combining methods interestingly increase the positive rate with ELISA, PCR and iPCR in Yersinia pestis diagnosis. Twenty‐seven ancient genomes of Y. pestis and one ancient genome of B. recurrentis were reconstructed. Comparing to the ancient bone, ancient teeth showed more advantage in septicemic diagnosis. Beside pathogen identification, ancient pulp help to distinguish species. Conclusions: Dental pulp with specific tissue is a suitable sample for detection of the blood infection in the past through DNA and protein identification with the correct preparation process, furthermore, it helps to more understand the pathogens of historic diseases and epidemics. [ABSTRACT FROM AUTHOR]

Essential protein plays a crucial role in the process of cell life. The identification of essential proteins not only promotes the development of drug target technology, but also contributes to the mechanism of biological evolution. There are plenty of scholars who pay attention to discover essential proteins according to the topological structure of protein network and biological information. The accuracy of protein recognition still demands to be improved. In this paper, we propose a method which integrates the clustering coefficient in protein complexes and topological properties to determine the essentiality of proteins. First, we give the definition of In-clustering coefficient (IC) to describe the properties of protein complexes. Then we propose a new method, complex edge and node clustering (CENC) coefficient, to identify essential proteins. Different Protein–Protein Interaction (PPI) networks of Saccharomyces cerevisiae, MIPS and DIP are used as experimental materials. Through some experiments of logistic regression model, the results show that the method of CENC can promote the ability of recognizing essential proteins by comparing with the existing methods DC, BC, EC, SC, LAC, NC and the recent UC method. [ABSTRACT FROM AUTHOR]

Background: Protein complexes are the cornerstones of many biological processes and gather them to form various types of molecular machinery that perform a vast array of biological functions. In fact, a protein may belong to multiple protein complexes. Most existing protein complex detection algorithms cannot reflect overlapping protein complexes. To solve this problem, a novel overlapping protein complexes identification algorithm is proposed. Results: In this paper, a new clustering algorithm based on overlay network chain in quotient space, marked as ONCQS, was proposed to detect overlapping protein complexes in weighted PPI networks. In the quotient space, a multilevel overlay network is constructed by using the maximal complete subgraph to mine overlapping protein complexes. The GO annotation data is used to weight the PPI network. According to the compatibility relation, the overlay network chain in quotient space was calculated. The protein complexes are contained in the last level of the overlay network. The experiments were carried out on four PPI databases, and compared ONCQS with five other state-of-the-art methods in the identification of protein complexes. Conclusions: We have applied ONCQS to four PPI databases DIP, Gavin, Krogan and MIPS, the results show that it is superior to other five existing algorithms MCODE, MCL, CORE, ClusterONE and COACH in detecting overlapping protein complexes. [ABSTRACT FROM AUTHOR]

Aim: This paper aimed to identify new candidate biomarkers in blood for early diagnosis of CRC. Background: Colorectal cancer (CRC) is the third most widespread malignancies increasing globally. The high mortality rate associated with colorectal cancer is due to the delayed diagnosis in an advanced stage while the metastasis has occurred. For better clinical management and subsequently to reduce mortality of CRC, early detection biomarkers are in high demand. Methods: A 2D-PAGE separation of proteins was performed followed by tandem mass Spectrometry (MALDI-TOF-TOF) to discover potential plasma protein markers for CRC and AA (advanced adenomas). Furthermore, western blot method was used to confirm a part of the results in colorectal tissue samples. Results: The significantly altered proteins including HPR, HP, ALB, KRT1, APOA1, FGB, IGJ and C4A were down-regulated in polyp relative to normal, and CRC compare to polyp surprisingly, and inversely, ORM2 was up-regulated with the fold change ≥ 2 and p-value ≤ 0.05. We also surveyed APOA1, FGB, and C4A for further confirmation of their expression changes by western blotting. All three of them showed a decreasing trend from normal toward CRC tissue samples as it mentioned before, but just changes of FGB and C4A were significant. Conclusion: The results demonstrated that plasma proteins can be less invasive markers for the detection of CRC. FGB and C4A can be considered as plasma potential biomarkers to early diagnosis of CRC patients and understanding the underlying procedures in tumorigenesis. Undoubtedly, the additional study must be conducted on large scale cohorts to verify the results. [ABSTRACT FROM AUTHOR]

Biological network figures are ubiquitous in the biology and medical literature. On the one hand, a good network figure can quickly provide information about the nature and degree of interactions between items and enable inferences about the reason for those interactions. On the other hand, good network figures are difficult to create. In this paper, we outline 10 simple rules for creating biological network figures for communication, from choosing layouts, to applying color or other channels to show attributes, to the use of layering and separation. These rules are accompanied by illustrative examples. We also provide a concise set of references and additional resources for each rule. [ABSTRACT FROM AUTHOR]

