Computational design of soluble and functional membrane protein analogues.

De novo design of complex protein folds using solely computational means remains a substantial challenge1. Here we use a robust deep learning pipeline to design complex folds and soluble analogues of integral membrane proteins. Unique membrane topologies, such as those from G-protein-coupled receptors2, are not found in the soluble proteome, and we demonstrate that their structural features can be recapitulated in solution. Biophysical analyses demonstrate the high thermal stability of the designs, and experimental structures show remarkable design accuracy. The soluble analogues were functionalized with native structural motifs, as a proof of concept for bringing membrane protein functions to the soluble proteome, potentially enabling new approaches in drug discovery. In summary, we have designed complex protein topologies and enriched them with functionalities from membrane proteins, with high experimental success rates, leading to a de facto expansion of the functional soluble fold space.

De novo design of high-affinity binders of bioactive helical peptides.

Many peptide hormones form an α-helix on binding their receptors1-4, and sensitive methods for their detection could contribute to better clinical management of disease5. De novo protein design can now generate binders with high affinity and specificity to structured proteins6,7. However, the design of interactions between proteins and short peptides with helical propensity is an unmet challenge. Here we describe parametric generation and deep learning-based methods for designing proteins to address this challenge. We show that by extending RFdiffusion8 to enable binder design to flexible targets, and to refining input structure models by successive noising and denoising (partial diffusion), picomolar-affinity binders can be generated to helical peptide targets by either refining designs generated with other methods, or completely de novo starting from random noise distributions without any subsequent experimental optimization. The RFdiffusion designs enable the enrichment and subsequent detection of parathyroid hormone and glucagon by mass spectrometry, and the construction of bioluminescence-based protein biosensors. The ability to design binders to conformationally variable targets, and to optimize by partial diffusion both natural and designed proteins, should be broadly useful.

Molecular insights into the biased signaling mechanism of the µ-opioid receptor.

GPCR functional selectivity opens new opportunities for the design of safer drugs. Ligands orchestrate GPCR signaling cascades by modulating the receptor conformational landscape. Our study provides insights into the dynamic mechanism enabling opioid ligands to preferentially activate the G protein over the ß-arrestin pathways through the µ-opioid receptor (µOR). We combine functional assays in living cells, solution NMR spectroscopy, and enhanced-sampling molecular dynamic simulations to identify the specific µOR conformations induced by G protein-biased agonists. In particular, we describe the dynamic and allosteric communications between the ligand-binding pocket and the receptor intracellular domains, through conserved motifs in class A GPCRs. Most strikingly, the biased agonists trigger µOR conformational changes in the intracellular loop 1 and helix 8 domains, which may impair ß-arrestin binding or signaling. The findings may apply to other GPCR families and provide key molecular information that could facilitate the design of biased ligands.

Reinforcement learning-driven exploration of peptide space: accelerating generation of drug-like peptides.

Using amino acid residues in peptide generation has solved several key problems, including precise control of amino acid sequence order, customized peptides for property modification, and large-scale peptide synthesis. Proteins contain unknown amino acid residues. Extracting them for the synthesis of drug-like peptides can create novel structures with unique properties, driving drug development. Computer-aided design of novel peptide drug molecules can solve the high-cost and low-efficiency problems in the traditional drug discovery process. Previous studies faced limitations in enhancing the bioactivity and drug-likeness of polypeptide drugs due to less emphasis on the connection relationships in amino acid structures. Thus, we proposed a reinforcement learning-driven generation model based on graph attention mechanisms for peptide generation. By harnessing the advantages of graph attention mechanisms, this model effectively captured the connectivity structures between amino acid residues in peptides. Simultaneously, leveraging reinforcement learning's strength in guiding optimal sequence searches provided a novel approach to peptide design and optimization. This model introduces an actor-critic framework with real-time feedback loops to achieve dynamic balance between attributes, which can customize the generation of multiple peptides for specific targets and enhance the affinity between peptides and targets. Experimental results demonstrate that the generated drug-like peptides meet specified absorption, distribution, metabolism, excretion, and toxicity properties and bioactivity with a success rate of over 90$\%$, thereby significantly accelerating the process of drug-like peptide generation.

