Macromolecular modeling with rosetta

Strategic lead compound design and development utilizing computer-aided drug discovery (CADD) to address herbicide-resistant Phalaris minor in wheat fields

Wheat (Triticum aestivum) is a vital cereal crop and a staple food source worldwide. However, wheat grain productivity has significantly declined as a consequence of infestations by Phalaris minor. Traditional weed control methods have proven inadequate owing to the physiological similarities between P. minor and wheat during early growth stages. Consequently, farmers have turned to herbicides, targeting acetyl-CoA carboxylase (ACCase), acetolactate synthase (ALS) and photosystem II (PSII). Isoproturon targeting PSII was introduced in mid-1970s, to manage P. minor infestations. Despite their effectiveness, the repetitive use of these herbicides has led to the development of herbicide-resistant P. minor biotypes, posing a significant challenge to wheat productivity. To address this issue, there is a pressing need for innovative weed management strategies and the discovery of novel herbicide molecules. The integration of computer-aided drug discovery (CADD) techniques has emerged as a promising approach in herbicide research, that facilitates the identification of herbicide targets and enables the screening of large chemical libraries for potential herbicide-like molecules. By employing techniques such as homology modelling, molecular docking, molecular dynamics simulation and pharmacophore modelling, CADD has become a rapid and cost-effective medium to accelerate the herbicide discovery process significantly. This approach not only reduces the dependency on traditional experimental methods, but also enhances the precision and efficacy of herbicide development. This article underscores the critical role of bioinformatics and CADD in developing next-generation herbicides, offering new hope for sustainable weed management and improved wheat cultivation practices. © 2024 Society of Chemical Industry.

Target-Specific De Novo Peptide Binder Design with DiffPepBuilder

Despite the exciting progress in target-specific de novo protein binder design, peptide binder design remains challenging due to the flexibility of peptide structures and the scarcity of protein-peptide complex structure data. In this study, we curated a large synthetic data set, referred to as PepPC-F, from the abundant protein-protein interface data and developed DiffPepBuilder, a de novo target-specific peptide binder generation method that utilizes an SE(3)-equivariant diffusion model trained on PepPC-F to codesign peptide sequences and structures. DiffPepBuilder also introduces disulfide bonds to stabilize the generated peptide structures. We tested DiffPepBuilder on 30 experimentally verified strong peptide binders with available protein-peptide complex structures. DiffPepBuilder was able to effectively recall the native structures and sequences of the peptide ligands and to generate novel peptide binders with improved binding free energy. We subsequently conducted de novo generation case studies on three targets. In both the regeneration test and case studies, DiffPepBuilder outperformed AfDesign and RFdiffusion coupled with ProteinMPNN, in terms of sequence and structure recall, interface quality, and structural diversity. Molecular dynamics simulations confirmed that the introduction of disulfide bonds enhanced the structural rigidity and binding performance of the generated peptides. As a general peptide binder de novo design tool, DiffPepBuilder can be used to design peptide binders for given protein targets with three-dimensional and binding site information.

Advancing rational pesticide development against Drosophila suzukii: bioinformatics tools and applications-a systematic review

CONTEXT

Drosophila suzukii (Matsumura, 1931) is a widespread agricultural pest responsible for significant damage to various soft-skinned fruit hosts. The revolutionary potential of bioinformatics in agriculture emerges from its ability to provide extensive information on pests, fungi, chemical resistance, implications of non-target species, and other critical aspects. This wealth of information allows researchers to engage in projects and applied research in diverse agricultural domains that face these challenges. In this context, bioinformatics tools play a fundamental role. The negative impact of pests on crops, resulting in substantial economic losses, has highlighted the importance of in silico methods.

METHODS

To achieve this, we conducted a systematic search in scientific databases using as keywords "Drosophila suzukii," "biopesticides," "simulations computational," and "in-silico." After applying the filters of relevance and publication date, we organized the articles and prioritized those that directly addressed that matched the keywords and the use of bioinformatics tools. Additionally, we included studies focusing on in silico assays of biopesticides, such as molecular docking. Our review aimed to present a collection of recent literature on biopesticides against Drosophila suzukii, emphasizing bioinformatics methods. Through this work, we strive to contribute to the literature of new perspectives on the development and efficiency of biopesticides, along with to advance research that may improve pest control strategies.

RESULTS

In the results of the systematic review, we found 2734 articles related to the selected keywords. Six of these articles directly address Drosophila suzukii and the use of bioinformatics tools in the search for alternatives in pest control. In the selected studies, we observed that two articles tend to focus on phylogenetic approaches, searching for gene sequences, amino acids, and constructing phylogenetic trees. The other three articles used molecular modeling and docking of receptors such as GABA and TRP with plant-derived and synthetic compounds to study intermolecular interactions. However, we identified gaps in these studies that could lead to further research in the biorational development of biopesticides using bioinformatics tools.

Binding structures of SERF1a with NT17-polyQ peptides of huntingtin exon 1 revealed by SEC-SWAXS, NMR and molecular simulation

The aberrant fibrillization of huntingtin exon 1 (Httex1) characterized by an expanded polyglutamine (polyQ) tract is a defining feature of Huntington's disease, a neurodegenerative disorder. Recent investigations underscore the involvement of a small EDRK-rich factor 1a (SERF1a) in promoting Httex1 fibrillization through interactions with its N terminus. By establishing an integrated approach with size-exclusion-column-based small- and wide-angle X-ray scattering (SEC-SWAXS), NMR, and molecular simulations using Rosetta, the analysis here reveals a tight binding of two NT17 fragments of Httex1 (comprising the initial 17 amino acids at the N terminus) to the N-terminal region of SERF1a. In contrast, examination of the complex structure of SERF1a with a coiled NT17-polyQ peptide (33 amino acids in total) indicates sparse contacts of the NT17 and polyQ segments with the N-terminal side of SERF1a. Furthermore, the integrated SEC-SWAXS and molecular-simulation analysis suggests that the coiled NT17 segment can transform into a helical conformation when associated with a polyQ segment exhibiting high helical content. Intriguingly, NT17-polyQ peptides with enhanced secondary structures display diminished interactions with SERF1a. This insight into the conformation-dependent binding of NT17 provides clues to a catalytic association mechanism underlying SERF1a's facilitation of Httext1 fibrillization.

Computational design of serine hydrolases

Enzymes that proceed through multistep reaction mechanisms often utilize complex, polar active sites positioned with sub-angstrom precision to mediate distinct chemical steps, which makes their de novo construction extremely challenging. We sought to overcome this challenge using the classic catalytic triad and oxyanion hole of serine hydrolases as a model system. We used RFdiffusion1 to generate proteins housing catalytic sites of increasing complexity and varying geometry, and a newly developed ensemble generation method called ChemNet to assess active site geometry and preorganization at each step of the reaction. Experimental characterization revealed novel serine hydrolases that catalyze ester hydrolysis with catalytic efficiencies (k cat /K m ) up to 3.8 x 103 M-1 s-1, closely match the design models (Cα RMSDs < 1 Å), and have folds distinct from natural serine hydrolases. In silico selection of designs based on active site preorganization across the reaction coordinate considerably increased success rates, enabling identification of new catalysts in screens of as few as 20 designs. Our de novo buildup approach provides insight into the geometric determinants of catalysis that complements what can be obtained from structural and mutational studies of native enzymes (in which catalytic group geometry and active site makeup cannot be so systematically varied), and provides a roadmap for the design of industrially relevant serine hydrolases and, more generally, for designing complex enzymes that catalyze multi-step transformations.