Lignocellulosic materials from municipal solid waste emerge as attractive resources for anaerobic digestion biorefinery. To increase the knowledge required for establishing efficient bioprocesses, dynamics of batch fermentation by the cellulolytic bacterium Ruminiclostridium cellulolyticum were compared using three cellulosic materials, paper handkerchief, cotton discs and Whatman filter paper. Fermentation of paper handkerchief occurred the fastest and resulted in a specific metabolic profile: it resulted in the lowest acetate-to-lactate and acetate-to-ethanol ratios. By shotgun proteomic analyses of paper handkerchief and Whatman paper incubations, 151 proteins with significantly different levels were detected, including 20 of the 65 cellulosomal components, 8 non-cellulosomal CAZymes and 44 distinct extracytoplasmic proteins. Consistent with the specific metabolic profile observed, many enzymes from the central carbon catabolic pathways had higher levels in paper handkerchief incubations. Among the quantified CAZymes and cellulosomal components, 10 endoglucanases mainly from the GH9 families and 7 other cellulosomal subunits had lower levels in paper handkerchief incubations. An in-depth characterization of the materials used showed that the lower levels of endoglucanases in paper handkerchief incubations could hypothetically result from its lower crystallinity index (50%) and degree of polymerization (970). By contrast, the higher hemicellulose rate in paper handkerchief (13.87%) did not result in the enhanced expression of enzyme with xylanase as primary activity, including enzymes from the “xyl-doc” cluster. It suggests the absence, in this material, of molecular structures that specifically lead to xylanase induction. The integrated approach developed in this work shows that subtle differences among cellulosic materials regarding chemical and structural characteristics have significant effects on expressed bacterial functions, in particular the cellulolysis machinery, resulting in different metabolic patterns and degradation dynamics. [ABSTRACT FROM AUTHOR]

Cellular processes are typically carried out by protein complexes rather than individual proteins. Identifying protein complexes is one of the keys to understanding principles of cellular organization and function. Also, protein complexes are a group of interacting genes underlying similar diseases, which points out the therapeutic importance of protein complexes. With the development of life science and computing science, an increasing amount of protein-protein interaction (PPI) data becomes available, which makes it possible to predict protein complexes from PPI networks. However, most PPI data produced by high-throughput experiments often has many false positive interactions and false negative edge loss, which makes it difficult to predict complexes accurately. In this paper, we present a new method, named as MEMO (Multiple network Embedding for coMplex detectiOn), to detect protein complexes. MEMO integrates multiple PPI datasets from different species into a single PPI network by using functional orthology information across multiple species and then uses a graph embedding technology to embed protein nodes of the network into continuous vector spaces, so as to quantify the relationships between nodes and better guild the protein complex detection process. Finally, it utilizes a seed-and-extend strategy to identify protein complexes from multiple PPI networks based on the similarities of their corresponding protein representations. As part of our approach, we also define a new quality measure which combines the cluster cohesiveness and cluster density to measure the likelihood of a detected protein complex being a real protein complex. Extensive experimental results demonstrate the proposed method outperforms state-of-the-art complex detection techniques. [ABSTRACT FROM AUTHOR]

Blocking cytokine interleukin 10 (IL-10) at the time of immunisation enhances vaccine induced T cell responses and improves control of tumour cell growth in vivo. However, the effect of an IL-10 blockade on the biological function of macrophages has not been explored. In the current paper, a macrophage precursor cell line, U937 cells, was selected to investigate the differential expression of proteins and relevant cell signalling pathway changes, when stimulated with lipopolysaccharide (LPS) in the presence of antibodies to IL-10 or IL-10 receptor. We used a quantitative proteomic strategy to investigate variations in protein profiles of U937 cells following the treatments with LPS, LPS plus human anti-IL10 antibody and anti-IL10R antibody in 24hrs, respectively. The LPS treatment significantly activated actin-related cell matrix formation and immune response pathways. The addition of anti-IL10 and anti-IL10R antibody further promoted the immune response and potentially effect macrophage survival through PI3K/AKT signalling; however, the latter appeared to also upregulated oncogene XRCC5 and Cajal body associated processes. [ABSTRACT FROM AUTHOR]

Background: Naja annulifera is a medically important venomous snake occurring in some of the countries in Sub-Saharan Africa. Accidental bites result in severe coagulation disturbances, systemic inflammation and heart damage, as reported in dogs, and death, by respiratory arrest, in humans. Despite the medical importance of N. annulifera, little is known about its venom composition and the pathogenesis of envenomation. In this paper, the toxic, inflammatory and immunogenic properties of N. annulifera venom were analyzed. Methodology/Principal findings: Venom proteomic analysis identified 79 different proteins, including Three Finger Toxins, Cysteine Rich Secretory Proteins, Metalloproteinases, Phospholipases A2 (PLA2), Hyaluronidase, L-amino-acid oxidase, Cobra Venom Factor and Serine Proteinase. The presence of PLA2, hyaluronidase, fibrinogenolytic and anticoagulant activities was detected using functional assays. The venom was cytotoxic to human keratinocytes. In an experimental murine model of envenomation, it was found that the venom induced local changes, such as swelling, which was controlled by anti-inflammatory drugs. Moreover, the venom caused death, which was preceded by systemic inflammation and pulmonary hemorrhage. The venom was shown to be immunogenic, inducing a strong humoral immune response, with the production of antibodies able to recognize venom components with high molecular weight and to neutralize its lethal activity. Conclusions/Significance: The results obtained in this study demonstrate that N. annulifera venom contains toxins able to induce local and systemic inflammation, which can contribute to lung damage and death. Moreover, the venom is immunogenic, an important feature that must be considered during the production of a therapeutic anti-N. annulifera antivenom. [ABSTRACT FROM AUTHOR]

LncRNA-protein interactions play important roles in post-transcriptional gene regulation, poly-adenylation, splicing and translation. Identification of lncRNA-protein interactions helps to understand lncRNA-related activities. Existing computational methods utilize multiple lncRNA features or multiple protein features to predict lncRNA-protein interactions, but features are not available for all lncRNAs or proteins; most of existing methods are not capable of predicting interacting proteins (or lncRNAs) for new lncRNAs (or proteins), which don’t have known interactions. In this paper, we propose the sequence-based feature projection ensemble learning method, “SFPEL-LPI”, to predict lncRNA-protein interactions. First, SFPEL-LPI extracts lncRNA sequence-based features and protein sequence-based features. Second, SFPEL-LPI calculates multiple lncRNA-lncRNA similarities and protein-protein similarities by using lncRNA sequences, protein sequences and known lncRNA-protein interactions. Then, SFPEL-LPI combines multiple similarities and multiple features with a feature projection ensemble learning frame. In computational experiments, SFPEL-LPI accurately predicts lncRNA-protein associations and outperforms other state-of-the-art methods. More importantly, SFPEL-LPI can be applied to new lncRNAs (or proteins). The case studies demonstrate that our method can find out novel lncRNA-protein interactions, which are confirmed by literature. Finally, we construct a user-friendly web server, available at . [ABSTRACT FROM AUTHOR]

