Primary Sequence Research Articles

A New Phase of Networking: The Molecular Composition and Regulatory Dynamics of Mammalian Stress Granules.

Stress granules (SGs) are cytosolic biomolecular condensates that form in response to cellular stress. Weak, multivalent interactions between their protein and RNA constituents drive their rapid, dynamic assembly through phase separation coupled to percolation. Though a consensus model of SG function has yet to be determined, their perceived implication in cytoprotective processes (e.g., antiviral responses and inhibition of apoptosis) and possible role in the pathogenesis of various neurodegenerative diseases (e.g., amyotrophic lateral sclerosis and frontotemporal dementia) have drawn great interest. Consequently, new studies using numerous cell biological, genetic, and proteomic methods have been performed to unravel the mechanisms underlying SG formation, organization, and function and, with them, a more clearly defined SG proteome. Here, we provide a consensus SG proteome through literature curation and an update of the user-friendly database RNAgranuleDB to version 2.0 (http://rnagranuledb.lunenfeld.ca/). With this updated SG proteome, we use next-generation phase separation prediction tools to assess the predisposition of SG proteins for phase separation and aggregation. Next, we analyze the primary sequence features of intrinsically disordered regions (IDRs) within SG-resident proteins. Finally, we review the protein- and RNA-level determinants, including post-translational modifications (PTMs), that regulate SG composition and assembly/disassembly dynamics.

The FANCC-FANCE-FANCF complex is evolutionarily conserved and regulates meiotic recombination.

At meiosis, programmed meiotic DNA double-strand breaks are repaired via homologous recombination, resulting in crossovers (COs). From a large excess of DNA double-strand breaks that are formed, only a small proportion gets converted into COs because of active mechanisms that restrict CO formation. The Fanconi anemia (FA) complex proteins AtFANCM, MHF1and MHF2 were previously identified in a genetic screen as anti-CO factors that function during meiosis in Arabidopsis thaliana. Here, pursuing the same screen, we identify FANCC as a new anti-CO gene. FANCC was previously only identified in mammals because of low primary sequence conservation. We show that FANCC, and its physical interaction with FANCE-FANCF, is conserved from vertebrates to plants. Further, we show that FANCC, together with its subcomplex partners FANCE and FANCF, regulates meiotic recombination. Mutations of any of these three genes partially rescues CO-defective mutants, which is particularly marked in female meiosis. Functional loss of FANCC, FANCE, or FANCF results in synthetic meiotic catastrophe with the pro-CO factor MUS81. This work reveals that FANCC is conserved outside mammals and has an anti-CO role during meiosis together with FANCE and FANCF.

Tree visualizations of protein sequence embedding space enable improved functional clustering of diverse protein superfamilies.

Protein language models, trained on millions of biologically observed sequences, generate feature-rich numerical representations of protein sequences. These representations, called sequence embeddings, can infer structure-functional properties, despite protein language models being trained on primary sequence alone. While sequence embeddings have been applied toward tasks such as structure and function prediction, applications toward alignment-free sequence classification have been hindered by the lack of studies to derive, quantify and evaluate relationships between protein sequence embeddings. Here, we develop workflows and visualization methods for the classification of protein families using sequence embedding derived from protein language models. A benchmark of manifold visualization methods reveals that Neighbor Joining (NJ) embedding trees are highly effective in capturing global structure while achieving similar performance in capturing local structure compared with popular dimensionality reduction techniques such as t-SNE and UMAP. The statistical significance of hierarchical clusters on a tree is evaluated by resampling embeddings using a variational autoencoder (VAE). We demonstrate the application of our methods in the classification of two well-studied enzyme superfamilies, phosphatases and protein kinases. Our embedding-based classifications remain consistent with and extend upon previously published sequence alignment-based classifications. We also propose a new hierarchical classification for the S-Adenosyl-L-Methionine (SAM) enzyme superfamily which has been difficult to classify using traditional alignment-based approaches. Beyond applications in sequence classification, our results further suggest NJ trees are a promising general method for visualizing high-dimensional data sets.

