Recent Progress Using De Novo Design to Study Protein Structure, Design and Binding Interactions

Controlling heme redox properties in peptide amphiphile fibers with sequence and heme loading ratio

Controlling the reduction midpoint potential of heme B is a key factor in many bioelectrochemical reactions, including long-range electron transport. Currently, there are a number of globular model protein systems to study this biophysical parameter; however, there are none for large polymeric protein model systems (e.g., the OmcS protein from G. sulfurreducens). Peptide amphiphiles, short peptides with a lipid tail that polymerize into fibrous structures, fill this gap. Here, we show a peptide amphiphile model system where one can tune the electrochemical potential of heme B by changing the loading ratio and peptide sequence. Changing the loading ratio resulted in the most significant increase, with values as high as -22 mV down to -224 mV. Circular dichroism spectra of certain sequences show Cotton effects at lower loading ratios that disappear as more heme B is added, indicating an ordered environment that becomes disrupted if heme B is overpacked. These findings can contribute to the design of functional self-assembling biomaterials.

Genome-wide identification and in-silico expression analysis of CCO gene family in sunflower (Helianthus annnus) against abiotic stress

Carotenoid cleavage oxygenases (CCOs) enzymes play an important role in plant growth and development by producing a wide array of apocarotenoids and their derivatives. These compounds are vital for colouring flowers and fruits and synthesizing plant hormones such as abscisic acid and strigolactones. Despite their importance, the gene family responsible for CCO enzymes in sunflowers has not been identified. In this study, we identify the CCO genes of the sunflower plant to fill this knowledge gap. Phylogenetic and synteny analysis indicated that the Helianthus annnus CCO (HaCCO) genes were conserved in different plant species and they could be divided into three subgroups based on their conserved domains. Analysis using MEME tool and multiple sequence alignment identified conserved motifs in the HaCCO gene sequence. Cis-regulatory elements (CREs) analysis of the HaCCO genes indicated the presence of various responsive elements related to plant hormones, development, and responses to both biotic and abiotic stresses. This implies that these genes may respond to plant hormones, developmental cues, and drought stress, offering potential applications in the development of more resistant crops. Genes belonging to the 9-cis-epoxy carotenoid dioxygenases (NCED) subgroups predominantly exhibited chloroplast localization, whereas the genes found in other groups are primarily localized in the cytoplasm. These 21 identified HaCCOs were regulated by 60 miRNAs, indicating the crucial role of microRNAs in gene regulation in sunflowers. Gene expression analysis under drought stress revealed significant up-regulation of HaNCED16 and HaNCED19, genes that are pivotal in ABA hormone biosynthesis. During organ-specific gene expression analysis, HaCCD12 and HaCCD20 genes exhibit higher activity in leaves, indicating a potential role in leaf pigmentation. This study provides a foundation for future research on the regulation and functions of the CCO gene family in sunflower and beyond. There is potential for developing molecular markers that could be employed in breeding programs to create new sunflower lines resistant to biotic and abiotic stresses.

Role of Artificial Intelligence in Drug Discovery and Target Identification in Cancer

Drug discovery and development (DDD) is a highly complex process that necessitates precise monitoring and extensive data analysis at each stage. Furthermore, the DDD process is both timeconsuming and costly. To tackle these concerns, artificial intelligence (AI) technology can be used, which facilitates rapid and precise analysis of extensive datasets within a limited timeframe. The pathophysiology of cancer disease is complicated and requires extensive research for novel drug discovery and development. The first stage in the process of drug discovery and development involves identifying targets. Cell structure and molecular functioning are complex due to the vast number of molecules that function constantly, performing various roles. Furthermore, scientists are continually discovering novel cellular mechanisms and molecules, expanding the range of potential targets. Accurately identifying the correct target is a crucial step in the preparation of a treatment strategy. Various forms of AI, such as machine learning, neural-based learning, deep learning, and network-based learning, are currently being utilised in applications, online services, and databases. These technologies facilitate the identification and validation of targets, ultimately contributing to the success of projects. This review focuses on the different types and subcategories of AI databases utilised in the field of drug discovery and target identification for cancer.

