Biomineralization through a Symmetry-Controlled Oligomeric Peptide.

Biomineralization peptides are versatile tools for generating nanostructures since they can make specific interactions with various inorganic metals, which can lead to the formation of intricate nanostructures. Previously, we examined the influence that multivalency has on inorganic structures formed by p53 tetramer-based biomineralization peptides and noted a connection between the geometry of the peptide and its ability to regulate nanostructure formation. To investigate the role of multivalency in nanostructure formation by biomineralization peptides more thoroughly, silver biomineralization peptides were engineered by linking them to additional self-assembling molecules based on coiled-coil peptides and multistranded DNA oligomers. Under mild reducing conditions at room temperature, these engineered biomineralization peptides self-assembled and formed silver nanostructures. The trimeric forms of the biomineralization peptides were the most efficient in forming a hexagonal disk nanostructure, with both the coiled-coil peptide and DNA-based multimeric forms. Together, the results suggest that the spatial arrangement of biomineralization peptides plays a more important role in regulating nanostructure formation than their valency.

Bilayer Hydrogel Composed of Elastin-Mimetic Polypeptides as a Bio-Actuator with Bidirectional and Reversible Bending Behaviors.

Biologically derived hydrogels have attracted attention as promising polymers for use in biomedical applications because of their high biocompatibility, biodegradability, and low toxicity. Elastin-mimetic polypeptides (EMPs), which contain a repeated amino acid sequence derived from the hydrophobic domain of tropoelastin, exhibit reversible phase transition behavior, and thus, represent an interesting starting point for the development of biologically derived hydrogels. In this study, we succeeded in developing functional EMP-conjugated hydrogels that displayed temperature-responsive swelling/shrinking properties. The EMP-conjugated hydrogels were prepared through the polymerization of acrylated EMP with acrylamide. The EMP hydrogel swelled and shrank in response to temperature changes, and the swelling/shrinking capacity of the EMP hydrogels could be controlled by altering either the amount of EMP or the salt concentration in the buffer. The EMP hydrogels were able to select a uniform component of EMPs with a desired and specific repeat number of the EMP sequence, which could control the swelling/shrinking property of the EMP hydrogel. Moreover, we developed a smart hydrogel actuator based on EMP crosslinked hydrogels and non-crosslinked hydrogels that exhibited bidirectional curvature behavior in response to changes in temperature. These thermally responsive EMP hydrogels have potential use as bio-actuators for a number of biomedical applications.

Sequence rules for gold-binding peptides.

Metal-binding peptides play a central role in bionanotechnology, wherein they are responsible for directing growth and influencing the resulting properties of inorganic nanomaterials. One of the key advantages of using peptides to create nanomaterials is their versatility, wherein subtle changes in the sequence can have a dramatic effect on the structure and properties of the nanomaterial. However, precisely knowing which position and which amino acid should be modified within a given sequence to enhance a specific property can be a daunting challenge owing to combinatorial complexity. In this study, classification based on association rules was performed using 860 gold-binding peptides. Using a minimum support threshold of 0.035 and confidence of 0.9, 30 rules with confidence and lift values greater than 0.9 and 1, respectively, were extracted that can differentiate high-binding from low-binding peptides. The test performance of these rules for categorizing the peptides was found to be satisfactory, as characterized by accuracy = 0.942, F1 = 0.941, MCC = 0.884. What stands out from the extracted rules are the importance of tryptophan and arginine residues in differentiating peptides with high binding affinity from those with low affinity. In addition, the association rules revealed that positions 2 and 4 within a decapeptide are frequently involved in the rules, thus suggesting their importance in influencing peptide binding affinity to AuNPs. Collectively, this study identified sequence rules that may be used to design peptides with high binding affinity.

Non-endoscopic Applications of Machine Learning in Gastric Cancer: A Systematic Review.

PURPOSE: Gastric cancer is an important health burden characterized by high prevalence and mortality rate. Upper gastrointestinal endoscopy coupled with biopsy is the primary means in which gastric cancer is diagnosed, and most of machine learning (ML) tools are developed in this area. This systematic review focuses on the applications of ML in gastric cancer that do not involve endoscopic image recognition. METHODS: A systematic review of ML applications that do not involve endoscopy and are relevant to gastric cancer was performed in two databases and independently evaluated by the two authors. Information collected from the included studies are year of publication, ML algorithm, ML performance, specimen used to create the ML model, and clinical application of the model. RESULTS: From 791 screened studies, 63 studies were included in the systematic review. The included studies demonstrate that the non-endoscopic applications of ML can be divided into three main categories, which are diagnostics, predicting response to therapy, and prognosis prediction. Various specimen and algorithms were found to be used for these applications. Most of its clinical use includes histopathologic slide reading in the diagnosis of gastric cancer and a risk scoring system to determine the survival of patients or to determine the important variables that may affect the patient's prognosis. CONCLUSION: The systematic review suggests that there are numerous examples of non-endoscopic applications of ML that are relevant to gastric cancer. These studies have utilized various specimens, even non-conventional ones, thus showing great promise for the development of more non-invasive techniques. However, most of these studies are still in the early stages and will take more time before they can be clinically deployed. Moving forward, researchers in this field of study are encouraged to improve data curation and annotation, improve model interpretability, and compare model performance with the currently accepted standard in the clinical practice.

