Search

Intrinsically disordered proteins (IDPs) and proteins containing intrinsically disordered protein regions (IDPRs) often affect protein structure determination. IDP/IDPRs cause failures in the protein structure determination pipeline leading to enhanced experimental cost and time. Hence the primary sequence of proteins is often analyzed using disorder prediction servers so that manipulations can be made to the protein sequence to aid its expression, purification, and crystallization. Assessment of existing IDP/IDPR prediction methods with CASP 10 targets shows the scope of improvement of prediction. In this paper, an attempt has been made to develop a hydrophobicity and net charge based artificial neural network model for protein disorder prediction. Subsequently, a prediction tool has been developed using Java using the Neural Network Schema. The prediction tool has been tested with CASP 10 targets and the developed predictor is found to outperform other predictors i.e RONN, PONDR VLXT, DisEMBL, FOLDINDEX, and GLOBPLOT in Sensitivity (0.983), Precision (0.854), Accuracy (0.937), MCC (0.840), and AUC (0.937). DisEMBL and GLOBPLOT are found to have better specificity i.e 0.909 and 0.979 respectively than the proposed predictor i.e 0.890. [ABSTRACT FROM AUTHOR]

Background: Catalase (EC 1.11.1.6) is a heme-containing tetrameric enzyme that plays a critical role in signaling and hydrogen peroxide metabolism. It was the first enzyme to be crystallized and isolated. Catalase is a well-known industrial enzyme used in diagnostic and analytical methods in the form of biomarkers and biosensors, as well as in the textile, paper, food, and pharmaceutical industries. In silico analysis of CAT genes and proteins has gained increased interest, emphasizing the development of biomarkers and drug designs. The present work aims to understand the catalase evolutionary relationship of plant species and analyze its physicochemical characteristics, homology, phylogenetic tree construction, secondary structure prediction, and 3D modeling of protein sequences and its validation using a variety of conventional computational methods to assist researchers in better understanding the structure of proteins. Results: Around 65 plant catalase sequences were computationally evaluated and subjected to bioinformatics assessment for physicochemical characterization, multiple sequence alignment, phylogenetic construction, motif and domain identification, and secondary and tertiary structure prediction. The phylogenetic tree revealed six unique clusters where diversity of plant catalases was found to be the largest for Oryza sativa. The thermostability and hydrophilic nature of these proteins were primarily observed, as evidenced by a relatively high aliphatic index and negative GRAVY value. The distribution of 5 sequence motifs was uniformly distributed with a width length of 50 with the best possible amino residue sequences that resemble the plant catalase PLN02609 superfamily. Using SOPMA, the predicted secondary structure of the protein sequences revealed the predominance of the random coil. The predicted 3D CAT model from Arabidopsis thaliana was a homotetramer, thermostable protein with 59-KDa weight, and its structural validation was confirmed by PROCHECK, ERRAT, Verify3D, and Ramachandran plot. The functional relationships of our query sequence revealed the glutathione reductase as the closest interacting protein of query protein. Conclusions: This theoretical plant catalases in silico analysis provide insight into its physiochemical characteristics and functional and structural understanding and its evolutionary behavior and exploring protein structure-function relationships when crystal structures are unavailable. [ABSTRACT FROM AUTHOR]

Identifying protein-protein interactions (PPIs) represents a challenging research problem in computational biology. Even though machine learning methods have significantly advanced the research in this field, current approaches struggle to accurately predict interactions for previously unseen proteins. Supervised autoencoders, which are neural networks trained to simultaneously predict labels and reconstruct their inputs, have been proved to offer generalization guarantees in the linear case. Moreover, the addition of a reconstruction branch was empirically shown to improve the performance of standard neural networks classifiers on several tasks. In this paper, we introduce a two-stage sequence-based PPI prediction method based on supervised autoencoders. The proposed approach consists of initially training a denoising autoencoder on protein sequences, followed by a supervised training stage in which the model learns to both predict whether two proteins interact and to reconstruct the two proteins in the pair. An experimental analysis was performed on two public PPI data sets containing testing pairs formed using both seen and unseen protein sequences. The results show that our approach surpasses, on the two data sets, multiple machine learning classifiers proposed in the literature. [ABSTRACT FROM AUTHOR]

