Computational redesign of endonuclease DNA binding and cleavage specificity

Observing one-divalent-metal-ion-dependent and histidine-promoted His-Me family I-PpoI nuclease catalysis in crystallo

Metal-ion-dependent nucleases play crucial roles in cellular defense and biotechnological applications. Time-resolved crystallography has resolved catalytic details of metal-ion-dependent DNA hydrolysis and synthesis, uncovering the essential roles of multiple metal ions during catalysis. The histidine-metal (His-Me) superfamily nucleases are renowned for binding one divalent metal ion and requiring a conserved histidine to promote catalysis. Many His-Me family nucleases, including homing endonucleases and Cas9 nuclease, have been adapted for biotechnological and biomedical applications. However, it remains unclear how the single metal ion in His-Me nucleases, together with the histidine, promotes water deprotonation, nucleophilic attack, and phosphodiester bond breakage. By observing DNA hydrolysis in crystallo with His-Me I-PpoI nuclease as a model system, we proved that only one divalent metal ion is required during its catalysis. Moreover, we uncovered several possible deprotonation pathways for the nucleophilic water. Interestingly, binding of the single metal ion and water deprotonation are concerted during catalysis. Our results reveal catalytic details of His-Me nucleases, which is distinct from multi-metal-ion-dependent DNA polymerases and nucleases.

Observing one-divalent-metal-ion dependent and histidine-promoted His-Me family I-PpoI nuclease catalysis in crystallo

Metal-ion-dependent nucleases play crucial roles in cellular defense and biotechnological applications. Time-resolved crystallography has resolved catalytic details of metal-ion-dependent DNA hydrolysis and synthesis, uncovering the essential roles of multiple metal ions during catalysis. The histidine-metal (His-Me) superfamily nucleases are renowned for binding one divalent metal ion and requiring a conserved histidine to promote catalysis. Many His-Me family nucleases, including homing endonucleases and Cas9 nuclease, have been adapted for biotechnological and biomedical applications. However, it remains unclear how the single metal ion in His-Me nucleases, together with the histidine, promotes water deprotonation, nucleophilic attack, and phosphodiester bond breakage. By observing DNA hydrolysis in crystallo with His-Me I-PpoI nuclease as a model system, we proved that only one divalent metal ion is required during its catalysis. Moreover, we uncovered several possible deprotonation pathways for the nucleophilic water. Interestingly, binding of the single metal ion and water deprotonation are concerted during catalysis. Our results reveal catalytic details of His-Me nucleases, which is distinct from multi-metal-ion-dependent DNA polymerases and nucleases.

Integrating machine learning and genome editing for crop improvement

Genome editing is a promising technique that has been broadly utilized for basic gene function studies and trait improvements. Simultaneously, the exponential growth of computational power and big data now promote the application of machine learning for biological research. In this regard, machine learning shows great potential in the refinement of genome editing systems and crop improvement. Here, we review the advances of machine learning to genome editing optimization, with emphasis placed on editing efficiency and specificity enhancement. Additionally, we demonstrate how machine learning bridges genome editing and crop breeding, by accurate key site detection and guide RNA design. Finally, we discuss the current challenges and prospects of these two techniques in crop improvement. By integrating advanced genome editing techniques with machine learning, progress in crop breeding will be further accelerated in the future.

Non-clinical safety assessment of novel drug modalities: Genome safety perspectives on viral-, nuclease- and nucleotide-based gene therapies

Gene therapies have emerged as promising treatments for various conditions including inherited diseases as well as cancer. Ensuring their safe clinical application requires the development of appropriate safety testing strategies. Several guidelines have been provided by health authorities to address these concerns. These guidelines state that non-clinical testing should be carried out on a case-by-case basis depending on the modality. This review focuses on the genome safety assessment of frequently used gene therapy modalities, namely Adeno Associated Viruses (AAVs), Lentiviruses, designer nucleases and mRNAs. Important safety considerations for these modalities, amongst others, are vector integrations into the patient genome (insertional mutagenesis) and off-target editing. Taking into account the constraints of in vivo studies, health authorities endorse the development of novel approach methodologies (NAMs), which are innovative in vitro strategies for genotoxicity testing. This review provides an overview of NAMs applied to viral and CRISPR/Cas9 safety, including next generation sequencing-based methods for integration site analysis and off-target editing. Additionally, NAMs to evaluate the oncogenicity risk arising from unwanted genomic modifications are discussed. Thus, a range of promising techniques are available to support the safe development of gene therapies. Thorough validation, comparisons and correlations with clinical outcomes are essential to identify the most reliable safety testing strategies. By providing a comprehensive overview of these NAMs, this review aims to contribute to a better understanding of the genome safety perspectives of gene therapies.

Perspectives on Computational Enzyme Modeling: From Mechanisms to Design and Drug Development

Understanding enzyme mechanisms is essential for unraveling the complex molecular machinery of life. In this review, we survey the field of computational enzymology, highlighting key principles governing enzyme mechanisms and discussing ongoing challenges and promising advances. Over the years, computer simulations have become indispensable in the study of enzyme mechanisms, with the integration of experimental and computational exploration now established as a holistic approach to gain deep insights into enzymatic catalysis. Numerous studies have demonstrated the power of computer simulations in characterizing reaction pathways, transition states, substrate selectivity, product distribution, and dynamic conformational changes for various enzymes. Nevertheless, significant challenges remain in investigating the mechanisms of complex multistep reactions, large-scale conformational changes, and allosteric regulation. Beyond mechanistic studies, computational enzyme modeling has emerged as an essential tool for computer-aided enzyme design and the rational discovery of covalent drugs for targeted therapies. Overall, enzyme design/engineering and covalent drug development can greatly benefit from our understanding of the detailed mechanisms of enzymes, such as protein dynamics, entropy contributions, and allostery, as revealed by computational studies. Such a convergence of different research approaches is expected to continue, creating synergies in enzyme research. This review, by outlining the ever-expanding field of enzyme research, aims to provide guidance for future research directions and facilitate new developments in this important and evolving field.

