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The conformation of three amyloidogenic peptides; amyloid beta 1–40 (Aβ1−40), alpha synuclein (α-syn), and beta-2-microglobulin (β2m) are closely associated with the process of causing neurodegenerative diseases. Use of a peptide adsorbed gold nano-particle system allowed us to investigate the interactive segment of each peptide responsible for peptide-peptide networking, which is crucial to initiate the formation of an oligomer and leads to fibrillogenesis. The adsorption orientationof theamyloidogenic peptides onthe nano-goldcolloid spherical surface wasexplainedbysimulatinghowmuchareaofthemetalsurfacewascoveredbythepeptides,i.e.,coverageratio,Θ. The empirically extracted Θ was explained by the summation of the 1st layer and the 2nd layer with a spikingout orientation of the prolate. Of note, the involvement of the 2nd layer was peptide type dependent. The nanosize dependence of Θ was linearly correlated with available spacing between adjacent peptides, Sd, which were approximated as prolates, For Aβ1−40 and α-syn, the 2nd layer was more included as Sd increased. In contrast, β2m was found to gyrate over the gold surface as Sd increased, creating a partially positive (δ+) region and repelling the extra β2m from the surface. Thus, as Sd decreased, it prohibited the gyration of β2m resulting in less δ+ region, and more β2m monomers were adsorbed with δ+ segment as the 2nd layer than the case where β2m gyrates more under relatively larger Sd. Based on the trend found in an experimentally extracted Θ as a function of Sd, optimized charge distribution of Aβ1−40 and α-syn were concluded to be negative partial charge (δ−) region that was covered by the positive partial charge (δ+) region. However, β2m may have a relatively large and/or distributed δ+ region with a small portion of δ− at the one end of a prolate. The characterization made in this work confirmed current understandings on the formation of the protein corona over nano-particles.

The PDBsum web server provides structural analyses of the entries in the Protein Data Bank (PDB). Two recent additions are described here. The first is the detailed analysis of the SARS‐CoV‐2 virus protein structures in the PDB. These include the variants of concern, which are shown both on the sequences and 3D structures of the proteins. The second addition is the inclusion of the available AlphaFold models for human proteins. The pages allow a search of the protein against existing structures in the PDB via the Sequence Annotated by Structure (SAS) server, so one can easily compare the predicted model against experimentally determined structures. The server is freely accessible to all at http://www.ebi.ac.uk/pdbsum.

Aim: Both bacterial infection and viral infection involve a large number of protein-protein interactions (PPIs) between a pathogen and its target host. Background: So far, many computational methods have focused on predicting PPIs within the same species rather than PPIs across different species. Methods: From the extensive analysis of PPIs between Yersinia pestis bacteria and humans, we recently discovered an interesting relation; a linear relation between amino acid composition and sequence length was observed in many proteins involved in PPIs. We have built a support vector machine (SVM) model, which predicts PPIs between human and bacteria using two feature types derived from the relation. The two feature types used in the SVM are the amino acid composition group (AACG) and the difference in amino acid composition between host and pathogen proteins. Result: The SVM model achieved high performance in predicting bacteria-human PPIs. The model showed an accuracy of 96%, sensitivity of 94%, and specificity of 98% in predicting PPIs between humans and Yersinia pestis, in which there is a strong relation between amino acid composition and sequence length. The SVM model was also tested in predicting PPIs between human and viruses, which include Ebola, HCV, and SARS-CoV-2, and showed a good performance. Conclusion: The feature types identified in our study are simple yet powerful in predicting pathogenhuman PPIs. Although preliminary, our method will be useful for finding unknown target host proteins or pathogen proteins and designing in vitro or in vivo experiments.

Heavy metals accumulating in the human body produce physiological toxicity by interfering with the transport of human proteins and enzymes. Heavy metals detection is significant for food safety ass...

Using RNA antisense purification and mass spectrometry, we identified more than 100 human proteins that directly and specifically bind SARS-CoV-2 RNA in infected cells. To gain insights into the functions of selected RNA interactors, we applied genetic perturbation and pharmacological inhibition experiments, and mapped the contact sites on the viral RNA. This led to the identification of host dependency factors and defense strategies, which can guide the design of novel therapeutics against SARS-CoV-2.

Minimal absent words (MAWs) are minimal-length oligomers absent from a genome or proteome. Although some artificially synthesized MAWs have deleterious effects, there is still a lack of a strategy for the classification of non-occurring sequences as potentially malicious or benign. In this work, by using Markovian models with multiple-testing correction, we reveal significant absent oligomers, which are statistically expected to exist. This suggests that their absence is due to negative selection. We survey genomes and proteomes covering the diversity of life and find thousands of significant absent sequences. Common significant MAWs are often mono- or dinucleotide tracts, or palindromic. Significant viral MAWs are often restriction sites and may indicate unknown restriction motifs. Surprisingly, significant mammal genome MAWs are often present, but rare, in other mammals, suggesting that they are suppressed but not completely forbidden. Significant human MAWs are frequently present in prokaryotes, suggesting immune function, but rarely present in human viruses, indicating viral mimicry of the host. More than one-fourth of human proteins are one substitution away from containing a significant MAW, with the majority of replacements being predicted harmful. We provide a web-based, interactive database of significant MAWs across genomes and proteomes.