Abstract Cereals are an important source of calories and protein in the human diet. Endosperm makes up a large part of cereal grains and plays an important role in determining economic and nutritional value of cereals. Identification and characterization of endosperm proteins and determining factors influencing their synthesis and localization can facilitate enhancement of amino acid content and starch accessibility for improved grain nutritional value. In recent years, quantitative proteomics studies using mass spectrometry techniques have been contributing to information on plant growth, development and interactions with the environment. The combination of natural drought tolerance traits in sorghum and its complete genome sequencing in 2009 makes it a model plant for proteomics and genomics research. This paper reviewed recent developments in grain proteomics research of sorghum and other cereals such as maize, rice and barley. Significant progress is evident in proteomics of drought tolerance, grain development, grain proteins and amino acid composition of sorghum. The ideal would be to use this information to develop sorghum genotypes with characteristics such as high protein digestibility and improved amino acid composition, leading to increased nutritional value, especially for poor consumers in Africa. Highlights • Sorghum is an important food security crop. • Proteomics of cereals has advanced significantly. • A number of proteomic studies have been done on kafirins. • High lysine sorghum mutants were analysed using proteomics. • There is large scope for application of proteomics techniques in sorghum grains. [ABSTRACT FROM AUTHOR]

The analysis of the 3D structures of proteins is a very important problem in life sciences, since the geometric set-up of proteins has a deep relevance in many biological processes. The complexity of the analysis and the continuous increase in the number of proteins whose 3D structure is known, call for efficient and quick algorithms. Parallel processing is becoming an enabling tool for such research. A key component in the geometric description of a protein is the structural motif, a 3D element which appears in a variety of molecules and is usually made of just a few simpler structures, the secondary structures elements (SSEs). This paper is an extended version of Ferretti and Musci (2013), and presents the Cross Motif Search (CMS) and the Complete CMS (CCMS) algorithms, two highly optimized and efficient parallel methods to detect the presence and location of all common motifs of secondary structures in a given protein pair (CMS) or across an arbitrary large dataset of proteins (CCMS). The analysis builds on existing approaches, such as Secondary Structure Co-Occurrences (SSC), based on the General Hough Transform (GHT) technique. The main difference between our proposal and the state of the art is the innovative focus that CMS puts on the geometric description of the structural motifs, which could be simply viewed as vectors in a 3D space, rather than on the topological/biological description employed by competing algorithms, such as ProSMoS, PROMOTIF or MASS. The advantage of a geometrical approach is that it enables to retrieve the exact location of the common substructures in a protein pair. The paper analyzes all possible forms of serial and parallelism optimization of the proposed algorithms, both shared memory and message passing. It introduces a complete parallel implementation of CMS, based on OpenMP, and discusses its scalability on shared-memory architectures. Both small-scale and medium-scale testing shows that the methods produces very interesting results in real applications, and scales nicely up to the eight-processor limit. More in-depth testing also shows that the scalability limit is due to the inner structure of the problem, and that the similarities among proteins and the chosen tolerance for the analysis highly affect the overall performance. [ABSTRACT FROM AUTHOR]

The basidiomycete fungus Gloeophyllum trabeum causes a typical brown rot and is known to use reactive oxygen species in the degradation of cellulose. The extracellular Cel12A is one of the few endo-1,4-β-glucanase produced by G. trabeum. Here we cloned cel12A and heterologously expressed it in Aspergillus niger. The identity of the resulting recombinant protein was confirmed by mass spectrometry. We used the purified GtCel12A to determine its substrate specificity and basic biochemical properties. The G. trabeum Cel12A showed highest activity on β-glucan, followed by lichenan, carboxymethylcellulose, phosphoric acid swollen cellulose, microcrystalline cellulose, and filter paper. The optimal pH and temperature for enzymatic activity were, respectively, 4.5 and 50°C on β-glucan. Under these conditions specific activity was 239.2±9.1 U mg−1 and the half-life of the enzyme was 84.6±3.5 hours. Thermofluor studies revealed that the enzyme was most thermal stable at pH 3. Using β-glucan as a substrate, the Km was 3.2±0.5 mg mL−1 and the Vmax was 0.41±0.02 µmol min−1. Analysis of the effects of GtCel12A on oat spelt and filter paper by scanning electron microscopy revealed the morphological changes taking place during the process. [ABSTRACT FROM AUTHOR]

Objective: Changes in the protein profile of acquired enamel pellicles (AEP) formed in vivo over different time periods were evaluated after the application of hydrochloric acid (HCl).Methods: Nine subjects were submitted to dental prophylaxis with pumice. After 3 or 120 min, the teeth were isolated with cotton rolls and 50 μL of 0.1 M HCl (pH 1.0), 0.01 M HCl (pH 2.0), or deionized water were applied on the buccal surface of the teeth for 10 s. The AEP was then collected using an electrode filter paper presoaked in 3% citric acid. After protein extraction, the samples were submitted to reverse-phase liquid chromatography coupled to mass spectrometry (nano LC-ESI-MS/MS). Label-free quantification was performed (Protein Lynx Global Service software).Results: A total of 180 proteins were successfully identified in the AEP samples. The number of identified proteins increased with the time of pellicle formation. Only 4 proteins were present in all the groups (isoforms of IgA, serum albumin, and statherin). The greatest number of proteins identified uniquely in one of the groups was obtained for the groups treated with HCl after 2 h of pellicle formation (approx. 50 proteins).Conclusion: Proteins resistant to removal by HCl, such as serum albumin and statherin, were identified even in the short-term AEP. In addition, 120-min pellicles present many proteins that are resistant to removal by HCl. This suggests an increase in protection against intrinsic acids with the time of pellicle formation, which should be evaluated in future studies. [ABSTRACT FROM AUTHOR]