Copyright of Informatica (03505596) is the property of Slovene Society Informatika and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The usage of antigen-functionalized nanoparticles has become a major focus in the field of experimental HIV-1 vaccine research during the last decade. Various molecular mechanisms to couple native-like trimers of the HIV-1 envelope protein (Env) onto nanoparticle surfaces have been reported, but many come with disadvantages regarding the coupling efficiency and stability. In this study, a short amino acid sequence ("aldehyde-tag") was introduced at the C-terminus of a conformationally stabilized native-like Env. The post-translational conversion of a tag-associated cysteine to formylglycine creates a site-specific aldehyde group without alteration of the Env antigenicity. This aldehyde group was further utilized for bioconjugation of Env trimers. We demonstrated that the low acidic environment necessary for this bioconjugation is not affecting the trimer conformation. Furthermore, we developed a two-step coupling method for pH-sensitive nanoparticles. To this end, we conjugated aldehyde-tagged Env with Propargyl-PEG 3 -aminooxy linker (oxime ligation; Step-one) and coupled these conjugates by copper-catalyzed azide-alkyne cycloaddition (Click reaction; Step-two) to calcium phosphate nanoparticles (CaPs) functionalized with terminal azide groups. CaPs displaying orthogonally arranged Env trimers on their surface (o-CaPs) were superior in activation of Env-specific B-cells (in vitro) and induction of Env-specific antibody responses (in vivo) compared to CaPs with Env trimers coupled in a randomly oriented manner. Taken together, we present a reliable method for the site-specific, covalent coupling of HIV-1 Env native-like trimers to the surface of nanoparticle delivery systems. This method can be broadly applied for functionalization of nanoparticle platforms with conformationally stabilized candidate antigens for both vaccination and diagnostic approaches. During the last decade antigen-functionalized nanoparticles have become a major focus in the field of experimental HIV-1 vaccines. Rational design led to the production of conformationally stabilized HIV-1 envelope protein (Env) trimers - the only target for the humoral immune system. Various molecular mechanisms to couple Env trimers onto nanoparticle surfaces have been reported, but many come with disadvantages regarding the coupling efficiency and stability. In this paper, we describe a highly selective bio-conjugation of Env trimers to the surface of medically relevant calcium phosphate nanoparticles. This method maintains the native-like protein conformation and has a broad potential application in functionalization of nanoparticle platforms with stabilized candidate antigens (including stabilized spike proteins of coronaviruses) for both vaccination and diagnostic approaches. [Display omitted] [ABSTRACT FROM AUTHOR]

The study and identification of protein post-translational modification(PTM) sites in rice seeds plays an important role in breeding and yield increasing in intelligent agriculture. Deep learning, especially deep convolutional neural networks(CNN) become more and more popular in intelligent agriculture because of its excellent image processing and data analysis capabilities. In this paper, the Denoised-Oversampling is adopted to denoise the samples and balance the data. The protein sequences are transformed into corresponding numerical feature vectors. Then, the processed feature subset is input to the deep convolution network. In fully connected layers, we used the L2 regularization method to prevent the overfitting problem. The phosphorylation sites are predicted by the sigmoid classifier. Finally, in the experimental part, in order to truly reflect the predictive power of the model, we divided the dataset into training set, validation set and independent test set. The prediction accuracy of the independent test set is 83.20%. The test result shows that the proposed algorithm has better prediction performance than the existing phosphorylation prediction algorithms. [ABSTRACT FROM AUTHOR]

Understanding the replication machinery of viruses contributes to suggest and try effective antiviral strategies. Exhaustive knowledge about the proteins structure, their function, or their interaction is one of the preconditions for successfully modeling it. In this context, modeling methods based on a formal representation with a high semantic expressiveness would be relevant to extract proteins and their nucleotide or amino acid sequences as an element from the replication process. Consequently, our approach relies on the use of semantic technologies to design the SARS-CoV-2 replication machinery. This provides the ability to infer new knowledge related to each step of the virus replication. More specifically, we developed an ontology-based approach enriched with reasoning process of a complete replication machinery process for SARS-CoV-2. We present in this paper a partial overview of our ontology OntoRepliCov to describe one step of this process, namely, the continuous translation or protein synthesis, through classes, properties, axioms, and SWRL (Semantic Web Rule Language) rules. [ABSTRACT FROM AUTHOR]

Copyright of Informatica (03505596) is the property of Slovene Society Informatika and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Wet Lab methods used to resolve the native structure of a given protein such as X-ray Diffraction and NMR spectroscopy are time-consuming, expensive, and could be done multiple times due to failure. Those pitfalls led to the development of computationally automated prediction methods. Predicting the structure of a protein using its peptide sequence only — also known as ab initio Protein Structure Prediction "PSP"— is computationally challenging because of the large conformational space to be searched and the complexity of energy functions. Some successful predictive methods have been developed to solve PSP problem. In this paper, by using a set of peptide sequences, we compare a collection of PSP ab initio-based methods. Experiments show that using a metaheuristic-based search method that utilizes genetic algorithm can achieve same or better results than time consuming methods. [ABSTRACT FROM AUTHOR]

