Procleave: Predicting Protease-specific Substrate Cleavage Sites by Combining Sequence and Structural Information

Structure of the human heparan-α-glucosaminide N-acetyltransferase (HGSNAT)

Degradation of heparan sulfate (HS), a glycosaminoglycan (GAG) comprised of repeating units of N-acetylglucosamine and glucuronic acid, begins in the cytosol and is completed in the lysosomes. Acetylation of the terminal non-reducing amino group of a-D-glucosamine of HS is essential for its complete breakdown into monosaccharides and free sulfate. Heparan-a-glucosaminide N-acetyltransferase (HGSNAT), a resident of the lysosomal membrane, catalyzes this essential acetylation reaction by accepting and transferring the acetyl group from cytosolic acetyl-CoA to terminal a-D-glucosamine of HS in the lysosomal lumen. Mutation-induced dysfunction in HGSNAT causes abnormal accumulation of HS within the lysosomes and leads to an autosomal recessive neurodegenerative lysosomal storage disorder called mucopolysaccharidosis IIIC (MPS IIIC). There are no approved drugs or treatment strategies to cure or manage the symptoms of, MPS IIIC. Here, we use cryo-electron microscopy (cryo-EM) to determine a high-resolution structure of the HGSNAT-acetyl-CoA complex, the first step in HGSNAT catalyzed acetyltransferase reaction. In addition, we map the known MPS IIIC mutations onto the structure and elucidate the molecular basis for mutation-induced HGSNAT dysfunction.

m6A-TCPred: a web server to predict tissue-conserved human m6A sites using machine learning approach

BACKGROUND

N6-methyladenosine (m6A) is the most prevalent post-transcriptional modification in eukaryotic cells that plays a crucial role in regulating various biological processes, and dysregulation of m6A status is involved in multiple human diseases including cancer contexts. A number of prediction frameworks have been proposed for high-accuracy identification of putative m6A sites, however, none have targeted for direct prediction of tissue-conserved m6A modified residues from non-conserved ones at base-resolution level.

RESULTS

We report here m6A-TCPred, a computational tool for predicting tissue-conserved m6A residues using m6A profiling data from 23 human tissues. By taking advantage of the traditional sequence-based characteristics and additional genome-derived information, m6A-TCPred successfully captured distinct patterns between potentially tissue-conserved m6A modifications and non-conserved ones, with an average AUROC of 0.871 and 0.879 tested on cross-validation and independent datasets, respectively.

CONCLUSION

Our results have been integrated into an online platform: a database holding 268,115 high confidence m6A sites with their conserved information across 23 human tissues; and a web server to predict the conserved status of user-provided m6A collections. The web interface of m6A-TCPred is freely accessible at: www.rnamd.org/m6ATCPred .

Modular and integrative activity reporters enhance biochemical studies in the yeast ER

The yeast endoplasmic reticulum sequestration and screening (YESS) system is a broadly applicable platform to perform high-throughput biochemical studies of post-translational modification enzymes (PTM-enzymes). This system enables researchers to profile and engineer the activity and substrate specificity of PTM-enzymes and to discover inhibitor-resistant enzyme mutants. In this study, we expand the capabilities of YESS by transferring its functional components to integrative plasmids. The YESS integrative system yields uniform protein expression and protease activities in various configurations, allows one to integrate activity reporters at two independent loci and to split the system between integrative and centromeric plasmids. We characterize these integrative reporters with two viral proteases, Tobacco etch virus (TEVp) and 3-chymotrypsin like protease (3CLpro), in terms of coefficient of variance, signal-to-noise ratio and fold-activation. Overall, we provide a framework for chromosomal-based studies that is modular, enabling rigorous high-throughput assays of PTM-enzymes in yeast.

ADCdb: the database of antibody-drug conjugates

Antibody-drug conjugates (ADCs) are a class of innovative biopharmaceutical drugs, which, via their antibody (mAb) component, deliver and release their potent warhead (a.k.a. payload) at the disease site, thereby simultaneously improving the efficacy of delivered therapy and reducing its off-target toxicity. To design ADCs of promising efficacy, it is crucial to have the critical data of pharma-information and biological activities for each ADC. However, no such database has been constructed yet. In this study, a database named ADCdb focusing on providing ADC information (especially its pharma-information and biological activities) from multiple perspectives was thus developed. Particularly, a total of 6572 ADCs (359 approved by FDA or in clinical trial pipeline, 501 in preclinical test, 819 with in-vivo testing data, 1868 with cell line/target testing data, 3025 without in-vivo/cell line/target testing data) together with their explicit pharma-information was collected and provided. Moreover, a total of 9171 literature-reported activities were discovered, which were identified from diverse clinical trial pipelines, model organisms, patient/cell-derived xenograft models, etc. Due to the significance of ADCs and their relevant data, this new database was expected to attract broad interests from diverse research fields of current biopharmaceutical drug discovery. The ADCdb is now publicly accessible at: https://idrblab.org/adcdb/.

Selenium-More than Just a Fortuitous Sulfur Substitute in Redox Biology

Living organisms use selenium mainly in the form of selenocysteine in the active site of oxidoreductases. Here, selenium's unique chemistry is believed to modulate the reaction mechanism and enhance the catalytic efficiency of specific enzymes in ways not achievable with a sulfur-containing cysteine. However, despite the fact that selenium/sulfur have different physicochemical properties, several selenoproteins have fully functional cysteine-containing homologues and some organisms do not use selenocysteine at all. In this review, selected selenocysteine-containing proteins will be discussed to showcase both situations: (i) selenium as an obligatory element for the protein's physiological function, and (ii) selenium presenting no clear advantage over sulfur (functional proteins with either selenium or sulfur). Selenium's physiological roles in antioxidant defence (to maintain cellular redox status/hinder oxidative stress), hormone metabolism, DNA synthesis, and repair (maintain genetic stability) will be also highlighted, as well as selenium's role in human health. Formate dehydrogenases, hydrogenases, glutathione peroxidases, thioredoxin reductases, and iodothyronine deiodinases will be herein featured.

The generation of detergent-insoluble clipped fragments from an ERAD substrate in mammalian cells

Proteostasis ensures the proper synthesis, folding, and trafficking of proteins and is required for cellular and organellar homeostasis. This network also oversees protein quality control within the cell and prevents accumulation of aberrant proteins, which can lead to cellular dysfunction and disease. For example, protein aggregates irreversibly disrupt proteostasis and can exert gain-of-function toxic effects. Although this process has been examined in detail for cytosolic proteins, how endoplasmic reticulum (ER)-tethered, aggregation-prone proteins are handled is ill-defined. To determine how a membrane protein with a cytoplasmic aggregation-prone domain is routed for ER-associated degradation (ERAD), we analyzed a new model substrate, TM-Ubc9ts. In yeast, we previously showed that TM-Ubc9ts ERAD requires Hsp104, which is absent in higher cells. In transient and stable HEK293 cells, we now report that TM-Ubc9ts degradation is largely proteasome-dependent, especially at elevated temperatures. In contrast to yeast, clipped TM-Ubc9ts polypeptides, which are stabilized upon proteasome inhibition, accumulate and are insoluble at elevated temperatures. TM-Ubc9ts cleavage is independent of the intramembrane protease RHBDL4, which clips other classes of ERAD substrates. These studies highlight an unappreciated mechanism underlying the degradation of aggregation-prone substrates in the ER and invite further work on other proteases that contribute to ERAD.