Multiple Functions of Compatible Solute Ectoine and Strategies for Constructing Overproducers for Biobased Production

Ectoine and its derivative 5-hydroxyectoine are compatible solutes initially found in the hyperhalophilic bacterium Ectothiorhodospira halochloris, which inhabits the desert in Egypt. The habitat of ectoine producers implies the primary function of ectoine as a cytoprotectant against harsh conditions such as high salinity, drought, and high radiation. More extensive and in-depth studies have revealed the multiple functions of ectoine in its native producer bacterial cells and other types of cells and its biomolecular components (such as proteins and DNA) as a general protective agent. Its chemical properties as a bio-based amino acid derivative make it attractive for basic scientific research and related industries, such as the food/agricultural industry, cosmetic manufacturing, biologics, and therapeutic agent preparation. This article first discusses the functions and applications of ectoine and 5-hydroxyectoine. Subsequently, more emphasis was placed on advances in bio-based ectoine and/or 5-hydroxyectoine production. Strategies for developing more robust cell factories for highly efficient ectoine and/or 5-hydroxyectoine production are further discussed. We hope this review will provide a valuable reference for studies on the bio-based production of ectoine and 5-hydroxyectoine.

An in silico investigation on the binding site preference of PD-1 and PD-L1 for designing antibodies for targeted cancer therapy

Cancer control and treatment remain a significant challenge in cancer therapy and recently immune checkpoints has considered as a novel treatment strategy to develop anti-cancer drugs. Many cancer types use the immune checkpoints and its ligand, PD-1/PD-L1 pathway, to evade detection and destruction by the immune system, which is associated with altered effector function of PD-1 and PD-L1 overexpression on cancer cells to deactivate T cells. In recent years, mAbs have been employed to block immune checkpoints, therefore normalization of the anti-tumor response has enabled the scientists to develop novel biopharmaceuticals. In vivo affinity maturation of antibodies in targeted therapy has sometimes failed, and current experimental methods cannot accommodate the accurate structural details of protein-protein interactions. Therefore, determining favorable binding sites on the protein surface for modulator design of these interactions is a major challenge. In this study, we used the in silico methods to identify favorable binding sites on the PD-1 and PD-L1 and to optimize mAb variants on a large scale. At first, all the binding areas on PD-1 and PD-L1 have been identified. Then, using the RosettaDesign protocol, thousands of antibodies have been generated for 11 different regions on PD-1 and PD-L1 and then the designs with higher stability, affinity, and shape complementarity were selected. Next, molecular dynamics simulations and MM-PBSA analysis were employed to understand the dynamic, structural features of the complexes and measure the binding affinity of the final designs. Our results suggest that binding sites 1, 3 and 6 on PD-1 and binding sites 9 and 11 on PD-L1 can be regarded as the most appropriate sites for the inhibition of PD-1-PD-L1 interaction by the designed antibodies. This study provides comprehensive information regarding the potential binding epitopes on PD-1 which could be considered as hotspots for designing potential biopharmaceuticals. We also showed that mutations in the CDRs regions will rearrange the interaction pattern between the designed antibodies and targets (PD-1 and PD-L1) with improved affinity to effectively inhibit protein-protein interaction and block the immune checkpoint.

Generation of antitumor chimeric antigen receptors incorporating T cell signaling motifs

Chimeric antigen receptor (CAR) T cells have been used to successfully treat various blood cancers, but adverse effects have limited their potential. Here, we developed chimeric adaptor proteins (CAPs) and CAR tyrosine kinases (CAR-TKs) in which the intracellular ζ T cell receptor (TCRζ) chain was replaced with intracellular protein domains to stimulate signaling downstream of the TCRζ chain. CAPs contain adaptor domains and the kinase domain of ZAP70, whereas CAR-TKs contain only ZAP70 domains. We hypothesized that CAPs and CAR-TKs would be more potent than CARs because they would bypass both the steps that define the signaling threshold of TCRζ and the inhibitory regulation of upstream molecules. CAPs were too potent and exhibited high tonic signaling in vitro. In contrast, CAR-TKs exhibited high antitumor efficacy and significantly enhanced long-term tumor clearance in leukemia-bearing NSG mice as compared with the conventional CD19-28ζ-CAR-T cells. CAR-TKs were activated in a manner independent of the kinase Lck and displayed slower phosphorylation kinetics and prolonged signaling compared with the 28ζ-CAR. Lck inhibition attenuated CAR-TK cell exhaustion and improved long-term function. The distinct signaling properties of CAR-TKs may therefore be harnessed to improve the in vivo efficacy of T cells engineered to express an antitumor chimeric receptor.

In Silico Discovery of Stapled Peptide Inhibitor Targeting the Nur77-PPARγ Interaction and Its Anti-Breast-Cancer Efficacy

The binding of peroxisome proliferator-activated receptor γ (PPARγ) to the orphan nuclear receptor Nur77 facilitates the ubiquitination and degradation of Nur77, and leads to aberrant fatty acid uptake for breast cancer progression. Because of its crucial role in clinical prognosis, the interaction between Nur77 and PPARγ is an attractive target for anti-breast-cancer therapy. However, developing an inhibitor of the Nur77-PPARγ interaction poses a technical challenge due to the absence of the crystal structure of PPARγ and its corresponding interactive model with Nur77. Here, ST-CY14, a stapled peptide, is identified as a potent modulator of Nur77 with a KD value of 3.247 × 10-8 M by in silico analysis, rational design, and structural modification. ST-CY14 effectively increases Nur77 protein levels by blocking the Nur77-PPARγ interaction, thereby inhibiting lipid metabolism in breast tumor cells. Notably, ST-CY14 significantly suppresses breast cancer growth and bone metastasis in mice. The findings demonstrate the feasibility of exploiting directly Nur77-PPARγ interaction in breast cancer, and generate what to the best knowledge is the first direct inhibitor of the Nur77-PPARγ interaction available for impeding fatty acid uptake and therapeutic development.

Mapping the Intersubunit Interdomain FMN-Heme Interactions in Neuronal Nitric Oxide Synthase by Targeted Quantitative Cross-Linking Mass Spectrometry

Nitric oxide synthase (NOS) in mammals is a family of multidomain proteins in which interdomain electron transfer (IET) is controlled by domain-domain interactions. Calmodulin (CaM) binds to the canonical CaM-binding site in the linker region between the FMN and heme domains of NOS and allows tethered FMN domain motions, enabling an intersubunit FMN-heme IET in the output state for NO production. Our previous cross-linking mass spectrometric (XL MS) results demonstrated site-specific protein dynamics in the CaM-responsive regions of rat neuronal NOS (nNOS) reductase construct, a monomeric protein [Jiang et al., Biochemistry, 2023, 62, 2232-2237]. In this work, we have extended our combined approach of XL MS structural mapping and AlphaFold structural prediction to examine the homodimeric nNOS oxygenase/FMN (oxyFMN) construct, an established model of the NOS output state. We employed parallel reaction monitoring (PRM) based quantitative XL MS (qXL MS) to assess the CaM-induced changes in interdomain dynamics and interactions. Intersubunit cross-links were identified by mapping the cross-links onto top AlphaFold structural models, which was complemented by comparing their relative abundances in the cross-linked dimeric and monomeric bands. Furthermore, contrasting the CaM-free and CaM-bound nNOS samples shows that CaM enables the formation of the intersubunit FMN-heme docking complex and that CaM binding induces extensive, allosteric conformational changes across the NOS regions. Moreover, the observed cross-links sites specifically respond to changes in ionic strength. This indicates that interdomain salt bridges are responsible for stabilizing and orienting the output state for efficient FMN-heme IET. Taken together, our targeted qXL MS results have revealed that CaM and ionic strength modulate specific dynamic changes in the CaM/FMN/heme complexes, particularly in the context of intersubunit interdomain FMN-heme interactions.