In this paper, we describe SAFlex (Structural Alphabet Flexibility), an extension of an existing structural alphabet (HMM-SA), to better explore increasing protein three dimensional structure information by encoding conformations of proteins in case of missing residues or uncertainties. An SA aims to reduce three dimensional conformations of proteins as well as their analysis and comparison complexity by simplifying any conformation in a series of structural letters. Our methodology presents several novelties. Firstly, it can account for the encoding uncertainty by providing a wide range of encoding options: the maximum a posteriori, the marginal posterior distribution, and the effective number of letters at each given position. Secondly, our new algorithm deals with the missing data in the protein structure files (concerning more than 75% of the proteins from the Protein Data Bank) in a rigorous probabilistic framework. Thirdly, SAFlex is able to encode and to build a consensus encoding from different replicates of a single protein such as several homomer chains. This allows localizing structural differences between different chains and detecting structural variability, which is essential for protein flexibility identification. These improvements are illustrated on different proteins, such as the crystal structure of an eukaryotic small heat shock protein. They are promising to explore increasing protein redundancy data and obtain useful quantification of their flexibility. [ABSTRACT FROM AUTHOR]

Essential proteins are crucial to living cells. Identification of essential proteins from protein-protein interaction (PPI) networks can be applied to pathway analysis and function prediction, furthermore, it can contribute to disease diagnosis and drug design. There have been some experimental and computational methods designed to identify essential proteins, however, the prediction precision remains to be improved. In this paper, we propose a new method for identifying essential proteins based on Participation degree of a protein in protein Complexes and Subgraph Density, named as PCSD. In order to test the performance of PCSD, four PPI datasets (DIP, Krogan, MIPS and Gavin) are used to conduct experiments. The experiment results have demonstrated that PCSD achieves a better performance for predicting essential proteins compared with some competing methods including DC, SC, EC, IC, LAC, NC, WDC, PeC, UDoNC, and compared with the most recent method LBCC, PCSD can correctly predict more essential proteins from certain numbers of top ranked proteins on the DIP dataset, which indicates that PCSD is very effective in discovering essential proteins in most case. [ABSTRACT FROM AUTHOR]

This study detected changes in the protein profile of the acquired enamel pellicle (AEP) formed in vivo after rinsing with whole milk, fat-free milk, or water. Nine subjects in good oral condition took part in the study. The acquired pellicle was formed in the morning, for 120 min, after prophylaxis with pumice. Following this, the volunteers rinsed with 10 mL of whole milk, fat-free milk, or deionized water for 30 s, following a blinded crossover protocol. After 60 min, the pellicle was collected with filter paper soaked in 3% citric acid and processed for analysis by liquid chromatography-electrospray ionization tandem mass spectrometry. The obtained tandem mass spectrometry spectra were searched against a human protein database (Swiss-Prot). The proteomic data related to protein quantification were analysed using the PLGS software. A total of 260 proteins were successfully identified in the AEP samples collected from all groups. Forty-nine were common to all 3 groups, while 72, 62, and 49 were specific to the groups rinsing with whole milk, fat-free milk, and water, respectively. Some were typical components of the AEP, such as cystatin-B, cystatin-SN, isoforms of α-amylase, IgA and IgG, lysozyme C, protein S100 A78, histatin-1, proline-rich protein 27, statherin, and lactotransferrin. Other proteins are not commonly described as part of the AEP but could act in defence of the organism against pathogens. Distinct proteomic profiles were found in the AEP after rinsing with whole or fat-free milk, which could have an impact on bacterial adhesion and tooth dissolution. The use of fat-free milk could favourably modulate the adhesion of bacteria to the AEP as well as biofilm formation when compared with whole milk. [ABSTRACT FROM AUTHOR]

Background: Due to the surrounding bone, the human inner ear is relatively inaccessible and difficult to reach for cellular and molecular analyses. However, these types of investigations are needed to better understand the etiology, pathophysiology and progression of several inner ear disorders. Moreover, the fluid from the inner ear cannot be sampled for micro-chemical analyses from healthy individuals in vivo. Therefore, in the present paper, we studied patients with vestibular schwannoma (VS) undergoing trans-labyrinthine surgery (TLS). Our primary aim was to identify perilymph proteins in patients with VS on an individual level. Our second aim was to investigate the proteins identified at a functional level and our final aim was to search for biological markers for tumor-associated hearing loss and tumor diameter. Methods and findings: Sixteen patients underwent TLS for sporadic VS. Perilymph was aspirated through the round window before opening the labyrinth. One sample was contaminated and excluded resulting in 15 usable samples. Perilymph samples were analyzed with an online tandem LTQ-Orbitrap mass spectrometer. Data were analyzed with MaxQuant software to identify the total number of proteins and to quantify proteins in individual samples. Protein function was analyzed using the PANTHER Overrepresentation tool. Associations between perilymph protein content, clinical parameters, tumor-associated hearing loss and tumor diameter were assessed using Random Forest and Boruta. In total, 314 proteins were identified; 60 in all 15 patients and 130 proteins only once in 15 patients. Ninety-one proteins were detected in at least 12 out of 15 patients. Random Forest followed by Boruta analysis confirmed that alpha-2-HS-glycoprotein (P02765) was an independent variable for tumor-associated hearing loss. In addition, functional analysis showed that numerous processes were significantly increased in the perilymph. The top three enriched biological processes were: 1) secondary metabolic processes; 2) complement activation and 3) cell recognition. Conclusions: The proteome of perilymph in patients with vestibular schwannoma has an inter-individual stable section. However, even in a cohort with homogenous disease, the variation between individuals represented the majority of the detected proteins. Alpha-2-HS-glycoprotein, P02765, was shown to be an independent variable for tumor-associated hearing loss, a finding that needs to be verified in other studies. In pathway analysis perilymph had highly enriched functions, particularly in terms of increased immune and metabolic processes. [ABSTRACT FROM AUTHOR]