Prediction and design of protease enzyme specificity using a structure-aware graph convolutional network

Site-specific proteolysis by the enzymatic cleavage of small linear sequence motifs is a key posttranslational modification involved in physiology and disease. The ability to robustly and rapidly predict protease-substrate specificity would also enable targeted proteolytic cleavage by designed proteases. Current methods for predicting protease specificity are limited to sequence pattern recognition in experimentally derived cleavage data obtained for libraries of potential substrates and generated separately for each protease variant. We reasoned that a more semantically rich and robust model of protease specificity could be developed by incorporating the energetics of molecular interactions between protease and substrates into machine learning workflows. We present Protein Graph Convolutional Network (PGCN), which develops a physically grounded, structure-based molecular interaction graph representation that describes molecular topology and interaction energetics to predict enzyme specificity. We show that PGCN accurately predicts the specificity landscapes of several variants of two model proteases. Node and edge ablation tests identified key graph elements for specificity prediction, some of which are consistent with known biochemical constraints for protease:substrate recognition. We used a pretrained PGCN model to guide the design of protease libraries for cleaving two noncanonical substrates, and found good agreement with experimental cleavage results. Importantly, the model can accurately assess designs featuring diversity at positions not present in the training data. The described methodology should enable the structure-based prediction of specificity landscapes of a wide variety of proteases and the construction of tailor-made protease editors for site-selectively and irreversibly modifying chosen target proteins.

ProsperousPlus: a one-stop and comprehensive platform for accurate protease-specific substrate cleavage prediction and machine-learning model construction

Proteases contribute to a broad spectrum of cellular functions. Given a relatively limited amount of experimental data, developing accurate sequence-based predictors of substrate cleavage sites facilitates a better understanding of protease functions and substrate specificity. While many protease-specific predictors of substrate cleavage sites were developed, these efforts are outpaced by the growth of the protease substrate cleavage data. In particular, since data for 100+ protease types are available and this number continues to grow, it becomes impractical to publish predictors for new protease types, and instead it might be better to provide a computational platform that helps users to quickly and efficiently build predictors that address their specific needs. To this end, we conceptualized, developed, tested and released a versatile bioinformatics platform, ProsperousPlus, that empowers users, even those with no programming or little bioinformatics background, to build fast and accurate predictors of substrate cleavage sites. ProsperousPlus facilitates the use of the rapidly accumulating substrate cleavage data to train, empirically assess and deploy predictive models for user-selected substrate types. Benchmarking tests on test datasets show that our platform produces predictors that on average exceed the predictive performance of current state-of-the-art approaches. ProsperousPlus is available as a webserver and a stand-alone software package at http://prosperousplus.unimelb-biotools.cloud.edu.au/.

Molecular de-extinction of ancient antimicrobial peptides enabled by machine learning

Molecular de-extinction could offer avenues for drug discovery by reintroducing bioactive molecules that are no longer encoded by extant organisms. To prospect for antimicrobial peptides encrypted within extinct and extant human proteins, we introduce the panCleave random forest model for proteome-wide cleavage site prediction. Our model outperformed multiple protease-specific cleavage site classifiers for three modern human caspases, despite its pan-protease design. Antimicrobial activity was observed in vitro for modern and archaic protein fragments identified with panCleave. Lead peptides showed resistance to proteolysis and exhibited variable membrane permeabilization. Additionally, representative modern and archaic protein fragments showed anti-infective efficacy against A. baumannii in both a skin abscess infection model and a preclinical murine thigh infection model. These results suggest that machine-learning-based encrypted peptide prospection can identify stable, nontoxic peptide antibiotics. Moreover, we establish molecular de-extinction through paleoproteome mining as a framework for antibacterial drug discovery.

De Novo Design of a β-Helix Tau Protein Scaffold: An Oligomer-Selective Vaccine Immunogen Candidate for Alzheimer's Disease

Tau pathology is associated with many neurodegenerative disorders, including Alzheimer's disease (AD), where the spatio-temporal pattern of tau neurofibrillary tangles strongly correlates with disease progression, which motivates therapeutics selective for misfolded tau. Here, we introduce a new avidity-enhanced, multi-epitope approach for protein-misfolding immunogen design, which is predicted to mimic the conformational state of an exposed epitope in toxic tau oligomers. A predicted oligomer-selective tau epitope 343KLDFK347 was scaffolded by designing a β-helix structure that incorporated multiple instances of the 16-residue tau fragment 339VKSEKLDFKDRVQSKI354. Large-scale conformational ensemble analyses involving Jensen-Shannon Divergence and the embedding depth D showed that the multi-epitope scaffolding approach, employed in designing the β-helix scaffold, was predicted to better discriminate toxic tau oligomers than other "monovalent" strategies utilizing a single instance of an epitope for vaccine immunogen design. Using Rosetta, 10,000 sequences were designed and screened for the linker portions of the β-helix scaffold, along with a C-terminal stabilizing α-helix that interacts with the linkers, to optimize the folded structure and stability of the scaffold. Structures were ranked by energy, and the lowest 1% (82 unique sequences) were verified using AlphaFold. Several selection criteria involving AlphaFold are implemented to obtain a lead-designed sequence. The structure was further predicted to have free energetic stability by using Hamiltonian replica exchange molecular dynamics (MD) simulations. The synthesized β-helix scaffold showed direct binding in surface plasmon resonance (SPR) experiments to several antibodies that were raised to the structured epitope using a designed cyclic peptide. Moreover, the strength of binding of these antibodies to in vitro tau oligomers correlated with the strength of binding to the β-helix construct, suggesting that the construct presents an oligomer-like conformation and may thus constitute an effective oligomer-selective immunogen.

Computational design of matrix metalloprotenaise-9 (MMP-9) resistant to auto-cleavage

Matrix metalloproteinase-9 (MMP-9) is an endopeptidase that remodels the extracellular matrix. MMP-9 has been implicated in several diseases including neurodegeneration, arthritis, cardiovascular diseases, fibrosis and several types of cancer, resulting in a high demand for MMP-9 inhibitors for therapeutic purposes. For such drug design efforts, large amounts of MMP-9 are required. Yet, the catalytic domain of MMP-9 (MMP-9Cat) is an intrinsically unstable enzyme that tends to auto-cleave within minutes, making it difficult to use in drug design experiments and other biophysical studies. We set our goal to design MMP-9Cat variant that is active but stable to auto-cleavage. For this purpose, we first identified potential auto-cleavage sites on MMP-9Cat using mass spectroscopy and then eliminated the auto-cleavage site by predicting mutations that minimize auto-cleavage potential without reducing enzyme stability. Four computationally designed MMP-9Cat variants were experimentally constructed and evaluated for auto-cleavage and enzyme activity. Our best variant, Des2, with 2 mutations, was as active as the wild-type enzyme but did not exhibit auto-cleavage after 7 days of incubation at 37°C. This MMP-9Cat variant, with an identical with MMP-9Cat WT active site, is an ideal candidate for drug design experiments targeting MMP-9 and enzyme crystallization experiments. The developed strategy for MMP-9CAT stabilization could be applied to redesign other proteases to improve their stability for various biotechnological applications.

Matrix metalloproteinase-2 proteolyzes mitofusin-2 and impairs mitochondrial function during myocardial ischemia-reperfusion injury

During myocardial ischemia and reperfusion (IR) injury matrix metalloproteinase-2 (MMP-2) is rapidly activated in response to oxidative stress. MMP-2 is a multifunctional protease that cleaves both extracellular and intracellular proteins. Oxidative stress also impairs mitochondrial function which is regulated by different proteins, including mitofusin-2 (Mfn-2), which is lost in IR injury. Oxidative stress and mitochondrial dysfunction trigger the NLRP3 inflammasome and the innate immune response which invokes the de novo expression of an N-terminal truncated isoform of MMP-2 (NTT-MMP-2) at or near mitochondria. We hypothesized that MMP-2 proteolyzes Mfn-2 during myocardial IR injury, impairing mitochondrial function and enhancing the inflammasome response. Isolated hearts from mice subjected to IR injury (30 min ischemia/40 min reperfusion) showed a significant reduction in left ventricular developed pressure (LVDP) compared to aerobically perfused hearts. IR injury increased MMP-2 activity as observed by gelatin zymography and increased degradation of troponin I, an intracellular MMP-2 target. MMP-2 preferring inhibitors, ARP-100 or ONO-4817, improved post-ischemic recovery of LVDP compared to vehicle perfused IR hearts. In muscle fibers isolated from IR hearts the rates of mitochondrial oxygen consumption and ATP production were impaired compared to those from aerobic hearts, whereas ARP-100 or ONO-4817 attenuated these reductions. IR hearts showed higher levels of NLRP3, cleaved caspase-1 and interleukin-1β in the cytosolic fraction, while the mitochondria-enriched fraction showed reduced levels of Mfn-2, compared to aerobic hearts. ARP-100 or ONO-4817 attenuated these changes. Co-immunoprecipitation showed that MMP-2 is associated with Mfn-2 in aerobic and IR hearts. ARP-100 or ONO-4817 also reduced infarct size and cell death in hearts subjected to 45 min ischemia/120 min reperfusion. Following myocardial IR injury, impaired contractile function and mitochondrial respiration and elevated inflammasome response could be attributed, at least in part, to MMP-2 activation, which targets and cleaves mitochondrial Mfn-2. Inhibition of MMP-2 activity protects against cardiac contractile dysfunction in IR injury in part by preserving Mfn-2 and suppressing inflammation.