Abstract A021: Divergent roles for BNIP3 and NIX in tumor progression and metastasis

Abstract BNIP3 and NIX (BNIP3L) are mitochondrial cargo receptors (MCRs) induced by physiological stresses, including hypoxia and nutrient deprivation, to promote turnover of mitochondria at the autolysosome in a process known as mitophagy. We and others have shown that BNIP3 and NIX function similarly in mitophagy by integrating into the outer mitochondrial membrane (OMM) via their conserved C-terminal transmembrane domain while interacting via conserved N-terminal LIR motifs with processed LC3 at nascent phagophores. To address functional roles of BNIP3 and NIX in tumorigenesis and metastasis in vivo, we examined the effect of loss of BNIP3 and/or NIX in two different mouse models: (1) the 4T1 orthotopic model of mammary tumorigenesis, and (2) the KRasG12D driven autochthonous model of pancreatic ductal adenocarcinoma (PDAC). Using CRISPR/Cas9 gene editing to delete BNIP3 (DBNIP3), NIX (DNIX) or both BNIP3 and NIX (DKO) in 4T1 cells, we showed that while loss of BNIP3 increased tumor growth, promoted invasion/migration and progression to metastasis in vivo, loss of NIX decreased primary tumor growth, inhibited invasion/migration and markedly attenuated metastasis to the lungs. Intriguingly, the effect of NIX loss was dominant over the effect of BNIP3 loss, such that DKO 4T1 cells phenocopied DNIX 4T1 cells in exhibiting decreased growth, invasion/migration properties and inability to metastasize from the mammary gland. Similar findings were observed in Pdx1-Cre;LSL-KRasG12D (KC) mice lacking BNIP3 and/or NIX with KC-DBNIP3 mice showing accelerated progression from PanIN to PDAC, increased organoid growth in vitro and increased metastasis to the liver compared to control KC mice, while KC-DNIX and KC-DKO mice showed increased tumor latency and absence of metastases. Thus, despite 55% homology in primary amino acid sequence and a shared role in mitophagy, our work indicates that BNIP3 and NIX do not function analogously in vivo in tumorigenesis and metastasis. In summary, our work shows that BNIP3 suppresses tumorigenesis in mouse models of breast, pancreas (and liver) cancer and that loss of BNIP3 correlates with worse prognosis in several human cancers. Conversely, NIX is required for progression to malignancy in models of both breast and pancreatic ductal adenocarcinoma (PDAC), and elevated NIX levels correlated with worse overall patient survival. These opposing roles for BNIP3 and NIX in progression to malignancy were unexpected and work presented will discuss mechanistic insight to their differential roles in tumorigenesis and metastasis, including a novel role for BNIP3 in suppressing RAS pathway signaling and a new role for NIX in promoting EMT. These novel functions are in addition to their canonical roles in mitophagy. Citation Format: Damian Berardi, Kay Macleod. Divergent roles for BNIP3 and NIX in tumor progression and metastasis [abstract]. In: Proceedings of the AACR Special Conference: Cancer Metastasis; 2022 Nov 14-17; Portland, OR. Philadelphia (PA): AACR; Cancer Res 2022;83(2 Suppl_2):Abstract nr A021.

Non-Conventional Peptide Self-Assembly into a Conductive Supramolecular Rope.