Targeting Progranulin as an Immuno-Neurology Therapeutic Approach

Immuno-neurology is an emerging therapeutic strategy for dementia and neurodegeneration designed to address immune surveillance failure in the brain. Microglia, as central nervous system (CNS)-resident myeloid cells, routinely perform surveillance of the brain and support neuronal function. Loss-of-function (LOF) mutations causing decreased levels of progranulin (PGRN), an immune regulatory protein, lead to dysfunctional microglia and are associated with multiple neurodegenerative diseases, including frontotemporal dementia caused by the progranulin gene (GRN) mutation (FTD-GRN), Alzheimer's disease (AD), Parkinson's disease (PD), limbic-predominant age-related transactivation response deoxyribonucleic acid binding protein 43 (TDP-43) encephalopathy (LATE), and amyotrophic lateral sclerosis (ALS). Immuno-neurology targets immune checkpoint-like proteins, offering the potential to convert aging and dysfunctional microglia into disease-fighting cells that counteract multiple disease pathologies, clear misfolded proteins and debris, promote myelin and synapse repair, optimize neuronal function, support astrocytes and oligodendrocytes, and maintain brain vasculature. Several clinical trials are underway to elevate PGRN levels as one strategy to modulate the function of microglia and counteract neurodegenerative changes associated with various disease states. If successful, these and other immuno-neurology drugs have the potential to revolutionize the treatment of neurodegenerative disorders by harnessing the brain's immune system and shifting it from an inflammatory/pathological state to an enhanced physiological/homeostatic state.

Computational design of sequence-specific DNA-binding proteins

Sequence-specific DNA-binding proteins (DBPs) play critical roles in biology and biotechnology, and there has been considerable interest in the engineering of DBPs with new or altered specificities for genome editing and other applications. While there has been some success in reprogramming naturally occurring DBPs using selection methods, the computational design of new DBPs that recognize arbitrary target sites remains an outstanding challenge. We describe a computational method for the design of small DBPs that recognize specific target sequences through interactions with bases in the major groove, and employ this method in conjunction with experimental screening to generate binders for 5 distinct DNA targets. These binders exhibit specificity closely matching the computational models for the target DNA sequences at as many as 6 base positions and affinities as low as 30-100 nM. The crystal structure of a designed DBP-target site complex is in close agreement with the design model, highlighting the accuracy of the design method. The designed DBPs function in both Escherichia coli and mammalian cells to repress and activate transcription of neighboring genes. Our method is a substantial step towards a general route to small and hence readily deliverable sequence-specific DBPs for gene regulation and editing.

Multiple adaptations underly co-option of a CRISPR surveillance complex for RNA-guided DNA transposition

CRISPR-associated transposons (CASTs) are natural RNA-directed transposition systems. We demonstrate that transposon protein TniQ plays a central role in promoting R-loop formation by RNA-guided DNA-targeting modules. TniQ residues, proximal to CRISPR RNA (crRNA), are required for recognizing different crRNA categories, revealing an unappreciated role of TniQ to direct transposition into different classes of crRNA targets. To investigate adaptations allowing CAST elements to utilize attachment sites inaccessible to CRISPR-Cas surveillance complexes, we compared and contrasted PAM sequence requirements in both I-F3b CAST and I-F1 CRISPR-Cas systems. We identify specific amino acids that enable a wider range of PAM sequences to be accommodated in I-F3b CAST elements compared with I-F1 CRISPR-Cas, enabling CAST elements to access attachment sites as sequences drift and evade host surveillance. Together, this evidence points to the central role of TniQ in facilitating the acquisition of CRISPR effector complexes for RNA-guided DNA transposition.

Decoding CRISPR-Cas PAM recognition with UniDesign

The critical first step in Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR)-associated (CRISPR-Cas) protein-mediated gene editing is recognizing a preferred protospacer adjacent motif (PAM) on target DNAs by the protein's PAM-interacting amino acids (PIAAs). Thus, accurate computational modeling of PAM recognition is useful in assisting CRISPR-Cas engineering to relax or tighten PAM requirements for subsequent applications. Here, we describe a universal computational protein design framework (UniDesign) for designing protein-nucleic acid interactions. As a proof of concept, we applied UniDesign to decode the PAM-PIAA interactions for eight Cas9 and two Cas12a proteins. We show that, given native PIAAs, the UniDesign-predicted PAMs are largely identical to the natural PAMs of all Cas proteins. In turn, given natural PAMs, the computationally redesigned PIAA residues largely recapitulated the native PIAAs (74% and 86% in terms of identity and similarity, respectively). These results demonstrate that UniDesign faithfully captures the mutual preference between natural PAMs and native PIAAs, suggesting it is a useful tool for engineering CRISPR-Cas and other nucleic acid-interacting proteins. UniDesign is open-sourced at https://github.com/tommyhuangthu/UniDesign.

Accelerating therapeutic protein design

Protein structures provide for defined microenvironments that can support complex pharmacological functions, otherwise unachievable by small molecules. The advent of therapeutic proteins has thus greatly broadened the range of manageable disorders. Leveraging the knowledge and recent advances in de novo protein design methods has the prospect of revolutionizing how protein drugs are discovered and developed. This review lays out the main challenges facing therapeutic proteins discovery and development, and how present and future advancements of protein design can accelerate the protein drug pipelines.