Structural journey of an insecticidal protein against western corn rootworm

The broad adoption of transgenic crops has revolutionized agriculture. However, resistance to insecticidal proteins by agricultural pests poses a continuous challenge to maintaining crop productivity and new proteins are urgently needed to replace those utilized for existing transgenic traits. We identified an insecticidal membrane attack complex/perforin (MACPF) protein, Mpf2Ba1, with strong activity against the devastating coleopteran pest western corn rootworm (WCR) and a novel site of action. Using an integrative structural biology approach, we determined monomeric, pre-pore and pore structures, revealing changes between structural states at high resolution. We discovered an assembly inhibition mechanism, a molecular switch that activates pre-pore oligomerization upon gut fluid incubation and solved the highest resolution MACPF pore structure to-date. Our findings demonstrate not only the utility of Mpf2Ba1 in the development of biotechnology solutions for protecting maize from WCR to promote food security, but also uncover previously unknown mechanistic principles of bacterial MACPF assembly.

Modular and integrative activity reporters enhance biochemical studies in the yeast ER

The yeast endoplasmic reticulum sequestration and screening (YESS) system is a generalizable platform that has become highly useful to investigate post-translational modification enzymes (PTM-enzymes). This system enables researchers to profile and engineer the activity and substrate specificity of PTM-enzymes and to discover inhibitor-resistant enzyme mutants. In this study, we expand the capabilities of YESS by transferring its functional components to integrative plasmids. The YESS integrative system yields uniform protein expression and protease activities in various configurations, allows one to integrate activity reporters at two independent loci and to split the system between integrative and centromeric plasmids. We characterize these integrative reporters with two viral proteases, Tobacco etch virus (TEVp) and 3-chymotrypsin like protease (3CL pro ), in terms of coefficient of variance, signal-to-noise ratio and fold-activation. Overall, we provide a framework for chromosomal-based studies that is modular, enabling rigorous high-throughput assays of PTM-enzymes in yeast.

Attenuation hotspots in neurotropic human astroviruses

During the last decade, the detection of neurotropic astroviruses has increased dramatically. The MLB genogroup of astroviruses represents a genetically distinct group of zoonotic astroviruses associated with gastroenteritis and severe neurological complications in young children, the immunocompromised, and the elderly. Using different virus evolution approaches, we identified dispensable regions in the 3' end of the capsid-coding region responsible for attenuation of MLB astroviruses in susceptible cell lines. To create recombinant viruses with identified deletions, MLB reverse genetics (RG) and replicon systems were developed. Recombinant truncated MLB viruses resulted in imbalanced RNA synthesis and strong attenuation in iPSC-derived neuronal cultures confirming the location of neurotropism determinants. This approach can be used for the development of vaccine candidates using attenuated astroviruses that infect humans, livestock animals, and poultry.

Predicting Structural Susceptibility of Proteins to Proteolytic Processing

The importance of 3D protein structure in proteolytic processing is well known. However, despite the plethora of existing methods for predicting proteolytic sites, only a few of them utilize the structural features of potential substrates as predictors. Moreover, to our knowledge, there is currently no method available for predicting the structural susceptibility of protein regions to proteolysis. We developed such a method using data from CutDB, a database that contains experimentally verified proteolytic events. For prediction, we utilized structural features that have been shown to influence proteolysis in earlier studies, such as solvent accessibility, secondary structure, and temperature factor. Additionally, we introduced new structural features, including length of protruded loops and flexibility of protein termini. To maximize the prediction quality of the method, we carefully curated the training set, selected an appropriate machine learning method, and sampled negative examples to determine the optimal positive-to-negative class size ratio. We demonstrated that combining our method with models of protease primary specificity can outperform existing bioinformatics methods for the prediction of proteolytic sites. We also discussed the possibility of utilizing this method for bioinformatics prediction of other post-translational modifications.

The MMP-2 histone H3 N-terminal tail protease is selectively targeted to the transcription start sites of active genes

BACKGROUND

Proteolysis of the histone H3 N-terminal tail (H3NT) is an evolutionarily conserved epigenomic feature of nearly all eukaryotes, generating a cleaved H3 product that is retained in ~ 5-10% of the genome. Although H3NT proteolysis within chromatin was first reported over 60 years ago, the genomic sites targeted for H3NT proteolysis and the impact of this histone modification on chromatin structure and function remain largely unknown. The goal of this study was to identify the specific regions targeted for H3NT proteolysis and investigate the consequence of H3NT "clipping" on local histone post-translational modification (PTM) dynamics.

RESULTS

Leveraging recent findings that matrix metalloproteinase 2 (MMP-2) functions as the principal nuclear H3NT protease in the human U2OS osteosarcoma cell line, a ChIP-Seq approach was used to map MMP-2 localization genome wide. The results indicate that MMP-2 is selectively targeted to the transcription start sites (TSSs) of protein coding genes, primarily at the + 1 nucleosome. MMP-2 localization was exclusive to highly expressed genes, further supporting a functional role for H3NT proteolysis in transcriptional regulation. MMP-2 dependent H3NT proteolysis at the TSSs of these genes resulted in a > twofold reduction of activation-associated histone H3 PTMs, including H3K4me3, H3K9ac and H3K18ac. One of genes requiring MMP-2 mediated H3NT proteolysis for proficient expression was the lysosomal cathepsin B protease (CTSB), which we discovered functions as a secondary nuclear H3NT protease in U2OS cells.

CONCLUSIONS

This study revealed that the MMP-2 H3NT protease is selectively targeted to the TSSs of active protein coding genes in U2OS cells. The resulting H3NT proteolysis directly alters local histone H3 PTM patterns at TSSs, which likely functions to regulate transcription. MMP-2 mediated H3NT proteolysis directly activates CTSB, a secondary H3NT protease that generates additional cleaved H3 products within chromatin.

Computational design of Matrix Metalloprotenaise-9 (MMP-9) resistant to auto-cleavage

Matrix metalloproteinase-9 (MMP-9) is an endopeptidase that remodels the extracellular matrix and has been implicated as a major driver in cancer metastasis. Hence, there is a high demand for MMP-9 inhibitors for therapeutic purposes. For such drug design efforts, large amounts of MMP-9 are required. Yet, the catalytic domain of MMP-9 (MMP-9 Cat ) is an intrinsically unstable enzyme that tends to auto-cleave within minutes, making it difficult to use in drug design experiments and other biophysical studies. We set our goal to design MMP-9 Cat variant that is active but stable to autocleavage. For this purpose, we first identified potential autocleavage sites on MMP-9 Cat using mass spectroscopy and then eliminated the autocleavage site by predicting mutations that minimize autocleavage potential without reducing enzyme stability. Four computationally designed MMP-9 Cat variants were experimentally constructed and evaluated for auto-cleavage and enzyme activity. Our best variant, Des2, with 2 mutations, was as active as the wild-type enzyme but did not exhibit auto-cleavage after seven days of incubation at 37°C. This MMP-9 Cat variant, with an identical to MMP- 9 Cat WT active site, is an ideal candidate for drug design experiments targeting MMP-9 and enzyme crystallization experiments. The developed strategy for MMP-9 CAT stabilization could be applied to redesign of other proteases to improve their stability for various biotechnological applications.