STC1 competitively binding βPIX enhances melanoma progression via YAP nuclear translocation and M2 macrophage recruitment through the YAP/CCL2/VEGFA/AKT feedback loop

This study investigates the role of Stanniocalcin-1 (STC1) in melanoma progression, with a focus on its impact on metastasis, angiogenesis, and immune evasion. Systematic bioinformatics analysis revealed the potential influence of STC1 dysregulation on prognosis, immune cell infiltration, response to immune therapy, and cellular functions. In vitro assays were conducted to assess the proliferation, invasion, migration, and angiogenesis capabilities of A375 cells. In vivo experiments utilizing C57BL/6 J mice established a lung metastasis model using B16-F10 cells to evaluate macrophage infiltration and M2 polarization. A Transwell co-culture system was employed to explore the crosstalk between melanoma and macrophages. Molecular interactions among STC1, YAP, βPIX, and CCL2 are investigated using mass spectrometry, Co-Immunoprecipitation, Dual-Luciferase Reporter Assay, and Chromatin Immunoprecipitation experiments. STC1 was found to enhance lung metastasis by promoting the recruitment and polarization of M2 macrophages, thereby fostering an immunosuppressive microenvironment. Mechanistically, STC1 competes with YAP for binding to βPIX within the KER domain in melanoma cells, leading to YAP activation and subsequent CCL2 upregulation. CCL2-induced M2 macrophages secrete VEGFA, which enhances tumor vascularization and increases STC1 expression via the AKT signaling pathway in melanoma cells, establishing a pro-metastatic feedback loop. Notably, STC1-induced YAP activation increases PD-L1 expression, promoting immune evasion. Silencing STC1 enhances the efficacy of PD-1 immune checkpoint therapy in mice. This research elucidates STC1's role in melanoma metastasis and its complex interactions with tumor-associated macrophages, proposing STC1 as a potential therapeutic target for countering melanoma metastasis and augmenting the efficacy of PD-1 immunotherapy.

Intermolecular Interactions between Cysteine and Aromatic Amino Acids with a Phenyl Moiety in the DNA-Binding Domain of Heat Shock Factor 1 Regulate Thermal Stress-Induced Trimerization

In this study, we investigated the trimerization mechanism and structure of heat shock factor 1 (HSF1) using western blotting, tryptophan (Trp) fluorescence spectroscopy, and molecular modeling. First, we examined the DNA-binding domains of human (Homo sapiens), goldfish (Carassius auratus), and walleye pollock (Gadus chalcogrammus) HSF1s by mutating key residues (36 and 103) that are thought to directly affect trimer formation. Human, goldfish, and walleye pollock HSF1s contain cysteine at residue 36 but cysteine (C), tyrosine (Y), and phenylalanine (F), respectively, at residue 103. The optimal trimerization temperatures for the wild-type HSF1s of each species were found to be 42, 37, and 20 °C, respectively. Interestingly, a mutation experiment revealed that trimerization occurred at 42 °C when residue 103 was cysteine, at 37 °C when it was tyrosine, and at 20 °C when it was phenylalanine, regardless of the species. In addition, it was confirmed that when residue 103 of the three species was mutated to alanine, trimerization did not occur. This suggests that in addition to trimerization via disulfide bond formation between the cysteine residues in human HSF1, trimerization can also occur via the formation of a different type of bond between cysteine and aromatic ring residues such as tyrosine and phenylalanine. We also confirmed that at least one cysteine is required for the trimerization of HSF1s, regardless of its position (residue 36 or 103). Additionally, it was shown that the trimer formation temperature is related to growth and survival in fish.

Generalized biomolecular modeling and design with RoseTTAFold All-Atom

Deep-learning methods have revolutionized protein structure prediction and design but are presently limited to protein-only systems. We describe RoseTTAFold All-Atom (RFAA), which combines a residue-based representation of amino acids and DNA bases with an atomic representation of all other groups to model assemblies that contain proteins, nucleic acids, small molecules, metals, and covalent modifications, given their sequences and chemical structures. By fine-tuning on denoising tasks, we developed RFdiffusion All-Atom (RFdiffusionAA), which builds protein structures around small molecules. Starting from random distributions of amino acid residues surrounding target small molecules, we designed and experimentally validated, through crystallography and binding measurements, proteins that bind the cardiac disease therapeutic digoxigenin, the enzymatic cofactor heme, and the light-harvesting molecule bilin.

State-of-the-art in engineering small molecule biosensors and their applications in metabolic engineering

Genetically encoded biosensors are crucial for enhancing our understanding of how molecules regulate biological systems. Small molecule biosensors, in particular, help us understand the interaction between chemicals and biological processes. They also accelerate metabolic engineering by increasing screening throughput and eliminating the need for sample preparation through traditional chemical analysis. Additionally, they offer significantly higher spatial and temporal resolution in cellular analyte measurements. In this review, we discuss recent progress in in vivo biosensors and control systems-biosensor-based controllers-for metabolic engineering. We also specifically explore protein-based biosensors that utilize less commonly exploited signaling mechanisms, such as protein stability and induced degradation, compared to more prevalent transcription factor and allosteric regulation mechanism. We propose that these lesser-used mechanisms will be significant for engineering eukaryotic systems and slower-growing prokaryotic systems where protein turnover may facilitate more rapid and reliable measurement and regulation of the current cellular state. Lastly, we emphasize the utilization of cutting-edge and state-of-the-art techniques in the development of protein-based biosensors, achieved through rational design, directed evolution, and collaborative approaches.

Cryo-EM structures of Mycobacterium tuberculosis polynucleotide phosphorylase suggest a potential mechanism for its RNA substrate degradation

As one of the oldest infectious diseases in the world, tuberculosis (TB) is the second most deadly infectious disease after COVID-19. Tuberculosis is caused by Mycobacterium tuberculosis (Mtb), which can attack various organs of the human body. Up to now, drug-resistant TB continues to be a public health threat. Pyrazinamide (PZA) is regarded as a sterilizing drug in the treatment of TB due to its distinct ability to target Mtb persisters. Previously we demonstrated that a D67N mutation in Mycobacterium tuberculosis polynucleotide phosphorylase (MtbPNPase, Rv2783c) confers resistance to PZA and Rv2783c is a potential target for PZA, but the mechanism leading to PZA resistance remains unclear. To gain further insight into the MtbPNPase, we determined the cryo-EM structures of apo Rv2783c, its mutant form and its complex with RNA. Our studies revealed the Rv2783c structure at atomic resolution and identified its enzymatic functional groups essential for its phosphorylase activities. We also investigated the molecular mechanisms underlying the resistance to PZA conferred by the mutation. Our research findings provide structural and functional insights enabling the development of new anti-tuberculosis drugs.

A Protein Misfolding Shaking Amplification-based method for the spontaneous generation of hundreds of bona fide prions

Prion diseases are a group of rapidly progressing neurodegenerative disorders caused by the misfolding of the endogenous prion protein (PrPC) into a pathogenic form (PrPSc). This process, despite being the central event underlying these disorders, remains largely unknown at a molecular level, precluding the prediction of new potential outbreaks or interspecies transmission incidents. In this work, we present a method to generate bona fide recombinant prions de novo, allowing a comprehensive analysis of protein misfolding across a wide range of prion proteins from mammalian species. We study more than 380 different prion proteins from mammals and classify them according to their spontaneous misfolding propensity and their conformational variability. This study aims to address fundamental questions in the prion research field such as defining infectivity determinants, interspecies transmission barriers or the structural influence of specific amino acids and provide invaluable information for future diagnosis and therapy applications.

Sulfated disaccharide protects membrane and DNA damages from arginine-rich dipeptide repeats in ALS

Hexanucleotide repeat expansion in C9ORF72 (C9) is the most prevalent mutation among amyotrophic lateral sclerosis (ALS) patients. The patients carry over ~30 to hundreds or thousands of repeats translated to dipeptide repeats (DPRs) where poly-glycine-arginine (GR) and poly-proline-arginine (PR) are most toxic. The structure-function relationship is still unknown. Here, we examined the minimal neurotoxic repeat number of poly-GR and found that extension of the repeat number led to a loose helical structure disrupting plasma and nuclear membrane. Poly-GR/PR bound to nucleotides and interfered with transcription. We screened and identified a sulfated disaccharide that bound to poly-GR/PR and rescued poly-GR/PR-induced toxicity in neuroblastoma and C9-ALS-iPSC-derived motor neurons. The compound rescued the shortened life span and defective locomotion in poly-GR/PR expressing Drosophila model and improved motor behavior in poly-GR-injected mouse model. Overall, our results reveal structural and toxicity mechanisms for poly-GR/PR and facilitate therapeutic development for C9-ALS.

