Query Protein Research Articles

Seq2Enz: An application of mask BLAST methodology with a new chemical logic of amino acids for improved enzyme function prediction

Seq2Enz method is a new way to identify whether a query protein sequence is an enzyme and to assign an enzyme class to the protein sequence. The method is based on mask BLAST fortified with some novel structural-chemical properties (NCL) of the building blocks of proteins. All available reviewed enyme sequences (267,276 in number) in Uniprot/SwissProt and most recent depositions (7062) not used for training in ECPred, a state of the art software for enzyme class prediction, are taken for assessment and the results are compared with those from conventional BLAST and ECPred respectively. Seq2Enz is seen to perform consistently better for all the enzyme classes to all the four levels. Seq2Enz methodology is converted into an easy to use web-server and made freely accessible at http://www.scfbio-iitd.res.in Seq2Enz/.

ATPdock: a template-based method for ATP-specific protein-ligand docking.

Accurately identifying protein-ATP binding poses is significantly valuable for both basic structure biology and drug discovery. Although many docking methods have been designed, most of them require a user-defined binding site and are difficult to achieve a high-quality protein-ATP docking result. It is critical to develop a protein-ATP-specific blind docking method without user-defined binding sites. Here, we present ATPdock, a template-based method for docking ATP into protein. For each query protein, if no pocket site is given, ATPdock first identifies its most potential pocket using ATPbind, an ATP-binding site predictor; then, the template pocket, which is most similar to the given or identified pocket, is searched from the database of pocket-ligand structures using APoc, a pocket structural alignment tool; thirdly, the rough docking pose of ATP (rdATP) is generated using LS-align, a ligand structural alignment tool, to align the initial ATP pose to the template ligand corresponding to template pocket; finally, the Metropolis Monte Carlo simulation is used to fine-tune the rdATP under the guidance of AutoDock Vina energy function. Benchmark tests show that ATPdock significantly outperforms other state-of-the-art methods in docking accuracy. https://jun-csbio.github.io/atpdock/. Supplementary data are available at Bioinformatics online.

Functional annotation of hypothetical proteins involved in multiple stress response in Oryza sativa

Plants being sessile in nature face multiple stresses during its growth and development. Understanding the dynamic tolerance mechanism of plants to changing environment is the need of the hour and developing climate smart crops is still a nightmare. Functional characterization of relevant genes is a prerequisite when identifying candidates for crop improvement, in this study we identified 66 multiple stress responsive genes in rice that could serve as potential candidates for breeding for stress tolerance. The 15 hypothetical genes rendering multiple stress tolerance were functionally annotated using a rich set of tools ranging from sequence similarity search to fold recognition methods. It is predicted that the query protein might be a copper chaperone for superoxide dismutase. A novel approach to annotate hypothetical proteins with different remote homology search and structure-based threading approaches was designed. In addition to identified multiple stress responsive proteins in rice (Oryza sativa L.), our study also provides a robust toolkit to annotate other hypothetical proteins in plants.

Duo: A Signature Based Method to Batch-Analyze Functional Similarities of Proteins.

With the rapid advancement of sequencing technology, handling of large sequencing data to analyze for protein coding capacity and functionality of predicted proteins has become an urgent demand. There is a lack of simple and effective tools to functionally annotate large number of unknown proteins in a personalized and customized workflow. To address this, we developed Duo, which batch-analyze functional similarities of predicted proteins. Duo can screen query proteins with specific characteristics based on highly flexible and customizable reference inputs from the user. In the current study, Duo was applied to screen for virulence associated proteins in the genome-sequence of Salmonella Typhimurium. Based on the analysis, recommendation for choice of Seed_database in order to get a reasonable number of predicted proteins for further analysis, and recommendation for preparing a Reference_proteins set for Duo was given. Delta-bitscore analysis was shown to be useful tool to focus the follow-up on predicted proteins. A successful screen for virulence proteins in the bacterial genome-sequence was further performed in a selection of 32 pathogenic bacteria, documenting the ability of Duo to work on a broad collection of bacteria. We anticipate that Duo will be a useful auxiliary tool for personalized and customized protein function research in the future.