Proteome integral solubility alteration high-throughput proteomics assay identifies Collectin-12 as a non-apoptotic microglial caspase-3 substrate

Caspases are a family of proteins mostly known for their role in the activation of the apoptotic pathway leading to cell death. In the last decade, caspases have been found to fulfill other tasks regulating the cell phenotype independently to cell death. Microglia are the immune cells of the brain responsible for the maintenance of physiological brain functions but can also be involved in disease progression when overactivated. We have previously described non-apoptotic roles of caspase-3 (CASP3) in the regulation of the inflammatory phenotype of microglial cells or pro-tumoral activation in the context of brain tumors. CASP3 can regulate protein functions by cleavage of their target and therefore could have multiple substrates. So far, identification of CASP3 substrates has been performed mostly in apoptotic conditions where CASP3 activity is highly upregulated and these approaches do not have the capacity to uncover CASP3 substrates at the physiological level. In our study, we aim at discovering novel substrates of CASP3 involved in the normal regulation of the cell. We used an unconventional approach by chemically reducing the basal level CASP3-like activity (by DEVD-fmk treatment) coupled to a Mass Spectrometry screen (PISA) to identify proteins with different soluble amounts, and consequently, non-cleaved proteins in microglia cells. PISA assay identified several proteins with significant change in their solubility after DEVD-fmk treatment, including a few already known CASP3 substrates which validated our approach. Among them, we focused on the Collectin-12 (COLEC12 or CL-P1) transmembrane receptor and uncovered a potential role for CASP3 cleavage of COLEC12 in the regulation of the phagocytic capacity of microglial cells. Taken together, these findings suggest a new way to uncover non-apoptotic substrates of CASP3 important for the modulation of microglia cell physiology.

Prediction and Design of Protease Enzyme Specificity Using a Structure-Aware Graph Convolutional Network

Site-specific proteolysis by the enzymatic cleavage of small linear sequence motifs is a key post-translational modification involved in physiology and disease. The ability to robustly and rapidly predict protease substrate specificity would also enable targeted proteolytic cleavage - editing - of a target protein by designed proteases. Current methods for predicting protease specificity are limited to sequence pattern recognition in experimentally-derived cleavage data obtained for libraries of potential substrates and generated separately for each protease variant. We reasoned that a more semantically rich and robust model of protease specificity could be developed by incorporating the three-dimensional structure and energetics of molecular interactions between protease and substrates into machine learning workflows. We present Protein Graph Convolutional Network (PGCN), which develops a physically-grounded, structure-based molecular interaction graph representation that describes molecular topology and interaction energetics to predict enzyme specificity. We show that PGCN accurately predicts the specificity landscapes of several variants of two model proteases: the NS3/4 protease from the Hepatitis C virus (HCV) and the Tobacco Etch Virus (TEV) proteases. Node and edge ablation tests identified key graph elements for specificity prediction, some of which are consistent with known biochemical constraints for protease:substrate recognition. We used a pre-trained PGCN model to guide the design of TEV protease libraries for cleaving two non-canonical substrates, and found good agreement with experimental cleavage results. Importantly, the model can accurately assess designs featuring diversity at positions not present in the training data. The described methodology should enable the structure-based prediction of specificity landscapes of a wide variety of proteases and the construction of tailor-made protease editors for site-selectively and irreversibly modifying chosen target proteins.

Predicting Pseudouridine Sites with Porpoise

Pseudouridine is a ubiquitous RNA modification and plays a crucial role in many biological processes. However, it remains a challenging task to identify pseudouridine sites using expensive and time-consuming experimental research. To this end, we present Porpoise, a computational approach to identify pseudouridine sites from RNA sequence data. Porpoise builds on a stacking ensemble learning framework with several informative features and achieves competitive performance compared with state-of-the-art approaches. This protocol elaborates on step-by-step use and execution of the local stand-alone version and the webserver of Porpoise. In addition, we also provide a general machine learning framework that can help identify the optimal stacking ensemble learning model using different combinations of feature-based features. This general machine learning framework can facilitate users to build their pseudouridine predictors using their in-house datasets.

Approaches to Avoid Proteolysis During Protein Expression and Purification

All cells contain proteases, which hydrolyze the peptide bonds between amino acids of a protein backbone. Typically, proteases are prevented from nonspecific proteolysis by regulation and by their physical separation into different subcellular compartments; however, this segregation is not retained during cell lysis, which is the initial step in any protein isolation procedure. Prevention of proteolysis during protein purification often takes the form of a two-pronged approach: first, inhibition of proteolysis in situ, followed by the early separation of the protease from the protein of interest via chromatographic purification. Protease inhibitors are routinely used to limit the effect of the proteases before they are physically separated from the protein of interest via column chromatography. In this chapter, commonly used approaches to reducing or avoiding proteolysis during protein expression and purification are reviewed.

The cholesteryl-ester transfer protein isoform (CETPI) and derived peptides: new targets in the study of Gram-negative sepsis

BACKGROUND

Sepsis is a syndrome where the dysregulated host response to infection threatens the life of the patient. The isoform of the cholesteryl-ester transfer protein (CETPI) is synthesized in the small intestine, and it is present in human plasma. CETPI and peptides derived from its C-terminal sequence present the ability to bind and deactivate bacterial lipopolysaccharides (LPS). The present study establishes the relationship between the plasma levels of CETPI and disease severity of sepsis due to Gram-negative bacteria.

METHODS

Plasma samples from healthy subjects and patients with positive blood culture for Gram-negative bacteria were collected at the Intensive Care Unit (ICU) of INCMNSZ (Mexico City). 47 healthy subjects, 50 patients with infection, and 55 patients with sepsis and septic shock, were enrolled in this study. CETPI plasma levels were measured by an enzyme-linked immunosorbent assay and its expression confirmed by Western Blot analysis. Plasma cytokines (IL-1β, TNFα, IL-6, IL-8, IL-12p70, IFNγ, and IL-10) were measured in both, healthy subjects, and patients, and directly correlated with their CETPI plasma levels and severity of clinical parameters. Sequential Organ Failure Assessment (SOFA) scores were evaluated at ICU admission and within 24 h of admission. Plasma LPS and CETPI levels were also measured and studied in patients  with liver dysfunction.

RESULTS

The level of CETPI in plasma was found to be higher in patients with positive blood culture for Gram-negative bacteria that in control subjects, showing a direct correlation with their SOFA values. Accordingly, septic shock patients showing a high CETPI plasma concentration, presented a negative correlation with cytokines IL-8, IL-1β, and IL-10. Also, in patients  with liver dysfunction, since higher CETPI levels correlated with a high plasma LPS concentration, LPS neutralization carried out by CETPI might be considered a physiological response that will have to be studied in detail.

CONCLUSIONS

Elevated levels of plasma CETPI were associated with disease severity and organ failure in patients  with Gram-negative bacteraemia, defining CETPI as a protein implicated in the systemic response to LPS.

Prediction of HIV-1 protease cleavage site from octapeptide sequence information using selected classifiers and hybrid descriptors

Background

In most parts of the world, especially in underdeveloped countries, acquired immunodeficiency syndrome (AIDS) still remains a major cause of death, disability, and unfavorable economic outcomes. This has necessitated intensive research to develop effective therapeutic agents for the treatment of human immunodeficiency virus (HIV) infection, which is responsible for AIDS. Peptide cleavage by HIV-1 protease is an essential step in the replication of HIV-1. Thus, correct and timely prediction of the cleavage site of HIV-1 protease can significantly speed up and optimize the drug discovery process of novel HIV-1 protease inhibitors. In this work, we built and compared the performance of selected machine learning models for the prediction of HIV-1 protease cleavage site utilizing a hybrid of octapeptide sequence information comprising bond composition, amino acid binary profile (AABP), and physicochemical properties as numerical descriptors serving as input variables for some selected machine learning algorithms. Our work differs from antecedent studies exploring the same subject in the combination of octapeptide descriptors and method used. Instead of using various subsets of the dataset for training and testing the models, we combined the dataset, applied a 3-way data split, and then used a "stratified" 10-fold cross-validation technique alongside the testing set to evaluate the models.