Interactions between marine megafauna and plastic pollution in Southeast Asia.

Southeast (SE) Asia is a highly biodiverse region, yet it is also estimated to cumulatively contribute a third of the total global marine plastic pollution. This threat is known to have adverse impacts on marine megafauna, however, understanding of its impacts has recently been highlighted as a priority for research in the region. To address this knowledge gap, a structured literature review was conducted for species of cartilaginous fishes, marine mammals, marine reptiles, and seabirds present in SE Asia, collating cases on a global scale to allow for comparison, coupled with a regional expert elicitation to gather additional published and grey literature cases which would have been omitted during the structured literature review. Of the 380 marine megafauna species present in SE Asia, but also studied elsewhere, we found that 9.1 % and 4.5 % of all publications documenting plastic entanglement (n = 55) and ingestion (n = 291) were conducted in SE Asian countries. At the species level, published cases of entanglement from SE Asian countries were available for 10 % or less of species within each taxonomic group. Additionally, published ingestion cases were available primarily for marine mammals and were lacking entirely for seabirds in the region. The regional expert elicitation led to entanglement and ingestion cases from SE Asian countries being documented in 10 and 15 additional species respectively, highlighting the utility of a broader approach to data synthesis. While the scale of the plastic pollution in SE Asia is of particular concern for marine ecosystems, knowledge of its interactions and impacts on marine megafauna lags behind other areas of the world, even after the inclusion of a regional expert elicitation. Additional funding to help collate baseline data are critically needed to inform policy and solutions towards limiting the interactions of marine megafauna and plastic pollution in SE Asia.

Ribosomal protein uL30 undergoes phase separation with nucleophosmin and regulates nucleolar formation in the absence of RNA.

The nucleolus is a membrane-less structure that exists in the nucleus of cells and plays a crucial role in ribosome biogenesis. It is known to be formed through liquid-liquid phase separation (LLPS) caused by the interaction of various nucleolar proteins and nucleic acids. Recently, many studies on LLPS with nucleolar proteins in the presence of RNA showed the importance of electrostatic interactions and cation-pi interactions among RNA and intrinsically disordered regions of proteins. However, it is reported that the initiation of nucleolar formation is RNA polymerase I-independent. The mechanism of nucleolar formation in the early stage remains obscure. In this study, we showed for the first time that the ribosomal protein uL30 and a major nucleolar protein, nucleophosmin (NPM) formed liquid droplets in vitro in the absence of RNA. The liquid droplet formation with uL30 and NPM may be derived from the interaction between the basic regions of uL30 and acidic regions of the oligomeric NPM. The knockdown of uL30 in cells significantly reduced the number of nucleoli, while it did not alter the protein level of NPM. The results showed that LLPS and nucleolar formation were affected by changes in uL30 levels. Our results suggest that the protein-protein interaction between nucleolar proteins may play an important role in nucleolar formation in the early stages when the rRNA content is very low.

Machine Learning Model for Biomimetic Chromatography Peptide Ligands.

Purification is an essential part of antibody production, which are important therapeutic biomolecules. Common methods of antibody purification rely on affinity chromatography (AC), wherein whole proteins are oftentimes used as ligands to catch the antibodies to be purified. While AC has been successful in purifying antibodies, it is associated with multiple challenges such as high cost and low stability, among others. A promising alternative is using short peptide sequences in place of whole proteins as the stationary phase for the chromatographic separation of the antibodies. In an effort to accelerate the discovery and development of short peptides for biomimetic chromatography, this study reports the creation of a machine learning classification which was trained and tested on 480 tetrapeptides. The optimized logistic regression model uses Cruciani properties as the input variables and can categorize peptides into one of two classes based on their binding affinity with immunoglobulin G (IgG). The externally validated model demonstrates satisfactory predictive performance and excellent discrimination as demonstrated by performance metrics such as AUC = 0.874, Balanced Accuracy = 0.874, F1 = 0.871, Precision = 0.884, and Recall = 0.859. Apart from this, the classifier has also provided valuable insights into important variables that influence the classification, such as electrostatic and hydrophobic interactions. Overall, the classifier can be regarded as a welcome development for biomimetic chromatography and is the first study that aims to integrate machine learning in the biomimetic chromatography peptide development process.