Changes in the glycome of human proteins and cells are associated with the progression of multiple diseases such as Alzheimer's, diabetes mellitus, many types of cancer, and those caused by viruses. Consequently, several studies have shown essential modifications to the isomeric glycan moieties for diseases in different stages. However, the elucidation of extensive isomeric glycan profiles remains challenging because of the lack of analytical techniques with sufficient resolution power to separate all glycan and glycopeptide iso-forms. Therefore, the development of sensitive and accurate approaches for the characterization of all the isomeric forms of glycans and glycopeptides is essential to tracking the progression of pathology in glycoprotein-related diseases. This review describes the isomeric separation achievements reported in glycomics and glycoproteomics in the last decade. It focuses on the mass spectrometry-based analytical strategies, stationary phases, and derivatization techniques that have been developed to enhance the separation mechanisms in liquid chromatography systems and the detection capabilities of mass spectrometry systems.

Introns are excised from pre-messenger RNAs by the spliceosome, which produces mRNAs with continuous protein-coding information. In humans, most pre-mRNAs undergo alternative splicing to expand proteomic diversity. Cryo-electron microscopy (cryo-EM) structures of the yeast spliceosome elucidated how proteins stabilize and remodel an RNA-based active site to effect splicing catalysis. More recent cryo-EM snapshots of the human spliceosome reveal a complex protein scaffold and provide insights into the role of specific human proteins in modulating spliceosome activation, splice site positioning, and the ATPase-mediated dynamics of the active site. The emerging molecular picture highlights how, compared to its yeast counterpart, the human spliceosome has coopted additional protein factors to allow increased plasticity of splice site recognition and remodeling, and potentially to regulate alternative splicing.

Due to the rapidness in research, accumulation of biological data is happening at an overwhelming rate. Advanced computation techniques are required to gather the useful information from this enormous amount of protein data such that the knowledge is practically useful and easily interpretable. For instance, drug discoverers need biological or computational methods to predict the functions of proteins, responsible for different sort of diseases in human body. Since traditional biological methods were time consuming and comparatively expensive, various computational methods have been introduced in the respective research areas. In this project, we have tried to generate machine learning models that predict the protein function of unknown proteins and analyze their performance to get a model with highest accuracy. Protein function's sequence annotations such as Amino Acid modifications, Molecule Processing and other structural features like Active Site, Beta strand, Chain, etc. along with it even protein mass and length are considered for prediction of protein functions. To further improve the accuracy feature selection has been performed. According to the enzyme nomenclature scheme the protein are classified into 6 groups. This enzyme classes is nothing but the crystalize reactions of proteins and shows the functions of it.

Cryptic sites are binding pockets that are transiently formed in an apo form or that are induced by ligand binding. The investigation of cryptic sites is crucial for drug discovery, since these sites are ubiquitous in disease-related human proteins, and targeting them expands the number of drug targets greatly. However, although many computational studies have attempted to identify cryptic sites, the detection remains challenging. Here, we aimed to characterize and detect cryptic sites in terms of structural fluctuations in an apo form, investigating proteins each of which possesses a cryptic site. From their X-ray structures, we saw that aromatic residues tended to be found in cryptic sites. To examine structural fluctuations of the apo forms, we performed molecular dynamics (MD) simulations, producing probability distributions of the solvent-accessible surface area per aromatic residue. To detect aromatic residues in cryptic sites, we have proposed a "cryptic-site index" based on the distribution, demonstrating the performance via several measures, such as recall and specificity. Besides, we found that high-ranking aromatic residues were likely to probe concaves in a cryptic site. This implies that such fluctuations provide a profile of scaffolds of compounds with the potential to bind to a particular cryptic site.

The huge space of experimental data on biological and chemical objects and their interactions contributes to the rapid growth of scientific publications containing their analysis. Such data may include characteristics of low-molecular-weight compounds, results of their biological activity evaluation, and their interaction with human and animal proteins, methods of synthesis of organic compounds, and their classification. The past decades saw the development of methods for automated extraction of data from texts of scientific publications, including those for retrieval of names of organic compounds. These data can be used for the automatic identification of the names of organic compounds, including all possible synonyms. Since the topics of scientific publications are diverse, the extracted data can be applied to obtain information about (1) classification of organic compounds (2) methods of synthesis of a given organic compound; (3) physicochemical properties of this compound; (4) its interaction with high-molecular-weight compounds (including proteins, mRNA of animals and humans); and (5) the therapeutic properties of organic compounds, the active substance of the drug, and data on clinical trials. This review considers the methods aimed at searching and extracting data on names of low-molecular-weight compounds and interactions between them and animal and human proteins (biological objects), as well as data on experimentally confirmed biological activity and the effects of organic compounds (including drugs) on pathological processes. Here, we discuss the methods developed and the results of their application published over the past 10 years.

This issue of the Biomedical Journal acquaints us with the compelling hypothesis that the vascular glycocalyx lies at the intersection of severe COVID-19 risk factors and damages, and the ways used by artificial intelligence to predict interactions between SARS-CoV-2 and human proteins. Furthermore, we explore the antiviral potential of valinomycin and the long list of COVID-19-related clinical trials, and learn how (not) to fix a broken femoral head. Last but not least, we get to enjoy the tale of the cellular oxygen-sensing system as well as the role of the host complement system during Leptospira infection, and learn that SARS-CoV-2 can sometimes come with a pathogenic plus one.

The DARPin® drug platform was established with a vision to expand the medical use of biologics beyond what was possible with monoclonal antibodies. It is based on naturally occurring ankyrin repeat domains that are typically building blocks of multifunctional human proteins. The platform allows for the generation of diverse, well-behaved, multifunctional drug candidates. Recent clinical data illustrate the favorable safety profile of the first DARPin® molecules tested in patients. With the positive phase III results of the most advanced DARPin® drug candidate, abicipar, the DARPin® drug platform is potentially about to achieve its first marketing approval. This review highlights some of the key milestones and decisions encountered when transforming the DARPin® platform from an academic concept to a biotech drug pipeline engine.

