What Have We Learned from Design of Function in Large Proteins?

Laccase based per- and polyfluoroalkyl substances degradation: Status and future perspectives

Per- and polyfluoroalkyl substances (PFAS) with stable carbon-fluorine bonds are used in a wide range of industrial and commercial applications. Due to their extreme environmental persistence, PFAS have the potential to bioaccumulate, cause adverse effects, and present challenges regarding remediation. Recently, microbial and enzymatic reactions for sustainable degradation of PFAS have gained attention from researchers, although biological decomposition of PFAS remains challenging. Surprisingly, laccases, the multi-copper oxidases produced by various organisms, showed potential for PFAS degradation. Mediators play key roles in initiating laccase induced PFAS degradation and defluorination reactions. The laccase-catalyzed PFAS degradation reactions are relatively slower than normal biocatalytic reactions and the low activity of native laccases constrains their capacity to complete defluorination. With their low redox potential and narrow substrate scope, an innovative remediation strategy must be taken to accelerate this reaction. In this review we have summarized the status, challenges, and future perspectives of enzymatic PFAS degradation. The knowledge of laccase-based defluorination and the molecular basis of the reaction mechanisms overviewed in this study could inform future applications of laccases for sustainable PFAS remediation.

Stabilized designs of the malaria adhesin protein PvRBP2b for use as a potential diagnostic for Plasmodium vivax

Plasmodium vivax is emerging as the most prevalent species causing malaria outside Africa. Most P. vivax infections are relapses due to the reactivation of the dormant liver stage parasites (hypnozoites). Hypnozoites are a major reservoir for transmission but undetectable by commercial diagnostic tests. Antibodies against P. vivax Reticulocyte Binding Protein 2b (PvRBP2b) are among the most reliable serological biomarkers for recent P. vivax infections in the prior nine months and act as indirect biomarkers for risk of relapse. We sought to design stabilized variants of PvRBP2b, under stringent conditions of minimally perturbing the solvent-accessible surfaces to maintain its antigenicity profile. Furthermore, for some of the designs, due to limited diversity of natural PvRBP2b homologs, we combined AI-based ProteinMPNN and PROSS atomistic design calculations. The best, bearing 19 core mutations relative to PvRBP2b, expressed 16-fold greater amounts (up to 11 mg per L), and had 14 °C higher thermal tolerance than the parental protein. Critically, the stabilized designs retained binding to naturally acquired human monoclonal antibodies with nanomolar affinities, suggesting that the immunologically competent surfaces were retained as was confirmed by crystallographic analyses. Using longitudinal observational cohorts from malaria endemic regions of Thailand, Brazil and the Solomon Islands, we show that antibody responses against the designs are highly correlated with those against the parental protein and can classify individuals as recently infected with P. vivax. This efficient computational stability design methodology can be used to enhance the biophysical properties of other recalcitrant proteins for use as diagnostics or vaccine immunogens.

Patient-derived xenograft model in cancer: establishment and applications

The patient-derived xenograft (PDX) model is a crucial in vivo model extensively employed in cancer research that has been shown to maintain the genomic characteristics and pathological structure of patients across various subtypes, metastatic, and diverse treatment histories. Various treatment strategies utilized in PDX models can offer valuable insights into the mechanisms of tumor progression, drug resistance, and the development of novel therapies. This review provides a comprehensive overview of the establishment and applications of PDX models. We present an overview of the history and current status of PDX models, elucidate the diverse construction methodologies employed for different tumors, and conduct a comparative analysis to highlight the distinct advantages and limitations of this model in relation to other in vivo models. The applications are elucidated in the domain of comprehending the mechanisms underlying tumor development and cancer therapy, which highlights broad applications in the fields of chemotherapy, targeted therapy, delivery systems, combination therapy, antibody-drug conjugates and radiotherapy. Furthermore, the combination of the PDX model with multiomics and single-cell analyses for cancer research has also been emphasized. The application of the PDX model in clinical treatment and personalized medicine is additionally emphasized.