Structures composed of alternating α and β amino acids can give rise to peculiar secondary structural motifs, which could self-assemble into complex structures of controlled geometries. This work describes the self-assembly properties of an α,β-peptide, containing three units of syn H2-(2-F-Phe)-h-PheGly-OH, able to self-organize on surfaces into a fascinating supramolecular rope. This material was characterized by AFM, electronic conduction and fluorescence measurements. Molecular dynamics simulations showed that this hexapeptide can self-assemble into an antiparallel β-sheet layer, stabilized by intermolecular H-bonds, which, in turn, can self-assemble into many side-by-side layers, due to π-π interactions. As a matter of fact, we demonstrated that in this system, the presence of aromatic residues at the intramolecular interface promoted by the alternation of α,β-amino-acids in the primary sequence, endorses the formation of a super-secondary structure where the aromatic groups are close to each other, conferring to the system good electron conduction properties. This work demonstrates the capability and future potential of designing and fabricating distinctive nanostructures and efficient bioelectronic interfaces based on an α,β-peptide, by controlling structure and interaction processes beyond those obtained with α- or β-peptides alone.

Oncogenic mutations of PIK3CA lead to increased membrane recruitment driven by reorientation of the ABD, p85 and C-terminus

PIK3CA encoding the phosphoinositide 3-kinase (PI3K) p110α catalytic subunit is frequently mutated in cancer, with mutations occurring widely throughout the primary sequence. The full set of mechanisms underlying how PI3Ks are activated by all oncogenic mutations on membranes are unclear. Using a synergy of biochemical assays and hydrogen deuterium exchange mass spectrometry (HDX-MS), we reveal unique regulatory mechanisms underlying PI3K activation. Engagement of p110α on membranes leads to disengagement of the ABD of p110α from the catalytic core, and the C2 domain from the iSH2 domain of the p85 regulatory subunit. PI3K activation also requires reorientation of the p110α C-terminus, with mutations that alter the inhibited conformation of the C-terminus increasing membrane binding. Mutations at the C-terminus (M1043I/L, H1047R, G1049R, and N1068KLKR) activate p110α through distinct mechanisms, with this having important implications for mutant selective inhibitor development. This work reveals unique mechanisms underlying how PI3K is activated by oncogenic mutations, and explains how double mutants can synergistically increase PI3K activity.

The Cynosure of CtBP: Evolution of a Bilaterian Transcriptional Corepressor.

Evolution of sequence-specific transcription factors clearly drives lineage-specific innovations, but less is known about how changes in the central transcriptional machinery may contribute to evolutionary transformations. In particular, transcriptional regulators are rich in intrinsically disordered regions that appear to be magnets for evolutionary innovation. The C-terminal Binding Protein (CtBP) is a transcriptional corepressor derived from an ancestral lineage of alpha hydroxyacid dehydrogenases; it is found in mammals and invertebrates, and features a core NAD-binding domain as well as an unstructured C-terminus (CTD) of unknown function. CtBP can act on promoters and enhancers to repress transcription through chromatin-linked mechanisms. Our comparative phylogenetic study shows that CtBP is a bilaterian innovation whose CTD of about 100 residues is present in almost all orthologs. CtBP CTDs contain conserved blocks of residues and retain a predicted disordered property, despite having variations in the primary sequence. Interestingly, the structure of the C-terminus has undergone radical transformation independently in certain lineages including flatworms and nematodes. Also contributing to CTD diversity is the production of myriad alternative RNA splicing products, including the production of "short" tailless forms of CtBP in Drosophila. Additional diversity stems from multiple gene duplications in vertebrates, where up to five CtBP orthologs have been observed. Vertebrate lineages show fewer major modifications in the unstructured CTD, possibly because gene regulatory constraints of the vertebrate body plan place specific constraints on this domain. Our study highlights the rich regulatory potential of this previously unstudied domain of a central transcriptional regulator.

Machine Learning Directed Aptamer Search from Conserved Primary Sequences and Secondary Structures.