Inhibition of Uridine 5'-diphospho-glucuronosyltransferases A10 and B7 by vitamins: insights from in silico and in vitro studies

Uridine 5'-diphospho-glucuronosyltransferases (UGTs) have been considered as a family of enzymes responsible for the glucuronidation process, a crucial phase II detoxification reaction. Among the various UGT isoforms, UGTs A10 and B7 have garnered significant attention due to their broad substrate specificity and involvement in the metabolism of numerous compounds. Recent studies have suggested that certain vitamins may exert inhibitory effects on UGT activity, thereby influencing the metabolism of drugs, environmental toxins, and endogenous substances, ultimately impacting their biological activities. In the present study, the inhibition potential of vitamins (A, B1, B2, B3, B5, B6, B7, B9, D3, E, and C) on UGT1A10 and UGT2B7 was determined using in silico and in vitro approaches. A 3-dimensional model of UGT1A10 and UGT2B7 enzymes was built using Swiss Model, ITASSER, and ROSETTA and verified using Ramachandran plot and SAVES tools. Molecular docking studies revealed that vitamins interact with UGT1A10 and UGT2B7 enzymes by binding within the active site pocket and interacting with residues. Among all vitamins, the highest binding affinity predicted by molecular docking was - 8.61 kcal/mol with vitamin B1. The in vitro studies results demonstrated the inhibition of the glucuronidation activity of UGTs by vitamins A, B1, B2, B6, B9, C, D, and E, with IC50 values of 3.28 ± 1.07 µg/mL, 24.21 ± 1.11 µg/mL, 3.69 ± 1.02 µg/mL, 23.60 ± 1.08 µg/mL, 6.77 ± 1.08 µg/mL, 83.95 ± 1.09 µg/ml, 3.27 ± 1.13 µg/mL and 3.89 ± 1.12 µg/mL, respectively. These studies provided the valuable insights into the mechanisms underlying drug-vitamins interactions and have the potential to guide personalized medicine approaches, optimizing therapeutic outcomes, and ensuring patient safety. Indeed, further research in the area of UGT (UDP-glucuronosyltransferase) inhibition by vitamins is essential to fully understand the clinical relevance and implications of these interactions. UGTs play a crucial role in the metabolism and elimination of various drugs, toxins, and endogenous compounds in the body. Therefore, any factors that can modulate UGT activity, including vitamins, can have implications for drug metabolism, drug-drug interactions, and overall health.

Supplementary Information

The online version contains supplementary material available at 10.1007/s40203-023-00182-0.

Predicting the Structural Impact of Human Alternative Splicing

Protein structure prediction with neural networks is a powerful new method for linking protein sequence, structure, and function, but structures have generally been predicted for only a single isoform of each gene, neglecting splice variants. To investigate the structural implications of alternative splicing, we used AlphaFold2 to predict the structures of more than 11,000 human isoforms. We employed multiple metrics to identify splicing-induced structural alterations, including template matching score, secondary structure composition, surface charge distribution, radius of gyration, accessibility of post-translational modification sites, and structure-based function prediction. We identified examples of how alternative splicing induced clear changes in each of these properties. Structural similarity between isoforms largely correlated with degree of sequence identity, but we identified a subset of isoforms with low structural similarity despite high sequence similarity. Exon skipping and alternative last exons tended to increase the surface charge and radius of gyration. Splicing also buried or exposed numerous post-translational modification sites, most notably among the isoforms of BAX. Functional prediction nominated numerous functional differences among isoforms of the same gene, with loss of function compared to the reference predominating. Finally, we used single-cell RNA-seq data from the Tabula Sapiens to determine the cell types in which each structure is expressed. Our work represents an important resource for studying the structure and function of splice isoforms across the cell types of the human body.

Biological and mutational analyses of CXCR4-antagonist interactions and design of new antagonistic analogs

The chemokine receptor CXCR4 has become an attractive therapeutic target for HIV-1 infection, hematopoietic stem cell mobilization, and cancer metastasis. A wide variety of synthetic antagonists of CXCR4 have been developed and studied for a growing list of clinical applications. To compare the biological effects of different antagonists on CXCR4 functions and their common and/or distinctive molecular interactions with the receptor, we conducted head-to-head comparative cell-based biological and mutational analyses of the interactions with CXCR4 of eleven reported antagonists, including HC4319, DV3, DV1, DV1 dimer, V1, vMIP-II, CVX15, LY2510924, IT1t, AMD3100, and AMD11070 that were representative of different structural classes of D-peptides, L-peptide, natural chemokine, cyclic peptides, and small molecules. The results were rationalized by molecular modeling of CXCR4-antagonist interactions from which the common as well as different receptor binding sites of these antagonists were derived, revealing a number of important residues such as W94, D97, H113, D171, D262, and E288, mostly of negative charge. To further examine this finding, we designed and synthesized new antagonistic analogs by adding positively charged residues Arg to a D-peptide template to enhance the postulated charge-charge interactions. The newly designed analogs displayed significantly increased binding to CXCR4, which supports the notion that negatively charged residues of CXCR4 can engage in interactions with moieties of positive charge of the antagonistic ligands. The results from these mutational, modeling and new analog design studies shed new insight into the molecular mechanisms of different types of antagonists in recognizing CXCR4 and guide the development of new therapeutic agents.

Designing Multivalent and Multispecific Biologics

In the era of precision medicine, multivalent and multispecific therapeutics present a promising approach for targeted disease intervention. These therapeutics are designed to interact with multiple targets simultaneously, promising enhanced efficacy, reduced side effects, and resilience against drug resistance. We dissect the principles guiding the design of multivalent biologics, highlighting challenges and strategies that must be considered to maximize therapeutic effect. Engineerable elements in multivalent and multispecific biologic design-domain affinities, valency, and spatial presentation-must be considered in the context of the molecular targets as well as the balance of important properties such as target avidity and specificity. We illuminate recent applications of these principles in designing protein and cell therapies and identify exciting future directions in this field, underscored by advances in biomolecular and cellular engineering and computational approaches. Expected final online publication date for the Annual Review of Chemical and Biomolecular Engineering , Volume 15 is June 2024. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Insights into the recognition mechanism in the UBR box of UBR4 for its specific substrates

The N-end rule pathway is a proteolytic system involving the destabilization of N-terminal amino acids, known as N-degrons, which are recognized by N-recognins. Dysregulation of the N-end rule pathway results in the accumulation of undesired proteins, causing various diseases. The E3 ligases of the UBR subfamily recognize and degrade N-degrons through the ubiquitin-proteasome system. Herein, we investigated UBR4, which has a distinct mechanism for recognizing type-2 N-degrons. Structural analysis revealed that the UBR box of UBR4 differs from other UBR boxes in the N-degron binding sites. It recognizes type-2 N-terminal amino acids containing an aromatic ring and type-1 N-terminal arginine through two phenylalanines on its hydrophobic surface. We also characterized the binding mechanism for the second ligand residue. This is the report on the structural basis underlying the recognition of type-2 N-degrons by the UBR box with implications for understanding the N-end rule pathway.

Equivariant Flexible Modeling of the Protein-Ligand Binding Pose with Geometric Deep Learning

Flexible modeling of the protein-ligand complex structure is a fundamental challenge for in silico drug development. Recent studies have improved commonly used docking tools by incorporating extra-deep learning-based steps. However, such strategies limit their accuracy and efficiency because they retain massive sampling pressure and lack consideration for flexible biomolecular changes. In this study, we propose FlexPose, a geometric graph network capable of direct flexible modeling of complex structures in Euclidean space without the following conventional sampling and scoring strategies. Our model adopts two key designs: scalar-vector dual feature representation and SE(3)-equivariant network, to manage dynamic structural changes, as well as two strategies: conformation-aware pretraining and weakly supervised learning, to boost model generalizability in unseen chemical space. Benefiting from these paradigms, our model dramatically outperforms all tested popular docking tools and recently advanced deep learning methods, especially in tasks involving protein conformation changes. We further investigate the impact of protein and ligand similarity on the model performance with two conformation-aware strategies. Moreover, FlexPose provides an affinity estimation and model confidence for postanalysis.