The Past, Present, and Future of Biomarkers for the Early Diagnosis of Pancreatic Cancer

Pancreatic cancer is one of the most aggressive cancers with a very poor 5-year survival rate and reduced therapeutic options when diagnosed in an advanced stage. The dismal prognosis of pancreatic cancer has guided significant efforts to discover novel biomarkers in order to anticipate diagnosis, increasing the population of patients who can benefit from curative surgical treatment. CA 19-9 is the reference biomarker that supports the diagnosis and guides the response to treatments. However, it has significant limitations, a low specificity, and is inefficient as a screening tool. Several potential biomarkers have been discovered in the serum, urine, feces, and pancreatic juice of patients. However, most of this evidence needs further validation in larger cohorts. The advent of advanced omics sciences and liquid biopsy techniques has further enhanced this field of research. The aim of this review is to analyze the historical evolution of the research on novel biomarkers for the early diagnosis of pancreatic cancer, focusing on the current evidence for the most promising biomarkers from different body fluids and the novel trends in research, such as omics sciences and liquid biopsy, in order to favor the application of modern personalized medicine.

Sirtuins as Key Regulators in Pancreatic Cancer: Insights into Signaling Mechanisms and Therapeutic Implications

Pancreatic ductal adenocarcinoma (PDAC) stands as one of the most lethal cancers, marked by rapid progression, pronounced chemoresistance, and a complex network of genetic and epigenetic dysregulation. Within this challenging context, sirtuins, NAD+-dependent deacetylases, have emerged as pivotal modulators of key cellular processes that drive pancreatic cancer progression. Each sirtuin contributes uniquely to PDAC pathogenesis. SIRT1 influences apoptosis and chemoresistance through hypoxia, enhancing glycolytic metabolism and HIF-1α signaling, which sustain tumor survival against drugs like gemcitabine. SIRT2, conversely, disrupts cancer cell proliferation by inhibiting eIF5A, while SIRT3 exerts tumor-suppressive effects by regulating mitochondrial ROS and glycolysis. SIRT4 inhibits aerobic glycolysis, and its therapeutic upregulation has shown promise in curbing PDAC progression. Furthermore, SIRT5 modulates glutamine and glutathione metabolism, offering an avenue to disrupt PDAC's metabolic dependencies. SIRT6 and SIRT7, through their roles in angiogenesis, EMT, and metastasis, represent additional targets, with modulators of SIRT6, such as JYQ-42, showing potential to reduce tumor invasiveness. This review aims to provide a comprehensive exploration of the emerging roles of sirtuins, a family of NAD+-dependent enzymes, as critical regulators within the oncogenic landscape of pancreatic cancer. This review meticulously explores the nuanced involvement of sirtuins in pancreatic cancer, elucidating their contributions to tumorigenesis and suppression through mechanisms such as metabolic reprogramming, the maintenance of genomic integrity and epigenetic modulation. Furthermore, it emphasizes the urgent need for the development of targeted therapeutic interventions aimed at precisely modulating sirtuin activity, thereby enhancing therapeutic efficacy and optimizing patient outcomes in the context of pancreatic malignancies.

Advances in the understanding of the production, modification and applications of xylanases in the food industry

Xylanases have broad applications in the food industry to decompose the complex carbohydrate xylan. This is applicable to enhance juice clarity, improve dough softness, or reduce beer turbidity. It can also be used to produce prebiotics and increase the nutritional value in foodstuff. However, the low yield and poor stability of most natural xylanases hinders their further applications. Therefore, it is imperative to explore higher-quality xylanases to address the potential challenges that appear in the food industry and to comprehensively improve the production, modification, and utilization of xylanases. Xylanases, due to their various sources, exhibit diverse characteristics that affect production and activity. Most fungi are suitable for solid-state fermentation to produce xylanases, but in liquid fermentation, microbial metabolism is more vigorous, resulting in higher yield. Fungi produce higher xylanase activity, but bacterial xylanases perform better than fungal ones under certain extreme conditions (high temperature, extreme pH). Gene and protein engineering technology helps to improve the production efficiency of xylanases and enhances their thermal stability and catalytic properties.

Opportunities and challenges in design and optimization of protein function

The field of protein design has made remarkable progress over the past decade. Historically, the low reliability of purely structure-based design methods limited their application, but recent strategies that combine structure-based and sequence-based calculations, as well as machine learning tools, have dramatically improved protein engineering and design. In this Review, we discuss how these methods have enabled the design of increasingly complex structures and therapeutically relevant activities. Additionally, protein optimization methods have improved the stability and activity of complex eukaryotic proteins. Thanks to their increased reliability, computational design methods have been applied to improve therapeutics and enzymes for green chemistry and have generated vaccine antigens, antivirals and drug-delivery nano-vehicles. Moreover, the high success of design methods reflects an increased understanding of basic rules that govern the relationships among protein sequence, structure and function. However, de novo design is still limited mostly to α-helix bundles, restricting its potential to generate sophisticated enzymes and diverse protein and small-molecule binders. Designing complex protein structures is a challenging but necessary next step if we are to realize our objective of generating new-to-nature activities.