Massively parallel, computationally guided design of a proenzyme.

Confining the activity of a designed protein to a specific microenvironment would have broad-ranging applications, such as enabling cell type-specific therapeutic action by enzymes while avoiding off-target effects. While many natural enzymes are synthesized as inactive zymogens that can be activated by proteolysis, it has been challenging to redesign any chosen enzyme to be similarly stimulus responsive. Here, we develop a massively parallel computational design, screening, and next-generation sequencing-based approach for proenzyme design. For a model system, we employ carboxypeptidase G2 (CPG2), a clinically approved enzyme that has applications in both the treatment of cancer and controlling drug toxicity. Detailed kinetic characterization of the most effectively designed variants shows that they are inhibited by ∼80% compared to the unmodified protein, and their activity is fully restored following incubation with site-specific proteases. Introducing disulfide bonds between the pro- and catalytic domains based on the design models increases the degree of inhibition to 98% but decreases the degree of restoration of activity by proteolysis. A selected disulfide-containing proenzyme exhibits significantly lower activity relative to the fully activated enzyme when evaluated in cell culture. Structural and thermodynamic characterization provides detailed insights into the prodomain binding and inhibition mechanisms. The described methodology is general and could enable the design of a variety of proproteins with precise spatial regulation.

Accurate positioning of functional residues with robotics-inspired computational protein design.

SignificanceComputational protein design promises to advance applications in medicine and biotechnology by creating proteins with many new and useful functions. However, new functions require the design of specific and often irregular atom-level geometries, which remains a major challenge. Here, we develop computational methods that design and predict local protein geometries with greater accuracy than existing methods. Then, as a proof of concept, we leverage these methods to design new protein conformations in the enzyme ketosteroid isomerase that change the protein's preference for a key functional residue. Our computational methods are openly accessible and can be applied to the design of other intricate geometries customized for new user-defined protein functions.

Computer-aided nanodrug discovery: recent progress and future prospects.

Nanodrugs, which utilise nanomaterials in disease prevention and therapy, have attracted considerable interest since their initial conceptualisation in the 1990s. Substantial efforts have been made to develop nanodrugs for overcoming the limitations of conventional drugs, such as low targeting efficacy, high dosage and toxicity, and potential drug resistance. Despite the significant progress that has been made in nanodrug discovery, the precise design or screening of nanomaterials with desired biomedical functions prior to experimentation remains a significant challenge. This is particularly the case with regard to personalised precision nanodrugs, which require the simultaneous optimisation of the structures, compositions, and surface functionalities of nanodrugs. The development of powerful computer clusters and algorithms has made it possible to overcome this challenge through in silico methods, which provide a comprehensive understanding of the medical functions of nanodrugs in relation to their physicochemical properties. In addition, machine learning techniques have been widely employed in nanodrug research, significantly accelerating the understanding of bio-nano interactions and the development of nanodrugs. This review will present a summary of the computational advances in nanodrug discovery, focusing on the understanding of how the key interfacial interactions, namely, surface adsorption, supramolecular recognition, surface catalysis, and chemical conversion, affect the therapeutic efficacy of nanodrugs. Furthermore, this review will discuss the challenges and opportunities in computer-aided nanodrug discovery, with particular emphasis on the integrated "computation + machine learning + experimentation" strategy that can potentially accelerate the discovery of precision nanodrugs.

Elucidating gastric cancer mechanisms and therapeutic potential of Adociaquinone A targeting EGFR: A genomic analysis and Computer Aided Drug Design (CADD) approach.

