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Long-acting delivery (LAD) of ocular therapeutics has potential to improve the standard of care for ocular diseases, such as age-related macular degeneration (AMD), by increasing patient compliance and reducing overall treatment burden on patients and healthcare providers. Although relatively few ocular LAD technologies are currently on the market, a variety of emergent and novel protein engineering-based technologies are being investigated in both the laboratory and clinical settings. Here, we review some of the key indications and treatments that would benefit from the development of LAD for the treatment of ocular diseases and examine the current state of LAD technologies that leverage protein-engineering approaches as well as nascent technologies with potential for future impact.

Fr10 is a secreted freeze-responsive protein found in the wood frog (Rana sylvatica). This protein has gained notable research attention for its highly dynamic expression in response to seasonal freezing stress, while its over-expression has been documented to enhance freeze tolerance in cold-susceptible cultured cells. This study further characterizes the properties of this novel protein with regards to thermal stability and ice recrystallization inhibition (i.e. IRI) activity. Thermal stability was assessed using differential scanning fluorimetry, with an experimental Tm value of 50.8 ± 0.1 °C. Potential IRI activity of Fr10 was evaluated using a recently developed nanoparticle-based colorimetric assay, where Fr10 displayed the ability to prevent freeze-induced aggregation of gold nanoparticles. Based upon this assay, Fr10 protein appeared to have a low level of IRI activity and it was therefore predicted that one of Fr10's biological functions may be to inhibit ice crystal growth via recrystallization. A SPLAT cooling assay was then employed to directly characterize the IRI properties of Fr10 and provide further insight into this hypothesis. In the presence of 30 μM of Fr10, a 40% reduction in the mean grain size of ice crystals relative to the control samples was observed, thus introducing the possibility of Fr10 to inhibit ice recrystallization. Collectively, the results from this study provide new insight into the potential of further exploring the potential of this vertebrate freeze-responsive protein in cryoprotection.

S100A4 is a multiple-function protein highly expressed in tumor or stem cells. We found S100A4 was a novel protein partner for heat shock protein 47 (HSP47) in deer antlerogenic periosteum cells (AP cells), indicating that S100A4 could bind with HSP47. S100A4 had both calcium-dependent and calcium-independent patterns (labeled as SCd and SCi, respectively) to execute different biological activities. Homology models of HSP47, SCd and SCi were constructed. HSP47:collagen model, HSP47:collagen I-V, HSP47:SCd and HSP47:SCi complexes were built using ZDOCK software. Together with free SCd and SCi, 200 ns molecular dynamic (MD) simulations were performed to analyze binding free energies and SCi/SCd conformational changes. The energetic results showed that SCi had the strongest affinity to HSP47, and followed by collagens. SCd had little interaction with HSP47. Decomposition energy results showed that collagen model interacted with HSP47 mainly though neutral amino acids. When SCi bound with HSP47, the majority of mediated amino acids were charged. These results indicated that SCi could compete with collagen on the binding site of HSP47. Root mean square fluctuation (RMSF) values and cross-correlation matrices of principal component analysis (PCA) were calculated to evaluate the SCi/SCd structural variation during MD simulation. Both HSP47 and Ca2+ could stabilize the conformation of SCi/SCd. The loops interacting with Ca2+s and linking the two EF-hand motifs were impacted particularly. The relative moving directions of α-helices in EF-hands were distinct by the binding effect of HSP47 and Ca2+. We found that SCi may regulate the differentiation of AP cells by disturbing the interaction between HSP47 and collagen. Communicated by Ramaswamy H. Sarma.

Determining the stability of downstream process (DSP) intermediates is an extremely important parameter used to maintain product quality attributes within their acceptance ranges. The IgG4 monoclonal antibody studied (mAb1) showed aggregation under acidic conditions, inhibiting the use of low pH treatment to inactivate endogenous retroviruses, and poor virus filtration performance. Both manufacturing steps are included in mAb DSP for viral clearance. The impact of several new compounds on the aggregation and stabilization of mAb1 in process intermediate pools encountered during these critical DSP steps was investigated. Results showed that, in the presence of a protein stabilizer at pH 3.2, 27% less aggregation was observed compared to controls, during the low pH treatment for viral inactivation. The impact of a novel protein stabilizer on virus filter throughput during mAb1 filtration was compared to L-arginine using an innovative high-throughput automation technique. Compared to control experiments without additives, conditions were found where a 70% increase in filter volumetric throughput was achieved in the presence of the novel stabilizer, and a 56% decrease in volumetric throughput observed with L-arginine. These findings present the possibility of using these novel compounds to stabilize proteins during DSP and permitting the use of platform DSP elements such as low pH treatment and high-throughput virus filtration to challenging and unstable proteins.

Here, we reported a novel secreted protein elicitor PeBL2 from Brevibacillus laterosporus A60, which can induce hypersensitive response in tobacco (Nicotiana benthamiana). The ion-exchange chromatography, high-performance liquid chromatography (HPLC) and mass spectrometry were performed for identification of protein elicitor. The 471 bp PeBL2 gene produces a 17.22 kDa protein with 156 amino acids containing an 84-residue signal peptide. Consistent with endogenous protein, the recombinant protein expressed in Escherichia coli induced the typical hypersensitive response (HR) and necrosis in tobacco leaves. Additionally, PeBL2 also triggered early defensive response of generation of reactive oxygen species (H2O2 and O2 -) and systemic resistance against of B. cinerea. Our findings shed new light on a novel strategy for biocontrol using B. laterosporus A60.

