Tools for the Recognition of Sorting Signals and the Prediction of Subcellular Localization of Proteins From Their Amino Acid Sequences

In Silico and In Vitro Evaluation of the Molecular Mimicry of the SARS-CoV-2 Spike Protein by Common Short Constituent Sequences (cSCSs) in the Human Proteome: Toward Safer Epitope Design for Vaccine Development

Spike protein sequences in SARS-CoV-2 have been employed for vaccine epitopes, but many short constituent sequences (SCSs) in the spike protein are present in the human proteome, suggesting that some anti-spike antibodies induced by infection or vaccination may be autoantibodies against human proteins. To evaluate this possibility of "molecular mimicry" in silico and in vitro, we exhaustively identified common SCSs (cSCSs) found both in spike and human proteins bioinformatically. The commonality of SCSs between the two systems seemed to be coincidental, and only some cSCSs were likely to be relevant to potential self-epitopes based on three-dimensional information. Among three antibodies raised against cSCS-containing spike peptides, only the antibody against EPLDVL showed high affinity for the spike protein and reacted with an EPLDVL-containing peptide from the human unc-80 homolog protein. Western blot analysis revealed that this antibody also reacted with several human proteins expressed mainly in the small intestine, ovary, and stomach. Taken together, these results showed that most cSCSs are likely incapable of inducing autoantibodies but that at least EPLDVL functions as a self-epitope, suggesting a serious possibility of infection-induced or vaccine-induced autoantibodies in humans. High-risk cSCSs, including EPLDVL, should be excluded from vaccine epitopes to prevent potential autoimmune disorders.

Vislocas: Vision transformers for identifying protein subcellular mis-localization signatures of different cancer subtypes from immunohistochemistry images

Proteins must be sorted to specific subcellular compartments to perform their functions. Abnormal protein subcellular localizations are related to many diseases. Although many efforts have been made in predicting protein subcellular localization from various static information, including sequences, structures and interactions, such static information cannot predict protein mis-localization events in diseases. On the contrary, the IHC (immunohistochemistry) images, which have been widely applied in clinical diagnosis, contains information that can be used to find protein mis-localization events in disease states. In this study, we create the Vislocas method, which is capable of finding mis-localized proteins from IHC images as markers of cancer subtypes. By combining CNNs and vision transformer encoders, Vislocas can automatically extract image features at both global and local level. Vislocas can be trained with full-sized IHC images from scratch. It is the first attempt to create an end-to-end IHC image-based protein subcellular location predictor. Vislocas achieved comparable or better performances than state-of-the-art methods. We applied Vislocas to find significant protein mis-localization events in different subtypes of glioma, melanoma and skin cancer. The mis-localized proteins, which were found purely from IHC images by Vislocas, are in consistency with clinical or experimental results in literatures. All codes of Vislocas have been deposited in a Github repository (https://github.com/JingwenWen99/Vislocas). All datasets of Vislocas have been deposited in Zenodo (https://zenodo.org/records/10632698).

Hijacking of internal calcium dynamics by intracellularly residing viral rhodopsins

Rhodopsins are ubiquitous light-driven membrane proteins with diverse functions, including ion transport. Widely distributed, they are also coded in the genomes of giant viruses infecting phytoplankton where their function is not settled. Here, we examine the properties of OLPVR1 (Organic Lake Phycodnavirus Rhodopsin) and two other type 1 viral channelrhodopsins (VCR1s), and demonstrate that VCR1s accumulate exclusively intracellularly, and, upon illumination, induce calcium release from intracellular IP3-dependent stores. In vivo, this light-induced calcium release is sufficient to remote control muscle contraction in VCR1-expressing tadpoles. VCR1s natively confer light-induced Ca2+ release, suggesting a distinct mechanism for reshaping the response to light of virus-infected algae. The ability of VCR1s to photorelease calcium without altering plasma membrane electrical properties marks them as potential precursors for optogenetics tools, with potential applications in basic research and medicine.