Gastric cancer predominantly adenocarcinoma, accounts for over 85% of gastric cancer diagnoses. Current therapeutic options are limited, necessitating the discovery of novel drug targets and effective treatments. The Affymetrix gene expression microarray dataset (GSE64951) was retrieved from NCBI-GEO data normalization and DEGs identification was done by using R-Bioconductor package. Gene Ontology (GO) analysis of DEGs was performed using DAVID. The protein-protein interaction network was constructed by STRING database plugin in Cytoscape. Subclusters/modules of important interacting genes in main network were extracted by using MCODE. The hub genes from in the network were identified by using Cytohubba. The miRNet tool built a hub gene/mRNA-miRNA network and Kaplan-Meier-Plotter conducted survival analysis. AutoDock Vina and GROMACS MD simulations were used for docking and stability analysis of marine compounds against the 5CNN protein. Total 734 DEGs (507 up-regulated and 228 down-regulated) were identified. Differentially expressed genes (DEGs) were enriched in processes like cell-cell adhesion and ATP binding. Eight hub genes (EGFR, HSPA90AA1, MAPK1, HSPA4, PPP2CA, CDKN2A, CDC20, and ATM) were selected for further analysis. A total of 23 miRNAs associated with hub genes were identified, with 12 of them targeting PPP2CA. EGFR displayed the highest expression and hazard rate in survival analyses. The kinase domain of EGFR (PDBID: 5CNN) was chosen as the drug target. Adociaquinone A from Petrosia alfiani, docked with 5CNN, showed the lowest binding energy with stable interactions across a 50 ns MD simulation, highlighting its potential as a lead molecule against EGFR. This study has identified crucial DEGs and hub genes in gastric cancer, proposing novel therapeutic targets. Specifically, Adociaquinone A demonstrates promising potential as a bioactive drug against EGFR in gastric cancer, warranting further investigation. The predicted miRNA against the hub gene/proteins can also be used as potential therapeutic targets.

Computer-assisted semi-rational design enhanced the enzymatic activity and protein stability of Proteinase K in calcium-free conditions.

Wild-type Proteinase K binds to two Ca2+ ions, which play an important role in regulating enzymaticactivity and maintaining protein stability. Therefore, a predetermined concentration of Ca2+ must be added during the use of Proteinase K, which increases its commercial cost. Herein, we addressed this challenge using a computational strategy to engineer a Proteinase K mutant that does not require Ca2+ and exhibits high enzymatic activity and protein stability. In the absence of Ca2+, the best mutant, MT24 (S17W-S176N-D260F), displayed an activity approximately 9.2-fold higher than that of wild-type Proteinase K. It also exhibited excellent protein stability, retaining 56.2 % of its enzymatic activity after storage at 4 °C for 5 days. The residual enzymatic activity was 65-fold higher than that of the wild-type Proteinase K under the same storage conditions. Structural analysis and molecular dynamics simulations suggest that the introduction of new hydrogen bond and π-π stacking at the Ca2+ binding sites due to the mutation may be the reasons for the increased enzymatic activity and stability of MT24.

Improving the catalytic activity and thermostability of Aspergillus niger xylanase through computational design.

Xylanase plays the most important role in catalyzing xylan to xylose moieties. GH11 xylanases have been widely used in many fields, but most GH11 xylanases are mesophilic enzymes. To improve the catalytic activity and thermostability of Aspergillus niger xylanase (Xyn-WT), we predicted potential key mutation sites of Xyn-WT through multiple computer-aided enzyme engineering strategies. We introduce a simple and economical Ni affinity chromatography purification method to obtain high-purity xylanase and its mutants. Ten mutants (Xyn-A, Xyn-B, Xyn-C, E45T, Q93R, E45T/Q93R, A161P, Xyn-D, Xyn-E, Xyn-F) were identified. Among the ten mutants, four (Xyn-A, Xyn-C, A161P, Xyn-F) presented improved thermal stability and activity, with Xyn-F(A161P/E45T/Q93R) being the most thermally stable and active. Compared with Xyn-WT, after heat treatment at 55 °C and 60 °C for 10 min, the remaining enzyme activity of Xyn-F was 12 and 6 times greater than that of Xyn-WT, respectively, and Xyn-F was approximately 1.5 times greater than Xyn-WT when not heat treated. The pH adaptation of Xyn-F was also significantly enhanced. In summary, an improved catalytic activity and thermostability of the design variant Xyn-F has been reported.

Deep Learning-Based Chemical Similarity for Accelerated Organic Light-Emitting Diode Materials Discovery.