In 2019 a very powerful web-server, or AI (Artificial Intelligence) tool, has been developed for predicting the subcellular localization of human proteins purely according to their information for the multi-label systems, in which a same protein may appear or travel between two or more locations and hence its identification needs the multi-label mark.

Aim: HPV is an important cause of cervical cancer, but Chlamydia trachomatis (CT) is suspiciously involved in this disease ranging from direct to its involvement as a cofactor with HPV. We performed this study to understand the interaction of HPV and C. trachomatis with humans and its contribution to cervical cancer. Materials & methods: Host–pathogen and pathogen–pathogen protein–protein interaction maps of HPV/CT/human were prepared and compared to analyze interactions during single/coinfection of C. trachomatis and HPV. The interacting human proteins were detected by their involvement in cervical cancer. Results: C. trachomatis may interact with several cancer associated proteins while HPV and C. trachomatis largely interact with different human proteins, suggesting different pathogenesis. Conclusion: C. trachomatis coinfection with HPV may modulate cervical cancer development.

The functional and optimal expression of genes is crucial for survival of all living organisms. Numerous experiments and efforts have been performed to reveal the mechanisms required for the functional and optimal expression of human genes. The yeast Saccharomyces cerevisiae has evolved independently of humans for billions of years. Nevertheless, S. cerevisiae has many conserved genes and expression mechanisms that are similar to those in humans. Yeast is the most commonly used model organism for studying the function and expression mechanisms of human genes because it has a relatively simple genome structure, which is easy to manipulate. Many previous studies have focused on understanding the functions and mechanisms of human proteins using orthologous genes and biological systems of yeast. In this review, we mainly introduce two recent studies that replaced human genes and nucleosomes with those of yeast. Here, we suggest that, although yeast is a relatively small eukaryotic cell, its humanization is useful for the direct study of human proteins. In addition, yeast can be used as a model organism in a broader range of studies, including drug screening.

Relevance. The genetic recombination between viruses and men is known long ago. It can be divided on relict and ontogenic ones. For the host the recombination may display different consequences the nature of which is not exposed explicitly.Aim is to analyze (on the base of computer comparison of the primary structure of viral and human proteins ) the occurrence of twodirectional recombination by small genome fragments between viruses and men and describe its possible after-effects.Materials and methods. For this computer study human and virus protein sequences were used from data bases available in INTERNET.Results. It was indicated that recombination (cryptical and explicit) by small genome fragments between viruses and men occurred many times in the past and many viruses pathogenic for men were involved in it.Conclusion. The bioinformatics approach allows to look at the past of viruses and men and find the traces of genetic information changes between them that may predetermine the effects of vaccines and diagnostic immune tests.

Recently, a very useful method called “pLoc_Deep-mEuk” has been proposed for finding against the Pandemic COVID-19. Illustrated in this short report is a step-by-step guide for how to use its web-server.

Recently, a very useful method called “pLoc_Deep-mHum” has been proposed for finding against the Pandemic COVID-19. Illustrated in this short report is a step-by-step guide for how to use its web-server.

Recently, the life of human beings around the entire world has been endangering by the spreading of pneumonia-causing virus, such as Coronavirus, COVID-19, and H1N1. To develop effective drugs against Coronavirus, knowledge of protein subcellular localization is indispensable. In 2019, a predictor called “pLoc_bal-mHum” was developed for identifying the subcellular localization of human proteins. Its predicted results are significantly better than its counterparts, particularly for those proteins that may simultaneously occur or move between two or more subcellular location sites. However, more efforts are definitely needed to further improve its power since pLoc_bal-mHum was still not trained by a “deep learning”, a very powerful technique developed recently. The present study was devoted to incorporate the “deep-learning” technique and develop a new predictor called “pLoc_Deep-mHum”. The global absolute true rate achieved by the new predictor is over 81% and its local accuracy is over 90%. Both are overwhelmingly superior to its counterparts. Moreover, a user-friendly web-server for the new predictor has been well established at http://www.jci-bioinfo.cn/pLoc_Deep-mHum/, which will become a very useful tool for fighting pandemic coronavirus and save the mankind of this planet.

A cell contains numerous protein molecules. One of the fundamental goals in molecular cell biology is to determine their subcellular locations since this information is extremely important to both basic research and drug development. In this paper, we report a novel and very powerful predictor called "pLoc_bal-mHum" for predicting the subcellular localization of human proteins based on their sequence information alone. Cross-validation tests on exactly the same experiment-confirmed dataset have indicated that the new predictor is remarkably superior to the existing state-of-the-art predictor in identifying the subcellular localization of human proteins. To maximize the convenience for the majority of experimental scientists, a user-friendly web-server for the new predictor has been established at http://www.jci-bioinfo.cn/pLoc_bal-mHum/, by which users can easily get their desired results without the need to go through the detailed mathematics.

Using a IIIA radiation skin burn model in CD1 mice, we demonstrated that transdermal application of liposomes containing rhAFP; liposomes containing rhGCSF; and liposomes containing rhAFP and rhGCSF on the affected area of the skin within two weeks after irradiation led to a significant reduction in the healing time of the burn wound according to clinical indices. The most effective was the use of liposomes containing rhAFP: the duration of the latent period increased 70%, the duration of the moist epidermitis was reduced by 60%, the duration of the reparative phase was reduced by 32%, and the total healing time of the burn was reduced by 44%, compared to the values for mice with untreated radiation burns. Immunohistochemical studies have shown that application of the studied drugs led to an increase in the survival of stem cells in the hair follicles of the skin: the most pronounced effect on histological indices was noted in the group treated with liposomes containing rhAFP and rhGCSF.