Results

Among the 8 models evaluated in the “stratified” 10-fold CV experiment, logistic regression, multi-layer perceptron classifier, linear discriminant analysis, gradient boosting classifier, Naive Bayes classifier, and decision tree classifier with AUC, F-score, and B. Acc. scores in the ranges of 0.91–0.96, 0.81–0.88, and 80.1–86.4%, respectively, have the closest predictive performance to the state-of-the-art model (AUC 0.96, F-score 0.80 and B. Acc. ~ 80.0%). Whereas, the perceptron classifier and the K-nearest neighbors had statistically lower performance (AUC 0.77–0.82, F-score 0.53–0.69, and B. Acc. 60.0–68.5%) at p < 0.05. On the other hand, logistic regression, and multi-layer perceptron classifier (AUC of 0.97, F-score > 0.89, and B. Acc. > 90.0%) had the best performance on further evaluation on the testing set, though linear discriminant analysis, gradient boosting classifier, and Naive Bayes classifier equally performed well (AUC > 0.94, F-score > 0.87, and B. Acc. > 86.0%).

Conclusions

Logistic regression and multi-layer perceptron classifiers have comparable predictive performances to the state-of-the-art model when octapeptide sequence descriptors consisting of AABP, bond composition and standard physicochemical properties are used as input variables. In our future work, we hope to develop a standalone software for HIV-1 protease cleavage site prediction utilizing the linear regression algorithm and the aforementioned octapeptide sequence descriptors.

PreAcrs: a machine learning framework for identifying anti-CRISPR proteins

BACKGROUND

Anti-CRISPR proteins are potent modulators that inhibit the CRISPR-Cas immunity system and have huge potential in gene editing and gene therapy as a genome-editing tool. Extensive studies have shown that anti-CRISPR proteins are essential for modifying endogenous genes, promoting the RNA-guided binding and cleavage of DNA or RNA substrates. In recent years, identifying and characterizing anti-CRISPR proteins has become a hot and significant research topic in bioinformatics. However, as most anti-CRISPR proteins fall short in sharing similarities to those currently known, traditional screening methods are time-consuming and inefficient. Machine learning methods could fill this gap with powerful predictive capability and provide a new perspective for anti-CRISPR protein identification.

RESULTS

Here, we present a novel machine learning ensemble predictor, called PreAcrs, to identify anti-CRISPR proteins from protein sequences directly. Three features and eight different machine learning algorithms were used to train PreAcrs. PreAcrs outperformed other existing methods and significantly improved the prediction accuracy for identifying anti-CRISPR proteins.

CONCLUSIONS

In summary, the PreAcrs predictor achieved a competitive performance for predicting new anti-CRISPR proteins in terms of accuracy and robustness. We anticipate PreAcrs will be a valuable tool for researchers to speed up the research process. The source code is available at: https://github.com/Lyn-666/anti_CRISPR.git .

Nutrient smuggling: Commensal gut bacteria-derived extracellular vesicles scavenge vitamin B12 and related cobamides for microbe and host acquisition

The processes by which bacteria proactively scavenge essential nutrients in crowded environments such as the gastrointestinal tract are not fully understood. In this context, we observed that bacterial extracellular vesicles (BEVs) produced by the human commensal gut microbe Bacteroides thetaiotaomicron contain multiple high-affinity vitamin B12 binding proteins suggesting that the vesicles play a role in micronutrient scavenging. Vitamin B12 belongs to the cobamide family of cofactors that regulate microbial communities through their limited bioavailability. We show that B. thetaiotaomicron derived BEVs bind a variety of cobamides and not only deliver them back to the parental bacterium but also sequester the micronutrient from competing bacteria. Additionally, Caco-2 cells, representing a model intestinal epithelial barrier, acquire cobamide-bound vesicles and traffic them to lysosomes, thereby mimicking the physiological cobalamin-specific intrinsic factor-mediated uptake process. Our findings identify a novel cobamide binding activity associated with BEVs with far-reaching implications for microbiota and host health.

Strengthening of enterococcal biofilms by Esp

Multidrug-resistant (MDR) Enterococcus faecalis are major causes of hospital-acquired infections. Numerous clinical strains of E. faecalis harbor a large pathogenicity island that encodes enterococcal surface protein (Esp), which is suggested to promote biofilm production and virulence, but this remains controversial. To resolve this issue, we characterized the Esp N-terminal region, the portion implicated in biofilm production. Small angle X-ray scattering indicated that the N-terminal region had a globular head, which consisted of two DEv-Ig domains as visualized by X-ray crystallography, followed by an extended tail. The N-terminal region was not required for biofilm production but instead significantly strengthened biofilms against mechanical or degradative disruption, greatly increasing retention of Enterococcus within biofilms. Biofilm strengthening required low pH, which resulted in Esp unfolding, aggregating, and forming amyloid-like structures. The pH threshold for biofilm strengthening depended on protein stability. A truncated fragment of the first DEv-Ig domain, plausibly generated by a host protease, was the least stable and sufficient to strengthen biofilms at pH ≤ 5.0, while the entire N-terminal region and intact Esp on the enterococcal surface was more stable and required a pH ≤ 4.3. These results suggested a virulence role of Esp in strengthening enterococcal biofilms in acidic abiotic or host environments.

The bacterium Enterococcus faecalis is part of the normal microbiome but can also cause serious hospital-acquired infections. Enterococcus strains isolated from hospitals tend to have certain proteins not found in microbiome strains. Such proteins are therefore likely to be important in infection. We sought to understand the function of one such protein, Esp, through biochemical, biophysical, and microbiological techniques. We found that Esp, which is on the bacterial surface, formed amyloid-like fibrils that prevented removal of biofilms. Biofilms are bacterial communities enmeshed within a matrix, and form within the body or on inert objects like catheters. They promote infection by increasing resistance to antibiotics and interfering with clearance by the immune system. We observed that biofilms that lacked Esp could be disrupted much more easily than those that had Esp. We also found that Esp acted only at low pH (i.e., acidic conditions). Exactly how low a pH depended on whether Esp remained on the bacterial surface or was liberated from the surface by a protease, with a human intestinal protease being a likely cause of liberation. In summary, we found that Esp acts at acidic conditions and likely contributes to virulence by preventing the dispersal of biofilms.

PredPromoter-MF(2L): A Novel Approach of Promoter Prediction Based on Multi-source Feature Fusion and Deep Forest

Promoters short DNA sequences play vital roles in initiating gene transcription. However, it remains a challenge to identify promoters using conventional experiment techniques in a high-throughput manner. To this end, several computational predictors based on machine learning models have been developed, while their performance is unsatisfactory. In this study, we proposed a novel two-layer predictor, called PredPromoter-MF(2L), based on multi-source feature fusion and ensemble learning. PredPromoter-MF(2L) was developed based on various deep features learned by a pre-trained deep learning network model and sequence-derived features. Feature selection based on XGBoost was applied to reduce fused features dimensions, and a cascade deep forest model was trained on the selected feature subset for promoter prediction. The results both fivefold cross-validation and independent test demonstrated that PredPromoter-MF(2L) outperformed state-of-the-art methods.