Computer-aided engineering of a branching sucrase for the glucodiversification of a tetrasaccharide precursor of S. flexneri antigenic oligosaccharides

Enzyme engineering approaches have allowed to extend the collection of enzymatic tools available for synthetic purposes. However, controlling the regioselectivity of the reaction remains challenging, in particular when dealing with carbohydrates bearing numerous reactive hydroxyl groups as substrates. Here, we used a computer-aided design framework to engineer the active site of a sucrose-active [Formula: see text]-transglucosylase for the 1,2-cis-glucosylation of a lightly protected chemically synthesized tetrasaccharide, a common precursor for the synthesis of serotype-specific S. flexneri O-antigen fragments. By targeting 27 amino acid positions of the acceptor binding subsites of a GH70 branching sucrase, we used a RosettaDesign-based approach to propose 49 mutants containing up to 15 mutations scattered over the active site. Upon experimental evaluation, these mutants were found to produce up to six distinct pentasaccharides, whereas only two were synthesized by the parental enzyme. Interestingly, we showed that by introducing specific mutations in the active site of a same enzyme scaffold, it is possible to control the regiospecificity of the 1,2-cis glucosylation of the tetrasaccharide acceptor and produce a unique diversity of pentasaccharide bricks. This work offers novel opportunities for the development of highly convergent chemo-enzymatic routes toward S. flexneri haptens.

Structural-Based Modeling in Protein Engineering. A Must Do

Biotechnological solutions will be a key aspect in our immediate future society, where optimized enzymatic processes through enzyme engineering might be an important solution for waste transformation, clean energy production, biodegradable materials, and green chemistry, for example. Here we advocate the importance of structural-based bioinformatics and molecular modeling tools in such developments. We summarize our recent experiences indicating a great prediction/success ratio, and we suggest that an early in silico phase should be performed in enzyme engineering studies. Moreover, we demonstrate the potential of a new technique combining Rosetta and PELE, which could provide a faster and more automated procedure, an essential aspect for a broader use.

Molecular flexibility in computational protein design: an algorithmic perspective

Computational protein design (CPD) is a powerful technique for engineering new proteins, with both great fundamental implications and diverse practical interests. However, the approximations usually made for computational efficiency, using a single fixed backbone and a discrete set of side chain rotamers, tend to produce rigid and hyper-stable folds that may lack functionality. These approximations contrast with the demonstrated importance of molecular flexibility and motions in a wide range of protein functions. The integration of backbone flexibility and multiple conformational states in CPD, in order to relieve the inaccuracies resulting from these simplifications and to improve design reliability, are attracting increased attention. However, the greatly increased search space that needs to be explored in these extensions defines extremely challenging computational problems. In this review, we outline the principles of CPD and discuss recent effort in algorithmic developments for incorporating molecular flexibility in the design process.

History of genome editing: From meganucleases to CRISPR

CRISPR-mediated genome editing has undoubtedly revolutionized genetic engineering of animals. With the ability for virtually unlimited modification of almost any genome it is easy to forget which amazing discoveries paved the way for this ground-breaking technology. Here, we summarize the history of genome editing platforms, starting from enhanced integration of foreign DNA by meganuclease-mediated double-strand breaks to CRISPR/Cas9, the leading technology to date, and its re-engineered variants.

Recent advances in de novo protein design: Principles, methods, and applications

The computational de novo protein design is increasingly applied to address a number of key challenges in biomedicine and biological engineering. Successes in expanding applications are driven by advances in design principles and methods over several decades. Here, we review recent innovations in major aspects of the de novo protein design and include how these advances were informed by principles of protein architecture and interactions derived from the wealth of structures in the Protein Data Bank. We describe developments in de novo generation of designable backbone structures, optimization of sequences, design scoring functions, and the design of the function. The advances not only highlight design goals reachable now but also point to the challenges and opportunities for the future of the field.

Fluorescence imaging of intracellular nucleases-A review

Nucleases play crucial roles in maintaining genomic integrity. Visualization of intracellular distribution and translocation of nucleases are of great importance for understanding the in-vivo physiological functions of these enzymes and their roles in DNA repair and other cellular signaling pathways. Here we review the recently developed approaches for fluorescence imaging of nucleases in various eukaryotic cells. We mainly focused on the immunofluorescence techniques, the genetically encoded fluorescent probes and the chemically synthesized fluorescent DNA-substrate probes that enabled in-situ visualization of the subcellular localization of nucleases and their interactions with other protein/DNA molecules within cells. The targeted nucleases included important endonucleases, 3' exonucleases and 5' exonucleases that were involved in the DNA damage repair pathways and the intracellular DNA degradation. The advantages and limitations of the available tools were summarized and discussed.

Understanding the structural details of APOBEC3-DNA interactions using graph-based representations

Human APOBEC3 (A3; apolipoprotein B mRNA editing catalytic polypeptide-like 3) is a family of seven enzymes involved in generating mutations in nascent reverse transcripts of many retroviruses, as well as the human genome in a range of cancer types. The structural details of the interaction between A3 proteins and DNA molecules are only available for a few family members. Here we use homology modelling techniques to address the difference in structural coverage of human A3 enzymes interacting with different DNA substrates. A3-DNA interfaces are represented as residue networks ("graphs"), based on which features at these interfaces are compared and quantified. We demonstrate that graph-based representations are effective in highlighting structural features of A3-DNA interfaces. By large-scale in silico mutagenesis of the bound DNA chain, we predicted the preference of substrate DNA sequence for multiple A3 domains. These data suggested that computational modelling approaches could contribute in the exploration of the structural basis for sequence specificity in A3 substrate selection, and demonstrated the utility of graph-based approaches in evaluating a large number of structural models generated in silico.

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APOBEC3(A3)-DNA structures have been resolved with modified deaminase domains.

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Structural modelling of interaction between wild-type A3 domains and DNA substrates.

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Graph-based representations reveal structural differences across A3-DNA interfaces.

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Using in silico mutagenesis we compared substrate preference of multiple A3 domains.