Designed active-site library reveals thousands of functional GFP variants

Mutations in a protein active site can lead to dramatic and useful changes in protein activity. The active site, however, is sensitive to mutations due to a high density of molecular interactions, substantially reducing the likelihood of obtaining functional multipoint mutants. We introduce an atomistic and machine-learning-based approach, called high-throughput Functional Libraries (htFuncLib), that designs a sequence space in which mutations form low-energy combinations that mitigate the risk of incompatible interactions. We apply htFuncLib to the GFP chromophore-binding pocket, and, using fluorescence readout, recover >16,000 unique designs encoding as many as eight active-site mutations. Many designs exhibit substantial and useful diversity in functional thermostability (up to 96 °C), fluorescence lifetime, and quantum yield. By eliminating incompatible active-site mutations, htFuncLib generates a large diversity of functional sequences. We envision that htFuncLib will be used in one-shot optimization of activity in enzymes, binders, and other proteins.

De Novo Design and Synthesis of Polypeptide Immunomodulators for Resetting Macrophage Polarization

Modulating the extracellular matrix microenvironment is critical for achieving the desired macrophage phenotype in immune investigations or tumor therapy. Combining de novo protein design and biosynthesis techniques, herein, we designed a biomimetic polypeptide self-assembled nano-immunomodulator to trigger the activation of a specific macrophage phenotype. It was intended to be made up of (​GGS​GGP​GGG​PAS​AAA​NSA​SRA​TSN​SP)n, the RGD motif from collagen, and the IKVAV motif from laminin. The combination of these domains allows the biomimetic polypeptide to assemble into extracellular matrix-like nanofibrils, creating an extracellular matrix-like milieu for macrophages. Furthermore, changing the concentration further provides a facile route to fine-tune macrophage polarization, which enhances antitumor immune responses by precisely resetting tumor-associated macrophage immune responses into an M1-like phenotype, which is generally considered to be tumor-killing macrophages, primarily antitumor, and immune-promoting. Unlike metal or synthetic polymer-based nanoparticles, this polypeptide-based nanomaterial exhibits excellent biocompatibility, high efficacy, and precise tunability in immunomodulatory effectiveness. These encouraging findings motivate us to continue our research into cancer immunotherapy applications in the future.

Combinatorial assembly and design of enzymes

The design of structurally diverse enzymes is constrained by long-range interactions that are necessary for accurate folding. We introduce an atomistic and machine learning strategy for the combinatorial assembly and design of enzymes (CADENZ) to design fragments that combine with one another to generate diverse, low-energy structures with stable catalytic constellations. We applied CADENZ to endoxylanases and used activity-based protein profiling to recover thousands of structurally diverse enzymes. Functional designs exhibit high active-site preorganization and more stable and compact packing outside the active site. Implementing these lessons into CADENZ led to a 10-fold improved hit rate and more than 10,000 recovered enzymes. This design-test-learn loop can be applied, in principle, to any modular protein family, yielding huge diversity and general lessons on protein design principles.

Designed High-Redox Potential Laccases Exhibit High Functional Diversity

White-rot fungi secrete an impressive repertoire of high-redox potential laccases (HRPLs) and peroxidases for efficient oxidation and utilization of lignin. Laccases are attractive enzymes for the chemical industry due to their broad substrate range and low environmental impact. Since expression of functional recombinant HRPLs is challenging, however, iterative-directed evolution protocols have been applied to improve their expression, activity, and stability. We implement a rational, stabilize-and-diversify strategy to two HRPLs that we could not functionally express. First, we use the PROSS stability-design algorithm to allow functional expression in yeast. Second, we use the stabilized enzymes as starting points for FuncLib active-site design to improve their activity and substrate diversity. Four of the FuncLib-designed HRPLs and their PROSS progenitor exhibit substantial diversity in reactivity profiles against high-redox potential substrates, including lignin monomers. Combinations of 3-4 subtle mutations that change the polarity, solvation, and sterics of the substrate-oxidation site result in orders of magnitude changes in reactivity profiles. These stable and versatile HRPLs are a step toward generating an effective lignin-degrading consortium of enzymes that can be secreted from yeast. The stabilize-and-diversify strategy can be applied to other challenging enzyme families to study and expand the utility of natural enzymes.