Protease Activity Analysis: A Toolkit for Analyzing Enzyme Activity Data

Analyzing the activity of proteases and their substrates is critical to defining the biological functions of these enzymes and to designing new diagnostics and therapeutics that target protease dysregulation in disease. While a wide range of databases and algorithms have been created to better predict protease cleavage sites, there is a dearth of computational tools to automate analysis of in vitro and in vivo protease assays. This necessitates individual researchers to develop their own analytical pipelines, resulting in a lack of standardization across the field. To facilitate protease research, here we present Protease Activity Analysis (PAA), a toolkit for the preprocessing, visualization, machine learning analysis, and querying of protease activity data sets. PAA leverages a Python-based object-oriented implementation that provides a modular framework for streamlined analysis across three major components. First, PAA provides a facile framework to query data sets of synthetic peptide substrates and their cleavage susceptibilities across a diverse set of proteases. To complement the database functionality, PAA also includes tools for the automated analysis and visualization of user-input enzyme-substrate activity measurements generated through in vitro screens against synthetic peptide substrates. Finally, PAA supports a set of modular machine learning functions to analyze in vivo protease activity signatures that are generated by activity-based sensors. Overall, PAA offers the protease community a breadth of computational tools to streamline research, taking a step toward standardizing data analysis across the field and in chemical biology and biochemistry at large.

Deep Learning-Based Advances In Protein Posttranslational Modification Site and Protein Cleavage Prediction

Posttranslational modification (PTM ) is a ubiquitous phenomenon in both eukaryotes and prokaryotes which gives rise to enormous proteomic diversity. PTM mostly comes in two flavors: covalent modification to polypeptide chain and proteolytic cleavage. Understanding and characterization of PTM is a fundamental step toward understanding the underpinning of biology. Recent advances in experimental approaches, mainly mass-spectrometry-based approaches, have immensely helped in obtaining and characterizing PTMs. However, experimental approaches are not enough to understand and characterize more than 450 different types of PTMs and complementary computational approaches are becoming popular. Recently, due to the various advancements in the field of Deep Learning (DL), along with the explosion of applications of DL to various fields, the field of computational prediction of PTM has also witnessed the development of a plethora of deep learning (DL)-based approaches. In this book chapter, we first review some recent DL-based approaches in the field of PTM site prediction. In addition, we also review the recent advances in the not-so-studied PTM , that is, proteolytic cleavage predictions. We describe advances in PTM prediction by highlighting the Deep learning architecture, feature encoding, novelty of the approaches, and availability of the tools/approaches. Finally, we provide an outlook and possible future research directions for DL-based approaches for PTM prediction.

As in Real Estate, Location Matters: Cellular Expression of Complement Varies Between Macular and Peripheral Regions of the Retina and Supporting Tissues

The cellular events that dictate the initiation of the complement pathway in ocular degeneration, such as age-related macular degeneration (AMD), is poorly understood. Using gene expression analysis (single cell and bulk), mass spectrometry, and immunohistochemistry, we dissected the role of multiple retinal and choroidal cell types in determining the complement homeostasis. Our scRNA-seq data show that the cellular response to early AMD is more robust in the choroid, particularly in fibroblasts, pericytes and endothelial cells. In late AMD, complement changes were more prominent in the retina especially with the expression of the classical pathway initiators. Notably, we found a spatial preference for these differences. Overall, this study provides insights into the heterogeneity of cellular responses for complement expression and the cooperation of neighboring cells to complete the pathway in healthy and AMD eyes. Further, our findings provide new cellular targets for therapies directed at complement.

Matrikines as mediators of tissue remodelling

Extracellular matrix (ECM) proteins confer biomechanical properties, maintain cell phenotype and mediate tissue repair (via release of sequestered cytokines and proteases). In contrast to intracellular proteomes, where proteins are monitored and replaced over short time periods, many ECM proteins function for years (decades in humans) without replacement. The longevity of abundant ECM proteins, such as collagen I and elastin, leaves them vulnerable to damage accumulation and their host organs prone to chronic, age-related diseases. However, ECM protein fragmentation can potentially produce peptide cytokines (matrikines) which may exacerbate and/or ameliorate age- and disease-related ECM remodelling. In this review, we discuss ECM composition, function and degradation and highlight examples of endogenous matrikines. We then critically and comprehensively analyse published studies of matrix-derived peptides used as topical skin treatments, before considering the potential for improvements in the discovery and delivery of novel matrix-derived peptides to skin and internal organs. From this, we conclude that while the translational impact of matrix-derived peptide therapeutics is evident, the mechanisms of action of these peptides are poorly defined. Further, well-designed, multimodal studies are required.

Retinoic acid-induced protein 14 controls dendritic spine dynamics associated with depressive-like behaviors

Dendritic spines are the central postsynaptic machinery that determines synaptic function. The F-actin within dendritic spines regulates their dynamic formation and elimination. Rai14 is an F-actin-regulating protein with a membrane-shaping function. Here, we identified the roles of Rai14 for the regulation of dendritic spine dynamics associated with stress-induced depressive-like behaviors. Rai14-deficient neurons exhibit reduced dendritic spine density in the Rai14^+/- mouse brain, resulting in impaired functional synaptic activity. Rai14 was protected from degradation by complex formation with Tara, and accumulated in the dendritic spine neck, thereby enhancing spine maintenance. Concurrently, Rai14 deficiency in mice altered gene expression profile relevant to depressive conditions and increased depressive-like behaviors. Moreover, Rai14 expression was reduced in the prefrontal cortex of the mouse stress model, which was blocked by antidepressant treatment. Thus, we propose that Rai14-dependent regulation of dendritic spines may underlie the plastic changes of neuronal connections relevant to depressive-like behaviors.

Homotypic fibrillization of TMEM106B across diverse neurodegenerative diseases

Misfolding and aggregation of disease-specific proteins, resulting in the formation of filamentous cellular inclusions, is a hallmark of neurodegenerative disease with characteristic filament structures, or conformers, defining each proteinopathy. Here we show that a previously unsolved amyloid fibril composed of a 135 amino acid C-terminal fragment of TMEM106B is a common finding in distinct human neurodegenerative diseases, including cases characterized by abnormal aggregation of TDP-43, tau, or α-synuclein protein. A combination of cryoelectron microscopy and mass spectrometry was used to solve the structures of TMEM106B fibrils at a resolution of 2.7 Å from postmortem human brain tissue afflicted with frontotemporal lobar degeneration with TDP-43 pathology (FTLD-TDP, n = 8), progressive supranuclear palsy (PSP, n = 2), or dementia with Lewy bodies (DLB, n = 1). The commonality of abundant amyloid fibrils composed of TMEM106B, a lysosomal/endosomal protein, to a broad range of debilitating human disorders indicates a shared fibrillization pathway that may initiate or accelerate neurodegeneration.

Pannexin 2 is expressed in murine skin and promotes UVB-induced apoptosis of keratinocytes

Pannexins (PANX) are a family of three channel-forming membrane glycoproteins expressed in the skin. Previous studies have focused on the role of PANX1 and PANX3 in the regulation of cellular functions in skin cells while PANX2, the largest member of this protein family, has not been investigated. In the current study, we explored the temporal PANX2 expression in murine skin and found that one Panx2 splice variant (Panx2-202) tends to be more abundant at the protein level and is continuously expressed in developed skin. PANX2 was detected in the suprabasal layers of the mouse epidermis and up-regulated in an in vitro model of rat epidermal keratinocyte differentiation. Furthermore, we show that in apoptotic rat keratinocytes, upon UV light B (UVB)-induced caspase-3/7 activation, ectopically overexpressed PANX2 is cleaved in its C-terminal domain at the D416 residue without increasing the apoptotic rate measured by caspase-3/7 activation. Notably, CRISPR-Cas9 mediated genetic deletion of rat Panx2 delays but does not impair caspase-3/7 activation and cytotoxicity in UVB-irradiated keratinocytes. We propose that endogenous PANX2 expression in keratinocytes promotes cell death after UVB insult and may contribute to skin homeostasis.

Computational analysis and prediction of PE_PGRS proteins using machine learning

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PEPPER is the first machine learning-based predictor for PE_PGRS proteins.

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PEPPER is based on lightGBM and various sequence and physicochemical features.

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PEPPER can identify PE_PGRS proteins rapidly and accurately.

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The webserver of PEPPER and stand-alone tool are publicly available at http://web.unimelb-bioinfortools.cloud.edu.au/PEPPER/.