The aim of this review was discussion on the new protein and lipid factors and their role in the pathomechanism of preeclampsia. Sphingolipids discussed in the review: Sph (sphingosine), S1P (sphingosine- 1-phosphate), C20-Cer (ceramides containing arachidic acid), C18-Cer (ceramides containing stearic acid), C16-Cer (ceramides containing palmitic acid), C18: 1-Cer (ceramides containing oleic acid), C22-Cer (ceramide containing behenic acid) and C24: 1-Cer (ceramides containing nervonic acid). Angiogenic factors discussed in the review: Hb-EGF (heparin-binding EGF-like growth factor), HGF (hepatocyte growth factor), LIF (leukaemia inhibitory factor), leptin, PDGF-BB (platelet-derived growth factor BB) and follistatin. Based on literature findings, it seems that protein and lipid factors may play an important role in the pathogenesis of preeclampsia, and there are many proteins and lipids that have not been studied in plasma of women with PE to date. We showed a few new pathways in pathomechanism of preeclampsia.

Constrained peptide scaffolds that are tolerant to extensive sequence manipulation and amenable to bioactive peptide design are of great value to the development of novel protein binders and peptide therapeutics. In this work, we reported strategies for the design and synthesis of a kind of novel interchain doubly-bridged α-helical peptides, involving mutual stabilization of two peptide α-helices linked by two interchain bisthioether crosslinkers. By taking a MDM2-binding peptide with an α-helical tendency as a model, we demonstrated that α-helical dimers with significantly improved structural and proteolytic stability and nanomolar binding affinity to the target protein can be obtained. By modulating the surface charges on the dimeric peptides, we also obtained a dimeric peptide with enhanced cell-penetrating capability, which can efficiently penetrate into cancer cells and inhibit the intracellular MDM2-p53 interactions to promote cell apoptosis. Considering that many proteins take a surface α-helical segment as the binding motif to mediate their interactions with other proteins, we believe that our interchain doubly-bridged α-helical peptides would provide a promising scaffold for the development of novel high-affinity protein binders.

Proteins constitute the majority of nature’s worker biomolecules. Designed for specific functions, complex tertiary structures make proteins ideal candidates for analysing natural systems and creating novel biological tools. Owing to both their large size and the need for proper folding, de novo synthesis of proteins has been quite a challenge, leading scientists to focus on modifying protein templates already provided by nature. Recently developed methods for protein modification fall into two broad categories: those that can modify the natural protein template directly and those that require genetic manipulation of the amino acid sequence before modification. The goal of this Review is not only to provide a window through which to view the many opportunities created by novel protein modification techniques‚ but also to act as an initial guide to help scientists find direction and form ideas in an ever-growing field. In addition to highlighting methods reported in the past 5 years, we aim to provide a broader sense of the goals and outcomes of protein modification and bioconjugation in general. While the main body of this paper comprises reactions involving the direct modification of expressed proteins, some further functionalization strategies as well as biological applications are also acknowledged. The discussion concludes by speculating which trends and discoveries will most likely come next in the field. Over the past 5 years, many novel site-selective protein modification techniques have been reported. Key features of these various strategies as well as prominent examples are discussed in this Review.

Sepsis is a life-threatening syndrome characterized by a dysregulated host response to an infection resulting in multiple organ dysfunctions. Early diagnosis and management of sepsis is key to improve patient outcome but remains challenging. Despite extensive research, only few biomarkers have so far proven to be helpful in the diagnosis of sepsis. A novel protein biomarker, the pancreatic stone protein (PSP), is showing great promises. Several lines of evidences suggest that PSP has a higher diagnostic performance for the identification of sepsis than procalcitonin and C-reactive protein, and a strong prognostic value to predict unfavorable outcome at admission to intensive care unit. This review summarizes the current knowledge on the molecular mechanisms of PSP function and the clinical evidences available to highlight the relevance of this protein in the diagnosis and prognosis of sepsis.

Background Meat, an important source of protein and other nutrients in human diets, is one of the major drivers of global environmental change in terms of greenhouse gas emissions, land and water use, animal welfare, human health and directions of breeding. Novel alternatives, including novel meat proxies (cultured meat, plant-based meat alternatives), insects and novel protein sources (like algae) receive increasing attention. But plausible socio-technological pathways for their further development have not yet been compared in an integrative, interdisciplinary perspective. Scope and approach This paper applies an integrated conceptual framework – the Reflexive Integrative Comparative Heuristic (RICH) – to comparatively assess the nutritional implications, potential sustainability gains and required technological and social-institutional change of five meat alternatives. We formulate plausible pathways for each alternative and identify their pre-conditions and implications. Key findings and conclusions High levels of transformation and processing limit the environmental sustainability gains of cultured meat, highly processed plant-based meat alternatives, algae- and insect-based food. At the same time, a high degree of societal coordination is needed to enable the potentially disruptive level of technological, organisational and institutional innovations needed to make these novel alternatives viable. Widespread expectations that solutions require break-through novelties or high-tech alternatives imply a neglect of existing and viable alternatives. Our integrative analysis suggests that the priority given to meat alternatives with limited sustainability potential does not just raise questions of technological optimisation of production systems, but is also a second-order problem of the framing of search directions.

