Macromolecular modeling and design in Rosetta: recent methods and frameworks

Comprehensive screening of a light-inducible split Cre recombinase with domain insertion profiling

Splitting proteins with light- or chemically-inducible dimers provides a mechanism for post-translational control of protein function. However, current methods for engineering stimulus-responsive split proteins often require significant protein engineering expertise and laborious screening of individual constructs. To address this challenge, we use a pooled library approach that enables rapid generation and screening of nearly all possible split protein constructs in parallel, where results can be read out using sequencing. We perform our method on Cre recombinase with optogenetic dimers as a proof of concept, resulting in comprehensive data on split sites throughout the protein. To improve accuracy in predicting split protein behavior, we develop a Bayesian computational approach to contextualize errors inherent to experimental procedures. Overall, our method provides a streamlined approach for achieving inducible post-translational control of a protein of interest.

Combinatorial immune refocusing within the influenza hemagglutinin head elicits cross-neutralizing antibody responses

The head domain of influenza hemagglutinin (HA) elicits potently neutralizing yet mostly strain-specific antibodies during infection and vaccination. Here we evaluated a series of immunogens that combined several immunofocusing techniques for their ability to enhance the functional breadth of vaccine-elicited immune responses. We designed a series of "trihead" nanoparticle immunogens that display native-like closed trimeric heads from the HAs of several H1N1 influenza viruses, including hyperglycosylated variants and hypervariable variants that incorporate natural and designed sequence diversity at key positions in the periphery of the receptor binding site (RBS). Nanoparticle immunogens displaying triheads or hyperglycosylated triheads elicited higher HAI and neutralizing activity against vaccine-matched and -mismatched H1 viruses than corresponding immunogens lacking either trimer-stabilizing mutations or hyperglycosylation, indicating that both of these engineering strategies contributed to improved immunogenicity. By contrast, mosaic nanoparticle display and antigen hypervariation did not significantly alter the magnitude or breadth of vaccine-elicited antibodies. Serum competition assays and electron microscopy polyclonal epitope mapping revealed that the trihead immunogens, especially when hyperglycosylated, elicited a high proportion of antibodies targeting the RBS, as well as cross-reactive antibodies targeting a conserved epitope on the side of the head. Our results yield important insights into antibody responses against the HA head and the ability of several structure-based immunofocusing techniques to influence vaccine-elicited antibody responses.

Antigen spacing on protein nanoparticles influences antibody responses to vaccination

Immunogen design approaches aim to control the specificity and quality of antibody responses to enable the creation of next-generation vaccines with improved potency and breadth. However, our understanding of the relationship between immunogen structure and immunogenicity is limited. Here we use computational protein design to generate a self-assembling nanoparticle vaccine platform based on the head domain of influenza hemagglutinin (HA) that enables precise control of antigen conformation, flexibility, and spacing on the nanoparticle exterior. Domain-based HA head antigens were presented either as monomers or in a native-like closed trimeric conformation that prevents exposure of trimer interface epitopes. These antigens were connected to the underlying nanoparticle by a rigid linker that was modularly extended to precisely control antigen spacing. We found that nanoparticle immunogens with decreased spacing between closed trimeric head antigens elicited antibodies with improved hemagglutination inhibition (HAI) and neutralization potency as well as binding breadth across diverse HAs within a subtype. Our "trihead" nanoparticle immunogen platform thus enables new insights into anti-HA immunity, establishes antigen spacing as an important parameter in structure-based vaccine design, and embodies several design features that could be used to generate next-generation vaccines against influenza and other viruses.

Computational design of dynamic receptor-peptide signaling complexes applied to chemotaxis

Engineering protein biosensors that sensitively respond to specific biomolecules by triggering precise cellular responses is a major goal of diagnostics and synthetic cell biology. Previous biosensor designs have largely relied on binding structurally well-defined molecules. In contrast, approaches that couple the sensing of flexible compounds to intended cellular responses would greatly expand potential biosensor applications. Here, to address these challenges, we develop a computational strategy for designing signaling complexes between conformationally dynamic proteins and peptides. To demonstrate the power of the approach, we create ultrasensitive chemotactic receptor-peptide pairs capable of eliciting potent signaling responses and strong chemotaxis in primary human T cells. Unlike traditional approaches that engineer static binding complexes, our dynamic structure design strategy optimizes contacts with multiple binding and allosteric sites accessible through dynamic conformational ensembles to achieve strongly enhanced signaling efficacy and potency. Our study suggests that a conformationally adaptable binding interface coupled to a robust allosteric transmission region is a key evolutionary determinant of peptidergic GPCR signaling systems. The approach lays a foundation for designing peptide-sensing receptors and signaling peptide ligands for basic and therapeutic applications.

Improving the Activity and Stability of Serine Protease ThAPT3 by Alleviating Self-Cleavage and Its Application in Deproteinization of Shrimp Shells

The self-cleavage properties of proteases result in low activity and instability, which limit their industrial application. In this study, the serine protease ThAPT3 from Torrubiella hemipterigena was successfully expressed in Komagataella phaffii. We investigated the self-degradation mechanism of ThAPT3 and presented a rational strategy to alleviate self-cleavage. A major self-degradation site (Leu²³⁸-Met²³⁹) and a primary autolysis region were identified. The autolysis regions (loop₁₈, α₈-helix, and loop₁₉) were redesigned and optimized using loop transplantation, energy calculations, surface cavity optimization, and loop anchoring. A triple-superposition mutant, ThAPT3-M9 (M²³⁹GKDGAVAAGLC²⁵⁰ → M²³⁹TLNRTTAANAC²⁵⁰/A251E/A254Q/R259L/A267E/S280N), was obtained. Compared to the wild type, the autolysis of M9 was significantly alleviated, and its half-life at 60 °C was increased approximately 39-fold (from 1.6 to 62.4 min). The optimal temperature and specific activity of M9 increased by 5 °C (from 60 to 65 °C) and 62% (4985 vs 3078 U/mg), respectively. M9 showed significant advantages in shrimp shell deproteinization.

Guiding protein design choices by per-residue energy breakdown analysis with an interactive web application

Recent developments in machine learning have greatly facilitated the design of proteins with improved properties. However, accurately assessing the contributions of an individual or multiple amino acid mutations to overall protein stability to select the most promising mutants remains a challenge. Knowing the specific types of amino acid interactions that improve energetic stability is crucial for finding favorable combinations of mutations and deciding which mutants to test experimentally. In this work, we present an interactive workflow for assessing the energetic contributions of single and multi-mutant designs of proteins. The energy breakdown guided protein design (ENDURE) workflow includes several key algorithms, including per-residue energy analysis and the sum of interaction energies calculations, which are performed using the Rosetta energy function, as well as a residue depth analysis, which enables tracking the energetic contributions of mutations occurring in different spatial layers of the protein structure. ENDURE is available as a web application that integrates easy-to-read summary reports and interactive visualizations of the automated energy calculations and helps users selecting protein mutants for further experimental characterization. We demonstrate the effectiveness of the tool in identifying the mutations in a designed polyethylene terephthalate (PET)-degrading enzyme that add up to an improved thermodynamic stability. We expect that ENDURE can be a valuable resource for researchers and practitioners working in the field of protein design and optimization. ENDURE is freely available for academic use at: http://endure.kuenzelab.org.