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Graph-based approaches can efficiently compare a large number of structural models.

Macromolecular modeling and design in Rosetta: recent methods and frameworks

The Rosetta software for macromolecular modeling, docking and design is extensively used in laboratories worldwide. During two decades of development by a community of laboratories at more than 60 institutions, Rosetta has been continuously refactored and extended. Its advantages are its performance and interoperability between broad modeling capabilities. Here we review tools developed in the last 5 years, including over 80 methods. We discuss improvements to the score function, user interfaces and usability. Rosetta is available at http://www.rosettacommons.org.

Attempts to develop an enzyme converting DHIV to KIV

Dihydroxy-acid dehydratase (DHAD) catalyzes the dehydration of R-2,3-dihydroxyisovalerate (DHIV) to 2-ketoisovalerate (KIV) using an Fe-S cluster as a cofactor, which is sensitive to oxidation and expensive to synthesize. In contrast, sugar acid dehydratases catalyze the same chemical reactions using a magnesium ion. Here, we attempted to substitute the high-cost DHAD with a cost-efficient engineered sugar acid dehydratase using computational protein design (CPD). First, we tried without success to modify the binding pocket of a sugar acid dehydratase to accommodate the smaller, more hydrophobic DHIV. Then, we used a chemically activated substrate analog to react with sugar acid dehydratases or other enolase superfamily enzymes. Mandelate racemase from Pseudomonas putida (PpManR) and the putative sugar acid dehydratase from Salmonella typhimurium (StPutD) showed beta-elimination activity towards chlorolactate (CLD). CPD combined with medium-throughput selection improved the PpManR kcat/KM for CLD by four-fold. However, these enzyme variants did not show dehydration activity towards DHIV. Lastly, assuming phosphorylation could also be a good activation mechanism, we found that mevalonate-3-kinase (M3K) from Picrophilus torridus (PtM3K) exhibited adenosine triphosphate (ATP) hydrolysis activity when mixed with DHIV, indicating phosphorylation activity towards DHIV. Engineering PpManR or StPutD to accept 3-phospho-DHIV as a substrate was performed, but no variants with the desired activity were obtained.

Modifying a covarying protein-DNA interaction changes substrate preference of a site-specific endonuclease

Identifying and validating intermolecular covariation between proteins and their DNA-binding sites can provide insights into mechanisms that regulate selectivity and starting points for engineering new specificity. LAGLIDADG homing endonucleases (meganucleases) can be engineered to bind non-native target sites for gene-editing applications, but not all redesigns successfully reprogram specificity. To gain a global overview of residues that influence meganuclease specificity, we used information theory to identify protein-DNA covariation. Directed evolution experiments of one predicted pair, 227/+3, revealed variants with surprising shifts in I-OnuI substrate preference at the central 4 bases where cleavage occurs. Structural studies showed significant remodeling distant from the covarying position, including restructuring of an inter-hairpin loop, DNA distortions near the scissile phosphates, and new base-specific contacts. Our findings are consistent with a model whereby the functional impacts of covariation can be indirectly propagated to neighboring residues outside of direct contact range, allowing meganucleases to adapt to target site variation and indirectly expand the sequence space accessible for cleavage. We suggest that some engineered meganucleases may have unexpected cleavage profiles that were not rationally incorporated during the design process.

Chirality manifestation in elastic coupling between the layers of double-walled carbon nanotubes

The search for new relatively easy physicochemical methods for the structural identification of carbon nanotubes represents a key challenge. Here, analyzing the experimental data on double-walled carbon nanotubes (DWCNTs) obtained by us and taken from the literature, we have expressed the magnitude of elastic coupling between two tubular walls forming a DWCNT as a simple function dependent not only on DWCNT diameters but also on the difference between the chirality angles of the constituent nanotubes. To get this quite unexpected result, which allows us to relate more precisely the structural parameters of a DWCNT with frequencies of its radial breathing-like modes (RBLM), we have developed a new model for the RBLM dynamics that takes into account a possible deposition of water molecules from ambient air onto the DWCNT surface. The model constructed allows us to predict the higher prevalence of DWCNTs comprising two walls with identical handedness. The application of the results obtained for the identification of DWCNTs is also considered.

Microinjection and Micromanipulation: A Historical Perspective

Microinjection/micromanipulation is more than 100 years old. It is a technique that is instrumental in biomedical research and healthcare. Its longevity lies in its preciseness in mechanical retrieval, or delivery of biological materials, which in some cases is simply necessary or more effective than other retrieval/delivery means. Microinjection is favored for its straightforwardness in transferring contents from micromolecules to macromolecules and from organelles to cells. Microinjection/micromanipulation has been practiced over the century like an art form. Variations in handlings and instruments can be tolerated to a surprising degree with satisfactory outcomes. Throughout the century, microinjection developed as an indispensable tool along with the evolution of biomedical fields: from transgenics to gene targeting, from animal cloning to human infertility treatment, from nuclease-guided genetic engineering to RNA-guided genome editing (Fig. 1). The birth of the CRISPRology rejuvenated microinjection. For microinjection/micromanipulation, the second century has already begun with the early arrival of computerized instrumentation and lately of the high-throughput nanomanipulators potentially operable by artificial intelligence. As we yin-yang both systemic and precision approaches in research and medicine, microinjection will no doubt continue to find its unique place in the future.

Third generation antibody discovery methods: in silico rational design

Owing to their outstanding performances in molecular recognition, antibodies are extensively used in research and applications in molecular biology, biotechnology and medicine. Recent advances in experimental and computational methods are making it possible to complement well-established in vivo (first generation) and in vitro (second generation) methods of antibody discovery with novel in silico (third generation) approaches. Here we describe the principles of computational antibody design and review the state of the art in this field. We then present Modular, a method that implements the rational design of antibodies in a modular manner, and describe the opportunities offered by this approach.