HPAStainR: an R package and Shiny app to query protein patterns in the Human Protein Atlas

HPAStainR: an R package and Shiny app to query protein patterns in the Human Protein Atlas

Improving protein fold recognition using triplet network and ensemble deep learning.

Protein fold recognition is a critical step toward protein structure and function prediction, aiming at providing the most likely fold type of the query protein. In recent years, the development of deep learning (DL) technique has led to massive advances in this important field, and accordingly, the sensitivity of protein fold recognition has been dramatically improved. Most DL-based methods take an intermediate bottleneck layer as the feature representation of proteins with new fold types. However, this strategy is indirect, inefficient and conditional on the hypothesis that the bottleneck layer's representation is assumed as a good representation of proteins with new fold types. To address the above problem, in this work, we develop a new computational framework by combining triplet network and ensemble DL. We first train a DL-based model, termed FoldNet, which employs triplet loss to train the deep convolutional network. FoldNet directly optimizes the protein fold embedding itself, making the proteins with the same fold types be closer to each other than those with different fold types in the new protein embedding space. Subsequently, using the trained FoldNet, we implement a new residue-residue contact-assisted predictor, termed FoldTR, which improves protein fold recognition. Furthermore, we propose a new ensemble DL method, termed FSD_XGBoost, which combines protein fold embedding with the other two discriminative fold-specific features extracted by two DL-based methods SSAfold and DeepFR. The Top 1 sensitivity of FSD_XGBoost increases to 74.8% at the fold level, which is ~9% higher than that of the state-of-the-art method. Together, the results suggest that fold-specific features extracted by different DL methods complement with each other, and their combination can further improve fold recognition at the fold level. The implemented web server of FoldTR and benchmark datasets are publicly available at http://csbio.njust.edu.cn/bioinf/foldtr/.

SCLpred-MEM: Subcellular localization prediction of membrane proteins by deep N-to-1 convolutional neural networks.

The knowledge of the subcellular location of a protein is a valuable source of information in genomics, drug design, and various other theoretical and analytical perspectives of bioinformatics. Due to the expensive and time-consuming nature of experimental methods of protein subcellular location determination, various computational methods have been developed for subcellular localization prediction. We introduce "SCLpred-MEM," an ab initio protein subcellular localization predictor, powered by an ensemble of Deep N-to-1 Convolutional Neural Networks (N1-NN) trained and tested on strict redundancy reduced datasets. SCLpred-MEM is available as a web-server predicting query proteins into two classes, membrane and non-membrane proteins. SCLpred-MEM achieves a Matthews correlation coefficient of 0.52 on a strictly homology-reduced independent test set and 0.62 on a less strict homology reduced independent test set, surpassing or matching other state-of-the-art subcellular localization predictors.

Deep template-based protein structure prediction.

Protein structure prediction has been greatly improved by deep learning, but most efforts are devoted to template-free modeling. But very few deep learning methods are developed for TBM (template-based modeling), a popular technique for protein structure prediction. TBM has been studied extensively in the past, but its accuracy is not satisfactory when highly similar templates are not available. This paper presents a new method NDThreader (New Deep-learning Threader) to address the challenges of TBM. NDThreader first employs DRNF (deep convolutional residual neural fields), which is an integration of deep ResNet (convolutional residue neural networks) and CRF (conditional random fields), to align a query protein to templates without using any distance information. Then NDThreader uses ADMM (alternating direction method of multipliers) and DRNF to further improve sequence-template alignments by making use of predicted distance potential. Finally, NDThreader builds 3D models from a sequence-template alignment by feeding it and sequence coevolution information into a deep ResNet to predict inter-atom distance distribution, which is then fed into PyRosetta for 3D model construction. Our experimental results show that NDThreader greatly outperforms existing methods such as CNFpred, HHpred, DeepThreader and CEthreader. NDThreader was blindly tested in CASP14 as a part of RaptorX server, which obtained the best average GDT score among all CASP14 servers on the 58 TBM targets.