Osteosarcomas are characterized by highly disrupted genomes. Although osteosarcomas lack common fusions, we find evidence of many tumour specific gene-gene fusion transcripts, likely due to chromosomal rearrangements and expression of transcription-induced chimeras. Most of the fusions result in out-of-frame transcripts, potentially capable of producing long novel protein sequences and a plethora of neoantigens. To identify fusions, we explored RNA-sequencing data to obtain detailed knowledge of transcribed fusions, by creating a novel program to compare fusions identified by deFuse to de novo transcripts generated by Trinity. This allowed us to confirm the deFuse results and identify unusual splicing patterns associated with fusion events. Using various existing tools combined with this custom program, we developed a pipeline for the identification of fusion transcripts applicable as targets for immunotherapy. In addition to identifying candidate neoantigens associated with fusions, we were able to use the pipeline to establish a method for measuring the frequency of fusion events, which correlated to patient outcome, as well as highlight some similarities between canine and human osteosarcomas. The results of this study of osteosarcomas underscores the numerous benefits associated with conducting a thorough analysis of fusion events within cancer samples.

Knowing the catalytic turnover numbers of enzymes is essential for understanding the growth rate, proteome composition, and physiology of organisms, but experimental data on enzyme turnover numbers is sparse and noisy. Here, we demonstrate that machine learning can successfully predict catalytic turnover numbers in Escherichia coli based on integrated data on enzyme biochemistry, protein structure, and network context. We identify a diverse set of features that are consistently predictive for both in vivo and in vitro enzyme turnover rates, revealing novel protein structural correlates of catalytic turnover. We use our predictions to parameterize two mechanistic genome-scale modelling frameworks for proteome-limited metabolism, leading to significantly higher accuracy in the prediction of quantitative proteome data than previous approaches. The presented machine learning models thus provide a valuable tool for understanding metabolism and the proteome at the genome scale, and elucidate structural, biochemical, and network properties that underlie enzyme kinetics., Experimental data on enzyme turnover numbers is sparse and noisy. Here, the authors use machine learning to successfully predict enzyme turnover numbers for E. coli, and show that using these to parameterize mechanistic genome-scale models enhances their predictive accuracy.

With the ever-increasing demands for functional and sustainable foods from the general public, there is currently a paradigm shift in the food industry toward the production of novel protein-based diet. Food scientists are therefore motivated to search for natural protein sources and innovative technologies to modify their chemical structure for desirable functionality and thus utilization. Deamidation is a viable, efficient, and attractive approach for modifying proteins owing to its ease of operating, specificity, and cost-effective processes. Over the past three decades, the knowledge of protein deamidation for food applications has evolved drastically, including the development of novel approaches for deamidation, such as protein-glutaminase and ion exchange resin, and their practices in new protein substrate. Thanks to deamidation, enhanced functionalities of food proteins from cereals, legumes, milk, oil seeds and others, and thereby their processabilities as food ingredients have been achieved. Moreover, deamidated proteins have been used to fabricate engineered food colloids, including self-assembled protein particles, protein-metallic complexes, and protein-carbohydrate complexes, which have demonstrated tailored physicochemical properties to modulate oral perception, improve gastrointestinal digestion and bioavailability, and protect and/or deliver bioactive nutrients. Novel bioactivity, altered digestibility, and varied allergenicity of deamidated proteins are increasingly recognized. Therefore, deamidated proteins with novel techno-functional and biological properties hold both promise and challenges for future food applications, and a comprehensive review on this area is critically needed to update our knowledge and provide a better understanding on the protein deamidation and its emerging applications.

Objective Lowering serum phosphorus in people on hemodialysis may improve their survival. However, prior studies have shown that restricting dietary protein intake, a major source of phosphorus, is associated with higher mortality. We hypothesized that a novel metric that incorporates both these values commensurately can improve survival prediction. Methods We used serum phosphorous and normalized protein catabolic rate (nPCR), a surrogate of dietary protein intake, to form a new metric R that was used to examine the associations with mortality in 63,016 people on hemodialysis (HD) of one year after treatment initiation. Survival models were adjusted for case-mix, malnutrition-inflammation cachexia syndrome (MICS), and residual kidney function (RKF). Results Individuals treated with hemodialysis were divided into five groups in accordance with R value. Group 1 included sick individuals with high phosphorous and low nPCR. Group 5 included individuals with low phosphorous and high nPCR. After 1-year follow-up, survival difference between the groups reflected R value, where an increase in R was associated with improved survival. The association of R with mortality was strengthened by adjustment in demographic variables and attenuated after adjustment to MICS. Mortality associations in accordance with R were not influenced by residual kidney function (RKF). Conclusion The novel protein to phosphorus ratio score R predicts mortality in people on dialysis, probably reflecting both nutrition and inflammation state independent of RKF. The metric enables better phosphorus monitoring, although adequate dietary protein intake is ensured and may improve the prediction of outcomes in the clinical setting.

Meat analogue research and development focuses on the production of sustainable products that recreate conventional meat in its physical sensations (texture, appearance, taste, etc.) and nutritional aspects. Minced products, like burger patties and nuggets, muscle-type products, like chicken or steak-like cuts, and emulsion products, like Frankfurter and Mortadella type sausages, are the major categories of meat analogues. In this review, we discuss key ingredients for the production of these novel products, with special focus on protein sources, and underline the importance of ingredient functionality. Our observation is that structuring processes are optimized based on ingredients that were not originally designed for meat analogues applications. Therefore, mixing and blending different plant materials to obtain superior functionality is for now the common practice. We observed though that an alternative approach towards the use of ingredients such as flours, is gaining more interest. The emphasis, in this case, is on functionality towards use in meat analogues, rather than classical functionality such as purity and solubility. Another trend is the exploration of novel protein sources such as seaweed, algae and proteins produced via fermentation (cellular agriculture).