Highlights • This article proposes a new method for Prediction of Cleavage of Amino Acid Sequences by HIV-1. • The proposed method relies on a new encoding and model for prediction of amino acid sequence cleavage by HIV protease. • In the encoding stage, a combination of amino acids' spatial and structural features are taken into account. • The Final prediction model is developed with the combination of genetic programming and support vector machine. Abstract The human immunodeficiency virus (HIV) is the cause of acquired immunodeficiency syndrome (AIDS), which has profound implications in terms of both economic burden and loss of life. Modeling and examination of the HIV protease cleavage of amino acid sequences can contribute to control of this disease and production of more effective drugs. The present paper introduces a new method for encoding and characterization of amino acid sequences and a new model for the prediction of amino acid sequence cleavage by HIV protease. The proposed encoding scheme utilizes a combination of amino acids' spatial and structural features in conjunction with 20 amino acid sequences to make sure that their physicochemical and sequencing features are all taken into account. The proposed HIV-1 amino acid cleavage prediction model is developed with the combination of genetic programming and support vector machine. The results of evaluations performed on various datasets demonstrate the superior performance of the proposed encoding and better accuracy of the proposed HIV-1 cleavage prediction model as compared to the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

A protein is a linear chain containing a set of amino acids, which folds on itself to create a specific native structure, called the minimum energy conformation. It is the native structure that determines the functionality of each protein. The Protein Folding Problem (PFP) remains one of the most strenuous computational and chemical biology. The principal challenge of PFP is to predict the optimal conformation of a given protein by considering only its amino acid sequence. Since the conformational space contains a colossal number of possibilities, even when considering short sequences, different simplified models have been developed and applied to make the PFP less complex. Experimental methods can be used to predict the native structure of small and specific proteins. Given the limitations of experimental methods, in the last few years many computational approaches have been proposed to solve the PFP. Based on the folding process, the PFP was formulated as an optimization problem. They are based on simplified lattice models such as the hydrophobic-polar model. In this paper, we present a new Hybrid Cuckoo Search Algorithm (HCSA) to solve the 3D-HP protein folding optimization problem. Our proposed algorithm consists of combining the Cuckoo Search Algorithm (CSA) with the Hill Climbing (HC) algorithm. Simulation results on different benchmark sequences are presented and compared to the state-of-the-art algorithms. • This paper deals with a novel approach for the 3D-HP Protein Folding Problem. • The study uses an algorithm named HCSA, which combines the Cuckoo Search (CS) with the Hill Climbing (HC) algorithms. • Experimental results on different benchmark sequences are presented and compared with state-of-the-art results. [ABSTRACT FROM AUTHOR]

The discovery of protein tertiary structure is the basis of current genetic engineering, medicinal design, and other biological applications. Protein structural class plays a significant role in the tertiary structure folding and function analysis of protein. However, the growth rate of new amino acid sequence far exceeds the tertiary structure. Existing research methods of confirming protein folding cannot satisfy massive sequences and protein engineering. A high-accuracy prediction result of low-similarity protein dataset is particularly critical to generate the corresponding tertiary structure from the primary structure. In this paper, we construct a novel super-large-scale feature of the primary structure based on secondary structure, evolutionary information, chemical properties, and global descriptors. The diversified and massive features are utilized to predict the protein class based on a novel feature selection algorithm and a gradient boosting decision tree model. To testify the effectiveness and robustness of our proposed method, namely IDEGBM, we choose the 10-fold cross-validation for evaluating four benchmark datasets 25PDB, FC699, D1189 and D640. Experimental results exhibit that our method improves the accuracy in comparison with other state-of-the-art prediction models in terms of both accuracy and efficiency. Furthermore, a representative protein is used to validate that our proposed IDEGBM can be applied to improve the conformation prediction of protein tertiary structure. • A novel super-large-scale feature for protein structural class is conducted. • A novel feature selection and a gradient boosting decision tree model is proposed. • Extensive experimental results show the superiority of the proposed method. [ABSTRACT FROM AUTHOR]

Copyright of Revista Iberoamericana de Micologia is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Recent advancement in the field of structural biology has generated huge volume of data and analyzing such data is vital to know the hidden truths of life but such analysis is compute intensive in nature and requires huge computational power resulting in extensive use of high performance computing (Multi Core Computing, G2PU Computing, CPU-GPU Hybrid computing, Cluster, Grid) models. Grid is one of the widely used HPC model which is commonly used in computational biology and to execute a compute intensive tasks on grid, the applications must also be grid enabled. Performance of grid depends on appropriate selection of load balancing strategy (at server and node level) and maximum utilization of computational resources of nodes (multiple cores of CPU and execution units of GPU) by the grid enabled applications. In this paper an attempt has been made to establish an experimental grid, develop a grid enabled application and to operate the grid with its highest possible performance by the grid enabled applications. [ABSTRACT FROM AUTHOR]