Assigning protein subcellular distributions of vesicle associated tail‐anchored membrane proteins by image‐based machine learning

Subcellular localization of proteins is a key feature of eukaryotes. Traditionally, co-staining with known marker proteins (antibody based or fluorescence fusion protein) or organelle-specific dyes (e.g., MitoTrackerTM) have been used to study subcellular localization of proteins of interest, followed by, a final inspection of the fluorescence microscope images by an experimentalist. However, human visual examination is inclined to bias and membrane proteins are trafficked in vesicles between subcellular locations such that assignment to a specific organelle can be misleading. As an alternative way to assign localization we generated a reference library of confocal micrographs of EGFP fusion proteins localized at key subcellular organelles in murine and human cell lines. Rather than assign the localization of an unknown protein to a specific organelle we consider which reference protein has the most similar subcellular distribution from image sets that are optically validated and comprise 789,011 and 523,319 individual human and murine cell images, respectively. Both morphology and statistical features were computed to enable automated assignment of the subcellular distribution for query proteins by machine learning algorithms with high accuracy. By means of this tool we investigate subcellular transport and distribution for model tail-anchored proteins with randomly mutated C-terminal targeting sequences for the endoplasmic reticulum and through out the secretory pathway.

Global Dynamics Renders Protein Sites with High Functional Response.

Deep mutational scanning enables examination of the effects of many mutations at each amino acid position in a query protein, readily disclosing positions that are particularly sensitive. Mutations in these positions alter protein function the most. Here, on the premise that dynamics underlie function, we explore to what extent the measured sensitivity to mutations could be linked to-perhaps be explained by-the structural dynamics of the protein. We employ a minimalist perturbation-response approach based on the Gaussian Network Model (GNM) on a data set of seven proteins with deep mutational scanning data. The analysis shows that the mutation-sensitive positions are often of capacity to modulate the global dynamics and to intermediate allosteric interactions in the structure. With that, upon strain perturbation, these positions decrease residue fluctuations the most, affecting function via entropy changes. This is particularly relevant for positions that are distant from binding sites or other functional regions of the protein and are sensitive to mutations, nevertheless. Our results indicate that mutations in these positions allosterically manipulate protein function.

HPAStainR: a Bioconductor and Shiny app to query protein expression patterns in the Human Protein Atlas.

The Human Protein Atlas is a website of protein expression in human tissues. It is an excellent resource of tissue and cell type protein localization, but only allows the query of a single protein at a time. We introduce HPAStainR as a new Shiny app and Bioconductor/R package used to query the scored staining patterns in the Human Protein Atlas with multiple proteins/genes of interest. This allows the user to determine if an experimentally-generated protein/gene list associates with a particular cell type. We validated the tool using the Panglao Database cell type specific marker genes and a Genotype Expression (GTEx) tissue deconvolution dataset. HPAStainR identified 92% of the Panglao cell types in the top quartile of confidence scores limited to tissue type of origin results. It also appropriately identified the correct cell types from the GTEx dataset. HPAStainR fills a gap in available bioinformatics tools to identify cell type protein expression patterns and can assist in establishing ground truths and exploratory analysis. HPAStainR is available from: https://32tim32.shinyapps.io/HPAStainR/.

HPAStainR: a Bioconductor and Shiny app to query protein expression patterns in the Human Protein Atlas

The Human Protein Atlas is a website of protein expression in human tissues. It is an excellent resource of tissue and cell type protein localization, but only allows the query of a single protein at a time. We introduce HPAStainR as a new Shiny app and Bioconductor/R package used to query the scored staining patterns in the Human Protein Atlas with multiple proteins/genes of interest. This allows the user to determine if an experimentally-generated protein/gene list associates with a particular cell type. We validated the tool using the Panglao Database cell type specific marker genes and a Genotype Expression (GTEx) tissue deconvolution dataset. HPAStainR identified 92% of the Panglao cell types in the top quartile of confidence scores limited to tissue type of origin results. It also appropriately identified the correct cell types from the GTEx dataset. HPAStainR fills a gap in available bioinformatics tools to identify cell type protein expression patterns and can assist in establishing ground truths and exploratory analysis. HPAStainR is available from: https://32tim32.shinyapps.io/HPAStainR/

Central Proteins of Plasma in Response to Low-Level Laser Therapy Involve in Body Hemostasis and Wound Repair.