The cell surface and secreted proteins are the initial points of contact between Candida albicans and the host. Improvements in protein extraction approaches and mass spectrometers have allowed researchers to obtain a comprehensive knowledge of these external subproteomes. In this paper, we review the published proteomic studies that have examined C. albicans extracellular proteins, including the cell surface proteins or surfome and the secreted proteins or secretome. The use of different approaches to isolate cell wall and cell surface proteins, such as fractionation approaches or cell shaving, have resulted in different outcomes. Proteins with N-terminal signal peptide, known as classically secreted proteins, and those that lack the signal peptide, known as unconventionally secreted proteins, have been consistently identified. Existing studies on C. albicans extracellular vesicles reveal that they are relevant as an unconventional pathway of protein secretion and can help explain the presence of proteins without a signal peptide, including some moonlighting proteins, in the cell wall and the extracellular environment. According to the global view presented in this review, cell wall proteins, virulence factors such as adhesins or hydrolytic enzymes, metabolic enzymes and stress related-proteins are important groups of proteins in C. albicans surfome and secretome. Biological significance Candida albicans extracellular proteins are involved in biofilm formation, cell nutrient acquisition and cell wall integrity maintenance. Furthermore, these proteins include virulence factors and immunogenic proteins. This review is of outstanding interest, not only because it extends knowledge of the C. albicans surface and extracellular proteins that could be related with pathogenesis, but also because it presents insights that may facilitate the future development of new antifungal drugs and vaccines and contributes to efforts to identify new biomarkers that can be employed to diagnose candidiasis. Here, we list more than 570 C. albicans proteins that have been identified in extracellular locations to deliver the most extensive catalogue of this type of proteins to date. Moreover, we describe 16 proteins detected at all locations analysed in the works revised. These proteins include the glycophosphatidylinositol (GPI)-anchored proteins Ecm33, Pga4 and Phr2 and unconventional secretory proteins such as Eft2, Eno1, Hsp70, Pdc11, Pgk1 and Tdh3. Furthermore, 13 of these 16 proteins are immunogenic and could represent a set of interesting candidates for biomarker discovery. [ABSTRACT FROM AUTHOR]

The prediction of protein complexes from protein-protein interactions (PPIs) is a well-studied problem in bioinformatics. However, the currently available PPI data is not enough to describe all known protein complexes. In this paper, we express the problem of determining the minimum number of (additional) required protein-protein interactions as a graph theoretic problem under the constraint that each complex constitutes a connected component in a PPI network. For this problem, we develop two computational methods: one is based on integer linear programming (ILPMinPPI) and the other one is based on an existing greedy-type approximation algorithm (GreedyMinPPI) originally developed in the context of communication and social networks. Since the former method is only applicable to datasets of small size, we apply the latter method to a combination of the CYC2008 protein complex dataset and each of eight PPI datasets (STRING, MINT, BioGRID, IntAct, DIP, BIND, WI-PHI, iRefIndex). The results show that the minimum number of additional required PPIs ranges from 51 (STRING) to 964 (BIND), and that even the four best PPI databases, STRING (51), BioGRID (67), WI-PHI (93) and iRefIndex (85), do not include enough PPIs to form all CYC2008 protein complexes. We also demonstrate that the proposed problem framework and our solutions can enhance the prediction accuracy of existing PPI prediction methods. ILPMinPPI can be freely downloaded from . [ABSTRACT FROM AUTHOR]

Lizards are seasonal breeders. Cyclic reproductive nature makes lizard as a useful model for the study of the reproductively active protein secretions in the epididymis. During breeding season, the epididymides of the lizard secret proteins that mixes with the spermatozoa and create a favourable environment for sperm maturation. In this spectrum, the aim of this study is to identify and characterize proteins which are present in the lumen of the epididymis of the lizard, E. carinata during the active phase of reproduction. The identification and analysis of the proteins are done through the proteomic approaches. The epididymal luminal fluid sample was taken from the reproductively active and inactive phase and these are subjected to the size exclusion chromatography. Two major peaks (peak 1 and peak 2) were obtained in the epididymal luminal fluid sample taken during the reproductively active phase. On the other hand, the sample from the reproductively inactive phase showed one peak (peak 1) whereas, peak 2 is not present during this phase. The peak 2 belong to reproductively active phase was later subjected to the proteomic analysis. Appropriate gel electrophoresis separation and purification methods are combined with LC-MS/MS in order to identify and characterize the proteins that are presented during the reproductively active phase. Further, in this work, nine proteins are identified including three enzymes and three heat shock proteins. Among the identified proteins, bioinformatics analysis predicts that majority of them are localized in the cytoplasm. In addition to this, an observation is made in the endoplasmic reticulum where it is seen that a close protein–protein interaction network of three molecular chaperones are involved in protein processing. Overall, this paper opens up a new dimension search for epididymal markers for the first time in reptiles, particularly lizards. [ABSTRACT FROM AUTHOR]