TCRmodel2: high-resolution modeling of T cell receptor recognition using deep learning

The cellular immune system, which is a critical component of human immunity, uses T cell receptors (TCRs) to recognize antigenic proteins in the form of peptides presented by major histocompatibility complex (MHC) proteins. Accurate definition of the structural basis of TCRs and their engagement of peptide-MHCs can provide major insights into normal and aberrant immunity, and can help guide the design of vaccines and immunotherapeutics. Given the limited amount of experimentally determined TCR-peptide-MHC structures and the vast amount of TCRs within each individual as well as antigenic targets, accurate computational modeling approaches are needed. Here, we report a major update to our web server, TCRmodel, which was originally developed to model unbound TCRs from sequence, to now model TCR-peptide-MHC complexes from sequence, utilizing several adaptations of AlphaFold. This method, named TCRmodel2, allows users to submit sequences through an easy-to-use interface and shows similar or greater accuracy than AlphaFold and other methods to model TCR-peptide-MHC complexes based on benchmarking. It can generate models of complexes in 15 minutes, and output models are provided with confidence scores and an integrated molecular viewer. TCRmodel2 is available at https://tcrmodel.ibbr.umd.edu.

Fast and versatile sequence-independent protein docking for nanomaterials design using RPXDock

Computationally designed multi-subunit assemblies have shown considerable promise for a variety of applications, including a new generation of potent vaccines. One of the major routes to such materials is rigid body sequence-independent docking of cyclic oligomers into architectures with point group or lattice symmetries. Current methods for docking and designing such assemblies are tailored to specific classes of symmetry and are difficult to modify for novel applications. Here we describe RPXDock, a fast, flexible, and modular software package for sequence-independent rigid-body protein docking across a wide range of symmetric architectures that is easily customizable for further development. RPXDock uses an efficient hierarchical search and a residue-pair transform (RPX) scoring method to rapidly search through multidimensional docking space. We describe the structure of the software, provide practical guidelines for its use, and describe the available functionalities including a variety of score functions and filtering tools that can be used to guide and refine docking results towards desired configurations.

Accelerating therapeutic protein design with computational approaches toward the clinical stage

Therapeutic protein, represented by antibodies, is of increasing interest in human medicine. However, clinical translation of therapeutic protein is still largely hindered by different aspects of developability, including affinity and selectivity, stability and aggregation prevention, solubility and viscosity reduction, and deimmunization. Conventional optimization of the developability with widely used methods, like display technologies and library screening approaches, is a time and cost-intensive endeavor, and the efficiency in finding suitable solutions is still not enough to meet clinical needs. In recent years, the accelerated advancement of computational methodologies has ushered in a transformative era in the field of therapeutic protein design. Owing to their remarkable capabilities in feature extraction and modeling, the integration of cutting-edge computational strategies with conventional techniques presents a promising avenue to accelerate the progression of therapeutic protein design and optimization toward clinical implementation. Here, we compared the differences between therapeutic protein and small molecules in developability and provided an overview of the computational approaches applicable to the design or optimization of therapeutic protein in several developability issues.

Sequence-structure-function relationships in the microbial protein universe

For the past half-century, structural biologists relied on the notion that similar protein sequences give rise to similar structures and functions. While this assumption has driven research to explore certain parts of the protein universe, it disregards spaces that don't rely on this assumption. Here we explore areas of the protein universe where similar protein functions can be achieved by different sequences and different structures. We predict ~200,000 structures for diverse protein sequences from 1,003 representative genomes across the microbial tree of life and annotate them functionally on a per-residue basis. Structure prediction is accomplished using the World Community Grid, a large-scale citizen science initiative. The resulting database of structural models is complementary to the AlphaFold database, with regards to domains of life as well as sequence diversity and sequence length. We identify 148 novel folds and describe examples where we map specific functions to structural motifs. We also show that the structural space is continuous and largely saturated, highlighting the need for a shift in focus across all branches of biology, from obtaining structures to putting them into context and from sequence-based to sequence-structure-function based meta-omics analyses.

Designed Rubredoxin miniature in a fully artificial electron chain triggered by visible light

Designing metal sites into de novo proteins has significantly improved, recently. However, identifying the minimal coordination spheres, able to encompass the necessary information for metal binding and activity, still represents a great challenge, today. Here, we test our understanding with a benchmark, nevertheless difficult, case. We assemble into a miniature 28-residue protein, the quintessential elements required to fold properly around a FeCys₄ redox center, and to function efficiently in electron-transfer. This study addresses a challenge in de novo protein design, as it reports the crystal structure of a designed tetra-thiolate metal-binding protein in sub-Å agreement with the intended design. This allows us to well correlate structure to spectroscopic and electrochemical properties. Given its high reduction potential compared to natural and designed FeCys₄-containing proteins, we exploit it as terminal electron acceptor of a fully artificial chain triggered by visible light.

Recent Advances in NMR Protein Structure Prediction with ROSETTA

Nuclear magnetic resonance (NMR) spectroscopy is a powerful method for studying the structure and dynamics of proteins in their native state. For high-resolution NMR structure determination, the collection of a rich restraint dataset is necessary. This can be difficult to achieve for proteins with high molecular weight or a complex architecture. Computational modeling techniques can complement sparse NMR datasets (<1 restraint per residue) with additional structural information to elucidate protein structures in these difficult cases. The Rosetta software for protein structure modeling and design is used by structural biologists for structure determination tasks in which limited experimental data is available. This review gives an overview of the computational protocols available in the Rosetta framework for modeling protein structures from NMR data. We explain the computational algorithms used for the integration of different NMR data types in Rosetta. We also highlight new developments, including modeling tools for data from paramagnetic NMR and hydrogen-deuterium exchange, as well as chemical shifts in CS-Rosetta. Furthermore, strategies are discussed to complement and improve structure predictions made by the current state-of-the-art AlphaFold2 program using NMR-guided Rosetta modeling.