DiAMoNDBack: Diffusion-Denoising Autoregressive Model for Non-Deterministic Backmapping of Cα Protein Traces

Coarse-grained molecular models of proteins permit access to length and time scales unattainable by all-atom models and the simulation of processes that occur on long time scales, such as aggregation and folding. The reduced resolution realizes computational accelerations, but an atomistic representation can be vital for a complete understanding of mechanistic details. Backmapping is the process of restoring all-atom resolution to coarse-grained molecular models. In this work, we report DiAMoNDBack (Diffusion-denoising Autoregressive Model for Non-Deterministic Backmapping) as an autoregressive denoising diffusion probability model to restore all-atom details to coarse-grained protein representations retaining only Cα coordinates. The autoregressive generation process proceeds from the protein N-terminus to C-terminus in a residue-by-residue fashion conditioned on the Cα trace and previously backmapped backbone and side-chain atoms within the local neighborhood. The local and autoregressive nature of our model makes it transferable between proteins. The stochastic nature of the denoising diffusion process means that the model generates a realistic ensemble of backbone and side-chain all-atom configurations consistent with the coarse-grained Cα trace. We train DiAMoNDBack over 65k+ structures from the Protein Data Bank (PDB) and validate it in applications to a hold-out PDB test set, intrinsically disordered protein structures from the Protein Ensemble Database (PED), molecular dynamics simulations of fast-folding mini-proteins from DE Shaw Research, and coarse-grained simulation data. We achieve state-of-the-art reconstruction performance in terms of correct bond formation, avoidance of side-chain clashes, and the diversity of the generated side-chain configurational states. We make the DiAMoNDBack model publicly available as a free and open-source Python package.

Engineering of Therapeutic and Detoxifying Enzymes

Therapeutic enzymes present excellent opportunities for the treatment of human disease, modulation of metabolic pathways and system detoxification. However, current use of enzyme therapy in the clinic is limited as naturally occurring enzymes are seldom optimal for such applications and require substantial improvement by protein engineering. Engineering strategies such as design and directed evolution that have been successfully implemented for industrial biocatalysis can significantly advance the field of therapeutic enzymes, leading to biocatalysts with new-to-nature therapeutic activities, high selectivity, and suitability for medical applications. This minireview highlights case studies of how state-of-the-art and emerging methods in protein engineering are explored for the generation of therapeutic enzymes and discusses gaps and future opportunities in the field of enzyme therapy.

Molecular replacement for small-molecule crystal structure determination from X-ray and electron diffraction data with reduced resolution

The resolution of 3D electron diffraction (ED) data of small-molecule crystals is often relatively poor, due to either electron-beam radiation damage during data collection or poor crystallinity of the material. Direct methods, used as standard for crystal structure determination, are not applicable when the data resolution falls below the commonly accepted limit of 1.2 Å. Therefore an evaluation was carried out of the performance of molecular replacement (MR) procedures, regularly used for protein structure determination, for structure analysis of small-molecule crystal structures from 3D ED data. In the course of this study, two crystal structures of Bi-3812, a highly potent inhibitor of the oncogenic transcription factor BCL6, were determined: the structure of α-Bi-3812 was determined from single-crystal X-ray data, the structure of β-Bi-3812 from 3D ED data, using direct methods in both cases. These data were subsequently used for MR with different data types, varying the data resolution limit (1, 1.5 and 2 Å) and by using search models consisting of connected or disconnected fragments of BI-3812. MR was successful with 3D ED data at 2 Å resolution using a search model that represented 74% of the complete molecule.

Deep mutational scanning of influenza A virus neuraminidase facilitates the identification of drug resistance mutations in vivo

IMPORTANCE

NA is a crucial surface antigen and drug target of influenza A virus. A comprehensive understanding of NA's mutational effect and drug resistance profiles in vivo is essential for comprehending the evolutionary constraints and making informed choices regarding drug selection to combat resistance in clinical settings. In the current study, we established an efficient deep mutational screening system in mouse lung tissues and systematically evaluated the fitness effect and drug resistance to three neuraminidase inhibitors of NA single-nucleotide mutations. The fitness of NA mutants is generally correlated with a natural mutation in the database. The fitness of NA mutants is influenced by biophysical factors such as protein stability, complex formation, and the immune response triggered by viral infection. In addition to confirming previously reported drug-resistant mutations, novel mutations were identified. Interestingly, we identified an allosteric drug-resistance mutation that is not located within the drug-binding pocket but potentially affects drug binding by interfering with NA tetramerization. The dual assessments performed in this study provide a more accurate assessment of the evolutionary potential of drug-resistant mutations and offer guidance for the rational selection of antiviral drugs.

De novo prediction of explicit water molecule positions by a novel algorithm within the protein design software MUMBO

By mediating interatomic interactions, water molecules play a major role in protein-protein, protein-DNA and protein-ligand interfaces, significantly affecting affinity and specificity. This notwithstanding, explicit water molecules are usually not considered in protein design software because of high computational costs. To challenge this situation, we analyzed the binding characteristics of 60,000 waters from high resolution crystal structures and used the observed parameters to implement the prediction of water molecules in the protein design and side chain-packing software MUMBO. To reduce the complexity of the problem, we incorporated water molecules through the solvation of rotamer pairs instead of relying on solvated rotamer libraries. Our validation demonstrates the potential of our algorithm by achieving recovery rates of 67% for bridging water molecules and up to 86% for fully coordinated waters. The efficacy of our algorithm is highlighted further by the prediction of 3 different proteinligand complexes. Here, 91% of water-mediated interactions between protein and ligand are correctly predicted. These results suggest that the new algorithm could prove highly beneficial for structure-based protein design, particularly for the optimization of ligand-binding pockets or protein-protein interfaces.

Machine learning coarse-grained potentials of protein thermodynamics

A generalized understanding of protein dynamics is an unsolved scientific problem, the solution of which is critical to the interpretation of the structure-function relationships that govern essential biological processes. Here, we approach this problem by constructing coarse-grained molecular potentials based on artificial neural networks and grounded in statistical mechanics. For training, we build a unique dataset of unbiased all-atom molecular dynamics simulations of approximately 9 ms for twelve different proteins with multiple secondary structure arrangements. The coarse-grained models are capable of accelerating the dynamics by more than three orders of magnitude while preserving the thermodynamics of the systems. Coarse-grained simulations identify relevant structural states in the ensemble with comparable energetics to the all-atom systems. Furthermore, we show that a single coarse-grained potential can integrate all twelve proteins and can capture experimental structural features of mutated proteins. These results indicate that machine learning coarse-grained potentials could provide a feasible approach to simulate and understand protein dynamics.

Pathfinder: Protein folding pathway prediction based on conformational sampling

The study of protein folding mechanism is a challenge in molecular biology, which is of great significance for revealing the movement rules of biological macromolecules, understanding the pathogenic mechanism of folding diseases, and designing protein engineering materials. Based on the hypothesis that the conformational sampling trajectory contain the information of folding pathway, we propose a protein folding pathway prediction algorithm named Pathfinder. Firstly, Pathfinder performs large-scale sampling of the conformational space and clusters the decoys obtained in the sampling. The heterogeneous conformations obtained by clustering are named seed states. Then, a resampling algorithm that is not constrained by the local energy basin is designed to obtain the transition probabilities of seed states. Finally, protein folding pathways are inferred from the maximum transition probabilities of seed states. The proposed Pathfinder is tested on our developed test set (34 proteins). For 11 widely studied proteins, we correctly predicted their folding pathways and specifically analyzed 5 of them. For 13 proteins, we predicted their folding pathways to be further verified by biological experiments. For 6 proteins, we analyzed the reasons for the low prediction accuracy. For the other 4 proteins without biological experiment results, potential folding pathways were predicted to provide new insights into protein folding mechanism. The results reveal that structural analogs may have different folding pathways to express different biological functions, homologous proteins may contain common folding pathways, and α-helices may be more prone to early protein folding than β-strands.