Computer-aided prediction of aptamer sequences has been focused on primary sequence alignment and motif comparison. We observed that many aptamers have a conserved hairpin, yet the sequence of the hairpin can be highly variable. Taking such secondary structure information into consideration, a new algorithm combining conserved primary sequences and secondary structures is developed, which combines three scores based on sequence abundance, stability, and structure, respectively. This algorithm was used in the prediction of aptamers from the caffeine and theophylline selections. In the late rounds of the selections, when the libraries were converged, the predicted sequences matched well with the most abundant sequences. When the libraries were far from convergence and the sequences were deemed challenging for traditional analysis methods, this algorithm still predicted aptamer sequences that were experimentally verified by isothermal titration calorimetry. This algorithm paves a new way to look for patterns in aptamer selection libraries and mimics the sequence evolution process. It will help shorten the aptamer selection time and promote the biosensor and chemical biology applications of aptamers.

Empirical comparison and analysis of machine learning-based approaches for druggable protein identification.

Efficiently and precisely identifying drug targets is crucial for developing and discovering potential medications. While conventional experimental approaches can accurately pinpoint these targets, they suffer from time constraints and are not easily adaptable to high-throughput processes. On the other hand, computational approaches, particularly those utilizing machine learning (ML), offer an efficient means to accelerate the prediction of druggable proteins based solely on their primary sequences. Recently, several state-of-the-art computational methods have been developed for predicting and analyzing druggable proteins. These computational methods showed high diversity in terms of benchmark datasets, feature extraction schemes, ML algorithms, evaluation strategies and webserver/software usability. Thus, our objective is to reexamine these computational approaches and conduct a comprehensive assessment of their strengths and weaknesses across multiple aspects. In this study, we deliver the first comprehensive survey regarding the state-of-the-art computational approaches for in silico prediction of druggable proteins. First, we provided information regarding the existing benchmark datasets and the types of ML methods employed. Second, we investigated the effectiveness of these computational methods in druggable protein identification for each benchmark dataset. Third, we summarized the important features used in this field and the existing webserver/software. Finally, we addressed the present constraints of the existing methods and offer valuable guidance to the scientific community in designing and developing novel prediction models. We anticipate that this comprehensive review will provide crucial information for the development of more accurate and efficient druggable protein predictors.

Tools for protein structure prediction and for molecular docking applied to enzyme active site analysis: A case study using a BAHD hydroxycinnamoyltransferase.

Elucidating the structure of an enzyme and how substrates bind to the active site is an important step for understanding its reaction mechanism and function. Nevertheless, the methods available to obtain three-dimensional structures of proteins, such as x-ray crystallography and NMR, can be expensive and time-consuming. Considering this, an alternative is using structural bioinformatic tools to predict the tertiary structure of a protein from its primary sequence, followed by molecular docking of one or more substrates into the enzyme structure model. In the past few years, significant advances have been made in these computational tools, which can give useful information about the active site and enzyme-substrate interactions before the structure can be resolved using physical methods. Here, using common bean (Phaseolus vulgaris) hydroxycinnamoyl-coenzyme A:tetrahydroxyhexanedioic acid hydroxycinnamoyltransferase (HHHT) as an example, we describe methods and workflows for protein structure prediction and molecular docking that can be performed on a personal computer using only open-source tools.

IDRBP-EL: Identifying DNA- and RNA- Binding Proteins Based on Hierarchical Ensemble Learning.

Identification of DNA-binding proteins (DBPs) and RNA-binding proteins (RBPs) from the primary sequences is essential for further exploring protein-nucleic acid interactions. Previous studies have shown that machine-learning-based methods can efficiently identify DBPs or RBPs. However, the information used in these methods is slightly unitary, and most of them only can predict DBPs or RBPs. In this study, we proposed a computational predictor iDRBP-EL to identify DNA- and RNA- binding proteins, and introduced hierarchical ensemble learning to integrate three level information. The method can integrate the information of different features, machine learning algorithms and data into one multi-label model. The ablation experiment showed that the fusion of different information can improve the prediction performance and overcome the cross-prediction problem. Experimental results on the independent datasets showed that iDRBP-EL outperformed all the other competing methods. Moreover, we established a user-friendly webserver iDRBP-EL (http://bliulab.net/iDRBP-EL), which can predict both DBPs and RBPs only based on protein sequences.