The Human Proteome Organization’s (HUPO) Human Proteome Project (HPP) developed Mass Spectrometry (MS) Data Interpretation Guidelines that have been applied since 2016. These guidelines have helped ensure that the emerging draft of the complete human proteome is highly accurate and with low numbers of false-positive protein identifications. Here, we describe an update to these guidelines based on consensus-reaching discussions with the wider HPP community over the past year. The revised 3.0 guidelines address several major and minor identified gaps. We have added guidelines for emerging data independent acquisition (DIA) MS workflows and for use of the new Universal Spectrum Identifier (USI) system being developed by the HUPO Proteomics Standards Initiative (PSI). In addition, we discuss updates to the standard HPP pipeline for collecting MS evidence for all proteins in the HPP, including refinements to minimum evidence. We present a new plan for incorporating MassIVE-KB into the HPP pipeline for the next (HPP 2020) cycle in order to obtain more comprehensive coverage of public MS data sets. The main checklist has been reorganized under headings and subitems, and related guidelines have been grouped. In sum, Version 2.1 of the HPP MS Data Interpretation Guidelines has served well, and this timely update to version 3.0 will aid the HPP as it approaches its goal of collecting and curating MS evidence of translation and expression for all predicted ∼20 000 human proteins encoded by the human genome., This work was funded in part by the National Institutes of Health grants R01GM087221 (EWD/RLM), R24GM127667 (EWD), U54EB020406 (EWD), R01HL133135 (RLM), U19AG02312 (RLM), U54ES017885 (GSO), U24CA210967-01 (GSO), R01LM013115 (NB) and P41GM103484 (NB); National Science Foundation grants ABI-1759980 (NB), DBI-1933311 (EWD), and IOS-1922871 (EWD); Canadian Institutes of Health Research 148408 (CMO); Korean Ministry of Health and Welfare HI13C2098 (YKP); French Ministry of Higher Education, Research and Innovation, ProFI project, ANR-10-INBS-08 (YV); also in part by the National Eye Institute (NEI), National Human Genome Research Institute (NHGRI), National Heart, Lung, and Blood Institute (NHLBI), National Institute of Allergy and Infectious Diseases (NIAID), National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK), National Institute of General Medical Sciences (NIGMS), and National Institute of Mental Health (NIMH) of the National Institutes of Health under Award Number U24HG007822 (SO) (the content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health).

SummaryMissense mutations that change protein stability are strongly associated with human inherited genetic disease. With the recent availability of predicted structures for all human proteins generated using the AlphaFold2 prediction model, genome-wide assessment of the stability effects of genetic variation can, for the first time, be easily performed. This facilitates the interrogation of personal genetic variation for potentially pathogenic effects through the application of stability metrics. Here, we present a novel algorithm to prioritise variants predicted to strongly destabilise essential proteins, available as both a standalone software package and a web-based tool. We demonstrate the utility of this tool by showing that at values of the Stability Sort Z-score above 1.6, pathogenic, protein-destabilising variants from ClinVar are detected at a 58% enrichment, over and above the destabilising (but presumably non-pathogenic) variation already present in the HapMap NA12878 genome.Availability and ImplementationStabilitySort is available as both a web service (http://130.56.244.113/StabilitySort/) and can be deployed as a standalone system (https://gitlab.com/baaron/StabilitySort).ContactDan.Andrews@anu.edu.au

To fight the SARS-CoV-2 pandemic, much effort has been directed toward drug repurposing, testing investigational and approved drugs against several viral or human proteins in vitro. Here we investigate the impact of colloidal aggregation, a common artifact in early drug discovery, in these repurposing screens. We selected 56 drugs reported to be active in biochemical assays and tested them for aggregation by both dynamic light scattering and by enzyme counter screening with and without detergent; seventeen of these drugs formed colloids at concentrations similar to their literature reported IC50s. To investigate the occurrence of colloidal aggregators more generally in repurposing libraries, we further selected 15 drugs that had physical properties resembling known aggregators from a common repurposing library, and found that 6 of these aggregated at micromolar concentrations. An attraction of repurposing is that drugs active on one target are considered de-risked on another. This study suggests not only that many of the drugs repurposed for SARS-CoV-2 in biochemical assays are artifacts, but that, more generally, when screened at relevant concentrations, drugs can act artifactually via colloidal aggregation. Understanding the role of aggregation, and detecting its effects rapidly, will allow the community to focus on those drugs and leads that genuinely have potential for treating COVID-19.Abstract FigureTable of Contents Graphic

Mutations in human proteins lead to diseases. The structure of these proteins can help understand the mechanism of such diseases and develop therapeutics against them. With improved deep learning techniques such as RoseTTAFold and AlphaFold, we can predict the structure of proteins even in the absence of structural homologues. We modeled and extracted the domains from 553 disease-associated human proteins without known protein structures or close homologues in the Protein Databank (PDB). We noticed that the model quality was higher and the RMSD lower between AlphaFold and RoseTTAFold models for domains that could be assigned to CATH families as compared to those which could only be assigned to Pfam families of unknown structure or could not be assigned to either. We predicted ligand-binding sites, protein-protein interfaces, conserved residues in these predicted structures. We then explored whether the disease-associated missense mutations were in the proximity of these predicted functional sites, if they destabilized the protein structure based on ddG calculations or if they were predicted to be pathogenic. We could explain 80% of these disease-associated mutations based on proximity to functional sites, structural destabilization or pathogenicity. When compared to polymorphisms a larger percentage of disease associated missense mutations were buried, closer to predicted functional sites, predicted as destabilising and/or pathogenic. Usage of models from the two state-of-the-art techniques provide better confidence in our predictions, and we explain 93 additional mutations based on RoseTTAFold models which could not be explained based solely on AlphaFold models.