The sampling of scale-free networks in Molecular Biology is usually achieved by growing networks from a seed using recursive algorithms with elementary moves which include the addition and deletion of nodes and bonds. These algorithms include the Barabási-Albert algorithm. Later algorithms, such as the Duplication-Divergence algorithm, the Solé algorithm and the iSite algorithm, were inspired by biological processes underlying the evolution of protein networks, and the networks they produce differ essentially from networks grown by the Barabási-Albert algorithm. In this paper the mean field analysis of these algorithms is reconsidered, and extended to variant and modified implementations of the algorithms. The degree sequences of scale-free networks decay according to a powerlaw distribution, namely P(k) ∼ k− γ, where γ is a scaling exponent. We derive mean field expressions for γ, and test these by numerical simulations. Generally, good agreement is obtained. We also found that some algorithms do not produce scale-free networks (for example some variant Barabási-Albert and Solé networks). [ABSTRACT FROM AUTHOR]

How to identify protein complex is an important and challenging task in proteomics. It would make great contribution to our knowledge of molecular mechanism in cell life activities. However, the inherent organization and dynamic characteristic of cell system have rarely been incorporated into the existing algorithms for detecting protein complexes because of the limitation of protein-protein interaction (PPI) data produced by high throughput techniques. The availability of time course gene expression profile enables us to uncover the dynamics of molecular networks and improve the detection of protein complexes. In order to achieve this goal, this paper proposes a novel algorithm DCA (Dynamic Core-Attachment). It detects protein-complex core comprising of continually expressed and highly connected proteins in dynamic PPI network, and then the protein complex is formed by including the attachments with high adhesion into the core. The integration of core-attachment feature into the dynamic PPI network is responsible for the superiority of our algorithm. DCA has been applied on two different yeast dynamic PPI networks and the experimental results show that it performs significantly better than the state-of-the-art techniques in terms of prediction accuracy, hF-measure and statistical significance in biology. In addition, the identified complexes with strong biological significance provide potential candidate complexes for biologists to validate. [ABSTRACT FROM AUTHOR]

This paper provides a detailed overview of relevant developments in separation and analysis of proteins by capillary electromigration (CE) methods in the period 2017–mid 2021. The presented topics embrace sample preparation, suppression of protein sorption, control of electroosmotic flow, separations of proteins by the particular CE methods and protein identification and quantification by various detection techniques. To illustrate the wide usability of CE methods for protein analysis, the paper shows several novel applications of CE methods, such as quality control and characterization of protein biopharmaceuticals, determination of proteins in complex biomatrices, investigation of posttranslational modification, peptide mapping, interactions of proteins with other (bio)molecules and determination of their physicochemical and biochemical characteristics. [Display omitted] • Developments in separation and analysis of proteins by CE methods are described. • Methodology aspects of CE separations and analyses of proteins are discussed. • Particular CE methods applied for analysis of proteins are reported. • Detection techniques for identification & quantification of proteins are presented. • Wide applicability of CE methods for protein analysis & characterization is shown. [ABSTRACT FROM AUTHOR]

With the recent technological developments a vast amount of high-throughput data has been profiled to understand the mechanism of complex diseases. The current bioinformatics challenge is to interpret the data and underlying biology, where efficient algorithms for analyzing heterogeneous high-throughput data using biological networks are becoming increasingly valuable. In this paper, we propose a software package based on the Prize-collecting Steiner Forest graph optimization approach. The PCSF package performs fast and user-friendly network analysis of high-throughput data by mapping the data onto a biological networks such as protein-protein interaction, gene-gene interaction or any other correlation or coexpression based networks. Using the interaction networks as a template, it determines high-confidence subnetworks relevant to the data, which potentially leads to predictions of functional units. It also interactively visualizes the resulting subnetwork with functional enrichment analysis. [ABSTRACT FROM AUTHOR]

Post-translational modification (PTM) is a covalent and enzymatic modification of proteins, which contributes to diversify the proteome. Despite many reported PTMs with essential roles in cellular functioning, lysine succinylation has emerged as a subject of particular interest. Because its experimental identification remains a costly and time-consuming process, computational predictors have been recently proposed for tackling this important issue. However, the performance of current predictors is still very limited. In this paper, we propose a new predictor called PSSM-Suc which employs evolutionary information of amino acids for predicting succinylated lysine residues. Here we described each lysine residue in terms of profile bigrams extracted from position specific scoring matrices. We compared the performance of PSSM-Suc to that of existing predictors using a widely used benchmark dataset. PSSM-Suc showed a significant improvement in performance over state-of-the-art predictors. Its sensitivity, accuracy and Matthews correlation coefficient were 0.8159, 0.8199 and 0.6396, respectively. [ABSTRACT FROM AUTHOR]