Thermally activated delayed fluorescence (TADF) material has attracted great attention as a promising metal-free organic light-emitting diode material with a high theoretical efficiency. To accelerate the discovery of novel TADF materials, computer-aided material design strategies have been developed. However, they have clear limitations due to the accessibility of only a few computationally tractable properties. Here, we propose TADF-likeness, a quantitative score to evaluate the TADF potential of molecules based on a data-driven concept of chemical similarity to existing TADF molecules. We used a deep autoencoder to characterize the common features of existing TADF molecules with common chemical descriptors. The score was highly correlated with the four essential electronic properties of TADF molecules and had a high success rate in large-scale virtual screening of millions of molecules to identify promising candidates at almost no cost, validating its feasibility for accelerating TADF discovery. The concept of TADF-likeness can be extended to other fields of materials discovery.

Finding Relevant Retrosynthetic Disconnections for Stereocontrolled Reactions.

Machine learning-driven computer-aided synthesis planning (CASP) tools have become important tools for idea generation in the design of complex molecule synthesis but do not adequately address the stereochemical features of the target compounds. A novel approach to automated extraction of templates used in CASP that includes stereochemical information included in the US Patent and Trademark Office (USPTO) and an internal AstraZeneca database containing reactions from Reaxys, Pistachio, and AstraZeneca electronic lab notebooks is implemented in the freely available AiZynthFinder software. Three hundred sixty-seven templates covering reagent- and substrate-controlled as well as stereospecific reactions were extracted from the USPTO, while 20,724 templates were from the AstraZeneca database. The performance of these templates in multistep CASP is evaluated for 936 targets from the ChEMBL database and an in-house selection of 791 AZ designs. The potential and limitations are discussed for four case studies from ChEMBL and examples of FDA-approved drugs.

Molecule auto-correction to facilitate molecular design.

Ensuring that computationally designed molecules are chemically reasonable is at best cumbersome. We present a molecule correction algorithm that morphs invalid molecular graphs into structurally related valid analogs. The algorithm is implemented as a tree search, guided by a set of policies to minimize its cost. We showcase how the algorithm can be applied to molecular design, either as a post-processing step or as an integral part of molecule generators.

Computational design and experimental confirmation of a disulfide-stapled YAP helixα1-trap derived from TEAD4 helical hairpin to selectively capture YAP α1-helix with potent antitumor activity.

Human Hippo signaling pathway is an evolutionarily conserved regulator network that controls organ development and has been implicated in various cancers. Transcriptional enhanced associate domain-4 (TEAD4) is the final nuclear effector of Hippo pathway, which is activated by Yes-associated protein (YAP) through binding to two separated YAP regions of α1-helix and Ω-loop. Previous efforts have all been addressed on deriving peptide inhibitors from the YAP to target TEAD4. Instead, we herein attempted to rationally design a so-called 'YAP helixα1-trap' based on the TEAD4 to target YAP by using dynamics simulation and energetics analysis as well as experimental assays at molecular and cellular levels. The trap represents a native double-stranded helical hairpin covering a specific YAP-binding site on TEAD4 surface, which is expected to form a three-helix bundle with the α1-helical region of YAP, thus competitively disrupting TEAD4-YAP interaction. The hairpin was further stapled by a disulfide bridge across its two helical arms. Circular dichroism characterized that the stapling can effectively constrain the trap into a native-like structured conformation in free state, thus largely minimizing the entropy penalty upon its binding to YAP. Affinity assays revealed that the stapling can considerably improve the trap binding potency to YAP α1-helix by up to 8.5-fold at molecular level, which also exhibited a good tumor-suppressing effect at cellular level if fused with TAT cell permeation sequence. In this respect, it is considered that the YAP helixα1-trap-mediated blockade of Hippo pathway may be a new and promising therapeutic strategy against cancers.

Computer-aided designing of a novel multi­epitope DNA vaccine against severe fever with thrombocytopenia syndrome virus.