We investigate the use of confidence scores to predict the accuracy of a given AlphaFold model for drug discovery. Predicted accuracy is improved by eliminating confidence scores below 80, due to effects of disorder. 95% of models corresponding to a set of recent crystal structures are accurate at the fold level. Conformational discordance in the training set has a more significant effect on accuracy than sequence divergence. We propose criteria for models and residues that are possibly useful for virtual screening, by which AlphaFold provides models for half of understudied (dark) human proteins and two-thirds of residues in those models.

Background Identifying human protein-phenotype relationships has attracted researchers in bioinformatics and biomedical natural language processing due to its importance in uncovering rare and complex diseases. Since experimental validation of protein-phenotype associations is prohibitive, automated tools capable of accurately extracting these associations from the biomedical text are in high demand. However, while the manual annotation of protein-phenotype co-mentions required for training such models is highly resource-consuming, extracting millions of unlabeled co-mentions is straightforward. Results In this study, we propose a novel deep semi-supervised ensemble framework that combines deep neural networks, semi-supervised, and ensemble learning for classifying human protein-phenotype co-mentions with the help of unlabeled data. This framework allows the ability to incorporate an extensive collection of unlabeled sentence-level co-mentions of human proteins and phenotypes with a small labeled dataset to enhance overall performance. We develop PPPredSS, a prototype of our proposed semi-supervised framework that combines sophisticated language models, convolutional networks, and recurrent networks. Our experimental results demonstrate that the proposed approach provides a new state-of-the-art performance in classifying human protein-phenotype co-mentions by outperforming other supervised and semi-supervised counterparts. Furthermore, we highlight the utility of PPPredSS in powering a curation assistant system through case studies involving a group of biologists. Conclusions This article presents a novel approach for human protein-phenotype co-mention classification based on deep, semi-supervised, and ensemble learning. The insights and findings from this work have implications for biomedical researchers, biocurators, and the text mining community working on biomedical relationship extraction.

The role of the blood–brain barrier (BBB) in Alzheimer’s and other neurodegenerative diseases is still the subject of many studies. However, those studies using high-throughput methods have been compromised by the lack of Gene Ontology (GO) annotations describing the role of proteins in the normal function of the BBB. The GO Consortium provides a gold-standard bioinformatics resource used for analysis and interpretation of large biomedical data sets. However, the GO is also used by other research communities and, therefore, must meet a variety of demands on the breadth and depth of information that is provided. To meet the needs of the Alzheimer’s research community we have focused on the GO annotation of the BBB, with over 100 transport or junctional proteins prioritized for annotation. This project has led to a substantial increase in the number of human proteins associated with BBB-relevant GO terms as well as more comprehensive annotation of these proteins in many other processes. Furthermore, data describing the microRNAs that regulate the expression of these priority proteins have also been curated. Thus, this project has increased both the breadth and depth of annotation for these prioritized BBB proteins. Database URLhttps://www.ebi.ac.uk/QuickGO/

Computational identification of human-virus protein-protein interactions (PHIs) is a worthwhile step towards understanding infection mechanisms. Analysis of the PHI networks is important for the determination of path-ogenic diseases. Prediction of these interactions is a popular problem since experimental detection of PHIs is both time-consuming and expensive. The available methods use biological features like amino acid sequences, molecular structure, or biological activities for prediction. Recent studies show that the topological properties of proteins in protein-protein interaction (PPI) networks increase the performance of the predictions. The basic network projections, random-walk-based models, or graph neural networks are used for generating topologically enriched (hybrid) protein embeddings. In this study, we propose a three-stage machine learning pipeline that generates and uses hybrid embeddings for PHI prediction. In the first stage, numerical features are extracted from the amino acid sequences using the Doc2Vec and Byte Pair Encoding method. The amino acid embeddings are used as node features while training a modified GraphSAGE model, which is an improved version of the graph convolutional network. Lastly, the hybrid protein embeddings are used for training a binary interaction classifier model that predicts whether there is an interaction between the given two proteins or not. The proposed method is evaluated with comprehensive experiments to test its functionality and compare it with the state-of-art methods. The experimental results on the benchmark dataset prove the efficiency of the proposed model by having a 3–23% better area under curve (AUC) score than its competitors. Publisher's Version Q2 WOS:000858873400002 PMID: 36037723

Motivation Recent development of deep-learning methods has led to a breakthrough in the prediction accuracy of 3D protein structures. Extending these methods to protein pairs is expected to allow large-scale detection of protein–protein interactions (PPIs) and modeling protein complexes at the proteome level. Results We applied RoseTTAFold and AlphaFold, two of the latest deep-learning methods for structure predictions, to analyze coevolution of human proteins residing in mitochondria, an organelle of vital importance in many cellular processes including energy production, metabolism, cell death and antiviral response. Variations in mitochondrial proteins have been linked to a plethora of human diseases and genetic conditions. RoseTTAFold, with high computational speed, was used to predict the coevolution of about 95% of mitochondrial protein pairs. Top-ranked pairs were further subject to modeling of the complex structures by AlphaFold, which also produced contact probability with high precision and in many cases consistent with RoseTTAFold. Most top-ranked pairs with high contact probability were supported by known PPIs and/or similarities to experimental structural complexes. For high-scoring pairs without experimental complex structures, our coevolution analyses and structural models shed light on the details of their interfaces, including CHCHD4–AIFM1, MTERF3–TRUB2, FMC1–ATPAF2 and ECSIT–NDUFAF1. We also identified novel PPIs (PYURF–NDUFAF5, LYRM1–MTRF1L and COA8–COX10) for several proteins without experimentally characterized interaction partners, leading to predictions of their molecular functions and the biological processes they are involved in. Availability and implementation Data of mitochondrial proteins and their interactions are available at: http://conglab.swmed.edu/mitochondria. Supplementary information Supplementary data are available at Bioinformatics online.