Background: Recently we surveyed the dark-proteome, i.e., regions of proteins never observed by experimental structure determination and inaccessible to homology modelling. Surprisingly, we found that most of the dark proteome could not be accounted for by conventional explanations (e.g., intrinsic disorder, transmembrane domains, and compositional bias), and that nearly half of the dark proteome comprised dark proteins, in which the entire sequence lacked similarity to any known structure. In this paper we will present the Dark Proteome Database (DPD) and associated web services that provide access to updated information about the dark proteome. Results: We assembled DPD from several external web resources (primarily Aquaria and Swiss-Prot) and stored it in a relational database currently containing ~10 million entries and occupying ~2 GBytes of disk space. This database comprises two key tables: one giving information on the 'darkness' of each protein, and a second table that breaks each protein into dark and non-dark regions. In addition, a second version of the database is created using also information from the Protein Model Portal (PMP) to determine darkness. To provide access to DPD, a web server has been implemented giving access to all underlying data, as well as providing access to functional analyses derived from these data. Conclusions: Availability of this database and its web service will help focus future structural and computational biology efforts to study the dark proteome, thus providing a basis for understanding a wide variety of biological functions that currently remain unknown. Availability and implementation: DPD is available at http://darkproteome.ws. The complete database is also available upon request. Data use is permitted via the Creative Commons Attribution-NonCommercial International license (http://creativecommons. org/licenses/by-nc/4.0/). [ABSTRACT FROM AUTHOR]

With the increasing availability of high-throughput data, various computational methods have recently been developed for understanding the cell through protein-protein interaction (PPI) networks at a systems level. However, due to the incompleteness of the original PPI networks those efforts have been significantly hindered. In this paper, we propose a two stage method to predict underlying links between two originally unlinked protein pairs. First, we measure gene expression and gene functional similarly between unlinked protein pairs on Saccharomyces cerevisiae benchmark network and obtain new constructed networks. Then, we select the significant part of the new predicted links by analyzing the difference between essential proteins that have been identified based on the new constructed networks and the original network. Furthermore, we validate the performance of the new method by using the reliable and comprehensive PPI dataset obtained from the STRING database and compare the new proposed method with four other random walk-based methods. Comparing the results indicates that the new proposed strategy performs well in predicting underlying links. This study provides a general paradigm for predicting new interactions between protein pairs and offers new insights into identifying essential proteins. [ABSTRACT FROM AUTHOR]

Protein–protein interactions (PPIs) are essential for most living organisms’ process. Thus, detecting PPIs is extremely important to understand the molecular mechanisms of biological systems. Although many PPIs data have been generated by high-throughput technologies for a variety of organisms, the whole interatom is still far from complete. In addition, the high-throughput technologies for detecting PPIs has some unavoidable defects, including time consumption, high cost, and high error rate. In recent years, with the development of machine learning, computational methods have been broadly used to predict PPIs, and can achieve good prediction rate. In this paper, we present here PCVMZM, a computational method based on a Probabilistic Classification Vector Machines (PCVM) model and Zernike moments (ZM) descriptor for predicting the PPIs from protein amino acids sequences. Specifically, a Zernike moments (ZM) descriptor is used to extract protein evolutionary information from Position-Specific Scoring Matrix (PSSM) generated by Position-Specific Iterated Basic Local Alignment Search Tool (PSI-BLAST). Then, PCVM classifier is used to infer the interactions among protein. When performed on PPIs datasets of Yeast and H. Pylori, the proposed method can achieve the average prediction accuracy of 94.48% and 91.25%, respectively. In order to further evaluate the performance of the proposed method, the state-of-the-art support vector machines (SVM) classifier is used and compares with the PCVM model. Experimental results on the Yeast dataset show that the performance of PCVM classifier is better than that of SVM classifier. The experimental results indicate that our proposed method is robust, powerful and feasible, which can be used as a helpful tool for proteomics research. [ABSTRACT FROM AUTHOR]

Graphlet analysis is an approach to network analysis that is particularly popular in bioinformatics. We show how to set up a system of linear equations that relate the orbit counts and can be used in an algorithm that is significantly faster than the existing approaches based on direct enumeration of graphlets. The approach presented in this paper presents a generalization of the currently fastest method for counting 5-node graphlets in bioinformatics. The algorithm requires existence of a vertex with certain properties; we show that such vertex exists for graphlets of arbitrary size, except for complete graphs and a cycle with four nodes, which are treated separately. Empirical analysis of running time agrees with the theoretical results. [ABSTRACT FROM AUTHOR]

Single nucleotide polymorphisms (SNPs) are widely used in functional genomics and genetics research work. The high-quality sequence of rice genome has provided a genome-wide SNP and proteome resource. However, the impact of SNPs on protein phosphorylation status in rice is not fully understood. In this paper, we firstly updated rice SNP resource based on the new rice genome Ver. 7.0, then systematically analyzed the potential impact of Non-synonymous SNPs (nsSNPs) on the protein phosphorylation status. There were 3,897,312 SNPs in Ver. 7.0 rice genome, among which 9.9% was nsSNPs. Whilst, a total 2,508,261 phosphorylated sites were predicted in rice proteome. Interestingly, we observed that 150,197 (39.1%) nsSNPs could influence protein phosphorylation status, among which 52.2% might induce changes of protein kinase (PK) types for adjacent phosphorylation sites. We constructed a database, SNP_rice, to deposit the updated rice SNP resource and phosSNPs information. It was freely available to academic researchers at http://bioinformatics.fafu.edu.cn. As a case study, we detected five nsSNPs that potentially influenced heterotrimeric G proteins phosphorylation status in rice, indicating that genetic polymorphisms showed impact on the signal transduction by influencing the phosphorylation status of heterotrimeric G proteins. The results in this work could be a useful resource for future experimental identification and provide interesting information for better rice breeding. [ABSTRACT FROM AUTHOR]