Introduction: Low-level laser therapy (LLLT) is accompanied by protein expression change in the body. There are many efforts to find a clear relationship between the differentially expressed proteins. This study aims to find the central differentiated expressed proteins of plasma after LLLT. Methods: Six proteins are extracted from a proteomics study and the network including these query proteins plus 100 first neighbors was constructed. The central proteins were determined based on degree value, betweenness centrality, closeness centrality (CC), and stress (The centrality parameters). Results: Among 106 nodes of the network, 10 proteins were characterized with the most values of degree, betweenness centrality, CC, and stress. These proteins were determined as central proteins in response to LLLT in plasma. Conclusion: Three query proteins, AHSG, FGG, and SERPINA1, plus 7 first neighbors, namely FGA, ALB, KNG1, FN1, APP, TIMP1, and F5, were identified as central proteins which were dysregulated.

Mustguseal and Sister Web-Methods: A Practical Guide to Bioinformatic Analysis of Protein Superfamilies.

Bioinformatic analysis of functionally diverse superfamilies can help to study the structure-function relationship in proteins, but represents a methodological challenge. The Mustguseal web-server can build large structure-guided sequence alignments of thousands of homologs that cover all currently available sequence variants within a common structural fold. The input to the method is a PDB code of the query protein, which represents the protein superfamily of interest. The collection and subsequent alignment of protein sequences and structures is fully automated and driven by the particular choice of parameters. Four integrated sister web-methods-the Zebra, pocketZebra, visualCMAT, and Yosshi-are available to further analyze the resulting superimposition and identify conserved, subfamily-specific, and co-evolving residues, as well as to classify and study disulfide bonds in protein superfamilies. The integration of these web-based bioinformatic tools provides an out-of-the-box easy-to-use solution, first of its kind, to study protein function and regulation and design improved enzyme variants for practical applications and selective ligands to modulate their functional properties. In this chapter, we provide a step-by-step protocol for a comprehensive bioinformatic analysis of a protein superfamily using a web-browser as the main tool and notes on selecting the appropriate values for the key algorithm parameters depending on your research objective. The web-servers are freely available to all users at https://biokinet.belozersky.msu.ru/m-platform with no login requirement.

Structure-Based Deep Mining Reveals First-Time Annotations for 46 Percent of the Dark Annotation Space of the 9,671-Member Superproteome of the Nucleocytoplasmic Large DNA Viruses.