Mycobacterium tuberculosis genome comprises approximately 10% of two families of poorly characterised genes due to their high GC content and highly repetitive nature. The largest sub-group, the proline-glutamic acid polymorphic guanine-cytosine-rich sequence (PE_PGRS) family, is thought to be involved in host response and disease pathogenicity. Due to their high genetic variability and complexity of analysis, they are typically disregarded for further research in genomic studies. There are currently limited online resources and homology computational tools that can identify and analyse PE_PGRS proteins. In addition, they are computational-intensive and time-consuming, and lack sensitivity. Therefore, computational methods that can rapidly and accurately identify PE_PGRS proteins are valuable to facilitate the functional elucidation of the PE_PGRS family proteins. In this study, we developed the first machine learning-based bioinformatics approach, termed PEPPER, to allow users to identify PE_PGRS proteins rapidly and accurately. PEPPER was built upon a comprehensive evaluation of 13 popular machine learning algorithms with various sequence and physicochemical features. Empirical studies demonstrated that PEPPER achieved significantly better performance than alignment-based approaches, BLASTP and PHMMER, in both prediction accuracy and speed. PEPPER is anticipated to facilitate community-wide efforts to conduct high-throughput identification and analysis of PE_PGRS proteins.

Staem5: A novel computational approachfor accurate prediction of m5C site

5-Methylcytosine (m5C) is an important post-transcriptional modification that has been extensively found in multiple types of RNAs. Many studies have shown that m5C plays vital roles in many biological functions, such as RNA structure stability and metabolism. Computational approaches act as an efficient way to identify m5C sites from high-throughput RNA sequence data and help interpret the functional mechanism of this important modification. This study proposed a novel species-specific computational approach, Staem5, to accurately predict RNA m5C sites in Mus musculus and Arabidopsis thaliana. Staem5 was developed by employing feature fusion tactics to leverage informatic sequence profiles, and a stacking ensemble learning framework combined five popular machine learning algorithms. Extensive benchmarking tests demonstrated that Staem5 outperformed state-of-the-art approaches in both cross-validation and independent tests. We provide the source code of Staem5, which is publicly available at https://github.com/Cxd-626/Staem5.git.

VARIDT 2.0: structural variability of drug transporter

The structural variability data of drug transporter (DT) are key for research on precision medicine and rational drug use. However, these valuable data are not sufficiently covered by the available databases. In this study, a major update of VARIDT (a database previously constructed to provide DTs' variability data) was thus described. First, the experimentally resolved structures of all DTs reported in the original VARIDT were discovered from PubMed and Protein Data Bank. Second, the structural variability data of each DT were collected by literature review, which included: (a) mutation-induced spatial variations in folded state, (b) difference among DT structures of human and model organisms, (c) outward/inward-facing DT conformations and (d) xenobiotics-driven alterations in the 3D complexes. Third, for those DTs without experimentally resolved structural variabilities, homology modeling was further applied as well-established protocol to enrich such valuable data. As a result, 145 mutation-induced spatial variations of 42 DTs, 1622 inter-species structures originating from 292 DTs, 118 outward/inward-facing conformations belonging to 59 DTs, and 822 xenobiotics-regulated structures in complex with 57 DTs were updated to VARIDT (https://idrblab.org/varidt/ and http://varidt.idrblab.net/). All in all, the newly collected structural variabilities will be indispensable for explaining drug sensitivity/selectivity, bridging preclinical research with clinical trial, revealing the mechanism underlying drug-drug interaction, and so on.

Positive-unlabeled learning in bioinformatics and computational biology: a brief review

Conventional supervised binary classification algorithms have been widely applied to address significant research questions using biological and biomedical data. This classification scheme requires two fully labeled classes of data (e.g. positive and negative samples) to train a classification model. However, in many bioinformatics applications, labeling data is laborious, and the negative samples might be potentially mislabeled due to the limited sensitivity of the experimental equipment. The positive unlabeled (PU) learning scheme was therefore proposed to enable the classifier to learn directly from limited positive samples and a large number of unlabeled samples (i.e. a mixture of positive or negative samples). To date, several PU learning algorithms have been developed to address various biological questions, such as sequence identification, functional site characterization and interaction prediction. In this paper, we revisit a collection of 29 state-of-the-art PU learning bioinformatic applications to address various biological questions. Various important aspects are extensively discussed, including PU learning methodology, biological application, classifier design and evaluation strategy. We also comment on the existing issues of PU learning and offer our perspectives for the future development of PU learning applications. We anticipate that our work serves as an instrumental guideline for a better understanding of the PU learning framework in bioinformatics and further developing next-generation PU learning frameworks for critical biological applications.

The Development of Machine Learning Methods in discriminating Secretory Proteins of Malaria Parasite

Malaria caused by Plasmodium falciparum is one of the major infectious diseases in the world. It is essential to exploit an effective method to predict secretory proteins of malaria parasites to develop effective cures and treatment. Biochemical assays can provide details for accurate identification of the secretory proteins, but these methods are expensive and time-consuming. In this paper, we summarized the machine learning-based identification algorithms and compared the construction strategies between different computational methods. Also, we discussed the use of machine learning to improve the ability of algorithms to identify proteins secreted by malaria parasites.

SCO-spondin, a giant matricellular protein that regulates cerebrospinal fluid activity

Cerebrospinal fluid is a clear fluid that occupies the ventricular and subarachnoid spaces within and around the brain and spinal cord. Cerebrospinal fluid is a dynamic signaling milieu that transports nutrients, waste materials and neuroactive substances that are crucial for the development, homeostasis and functionality of the central nervous system. The mechanisms that enable cerebrospinal fluid to simultaneously exert these homeostatic/dynamic functions are not fully understood. SCO-spondin is a large glycoprotein secreted since the early stages of development into the cerebrospinal fluid. Its domain architecture resembles a combination of a matricellular protein and the ligand-binding region of LDL receptor family. The matricellular proteins are a group of extracellular proteins with the capacity to interact with different molecules, such as growth factors, cytokines and cellular receptors; enabling the integration of information to modulate various physiological and pathological processes. In the same way, the LDL receptor family interacts with many ligands, including β-amyloid peptide and different growth factors. The domains similarity suggests that SCO-spondin is a matricellular protein enabled to bind, modulate, and transport different cerebrospinal fluid molecules. SCO-spondin can be found soluble or polymerized into a dynamic threadlike structure called the Reissner fiber, which extends from the diencephalon to the caudal tip of the spinal cord. Reissner fiber continuously moves caudally as new SCO-spondin molecules are added at the cephalic end and are disaggregated at the caudal end. This movement, like a conveyor belt, allows the transport of the bound molecules, thereby increasing their lifespan and action radius. The binding of SCO-spondin to some relevant molecules has already been reported; however, in this review we suggest more than 30 possible binding partners, including peptide β-amyloid and several growth factors. This new perspective characterizes SCO-spondin as a regulator of cerebrospinal fluid activity, explaining its high evolutionary conservation, its apparent multifunctionality, and the lethality or severe malformations, such as hydrocephalus and curved body axis, of knockout embryos. Understanding the regulation and identifying binding partners of SCO-spondin are crucial for better comprehension of cerebrospinal fluid physiology.

Staurosporine-induced cleavage of apoptosis-inducing factor in human fibrosarcoma cells is independent of matrix metalloproteinase-2

Apoptosis-inducing factor (AIF) is a mitochondrial flavoprotein which mediates staurosporine (STS)-induced cell death. AIF cleavage and translocation to the cytosol is thought to be calpain-1-dependent as calpain inhibitors reduced AIF proteolysis. However, many calpain inhibitors also inhibit matrix metalloproteinase-2 (MMP-2) activity, an intracellular and extracellular protease implicated in apoptosis. Here we investigated whether MMP-2 activity is affected in response to STS and if contributes to AIF cleavage. Human fibrosarcoma HT1080 cells were treated with STS (0.1 µM, 0.25-24 hr). A significant increase in cellular MMP-2 activity was seen by gelatin zymography after 6 hr STS treatment, prior to induction of cell necrosis. Western blot showed the time-dependent appearance of two forms of AIF (~60 and 45 kDa) in the cytosol which were significantly increased at 6 hr. Surprisingly, knocking down MMP-2 or inhibiting its activity with MMP-2 preferring inhibitors ARP-100 or ONO-4817, or inhibiting calpain activity with ALLM or PD150606, did not prevent the STS-induced increase in cytosolic AIF. These results show that although STS rapidly increases MMP-2 activity, the cytosolic release of AIF may be independent of the proteolytic activities of MMP-2 or calpain.