Identification and production of novel potential pathogen-specific biomarkers for diagnosis of histoplasmosis

IMPORTANCE

Histoplasmosis is considered one of the most important mycoses due to the increasing number of individuals susceptible to develop severe clinical forms, particularly those with HIV/AIDS or receiving immunosuppressive biological therapies, the high mortality rates reported when antifungal treatment is not initiated in a timely manner, and the limitations of conventional diagnostic methods. In this context, there is a clear need to improve the capacity of diagnostic tools to specifically detect the fungal pathogen, regardless of the patient's clinical condition or the presence of other co-infections. The proposed novel pathogen-specific biomarkers have the potential to be used in immunodiagnostic platforms and antifungal treatment monitoring in histoplasmosis. In addition, the bioinformatics strategy used in this study could be applied to identify potential diagnostic biomarkers in other models of fungal infection of public health importance.

Unorthodox localization of P2X7 receptor in subcellular compartments of skeletal system cells

Identifying the subcellular localization of a protein within a cell is often an essential step in understanding its function. The main objective of this report was to determine the presence of the P2X7 receptor (P2X7R) in healthy human cells of skeletal system, specifically osteoblasts (OBs), chondrocytes (Chs) and intervertebral disc (IVD) cells. This receptor is a member of the ATP-gated ion channel family, known to be a main sensor of extracellular ATP, the prototype of the danger signal released at sites of tissue damage, and a ubiquitous player in inflammation and cancer, including bone and cartilaginous tissues. Despite overwhelming data supporting a role in immune cell responses and tumor growth and progression, a complete picture of the pathophysiological functions of P2X7R, especially when expressed by non-immune cells, is lacking. Here we show that human wild-type P2X7R (P2X7A) was expressed in different samples of human osteoblasts, chondrocytes and intervertebral disc cells. By fluorescence microscopy (LM) and immunogold transmission electron microscopy we localized P2X7R not only in the canonical sites (plasma membrane and cytoplasm), but also in the nucleus of all the 3 cell types, especially IVD cells and OBs. P2X7R mitochondrial immunoreactivity was predominantly detected in OBs and IVD cells, but not in Chs. Evidence of subcellular localization of P2X7R may help to i. understand the participation of P2X7R in as yet unidentified signaling pathways in the joint and bone microenvironment, ii. identify pathologies associated with P2X7R mislocalization and iii. design specific targeted therapies.

Pathogen-associated gene discovery workflows for novel antivirulence therapeutic development

Novel therapeutics to manage bacterial infections are urgently needed as the impact and prevalence of antimicrobial resistance (AMR) grows. Antivirulence therapeutics are an alternative approach to antibiotics that aim to attenuate virulence rather than target bacterial essential functions, while minimizing microbiota perturbation and the risk of AMR development. Beyond known virulence factors, pathogen-associated genes (PAGs; genes found only in pathogens to date) may play an important role in virulence or host association. Many identified PAGs encode uncharacterized hypothetical proteins and represent an untapped wealth of novel drug targets. Here, we review current advances in antivirulence drug research and development, including PAG identification, and provide a comprehensive workflow from the discovery of antivirulence drug targets to drug discovery. We highlight the importance of integrating bioinformatic/genomic-based methods for novel virulence factor discovery, coupled with experimental characterization, into existing drug screening platforms to develop novel and effective antivirulence drugs.

MTSviewer: A database to visualize mitochondrial targeting sequences, cleavage sites, and mutations on protein structures