Severe fever with thrombocytopenia syndrome (SFTS) is an emerging tick-borne viral disease caused by the SFTS virus (Dabie bandavirus), which has become a substantial risk to public health. No specific treatment is available now, that calls for an effective vaccine. Given this, we aimed to develop a multi-epitope DNA vaccine through the help of bioinformatics. The final DNA vaccine was inserted into a special plasmid vector pVAX1, consisting of CD8+ T cell epitopes, CD4+ T cell epitopes and B cell epitopes (six epitopes each) screened from four genome-encoded proteins--nuclear protein (NP), glycoprotein (GP), RNA-dependent RNA polymerase (RdRp), as well as nonstructural protein (NSs). To ascertain if the predicted structure would be stable and successful in preventing infection, an immunological simulation was run on it. In conclusion, we designed a multi-epitope DNA vaccine that is expected to be effective against Dabie bandavirus, but in vivo trials are needed to verify this claim.

Improved positional accuracy of dental implant placement using a haptic and machine-vision-controlled collaborative surgery robot: A pilot randomized controlled trial.

AIM: To compare the implant accuracy, safety and morbidity between robot-assisted and freehand dental implant placement. MATERIALS AND METHODS: Subjects requiring single-site dental implant placement were recruited. Patients were randomly allocated to freehand implant placement and robot-assisted implant placement. Differences in positional accuracy of the implant, surgical morbidity and complications were assessed. The significance of intergroup differences was tested with an intention-to-treat analysis and a per-protocol (PP) analysis (excluding one patient due to calibration error). RESULTS: Twenty patients (with a median age of 37, 13 female) were included. One subject assigned to the robotic arm was excluded from the PP analysis because of a large calibration error due to the dislodgement of the index. For robot-assisted and freehand implant placement, with the PP analysis, the median (25th-75th percentile) platform global deviation, apex global deviation and angular deviation were 1.23 (0.9-1.4) mm/1.9 (1.2-2.3) mm (p = .03, the Mann-Whitney U-test), 1.40 (1.1-1.6) mm/2.1 (1.7-3.9) mm (p < .01) and 3.0 (0.9-6.0)°/6.7 (2.2-13.9)° (p = .08), respectively. Both methods showed limited damage to the alveolar ridge and had similar peri- and post-operative morbidity and safety. CONCLUSIONS: Robot-assisted implant placement enabled greater positional accuracy of the implant compared to freehand placement in this pilot trial. The robotic system should be further developed to simplify surgical procedures and improve accuracy and be validated in properly sized trials assessing the full spectrum of relevant outcomes.

Three-dimensional imaging analysis of CAD/CAM custom-milled versus prefabricated allogeneic block remodelling at 6 months and long-term follow-up of dental implants: A retrospective cohort study.

AIM: This retrospective cohort study aimed to volumetrically investigate the bone stability rate of prefabricated allogeneic bone blocks (PBB) and computer-aided design (CAD)/computer-aided manufacturing (CAM) custom-milled allogeneic bone blocks (CCBB) for ridge augmentation. MATERIALS AND METHODS: Nineteen patients were treated with 20 allografts: 11 CCBB, 9 PBB; 10 in the maxilla and 10 in the mandible. Clinical treatment history and cone beam computed tomography scans before surgery (t0), directly after graft surgery (t1) and after 6 months of healing prior to implant insertion (t2) were evaluated using a three-dimensional evaluation software for absolute bone volume, stability as well as vertical and horizontal bone gain. Furthermore, the inserted implants were analysed for survival, marginal bone loss (MBL) and complications for a mean follow-up period of 43.75 (±33.94) months. RESULTS: A mean absolute volume of 2228.1 mm3 (±1205) was grafted at t1. The bone stability rate was 87.6% (±9.9) for CCBB and 83.0% (±14.5) for PBB. The stability was higher in the maxilla (91.6%) than in the mandible (79.53%). Surgery time of PBB was longer than for CCBB (mean Δ = 52 min). The survival rate of the inserted implants was 100% with a mean MBL of 0.41 mm (±0.37). CONCLUSION: The clinical performance of both allograft block designs was equally satisfactory for vertical and horizontal bone grafting prior to implant placement. CLINICAL TRIAL REGISTRATION: ClinicalTrials.gov: NCT06027710.

Automated Structure- and Sequence-Based Design of Proteins for High Bacterial Expression and Stability.