COVID-19 has threatened the whole world since December 2019 and has also infected millions of people around the globe. It has been transmitted through the SARS CoV-2 virus. Various proteins of the SARS CoV-2 virus have an important role in its interaction with human cells. Specifically, the interaction of S-protein with human ACE-2 protein helps in entering of SARS CoV-2 virus into a human cell. This interaction take-place at some specific amino-acid locations called as hot-spots. Understanding of this interaction is helpful for drug designing and vaccine development for new variants of COVID-19 disease. An attempt has been made in this paper for understanding this interaction by finding the characteristics frequency of SARS-related protein families using the resonance recognition model (RRM). Hardware implementation of Bandpass notch (BPN) lattice IIR filter system architecture is also carried out, which is used for hot-spots identification in SARS CoV-2 proteins. Various signal processing techniques like retiming, pipelining, etc. are explored for performance improvement. Synthesis of proposed BPN filter system has been done using Xilinx ISE EDA tool on Zynq-series (Zybo-board) FPGA family. It is found that retimed and pipelined architecture of hardware-implemented BPN lattice IIR filter-based hot-spots detection system improves the speed (computational time) by 14 to 31 times for different SARS CoV2 related proteins as compared to its MATLAB simulation with similar functionality. • Proposed BPN lattice IIR filter architecture for hot-spots identification in SARS CoV-2. • Hot-spot identification is useful for vaccine development of new variants of COVID-19. • Synthesis is done using Xilinx ISE and implemented on Zynq-series FPGA. • Experimentation is carried out on five proteins and identified hot-spots are reported. • Comparative analysis of proposed filter is done with MATLAB simulation. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to present in details, an extended case-study focused on emerging in vitro Diagnostics Technology as reflected on retrieved and evaluated Industrial Property Documents. More specific, this study comprises of the state-of-the-art and the perspectives of automatic Immunoassay Analyzers, it follows the innovation patent-trail from Flow Cytometry to Quantum Dots applications in the in vitro Diagnostics of tomorrow and finally, it provides an overview of the trends in Next Generation Nucleic Acid and Protein Sequencing, all of them as depicted on numerous relevant, retrieved and processed Industrial Property Documents. [ABSTRACT FROM AUTHOR]

Abstract: The medical use of nanoparticles (NPs) has to consider their interactions with the cells of the reticuloendothelial system. In this study the authors used gold nanorods coated by PEG chains bearing peptides or charged functional groups to study their influence on the uptake, subcellular distribution, and activation of human primary reticuloendothelial cells: monocytes, macrophages (MΦ), immature and mature dendritic cells (DC), and endothelial cells (EC). We found that beside MΦ and immature DC also EC internalize large quantities of NPs and observed an increased uptake of positively charged particles. Most notably, NPs accumulated in the MHC II compartment in mature DC that is involved in antigen processing. Furthermore, surface-coupled peptide sequences RGD and GLF altered the activation profile of DC, and modulated cytokine release in both DC and MΦ in a cell specific manner. These data suggest that the charge of NPs mainly influences their uptake, whereas conjugated peptides alter cell functions. From the Clinical Editor: In this paper the interactions between RES cells and nanoparticles is investigated, concluding that in the case of gold nanorods charge determines uptake characteristics, whereas conjugated peptides determine their function. [Copyright &y& Elsevier]

Arc-annotated sequences are useful in representing the structural information of RNA and protein sequences. In particular, arc-annotated sequences are useful in describing the secondary and tertiary structures of RNA and protein sequences. Structure comparison for RNA and for protein sequences has become a central computational problem bearing many challenging computer science questions. The longest arcpreserving common subsequence problem has been introduced as a framework for studying the similarity of arc-annotated sequences. It is a sound and meaningful mathematical formalization of comparing the secondary structures of molecular sequences. In this paper, we consider two special cases of the longest arc-preserving common subsequence problem, c- fragment LAPCS (unlimited, plain), c-fragment LAPCS (unlimited, unlimited). In particular, we consider a parameterized version of the 1-fragment LAPCS (unlimited, plain) problem, parameterized by the length l of the desired subsequence. We show W[1]-completeness of the problem. Also, we describe an approach to solve c-fragment LAPCS (unlimited, unlimited). This approach is based on constructing logical models for the problem. [ABSTRACT FROM AUTHOR]