An adaptive geometric search algorithm for macromolecular scaffold selection

A wide variety of protein and peptidomimetic design tasks require matching functional 3D motifs to potential oligomeric scaffolds. For example, during enzyme design, one aims to graft active-site patterns-typically consisting of 3-15 residues-onto new protein surfaces. Identifying protein scaffolds suitable for such active-site engraftment requires costly searches for protein folds that provide the correct side chain positioning to host the desired active site. Other examples of biodesign tasks that require similar fast exact geometric searches of potential side chain positioning include mimicking binding hotspots, design of metal binding clusters and the design of modular hydrogen binding networks for specificity. In these applications, the speed and scaling of geometric searches limits the scope of downstream design to small patterns. Here, we present an adaptive algorithm capable of searching for side chain take-off angles, which is compatible with an arbitrarily specified functional pattern and which enjoys substantive performance improvements over previous methods. We demonstrate this method in both genetically encoded (protein) and synthetic (peptidomimetic) design scenarios. Examples of using this method with the Rosetta framework for protein design are provided. Our implementation is compatible with multiple protein design frameworks and is freely available as a set of python scripts (https://github.com/JiangTian/adaptive-geometric-search-for-protein-design).

Engineering altered protein-DNA recognition specificity

Protein engineering is used to generate novel protein folds and assemblages, to impart new properties and functions onto existing proteins, and to enhance our understanding of principles that govern protein structure. While such approaches can be employed to reprogram protein-protein interactions, modifying protein-DNA interactions is more difficult. This may be related to the structural features of protein-DNA interfaces, which display more charged groups, directional hydrogen bonds, ordered solvent molecules and counterions than comparable protein interfaces. Nevertheless, progress has been made in the redesign of protein-DNA specificity, much of it driven by the development of engineered enzymes for genome modification. Here, we summarize the creation of novel DNA specificities for zinc finger proteins, meganucleases, TAL effectors, recombinases and restriction endonucleases. The ease of re-engineering each system is related both to the modularity of the protein and the extent to which the proteins have evolved to be capable of readily modifying their recognition specificities in response to natural selection. The development of engineered DNA binding proteins that display an ideal combination of activity, specificity, deliverability, and outcomes is not a fully solved problem, however each of the current platforms offers unique advantages, offset by behaviors and properties requiring further study and development.

Efficient minimization of multipole electrostatic potentials in torsion space

The development of models of macromolecular electrostatics capable of delivering improved fidelity to quantum mechanical calculations is an active field of research in computational chemistry. Most molecular force field development takes place in the context of models with full Cartesian coordinate degrees of freedom. Nevertheless, a number of macromolecular modeling programs use a reduced set of conformational variables limited to rotatable bonds. Efficient algorithms for minimizing the energies of macromolecular systems with torsional degrees of freedom have been developed with the assumption that all atom-atom interaction potentials are isotropic. We describe novel modifications to address the anisotropy of higher order multipole terms while retaining the efficiency of these approaches. In addition, we present a treatment for obtaining derivatives of atom-centered tensors with respect to torsional degrees of freedom. We apply these results to enable minimization of the Amoeba multipole electrostatics potential in a system with torsional degrees of freedom, and validate the correctness of the gradients by comparison to finite difference approximations. In the interest of enabling a complete model of electrostatics with implicit treatment of solvent-mediated effects, we also derive expressions for the derivative of solvent accessible surface area with respect to torsional degrees of freedom.

Gene Drive for Mosquito Control: Where Did It Come from and Where Are We Headed?

Mosquito-borne pathogens place an enormous burden on human health. The existing toolkit is insufficient to support ongoing vector-control efforts towards meeting disease elimination and eradication goals. The perspective that genetic approaches can potentially add a significant set of tools toward mosquito control is not new, but the recent improvements in site-specific gene editing with CRISPR/Cas9 systems have enhanced our ability to both study mosquito biology using reverse genetics and produce genetics-based tools. Cas9-mediated gene-editing is an efficient and adaptable platform for gene drive strategies, which have advantages over innundative release strategies for introgressing desirable suppression and pathogen-blocking genotypes into wild mosquito populations; until recently, an effective gene drive has been largely out of reach. Many considerations will inform the effective use of new genetic tools, including gene drives. Here we review the lengthy history of genetic advances in mosquito biology and discuss both the impact of efficient site-specific gene editing on vector biology and the resulting potential to deploy new genetic tools for the abatement of mosquito-borne disease.

Crystallographic analyses illustrate significant plasticity and efficient recoding of meganuclease target specificity

The retargeting of protein-DNA specificity, outside of extremely modular DNA binding proteins such as TAL effectors, has generally proved to be quite challenging. Here, we describe structural analyses of five different extensively retargeted variants of a single homing endonuclease, that have been shown to function efficiently in ex vivo and in vivo applications. The redesigned proteins harbor mutations at up to 53 residues (18%) of their amino acid sequence, primarily distributed across the DNA binding surface, making them among the most significantly reengineered ligand-binding proteins to date. Specificity is derived from the combined contributions of DNA-contacting residues and of neighboring residues that influence local structural organization. Changes in specificity are facilitated by the ability of all those residues to readily exchange both form and function. The fidelity of recognition is not precisely correlated with the fraction or total number of residues in the protein-DNA interface that are actually involved in DNA contacts, including directional hydrogen bonds. The plasticity of the DNA-recognition surface of this protein, which allows substantial retargeting of recognition specificity without requiring significant alteration of the surrounding protein architecture, reflects the ability of the corresponding genetic elements to maintain mobility and persistence in the face of genetic drift within potential host target sites.