Background: Zinc binding proteins make up a significant proportion of the proteomes of most organisms and, within those proteins, zinc performs rôles in catalysis and structure stabilisation. Identifying the ability to bind zinc in a novel protein can offer insights into its functions and the mechanism by which it carries out those functions. Computational means of doing so are faster than spectroscopic means, allowing for searching at much greater speeds and scales, and thereby guiding complimentary experimental approaches. Typically, computational models of zinc binding predict zinc binding for individual residues rather than as a single binding site, and typically do not distinguish between different classes of binding site—missing crucial properties indicative of zinc binding. Methods: Previously, we created ZincBindDB, a continuously updated database of known zinc binding sites, categorised by family (the set of liganding residues). Here, we use this dataset to create ZincBindPredict, a set of machine learning methods to predict the most common zinc binding site families for both structure and sequence. Results: The models all achieve an MCC ≥ 0.88, recall ≥0.93 and precision ≥0.91 for the structural models (mean MCC = 0.97), while the sequence models have MCC ≥ 0.64, recall ≥0.80 and precision ≥0.83 (mean MCC = 0.87), with the models for binding sites containing four liganding residues performing much better than this. Conclusions: The predictors outperform competing zinc binding site predictors and are available online via a web interface and a GraphQL API.

Recently developed language models (LMs) based on deep neural networks have demonstrated the ability to generate fluent natural language text. LMs pre-trained on protein sequences have shown state of the art performance on a variety of downstream tasks. Protein LMs have also been used to generate novel protein sequences. In the present work we use Gibbs sampling of BERT-style LMs, pre-trained on protein sequences using the masked language modeling task, to generate novel protein sequences. We evaluate the quality of the generated sequences by comparing them to natural sequences from the same family. In particular, we focus on proteins from the chorismate mutase type II family, which has been used in previous work as an example target for protein generative models. We find that the Gibbs sampling process on BERT-style models pretrained on millions to billions of protein sequences is able to generate novel sequences that retain key features of related natural sequences. Further, we find that smaller models fine-tuned or trained from scratch on family-specific data are able to equal or surpass the generation quality of large pre-trained models by some metrics. The ability to generate novel natural-like protein sequences could contribute to the development of improved protein therapeutics and protein-catalysts for industrial chemical production.

Plant-parasitic nematodes are a major threat to food security. The most economically important species have remarkable abilities to manipulate host physiology and immunity. This review highlights recent applications of biotechnological approaches to elucidate the underlying biology on both sides of the interaction. Their obligate biotrophic nature has hindered the development of simple nematode transformation protocols. Instead, transient or stable expression of the effector (native or tagged) in planta has been instrumental in elucidating the biology of plant-nematode interactions. Recent progress in the development of functional genetics tools 'in nematoda' promises further advances. Finally, we discuss how effector research has uncovered novel protein translocation routes in plant cells and may reveal additional unknown biological processes in the future.

The complexation of rice glutelin fibrils (RGFs) with cyanidin-3-O-glucoside (C3G) at acidic condition was investigated. The RGFs at pH 3.5 had a greatest protective effect on the thermal stability of C3G. The binding of C3G for RGFs was exothermic and driven by hydrophobic and electrostatic interactions. The RGFs exhibited a stronger binding interaction with C3G than rice glutelin (RG), resulting from the exposure of hydrophobic groups and positive charges on the fibrils surface, and thus RGFs exhibited better protective effect on C3G. The interaction with C3G resulted in the rearrangement of polypeptide chain, thereby reducing the β-sheet content. The larger aggregates were observed in RG/RGFs-C3G complexes due to protein-polyphenols aggregation. It was noteworthy that the pre-formed RGFs were restructured into entangled aggregates due to the interaction. This study proposed a novel protein fibril to protect anthocyanins, expanding the application of anthocyanins as stable and functional ingredients in acidic food systems.

Background Aberrant activation of the Hedgehog pathway drives tumorigenesis of many cancers, including glioblastoma. However, the sensitization mechanism of the G protein-coupled-like receptor smoothened (SMO), a key component of Hedgehog signaling, remains largely unknown. Results In this study, we describe a novel protein SMO-193a.a. that is essential for Hedgehog signaling activation in glioblastoma. Encoded by circular SMO (circ-SMO), SMO-193a.a. is required for sonic hedgehog (Shh) induced SMO activation, via interacting with SMO, enhancing SMO cholesterol modification, and releasing SMO from the inhibition of patched transmembrane receptors. Deprivation of SMO-193a.a. in brain cancer stem cells attenuates Hedgehog signaling intensity and suppresses self-renewal, proliferation in vitro, and tumorigenicity in vivo. Moreover, circ-SMO/SMO-193a.a. is positively regulated by FUS, a direct transcriptional target of Gli1. Shh/Gli1/FUS/SMO-193a.a. form a positive feedback loop to sustain Hedgehog signaling activation in glioblastoma. Clinically, SMO-193a.a. is more specifically expressed in glioblastoma than SMO and is relevant to Gli1 expression. Higher expression of SMO-193a.a. predicts worse overall survival of glioblastoma patients, indicating its prognostic value. Conclusions Our study reveals that SMO-193a.a., a novel protein encoded by circular SMO, is critical for Hedgehog signaling, drives glioblastoma tumorigenesis and is a novel target for glioblastoma treatment.

Considerable effort has been devoted to generating novel protein- and peptide-based nanomaterials with their applications in a wide range of fields. Specifically, the unique property of proteins to self-assemble has been utilized to create a variety of nanoassemblies, which offer significant possibilities for next-generation biomaterials. In this minireview, we describe self-assembled protein- and peptide-based nanomaterials with focus on nanofibers and nanoparticles. Their applications in delivering therapeutic drugs and genes are discussed.