Using Budding Yeast as a Model to Understand Dynein-Mediated Cargo Transport.

Cytoplasmic dynein-1 is a minus end-directed microtubule motor that transports numerous cargoes in cell types throughout the evolutionary spectrum. Dynein is regulated by various motor-intrinsic and motor-extrinsic factors that enhance its processivity, recruit it to various cellular sites, or otherwise promote or restrict its activity. Studying dynein activity in higher eukaryotes is complicated by various factors, including the myriad functions in which this motor participates, and the consequential pleotropic effects associated with disrupting its activity. Budding yeast has long been a powerful model system for understanding this enormous motor protein complex, which is highly conserved between yeast and humans at the primary sequence and structural levels. Studies in budding yeast are simplified by the fact that dynein only performs one known function in this organism: to position the mitotic spindle at the site of cell division. Monitoring dynein-mediated spindle movements in budding yeast provides a powerful tool for the quantitative measurements of various motility parameters, and a system with which to assess the consequence of mutations in dynein or its regulators. Here, we provide detailed protocols to perform quantitative measurements of dynein activity in live cells using a combination of fluorescence microscopy and computational methods to track and quantitate dynein-mediated spindle movements. These methods are broadly applicable to anyone that wishes to perform fluorescence microscopy on budding yeast.

Hemolytic-Pred: A machine learning-based predictor for hemolytic proteins using position and composition-based features.

The objective of this study is to propose a novel in-silico method called Hemolytic-Pred for identifying hemolytic proteins based on their sequences, using statistical moment-based features, along with position-relative and frequency-relative information. Primary sequences were transformed into feature vectors using statistical and position-relative moment-based features. Varying machine learning algorithms were employed for classification. Computational models were rigorously evaluated using four different validation. The Hemolytic-Pred webserver is available for further analysis at http://ec2-54-160-229-10.compute-1.amazonaws.com/. XGBoost outperformed the other six classifiers with an accuracy value of 0.99, 0.98, 0.97, and 0.98 for self-consistency test, 10-fold cross-validation, Jackknife test, and independent set test, respectively. The proposed method with the XGBoost classifier is a workable and robust solution for predicting hemolytic proteins efficiently and accurately. The proposed method of Hemolytic-Pred with XGBoost classifier is a reliable tool for the timely identification of hemolytic cells and diagnosis of various related severe disorders. The application of Hemolytic-Pred can yield profound benefits in the medical field.

Chamaesiphon fontinalis sp. nov., a new species of unicellular asymmetrically dividing cyanobacteria

Extensive brownish biofilms of algal-bacterial communities were found on stones in streams in the Valdaysky National Park (Russia). The dominant unicellular cyanobacteria in biofilms was determined as Chamaesiphon sp. Although species of this genus often occur in the periphyton of fast-flowing waters, many of them have a geographically limited distribution and occur mainly in mountain streams and rivers in Europe. The strain Chamaesiphon sp. CALU 1864 was isolated from epilithic biofilms in the spring Tekunok. We found that it differs from previously described species not only by its unusual purple pigmentation, but also by morphological features. Molecular-genetic analysis of the primary 16S rRNA sequence and secondary structures of the internal transcribed spacer of the ribosomal operon also confirmed its uniqueness. Phylogenetically, the strain represents an independent lineage on the evolutionary tree. Based on the differences found, which provide strong support for its delineation, we propose to consider strain CALU 1864 as a new species, Chamaesiphon fontinalis sp. nov., first discovered in Russia and described in accordance with the current rules of the International Code of Nomenclature for Algae, Fungi and Plants.

Artificial intelligence-based recognition for variant pathogenicity of BRCA1 using AlphaFold2-predicted structures.