Toward an Expanded Genome: Structural and Computational Characterization of an Artificially Expanded Genetic Information System

Although the fundamental properties of DNA as first proposed by Watson and Crick in 1953 provided a basic understanding of how duplex DNA was organized and might be replicated, it was not until the first crystal structures of DNA (Z-DNA in 1979, B-DNA in 1980, and A-DNA in 1982) that the true complexity of the molecule began to be appreciated. Many crystal structures of oligonucleotides have since shed light on the helical forms that "Watson-Crick" DNA can adopt, their associated groove widths, and the properties of the nucleobase pairs and their interactions in all three helical forms. Additional understanding of the properties of Watson-Crick DNA has been provided by computational studies employing a variety of theoretical methods. Together with these studies devoted to understanding Watson-Crick DNA, recent efforts to expand the genetic alphabet have founded a new field in synthetic biology. One of these efforts, the artificially expanded genetic information system (AEGIS) developed by Steven Benner and co-workers, takes advantage of orthogonal hydrogen bonding to produce DNA comprised of six nucleobase pairs, of which the most extensively studied is referred to as P:Z with P being 2-amino-imidazo[1,2-a]-1,3,5-triazin-4(8H)-one) and Z being 6-amino-5-nitro-2(1H)-pyridone. P:Z forms three edge-on hydrogen bonds that differ from standard Watson-Crick pairs in the arrangement of acceptors and donor groups; P presents acceptor, acceptor, donor, and Z presents donor, donor, acceptor. Z is unique among the AEGIS nucleobases in having a nitro group present in the major groove. PZ-containing DNA has been exploited in a number of clinical applications and is being used to develop receptors and catalysts. Ultimately, the grand challenge will be to create a semisynthetic organism with an expanded genome. Furthermore, just as our understanding of the properties of natural DNA have benefited from structural and computational characterization, so too will our understanding of artificial DNA. This Account focuses on the structural and biophysical properties of AEGIS DNA containing P:Z pairs. We begin with the fundamental properties of P:Z nucleobase pairs, including their electrostatic potential and hydrogen-bonding energies, as elucidated by quantum mechanical calculations. We then examine the impact of including multiple consecutive P:Z pairs into duplex DNA providing an opportunity to investigate stacking interactions between P:Z pairs. The self-complementary 5'-CTTATPPTAZZATAAG was crystallized in B-form using the host-guest system along with analogous natural sequences including Gs or As. Use of the host-guest system to characterize B-DNA obviates a number of limitations on the structural characterization of sequences of interest; these include the ability to crystallize the desired sequences and to distinguish structural effects imparted by the lattice constraints from those inherent in the sequence itself. On the other hand, 3/6ZP, 5'-CTTATPPPZZZATAAG, was crystallized in A-form in a DNA-only lattice allowing a comparative analysis of P:Z pairs in two of the biologically relevant helical forms: A- and B-DNA. Computational studies on the 3/6ZP sequence starting in A-form provide additional evidence for a more energetically favorable stacking interaction, which we term the "slide" conformer, observed in the A-form crystal structure; this unusual stacking interaction plays a major role in altering the conformational dynamics observed for the PZ-containing duplex as compared to a GC-containing "control" duplex in long time scale molecular dynamics simulations. This combined use of structural and computational strategies paves the way for obtaining a detailed description of artificial DNA, both in how it differs from Watson-Crick DNA and in the rational discovery of proteins, such as endonucleases, transcription factors, and polymerases, which can specifically manipulate DNA containing AEGIS nucleobase pairs.

Rosetta:MSF: a modular framework for multi-state computational protein design

Computational protein design (CPD) is a powerful technique to engineer existing proteins or to design novel ones that display desired properties. Rosetta is a software suite including algorithms for computational modeling and analysis of protein structures and offers many elaborate protocols created to solve highly specific tasks of protein engineering. Most of Rosetta’s protocols optimize sequences based on a single conformation (i. e. design state). However, challenging CPD objectives like multi-specificity design or the concurrent consideration of positive and negative design goals demand the simultaneous assessment of multiple states. This is why we have developed the multi-state framework MSF that facilitates the implementation of Rosetta’s single-state protocols in a multi-state environment and made available two frequently used protocols. Utilizing MSF, we demonstrated for one of these protocols that multi-state design yields a 15% higher performance than single-state design on a ligand-binding benchmark consisting of structural conformations. With this protocol, we designed de novo nine retro-aldolases on a conformational ensemble deduced from a (βα)₈-barrel protein. All variants displayed measurable catalytic activity, testifying to a high success rate for this concept of multi-state enzyme design.

Protein engineering, i. e. the targeted modification or design of proteins has tremendous potential for medical and industrial applications. One generally applicable strategy for protein engineering is rational protein design: based on detailed knowledge of structure and function, computer programs like Rosetta propose the sequence of a protein possessing the desired properties. So far, most computer protocols have used rigid structures for design, which is a simplification because a protein’s structure is more accurately specified by a conformational ensemble. We have now implemented a framework for computational protein design that allows certain design protocols of Rosetta to make use of multiple design states like structural ensembles. An in silico assessment simulating ligand-binding design showed that this new approach generates more reliably native-like sequences than a single-state approach. As a proof-of-concept, we introduced de novo retro-aldolase activity into a scaffold protein and characterized nine variants experimentally, all of which were catalytically active.