Abstract: The physiological function of fructosamine-3-kinase (FN3K) is relatively well understood. As shown in several studies, most conclusively by data on the FN3K-KO mouse , this enzyme breaks down compounds produced by the non-enzymatic glycation of proteins by d-glucose. In contrast with FN3K, very little is known about the function of the fructosamine-3-kinase-related-protein (FN3KRP) even though it has a 65% amino-acid sequence identity with FN3K. We do know that this enzyme is a kinase as evidenced by its ability to phosphorylate non-physiological compounds such a psicosamines, ribulosamines, erythrulosamines, and glucitolamines. However, FN3KRP does not phosphorylate any of the numerous Amadori products that are the physiological substrates of FN3K. The fact that FN3KRP is highly conserved in all vertebrates and present throughout nature suggests that it plays an important role in cellular metabolism and makes identification of its physiological substrates an important objective. In this paper, we propose that FN3KRP phosphorylates products resulting from a non-enzymatic glycation of amines by ketoses (fructation) that involves a 2,3-enolization and produces the stable Amadori intermediate, 2-amino-2-deoxy-d-ribo-hex-3-ulose (ADRH). This ketosamine is then phosphorylated to 2-amino-2-deoxy-d-ribo-hex-3-ulose-4-phosphate (ADRH-4-P). Since phosphates are much better leaving groups than hydroxyls, this destabilizes the C-2 amine bond and results in a spontaneous β-elimination of the phosphate to regenerate an unmodified amine with the concomitant production of 4-deoxy-2,3-diulose. Consequently, we postulate that the principal physiological function of FN3KRP is the breakdown of nonenzymatic fructation products. If confirmed in future studies, this hypothesis opens up new perspectives for an improved understanding of biological Maillard reactions and mechanisms for their control and/or reversal. [Copyright &y& Elsevier]

Abstract: Protein database search requests are generally being performed using the BLASTp algorithm, introduced by NCBI . Since it is computationally intensive, it becomes more and more ine_ective with today''s growth of sequence database sizes. The needs for an e_cient parallel implementation arise. In this paper, we focus on a massive parallelization using the FPGA-based hardware architecture RIVYERA . The aim is to reach speedups in orders of magnitude with a flexible implementation while saving energy costs compared to PC-based database search. We keep our implementation close to the structure published by Kasap et al. and include ideas from Sotiriades et al. such that all parts of the algorithm are organized in components of a long pipeline. We also use the idea of the two-hit method to keep the computational e_ort small. Besides the related work, we perform the very last step of the algorithm to produce a gapped alignment with the Needleman-Wunsch algorithm in software, only with the option of hardware processing after reconfiguration. This saves FPGA-resources and allows an even higher degree of parallelism. [Copyright &y& Elsevier]

Given an amino acid sequence with the a-carbon 3D coordinates on its backbone, the all-atom protein backbone reconstruction problem (PBRP) is to rebuild the 3D coordinates of all major atoms (N, C and O atoms) on the backbone. In this paper, we first build a 4-residue fragment library extracted from PDB. Then, to solve PBRP, we search for fragments with similar structures based on the inner distances. To test the performance of our method, we use two testing sets of target proteins, one was proposed by Maupetit et al. and the other is a subset extracted from CASP7. We compare the experimental results of our method with three previous works, MaxSprout, Adcock's method and SABBAC proposed by Maupetit et al. The reconstruction accuracy of our method is comparable to these previous works. And the solution of our method is more stable than the previous works in most target proteins. These previous works contain complicated energy computation, while our method does not. Thus, our method requires much less execution time than the previous works. [ABSTRACT FROM AUTHOR]

Copyright of Pediatric Endocrinology, Diabetes & Metabolism is the property of Termedia Publishing House and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Oleosins are structural proteins found in oil bodies, organelles found in the cells of plant tissues with a high oil content that undergo extreme desiccation as part of their maturation process. Oleosins stabilise oil bodies. In this paper, a fulllength cDNA sequence homologous to oleosin, a seed-storage oil-body protein, from coconut (Cocos nucifera L.) was identified from cDNA libraries during fruit development and characterised. The gene, termed Coco-Ole, contained an open reading frame of 375 bp encoding a polypeptide of 125 amino acids. The predicted amino acid sequence of coconut oleosin had a molecular mass of 13.0 kDa, and showed 92% (AAF76238.1) and 67% (AAC02239.1) sequence similarity to oil palm (Elaeis guineensis Jacq.) and rice (Oryza sativa) oleosin proteins, respectively. The amino acid sequence clustered in the same branch as oil palm in the cladogram, but was distant from other species. The results of semi-quantitative RT-PCR indicated that the Coco-Ole gene was expressed only in the pulp, and its expression increased significantly during pulp development. Compared with fluctuations in oil content, expression of the Coco-Ole gene was consistent with the anabolism of oil during pulp development. The cloning and sequencing of the Coco-ole gene provides a new marker for studies on oil body biogenesis and fruit development in coconut. [ABSTRACT FROM AUTHOR]

Many of todays database applications, including market basket analysis, web log analysis, DNA and protein sequence analysis utilize databases to store and retrieve sequential data. Commercial database management systems allow to store sequential data, but they do not support efficient querying of such data. To increase the efficiency of analysis of sequential data new index structures need to be developed. In this paper we propose an indexing scheme for non-timestamped sequences of sets, which supports set subsequence queries. Our contribution is threefold. First, we describe the index logical and physical structure, second, we provide algorithms for set subsequence queries utilizing this structure, and finally we perform experimental evaluation of the index, which proves its feasibility and advantages in set subsequence query processing. [ABSTRACT FROM AUTHOR]