Mitochondrial dysfunction is implicated in a wide array of human diseases ranging from neurodegenerative disorders to cardiovascular defects. The coordinated localization and import of proteins into mitochondria are essential processes that ensure mitochondrial homeostasis. The localization and import of most mitochondrial proteins are driven by N-terminal mitochondrial targeting sequences (MTS's), which interact with import machinery and are removed by the mitochondrial processing peptidase (MPP). The recent discovery of internal MTS's-those which are distributed throughout a protein and act as import regulators or secondary MPP cleavage sites-has expanded the role of both MTS's and MPP beyond conventional N-terminal regulatory pathways. Still, the global mutational landscape of MTS's remains poorly characterized, both from genetic and structural perspectives. To this end, we have integrated a variety of tools into one harmonized R/Shiny database called MTSviewer (https://neurobioinfo.github.io/MTSvieweR/), which combines MTS predictions, cleavage sites, genetic variants, pathogenicity predictions, and N-terminomics data with structural visualization using AlphaFold models of human and yeast mitochondrial proteomes. Using MTSviewer, we profiled all MTS-containing proteins across human and yeast mitochondrial proteomes and provide multiple case studies to highlight the utility of this database.

Thousands of human non-AUG extended proteoforms lack evidence of evolutionary selection among mammals

The synthesis of most proteins begins at AUG codons, yet a small number of non-AUG initiated proteoforms are also known. Here we analyse a large number of publicly available Ribo-seq datasets to identify novel, previously uncharacterised non-AUG proteoforms using Trips-Viz implementation of a novel algorithm for detecting translated ORFs. In parallel we analyse genomic alignment of 120 mammals to identify evidence of protein coding evolution in sequences encoding potential extensions. Unexpectedly we find that the number of non-AUG proteoforms identified with ribosome profiling data greatly exceeds those with strong phylogenetic support suggesting their recent evolution. Our study argues that the protein coding potential of human genome greatly exceeds that detectable through comparative genomics and exposes the existence of multiple proteins encoded by the same genomic loci.

Advances in experimental and computational methodologies for the study of microbial-surface interactions at different omics levels

The study of the biological response of microbial cells interacting with natural and synthetic interfaces has acquired a new dimension with the development and constant progress of advanced omics technologies. New methods allow the isolation and analysis of nucleic acids, proteins and metabolites from complex samples, of interest in diverse research areas, such as materials sciences, biomedical sciences, forensic sciences, biotechnology and archeology, among others. The study of the bacterial recognition and response to surface contact or the diagnosis and evolution of ancient pathogens contained in archeological tissues require, in many cases, the availability of specialized methods and tools. The current review describes advances in in vitro and in silico approaches to tackle existing challenges (e.g., low-quality sample, low amount, presence of inhibitors, chelators, etc.) in the isolation of high-quality samples and in the analysis of microbial cells at genomic, transcriptomic, proteomic and metabolomic levels, when present in complex interfaces. From the experimental point of view, tailored manual and automatized methodologies, commercial and in-house developed protocols, are described. The computational level focuses on the discussion of novel tools and approaches designed to solve associated issues, such as sample contamination, low quality reads, low coverage, etc. Finally, approaches to obtain a systems level understanding of these complex interactions by integrating multi omics datasets are presented.

Recent Advances in the Prediction of Subcellular Localization of Proteins and Related Topics

Prediction of subcellular localization of proteins from their amino acid sequences has a long history in bioinformatics and is still actively developing, incorporating the latest advances in machine learning and proteomics. Notably, deep learning-based methods for natural language processing have made great contributions. Here, we review recent advances in the field as well as its related fields, such as subcellular proteomics and the prediction/recognition of subcellular localization from image data.

Comparative proteome analysis of the tegument of male and female adult Schistosoma mansoni

The tegument, as the surface layer of adult male and female Schistosoma spp. represents the protective barrier of the worms to the hostile environment of the host bloodstream. Here we present the first comparative analysis of sex-specific tegument proteins of paired or virgin Schistosoma mansoni. We applied a new and highly sensitive workflow, allowing detection of even low abundance proteins. Therefore, a streptavidin–biotin affinity purification technique in combination with single pot solid-phase enhanced sample preparation was established for subsequent LC–MS/MS analysis. We were able to identify 1519 tegument proteins for male and female virgin and paired worms and categorized them by sex. Bioinformatic analysis revealed an involvement of female-specific tegument proteins in signaling pathways of cellular processes and antioxidant mechanisms. Male-specific proteins were found to be enriched in processes linked to phosphorylation and signal transduction. This suggests a task sharing between the sexes that might be necessary for survival in the host. Our datasets provide a basis for further studies to understand and ultimately decipher the strategies of the two worm sexes to evade the immune system.