Improving the Modeling of Extracellular Ligand Binding Pockets in RosettaGPCR for Conformational Selection

G protein-coupled receptors (GPCRs) are the largest class of drug targets and undergo substantial conformational changes in response to ligand binding. Despite recent progress in GPCR structure determination, static snapshots fail to reflect the conformational space of putative binding pocket geometries to which small molecule ligands can bind. In comparative modeling of GPCRs in the absence of a ligand, often a shrinking of the orthosteric binding pocket is observed. However, the exact prediction of the flexible orthosteric binding site is crucial for adequate structure-based drug discovery. In order to improve ligand docking and guide virtual screening experiments in computer-aided drug discovery, we developed RosettaGPCRPocketSize. The algorithm creates a conformational ensemble of biophysically realistic conformations of the GPCR binding pocket between the TM bundle, which is consistent with a knowledge base of expected pocket geometries. Specifically, tetrahedral volume restraints are defined based on information about critical residues in the orthosteric binding site and their experimentally observed range of Cα-Cα-distances. The output of RosettaGPCRPocketSize is an ensemble of binding pocket geometries that are filtered by energy to ensure biophysically probable arrangements, which can be used for docking simulations. In a benchmark set, pocket shrinkage observed in the default RosettaGPCR was reduced by up to 80% and the binding pocket volume range and geometric diversity were increased. Compared to models from four different GPCR homology model databases (RosettaGPCR, GPCR-Tasser, GPCR-SSFE, and GPCRdb), the here-created models showed more accurate volumes of the orthosteric pocket when evaluated with respect to the crystallographic reference structure. Furthermore, RosettaGPCRPocketSize was able to generate an improved realistic pocket distribution. However, while being superior to other homology models, the accuracy of generated model pockets was comparable to AlphaFold2 models. Furthermore, in a docking benchmark using small-molecule ligands with a higher molecular weight between 400 and 700 Da, a higher success rate in creating native-like binding poses was observed. In summary, RosettaGPCRPocketSize can generate GPCR models with realistic orthosteric pocket volumes, which are useful for structure-based drug discovery applications.

Rational peptide design for inhibition of the KIX-MLL interaction

The kinase-inducible domain interacting (KIX) domain is an integral part of the general transcriptional coactivator CREB-binding protein, and has been associated with leukemia, cancer, and various viral diseases. Hence, the KIX domain has attracted considerable attention in drug discovery and development. Here, we rationally designed a KIX inhibitor using a peptide fragment corresponding to the transactivation domain (TAD) of the transcriptional activator, mixed-lineage leukemia protein (MLL). We performed theoretical saturation mutagenesis using the Rosetta software to search for mutants expected to bind KIX more tightly than the wild-type MLL TAD. Mutant peptides with higher helical propensities were selected for experimental characterization. We found that the T2857W mutant of the MLL TAD peptide had the highest binding affinity for KIX compared to the other 12 peptides designed in this study. Moreover, the peptide had a high inhibitory effect on the KIX-MLL interaction with a half-maximal inhibitory concentration close to the dissociation constant for this interaction. To our knowledge, this peptide has the highest affinity for KIX among all previously reported inhibitors that target the MLL site of KIX. Thus, our approach may be useful for rationally developing helical peptides that inhibit protein-protein interactions implicated in the progression of various diseases.

Computational design of non-porous, pH-responsive antibody nanoparticles

Programming protein nanomaterials to respond to changes in environmental conditions is a current challenge for protein design and important for targeted delivery of biologics. We describe the design of octahedral non-porous nanoparticles with the three symmetry axes (four-fold, three-fold, and two-fold) occupied by three distinct protein homooligomers: a de novo designed tetramer, an antibody of interest, and a designed trimer programmed to disassemble below a tunable pH transition point. The nanoparticles assemble cooperatively from independently purified components, and a cryo-EM density map reveals that the structure is very close to the computational design model. The designed nanoparticles can package a variety of molecular payloads, are endocytosed following antibody-mediated targeting of cell surface receptors, and undergo tunable pH-dependent disassembly at pH values ranging between to 5.9-6.7. To our knowledge, these are the first designed nanoparticles with more than two structural components and with finely tunable environmental sensitivity, and they provide new routes to antibody-directed targeted delivery.

Identification of Fungal and Bacterial Denitrification Inhibitors Targeting Copper Nitrite Reductase

The usage of nitrification inhibitors is one of the strategies that reduce or slow down the denitrification process to prevent nitrogen loss to the atmosphere in the form of N₂O. Directly targeting microbial denitrification could be one of the mitigation strategies; however, until now little efforts have been devoted toward the development of denitrification inhibitors. Here, we have identified small-molecule inhibitors of one of the proteins involved in the fungal denitrification pathway. Specifically, virtual screening was employed to identify the inhibitors of copper-containing nitrite reductase (FoNirK) of the filamentous fungus Fusarium oxysporum. Three series of chemical compounds were identified, out of which compounds belonging to two chemical scaffolds inhibited FoNirK enzymatic activity in low micromolar ranges. Several compounds also displayed moderate inhibition of fungal denitrification activity in vivo. Evaluation of in vitro activity against NirK from denitrifying bacterium Achromobacter xylosoxidans (AxNirK) and in vivo bacterial denitrification revealed a similar inhibitory profile.

De novo design of modular peptide-binding proteins by superhelical matching

General approaches for designing sequence-specific peptide-binding proteins would have wide utility in proteomics and synthetic biology. However, designing peptide-binding proteins is challenging, as most peptides do not have defined structures in isolation, and hydrogen bonds must be made to the buried polar groups in the peptide backbone1-3. Here, inspired by natural and re-engineered protein-peptide systems4-11, we set out to design proteins made out of repeating units that bind peptides with repeating sequences, with a one-to-one correspondence between the repeat units of the protein and those of the peptide. We use geometric hashing to identify protein backbones and peptide-docking arrangements that are compatible with bidentate hydrogen bonds between the side chains of the protein and the peptide backbone12. The remainder of the protein sequence is then optimized for folding and peptide binding. We design repeat proteins to bind to six different tripeptide-repeat sequences in polyproline II conformations. The proteins are hyperstable and bind to four to six tandem repeats of their tripeptide targets with nanomolar to picomolar affinities in vitro and in living cells. Crystal structures reveal repeating interactions between protein and peptide interactions as designed, including ladders of hydrogen bonds from protein side chains to peptide backbones. By redesigning the binding interfaces of individual repeat units, specificity can be achieved for non-repeating peptide sequences and for disordered regions of native proteins.