A Machine Learning Classification Model for Gold-Binding Peptides.

There has been growing interest in using peptides for the controlled synthesis of nanomaterials. Peptides play a crucial role not only in regulating the nanostructure formation process but also in influencing the resulting properties of the nanomaterials. Leveraging machine learning (ML) in the biomimetic workflow is anticipated to accelerate peptide discovery, make the process more resource-efficient, and unravel associations among attributes that may be useful in peptide design. In this study, a binary ML classifier is formulated that was trained and tested on 1720 peptide examples. The support vector machine classifier uses Kidera factors to categorize peptides into one of two groups based on their binding ability. The classifier exhibits satisfactory performance, as demonstrated by various performance metrics. In addition, key variables that bear a huge impact on the model were identified, such as peptide hydrophobicity. As these trends were derived from a large and diverse dataset, the insights drawn from the data are expected to be generalizable and robust. Thus, the presented ML model is an important step toward the rational and predictive peptide design.

A machine learning regression model for the screening and design of potential SARS-CoV-2 protease inhibitors.

The widespread infection caused by the 2019 novel corona virus (SARS-CoV-2) has initiated global efforts to search for antiviral agents. Drug discovery is the first step in the development of commercially viable pharmaceutical products to deal with novel diseases. In an effort to accelerate the screening and drug discovery workflow for potential SARS-CoV-2 protease inhibitors, a machine learning model that can predict the binding free energies of compounds to the SARS-CoV-2 main protease is presented. The optimized multiple linear regression model, which was trained and tested on 226 natural compounds demonstrates reliable prediction performance (r 2 test = 0.81, RMSE test = 0.43), while only requiring five topological descriptors. The externally validated model can help conserve and maximize available resources by limiting biological assays to compounds that yielded favorable outcomes from the model. The emergence of highly infectious diseases will always be a threat to human health and development, which is why the development of computational tools for rapid response is very important. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s13721-021-00326-2.

Optimization of oxalate-free starch production from Taro flour by oxalate oxidase assisted process.

Taro (Colocasia esculenta) starch is known to possess unique physical and functional properties such as low amylose content, A-crystalline form, small granules, higher swelling power, etc. Due to the presence of significant amount of calcium oxalate crystals, the food industry is reluctant to explore this unique and cheap starch source for various food applications. Traditional processes utilizing various physical and chemical methods to remove oxalate content of starch inevitably change its physical and functional properties. However, using oxalate oxidase can effectively remove oxalates without altering the unique properties of starch. Hence, an attempt was made to optimize oxalate oxidase assisted starch extraction process from taro flour using response surface methodology. A central composite design comprising 20 experimental trials with 10 cube points augmented with six axial points and four replicates at the center point was applied. A mathematical model was developed to show the effect of taro flour concentration, enzyme load and incubation time on the oxalate removal. Validity of the model was experimentally verified and found that 98.3% of total oxalates can be removed under optimal conditions. This is the first report of optimization of the production of starch from taro flour using microbial oxalate oxidase.

Metal-dependent Ser/Thr protein phosphatase PPM family: Evolution, structures, diseases and inhibitors.

Protein phosphatases and kinases control multiple cellular events including proliferation, differentiation, and stress responses through regulating reversible protein phosphorylation, the most important post-translational modification. Members of metal-dependent protein phosphatase (PPM) family, also known as PP2C phosphatases, are Ser/Thr phosphatases that bind manganese/magnesium ions (Mn2+/Mg2+) in their active center and function as single subunit enzymes. In mammals, there are 20 isoforms of PPM phosphatases: PPM1A, PPM1B, PPM1D, PPM1E, PPM1F, PPM1G, PPM1H, PPM1J, PPM1K, PPM1L, PPM1M, PPM1N, ILKAP, PDP1, PDP2, PHLPP1, PHLPP2, PP2D1, PPTC7, and TAB1, whereas there are only 8 in yeast. Phylogenetic analysis of the DNA sequences of vertebrate PPM isoforms revealed that they can be divided into 12 different classes: PPM1A/PPM1B/PPM1N, PPM1D, PPM1E/PPM1F, PPM1G, PPM1H/PPM1J/PPM1M, PPM1K, PPM1L, ILKAP, PDP1/PDP2, PP2D1/PHLPP1/PHLPP2, TAB1, and PPTC7. PPM-family members have a conserved catalytic core region, which contains the metal-chelating residues. The different isoforms also have isoform specific regions within their catalytic core domain and terminal domains, and these regions may be involved in substrate recognition and/or functional regulation of the phosphatases. The twenty mammalian PPM phosphatases are involved in regulating diverse cellular functions, such as cell cycle control, cell differentiation, immune responses, and cell metabolism. Mutation, overexpression, or deletion of the PPM phosphatase gene results in abnormal cellular responses, which lead to various human diseases. This review focuses on the structures and biological functions of the PPM-phosphatase family and their associated diseases. The development of specific inhibitors against the PPM phosphatase family as a therapeutic strategy will also be discussed.