With the surge of the high-throughput sequencing technologies, many genetic variants have been identified in the past decade. The vast majority of these variants are defined as variants of uncertain significance (VUS), as their significance to the function or health of an organism is not known. It is urgently needed to develop intelligent models for the clinical interpretation of VUS. State-of-the-art artificial intelligence (AI)-based variant effect predictors only learn features from primary amino acid sequences, leaving out information about the most important three-dimensional structure that is more related to its function. Methods: We proposed a deep convolutional neural network model named variant effect recognition network for BRCA1 (vERnet-B) to recognize the clinical pathogenicity of missense single-nucleotide variants in the BRCT domain of BRCA1. vERnet-B learned features associated with the pathogenicity from the tertiary protein structures of variants predicted by AlphaFold2. Results: After performing a series of validation and analyses on vERnet-B, we discovered that it exhibited significant advances over previous works. Recognizing the phenotypic consequences of VUS is one of the most daunting challenges in genetic informatics; however, we achieved 85% accuracy in recognizing disease BRCA1 variants with an ideal balance of false-positive and true-positive detection rates. vERnet-B correctly recognized the pathogenicity of variant A1708E, which was poorly predicted by AlphaFold2 as previously described. The vERnet-B web server is freely available from URL: http://ai-lab.bjrz.org.cn/vERnet. Conclusions: We applied protein tertiary structures to successfully recognize the pathogenic missense SNVs, which were difficult to be addressed by classical approaches based on sequences. Our work demonstrated that AlphaFold2-predicted structures were expected to be used for rich feature learning and revealed unique insights into the clinical interpretation of VUS in disease-related genes, using vERnet-B as a discovery tool.

EnsembleDL-ATG: Identifying autophagy proteins by integrating their sequence and evolutionary information using an ensemble deep learning framework

Autophagy is a primary mechanism for maintaining cellular homeostasis. The synergistic actions of autophagy-related (ATG) proteins strictly regulate the whole autophagic process. Therefore, accurate identification of ATGs is a first and critical step to reveal the molecular mechanism underlying the regulation of autophagy. Current computational methods can predict ATGs from primary protein sequences, but owing to the limitations of algorithms, significant room for improvement still exists. In this research, we propose EnsembleDL-ATG, an ensemble deep learning framework that aggregates multiple deep learning models to predict ATGs from protein sequence and evolutionary information. We first evaluated the performance of individual networks for various feature descriptors to identify the most promising models. Then, we explored all possible combinations of independent models to select the most effective ensemble architecture. The final framework was built and maintained by an organization of four different deep learning models. Experimental results show that our proposed method achieves a prediction accuracy of 94.5 % and MCC of 0.890, which are nearly 4 % and 0.08 higher than ATGPred-FL, respectively. Overall, EnsembleDL-ATG is the first ATG machine learning predictor based on ensemble deep learning. The benchmark data and code utilized in this study can be accessed for free at https://github.com/jingry/autoBioSeqpy/tree/2.0/examples/EnsembleDL-ATG.

DNA methyltransferase (Dnmt) silencing causes increased Cdx2 and Nanog levels in surviving embryos.

Epigenetic mechanisms are one of the essential regulators of gene expression which do not involve altering the primary nucleotide sequence. DNA methylation is considered among the most prominent epigenetic mechanisms in controlling the functions of genes related to cell differentiation, cell cycle, cell survival, autophagy, and embryo development. DNA methyl transferases (Dnmts) control DNA methylation, the levels of which are differentially altered during embryonic development, and may determine cell differentiation fate as in the case of pluripotent inner cell mass (ICM) or trophectoderm (TE). In this study, we aimed to analyze the role of Dnmt1 and Dnmt3a enzymes in ICM (using the Nanog marker) and TE (using the Cdx2 marker) differentiation, autophagy (using p62 marker), reactive oxygen species (ROS) production, and apoptosis (using TUNEL) during mouse preimplantation embryo development. Following knockdown of Dnmt1 and Dnmt3a in zygotes, expression levels of Cdx2 in the trophectoderm and Nanog in the inner cell mass were measured, as well as p62 levels, reactive oxygen species (ROS) production, and apoptosis levels after 96 hours in embryo culture. We found that knockdown of Dnmt1 or Dnmt3a significantly induced Cdx2 and Nanog expression. Similarly, p62 expression, ROS levels and apoptosis significantly increased after silencing. This study shows that Dnmt genes are highly crucial for embryonic fate determination and survival. Further studies are required to reveal the specific targets of these methylation processes related to cell differentiation, survival, autophagy, and ROS production in mouse and human preimplantation embryos.