Identification of Bacterial Strains and Development of anmRNA-Based Vaccine to Combat Antibiotic Resistance in Staphylococcus aureus via In Vitro and In Silico Approaches

The emergence of antibiotic-resistant microorganisms is a significant concern in global health. Antibiotic resistance is attributed to various virulent factors and genetic elements. This study investigated the virulence factors of Staphylococcus aureus to create an mRNA-based vaccine that could help prevent antibiotic resistance. Distinct strains of the bacteria were selected for molecular identification of virulence genes, such as spa, fmhA, lukD, and hla-D, which were performed utilizing PCR techniques. DNA extraction from samples of Staphylococcus aureus was conducted using the Cetyl Trimethyl Ammonium Bromide (CTAB) method, which was confirmed and visualized using a gel doc; 16S rRNA was utilized to identify the bacterial strains, and primers of spa, lukD, fmhA, and hla-D genes were employed to identify the specific genes. Sequencing was carried out at Applied Bioscience International (ABI) in Malaysia. Phylogenetic analysis and alignment of the strains were subsequently constructed. We also performed an in silico analysis of the spa, fmhA, lukD, and hla-D genes to generate an antigen-specific vaccine. The virulence genes were translated into proteins, and a chimera was created using various linkers. The mRNA vaccine candidate was produced utilizing 18 epitopes, linkers, and an adjuvant, known as RpfE, to target the immune system. Testing determined that this design covered 90% of the population conservancy. An in silico immunological vaccine simulation was conducted to verify the hypothesis, including validating and predicting secondary and tertiary structures and molecular dynamics simulations to evaluate the vaccine’s long-term viability. This vaccine design may be further evaluated through in vivo and in vitro testing to assess its efficacy.

Machine learning for evolutionary-based and physics-inspired protein design: Current and future synergies

Computational protein design facilitates the discovery of novel proteins with prescribed structure and functionality. Exciting designs were recently reported using novel data-driven methodologies that can be roughly divided into two categories: evolutionary-based and physics-inspired approaches. The former infer characteristic sequence features shared by sets of evolutionary-related proteins, such as conserved or coevolving positions, and recombine them to generate candidates with similar structure and function. The latter approaches estimate key biochemical properties, such as structure free energy, conformational entropy, or binding affinities using machine learning surrogates, and optimize them to yield improved designs. Here, we review recent progress along both tracks, discuss their strengths and weaknesses, and highlight opportunities for synergistic approaches.

Molecular determinants of μ-conotoxin KIIIA interaction with the human voltage-gated sodium channel NaV1.7

The voltage-gated sodium (NaV) channel subtype NaV1.7 plays a critical role in pain signaling, making it an important drug target. Here we studied the molecular interactions between μ-Conotoxin KIIIA (KIIIA) and the human NaV1.7 channel (hNaV1.7). We developed a structural model of hNaV1.7 using Rosetta computational modeling and performed in silico docking of KIIIA using RosettaDock to predict residues forming specific pairwise contacts between KIIIA and hNaV1.7. We experimentally validated these contacts using mutant cycle analysis. Comparison between our KIIIA-hNaV1.7 model and the cryo-EM structure of KIIIA-hNaV1.2 revealed key similarities and differences between NaV channel subtypes with potential implications for the molecular mechanism of toxin block. The accuracy of our integrative approach, combining structural data with computational modeling, experimental validation, and molecular dynamics simulations, suggests that Rosetta structural predictions will be useful for rational design of novel biologics targeting specific NaV channels.

Modulation of FGF pathway signaling and vascular differentiation using designed oligomeric assemblies

Growth factors and cytokines signal by binding to the extracellular domains of their receptors and drive association and transphosphorylation of the receptor intracellular tyrosine kinase domains, initiating downstream signaling cascades. To enable systematic exploration of how receptor valency and geometry affects signaling outcomes, we designed cyclic homo-oligomers with up to 8 subunits using repeat protein building blocks that can be modularly extended. By incorporating a de novo designed fibroblast growth-factor receptor (FGFR) binding module into these scaffolds, we generated a series of synthetic signaling ligands that exhibit potent valency- and geometry-dependent Ca2+ release and MAPK pathway activation. The high specificity of the designed agonists reveal distinct roles for two FGFR splice variants in driving endothelial and mesenchymal cell fates during early vascular development. The ability to incorporate receptor binding domains and repeat extensions in a modular fashion makes our designed scaffolds broadly useful for probing and manipulating cellular signaling pathways.

Improving the secretion of designed protein assemblies through negative design of cryptic transmembrane domains

Computationally designed protein nanoparticles have recently emerged as a promising platform for the development of new vaccines and biologics. For many applications, secretion of designed nanoparticles from eukaryotic cells would be advantageous, but in practice, they often secrete poorly. Here we show that designed hydrophobic interfaces that drive nanoparticle assembly are often predicted to form cryptic transmembrane domains, suggesting that interaction with the membrane insertion machinery could limit efficient secretion. We develop a general computational protocol, the Degreaser, to design away cryptic transmembrane domains without sacrificing protein stability. The retroactive application of the Degreaser to previously designed nanoparticle components and nanoparticles considerably improves secretion, and modular integration of the Degreaser into design pipelines results in new nanoparticles that secrete as robustly as naturally occurring protein assemblies. Both the Degreaser protocol and the nanoparticles we describe may be broadly useful in biotechnological applications.

Artificial intelligence in cancer immunotherapy: Applications in neoantigen recognition, antibody design and immunotherapy response prediction

Cancer immunotherapy is a method of controlling and eliminating tumors by reactivating the body's cancer-immunity cycle and restoring its antitumor immune response. The increased availability of data, combined with advancements in high-performance computing and innovative artificial intelligence (AI) technology, has resulted in a rise in the use of AI in oncology research. State-of-the-art AI models for functional classification and prediction in immunotherapy research are increasingly used to support laboratory-based experiments. This review offers a glimpse of the current AI applications in immunotherapy, including neoantigen recognition, antibody design, and prediction of immunotherapy response. Advancing in this direction will result in more robust predictive models for developing better targets, drugs, and treatments, and these advancements will eventually make their way into the clinical setting, pushing AI forward in the field of precision oncology.

Computational pipeline provides mechanistic understanding of Omicron variant of concern neutralizing engineered ACE2 receptor traps

The SARS-CoV-2 Omicron variant, with 15 mutations in Spike receptor-binding domain (Spike-RBD), renders virtually all clinical monoclonal antibodies against WT SARS-CoV-2 ineffective. We recently engineered the SARS-CoV-2 host entry receptor, ACE2, to tightly bind WT-RBD and prevent viral entry into host cells ("receptor traps"). Here we determine cryo-EM structures of our receptor traps in complex with stabilized Spike ectodomain. We develop a multi-model pipeline combining Rosetta protein modeling software and cryo-EM to allow interface energy calculations even at limited resolution and identify interface side chains that allow for high-affinity interactions between our ACE2 receptor traps and Spike-RBD. Our structural analysis provides a mechanistic rationale for the high-affinity (0.53-4.2 nM) binding of our ACE2 receptor traps to Omicron-RBD confirmed with biolayer interferometry measurements. Finally, we show that ACE2 receptor traps potently neutralize Omicron and Delta pseudotyped viruses, providing alternative therapeutic routes to combat this evolving virus.