Porcine cells devoid of three major carbohydrate xenoantigens, αGal, Neu5GC, and SDa (TKO) exhibit markedly reduced binding of human natural antibodies. Therefore, it is anticipated that TKO pigs will be better donors for human xenotransplantation. However, previous studies on TKO pigs using old world monkeys (OWMs) have been disappointing because of higher anti-TKO pig antibodies in OWMs than humans. Here, we show that long-term survival of renal xenografts from TKO pigs that express additional human transgenes (hTGs) can be achieved in cynomolgus monkeys. Kidney xenografts from TKO-hTG pigs were transplanted into eight cynomolgus recipients without pre-screening for low anti-pig antibody titers. Two recipients of TKO-hTG xenografts with low expression of human complement regulatory proteins (CRPs) (TKO-A) survived for 2 and 61 days, whereas six recipients of TKO-hTG xenografts with high CRP expression (TKO-B) survived for 15, 20, 71, 135, 265, and 316 days. Prolonged CD4+ T cell depletion and low anti-pig antibody titers, which were previously reported important for long-term survival of αGal knock-out (GTKO) xenografts, were not always required for long-term survival of TKO-hTG renal xenografts. This study indicates that OWMs such as cynomolgus monkeys can be used as a relevant model for clinical application of xenotransplantation using TKO pigs.

The paralogous human proteins UPF3A and UPF3B are involved in recognizing mRNAs targeted by nonsense-mediated mRNA decay (NMD). While UPF3B has been demonstrated to support NMD, contradicting reports describe UPF3A either as an NMD activator or inhibitor. Here, we present a comprehensive functional analysis of UPF3A and UPF3B in human cells using combinatory experimental approaches. Overexpression or knockout of UPF3A as well as knockout of UPF3B did not detectably change global NMD activity. In contrast, the co-depletion of UPF3A and UPF3B resulted in a marked NMD inhibition and a transcriptome-wide upregulation of NMD substrates, demonstrating a functional redundancy between both NMD factors. Although current models assume that UPF3 bridges NMD-activating exon-junction complexes (EJC) to the NMD factor UPF2, UPF3B exhibited only slightly impaired NMD activity in rescue experiments when UPF2 or EJC binding was impaired. Further rescue experiments revealed partially redundant functions of UPF3B domains in supporting NMD, involving both UPF2 and EJC interaction sites and the central region of UPF3. Collectively, UPF3A and UPF3B serve as fault-tolerant NMD activators in human cells.

Recombinant therapeutic proteins are a broad class of biological products used to replace dysfunctional human proteins in individuals with genetic defects (e.g., factor VIII for hemophilia) or, in the case of monoclonal antibodies, bind to disease targets involved in cancers, autoimmune disorders, or other conditions. Unfortunately, immunogenicity (immune response to the drug) remains a key impediment, potentially affecting the safety and efficacy of these therapeutics. Immunogenicity risk is routinely evaluated during the licensure of therapeutic proteins. However, despite eliciting anti-drug immune responses in at least some patients, most protein drugs are nevertheless licensed as they address unmet medical needs. The pre-licensure immunogenicity assessments of therapeutic proteins are the subject of numerous reviews and white papers. However, observation and clinical management of the immunogenicity of approved therapeutic proteins face additional challenges. We survey the immunogenicity of approved therapeutic proteins, discuss the clinical management of immunogenicity, and identify the challenges to establishing clinically relevant immunogenicity assays for use in routine clinical practice.

Motivation Human proteins that are secreted into different body fluids from various cells and tissues can be promising disease indicators. Modern proteomics research empowered by both qualitative and quantitative profiling techniques has made great progress in protein discovery in various human fluids. However, due to the large number of proteins and diverse modifications present in the fluids, as well as the existing technical limits of major proteomics platforms (e.g. mass spectrometry), large discrepancies are often generated from different experimental studies. As a result, a comprehensive proteomics landscape across major human fluids are not well determined. Results To bridge this gap, we have developed a deep learning framework, named DeepSec, to identify secreted proteins in 12 types of human body fluids. DeepSec adopts an end-to-end sequence-based approach, where a Convolutional Neural Network is built to learn the abstract sequence features followed by a Bidirectional Gated Recurrent Unit with fully connected layer for protein classification. DeepSec has demonstrated promising performances with average area under the ROC curves of 0.85–0.94 on testing datasets in each type of fluids, which outperforms existing state-of-the-art methods available mostly on blood proteins. As an illustration of how to apply DeepSec in biomarker discovery research, we conducted a case study on kidney cancer by using genomics data from the cancer genome atlas and have identified 104 possible marker proteins. Availability DeepSec is available at https://bmbl.bmi.osumc.edu/deepsec/. Supplementary information Supplementary data are available at Bioinformatics online.