Achievements and Challenges in Computational Protein Design

Computational protein design (CPD), a yet evolving field, includes computer-aided engineering for partial or full de novo designs of proteins of interest. Designs are defined by a requested structure, function, or working environment. This chapter describes the birth and maturation of the field by presenting 101 CPD examples in a chronological order emphasizing achievements and pending challenges. Integrating these aspects presents the plethora of CPD approaches with the hope of providing a "CPD 101". These reflect on the broader structural bioinformatics and computational biophysics field and include: (1) integration of knowledge-based and energy-based methods, (2) hierarchical designated approach towards local, regional, and global motifs and the integration of high- and low-resolution design schemes that fit each such region, (3) systematic differential approaches towards different protein regions, (4) identification of key hot-spot residues and the relative effect of remote regions, (5) assessment of shape-complementarity, electrostatics and solvation effects, (6) integration of thermal plasticity and functional dynamics, (7) negative design, (8) systematic integration of experimental approaches, (9) objective cross-assessment of methods, and (10) successful ranking of potential designs. Future challenges also include dissemination of CPD software to the general use of life-sciences researchers and the emphasis of success within an in vivo milieu. CPD increases our understanding of protein structure and function and the relationships between the two along with the application of such know-how for the benefit of mankind. Applied aspects range from biological drugs, via healthier and tastier food products to nanotechnology and environmentally friendly enzymes replacing toxic chemicals utilized in the industry.

To CRISPR and beyond: the evolution of genome editing in stem cells

The goal of editing the genomes of stem cells to generate model organisms and cell lines for genetic and biological studies has been pursued for decades. There is also exciting potential for future clinical impact in humans. While recent, rapid advances in targeted nuclease technologies have led to unprecedented accessibility and ease of gene editing, biology has benefited from past directed gene modification via homologous recombination, gene traps and other transgenic methodologies. Here we review the history of genome editing in stem cells (including via zinc finger nucleases, transcription activator-like effector nucleases and CRISPR-Cas9), discuss recent developments leading to the implementation of stem cell gene therapies in clinical trials and consider the prospects for future advances in this rapidly evolving field.

In vivo genome editing as a potential treatment strategy for inherited retinal dystrophies

In vivo genome editing represents an emerging field in the treatment of monogenic disorders, as it may constitute a solution to the current hurdles in classic gene addition therapy, which are the low levels and limited duration of transgene expression. Following the introduction of a double strand break (DSB) at the mutational site by highly specific endonucleases, such as TALENs (transcription activator like effector nucleases) or RNA based nucleases (clustered regulatory interspaced short palindromic repeats - CRISPR-Cas), the cell's own DNA repair machinery restores integrity to the DNA strand and corrects the mutant sequence, thus allowing the cell to produce protein levels as needed. The DNA repair happens either through the error prone non-homologous end-joining (NHEJ) pathway or with high fidelity through homology directed repair (HDR) in the presence of a DNA donor template. A third pathway called microhomology mediated endjoining (MMEJ) has been recently discovered. In this review, the authors focus on the different DNA repair mechanisms, the current state of the art tools for genome editing and the particularities of the retina and photoreceptors with regard to in vivo therapeutic approaches. Finally, current attempts in the field of retinal in vivo genome editing are discussed and future directions of research identified.

Statistical analysis of structural determinants for protein-DNA-binding specificity

DNA-binding proteins play critical roles in biological processes including gene expression, DNA packaging and DNA repair. They bind to DNA target sequences with different degrees of binding specificity, ranging from highly specific (HS) to nonspecific (NS). Alterations of DNA-binding specificity, due to either genetic variation or somatic mutations, can lead to various diseases. In this study, a comparative analysis of protein-DNA complex structures was carried out to investigate the structural features that contribute to binding specificity. Protein-DNA complexes were grouped into three general classes based on degrees of binding specificity: HS, multispecific (MS), and NS. Our results show a clear trend of structural features among the three classes, including amino acid binding propensities, simple and complex hydrogen bonds, major/minor groove and base contacts, and DNA shape. We found that aspartate is enriched in HS DNA binding proteins and predominately binds to a cytosine through a single hydrogen bond or two consecutive cytosines through bidentate hydrogen bonds. Aromatic residues, histidine and tyrosine, are highly enriched in the HS and MS groups and may contribute to specific binding through different mechanisms. To further investigate the role of protein flexibility in specific protein-DNA recognition, we analyzed the conformational changes between the bound and unbound states of DNA-binding proteins and structural variations. The results indicate that HS and MS DNA-binding domains have larger conformational changes upon DNA-binding and larger degree of flexibility in both bound and unbound states. Proteins 2016; 84:1147-1161. © 2016 Wiley Periodicals, Inc.

Designing peptide sequences in flexible chain conformations to bind RNA: a search algorithm combining Monte Carlo, self-consistent mean field and concerted rotation techniques

A search algorithm combining Monte Carlo, self-consistent mean field, and concerted rotation techniques was developed to discover peptide sequences that are reasonable HIV drug candidates due to their exceptional binding to human tRNAUUU(Lys3), the primer of HIV replication. The search algorithm allows for iteration between sequence mutations and conformation changes during sequence evolution. Searches conducted for different classes of peptides identified several potential peptide candidates. Analysis of the energy revealed that the asparagine and cysteine at residues 11 and 12 play important roles in "recognizing" tRNA(Lys3) via van der Waals interactions, contributing to binding specificity. Arginines preferentially attract the phosphate linkage via charge-charge interaction, contributing to binding affinity. Evaluation of the RNA/peptide complex's structure revealed that adding conformation changes to the search algorithm yields peptides with better binding affinity and specificity to tRNA(Lys3) than a previous mutation-only algorithm.

Engineering Delivery Vehicles for Genome Editing

The field of genome engineering has created new possibilities for gene therapy, including improved animal models of disease, engineered cell therapies, and in vivo gene repair. The most significant challenge for the clinical translation of genome engineering is the development of safe and effective delivery vehicles. A large body of work has applied genome engineering to genetic modification in vitro, and clinical trials have begun using cells modified by genome editing. Now, promising preclinical work is beginning to apply these tools in vivo. This article summarizes the development of genome engineering platforms, including meganucleases, zinc finger nucleases, TALENs, and CRISPR/Cas9, and their flexibility for precise genetic modifications. The prospects for the development of safe and effective viral and nonviral delivery vehicles for genome editing are reviewed, and promising advances in particular therapeutic applications are discussed.