Computational and artificial intelligence-based methods for antibody development

Due to their high target specificity and binding affinity, therapeutic antibodies are currently the largest class of biotherapeutics. The traditional largely empirical antibody development process is, while mature and robust, cumbersome and has significant limitations. Substantial recent advances in computational and artificial intelligence (AI) technologies are now starting to overcome many of these limitations and are increasingly integrated into development pipelines. Here, we provide an overview of AI methods relevant for antibody development, including databases, computational predictors of antibody properties and structure, and computational antibody design methods with an emphasis on machine learning (ML) models, and the design of complementarity-determining region (CDR) loops, antibody structural components critical for binding.

Integrating comparative modeling and accelerated simulations reveals conformational and energetic basis of actomyosin force generation

Muscle contraction is performed by arrays of contractile proteins in the sarcomere. Serious heart diseases, such as cardiomyopathy, can often be results of mutations in myosin and actin. Direct characterization of how small changes in the myosin-actin complex impact its force production remains challenging. Molecular dynamics (MD) simulations, although capable of studying protein structure-function relationships, are limited owing to the slow timescale of the myosin cycle as well as a lack of various intermediate structures for the actomyosin complex. Here, employing comparative modeling and enhanced sampling MD simulations, we show how the human cardiac myosin generates force during the mechanochemical cycle. Initial conformational ensembles for different myosin-actin states are learned from multiple structural templates with Rosetta. This enables us to efficiently sample the energy landscape of the system using Gaussian accelerated MD. Key myosin loop residues, whose substitutions are related to cardiomyopathy, are identified to form stable or metastable interactions with the actin surface. We find that the actin-binding cleft closure is allosterically coupled to the myosin motor core transitions and ATP-hydrolysis product release from the active site. Furthermore, a gate between switch I and switch II is suggested to control phosphate release at the prepowerstroke state. Our approach demonstrates the ability to link sequence and structural information to motor functions.

Lessons Learnt from COVID-19: Computational Strategies for Facing Present and Future Pandemics

Since its outbreak in December 2019, the COVID-19 pandemic has caused the death of more than 6.5 million people around the world. The high transmissibility of its causative agent, the SARS-CoV-2 virus, coupled with its potentially lethal outcome, provoked a profound global economic and social crisis. The urgency of finding suitable pharmacological tools to tame the pandemic shed light on the ever-increasing importance of computer simulations in rationalizing and speeding up the design of new drugs, further stressing the need for developing quick and reliable methods to identify novel active molecules and characterize their mechanism of action. In the present work, we aim at providing the reader with a general overview of the COVID-19 pandemic, discussing the hallmarks in its management, from the initial attempts at drug repurposing to the commercialization of Paxlovid, the first orally available COVID-19 drug. Furthermore, we analyze and discuss the role of computer-aided drug discovery (CADD) techniques, especially those that fall in the structure-based drug design (SBDD) category, in facing present and future pandemics, by showcasing several successful examples of drug discovery campaigns where commonly used methods such as docking and molecular dynamics have been employed in the rational design of effective therapeutic entities against COVID-19.

Advancing Strategies for Proteolysis-Targeting Chimera Design

Proteolysis-targeting chimeras (PROTACs) have shown great therapeutic potential by degrading various disease-causing proteins, particularly those related to tumors. Therefore, the introduction of PROTACs has ushered in a new chapter of antitumor drug development, marked by significant advances over recent years. Herein, we describe recent developments in PROTAC technology, focusing on design strategy, development workflow, and future outlooks. We also discuss potential opportunities and challenges for PROTAC research.

Modern non-polarizable force fields diverge in modeling the enzyme-substrate complex of a canonical serine protease

Classical molecular dynamics simulation is a powerful and established method of modern computational chemistry. Being able to obtain accurate information on molecular behavior is crucial to get valuable insights into structure-function relationships that translate into fundamental findings and practical applications. Active sites of enzymes are known to be particularly intricate, therefore, simpler non-polarizable force fields may provide an inaccurate description. In this work, we addressed this hypothesis in a case of a canonical serine triad protease trypsin in its complex with a substrate-mimicking inhibitor. We tested six modern and popular force fields to find that significantly diverging results may be obtained. Amber FB-15 and OPLS-AA/M turned out to model the active site incorrectly. Amber ff19sb and ff15ipq demonstrated mixed performance. The best performing force fields were CHARMM36m and Amber ff99sb-ildn, therefore, they are recommended for use with this and related systems. We speculate that a similar lack of cross-force field convergence may be characteristic of other enzymatic systems. Therefore, we advocate for careful consideration of different force fields in any study within the field of computational enzymology.

Inside-Out Design of Zinc-Binding Proteins with Non-Native Backbones

The de novo design of functional proteins requires specification of tertiary structure and incorporation of molecular binding sites. Here, we develop an inside-out design strategy in the molecular modeling program Rosetta that begins with amino acid side chains from one or two α-helices making well-defined contacts with a ligand. A full-sized protein is then built around the ligand by adding additional helices that promote the formation of a protein core and allow additional contacts with the ligand. The protocol was tested by designing 12 zinc-binding proteins, each with 4-5 helices. Four of the designs were folded and bound to zinc with equilibrium dissociation constants varying between 95 nM and 1.1 μM. The design with the tightest affinity for zinc, N12, adopts a unique conformation in the folded state as assessed with nuclear magnetic resonance (NMR) and the design model closely matches (backbone root-mean-square deviation (RMSD) < 1 Å) an AlphaFold model of the sequence. Retrospective analysis with AlphaFold suggests that the sequences of many of the failed designs did not encode the desired tertiary packing.

Sulfamate Acetamides as Self-Immolative Electrophiles for Covalent Ligand-Directed Release Chemistry

Electrophiles for covalent inhibitors that are suitable for in vivo administration are rare. While acrylamides are prevalent in FDA-approved covalent drugs, chloroacetamides are considered too reactive for such purposes. We report sulfamate-based electrophiles that maintain chloroacetamide-like geometry with tunable reactivity. In the context of the BTK inhibitor ibrutinib, sulfamate analogues showed low reactivity with comparable potency in protein labeling, in vitro, and cellular kinase activity assays and were effective in a mouse model of CLL. In a second example, we converted a chloroacetamide Pin1 inhibitor to a potent and selective sulfamate acetamide with improved buffer stability. Finally, we show that sulfamate acetamides can be used for covalent ligand-directed release (CoLDR) chemistry, both for the generation of "turn-on" probes as well as for traceless ligand-directed site-specific labeling of proteins. Taken together, this chemistry represents a promising addition to the list of electrophiles suitable for in vivo covalent targeting.