Data on the sequence-derived properties of gastric cancer - binding peptides.

The article presents a dataset containing nine classes of calculated sequence-derived descriptors for 78 peptide sequences, 21 of which demonstrate the ability to bind with gastric cancer cells. The datasaet was used in the paper "A screening algorithm for gastric cancer binding peptides" [1] for the creation of a classification model that can predict the ability of a given peptide sequence to bind with gastric cancer cells. The 78 peptide sequences were extracted from a systematic literature search, and the various peptide descriptors were calculated using the R package "Peptides". The nine calculated sequence-derived descriptor classes are the Blosum indices, Cruciani properties, FASGAI vectors, Kidera factors, ProtFP, ST-scales, T-scales, VHSE scales, and Z-scales. The resulting dataset, which is composed of over 4000 data points, offers a rich resource for further protochemometric analyses of the curated peptide sequences relevant to cancer diagnostics and therapeutics.

Synergic Strategies for the Enhanced Self-Assembly of Biomineralization Peptides for the Synthesis of Functional Nanomaterials.

INTRODUCTION: Peptide-mediated biomineralization is a promising bioinspired technique of nanostructure formation. The biomineralization peptide is responsible for the regulation of the growth and morphology of the inorganic nanostructure. The 3D properties of the biomineralization peptide is a crucial factor in which the success of creating functional nanomaterials depends on. However, given the relatively short sequence of most biomineralization peptides, controlling the multivalency and spatial orientation of the peptide can be a challenging endeavor. OBJECTIVE: This mini-review details recent advances in enhancing the self-assembly and 3D properties of the biomineralization peptide. The design and creation of fusion peptides is highlighted, which involves the conjugation of the biomineralization peptide with a control element. The control element is responsible for directing the self-assembly of the biomineralization peptide. CONCLUSION: A variety of control elements that can direct the self-assembly of biomineralization peptides are available. The control element can be a small organic molecule such as a biphenyl, or a peptide segment such as the p53 tetramerization domain or the amyloid peptide. The high diversity of existing control elements further increases the ability of peptide-mediated biomineralization to create functional nanomaterials.

Oligomerization enhances the binding affinity of a silver biomineralization peptide and catalyzes nanostructure formation.

Binding affinity and specificity are crucial factors that influence nanostructure control by biomineralization peptides. In this paper, we analysed the role that the oligomeric state of a silver biomineralization peptide plays in regulating the morphology of silver nanostructure formation. Oligomerization was achieved by conjugating the silver specific TBP biomineralization peptide to the p53 tetramerization domain peptide (p53Tet). Interestingly, the TBP-p53Tet tetrameric peptide acted as a growth catalyst, controlling silver crystal growth, which resulted in the formation of hexagonal silver nanoplates without consuming the peptide. The TBP-p53Tet peptide caps the surface of the silver crystals, which enhances crystal growth on specific faces and thereby regulates silver nanostructure formation in a catalytic fashion. The present findings not only provide an efficient strategy for controlling silver nanostructure formation by biomineralization peptides, but they also demonstrate that in this case the oligomeric peptides play a unique catalytic role.

Sequence-dependent cluster analysis of biomineralization peptides.

A reliable and statistically valid classification of biomineralization peptides is herein presented. 27 biomineralization peptides (BMPep) were randomly selected as representative samples to establish the classification system using k-means method. These biomineralization peptides were either discovered through isolation from various organisms or via phage display. Our findings show that there are two types of biomineralization peptides based on their length, molecular weight, heterogeneity, and aliphatic residues. Type-1 BMPeps are more commonly found and exhibit higher values for these significant clustering variables. In contrast are the type-2 BMPeps, which have lower values for these parameters and are less common. Through our clustering analysis, a more efficient and systematic approach in BMPep selection is possible since previous methods of BMPep classification are unreliable.

Effects of biomineralization peptide topology on the structure and catalytic activity of Pd nanomaterials.

Highly branched, coral-like Pd nanostructures were formed using a biomineralization peptide conjugated to an oligomeric peptide that simultaneously controls the spatial orientation, arrangement and valency. The Pd nanocoral showed very high catalytic activity in the reduction of nitrophenol. The results highlight the importance of topological arrangement in nanostructure formation and catalytic activity.