Designed nanoparticles elicit cross-reactive antibody responses to conserved influenza virus hemagglutinin stem epitopes

Despite the availability of seasonal vaccines and antiviral medications, influenza virus continues to be a major health concern and pandemic threat due to the continually changing antigenic regions of the major surface glycoprotein, hemagglutinin (HA). One emerging strategy for the development of more efficacious seasonal and universal influenza vaccines is structure-guided design of nanoparticles that display conserved regions of HA, such as the stem. Using the H1 HA subtype to establish proof of concept, we found that tandem copies of an alpha-helical fragment from the conserved stem region (helix-A) can be displayed on the protruding spikes structures of a capsid scaffold. The stem region of HA on these designed chimeric nanoparticles is immunogenic and the nanoparticles are biochemically robust in that heat exposure did not destroy the particles and immunogenicity was retained. Furthermore, mice vaccinated with H1-nanoparticles were protected from lethal challenge with H1N1 influenza virus. By using a nanoparticle library approach with this helix-A nanoparticle design, we show that this vaccine nanoparticle construct design could be applicable to different influenza HA subtypes. Importantly, antibodies elicited by H1, H5, and H7 nanoparticles demonstrated homosubtypic and heterosubtypic cross-reactivity binding to different HA subtypes. Also, helix-A nanoparticle immunizations were used to isolate mouse monoclonal antibodies that demonstrated heterosubtypic cross-reactivity and provided protection to mice from viral challenge via passive-transfer. This tandem helix-A nanoparticle construct represents a novel design to display several hundred copies of non-trimeric conserved HA stem epitopes on vaccine nanoparticles. This design concept provides a new approach to universal influenza vaccine development strategies and opens opportunities for the development of nanoparticles with broad coverage over many antigenically diverse influenza HA subtypes.

Learning Correlations between Internal Coordinates to Improve 3D Cartesian Coordinates for Proteins

We consider a generic representation problem of internal coordinates (bond lengths, valence angles, and dihedral angles) and their transformation to 3-dimensional Cartesian coordinates of a biomolecule. We show that the internal-to-Cartesian process relies on correctly predicting chemically subtle correlations among the internal coordinates themselves, and learning these correlations increases the fidelity of the Cartesian representation. We developed a machine learning algorithm, Int2Cart, to predict bond lengths and bond angles from backbone torsion angles and residue types of a protein, which allows reconstruction of protein structures better than using fixed bond lengths and bond angles or a static library method that relies on backbone torsion angles and residue types in a local environment. The method is able to be used for structure validation, as we show that the agreement between Int2Cart-predicted bond geometries and those from an AlphaFold 2 model can be used to estimate model quality. Additionally, by using Int2Cart to reconstruct an IDP ensemble, we are able to decrease the clash rate during modeling. The Int2Cart algorithm has been implemented as a publicly accessible python package at https://github.com/THGLab/int2cart.

Structural basis for the ubiquitination of G protein βγ subunits by KCTD5/Cullin3 E3 ligase

G protein-coupled receptor (GPCR) signaling is precisely controlled to avoid overstimulation that results in detrimental consequences. Gβγ signaling is negatively regulated by a Cullin3 (Cul3)-dependent E3 ligase, KCTD5, which triggers ubiquitination and degradation of free Gβγ. Here, we report the cryo-electron microscopy structures of the KCTD5-Gβγ fusion complex and the KCTD7-Cul3 complex. KCTD5 in pentameric form engages symmetrically with five copies of Gβγ through its C-terminal domain. The unique pentameric assembly of the KCTD5/Cul3 E3 ligase places the ubiquitin-conjugating enzyme (E2) and the modification sites of Gβγ in close proximity and allows simultaneous transfer of ubiquitin from E2 to five Gβγ subunits. Moreover, we show that ubiquitination of Gβγ by KCTD5 is important for fine-tuning cyclic adenosine 3´,5´-monophosphate signaling of GPCRs. Our studies provide unprecedented insights into mechanisms of substrate recognition by unusual pentameric E3 ligases and highlight the KCTD family as emerging regulators of GPCR signaling.

De novo designed ice-binding proteins from twist-constrained helices

Attaining molecular-level control over solidification processes is a crucial aspect of materials science. To control ice formation, organisms have evolved bewildering arrays of ice-binding proteins (IBPs), but these have poorly understood structure-activity relationships. We propose that reverse engineering using de novo computational protein design can shed light on structure-activity relationships of IBPs. We hypothesized that the model alpha-helical winter flounder antifreeze protein uses an unusual undertwisting of its alpha-helix to align its putative ice-binding threonine residues in exactly the same direction. We test this hypothesis by designing a series of straight three-helix bundles with an ice-binding helix projecting threonines and two supporting helices constraining the twist of the ice-binding helix. Our findings show that ice-recrystallization inhibition by the designed proteins increases with the degree of designed undertwisting, thus validating our hypothesis, and opening up avenues for the computational design of IBPs.

Building protein structure-specific rotamer libraries

MOTIVATION

Identifying the probable positions of the protein side-chains is one of the protein modelling steps that can improve the prediction of protein-ligand and protein-protein interactions. Most of the strategies predicting the side-chain conformations use predetermined dihedral angle lists, also called rotamer libraries, that are usually generated from a subset of high-quality protein structures. Although these methods are fast to apply, they tend to average out geometries instead of taking into account the surrounding atoms and molecules and ignore structures not included in the selected subset. Such simplifications can result in inaccuracies when predicting possible side-chain atom positions.

RESULTS

We propose an approach that takes into account both of these circumstances by scanning through sterically accessible side-chain conformations and generating dihedral angle libraries specific to the target proteins. The method avoids the drawbacks of lacking conformations due to unusual or rare protein structures and successfully suggests potential rotamers with average RMSD closer to the experimentally determined side-chain atom positions than other widely used rotamer libraries.

AVAILABILITY AND IMPLEMENTATION

The technique is implemented in open-source software package rotag and available at GitHub: https://www.github.com/agrybauskas/rotag, under GNU Lesser General Public License.

Recent Advances in β-Glucosidase Sequence and Structure Engineering: A Brief Review

β-glucosidases (BGLs) play a crucial role in the degradation of lignocellulosic biomass as well as in industrial applications such as pharmaceuticals, foods, and flavors. However, the application of BGLs has been largely hindered by issues such as low enzyme activity, product inhibition, low stability, etc. Many approaches have been developed to engineer BGLs to improve these enzymatic characteristics to facilitate industrial production. In this article, we review the recent advances in BGL engineering in the field, including the efforts from our laboratory. We summarize and discuss the BGL engineering studies according to the targeted functions as well as the specific strategies used for BGL engineering.

The development of an anti-cancer peptide M1-21 targeting transcription factor FOXM1

BACKGROUND

Transcription factor FOXM1 is a potential target for anti-cancer drug development. An interfering peptide M1-21, targeting FOXM1 and FOXM1-interacting proteins, is developed and its anti-cancer efficacy is evaluated.

METHODS

FOXM1 C-terminus-binding peptides are screened by in silico protocols from the peptide library of FOXM1 (1-138aa) and confirmed by cellular experiments. The selected peptide is synthesized into its D-retro-inverso (DRI) form by fusing a TAT cell-penetrating sequence. Anti-cancer activities are evaluated in vitro and in vivo with tumor-grafted nude mice, spontaneous breast cancer mice, and wild-type metastasis-tracing mice. Anti-cancer mechanisms are analyzed. Distribution and safety profiles in mice are evaluated.