Hundreds of human proteins were found to establish transient interactions with rather degenerated consensus DNA sequences or motifs. Identifying these motifs and the genomic sites where interactions occur represent one of the most challenging research goals in modern molecular biology and bioinformatics. The last twenty years witnessed an explosion of computational tools designed to perform this task, whose performance has been last compared fifteen years ago. Here, we survey sixteen of them, benchmark their ability to identify known motifs nested in twenty-nine simulated sequence datasets, and finally report their strengths, weaknesses, and complementarity.

Summary QuartataWeb is a user-friendly server developed for polypharmacological and chemogenomics analyses. Users can easily obtain information on experimentally verified (known) and computationally predicted (new) interactions between 5494 drugs and 2807 human proteins in DrugBank, and between 315 514 chemicals and 9457 human proteins in the STITCH database. In addition, QuartataWeb links targets to KEGG pathways and GO annotations, completing the bridge from drugs/chemicals to function via protein targets and cellular pathways. It allows users to query a series of chemicals, drug combinations or multiple targets, to enable multi-drug, multi-target, multi-pathway analyses, toward facilitating the design of polypharmacological treatments for complex diseases. Availability and implementation QuartataWeb is freely accessible at http://quartata.csb.pitt.edu. Supplementary information Supplementary data are available at Bioinformatics online.

Proteins with homo-repeats of more than 4 amino acid residues in length were examined to understand whether some splicing sites in pre-mRNA may be attributed to homo-repeats in human proteins. The human proteome was found to contain a total of 404 proteins with homo-repeats that account for at least one splicing site in pre-mRNA. Pre-mRNA splicing sites were more often found in the C-terminal part (67%) than in the middle or N-terminal part of a homo-repeat. Ten homo-repeats were identified to have two splicing sites per repeat. The repeats were lysine homo-repeats in all but one case.

Curing malignant carcinomas is a grand ambition in the development of human health. Over the past decades, targeted therapies have become one of the most successful ways of achieving this. Of these approaches, small molecule inhibitors and monoclonal antibodies are two major methods, however several barriers to their development and clinical use still exist. The use of proteolysis-targeting chimeras (PROTACs) is a new technology through utilizing a intracellular ubiquitin-proteasome system to induce targeted protein degradation, is receiving much attention in the field of targeted therapies. Hetero-bifunctional PROTACs have the potential to eliminate the "undruggable" proteome that comprises about 85% of human proteins, which indicates their great prospects in therapeutic fields. However, there are some hurdles preventing current PROTACs moving from bench to clinic, such as delivery and bioavailability. This review provides an overview of the development of PROTAC technology and will briefly summarize the future possible directions of this approach.

Abstract CTCF belongs to a large family of transcription factors with clusters of C2H2-type zinc finger domains (C2H2 proteins) and is a main architectural protein in mammals. Human CTCF has a homodimerizing unstructured domain at the N-terminus which is involved in long-distance interactions. To test the presence of similar N-terminal domains in other human C2H2 proteins, a yeast two-hybrid system was used. In total, the ability of unstructured N-terminal domains to homodimerize was investigated for six human C2H2 proteins with an expression profile similar to CTCF. The data indicate the lack of the homodimerization ability of these domains. On the other hand, three C2H2 proteins containing the structured domain DUF3669 at the N-terminus demonstrated homo- and heterodimerization activity.

Understanding the interaction patterns between a particular virus and human proteins plays a crucial role in unveiling the underlying mechanism of viral infection. This could further help in developing treatments of viral diseases. The main issues in tackling it as a machine learning problem is the scarcity of training data as well input information of the viral proteins. We overcome these limitations by exploiting powerful statistical protein representations derived from a corpus of around 24 Million protein sequences in a multi task framework. Our experiments on 7 varied benchmark datasets support the superiority of our approach.

Most diseases disrupt multiple proteins, and drugs treat such diseases by restoring the functions of the disrupted proteins. How drugs restore these functions, however, is often unknown as a drug’s therapeutic effects are not limited to the proteins that the drug directly targets. Here, we develop the multiscale interactome, a powerful approach to explain disease treatment. We integrate disease-perturbed proteins, drug targets, and biological functions into a multiscale interactome network. We then develop a random walk-based method that captures how drug effects propagate through a hierarchy of biological functions and physical protein-protein interactions. On three key pharmacological tasks, the multiscale interactome predicts drug-disease treatment, identifies proteins and biological functions related to treatment, and predicts genes that alter a treatment’s efficacy and adverse reactions. Our results indicate that physical interactions between proteins alone cannot explain treatment since many drugs treat diseases by affecting the biological functions disrupted by the disease rather than directly targeting disease proteins or their regulators. We provide a general framework for explaining treatment, even when drugs seem unrelated to the diseases they are recommended for., Most diseases disrupt multiple proteins, and drugs treat such diseases by restoring the functions of the disrupted proteins; how drugs restore these functions, however, is often unknown. Here, the authors develop the multiscale interactome, a powerful approach to explain disease treatment.