Computational Protein Design for COVID-19 Research and Emerging Therapeutics

As the world struggles with the ongoing COVID-19 pandemic, unprecedented obstacles have continuously been traversed as new SARS-CoV-2 variants continually emerge. Infectious disease outbreaks are unavoidable, but the knowledge gained from the successes and failures will help create a robust health management system to deal with such pandemics. Previously, scientists required years to develop diagnostics, therapeutics, or vaccines; however, we have seen that, with the rapid deployment of high-throughput technologies and unprecedented scientific collaboration worldwide, breakthrough discoveries can be accelerated and insights broadened. Computational protein design (CPD) is a game-changing new technology that has provided alternative therapeutic strategies for pandemic management. In addition to the development of peptide-based inhibitors, miniprotein binders, decoys, biosensors, nanobodies, and monoclonal antibodies, CPD has also been used to redesign native SARS-CoV-2 proteins and human ACE2 receptors. We discuss how novel CPD strategies have been exploited to develop rationally designed and robust COVID-19 treatment strategies.

Current therapeutic approaches and promising perspectives of using bioengineered peptides in fighting chemoresistance in triple-negative breast cancer

Breast cancer is a collation of malignancies that manifest in the mammary glands at the early stages. Among breast cancer subtypes, triple-negative breast cancer (TNBC) shows the most aggressive behavior, with apparent stemness features. Owing to the lack of response to hormone therapy and specific targeted therapies, chemotherapy remains the first line of the TNBC treatment. However, the acquisition of resistance to chemotherapeutic agents increase therapy failure, and promotes cancer recurrence and distant metastasis. Invasive primary tumors are the birthplace of cancer burden, though metastasis is a key attribute of TNBC-associated morbidity and mortality. Targeting the chemoresistant metastases-initiating cells via specific therapeutic agents with affinity to the upregulated molecular targets is a promising step in the TNBC clinical management. Exploring the capacity of peptides as biocompatible entities with the specificity of action, low immunogenicity, and robust efficacy provides a principle for designing peptide-based drugs capable of increasing the efficacy of current chemotherapy agents for selective targeting of the drug-tolerant TNBC cells. Here, we first focus on the resistance mechanisms that TNBC cells acquire to evade the effect of chemotherapeutic agents. Next, the novel therapeutic approaches employing tumor-targeting peptides to exploit the mechanisms of drug resistance in chemorefractory TNBC are described.

Tailorable Tetrahelical Bundles as a Toolkit for Redox Studies

Oxidoreductases have evolved over millions of years to perform a variety of metabolic tasks crucial for life. Understanding how these tasks are engineered relies on delivering external electron donors or acceptors to initiate electron transfer reactions. This is a challenge. Small-molecule redox reagents can act indiscriminately, poisoning the cell. Natural redox proteins are more selective, but finding the right partner can be difficult due to the limited number of redox potentials and difficulty tuning them. De novo proteins offer an alternative path. They are robust and can withstand mutations that allow for tailorable changes. They are also devoid of evolutionary artifacts and readily bind redox cofactors. However, no reliable set of engineering principles have been developed that allow for these proteins to be fine-tuned so their redox midpoint potential (E_m) can form donor/acceptor pairs with any natural oxidoreductase. This work dissects protein-cofactor interactions that can be tuned to modulate redox potentials of acceptors and donors using a mutable de novo designed tetrahelical protein platform with iron tetrapyrrole cofactors as a test case. We show a series of engineered heme b-binding de novo proteins and quantify their resulting effect on E_m. By focusing on the surface charge and buried charges, as well as cofactor placement, chemical modification, and ligation of cofactors, we are able to achieve a broad range of E_m values spanning a range of 330 mV. We anticipate this work will guide the design of proteinaceous tools that can interface with natural oxidoreductases inside and outside the cell while shedding light on how natural proteins modulate E_m values of bound cofactors.