Fostering discoveries in the era of exascale computing: How the next generation of supercomputers empowers computational and experimental biophysics alike

Over a century ago, physicists started broadly relying on theoretical models to guide new experiments. Soon thereafter, chemists began doing the same. Now, biological research enters a new era when experiment and theory walk hand in hand. Novel software and specialized hardware became essential to understand experimental data and propose new models. In fact, current petascale computing resources already allow researchers to reach unprecedented levels of simulation throughput to connect in silico and in vitro experiments. The reduction in cost and improved access allowed a large number of research groups to adopt supercomputing resources and techniques. Here, we outline how large-scale computing has evolved to expand decades-old research, spark new research efforts, and continuously connect simulation and observation. For instance, multiple publicly and privately funded groups have dedicated extensive resources to develop artificial intelligence tools for computational biophysics, from accelerating quantum chemistry calculations to proposing protein structure models. Moreover, advances in computer hardware have accelerated data processing from single-molecule experimental observations and simulations of chemical reactions occurring throughout entire cells. The combination of software and hardware has opened the way for exascale computing and the production of the first public exascale supercomputer, Frontier, inaugurated by the Oak Ridge National Laboratory in 2022. Ultimately, the popularization and development of computational techniques and the training of researchers to use them will only accelerate the diversification of tools and learning resources for future generations.

Developing a multiepitope vaccine for the prevention of SARS-CoV-2 and monkeypox virus co-infection: A reverse vaccinology analysis

BACKGROUND

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and monkeypox virus (MPXV) severely threaten human health; however, currently, no vaccine can prevent a co-infection with both viruses.

METHODS

Five antigens were selected to predict dominant T and B cell epitopes screened for immunogenicity, antigenicity, toxicity, and sensitization. After screening, all antigens joined in the construction of a novel multiepitope vaccine. The physicochemical and immunological characteristics, and secondary and tertiary structures of the vaccine were predicted and analyzed using bio- and immunoinformatics. Finally, codon optimization and cloning in-silico were performed.

RESULTS

A new multiepitope vaccine, named S7M8, was constructed based on four helper T lymphocyte (HTL) epitopes, six cytotoxic T lymphocyte (CTL) epitopes, five B cell epitopes, as well as Toll-like receptor (TLR) agonists. The antigenicity and immunogenicity scores of the S7M8 vaccine were 0.907374 and 0.6552, respectively. The S7M8 vaccine was comprised of 26.96% α-helices, the optimized Z-value of the tertiary structure was -5.92, and the favored area after majorization in the Ramachandran plot was 84.54%. Molecular docking showed that the S7M8 vaccine could tightly bind to TLR2 (-1100.6 kcal/mol) and TLR4 (-950.3 kcal/mol). In addition, the immune stimulation prediction indicated that the S7M8 vaccine could activate T and B lymphocytes to produce high levels of Th1 cytokines and antibodies.

CONCLUSION

S7M8 is a promising biomarker with good antigenicity, immunogenicity, non-toxicity, and non-sensitization. The S7M8 vaccine can trigger significantly high levels of Th1 cytokines and antibodies and may be a potentially powerful tool in preventing SARS-CoV-2 and MPXV.

PLK1 regulates CtIP and DNA2 interplay in long-range DNA end resection

DNA double-strand break (DSB) repair is initiated by DNA end resection. CtIP acts in short-range resection to stimulate MRE11-RAD50-NBS1 (MRN) to endonucleolytically cleave 5'-terminated DNA to bypass protein blocks. CtIP also promotes the DNA2 helicase-nuclease to accelerate long-range resection downstream from MRN. Here, using AlphaFold2, we identified CtIP-F728E-Y736E as a separation-of-function mutant that is still proficient in conjunction with MRN but is not able to stimulate ssDNA degradation by DNA2. Accordingly, CtIP-F728E-Y736E impairs physical interaction with DNA2. Cellular assays revealed that CtIP-F728E-Y736E cells exhibit reduced DSB-dependent chromatin-bound RPA, impaired long-range resection, and increased sensitivity to DSB-inducing drugs. Previously, CtIP was shown to be targeted by PLK1 to inhibit long-range resection, yet the underlying mechanism was unclear. We show that the DNA2-interacting region in CtIP includes the PLK1 target site at S723. The integrity of S723 in CtIP is necessary for the stimulation of DNA2, and phosphorylation of CtIP by PLK1 in vitro is consequently inhibitory, explaining why PLK1 restricts long-range resection. Our data support a model in which CDK-dependent phosphorylation of CtIP activates resection by MRN in S phase, and PLK1-mediated phosphorylation of CtIP disrupts CtIP stimulation of DNA2 to attenuate long-range resection later at G2/M.

Towards de novo design of transmembrane α-helical assemblies using structural modelling and molecular dynamics simulation

Computational de novo protein design involves iterative processes consisting of amino acid sequence design, structural modelling and scoring, and design validation by synthesis and experimental characterisation. Recent advances in protein structure prediction and modelling methods have enabled the highly efficient and accurate design of water-soluble proteins. However, the design of membrane proteins remains a major challenge. To advance membrane protein design, considering the higher complexity of membrane protein folding, stability, and dynamic interactions between water, ions, lipids, and proteins is an important task. For introducing explicit solvents and membranes to these design methods, all-atom molecular dynamics (MD) simulations of designed proteins provide useful information that cannot be obtained experimentally. In this review, we first describe two major approaches to designing transmembrane α-helical assemblies, consensus and de novo design. We further illustrate recent MD studies of membrane protein folding related to protein design, as well as advanced treatments in molecular models and conformational sampling techniques in the simulations. Finally, we discuss the possibility to introduce MD simulations after the existing static modelling and screening of design decoys as an additional step for refinement of the design, which considers membrane protein folding dynamics and interactions with explicit membranes.

Neural network-derived Potts models for structure-based protein design using backbone atomic coordinates and tertiary motifs

Designing novel proteins to perform desired functions, such as binding or catalysis, is a major goal in synthetic biology. A variety of computational approaches can aid in this task. An energy-based framework rooted in the sequence-structure statistics of tertiary motifs (TERMs) can be used for sequence design on predefined backbones. Neural network models that use backbone coordinate-derived features provide another way to design new proteins. In this work, we combine the two methods to make neural structure-based models more suitable for protein design. Specifically, we supplement backbone-coordinate features with TERM-derived data, as inputs, and we generate energy functions as outputs. We present two architectures that generate Potts models over the sequence space: TERMinator, which uses both TERM-based and coordinate-based information, and COORDinator, which uses only coordinate-based information. Using these two models, we demonstrate that TERMs can be utilized to improve native sequence recovery performance of neural models. Furthermore, we demonstrate that sequences designed by TERMinator are predicted to fold to their target structures by AlphaFold. Finally, we show that both TERMinator and COORDinator learn notions of energetics, and these methods can be fine-tuned on experimental data to improve predictions. Our results suggest that using TERM-based and coordinate-based features together may be beneficial for protein design and that structure-based neural models that produce Potts energy tables have utility for flexible applications in protein science.