Theoretical and experimental advances in protein engineering have led to the creation of precisely defined, novel protein assemblies of great size and complexity, with diverse applications. One powerful approach involves designing a new attachment or binding interface between two simpler symmetric oligomeric protein components. The required methods of design, which present both similarities and key differences compared to problems in protein docking, remain challenging, and are not yet routine. With the aim of more fully enabling this emerging area of protein material engineering, we developed a computer program, Nanohedra, to introduce two key advances. First, we encoded in the program the construction rules (i.e. the search space parameters) that underlie all possible symmetric material constructions. Second, we developed algorithms for rapidly identifying favorable docking/interface arrangements based on tabulations of empirical patterns of known protein fragment-pair associations. As a result, the candidate poses that Nanohedra generates for subsequent amino acid interface design appear highly native-like (at the protein backbone level), while simultaneously conforming to the exacting requirements for symmetry-based assembly. A retrospective computational analysis of successful vs failed experimental studies supports the expectation that this should improve the success rate for this challenging area of protein engineering.

Using black soldier fly (BSF, Hermetia illucens) larvae as a novel protein source substituting fishmeal (FM) in animal feeds is globally gaining momentum. BSF can be reared on agro-industrial residues. However, incorporating BSF larval meal (BSFLM) into finisher pig diets has received inadequate attention. This study evaluated the effects of replacing dietary FM with BSFLM on growth, carcass traits and meat quality of finisher pigs. A control diet (including FM: 0% BSFLM) was compared with four dietary levels of replacement of FM with BSFLM at 25, 50, 75 or 100%. Forty hybrid pigs (crossbreeds of purebred Large White and Landrace) were randomly allocated to the five different dietary treatments. Feed intake, body weight gain and feed conversion ratio were measured. After 98 days of feeding, all pigs were slaughtered for the evaluation of carcass and nutritional content of the organ and muscle tissues. Diet significantly affected pig growth performance. Carcass weight of pigs fed diets with BSFLM replacing FM by 50, 75 or 100% (w/w) was higher than for pigs fed control diet with 100% FM as protein source. Crude protein content of pork tissues was high (65-93% on dry-matter basis) across all dietary groups. Therefore, BSFLM can replace FM in pig feed. This is relevant for commercial pig feed production and provides for the first time a nutritional analysis of pork derived from pigs raised on BSFLM.

The Staphylococcal Bap proteins sense environmental signals (such as pH, [Ca2+]) to build amyloid scaffold biofilm matrices via unknown mechanisms. We here report the crystal structure of the aggregation-prone region of Staphylococcus aureus Bap which adopts a dumbbell-shaped fold. The middle module (MM) connecting the N-terminal and C-terminal lobes consists of a tandem of novel double-Ca2+-binding motifs involved in cooperative interaction networks, which undergoes Ca2+-dependent order–disorder conformational switches. The N-terminal lobe is sufficient to mediate amyloid aggregation through liquid–liquid phase separation and maturation, and subsequent biofilm formation under acidic conditions. Such processes are promoted by disordered MM at low [Ca2+] but inhibited by ordered MM stabilized by Ca2+ binding, with inhibition efficiency depending on structural integrity of the interaction networks. These studies illustrate a novel protein switch in pathogenic bacteria and provide insights into the mechanistic understanding of Bap proteins in modulation of functional amyloid and biofilm formation, which could be implemented in the anti-biofilm drug design. This work was supported by grants from the National Natural Science Foundation of China (No. 31872712), the National Key Research and Development Project of China (2016YFA0500700), the Beijing Advanced Innovation Center for Structural Biology, the Tsinghua-Peking Joint Center for Life Sciences, to X.F.

Understanding the air-water and oil-water interfacial behavior of plant proteins is crucial for developing stable emulsions and foams in food systems. Plant crops are often processed into protein extracts with high purity, which primarily consist of globulins. These globulins are often unable to form stiff interfacial layers owing to their compact and highly aggregated state and have inferior functionality compared with animal-derived proteins from milk or eggs. Much of the current focus is on modifying these proteins, whereas better interface stabilizing functionality can also be obtained by choosing more targeted protein extraction methods. This review will highlight the benefits and drawbacks of current and novel protein sources and protein extraction methods with respect to interfacial properties.

Motivation Identification of interactions between bioactive small molecules and target proteins is crucial for novel drug discovery, drug repurposing and uncovering off-target effects. Due to the tremendous size of the chemical space, experimental bioactivity screening efforts require the aid of computational approaches. Although deep learning models have been successful in predicting bioactive compounds, effective and comprehensive featurization of proteins, to be given as input to deep neural networks, remains a challenge. Results Here, we present a novel protein featurization approach to be used in deep learning-based compound–target protein binding affinity prediction. In the proposed method, multiple types of protein features such as sequence, structural, evolutionary and physicochemical properties are incorporated within multiple 2D vectors, which is then fed to state-of-the-art pairwise input hybrid deep neural networks to predict the real-valued compound–target protein interactions. The method adopts the proteochemometric approach, where both the compound and target protein features are used at the input level to model their interaction. The whole system is called MDeePred and it is a new method to be used for the purposes of computational drug discovery and repositioning. We evaluated MDeePred on well-known benchmark datasets and compared its performance with the state-of-the-art methods. We also performed in vitro comparative analysis of MDeePred predictions with selected kinase inhibitors’ action on cancer cells. MDeePred is a scalable method with sufficiently high predictive performance. The featurization approach proposed here can also be utilized for other protein-related predictive tasks. Availability and implementation The source code, datasets, additional information and user instructions of MDeePred are available at https://github.com/cansyl/MDeePred. Supplementary information Supplementary data are available at Bioinformatics online.

