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Interferon (IFN)-γ is a central regulator of cell-mediated immunity in human health and disease, but reduced expression of the target receptors impairs signaling activity and leads to immunotherapy resistance. Although intracellular expression of IFN-γ restores the signaling and downstream functions, we lack the tools to activate the IFNG gene instead of cell surface receptors. This paper introduces the design and characterization of an artificial transcription factor (ATF) protein that recognizes the IFNG gene with six zinc finger domains, which are dovetailed to a VP64 signaling domain that promotes gene transcription and translation. Biological studies with human Jurkat T cells reveal that the ATF amplifies IFNG gene transcription and translation, and also stimulates gene transcription for multiple class I and II HLA alleles and interferon-stimulated genes (ISGs). Biophysical characterization showed the recombinant ATF protein recognizes the human IFNG gene with nanomolar affinity (KD = 5.27 ± 0.3 nM), adopts a protein secondary structure associated with the ββα-fold of zinc finger domains, and is resistant to thermal denaturation. These studies demonstrate that transcriptional targeting of cytokine genes, rather than surface receptors, activates cytokine expression and shows significant potential for directing immune function.

The “Histidine-brace” (His-brace) copper-binding site, composed of Cu(His)2 with a backbone amine, is found in metalloproteins with diverse functions. A primary example is lytic polysaccharide monooxygenase (LPMO), a class of enzymes that catalyze the oxidative depolymerization of polysaccharides, providing not only an energy source for native microorganisms but also a route to more effective industrial biomass conversion. Despite its importance, how the Cu His-brace site performs this unique and challenging oxidative depolymerization reaction remains to be understood. To answer this question, we have designed a biosynthetic model of LPMO by incorporating the Cu His-brace motif into azurin, an electron transfer protein. Spectroscopic studies, including ultraviolet-visible (UV–Vis) absorption and electron paramagnetic resonance, confirm copper binding at the designed His-brace site. Moreover, the designed protein is catalytically active towards both cellulose and starch, the native substrates of LPMO, generating degraded oligosaccharides with multiturnovers by C1 oxidation. It also performs oxidative cleavage of the model substrate 4-nitrophenyl-D-glucopyranoside, achieving a turnover number ~9% of that of a native LPMO assayed under identical conditions. This work presents a rationally designed artificial metalloenzyme that acts as a structural and functional mimic of LPMO, which provides a promising system for understanding the role of the Cu His-brace site in LPMO activity and potential application in polysaccharide degradation. [ABSTRACT FROM AUTHOR]