Three histone H1 variants were identified in medaka fish and their sequence characteristics were analyzed. This paper reports one of these variants, termed H1-2, because of its possible implication in erythrocyte maturation. The amino acid sequence of H1-2 was phylogenetically similar to that of other replication-dependent histones. The mRNA transcribed from the h1- 2 gene, however, possessed a poly(A) tail without a stem-loop structure, indicating that H1-2 may represent a replication-independent (RI) histone. Transcripts from the h1-2 gene were largely localized in erythrocytes, and knock-down of the h1-2 gene with morpholino antisense oligos resulted in failure to develop mature erythrocytes. In the morphants, residual erythrocytes showed severely impaired nuclear compaction. Although not structurally related to chicken RI histone H5, which is required for erythrocyte maturation, H1-2 may constitute its functional counterpart. Our findings may offer evolutionary insights into the function of H1 variants. [ABSTRACT FROM AUTHOR]

Bioinformatics is a large group of methods used in biology, mostly for analysis of gene sequences. The algorithms developed for this task have recently found a new application in network threat detection. This paper is an introduction to this area of research, presenting a survey of bioinformatics methods applied to this task, outlining the individual tasks and methods used to solve them. It is argued that the early conclusion that such methods are ineffective against polymorphic attacks is in fact too pessimistic. [ABSTRACT FROM AUTHOR]

Abstract: The diversity of type I polyketide synthases (PKSs) in cellulolytic myxobacterium Sorangium was explored by assaying the ketoacyl synthases (KSs) in 10 Sorangium strains with two degenerate primer sets and 64 different KS fragments were obtained. For their deduced amino acid sequences, eight were identical to three known KSs from Sorangium and Magnetospirillum, while the others showed 54–83% identities to the modular KS domains reported from various microorganisms. Parts of the Sorangium KSs tightly share the clade with Actinobacteria excluding any other analyzed myxobacterial KSs, or with Cyanobacteria /Myxobacteria. Parts are widely located in the three functional groups – “Loading”, “NRPS/PKS” and “Trans-AT”. Sorangium KSs in the Actinobacteria, Cyanobacteria/Myxobacteria, or “Loading” clade further evolved independently on its own genus. Notably, the modular KSs from other Myxobacteria genera, i.e. Myxococcus, Stigmatella, Melittangium, Cystobacter and Angiococcus are often distributed crosswise and form non-Sorangium blend subgroups. “NRPS/PKS” and “Trans-AT” are two rather diverse groups and the Sorangium KSs in these clades evolved crosswise with other taxa lineages. The results presented in this paper suggest that the inherent genetic strategies, together with frequent gene importing from many organisms (HGT) have contributed to the evolution of modular PKSs in Sorangium. These findings reinforce that Sorangium strains are really excellent creators for novel and diverse polyketides. [Copyright &y& Elsevier]

• A novel method (Fertility-LightGBM) to predict the fertility-related proteins. • The protein sequences are extracted by combining sequence features, physicochemical property features, and evolutionary features. • LASSO method remove the redundant and irrelevant feature information to select the optimal feature subset for the first time. • We investigate the effect of the eight different classifiers on the results, LightGBM supports parallel learning, which can process large-scale data and has higher learning efficiency. • The proposed method increases the prediction performance on the independent test dataset compared with other models. The identification of fertility-related proteins plays an essential part in understanding the embryogenesis of germ cell development. Since the traditional experimental methods are expensive and time-consuming to identify fertility-related proteins, the purposes of predicting protein functions from amino acid sequences appeared. In this paper, we propose a fertility-related protein prediction model. Firstly, the model combines protein physicochemical property information, evolutionary information and sequence information to construct the initial feature space 'ALL'. Then, the least absolute shrinkage and selection operator (LASSO) is used to remove redundant features. Finally, light gradient boosting machine (LightGBM) is used as a classifier to predict. The 5-fold cross-validation accuracy of the training dataset is 88.5 %, and the independent accuracy of the independent test dataset is 91.5 %. The results show that our model is more competitive for the prediction of fertility-related proteins, which is helpful for the study of fertility diseases and related drug targets. The source code and all datasets are available at https://github.com/QUST-AIBBDRC/Fertility-LightGBM/. [ABSTRACT FROM AUTHOR]