Upon heterologous overexpression, many proteins misfold or aggregate, thus resulting in low functional yields. Human acetylcholinesterase (hAChE), an enzyme mediating synaptic transmission, is a typical case of a human protein that necessitates mammalian systems to obtain functional expression. We developed a computational strategy and designed an AChE variant bearing 51 mutations that improved core packing, surface polarity, and backbone rigidity. This variant expressed at ∼2,000-fold higher levels in E. coli compared to wild-type hAChE and exhibited 20°C higher thermostability with no change in enzymatic properties or in the active-site configuration as determined by crystallography. To demonstrate broad utility, we similarly designed four other human and bacterial proteins. Testing at most three designs per protein, we obtained enhanced stability and/or higher yields of soluble and active protein in E. coli. Our algorithm requires only a 3D structure and several dozen sequences of naturally occurring homologs, and is available at http://pross.weizmann.ac.il.

Clinical use of digital applications for diagnostic and treatment planning in prosthodontics: A scoping review.

AIM: This scoping review aimed to compile and evaluate clinical trials investigating digital applications in prosthetic diagnostics and treatment planning by assessing their clinical relevance and future potential. METHODS: Following the PCC-framework for scoping reviews and combining the source of analysis (Population/P: "prosthodontics"), the technique of interest (Concept/C: "digital application") and the field of interest (Context/C: "diagnostics"), a three-pronged search strategy was applied in the database PubMed and Web of Science. Clinical trials (≥10 study participants, English/German) were considered until 2023-03-09. Reporting adhered to the PRISMA-ScR statement. RESULTS: The search identified 520 titles, of which 18 full-texts met the inclusion criteria for data extraction. The trials involved a total of 14,457 study participants and were mapped for prosthetic subdisciplines: fixed (n = 9; 50%) and removable (n = 4; 22%) prosthodontics, reconstructive dentistry in general (n = 3; 17%), and temporo-mandibular joint disorders (n = 2; 11%). Data merging of medical format files, as DICOM+STL, was the dominant digital application (n = 7; 39%); and virtual treatment simulation using digital smile design or digital wax-up represented the most frequent prosthetic diagnostics (n = 6; 33%). CONCLUSION: This scoping review identified a relatively low number of clinical trials. The future potential of digital diagnostics appears to be mostly related to the subdiscipline of fixed prosthodontics, especially regarding virtual treatment simulation for communication with the patient and among dental professionals. Artificial intelligence emerged as a key technology in many of the identified studies. Further research in this area is needed to explore the capabilities of digital technologies in prosthetic diagnostics and treatment planning.

Influence of sterilization on the retention forces of lithium disilicate and polymer-infiltrated ceramic-network crowns bonded to titanium base abutments: An in-vitro study.

OBJECTIVES: The aim of this study was to evaluate the effect of sterilization on the retention forces of lithium disilicate (LD) and polymer-infiltrated ceramic network (PICN) crowns bonded to titanium base (Ti-base) abutments. MATERIALS AND METHODS: Forty LD and 40 PICN crowns were milled and then bonded to 80 Ti-bases with two resin composite cements: Multilink Hybrid Abutment (mh) and Panavia V5 (pv) for a total of 8 groups (n = 10). Half of the specimens (test) underwent an autoclaving protocol (pressure 1.1 bar, 121°C, 20.5 min) and the other half not (control). Restorations were screw-retained to implants, and retention forces (N) were measured with a pull-off testing machine. The surfaces of the Ti-bases and the crowns were inspected for the analysis of the integrity of the marginal bonding interface and failure mode. Student's t-test, chi-square test, and univariate linear regression model were performed to analyze the data (α = 0.05). RESULTS: The mean pull-off retention forces ranged from 487.7 ± 73.4 N to 742.2 ± 150.3 N. Sterilized groups showed statistically significant overall higher maximum retention forces (p < .05), except for one combination (LD + mh). Sterilization led to an increased presence of marginal gaps and deformities compared to no-sterilization (p < .001), while no statistically significant relationship was found between failure mode and sterilization (p > .05). CONCLUSIONS: Sterilization may have a beneficial effect on the retention forces of LD and PICN crowns bonded to titanium base abutments, although it may negatively influence the integrity of the marginal bonding interface.