We conducted an exhaustive search for three-dimensional structural homologs to the proteins of 20 key phylogenetically distinct nucleocytoplasmic DNA viruses (NCLDV). Structural matches covered 429 known protein domain superfamilies, with the most highly represented being ankyrin repeat, P-loop NTPase, F-box, protein kinase, and membrane occupation and recognition nexus (MORN) repeat. Domain superfamily diversity correlated with genome size, but a diversity of around 200 superfamilies appeared to correlate with an abrupt switch to paralogization. Extensive structural homology was found across the range of eukaryotic RNA polymerase II subunits and their associated basal transcription factors, with the coordinated gain and loss of clusters of subunits on a virus-by-virus basis. The total number of predicted endonucleases across the 20 NCLDV was nearly quadrupled from 36 to 132, covering much of the structural and functional diversity of endonucleases throughout the biosphere in DNA restriction, repair, and homing. Unexpected findings included capsid protein-transcription factor chimeras; endonuclease chimeras; enzymes for detoxification; antimicrobial peptides and toxin-antitoxin systems associated with symbiosis, immunity, and addiction; and novel proteins for membrane abscission and protein turnover.IMPORTANCE We extended the known annotation space for the NCLDV by 46%, revealing high-probability structural matches for fully 45% of the 9,671 query proteins and confirming up to 98% of existing annotations per virus. The most prevalent protein families included ankyrin repeat- and MORN repeat-containing proteins, many of which included an F-box, suggesting extensive host cell modulation among the NCLDV. Regression suggested a minimum requirement for around 36 protein structural superfamilies for a viable NCLDV, and beyond around 200 superfamilies, genome expansion by the acquisition of new functions was abruptly replaced by paralogization. We found homologs to herpesvirus surface glycoprotein gB in cytoplasmic viruses. This study provided the first prediction of an endonuclease in 10 of the 20 viruses examined; the first report in a virus of a phenolic acid decarboxylase, proteasomal subunit, or cysteine knot (defensin) protein; and the first report of a prokaryotic-type ribosomal protein in a eukaryotic virus.

Isobaric Tag-Based Protein Profiling across Eight Human Cell Lines Using High-Field Asymmetric Ion Mobility Spectrometry and Real-Time Database Searching.

A vast number of human cell lines are available for cell culture model-based studies, and as such the potential exists for discrepancies in findings due to cell line selection. To investigate this concept, the authors determine the relative protein abundance profiles of a panel of eight diverse, but commonly studied human cell lines. This panel includes HAP1, HEK293T, HeLa, HepG2, Jurkat, Panc1, SH-SY5Y, and SVGp12. A mass spectrometry-based proteomics workflow designed to enhance quantitative accuracy while maintaining analytical depth is used. To this end, this strategy leverages TMTpro16-based sample multiplexing, high-field asymmetric ion mobility spectrometry, and real-time database searching. The data show that the differences in the relative protein abundance profiles reflect cell line diversity. The authors also determine several hundred proteins to be highly enriched for a given cell line, and perform gene ontology and pathway analysis on these cell line-enriched proteins. An R Shiny application is designed to query protein abundance profiles and retrieve proteins with similar patterns. The workflows used herein can be applied to additional cell lines to aid cell line selection for addressing a given scientific inquiry or for improving an experimental design.

HPAStainR: a Bioconductor and Shiny app to query protein expression patterns in the Human Protein Atlas.

The Human Protein Atlas is a website of protein expression in human tissues. It is an excellent resource of tissue and cell type protein localization, but only allows the query of a single protein at a time. We introduce HPAStainR as a new Shiny app and Bioconductor/R package used to query the scored staining patterns in the Human Protein Atlas with multiple proteins/genes of interest. This allows the user to determine if an experimentally-generated protein/gene list associates with a particular cell type. We validated the tool using the Panglao Database cell type specific marker genes and a Genotype Expression (GTEx) tissue deconvolution dataset. HPAStainR identified 92% of the Panglao cell types in the top quartile of confidence scores limited to tissue type of origin results. It also appropriately identified the correct cell types from the GTEx dataset. HPAStainR fills a gap in available bioinformatics tools to identify cell type protein expression patterns and can assist in establishing ground truths and exploratory analysis. HPAStainR is available from: https://32tim32.shinyapps.io/HPAStainR/.

Intrinsic disorder perspective of an interplay between the renin-angiotensin-aldosterone system and SARS-CoV-2.