Proteins are some of the most fascinating and challenging molecules in the universe, and they pose a big challenge for artificial intelligence. The implementation of machine learning/AI in protein science gives rise to a world of knowledge adventures in the workhorse of the cell and proteome homeostasis, which are essential for making life possible. This opens up epistemic horizons thanks to a coupling of human tacit–explicit knowledge with machine learning power, the benefits of which are already tangible, such as important advances in protein structure prediction. Moreover, the driving force behind the protein processes of self-organization, adjustment, and fitness requires a space corresponding to gigabytes of life data in its order of magnitude. There are many tasks such as novel protein design, protein folding pathways, and synthetic metabolic routes, as well as protein-aggregation mechanisms, pathogenesis of protein misfolding and disease, and proteostasis networks that are currently unexplored or unrevealed. In this systematic review and biochemical meta-analysis, we aim to contribute to bridging the gap between what we call binomial artificial intelligence (AI) and protein science (PS), a growing research enterprise with exciting and promising biotechnological and biomedical applications. We undertake our task by exploring “the state of the art” in AI and machine learning (ML) applications to protein science in the scientific literature to address some critical research questions in this domain, including What kind of tasks are already explored by ML approaches to protein sciences? What are the most common ML algorithms and databases used? What is the situational diagnostic of the AI–PS inter-field? What do ML processing steps have in common? We also formulate novel questions such as Is it possible to discover what the rules of protein evolution are with the binomial AI–PS? How do protein folding pathways evolve? What are the rules that dictate the folds? What are the minimal nuclear protein structures? How do protein aggregates form and why do they exhibit different toxicities? What are the structural properties of amyloid proteins? How can we design an effective proteostasis network to deal with misfolded proteins? We are a cross-functional group of scientists from several academic disciplines, and we have conducted the systematic review using a variant of the PICO and PRISMA approaches. The search was carried out in four databases (PubMed, Bireme, OVID, and EBSCO Web of Science), resulting in 144 research articles. After three rounds of quality screening, 93 articles were finally selected for further analysis. A summary of our findings is as follows: regarding AI applications, there are mainly four types: 1) genomics, 2) protein structure and function, 3) protein design and evolution, and 4) drug design. In terms of the ML algorithms and databases used, supervised learning was the most common approach (85%). As for the databases used for the ML models, PDB and UniprotKB/Swissprot were the most common ones (21 and 8%, respectively). Moreover, we identified that approximately 63% of the articles organized their results into three steps, which we labeled pre-process, process, and post-process. A few studies combined data from several databases or created their own databases after the pre-process. Our main finding is that, as of today, there are no research road maps serving as guides to address gaps in our knowledge of the AI–PS binomial. All research efforts to collect, integrate multidimensional data features, and then analyze and validate them are, so far, uncoordinated and scattered throughout the scientific literature without a clear epistemic goal or connection between the studies. Therefore, our main contribution to the scientific literature is to offer a road map to help solve problems in drug design, protein structures, design, and function prediction while also presenting the “state of the art” on research in the AI–PS binomial until February 2021. Thus, we pave the way toward future advances in the synthetic redesign of novel proteins and protein networks and artificial metabolic pathways, learning lessons from nature for the welfare of humankind. Many of the novel proteins and metabolic pathways are currently non-existent in nature, nor are they used in the chemical industry or biomedical field.

We employed mutual information (MI) analysis to detect motions affecting the mechanical resistance of the human-engineered protein Top7. The results are based on the MI analysis of pair contact correlations measured in steered molecular dynamics (SMD) trajectories and their statistical dependence on global unfolding. This study is the first application of the MI analysis to SMD forced unfolding, and we furnish specific SMD recommendations for the utility of parameters and options in the TimeScapes package. The MI analysis provided a global overview of the effect of perturbation on the stability of the protein. We also employed a more conventional trajectory analysis for a detailed description of the mechanical resistance of Top7. Specifically, we investigated 1) the hydropathy of the interactions of structural segments, 2) the H2O concentration near residues relevant for unfolding, and 3) the changing hydrogen bonding patterns and main chain dihedral angles. The results show that the application of MI in the study of protein mechanical resistance can be useful for the engineering of more resistant mutants when combined with conventional analysis. We propose a novel mutation design based on the hydropathy of residues that would stabilize the unfolding region by mimicking its more stable symmetry mate. The proposed design process does not involve the introduction of covalent crosslinks, so it has the potential to preserve the conformational space and unfolding pathway of the protein.

As a common protein modification, asparagine-linked (N-linked) glycosylation has the capacity to greatly influence the biological and biophysical properties of proteins. However, the routine use of glycosylation as a strategy for engineering proteins with advantageous properties is limited by our inability to construct and screen large collections of glycoproteins for cataloguing the consequences of glycan installation. To address this challenge, we describe a combinatorial strategy termed shotgun scanning glycomutagenesis in which DNA libraries encoding all possible glycosylation site variants of a given protein are constructed and subsequently expressed in glycosylation-competent bacteria, thereby enabling rapid determination of glycosylatable sites in the protein. The resulting neoglycoproteins can be readily subjected to available high-throughput assays, making it possible to systematically investigate the structural and functional consequences of glycan conjugation along a protein backbone. The utility of this approach was demonstrated with three different acceptor proteins, namely bacterial immunity protein Im7, bovine pancreatic ribonuclease A, and human anti-HER2 single-chain Fv antibody, all of which were found to tolerate N-glycan attachment at a large number of positions and with relatively high efficiency. The stability and activity of many glycovariants was measurably altered by N-linked glycans in a manner that critically depended on the precise location of the modification. Structural models suggested that affinity was improved by creating novel interfacial contacts with a glycan at the periphery of a protein-protein interface. Importantly, we anticipate that our glycomutagenesis workflow should provide access to unexplored regions of glycoprotein structural space and to custom-made neoglycoproteins with desirable properties. [ABSTRACT FROM AUTHOR]