Accelerating antibiotic discovery through artificial intelligence

By targeting invasive organisms, antibiotics insert themselves into the ancient struggle of the host-pathogen evolutionary arms race. As pathogens evolve tactics for evading antibiotics, therapies decline in efficacy and must be replaced, distinguishing antibiotics from most other forms of drug development. Together with a slow and expensive antibiotic development pipeline, the proliferation of drug-resistant pathogens drives urgent interest in computational methods that promise to expedite candidate discovery. Strides in artificial intelligence (AI) have encouraged its application to multiple dimensions of computer-aided drug design, with increasing application to antibiotic discovery. This review describes AI-facilitated advances in the discovery of both small molecule antibiotics and antimicrobial peptides. Beyond the essential prediction of antimicrobial activity, emphasis is also given to antimicrobial compound representation, determination of drug-likeness traits, antimicrobial resistance, and de novo molecular design. Given the urgency of the antimicrobial resistance crisis, we analyze uptake of open science best practices in AI-driven antibiotic discovery and argue for openness and reproducibility as a means of accelerating preclinical research. Finally, trends in the literature and areas for future inquiry are discussed, as artificially intelligent enhancements to drug discovery at large offer many opportunities for future applications in antibiotic development.

Large-scale comparative review and assessment of computational methods for anti-cancer peptide identification

Anti-cancer peptides (ACPs) are known as potential therapeutics for cancer. Due to their unique ability to target cancer cells without affecting healthy cells directly, they have been extensively studied. Many peptide-based drugs are currently evaluated in the preclinical and clinical trials. Accurate identification of ACPs has received considerable attention in recent years; as such, a number of machine learning-based methods for in silico identification of ACPs have been developed. These methods promote the research on the mechanism of ACPs therapeutics against cancer to some extent. There is a vast difference in these methods in terms of their training/testing datasets, machine learning algorithms, feature encoding schemes, feature selection methods and evaluation strategies used. Therefore, it is desirable to summarize the advantages and disadvantages of the existing methods, provide useful insights and suggestions for the development and improvement of novel computational tools to characterize and identify ACPs. With this in mind, we firstly comprehensively investigate 16 state-of-the-art predictors for ACPs in terms of their core algorithms, feature encoding schemes, performance evaluation metrics and webserver/software usability. Then, comprehensive performance assessment is conducted to evaluate the robustness and scalability of the existing predictors using a well-prepared benchmark dataset. We provide potential strategies for the model performance improvement. Moreover, we propose a novel ensemble learning framework, termed ACPredStackL, for the accurate identification of ACPs. ACPredStackL is developed based on the stacking ensemble strategy combined with SVM, Naïve Bayesian, lightGBM and KNN. Empirical benchmarking experiments against the state-of-the-art methods demonstrate that ACPredStackL achieves a comparative performance for predicting ACPs. The webserver and source code of ACPredStackL is freely available at http://bigdata.biocie.cn/ACPredStackL/ and https://github.com/liangxiaoq/ACPredStackL, respectively.

Identification of Disease-Related 2-Oxoglutarate/Fe (II)-Dependent Oxygenase Based on Reduced Amino Acid Cluster Strategy

The 2-oxoglutarate/Fe (II)-dependent (2OG) oxygenase superfamily is mainly responsible for protein modification, nucleic acid repair and/or modification, and fatty acid metabolism and plays important roles in cancer, cardiovascular disease, and other diseases. They are likely to become new targets for the treatment of cancer and other diseases, so the accurate identification of 2OG oxygenases is of great significance. Many computational methods have been proposed to predict functional proteins to compensate for the time-consuming and expensive experimental identification. However, machine learning has not been applied to the study of 2OG oxygenases. In this study, we developed OGFE_RAAC, a prediction model to identify whether a protein is a 2OG oxygenase. To improve the performance of OGFE_RAAC, 673 amino acid reduction alphabets were used to determine the optimal feature representation scheme by recoding the protein sequence. The 10-fold cross-validation test showed that the accuracy of the model in identifying 2OG oxygenases is 91.04%. Besides, the independent dataset results also proved that the model has excellent generalization and robustness. It is expected to become an effective tool for the identification of 2OG oxygenases. With further research, we have also found that the function of 2OG oxygenases may be related to their polarity and hydrophobicity, which will help the follow-up study on the catalytic mechanism of 2OG oxygenases and the way they interact with the substrate. Based on the model we built, a user-friendly web server was established and can be friendly accessed at http://bioinfor.imu.edu.cn/ogferaac.

Porpoise: a new approach for accurate prediction of RNA pseudouridine sites

Pseudouridine is a ubiquitous RNA modification type present in eukaryotes and prokaryotes, which plays a vital role in various biological processes. Almost all kinds of RNAs are subject to this modification. However, it remains a great challenge to identify pseudouridine sites via experimental approaches, requiring expensive and time-consuming experimental research. Therefore, computational approaches that can be used to perform accurate in silico identification of pseudouridine sites from the large amount of RNA sequence data are highly desirable and can aid in the functional elucidation of this critical modification. Here, we propose a new computational approach, termed Porpoise, to accurately identify pseudouridine sites from RNA sequence data. Porpoise builds upon a comprehensive evaluation of 18 frequently used feature encoding schemes based on the selection of four types of features, including binary features, pseudo k-tuple composition, nucleotide chemical property and position-specific trinucleotide propensity based on single-strand (PSTNPss). The selected features are fed into the stacked ensemble learning framework to enable the construction of an effective stacked model. Both cross-validation tests on the benchmark dataset and independent tests show that Porpoise achieves superior predictive performance than several state-of-the-art approaches. The application of model interpretation tools demonstrates the importance of PSTNPs for the performance of the trained models. This new method is anticipated to facilitate community-wide efforts to identify putative pseudouridine sites and formulate novel testable biological hypothesis.

SARS-CoV-2 and other human coronaviruses: Mapping of protease recognition sites, antigenic variation of spike protein and their grouping through molecular phylogenetics

In recent years, a total of seven human pathogenic coronaviruses (HCoVs) strains were identified, i.e., SARS-CoV, SARS-CoV-2, MERS-CoV, HCoV-OC43, HCoV-229E, HCoV-NL63, and HCoV-HKU1. Here, we performed an analysis of the protease recognition sites and antigenic variation of the S-protein of these HCoVs. We showed tissue-specific expression pattern, functions, and a number of recognition sites of proteases in S-proteins from seven strains of HCoVs. In the case of SARS-CoV-2, we found two new protease recognition sites, each of calpain-2, pepsin-A, and caspase-8, and one new protease recognition site each of caspase-6, caspase-3, and furin. Our antigenic mapping study of the S-protein of these HCoVs showed that the SARS-CoV-2 virus strain has the most potent antigenic epitopes (highest antigenicity score with maximum numbers of epitope regions). Additionally, the other six strains of HCoVs show common antigenic epitopes (both B-cell and T-cell), with low antigenicity scores compared to SARS-CoV-2. We suggest that the molecular evolution of structural proteins of human CoV can be classified, such as (i) HCoV-NL63 and HCoV-229E, (ii) SARS-CoV-2, and SARS-CoV and (iii) HCoV-OC43 and HCoV-HKU1. In conclusion, we can presume that our study might help to prepare the interventions for the possible HCoVs outbreaks in the future.