SARS-CoV-2 Spike Protein Post-Translational Modification Landscape and Its Impact on Protein Structure and Function via Computational Prediction.

To elucidate the role of post-translational modifications (PTMs) in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike protein's structure and virulence, we generated a high-resolution map of 87 PTMs using liquid chromatography with tandem mass spectrometry data on the extracted spike protein from SARS-CoV-2 virions and then reconstituted its structure heterogeneity caused by PTMs. Nonetheless, Alphafold2, a high-accuracy artificial intelligence tool to perform protein structure prediction, relies solely on primary amino acid sequence, whereas the impact of PTM, which often modulates critical protein structure and function, is much ignored. To overcome this challenge, we proposed the mutagenesis approach-an in silico, site-directed amino acid substitution to mimic the influence of PTMs on protein structure due to altered physicochemical properties in the post-translationally modified amino acids-and then reconstituted the spike protein's structure from the substituted sequences by Alphafold2. For the first time, the proposed method revealed predicted protein structures resulting from PTMs, a problem that Alphafold2 has yet to address. As an example, we performed computational analyses of the interaction of the post-translationally modified spike protein with its host factors such as angiotensin-converting enzyme 2 to illuminate binding affinity. Mechanistically, this study suggested the structural analysis of post-translationally modified protein via mutagenesis and deep learning. To summarize, the reconstructed spike protein structures showed that specific PTMs can be used to modulate host factor binding, guide antibody design, and pave the way for new therapeutic targets. The code and Supplementary Materials are freely available at https://github.com/LTZHKUSTGZ/SARS-CoV-2-spike-protein-PTM.

GUD-VE visualization tool for physicochemical properties of proteins

The physicochemical properties of primary sequences of proteins helps in determining both the structure and biological functions. The sequence analysis of the proteins and nucleic acids is most fundamental element of bioinformatics. Without these elements, it is impossible to gain insight deeper molecular and biochemical mechanisms. For this purpose, the computational methods like bioinformatics tools assist experts and novices alike in resolving issues relating to protein analysis. Similarly, this proposed work, for the graphical user interface (GUI) based prediction and visualization through the computations-based method done on Jupyter Notebook with tkinter package which allows the creation of a program on a local host platform and accessed by the programmer.•When it is queried with a protein sequence, it predicts physicochemical parameters of the peptides.•Users can choose to visualize the findings acquired either anonymously or on the user-specified email address and compare the biophysical properties of one protein with other using amino acids (AA) sequences.The aim of this paper is to meet the requirements of experimentalists, not just hardcore bioinformaticians related to biophysical properties prediction and comparison with other proteins. The code for it has been uploaded on GitHub (an online repository of codes) in private mode.

Enzymatic Sequencing of Heparin Oligosaccharides Using Exolyase.

Heparin/heparan sulfate (HP/HS) is a class of acidic polysaccharides with many potential medical applications, especially HP, and its derivatives, low molecular weight heparins (LMWHs), have been widely used as anticoagulants to treat thrombosis for decades. However, the complex structure endows HP/HS a variety of biological functions and hinders the structural and functional studies of HP/HS. Heparinases derived from bacteria are useful tools for the structural studies of HP/HS as well as the preparation of LMWHs. The enzymatic method for the structural analysis of HP/HS chains is easy to operate, requires less samples, and is low cost. Here, we describe an enzymatic approach to investigate the primary sequences of the HP/HS oligosaccharides using a recently discovered exotype heparinase.