Rahman RNZ, Tan JS, Raja Khairuddin RF, Salleh AB, Oslan SN. Determination of Putative Vacuolar Proteases, PEP4 and PRB1 in a Novel Yeast Expression Host Meyerozyma guilliermondii Strain SO Using Bioinformatics Tools

Meyerozyma guilliermondii strain SO, a newly isolated yeast species from spoilt orange, has been used as a host to express the recombinant proteins using methylotrophic yeast promoters. However, as a novel yeast expression system, the vacuolar proteases of this yeast have not been determined, which may have contributed to the low level of heterologous protein secretions. Thus, this study aimed to determine intra- and extracellular proteolytic activity and identify the putative vacuolar proteases using bioinformatics techniques. A clear zone was observed from the nutrient agar skimmed milk screening plate. Proteolytic activity of 117.30 U/ml and 75 U/ml were obtained after 72 h of cultivation for both extracellular and intracellular proteins, respectively. Next, the Hidden Markov model (HMM) was used to detect the presence of the vacuolar proteases (PEP4 and PRB1) from the strain SO proteome. Aspartyl protease (PEP4) with 97.55% identity to Meyerozyma sp. JA9 and a serine protease (PRB1) with 70.91% identity to Candida albicans were revealed. The homology with other yeast vacuolar proteases was confirmed via evolutionary analysis. PROSPER tool prediction of cleavage sites postulated that PEP4 and PRB1 might have caused proteolysis of heterologous proteins in strain SO. In conclusion, two putative vacuolar proteases (PEP4 and PRB1) were successfully identified in strain SO. Further characterization can be done to understand their specific properties, and their effects on heterologous protein expression can be conducted via genome editing.

Computational methods for protein localization prediction

The accurate annotation of protein localization is crucial in understanding protein function in tandem with a broad range of applications such as pathological analysis and drug design. Since most proteins do not have experimentally-determined localization information, the computational prediction of protein localization has been an active research area for more than two decades. In particular, recent machine-learning advancements have fueled the development of new methods in protein localization prediction. In this review paper, we first categorize the main features and algorithms used for protein localization prediction. Then, we summarize a list of protein localization prediction tools in terms of their coverage, characteristics, and accessibility to help users find suitable tools based on their needs. Next, we evaluate some of these tools on a benchmark dataset. Finally, we provide an outlook on the future exploration of protein localization methods.

Ensemble of Multiple Classifiers for Multilabel Classification of Plant Protein Subcellular Localization

The accurate prediction of protein localization is a critical step in any functional genome annotation process. This paper proposes an improved strategy for protein subcellular localization prediction in plants based on multiple classifiers, to improve prediction results in terms of both accuracy and reliability. The prediction of plant protein subcellular localization is challenging because the underlying problem is not only a multiclass, but also a multilabel problem. Generally, plant proteins can be found in 10–14 locations/compartments. The number of proteins in some compartments (nucleus, cytoplasm, and mitochondria) is generally much greater than that in other compartments (vacuole, peroxisome, Golgi, and cell wall). Therefore, the problem of imbalanced data usually arises. Therefore, we propose an ensemble machine learning method based on average voting among heterogeneous classifiers. We first extracted various types of features suitable for each type of protein localization to form a total of 479 feature spaces. Then, feature selection methods were used to reduce the dimensions of the features into smaller informative feature subsets. This reduced feature subset was then used to train/build three different individual models. In the process of combining the three distinct classifier models, we used an average voting approach to combine the results of these three different classifiers that we constructed to return the final probability prediction. The method could predict subcellular localizations in both single- and multilabel locations, based on the voting probability. Experimental results indicated that the proposed ensemble method could achieve correct classification with an overall accuracy of 84.58% for 11 compartments, on the basis of the testing dataset.