The formation of protein complexes is central to biology, with oligomeric proteins more prevalent than monomers. The coupling of functionally and even structurally distinct protein units can lead to new functional properties not accessible by monomeric proteins alone. While such complexes are driven by evolutionally needs in biology, the ability to link normally functionally and structurally disparate proteins can lead to new emergent properties for use in synthetic biology and the nanosciences. Here we demonstrate how two disparate proteins, the haem binding helical bundle protein cytochrome b562 and the β-barrel green fluorescent protein can be combined to form a heterodimer linked together by an unnatural triazole linkage. The complex was designed using computational docking approaches to predict compatible interfaces between the two proteins. Models of the complexes where then used to engineer residue coupling sites in each protein to link them together. Genetic code expansion was used to incorporate azide chemistry in cytochrome b562 and alkyne chemistry in GFP so that a permanent triazole covalent linkage can be made between the two proteins. Two linkage sites with respect to GFP were sampled. Spectral analysis of the new heterodimer revealed that haem binding and fluorescent protein chromophore properties were retained. Functional coupling was confirmed through changes in GFP absorbance and fluorescence, with linkage site determining the extent of communication between the two proteins. We have thus shown here that is possible to design and build heterodimeric proteins that couple structurally and functionally disparate proteins to form a new complex with new functional properties.

Software to predict the change in protein stability upon point mutation is a valuable tool for a number of biotechnological and scientific problems. To facilitate the development of such software and provide easy access to the available experimental data, the ProTherm database was created. Biases in the methods and types of information collected has led to disparity in the types of mutations for which experimental data is available. For example, mutations to alanine are hugely overrepresented whereas those involving charged residues, especially from one charged residue to another, are underrepresented. ProTherm subsets created as benchmark sets that do not account for this often underrepresent tense certain mutational types. This issue introduces systematic biases into previously published protocols’ ability to accurately predict the change in folding energy on these classes of mutations. To resolve this issue, we have generated a new benchmark set with these problems corrected. We have then used the benchmark set to test a number of improvements to the point mutation energetics tools in the Rosetta software suite.

The main features of the triple helical structure of collagen were deduced in the mid-1950s from fibre X-ray diffraction of tendons. Yet, the resulting models only could offer an average description of the molecular conformation. A critical advance came about 20 years later with the chemical synthesis of sufficiently long and homogeneous peptides with collagen-like sequences. The availability of these collagen model peptides resulted in a large number of biochemical, crystallographic and NMR studies that have revolutionized our understanding of collagen structure. High-resolution crystal structures from collagen model peptides have provided a wealth of data on collagen conformational variability, interaction with water, collagen stability or the effects of interruptions. Furthermore, a large increase in the number of structures of collagen model peptides in complex with domains from receptors or collagen-binding proteins has shed light on the mechanisms of collagen recognition. In recent years, collagen biochemistry has escaped the boundaries of natural collagen sequences. Detailed knowledge of collagen structure has opened the field for protein engineers who have used chemical biology approaches to produce hyperstable collagens with unnatural residues, rationally designed collagen heterotrimers, self-assembling collagen peptides, etc. This review summarizes our current understanding of the structure of the collagen triple helical domain (COLx3) and gives an overview of some of the new developments in collagen molecular engineering aiming to produce novel collagen-based materials with superior properties. [ABSTRACT FROM AUTHOR]