Persistent spectral theory-guided protein engineering

While protein engineering, which iteratively optimizes protein fitness by screening the gigantic mutational space, is constrained by experimental capacity, various machine learning models have substantially expedited protein engineering. Three-dimensional protein structures promise further advantages, but their intricate geometric complexity hinders their applications in deep mutational screening. Persistent homology, an established algebraic topology tool for protein structural complexity reduction, fails to capture the homotopic shape evolution during the filtration of a given data. This work introduces a Topology-offered protein Fitness (TopFit) framework to complement protein sequence and structure embeddings. Equipped with an ensemble regression strategy, TopFit integrates the persistent spectral theory, a new topological Laplacian, and two auxiliary sequence embeddings to capture mutation-induced topological invariant, shape evolution, and sequence disparity in the protein fitness landscape. The performance of TopFit is assessed by 34 benchmark datasets with 128,634 variants, involving a vast variety of protein structure acquisition modalities and training set size variations.

Multi-input drug-controlled switches of mammalian gene expression based on engineered nuclear hormone receptors

Protein-based switches that respond to different inputs to regulate cellular outputs, such as gene expression, are central to synthetic biology. For increased controllability, multi-input switches that integrate several cooperating and competing signals for the regulation of a shared output are of particular interest. The nuclear hormone receptor (NHR) superfamily offers promising starting points for engineering multi-input-controlled responses to clinically approved drugs. Starting from the VgEcR/RXR pair, we demonstrate that novel (multi-)drug regulation can be achieved by exchange of the ecdysone receptor (EcR) ligand binding domain (LBD) for other human NHR-derived LBDs. For responses activated to saturation by an agonist for the first LBD, we show that outputs can be boosted by an agonist targeting the second LBD. In combination with an antagonist, output levels are tunable by up to three simultaneously present small-molecule drugs. Such high-level control validates NHRs as a versatile, engineerable platform for programming multi-drug-controlled responses.

Structural evolution of Delta lineage of SARS-CoV-2

One of the main obstacles in prevention and treatment of COVID-19 is the rapid evolution of the SARS-CoV-2 Spike protein. Given that Spike is the main target of common treatments of COVID-19, mutations occurring at this virulent factor can affect the effectiveness of treatments. The B.1.617.2 lineage of SARS-CoV-2, being characterized by many Spike mutations inside and outside of its receptor-binding domain (RBD), shows high infectivity and relative resistance to existing cures. Here, utilizing a wide range of computational biology approaches, such as immunoinformatics, molecular dynamics (MD), analysis of intrinsically disordered regions (IDRs), protein-protein interaction analyses, residue scanning, and free energy calculations, we examine the structural and biological attributes of the B.1.617.2 Spike protein. Furthermore, the antibody design protocol of Rosetta was implemented for evaluation the stability and affinity improvement of the Bamlanivimab (LY-CoV55) antibody, which is not capable of interactions with the B.1.617.2 Spike. We observed that the detected mutations in the Spike of the B1.617.2 variant of concern can cause extensive structural changes compatible with the described variation in immunogenicity, secondary and tertiary structure, oligomerization potency, Furin cleavability, and drug targetability. Compared to the Spike of Wuhan lineage, the B.1.617.2 Spike is more stable and binds to the Angiotensin-converting enzyme 2 (ACE2) with higher affinity.

Decoupling peptide binding from T cell receptor recognition with engineered chimeric MHC-I molecules

Major Histocompatibility Complex class I (MHC-I) molecules display self, viral or aberrant epitopic peptides to T cell receptors (TCRs), which employ interactions between complementarity-determining regions with both peptide and MHC-I heavy chain 'framework' residues to recognize specific Human Leucocyte Antigens (HLAs). The highly polymorphic nature of the HLA peptide-binding groove suggests a malleability of interactions within a common structural scaffold. Here, using structural data from peptide:MHC-I and pMHC:TCR structures, we first identify residues important for peptide and/or TCR binding. We then outline a fixed-backbone computational design approach for engineering synthetic molecules that combine peptide binding and TCR recognition surfaces from existing HLA allotypes. X-ray crystallography demonstrates that chimeric molecules bridging divergent HLA alleles can bind selected peptide antigens in a specified backbone conformation. Finally, in vitro tetramer staining and biophysical binding experiments using chimeric pMHC-I molecules presenting established antigens further demonstrate the requirement of TCR recognition on interactions with HLA framework residues, as opposed to interactions with peptide-centric Chimeric Antigen Receptors (CARs). Our results underscore a novel, structure-guided platform for developing synthetic HLA molecules with desired properties as screening probes for peptide-centric interactions with TCRs and other therapeutic modalities.

Enzyme redesign and genetic code expansion

Enzyme design is an important application of computational protein design (CPD). It can benefit enormously from the additional chemistries provided by noncanonical amino acids (ncAAs). These can be incorporated into an 'expanded' genetic code, and introduced in vivo into target proteins. The key step for genetic code expansion is to engineer an aminoacyl-transfer RNA (tRNA) synthetase (aaRS) and an associated tRNA that handles the ncAA. Experimental directed evolution has been successfully used to engineer aaRSs and incorporate over 200 ncAAs into expanded codes. But directed evolution has severe limits, and is not yet applicable to noncanonical AA backbones. CPD can help address several of its limitations, and has begun to be applied to this problem. We review efforts to redesign aaRSs, studies that designed new proteins and functionalities with the help of ncAAs, and some of the method developments that have been used, such as adaptive landscape flattening Monte Carlo, which allows an enzyme to be redesigned with substrate or transition state binding as the design target.

Multienzyme deep learning models improve peptide de novo sequencing by mass spectrometry proteomics

Generating and analyzing overlapping peptides through multienzymatic digestion is an efficient procedure for de novo protein using from bottom-up mass spectrometry (MS). Despite improved instrumentation and software, de novo MS data analysis remains challenging. In recent years, deep learning models have represented a performance breakthrough. Incorporating that technology into de novo protein sequencing workflows require machine-learning models capable of handling highly diverse MS data. In this study, we analyzed the requirements for assembling such generalizable deep learning models by systemcally varying the composition and size of the training set. We assessed the generated models' performances using two test sets composed of peptides originating from the multienzyme digestion of samples from various species. The peptide recall values on the test sets showed that the deep learning models generated from a collection of highly N- and C-termini diverse peptides generalized 76% more over the termini-restricted ones. Moreover, expanding the training set's size by adding peptides from the multienzymatic digestion with five proteases of several species samples led to a 2-3 fold generalizability gain. Furthermore, we tested the applicability of these multienzyme deep learning (MEM) models by fully de novo sequencing the heavy and light monomeric chains of five commercial antibodies (mAbs). MEMs extracted over 10000 matching and overlapped peptides across six different proteases mAb samples, achieving a 100% sequence coverage for 8 of the ten polypeptide chains. We foretell that the MEMs' proven improvements to de novo analysis will positively impact several applications, such as analyzing samples of high complexity, unknown nature, or the peptidomics field.