DNA-binding proteins (DBPs) not only play an important role in all aspects of genetic activities such as DNA replication, recombination, repair, and modification but also are used as key components of antibiotics, steroids, and anticancer drugs in the field of drug discovery. Identifying DBPs becomes one of the most challenging problems in the domain of proteomics research. Considering the high-priced and inefficient of the experimental method, constructing a detailed DBPs prediction model becomes an urgent problem for researchers. In this paper, we propose a stacked ensemble classifier based method for predicting DBPs called StackPDB. Firstly, pseudo amino acid composition (PseAAC), pseudo-position-specific scoring matrix (PsePSSM), position-specific scoring matrix-transition probability composition (PSSM-TPC), evolutionary distance transformation (EDT), and residue probing transformation (RPT) are applied to extract protein sequence features. Secondly, extreme gradient boosting-recursive feature elimination (XGB-RFE) is employed to gain an excellent feature subset. Finally, the best features are applied to the stacked ensemble classifier composed of XGBoost, LightGBM, and SVM to construct StackPDB. After applying leave-one-out cross-validation (LOOCV), StackPDB obtains high ACC and MCC on PDB1075, 93.44% and 0.8687, respectively. Besides, the ACC of the independent test datasets PDB186 and PDB180 are 84.41% and 90.00%, respectively. The MCC of the independent test datasets PDB186 and PDB180 are 0.6882 and 0.7997, respectively. The results on the training dataset and the independent test dataset show that StackPDB has a great predictive ability to predict DBPs. • A novel method (StackPDB) to predict DNA-binding proteins. • Fusing the PSSM-TPC, PsePSSM, EDT, RPT and PseAAC methods to extract feature information. • XGB-RFE is employed to eliminate redundant and retain the optimal feature subset for the first time. • We build up a stacked ensemble classifier using LightGBM, XGBoost, and SVM for the first time. [ABSTRACT FROM AUTHOR]

Abstract: As possible mechanisms to explain the emergence of autoimmune diseases, the current author has suggested in earlier papers two new pathways: the “protein localization hypothesis” and the “protein traffic hypothesis”. The “protein localization hypothesis” states that an autoimmune disease develops if a protein accumulates in a previously unoccupied compartment, that did not previously contain that protein. Similarly, the “protein traffic hypothesis” states that a sudden error within the transport of a certain protein leads to the emergence of an autoimmune disease. The current article discusses the usefulness of the different commercially available transgenic murine models of diabetes mellitus type 1 to confirm the aforementioned hypotheses. This discussion shows that several transgenic murine models of diabetes mellitus type 1 are in-line and confirm the aforementioned hypotheses. Furthermore, these hypotheses are additionally inline with the occurrence of several newly discovered protein sequences, the so-called trepitope sequences. These sequences modulate the immune response to certain proteins. The current study analyzed to what extent the hypotheses are supported by the occurrence of these new sequences. Thereby the occurrence of the trepitope sequences provides additional evidence supporting the aforementioned hypotheses. Both the “protein localization hypothesis” and the “protein traffic hypothesis” have the potential to lead to new causal therapy concepts. The “protein localization hypothesis” and the “protein traffic hypothesis” provide conceptional explanations for the diabetes mouse models as well as for the newly discovered trepitope sequences. [Copyright &y& Elsevier]

This paper presents a two-phase protein folding optimization on a three-dimensional AB off-lattice model. The first phase is responsible for forming conformations with a good hydrophobic core or a set of compact hydrophobic amino acid positions. These conformations are forwarded to the second phase, where an accurate search is performed with the aim of locating conformations with the best energy value. The optimization process switches between these two phases until the stopping condition is satisfied. An auxiliary fitness function was designed for the first phase, while the original fitness function is used in the second phase. The auxiliary fitness function includes an expression about the quality of the hydrophobic core. This expression is crucial for leading the search process to the promising solutions that have a good hydrophobic core and, consequently, improves the efficiency of the whole optimization process. Our differential evolution algorithm was used for demonstrating the efficiency of two-phase optimization. It was analyzed on well-known amino acid sequences that are used frequently in the literature. The obtained experimental results show that the employed two-phase optimization improves the efficiency of our algorithm significantly and that the proposed algorithm is superior to other state-of-the-art algorithms. Image 1 • The two-phase protein folding optimization. • The improved version of differential evolution for protein folding optimization. • The auxiliary fitness function. • The frontiers of finding the best-known solutions with a success rate of 100% are pushed to the sequences with 25 monomers. • The new best-known conformations for all sequences with 29 or more monomers. [ABSTRACT FROM AUTHOR]