Functional roles of enzyme dynamics in accelerating active site chemistry: Emerging techniques and changing concepts

With the growing acceptance of the contribution of protein conformational ensembles to enzyme catalysis and regulation, research in the field of protein dynamics has shifted toward an understanding of the atomistic properties of protein dynamical networks and the mechanisms and time scales that control such behavior. A full description of an enzymatic reaction coordinate is expected to extend beyond the active site and include site-specific networks that communicate with the protein/water interface. Advances in experimental tools for the spatial resolution of thermal activation pathways are being complemented by biophysical methods for visualizing dynamics in real time. An emerging multidimensional model integrates the impacts of bound substrate/effector on the distribution of protein substates that are in rapid equilibration near room temperature with reaction-specific protein embedded heat transfer conduits.

Molecular Properties of β-Carotene Oxygenases and Their Potential in Industrial Production of Vitamin A and Its Derivatives

β-Carotene 15,15′-oxygenase (BCO1) and β-carotene 9′,10′-oxygenase (BCO2) are potential producers of vitamin A derivatives, since they can catalyze the oxidative cleavage of dietary provitamin A carotenoids to retinoids and derivative such as apocarotenal. Retinoids are a class of chemical compounds that are vitamers of vitamin A or are chemically related to it, and are essential nutrients for humans and highly valuable in the food and cosmetics industries. β-carotene oxygenases (BCOs) from various organisms have been overexpressed in heterogeneous bacteria, such as Escherichia coli, and their biochemical properties have been studied. For the industrial production of retinal, there is a need for increased production of a retinal producer and biosynthesis of retinal using biocatalyst systems improved by enzyme engineering. The current review aims to discuss BCOs from animal, plants, and bacteria, and to elaborate on the recent progress in our understanding of their functions, biochemical properties, substrate specificity, and enzyme activities with respect to the production of retinoids in whole-cell conditions. Moreover, we specifically propose ways to integrate BCOs into retinal biosynthetic bacterial systems to improve the performance of retinal production.

Protein Engineering in the Design of Protein-Protein Interactions: SARS-CoV-2 Inhibitors as a Test Case

The formation of specific protein-protein interactions (PPIs) drive most biological processes. Malfunction of such interactions is the molecular driver of many diseases. Our ability to engineer existing PPIs or create new ones has become a vital research tool. In addition, engineered proteins with new or altered interactions are among the most critical drugs that have been developed in recent years. These include antibodies, cytokines, inhibitors, and others. Here, we provide a perspective on the current status of the methods used to engineer new or altered PPIs. The emergence of the COVID-19 pandemic, which resulted in a worldwide quest to develop specific PPI inhibitors as drugs, provided an up-to-date and state-of-the-art status report on the methodologies for engineering PPIs targeting the interaction of the viral spike protein with its cellular target, ACE2. Multiple, very high affinity binders were generated within a few months using in vitro evolution by itself, or in combination with computational design. The different experimental and computational methods used to block this interaction provide a road map for the future of PPI engineering.

Unravelling the Structure of the Tetrahedral Metal-Binding Site in METP3 through an Experimental and Computational Approach

Understanding the structural determinants for metal ion coordination in metalloproteins is a fundamental issue for designing metal binding sites with predetermined geometry and activity. In order to achieve this, we report in this paper the design, synthesis and metal binding properties of METP3, a homodimer made up of a small peptide, which self assembles in the presence of tetrahedrally coordinating metal ions. METP3 was obtained through a redesign approach, starting from the previously developed METP molecule. The undecapeptide sequence of METP, which dimerizes to house a Cys₄ tetrahedral binding site, was redesigned in order to accommodate a Cys₂His₂ site. The binding properties of METP3 were determined toward different metal ions. Successful assembly of METP3 with Co(II), Zn(II) and Cd(II), in the expected 2:1 stoichiometry and tetrahedral geometry was proven by UV-visible spectroscopy. CD measurements on both the free and metal-bound forms revealed that the metal coordination drives the peptide chain to fold into a turned conformation. Finally, NMR data of the Zn(II)-METP3 complex, together with a retrostructural analysis of the Cys-X-X-His motif in metalloproteins, allowed us to define the model structure. All the results establish the suitability of the short METP sequence for accommodating tetrahedral metal binding sites, regardless of the first coordination ligands.