Accurate, reliable and sensitive detection methods for gluten are required to support current EU regulations. The enforcement of legislative levels requires that measurement results are comparable over time and between methods. This is not a trivial task for gluten which comprises a large number of protein targets. This paper describes a strategy for defining a set of specific analytical targets for wheat gluten. A comprehensive proteomic approach was applied by fractionating wheat gluten using RP-HPLC (reversed phase high performance liquid chromatography) followed by a multi-enzymatic digestion (LysC, trypsin and chymotrypsin) with subsequent mass spectrometric analysis. This approach identified 434 peptide sequences from gluten. Peptides were grouped based on two criteria: unique to a single gluten protein sequence; contained known immunogenic and toxic sequences in the context of coeliac disease. An LC-MS/MS method based on selected reaction monitoring (SRM) was developed on a triple quadrupole mass spectrometer for the specific detection of the target peptides. The SRM based screening approach was applied to gluten containing cereals (wheat, rye, barley and oats) and non-gluten containing flours (corn, soy and rice). A unique set of wheat gluten marker peptides were identified and are proposed as wheat specific markers. Significance The measurement of gluten in processed food products in support of regulatory limits is performed routinely. Mass spectrometry is emerging as a viable alternative to ELISA based methods. Here we outline a set of peptide markers that are representative of gluten and consider the end user's needs in protecting those with coeliac disease. The approach taken has been applied to wheat but can be easily extended to include other species potentially enabling the MS quantification of different gluten containing species from the identified markers. [ABSTRACT FROM AUTHOR]

• Myofibrillar protein of S-nitrosylation in pork longissimus thoracis was identified. • Differential SNO-modified proteins between PSE and RFN meat were analyzed. • S-nitrocysteines of myosin and actin were differentially detected in two meat groups. • Sarcomeric protein S-nitrosylation could be an alternative pathway of NO affecting meat quality. In this paper, the S-nitrosylated myofibrillar protein in pork was comparatively analyzed between pale, soft, and exudative (PSE) and red, firm, and non-exudative (RFN) meat. The S-nitrosothiol and immunoblot of S-nitrosylated protein indicated that the overall protein S-nitrosylation level in PSE pork was higher than that in RFN pork. Proteomics showed that 114 SNO-modified cysteines corresponding to 65 proteins were over-expressed in PSE samples while 74 nitrosylated cysteines of 20 proteins were over-expressed in RFN samples. Differential proteins including myosin, actin, actinin, nebulin, titin, troponin-I, and filamin were distributed in the cytoskeleton and muscle fibers, participating in muscle contraction, cell development, and myofibril assembly by exerting binding activity. The enriched KEGG pathways included tight junction, regulation of actin cytoskeleton, glycolysis/gluconeogenesis, AMPK signaling pathway, and HIF-1 signaling pathway. Our data suggest that S-nitrosylation of myofibrillar protein could be an alternative pathway of NO involved in the regulation of fresh meat quality. [ABSTRACT FROM AUTHOR]

Essential proteins are indispensable to the viability and reproduction of an organism. The identification of essential proteins is necessary not only for understanding the molecular mechanisms of cellular life but also for disease diagnosis, medical treatments and drug design. Many computational methods have been proposed for discovering essential proteins, but the precision of the prediction of essential proteins remains to be improved. In this paper, we propose a new method, LBCC, which is based on the combination of local density, betweenness centrality (BC) and in-degree centrality of complex (IDC). First, we introduce the common centrality measures; second, we propose the densities Den1(v) and Den2(v) of a node v to describe its local properties in the network; and finally, the combined strategy of Den1, Den2, BC and IDC is developed to improve the prediction precision. The experimental results demonstrate that LBCC outperforms traditional topological measures for predicting essential proteins, including degree centrality (DC), BC, subgraph centrality (SC), eigenvector centrality (EC), network centrality (NC), and the local average connectivity-based method (LAC). LBCC also improves the prediction precision by approximately 10 percent on the YMIPS and YMBD datasets compared to the most recently developed method, LIDC. [ABSTRACT FROM AUTHOR]

In this paper we apply machine learning methods for predicting protein interactions in fungal secretion pathways. We assume an inter-species transfer setting, where training data is obtained from a single species and the objective is to predict protein interactions in other, related species. In our methodology, we combine several state of the art machine learning approaches, namely, multiple kernel learning (MKL), pairwise kernels and kernelized structured output prediction in the supervised graph inference framework. For MKL, we apply recently proposed centered kernel alignment and p-norm path following approaches to integrate several feature sets describing the proteins, demonstrating improved performance. For graph inference, we apply input-output kernel regression (IOKR) in supervised and semi-supervised modes as well as output kernel trees (OK3). In our experiments simulating increasing genetic distance, Input-Output Kernel Regression proved to be the most robust prediction approach. We also show that the MKL approaches improve the predictions compared to uniform combination of the kernels. We evaluate the methods on the task of predicting protein-protein-interactions in the secretion pathways in fungi, S.cerevisiae, baker’s yeast, being the source, T. reesei being the target of the inter-species transfer learning. We identify completely novel candidate secretion proteins conserved in filamentous fungi. These proteins could contribute to their unique secretion capabilities. [ABSTRACT FROM AUTHOR]

High-resolution C solid-state NMR stands out as one of the most promising techniques to solve the structure of insoluble proteins featuring biological and technological importance. The simplest nuclear magnetic resonance (NMR) spectroscopy method to quantify the secondary structure of proteins uses the areas of carbonyl and alpha carbon peaks. The quantification obtained by fitting procedures depends on the assignment of the peaks to the structure, type of line shape, number of peaks to be used, and other parameters that are set by the operator. In this paper, we demonstrate that the analysis of C NMR spectra by a pattern recognition method-based on the singular value decomposition (SVD) regression, which does not depend on the operator-shows higher correlation coefficients for α-helix and β-sheet (0.96 and 0.91, respectively) than Fourier transform infrared spectroscopy (FTIR) method. Therefore, the use of C solid-state NMR spectra and SVD is a simple and reliable method for quantifying the secondary structures of insoluble proteins in solid-state. [ABSTRACT FROM AUTHOR]