• Random match probability from peptide sequences. • Accommodates LD. • Robust to drop-in events. • Open source. For the past three decades, forensic genetic investigations have focused on elucidating DNA signatures. While DNA has a number of desirable properties (e.g., presence in most biological materials, an amenable chemistry for analysis and well-developed statistics), DNA also has limitations. DNA may be in low quantity in some tissues, such as hair, and in some tissues it may degrade more readily than its protein counterparts. Recent research efforts have shown the feasibility of performing protein-based human identification in cases in which recovery of DNA is challenged; however, the methods involved in assessing the rarity of a given protein profile have not been addressed adequately. In this paper an algorithm is proposed that describes the computation of a random match probability (RMP) resulting from a genetically variable peptide signature. The approach described herein explicitly models proteomic error and genetic linkage, makes no assumptions as to allelic drop-out, and maps the observed proteomic alleles to their expected protein products from DNA which, in turn, permits standard corrections for population structure and finite database sizes. To assess the feasibility of this approach, RMPs were estimated from peptide profiles of skin samples from 25 individuals of European ancestry. 126 common peptide alleles were used in this approach, yielding a mean RMP of approximately 10−2. [ABSTRACT FROM AUTHOR]

In this paper, we present a knowledge-based memetic algorithm to tackle the three-dimensional protein structure prediction problem without the explicit use of experimentally determined protein structures' templates. Our algorithm proposal was divided into two main prediction steps: (i) solutions sampling and initialization; and (ii) structural models' optimization coming from the previous stage. The first step generates and classifies several structural models for a given target protein, through the Angle Probability List strategy, to identify distinct structural patterns and consider reasonable solutions in the memetic algorithm initialization. The Angle Probability List takes advantage of structural knowledge stored in the Protein Data Bank to reduce the size and, consequently, the conformational search space complexity. The second step of the method consists in the optimization of the structures generated in the first stage by the proposed memetic algorithm. It uses a tree-based population where each node can be seen as an independent subpopulation that interacts with each other over global search operations, aiming at knowledge sharing, population diversity, and better exploration of the multimodal search space. The method also encompasses ad hoc global search operators, whose objective is to increase the method exploration ability focusing on specific characteristics of the protein structure prediction problem, combined with the artificial bee colony algorithm used as an exploitation technique applied to each node of the tree. The proposed algorithm was tested on a set of 24 amino acid sequences, as well as compared to the reference method in the protein structure prediction area, the method of Rosetta. The obtained results show the ability of our method to predict three-dimensional protein structures with similar folding to the experimentally determined ones, regarding the structural metrics Root-Mean-Square Deviation and Global Distance Total Score Test. We also show that our method was able to reach comparable results to Rosetta, and in some cases, it outperformed Rosetta, corroborating the effectiveness of our proposal. [ABSTRACT FROM AUTHOR]

The veracity present in molecular data available in biological databases possesses new challenges for data analytics. The analysis of molecular data of various diseases can provide vital information for developing better understanding of the molecular mechanism of a disease. In this paper, an attempt has been made to propose a model that addresses the issue of veracity in data analytics for amino acid association patterns in protein sequences of Swine Influenza Virus. The veracity is caused by intra-sequential and inter-sequential biases present in the sequences due to varying degrees of relationships among amino acids. A complete dataset of 63,682 protein sequences is downloaded from NCBI and is refined. The refined dataset consists of 26,594 sequences which are employed in the present study. The type I fuzzy set is employed to explore amino acid association patterns in the dataset. The type I fuzzy support is refined to partially remove the inter-sequential biases causing veracity in data. The remaining inter-sequential biases present in refined fuzzy support are evaluated and eliminated using type II fuzzy set. Hence, it is concluded that a combination of type II fuzzy & refined fuzzy approach is the optimal approach for extracting a better picture of amino acid association patterns in the molecular dataset. • The Type I fuzzy set is employed to explore amino acid association patterns. • Type I fuzzy support is refined to remove the inter-sequential bias causing veracity. • Other biases present in refined fuzzy support are eliminated using Type II fuzzy set. • Combination of Type II & Refined Fuzzy is concluded as optimal approach. [ABSTRACT FROM AUTHOR]

In this paper, we propose a systematic design of evolutionary optimization, namely Multimodal Memetic Framework (MMF), to effectively search the vast complex energy landscape. Our proposed memetic framework is implemented in hierarchical stages with the optimization of each stage performed in parallel in three different states: Exploratory, Exploitative and Central. Each state, with its own set of sub-populations, either explores or exploits by beneficial mixing of potential solutions to direct the search towards a global solution. Instead of implementing identical genetic operators, the proposed approach employs different selection and survival criteria in each state according to their designated task. The Exploratory state employs a knowledge-based initial population generation technique with appropriately tuned genetic operators to guide the search to the "nearest peak". The Exploitative state fine-tunes the individuals representing different regions by applying a building block based local search. Finally, by utilizing the imbibed knowledge from different peaks, the Central state carries out information-exchange among the highly fit solutions for exploring the undiscovered regions. The information exchange employs a novel non-random parental selection technique to distribute the reproduction opportunity intelligently among the individuals for making cross-over more effective. The method has been tested on a set of various benchmark protein sequences for 2D and 3D lattice models. The experimental results demonstrate the superiority of the proposed method over other state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]