Systematic exploration of the dynamic human plasma proteome enables the discovery of novel protein biomarkers. Using state-of-the-art technologies holds the promise to facilitate a better diagnosis and risk prediction of diseases. Cardiovascular disease (CVD) pathophysiology is characterized for unbalancing of processes such as vascular inflammation, endothelial dysfunction, or lipid profiles among others. Such processes have a direct impact on the dynamic and complex composition of blood and hence the plasma proteome. Therefore, the study of the plasma proteome comprises an excellent exploratory source of biomarker research particularly for CVD. We describe the protocol for performing the discovery of protein biomarker candidates using the suspension bead array technology. The process does not require depletion steps to remove abundant proteins and consumes only a few microliters of sample from the body fluid of interest. The approach is scalable to measure many analytes as well as large numbers of samples. Moreover, we describe a bead-assisted antibody-labeling process that helps to develop quantitative assays for validation purposes and facilitate the translation of the identified candidates into clinical studies.

Recognizing object of unseen novel classes is of great importance in real-world incremental data scenarios. Additionally, novel classes arise frequently in data stream mining, e.g., new topics in opinion monitoring, and novel protein families in protein sequence classification. Conducting streaming novel class detection is a complex problem composed of detection and model update. However, when updating the model solely with detected novel instances, it concentrates more on the novel patterns than known ones; thus the detection ability of the model may degrade consequently. This would exert harmful affections to further classification as the data evolving. To this end, in this paper, we consider the accuracy of the detection along with the robustness of model updating, and propose DEtecting Sequentially Novel clAsses with Stable generalization Ability (DesNasa). Specifically, DesNasa utilizes a prototypical network to reflect the structure information between scattered prototypes for novel class detection. Furthermore, the well-designed data augmentation method can help the model learning novel patterns robustly without degrading detection ability. Experiments on various datasets successfully validate the effectiveness of our proposed method.

Simple Summary Livestock feed contains components that can also be consumed by humans, which may become less available for livestock. Proteins are such components that may become less available for livestock feed. This review focuses on using alternative protein sources in feed. We may expect protein efficiency problems and we discuss how these could be solved using a combination of alternative protein sources and animal breeding. We make recommendations for the use and optimization of novel protein sources. Abstract Livestock feed encompasses both human edible and human inedible components. Human edible feed components may become less available for livestock. Especially for proteins, this calls for action. This review focuses on using alternative protein sources in feed and protein efficiency, the expected problems, and how these problems could be solved. Breeding for higher protein efficiency leading to less use of the protein sources may be one strategy. Replacing (part of) the human edible feed components with human inedible components may be another strategy, which could be combined with breeding for livestock that can efficiently digest novel protein feed sources. The potential use of novel protein sources is discussed. We discuss the present knowledge on novel protein sources, including the consequences for animal performance and production costs, and make recommendations for the use and optimization of novel protein sources (1) to improve our knowledge on the inclusion of human inedible protein into the diet of livestock, (2) because cooperation between animal breeders and nutritionists is needed to share knowledge and combine expertise, and (3) to investigate the effect of animal-specific digestibility of protein sources for selective breeding for each protein source and for precision feeding. Nutrigenetics and nutrigenomics will be important tools.

Recently, it was demonstrated that it is possible to create highly fibrous meat analogues, using simple shear structuring. We investigated how the model meat analogue responds to sterilization and storage in media (water, jelly, or gravy) with pet food and beef chunks as reference. Chunks were analysed before and after sterilization, and after storage for textural properties and proximate composition. The fibrous appearance of the model meat analogue was retained after sterilization and storage. Moisture uptake of the model meat analogue was about twice the amount of that in pet food, resulting in textural properties approaching pet food after sterilization. Proximate composition and texture of the model meat analogue remained stable during storage, except for in gravy, which was probably caused by the salt content in the gravy. Due to the fibrousness and pet food-like texture after sterilization, the model meat analogue can be considered promising for the development of plant-based pet foods.

The synthesis of Cox1, the conserved catalytic-core subunit of Complex IV, a multisubunit machinery of the mitochondrial oxidative phosphorylation (OXPHOS) system under environmental stress, has not been sufficiently addressed. In this study, we show that the putative YihA superfamily GTPase, Mrx8, is a bona fide mitochondrial protein required for Cox1 translation initiation and elongation during suboptimal growth condition at 16°C. Mrx8 was found in a complex with mitochondrial ribosomes, consistent with a role in protein synthesis. Cells expressing mutant Mrx8 predicted to be defective in guanine nucleotide binding and hydrolysis were compromised for robust cellular respiration. We show that the requirement of Pet309 and Mss51 for cellular respiration is not bypassed by overexpression of Mrx8 and vice versa. Consistently the ribosomal association of Mss51 is independent of Mrx8. Significantly, we find that GTPBP8, the human orthologue, complements the loss of cellular respiration in Δmrx8 cells and GTPBP8 localizes to the mitochondria in mammalian cells. This strongly suggests a universal role of the MRX8 family of proteins in regulating mitochondrial function.

Insertions/deletions of amino acids in the protein backbone potentially result in altered structural/functional specifications. They can either contribute positively to the evolutionary process or can result in disease conditions. Despite being the second most prevalent form of protein modification, there are no databases or computational frameworks that delineate harmful multipoint deletions (MPD) from beneficial ones. We introduce a positive unlabeled learning-based prediction framework (PROFOUND) that utilizes fold-level attributes, environment-specific properties, and deletion site-specific properties to predict the change in foldability arising from such MPDs, both in the non-loop and loop regions of protein structures. In the absence of any protein structure dataset to study MPDs, we introduce a dataset with 153 MPD instances that lead to native-like folded structures and 7650 unlabeled MPD instances whose effect on the foldability of the corresponding proteins is unknown. PROFOUND on 10-fold cross-validation on our newly introduced dataset reports a recall of 82.2% (86.6%) and a fall out rate (FR) of 14.2% (20.6%), corresponding to MPDs in the protein loop (non-loop) region. The low FR suggests that the foldability in proteins subject to MPDs is not random and necessitates unique specifications of the deleted region. In addition, we find that additional evolutionary attributes contribute to higher recall and lower FR. The first of a kind foldability prediction system owing to MPD instances and the newly introduced dataset will potentially aid in novel protein engineering endeavors.