Abstract: The activator protein-1 (AP-1) complex plays a crucial role in numerous pathways, and its ability to induce tumorigenesis is well documented. Thus, AP-1 represents an interesting therapeutic target. We selected peptides from phage display and compared their ability to disrupt the cFos/cJun interaction to a previously described in vivo protein-fragment complementation assay (PCA). A cJun-based library was screened to enrich for peptides that disrupt the AP-1 complex by binding to the cFos coiled-coil domain. Interestingly, phage display identified one helix, JunWPh1 [phage-selected winning peptide (clone 1) targeting cFos], which differs in only 2 out of 10 randomized positions to JunW (PCA-selected winning peptide targeting cFos). Phage-selected peptides revealed higher affinity to cFos than wild-type cJun, harboring a T m of 53 °C compared to 16 °C for cFos/cJun or 44 °C for cFos/JunW. In PCA growth assays in the presence of cJun as competitor, phage-selected JunWPh1 conferred shorter generation times than JunW. Bacterial growth was barely detectable, using JunWPh1 as a competitor for the wild-type cJun/cFos interaction, indicating efficient cFos removal from the dimeric wild-type complex. Importantly, all inhibitory peptides were able to interfere with DNA binding as demonstrated in gel shift assays. The selected sequences have consequently improved our ‘bZIP coiled-coil interaction prediction algorithm’ in distinguishing interacting from noninteracting coiled-coil sequences. Predicting and manipulating protein interaction will accelerate the systems biology field, and generated peptides will be valuable tools for analytical and biomedical applications. [Copyright &y& Elsevier]

The standard genetic code is a set of rules that relates the 20 canonical amino acids in proteins to groups of three bases in the mRNA. It evolved from a more primitive form and the attempts to reconstruct its natural history are based on its present-day features. Genetic code engineering as a new research field was developed independently in a few laboratories during the last 15 years. The main intention is to re-program protein synthesis by expanding the coding capacities of the genetic code via re-assignment of specific codons to un-natural amino acids. This article focuses on the question as to which extent hypothetical scenarios that led to codon re-assignments during the evolution of the genetic code are relevant for its further evolution in the laboratory. Current attempts to engineer the genetic code are reviewed with reference to theoretical works on its natural history. Integration of the theoretical considerations into experimental concepts will bring us closer to designer cells with target-engineered genetic codes that should open not only tremendous possibilities for the biotechnology of the twenty-first century but will also provide a basis for the design of novel life forms. [ABSTRACT FROM AUTHOR]

Recently, it has become possible to reprogram the protein synthesis machinery such that numerous noncanonical amino acids can be translated into target sequences yielding tailor-made proteins. The canonical amino acid tryptophan (Trp) encoded by a single nucleotide triplet (UGG) is a particularly interesting target for protein engineering and design. Trp-residues can be substituted with a variety of analogs and surrogates generated biosynthetically or by organic chemistry. Among them, nitrogen-containing tryptophan analogs occupy a central position, as they have distinct chemical properties in comparison with aliphatic amines and imines. They resemble purine bases of DNA and share their capacity for pH-sensitive intramolecular charge transfer. These special properties of the analogs can be directly transmitted into related protein structures via in vivo ribosome-mediated translation. Proteins expressed in this way are further endowed with unique properties like new spectral, altered redox and titration features or might serve as useful biomaterials. We present and discuss current works and future developments in protein engineering with nitrogen-containing tryptophan analogs and related compounds as well as their relevance for academic and applicative research. [ABSTRACT FROM AUTHOR]

As a versatile intracellular second messenger, calcium ion (Ca 2+ ) regulates a plethora of physiological processes. To achieve precise control over Ca 2+ signals in living cells and organisms, a set of optogenetic tools have recently been crafted by engineering photosensitive domains into intracellular signaling proteins, G-protein coupled receptors (GPCRs), receptor tyrosine kinases (RTKs), and Ca 2+ channels. We highlight herein the optogenetic engineering strategies, kinetic properties, advantages and limitations of these genetically-encoded Ca 2+ channel actuators (GECAs) and modulators. In parallel, we present exemplary applications in both excitable and non-excitable cells and tissues. Furthermore, we briefly discuss potential solutions for wireless optogenetics to accelerate the in vivo applications of GECAs under physiological conditions, with an emphasis on integrating near-infrared (NIR) light-excitable upconversion nanoparticles (UCNPs) and bioluminescence with optogenetics., Competing Interests: Conflict of interest statement The authors have no competing interests or conflict of interest to declare.