The novel severe acute respiratory syndrome (SARS) coronavirus SARS-CoV-2 walks the planet causing the rapid spread of the CoV disease 2019 (COVID-19) that has especially deleterious consequences for the patients with underlying cardiovascular diseases (CVDs). Entry of the SARS-CoV-2 into the host cell involves interaction of the virus (via the receptor-binding domain (RBD) of its spike glycoprotein) with the membrane-bound form of angiotensin-converting enzyme 2 (ACE2) followed by the virus-ACE2 complex internalization by the cell. Since ACE2 is expressed in various tissues, such as brain, gut, heart, kidney, and lung, and since these organs represent obvious targets for the SARS-CoV-2 infection, therapeutic approaches were developed to either inhibit ACE2 or reduce its expression as a means of prevention of the virus entry into the corresponding host cells. The problem here is that in addition to be a receptor for the SARS-CoV-2 entry into the host cells, ACE2 acts as a key component of the renin-angiotensin-aldosterone system (RAAS) aimed at the generation of a cascade of vasoactive peptides coordinating several physiological processes. In RAAS, ACE2 degrades angiotensin II, which is a multifunctional CVD-promoting peptide hormone and converts it to a heptapeptide angiotensin-(1–7) acting as the angiotensin II antagonist. As protein multifunctionality is commonly associated with the presence of flexible or disordered regions, we analyze here the intrinsic disorder predisposition of major players related to the SARS-CoV-2 – RAAS axis. We show that all considered proteins contain intrinsically disordered regions that might have specific functions. Since intrinsic disorder might play a role in the functionality of query proteins and be related to the COVID-19 pathogenesis, this work represents an important disorder-based outlook of an interplay between the renin-angiotensin-aldosterone system and SARS-CoV-2. It also suggests that consideration of the intrinsic disorder phenomenon should be added to the modern arsenal of means for drug development.

Computational-based insights into the phylogeny, structure, and function of Rhodococcus alkane-1-monooxygenase.

Alkane-1-monooxygenase of alkanotrophic Rhodococcus species has been characterized using standard bioinformatics tools to investigate phylogenetic relationships, and three-dimensional structure and functions. Results revealed that activity of the Rhodococcus alkane-1-monooxygenase would be optimum in alkaline pH as their isoelectric points were in the range of 7.5 to 9. Higher aliphatic index (87 to 95) indicated that these enzymes are thermostable. Extinction coefficient of the enzyme varied from 68,793 to 1,25,820M-1cm-1 and average molecular weight was 45kDa. Secondary structures predicted maximum alpha-helical content rather than the other conformations such as sheets or turns. The instability index (II) of most stable query protein was 39.7% which was lowest among all 76 proteins analysed in this study. Predicted 3D structure of query protein revealed that it contains homodimer polypeptides. The suitable template for query protein was Flavin-dependent luciferase-type alkane monooxygenase. The presence of 98.3% amino acid residues in Ramachandran plot was determined in 3-D protein model which confirmed the model feasibility. The predicted model contains 12% more α-helix than template protein which indicated towards membrane localization of the protein. The protein interactome partners of predicted model were determined as FMN-dependent oxidoreductase, molybdopterin, nuclear transport factor, and peroxiredoxin. The predicted tertiary model of R. rhodochrous alkane-1-monooxygenase (OOL33526.1) was deposited in Protein Model Database (Accession No.: PM0083166). The overall report is unique to best of our knowledge, and the importance of this study is to understand the theoretical aspects of structure and functions of alkane-1-monooxygenase of hydrocarbonoclastic strains of Rhodococcus.

Identifying the Mechanisms of α-Synuclein-Mediated Cytotoxicity in Parkinson’s Disease: New Insights from a Bioinformatics-Based Approach

Aim: A large body of evidence has implicated the cytotoxicity of α-synuclein in Parkinson’s disease (PD). We planned to use a bioinformatics-based approach to gain further insight into this process. Materials & methods: Using STRING version 10, we identified interacting proteins of α-synuclein. Using α-synuclein and one of these interactors involved in apoptosis as query proteins, we identified other linked proteins. We further analyzed the interactions between some of these proteins by Protein–Protein Docking using ClusPro. Results: We identified BAX as an interacting protein of α-synuclein. Interactions of α-synuclein and BAX as well as BAX and BCL2L1 were determined. Conclusion: The interaction of α-synuclein and BAX could play a crucial role in the cell death process of PD where apoptosis and mitochondrial permeability transition-driven necrosis may coexist.