The recent pandemic caused by the novel coronavirus resulted in the greatest global health crisis since the Spanish flu pandemic of 1918. There is limited knowledge of whether SARS-CoV-2 is physically associated with human metalloproteins. Recently, high-confidence, experimentally supported protein-protein interactions between SARS-CoV-2 and human proteins were reported. In this work, 58 metalloproteins among these human targets have been identified by a structure-based approach. This study reveals that most human metalloproteins interact with the recently discovered SARS-CoV-2 orf8 protein, whose antibodies are one of the principal markers of SARS-CoV-2 infections. Furthermore, this work provides sufficient evidence to conclude that Zn2+ plays an important role in the interplay between the novel coronavirus and humans. First, the content of Zn-binding proteins in the involved human metalloproteome is significantly higher than that of the other metal ions. Second, a molecular linkage between the identified human Zn-binding proteome with underlying medical conditions that might increase the risk of severe illness from the SARS-CoV-2 virus has been found. Likely perturbations of host cellular metal homeostasis by SARS-CoV-2 infection are highlighted., Graphical abstract Unlabelled Image

Commonly, protein-based drugs are referred as therapeutic proteins. However, the term was first used to the biologics that are genetically engineered version of naturally occurring human proteins. Therapeutic proteins are large molecules consisting of long chain amino acids with multi-folded structural entity responsible for variety of biological functions. Therapeutic proteins can be clinically used in replacing an abnormally behaved protein responsible for a specific disease. They can also be supplemented with body’s supply of a beneficial protein to minimize the impact of diseases or chemotherapy. The genetically engineered proteins are approximately similar to natural proteins, and their functional efficacy can be increased by conjugating with glycans or unnatural amino acids.

About 10% of human proteins have no annotated function in protein knowledge bases. A workflow to generate hypotheses for the function of these uncharacterized proteins has been developed, based on predicted and experimental information on protein properties, interactions, tissular expression, subcellular localization, conservation in other organisms, as well as phenotypic data in mutant model organisms. This workflow has been applied to seven uncharacterized human proteins (C6orf118, C7orf25, CXorf58, RSRP1, SMLR1, TMEM53 and TMEM232) in the frame of a course-based undergraduate research experience named Functionathon organized at the University of Geneva to teach undergraduate students how to use biological databases and bioinformatics tools and interpret the results. C6orf118, CXorf58 and TMEM232 were proposed to be involved in cilia-related functions; TMEM53 and SMLR1 were proposed to be involved in lipid metabolism and C7orf25 and RSRP1 were proposed to be involved in RNA metabolism and gene expression. Experimental strategies to test these hypotheses were also discussed. The results of this manual data mining study may contribute to the project recently launched by the Human Proteome Organization (HUPO) Human Proteome Project aiming to fill gaps in the functional annotation of human proteins. Database URL: http://www.nextprot.org

Cell-free protein production technology can easily produce recombinant proteins from cDNA temples, because it is synthesized in a tube without cell culture. We developed a wheat cell-free protein production system from washed wheat embryos. Since our cell-free system is based on eukaryotic translational machinery, it is very suitable for synthesis of eukaryotic proteins such as human protein. Using this system, recently we made a protein array technology consisting of proteins synthesized in 384-well formatted plates, and then constructed human protein array consisting of more than 20,000 recombinant human proteins (20K-HuPA). In addition, combinations with AlphaScreen or magnetic plate technology promotes development of a new high-throughput and high-sensitive approach for identification of protein–protein or protein–antibody interaction. Herein, we demonstrate the results of protein interactomes and antibody validation by using protein array.

The full might of a world-leading artificial-intelligence laboratory has been brought to bear on protein-structure prediction. The resulting method, AlphaFold2, promises to transform our understanding of proteins. Accurate predictions of the structures of almost all human proteins.

According to the amino acid composition of natural proteins, it could be expected that all possible sequences of three or four amino acids will occur at least once in large protein datasets purely by chance. However, in some species or cellular context, specific short amino acid motifs are missing due to unknown reasons. We describe these as Avoided Motifs, short amino acid combinations missing from biological sequences. Here we identify 209 human and 154 bacterial Avoided Motifs of length four amino acids, and discuss their possible functionality according to their presence in other species. Furthermore, we determine two Avoided Motifs of length three amino acids in human proteins specifically located in the cytoplasm, and two more in secreted proteins. Our results support the hypothesis that the characterization of Avoided Motifs in particular contexts can provide us with information about functional motifs, pointing to a new approach in the use of molecular sequences for the discovery of protein function.

The Human Proteome Project (HPP) is leading the international effort to characterize the human proteome. Although the main goal of this project was first focused on the detection of missing proteins, a new challenge arose from the need to assign biological functions to the uncharacterized human proteins and describe their implications in human diseases. Not only the proteins with experimental evidence (uPE1 proteins) but also the uncharacterized missing proteins (uMPs) were the objects of study in this challenge, neXt-CP50. In this work, we developed a new bioinformatic approach to infer biological annotations for the uPE1 proteins and uMPs based on a “guilt-by-association” analysis using public RNA-Seq data sets. We used the correlation of these proteins with the well-characterized PE1 proteins to construct a network. In this way, we applied the PageRank algorithm to this network to identify the most relevant nodes, which were the biological annotations of the uncharacterized proteins. All of the generated information was stored in a database. In addition, we implemented the web application UPEFinder (https://upefinder.proteored.org) to facilitate the access to this new resource. This information is especially relevant for the researchers of the HPP who are interested in the generation and validation of new hypotheses about the functions of these proteins. Both the database and the web application are publicly available, This work was supported by PRB3-ISCIII (PT17/0019/0013) and MCIU/AEI/FEDER, UE Ministerio de Ciencia, Innovación y Universidades (RTI2018-101481-B-100), cofinanced by FEDER funds.

Structural genomics groups have identified the need to generate multiple truncated versions of each target to improve their success in producing a well-expressed, soluble, and stable protein and one that crystallizes and diffracts to a sufficient resolution for structural determination. At the Structural Genomics Consortium, we opted for the ligation-independent cloning (LIC) method which provides the throughput we desire to produce and screen many proteins in a parallel process. Here, we describe our LIC protocol for generating constructs in 96-well format and provide a choice of vectors suitable for expressing proteins in both E. coli and the baculovirus expression vector system (BEVS).