The FANCC-FANCE-FANCF complex is evolutionarily conserved and regulates meiotic recombination

At meiosis, programmed meiotic DNA double-strand breaks are repaired via homologous recombination, resulting in crossovers (COs). From a large excess of DNA double-strand breaks that are formed, only a small proportion gets converted into COs because of active mechanisms that restrict CO formation. The Fanconi anemia (FA) complex proteins AtFANCM, MHF1 and MHF2 were previously identified in a genetic screen as anti-CO factors that function during meiosis in Arabidopsis thaliana. Here, pursuing the same screen, we identify FANCC as a new anti-CO gene. FANCC was previously only identified in mammals because of low primary sequence conservation. We show that FANCC, and its physical interaction with FANCE-FANCF, is conserved from vertebrates to plants. Further, we show that FANCC, together with its subcomplex partners FANCE and FANCF, regulates meiotic recombination. Mutations of any of these three genes partially rescues CO-defective mutants, which is particularly marked in female meiosis. Functional loss of FANCC, FANCE, or FANCF results in synthetic meiotic catastrophe with the pro-CO factor MUS81. This work reveals that FANCC is conserved outside mammals and has an anti-CO role during meiosis together with FANCE and FANCF.

Automating the High-Throughput Screening of Protein-Based Optical Indicators and Actuators

Over the last 25 years, protein engineers have developed an impressive collection of optical tools to interface with biological systems: indicators to eavesdrop on cellular activity and actuators to poke and prod native processes. To reach the performance level required for their downstream applications, protein-based tools are usually sculpted by iterative rounds of mutagenesis. In each round, libraries of variants are made and evaluated, and the most promising hits are then retrieved, sequenced, and further characterized. Early efforts to engineer protein-based optical tools were largely manual, suffering from low throughput, human error, and tedium. Here, we describe approaches to automating the screening of libraries generated as colonies on agar, multiwell plates, and pooled populations of single-cell variants. We also briefly discuss emerging approaches for screening, including cell-free systems and machine learning.

Improved Repeat Protein Stability by Combined Consensus and Computational Protein Design

High protein stability is an important feature for proteins used as therapeutics, as diagnostics, and in basic research. We have previously employed consensus design to engineer optimized Armadillo repeat proteins (ArmRPs) for sequence-specific recognition of linear epitopes with a modular binding mode. These designed ArmRPs (dArmRPs) feature high stability and are composed of M-type internal repeats that are flanked by N- and C-terminal capping repeats that protect the hydrophobic core from solvent exposure. While the overall stability of the designed ArmRPs is remarkably high, subsequent biochemical and biophysical experiments revealed that the N-capping repeat assumes a partially unfolded, solvent-accessible conformation for a small fraction of time that renders it vulnerable to proteolysis and aggregation. To overcome this problem, we have designed new N-caps starting from an M-type internal repeat using the Rosetta software. The superior stability of the computationally refined models was experimentally verified by circular dichroism and nuclear magnetic resonance spectroscopy. A crystal structure of a dArmRP containing the novel N-cap revealed that the enhanced stability correlates with an improved packing of this N-cap onto the hydrophobic core of the dArmRP. Hydrogen exchange experiments further show that the level of local unfolding of the N-cap is reduced by several orders of magnitude, resulting in increased resistance to proteolysis and weakened aggregation. As a first application of the novel N-cap, we determined the solution structure of a dArmRP with four internal repeats, which was previously impeded by the instability of the original N-cap.

Improving the catalytic activity of a detergent-compatible serine protease by rational design

Serine proteases are among the most important biological additives in various industries such as detergents, leather, animal feed and food. A serine protease gene, Fgapt4, from Fusarium graminearum 2697 was identified, cloned and expressed in Pichia pastoris. The optimal pH and temperature of FgAPT4 were 8.5 and 40°C, respectively. The relative activity was >30% even at 10°C. It had a wide range of pH stability (4.0-12.0) and detergent compatibility. To improve the catalytic activity, a strategy combining molecular docking and evolutionary analysis was adopted. Twelve amino acid residue sites and three loops (A, B and C) were selected as potential hot spots that might play critical roles in the enzyme's functional properties. Twenty-eight mutants targeting changes in individual sites or loops were designed, and mutations with good performance were combined. The best mutant was FgAPT4-M3 (Q70N/D142S/A143S/loop C). The specific activity and catalytic efficiency of FgAPT4-M3 increased by 1.6 (1008.5 vs. 385.9 U/mg) and 2.2-fold (3565.1 vs. 1106.3/s/mM), respectively. Computational analyses showed that the greater flexibility of the substrate pocket may be responsible for the increased catalytic activity. In addition, its application in detergents indicated that FgAPT4-M3 has great potential in washing.

Computational protein design repurposed to explore enzyme vitality and help predict antibiotic resistance

In response to antibiotics that inhibit a bacterial enzyme, resistance mutations inevitably arise. Predicting them ahead of time would aid target selection and drug design. The simplest resistance mechanism would be to reduce antibiotic binding without sacrificing too much substrate binding. The property that reflects this is the enzyme "vitality", defined here as the difference between the inhibitor and substrate binding free energies. To predict such mutations, we borrow methodology from computational protein design. We use a Monte Carlo exploration of mutation space and vitality changes, allowing us to rank thousands of mutations and identify ones that might provide resistance through the simple mechanism considered. As an illustration, we chose dihydrofolate reductase, an essential enzyme targeted by several antibiotics. We simulated its complexes with the inhibitor trimethoprim and the substrate dihydrofolate. 20 active site positions were mutated, or "redesigned" individually, then in pairs or quartets. We computed the resulting binding free energy and vitality changes. Out of seven known resistance mutations involving active site positions, five were correctly recovered. Ten positions exhibited mutations with significant predicted vitality gains. Direct couplings between designed positions were predicted to be small, which reduces the combinatorial complexity of the mutation space to be explored. It also suggests that over the course of evolution, resistance mutations involving several positions do not need the underlying point mutations to arise all at once: they can appear and become fixed one after the other.

Cellulose synthesis in land plants

All plant cells are surrounded by a cell wall that provides cohesion, protection, and a means of directional growth to plants. Cellulose microfibrils contribute the main biomechanical scaffold for most of these walls. The biosynthesis of cellulose, which typically is the most prominent constituent of the cell wall and therefore Earth's most abundant biopolymer, is finely attuned to developmental and environmental cues. Our understanding of the machinery that catalyzes and regulates cellulose biosynthesis has substantially improved due to recent technological advances in, for example, structural biology and microscopy. Here, we provide a comprehensive overview of the structure, function, and regulation of the cellulose synthesis machinery and its regulatory interactors. We aim to highlight important knowledge gaps in the field, and outline emerging approaches that promise a means to close those gaps.