RESULTS

With improved stability and cell inhibitory activity compared to the parent peptide, M1-21 binds to multiple regions of FOXM1 and interferes with protein-protein interactions between FOXM1 and its various known partner proteins, including PLK1, LIN9 and B-MYB of the MuvB complex, and β-catenin. Consequently, M1-21 inhibits FOXM1-related transcriptional activities and FOXM1-mediated nuclear importation of β-catenin and β-catenin transcriptional activities. M1-21 inhibits multiple types of cancer (20 µM in vitro or 30 mg/kg in vivo) by preventing proliferation, migration, and WNT signaling. Distribution and safety profiles of M1-21 are favorable (broad distribution and > 15 h stability in mice) and the tested non-severely toxic dose reaches 200 mg/kg in mice. M1-21 also has low hemolytic toxicity and immunogenicity in mice.

CONCLUSIONS

M1-21 is a promising interfering peptide targeting FOXM1 for the development of anti-cancer drugs.

Validation of X-ray Crystal Structure Ensemble Representations of SARS-CoV-2 Main Protease by Solution NMR Residual Dipolar Couplings

Considerable debate has focused on whether sampling of molecular dynamics trajectories restrained by crystallographic data can be used to develop realistic ensemble models for proteins in their natural, solution state. For the SARS-CoV-2 main protease, Mpro, we evaluated agreement between solution residual dipolar couplings (RDCs) and various recently reported multi-conformer and dynamic-ensemble crystallographic models. Although Phenix-derived ensemble models showed only small improvements in crystallographic Rfree, substantially improved RDC agreement over fits to a conventionally refined 1.2-Å X-ray structure was observed, in particular for residues with above average disorder in the ensemble. For a set of six lower resolution (1.55-2.19 Å) Mpro X-ray ensembles, obtained at temperatures ranging from 100 to 310 K, no significant improvement over conventional two-conformer representations was found. At the residue level, large differences in motions were observed among these ensembles, suggesting high uncertainties in the X-ray derived dynamics. Indeed, combining the six ensembles from the temperature series with the two 1.2-Å X-ray ensembles into a single 381-member "super ensemble" averaged these uncertainties and substantially improved agreement with RDCs. However, all ensembles showed excursions that were too large for the most dynamic fraction of residues. Our results suggest that further improvements to X-ray ensemble refinement are feasible, and that RDCs provide a sensitive benchmark in such endeavors. Remarkably, a weighted ensemble of 350 PDB Mpro X-ray structures provided slightly better cross-validated agreement with RDCs than any individual ensemble refinement, implying that differences in lattice confinement also limit the fit of RDCs to X-ray coordinates.

Phosphatidylserine-dependent structure of synaptogyrin remodels the synaptic vesicle membrane

Synaptic vesicles are small membrane-enclosed organelles that store neurotransmitters at presynaptic terminals. The uniform morphology of synaptic vesicles is important for brain function, because it enables the storage of well-defined amounts of neurotransmitters and thus reliable synaptic transmission. Here, we show that the synaptic vesicle membrane protein synaptogyrin cooperates with the lipid phosphatidylserine to remodel the synaptic vesicle membrane. Using NMR spectroscopy, we determine the high-resolution structure of synaptogyrin and identify specific binding sites for phosphatidylserine. We further show that phosphatidylserine binding changes the transmembrane structure of synaptogyrin and is critical for membrane bending and the formation of small vesicles. Cooperative binding of phosphatidylserine to both a cytoplasmic and intravesicular lysine-arginine cluster in synaptogyrin is required for the formation of small vesicles. Together with other synaptic vesicle proteins, synaptogyrin thus can sculpt the membrane of synaptic vesicles.

CXCR4 Recognition by L- and D-Peptides Containing the Full-Length V3 Loop of HIV-1 gp120

Human immunodeficiency virus-1 (HIV-1) recognizes one of its principal coreceptors, CXC chemokine receptor 4 (CXCR4), on the host cell via the third variable loop (V3 loop) of HIV-1 envelope glycoprotein gp120 during the viral entry process. Here, the mechanism of the molecular recognition of HIV-1 gp120 V3 loop by coreceptor CXCR4 was probed by synthetic peptides containing the full-length V3 loop. The two ends of the V3 loop were covalently linked by a disulfide bond to form a cyclic peptide with better conformational integrity. In addition, to probe the effect of the changed side-chain conformations of the peptide on CXCR4 recognition, an all-D-amino acid analog of the L-V3 loop peptide was generated. Both of these cyclic L- and D-V3 loop peptides displayed comparable binding recognition to the CXCR4 receptor, but not to another chemokine receptor, CCR5, suggesting their selective interactions with CXCR4. Molecular modeling studies revealed the important roles played by many negative-charged Asp and Glu residues on CXCR4 that probably engaged in favorable electrostatic interactions with the positive-charged Arg residues present in these peptides. These results support the notion that the HIV-1 gp120 V3 loop-CXCR4 interface is flexible for ligands of different chiralities, which might be relevant in terms of the ability of the virus to retain coreceptor recognition despite the mutations at the V3 loop.

Computational design of nanomolar-binding antibodies specific to multiple SARS-CoV-2 variants by engineering a specificity switch of antibody 80R using RosettaAntibodyDesign (RAbD) results in potential generalizable therapeutic antibodies for novel SARS-CoV-2 virus

The human infectious disease COVID-19 caused by the SARS-CoV-2 virus has become a major threat to global public health. Developing a vaccine is the preferred prophylactic response to epidemics and pandemics. However, for individuals who have contracted the disease, the rapid design of antibodies that can target the SARS-CoV-2 virus fulfils a critical need. Further, discovering antibodies that bind multiple variants of SARS-CoV-2 can aid in the development of rapid antigen tests (RATs) which are critical for the identification and isolation of individuals currently carrying COVID-19. Here we provide a proof-of-concept study for the computational design of high-affinity antibodies that bind to multiple variants of the SARS-CoV-2 spike protein using RosettaAntibodyDesign (RAbD). Well characterized antibodies that bind with high affinity to the SARS-CoV-1 (but not SARS-CoV-2) spike protein were used as templates and re-designed to bind the SARS-CoV-2 spike protein with high affinity, resulting in a specificity switch. A panel of designed antibodies were experimentally validated. One design bound to a broad range of variants of concern including the Omicron, Delta, Wuhan, and South African spike protein variants.

The in-silico evaluation of important GLUT9 residue for uric acid transport based on renal hypouricemia type 2

Uric acid is the end product of purine metabolism. Uric acid transporters in the renal proximal tubule plays a key role in uric acid transport. Functional abnormalities in these transporters could lead to high or low levels of uric acid in the blood plasma, known as hyperuricemia and hypouricemia, respectively. GLUT9 has been reported as a key transporter for uric acid reuptake in renal proximal tubule. GLUT9 mutation is known as causal gene for renal hypouricemia due to defective uric acid uptake, with more severe cases resulting in urolithiasis and exercise induced acute kidney injury (EIAKI). However, the effect of mutation is not fully investigated and hard to predict the change of binding affinity. We comprehensively described the effect of GLUT9 mutation for uric acid transport using molecular dynamics and investigated the specific site for uric acid binding differences. R171C and R380W showed the significant disruption of the structure not affecting transport dynamics whereas L75R, G216R, N333S, and P412R showed the reduced affinity of the extracellular vestibular area towards urate. Interestingly, T125 M showed a significant increase in intracellular binding energy, associated with distorted geometries. We can use this classification to consider the effect mutations by comparing the transport profiles of mutants against those of chemical candidates for transport and providing new perspectives to urate lowering drug discovery using GLUT9.

Benchmarking TriadAb using targets from the second antibody modeling assessment

Computational modeling and design of antibodies has become an integral part of today's research and development in antibody therapeutics. Here we describe the Triad Antibody Homology Modeling (TriadAb) package, a functionality of the Triad protein design platform that predicts the structure of any heavy and light chain sequences of an antibody Fv domain using template-based modeling. To gauge the performance of TriadAb, we benchmarked against the results of the Second Antibody Modeling Assessment (AMA-II). On average, TriadAb produced main-chain carbonyl root-mean-square deviations between models and experimentally determined structures at 1.10 Å, 1.45 Å, 1.41 Å, 3.04 Å, 1.47 Å, 1.27 Å, 1.63 Å in the framework and the six complementarity-determining regions (H1, H2, H3, L1, L2, L3), respectively. The inaugural results are comparable to those reported in AMA-II, corroborating with our internal bench-based experiences that models generated using TriadAb are sufficiently accurate and useful for antibody engineering using the sequence design capabilities provided by Triad.