Nucleotide-activated effector deployment, prototyped by interferon-dependent immunity, is a common mechanistic theme shared by immune systems of several animals and prokaryotes. Prokaryotic versions include CRISPR-Cas with the CRISPR polymerase domain, their minimal variants, and systems with second messenger oligonucleotide or dinucleotide synthetase (SMODS). Cyclic or linear oligonucleotide signals in these systems help set a threshold for the activation of potentially deleterious downstream effectors in response to invader detection. We establish such a regulatory mechanism to be a more general principle of immune systems, which can also operate independently of such messengers. Using sensitive sequence analysis and comparative genomics, we identify 12 new prokaryotic immune systems, which we unify by this principle of threshold-dependent effector activation. These display regulatory mechanisms paralleling physiological signaling based on 3'-5' cyclic mononucleotides, NAD

Recombinant proteins used in biomedical research, diagnostics and different therapies are mostly produced in Chinese hamster ovary cells in the pharmaceutical industry. These biotherapeutics, monoclonal antibodies in particular, have shown remarkable market growth in the past few decades. The increasing demand for high amounts of biologics requires continuous optimization and improvement of production technologies. Research aims at discovering better means and methods for reaching higher volumetric capacity, while maintaining stable product quality. An increasing number of complex novel protein therapeutics, such as viral antigens, vaccines, bi- and tri-specific monoclonal antibodies, are currently entering industrial production pipelines. These biomolecules are, in many cases, difficult to express and require tailored product-specific solutions to improve their transient or stable production. All these requirements boost the development of more efficient expression optimization systems and high-throughput screening platforms to facilitate the design of product-specific cell line engineering and production strategies. In this minireview, we provide an overview on recent advances in CHO cell line development, targeted genome manipulation techniques, selection systems and screening methods currently used in recombinant protein production.

Labelling proteins at internal sites holds promise for generating novel protein conjugates in a programmable fashion. Now, a chemoenzymatic approach, dubbed LACE, enables the site-specific modification of recombinant proteins that contain a short genetically encoded tag.

The COVID19 pandemic has resulted in 1,092,342 deaths as of 14thOctober 2020, indicating the urgent need for a vaccine. This study highlights novel protein sequences generated by shot gun sequencing protocols that could serve as potential antigens in the development of novel subunit vaccines and through a stringent inclusion criterion, we characterized these protein sequences and predicted their 3D structures. We found distinctly antigenic sequences from the SARS-CoV-2 that have led to identification of 4 proteins that demonstrate an advantageous binding with Human leukocyte antigen-1 molecules. Results show how previously unexplored proteins may serve as better candidates for subunit vaccine development due to their high stability and immunogenicity, reinforce by their HLA-1 binding propensities and low global binding energies. This study thus takes a unique approach towards furthering the development of vaccines by employing multiple consensus strategies involved in immuno-informatics technique.

The Golgi is surrounded by a ribosome‐excluding matrix. Recently, we reported that the cis‐Golgi‐localized golgin GM130 can phase‐separate to form dynamic, liquid‐like condensates in vitro and in vivo. Here, we show that the overexpression of each of the remaining cis (golgin160, GMAP210)‐ and trans (golgin97, golgin245, GCC88, GCC185)‐golgins results in novel protein condensates. Focused ion beam scanning electron microscopy (FIB‐SEM) images of GM130 condensates reveal a complex internal organization with branching aqueous channels. Pairs of golgins overexpressed in the same cell form distinct juxtaposed condensates. These findings support the hypothesis that, in addition to their established roles as vesicle tethers, phase separation may be a common feature of the golgin family that contributes to Golgi organization.

Comparative evolutionary genomics has revealed that novel protein coding genes can emerge randomly from non-coding DNA. While most of the myriad of transcripts which continuously emerge vanish rapidly, some attain regulatory regions, become translated and survive. More surprisingly, sequence properties of de novo proteins are almost indistinguishable from randomly obtained sequences, yet de novo proteins may gain functions and integrate into eukaryotic cellular networks quite easily. We here discuss current knowledge on de novo proteins, their structures, functions and evolution. Since the existence of de novo proteins seems at odds with decade-long attempts to construct proteins with novel structures and functions from scratch, we suggest that a better understanding of de novo protein evolution may fuel new strategies for protein design.

This review article provides an update for the role of prostaglandin E2 receptors (EP1, EP2, EP3 and EP4) in cardiovascular disease. Where possible we have reported citations from the last decade although this was not possible for all of the topics covered due to the paucity of publications. The authors have attempted to cover the subjects of ischemia–reperfusion injury, arrhythmias, hypertension, novel protein binding partners of the EP receptors and their pathophysiological significance, and cardiac regeneration. These latter two topics bring studies of the EP receptors into new and exciting areas of research that are just beginning to be explored. Where there is peer-reviewed literature, the authors have placed particular emphasis on clinical studies although these are limited in number.