BetaSCPWeb: side-chain prediction for protein structures using Voronoi diagrams and geometry prioritization

Many applications, such as protein design, homology modeling, flexible docking, etc. require the prediction of a protein's optimal side-chain conformations from just its amino acid sequence and backbone structure. Side-chain prediction (SCP) is an NP-hard energy minimization problem. Here, we present BetaSCPWeb which efficiently computes a conformation close to optimal using a geometry-prioritization method based on the Voronoi diagram of spherical atoms. Its outputs are visual, textual and PDB file format. The web server is free and open to all users at http://voronoi.hanyang.ac.kr/betascpweb with no login requirement.

Structure of Type IIL Restriction-Modification Enzyme MmeI in Complex with DNA Has Implications for Engineering New Specificities

The creation of restriction enzymes with programmable DNA-binding and -cleavage specificities has long been a goal of modern biology. The recently discovered Type IIL MmeI family of restriction-and-modification (RM) enzymes that possess a shared target recognition domain provides a framework for engineering such new specificities. However, a lack of structural information on Type IIL enzymes has limited the repertoire that can be rationally engineered. We report here a crystal structure of MmeI in complex with its DNA substrate and an S-adenosylmethionine analog (Sinefungin). The structure uncovers for the first time the interactions that underlie MmeI-DNA recognition and methylation (5’-TCCRAC-3’; R = purine) and provides a molecular basis for changing specificity at four of the six base pairs of the recognition sequence (5’-TCCRAC-3’). Surprisingly, the enzyme is resilient to specificity changes at the first position of the recognition sequence (5’-TCCRAC-3’). Collectively, the structure provides a basis for engineering further derivatives of MmeI and delineates which base pairs of the recognition sequence are more amenable to alterations than others.

The structure of the bifunctional Type IIL restriction-and-modification enzyme MmeI provides a basis for understanding how such enzymes recognize their substrates and a framework for manipulating their specificities.

Type II restriction endonucleases (REases) are the bedrock of modern biotechnology. Type II REases were essential for the recombinant DNA revolution and the development of gene technology. However, despite the discovery of more than 4,000 REases, the DNA recognition specificities are limited to only ~365. The recently discovered Type IIL MmeI family of restriction-and-modification (RM) enzymes provides a framework for understanding and engineering new specificities. We report here a crystal structure of MmeI in complex with its DNA substrate and an S-adenosylmethionine analog (Sinefungin). The structure uncovers for the first time the interactions that underlie MmeI-DNA recognition and methylation. The results establish a platform for rationally engineering further derivatives from MmeI and its homologs that will possess new, intentionally chosen, specificities.

Protein side chain conformation predictions with an MMGBSA energy function

The prediction of protein side chain conformations from backbone coordinates is an important task in structural biology, with applications in structure prediction and protein design. It is a difficult problem due to its combinatorial nature. We study the performance of an "MMGBSA" energy function, implemented in our protein design program Proteus, which combines molecular mechanics terms, a Generalized Born and Surface Area (GBSA) solvent model, with approximations that make the model pairwise additive. Proteus is not a competitor to specialized side chain prediction programs due to its cost, but it allows protein design applications, where side chain prediction is an important step and MMGBSA an effective energy model. We predict the side chain conformations for 18 proteins. The side chains are first predicted individually, with the rest of the protein in its crystallographic conformation. Next, all side chains are predicted together. The contributions of individual energy terms are evaluated and various parameterizations are compared. We find that the GB and SA terms, with an appropriate choice of the dielectric constant and surface energy coefficients, are beneficial for single side chain predictions. For the prediction of all side chains, however, errors due to the pairwise additive approximation overcome the improvement brought by these terms. We also show the crucial contribution of side chain minimization to alleviate the rigid rotamer approximation. Even without GB and SA terms, we obtain accuracies comparable to SCWRL4, a specialized side chain prediction program. In particular, we obtain a better RMSD than SCWRL4 for core residues (at a higher cost), despite our simpler rotamer library. Proteins 2016; 84:803-819. © 2016 Wiley Periodicals, Inc.

Principles and Overview of Sampling Methods for Modeling Macromolecular Structure and Dynamics

Investigation of macromolecular structure and dynamics is fundamental to understanding how macromolecules carry out their functions in the cell. Significant advances have been made toward this end in silico, with a growing number of computational methods proposed yearly to study and simulate various aspects of macromolecular structure and dynamics. This review aims to provide an overview of recent advances, focusing primarily on methods proposed for exploring the structure space of macromolecules in isolation and in assemblies for the purpose of characterizing equilibrium structure and dynamics. In addition to surveying recent applications that showcase current capabilities of computational methods, this review highlights state-of-the-art algorithmic techniques proposed to overcome challenges posed in silico by the disparate spatial and time scales accessed by dynamic macromolecules. This review is not meant to be exhaustive, as such an endeavor is impossible, but rather aims to balance breadth and depth of strategies for modeling macromolecular structure and dynamics for a broad audience of novices and experts.

Generation of artificial sequence-specific nucleases via a preassembled inert-template

Sequence specific nucleases are important tools for processing nucleic acids in a predictable way. Herein, we demonstrate a conceptually new approach for generating sequence-specific nucleases via a preassembled inert-template (PAIT). A fairly stable DNase I/inert-DNA complex was prepared with a customized sequence specificity for a target DNA which contains a sequence complementary to the inert-DNA template. The complex could efficiently cleave the targeted sequence within either a long double-stranded DNA or a single-stranded DNA without affecting other unrelated DNA strands. The discrimination factor against single-base mismatch strands within a 14 nt target region was as high as 25.3. The strategy was also successfully applied to RNase A. Our findings may hold great potential for the development of a number of new powerful enzymatic tools.