What Can De Novo Protein Design Bring to the Treatment of Hematological Disorders?

Protein therapeutics have been widely used to treat hematological disorders. With the advent of de novo protein design, protein therapeutics are not limited to ameliorating natural proteins but also produce novel protein sequences, folds, and functions with shapes and functions customized to bind to the therapeutic targets. De novo protein techniques have been widely used biomedically to design novel diagnostic and therapeutic drugs, novel vaccines, and novel biological materials. In addition, de novo protein design has provided new options for treating hematological disorders. Scientists have designed protein switches called Colocalization-dependent Latching Orthogonal Cage-Key pRoteins (Co-LOCKR) that perform computations on the surface of cells. De novo designed molecules exhibit a better capacity than the currently available tyrosine kinase inhibitors in chronic myeloid leukemia therapy. De novo designed protein neoleukin-2/15 enhances chimeric antigen receptor T-cell activity. This new technique has great biomedical potential, especially in exploring new treatment methods for hematological disorders. This review discusses the development of de novo protein design and its biological applications, with emphasis on the treatment of hematological disorders.

Accurate Prediction of Enzyme Thermostabilization with Rosetta Using AlphaFold Ensembles

Thermostability enhancement is a fundamental aspect of protein engineering as a biocatalyst's half-life is key for its industrial and biotechnological application, particularly at high temperatures and under harsh conditions. Thermostability changes upon mutation originate from modifications of the free energy of unfolding (ΔG_u), making thermostabilization extremely challenging to predict with computational methods. In this contribution, we combine global conformational sampling with energy prediction using AlphaFold and Rosetta to develop a new computational protocol for the quantitative prediction of thermostability changes upon laboratory evolution of acyltransferase LovD and lipase LipA. We highlight how using an ensemble of protein conformations rather than a single three-dimensional model is mandatory for accurate thermostability predictions. By comparing our approaches with existing ones, we show that ensembles based on AlphaFold models provide more accurate and robust calculated thermostability trends than ensembles based solely on crystallographic structures as the latter introduce a strong distortion (scaffold bias) in computed thermostabilities. Eliminating this bias is critical for computer-guided enzyme design and evaluating the effect of multiple mutations on protein stability.

Full-Length Model of SaCas9-sgRNA-DNA Complex in Cleavage State

Staphylococcus aureus Cas9 (SaCas9) is a widely used genome editing tool. Understanding its molecular mechanisms of DNA cleavage could effectively guide the engineering optimization of this system. Here, we determined the first cryo-electron microscopy structure of the SaCas9-sgRNA-DNA ternary complex. This structure reveals that the HNH nuclease domain is tightly bound to the cleavage site of the target DNA strand, and is in close contact with the WED and REC domains. Moreover, it captures the complete structure of the sgRNA, including the previously unresolved stem-loop 2. Based on this structure, we build a full-length model for the ternary complex in cleavage state. This model enables identification of the residues for the interactions between the HNH domain and the WED and REC domains. Moreover, we found that the stem-loop 2 of the sgRNA tightly binds to the PI and RuvC domains and may also regulate the position shift of the RuvC domain. Further mutagenesis and molecular dynamics simulations supported the idea that the interactions of the HNH domain with the WED and REC domains play an important role in the DNA cleavage. Thus, this study provides new mechanistic insights into the DNA cleavage of SaCas9 and is also useful for guiding the future engineering of SaCas9-mediated gene editing systems.

Scavenger receptor B1 facilitates the endocytosis of Escherichia coli via TLR4 signaling in mammary gland infection

SCARB1 belongs to class B of Scavenger receptors (SRs) that are known to be involved in binding and endocytosis of various pathogens. SRs have emerging role in regulating innate immunity and host-pathogen interactions by acting in co-ordination with Toll-like receptors.Query Little is known about the function of SCARB1 in milk-derived mammary epithelial cells (MECs). This study reports the role of SCARB1 in infection and its potential association in TLR4 signaling on bacterial challenge in Goat mammary epithelial cells (GMECs). The novelty in the establishment of MEC culture lies in the method that aims to enhance the viability of the cells with intact characteristics upto a higher passage number. We represent MEC culture to be used as a potential infection model for deeper understanding of animal physiology especially around the mammary gland. On E.coli challenge the expression of SCARB1 was significant in induced GMECs at 6 h. Endoribonuclease-esiRNA based silencing of SCARB1 affects the expression of TLR4 and its pathways i.e. MyD88 and TRIF pathways on infection. Knockdown also affected the endocytosis of E.coli in GMECs demonstrating that E.coli uses SCARB1 function to gain entry in cells. Furthermore, we predict 3 unique protein structures of uncharacterized SCARB1 (Capra hircus) protein. Overall, we highlight SCARB1 as a main participant in host defence and its function in antibacterial advances to check mammary gland infections. Video Abstract.

PDBench: evaluating computational methods for protein-sequence design

SUMMARY

Ever increasing amounts of protein structure data, combined with advances in machine learning, have led to the rapid proliferation of methods available for protein-sequence design. In order to utilize a design method effectively, it is important to understand the nuances of its performance and how it varies by design target. Here, we present PDBench, a set of proteins and a number of standard tests for assessing the performance of sequence-design methods. PDBench aims to maximize the structural diversity of the benchmark, compared with previous benchmarking sets, in order to provide useful biological insight into the behaviour of sequence-design methods, which is essential for evaluating their performance and practical utility. We believe that these tools are useful for guiding the development of novel sequence design algorithms and will enable users to choose a method that best suits their design target.

AVAILABILITY AND IMPLEMENTATION

https://github.com/wells-wood-research/PDBench.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

A quantum walks assisted algorithm for peptide and protein folding prediction

Proteins are considered as the working force of cells. Their functionality is determined by their spatial form. In 1973 Anfinsen proposed that the spatial form is determined by the sequence of amino acids in the protein backbone. Yet, the number of possible sequences as well as the possible configurations is very large, making the task of predicting the protein's spatial form very difficult. Many approaches have been proposed, both classical and hybrid quantum - classical ones. We propose a novel hybrid algorithm. In our approach we utilized quantum walks, a proven model for universal quantum computation. We considered a simplified version of the protein backbone to be the evolution space of the quantum walk. The dihedral angles φ and ψ are introduced as phase factors to the quantum walk evolution. We also utilized a cost function to describe the system, where the R - chain, describing the specific amino acid, corresponds to a discrete value, affecting the cost functions value. Our aim is to minimize the cost function value, by updating the dihedral angles for specific regions of the Ramachandran plot, using a Metropolis algorithm.

From sequence to function through structure: Deep learning for protein design

The process of designing biomolecules, in particular proteins, is witnessing a rapid change in available tooling and approaches, moving from design through physicochemical force fields, to producing plausible, complex sequences fast via end-to-end differentiable statistical models. To achieve conditional and controllable protein design, researchers at the interface of artificial intelligence and biology leverage advances in natural language processing (NLP) and computer vision techniques, coupled with advances in computing hardware to learn patterns from growing biological databases, curated annotations thereof, or both. Once learned, these patterns can be used to provide novel insights into mechanistic biology and the design of biomolecules. However, navigating and understanding the practical applications for the many recent protein design tools is complex. To facilitate this, we 1) document recent advances in deep learning (DL) assisted protein design from the last three years, 2) present a practical pipeline that allows to go from de novo-generated sequences to their predicted properties and web-powered visualization within minutes, and 3) leverage it to suggest a generated protein sequence which might be used to engineer a biosynthetic gene cluster to produce a molecular glue-like compound. Lastly, we discuss challenges and highlight opportunities for the protein design field.