Protein-protein communications govern cellular processes via complex regulatory networks, that are still far from being understood. Thus, identifying novel interactions between proteins can significantly facilitate our comprehension of the mechanistic principles of protein functions. Co-evolution between proteins is a sign of functional communication and, as such, provides a powerful approach to search for novel direct or indirect molecular partners. However, evolutionary analysis of large arrays of proteins, in silico, is a highly time-consuming effort, which has limited the usage of this method to protein pairs or small protein groups. Here, we developed AutoCoEv, a user-friendly computational pipeline for the search of co-evolution between a large number of proteins. By driving 15 individual programs, culminating in CAPS2 as the software for detecting co-evolution, AutoCoEv achieves seamless automation and parallelization of the workflow. Importantly, we provide a patch to CAPS2 source code to strengthen its statistical output, allowing for multiple comparisons correction and enhanced analysis of the results. We apply the pipeline to inspect co-evolution among 324 proteins identified to locate at the vicinity of the lipid rafts of B lymphocytes. We successfully detected multiple strong coevolutionary relations between the proteins, predicting many novel partners and previously unidentified clusters of functionally related molecules. We conclude that AutoCoEv, available at https://github.com/mattilalab/autocoev, can be used to predict functional interactions from large datasets in a time and cost-efficient manner.

Despite its potential importance for bacterial virulence, protein rhamnosylation has not yet been sufficiently studied. Specific anti-SerRha, anti-ThrRha and anti-AsnRha antibodies allowed the identification of previously unknown monorhamnosylated proteins in cytosol and membrane fractions of bacterial cell lysates. Mapping of the complete rhamnoproteome in pathogens should facilitate development of targeted therapies against bacterial infections.

Supplying novel feed ingredients for pig production is crucial to enhance food security and decrease the environmental impact of meat production. Several studies have focused on evaluating the beneficial health effects of yeast in pigs. However, its use as a protein source has been partially addressed. Previously, we have shown that yeast at high inclusion levels maintains growth performance and digestibility, while nutrient digestibility, intestinal villi height and fecal consistency were improved. The present study combined microbiome, short-chain fatty acid, and immune parameter analysis to investigate the effect of high inclusion of yeast in diets for post-weaning piglets. Our results showed that yeast did not have a significant impact on the hematological or biochemical parameters in blood. The different immune cell subpopulations isolated from blood and distal jejunal lymph nodes (DJLN) were analyzed by flow cytometry and showed that yeast diet induced an increased number of the subtype of leukocytes CD45+/CD3-/CD8+, a special type of Natural Killer (NK) cells. Also, a very mild to moderate infiltration of neutrophilic granulocytes and lower IgA level were observed in the colon of yeast fed piglets. The microbiome profiling in different compartments of the gastrointestinal tract of piglets was performed using 16S rRNA metabarcoding. The results showed that 40% replacement of dietary protein had a statistically significant effect on the microbial communities in cecum and colon, while the microbial population in ileum and jejunum were not affected. Analysis of predicted microbial metabolic pathways analysis revealed significant upregulation of short-chain fatty acids, ether lipid metabolisms, secondary bile acids, and several other important biosynthesis pathways in cecum and colon of pigs fed yeast. In conclusion, the results showed that diet containing 40% of yeast protein positively shaped microbial community in the large intestine and increased the number of a specific subpopulation of NK cells in the DJLN. These results showed that yeast modulates the microbiome and decreases the secretion of IgA in the colon of post-weaning pigs.

Endogenous or surrogate ligands of a vast number of proteins remain unknown. Identification of small molecules that bind to these orphan proteins will not only shed new light into their biological functions but also provide new opportunities for drug discovery. Deep learning plays an increasing role in the prediction of chemical-protein interactions, but it faces several challenges in protein deorphanization. Bioassay data are highly biased to certain proteins, making it difficult to train a generalizable machine learning model for the proteins that are dissimilar from the ones in the training data set. Pre-training offers a general solution to improving the model generalization, but needs incorporation of domain knowledge and customization of task-specific supervised learning. To address these challenges, we develop a novel protein pre-training method, DIstilled Sequence Alignment Embedding (DISAE), and a module-based fine-tuning strategy for the protein deorphanization. In the benchmark studies, DISAE significantly improves the generalizability and outperforms the state-of-the-art methods with a large margin. The interpretability analysis of pre-trained model suggests that it learns biologically meaningful information. We further use DISAE to assign ligands to 649 human orphan G-Protein Coupled Receptors (GPCRs) and to cluster the human GPCRome by integrating their phylogenetic and ligand relationships. The promising results of DISAE open an avenue for exploring the chemical landscape of entire sequenced genomes.

Plasmodium vivaxhas been largely neglected over the past century, despite a widespread recognition of its burden across region where it is endemic. The parasite invades reticulocytes, employing the interaction betweenPlasmodium vivaxDuffy binding protein (PvDBP) and human Duffy antigen receptor for chemokines (DARC). However,P. vivaxhas now been observed in Duffy-negative individuals, presenting a potentially serious public health problem as the majority of African populations are Duffy-negative. Invasion of Duffy-negative reticulocytes is suggested to be through duplication of the PvDBP and a novel protein encoded byP. vivaxerythrocyte binding protein (EBP) genes. The emergence and spread of specificP. vivaxstrains with ability to invade Duffy-negative reticulocytes has, therefore, drawn substantial attention and further complicated the epidemiology and public health implication of vivax malaria. Given the right environment and vectorial capacity for transmission coupled with the parasite’s ability to invade Duffy-negative individuals,P. vivaxcould increase its epidemiological significance in Africa. In this review, authors present accruing knowledge on the paradigm shift inP. vivaxinvasion of Duffy-negative reticulocytes against the established mechanism of invading only Duffy-positive individuals and offer a perspective on the epidemiological diagnostic and public health implication in Africa.