Search Results (204)

Plasmid DNA (pDNA) purification plays a key role in the development of vaccines and gene therapies. Affinity chromatography stands out as a promising method for plasmid purification, leveraging a range of biological and synthetic ligands to achieve selectivity. This study investigates the potential of a synthetic ligand library consisting of triazine-based bifunctional compounds designed to mimic the side chains of amino acids that are known to bind nucleic acids. A high-throughput screening method was employed to assess the binding ability of 158 ligands within the library to single-stranded, FITC-labeled homo-oligonucleotides (G and T), each comprising 20 nucleotides, under both hydrophilic and hydrophobic conditions. High-affinity ligands were identified for both T and G oligonucleotides. Follow-up microscale chromatographic screening uncovered some false positives from the initial FITC-based screening, narrowing the selection to 22 ligands for further investigation. In the next phase of the study, the binding affinity of these ligands towards double-stranded oligonucleotides (AT and CG) was assessed. Ligand 1/2, a mimic of Ala-Lys or Gly-Lys, and ligand 2/3, a mimic of Lys-Tyr, were chosen as initial candidates for evaluating plasmid DNA purification from an Escherichia coli crude extract. The results obtained with 0.4 M ammonium sulfate in 20 mM Tris-HCl (pH 8.0) as the binding buffer were similar to those observed when purifying plasmid DNA from E. coli clarified lysates by hydrophobic interaction chromatography. The affinity resins retained RNA, while the less hydrophobic plasmid DNA was excluded in the initial fractions. Future research will be directed towards exploring the potential of the most promising ligands to separate pDNA isoforms. Full article

The SpyTag–SpyCatcher system, developed by the Howarth lab, is based on splitting the CnaB2 domain from Streptococcus pyogenes into two parts: a 13-amino-acid SpyTag and a 116-amino-acid SpyCatcher. Upon incubation, they spontaneously form a covalent isopeptide bond between Asp7 (SpyTag) and Lys31 (SpyCatcher). This study explores whether the interaction specificity can be modulated by altering hydrophobic residues within the SpyCatcher binding pocket and corresponding SpyTag positions, potentially to create orthogonal SpyTag–SpyCatcher pairs. Libraries of SpyCatcher and SpyTag were created by partial saturation mutagenesis using overlap PCR and MAX randomisation, respectively. To assess the specificity of the SpyCatcher–SpyTag interaction within the resulting protein mixtures, a novel screening strategy based on mass photometry was developed to detect isopeptide bond formation. We demonstrate tolerance to mutation in the hydrophobic binding pocket of SpyCatcher in terms of binding native SpyTag and demonstrate what to our knowledge constitutes the first example of using mass photometry to examine the interactions of small libraries of proteins with a given ligand. Mass photometry detects stable interactions whether covalent or not and so this study suggests the prospect of employing mass photometry for more general application in protein engineering. Full article

The increasing complexity of real-world engineering problems, ranging from manufacturing scheduling to resource optimization in smart grids, has driven demand for adaptive and high-performing heuristic methods. Automatic Heuristic Design (AHD) and neural-enhanced metaheuristics have shown promise in automating strategy development, but often suffer from limited flexibility and scalability due to static operator libraries or high retraining costs. Recently, Large Language Models (LLMs) have emerged as a powerful alternative for exploring and evolving heuristics through natural language and program synthesis. This paper proposes a novel LLM-based memetic framework that synergizes LLM-driven exploration with domain-specific local refinement and memory-aware reflection, enabling a dynamic balance between heuristic creativity and effectiveness. In the experiments, the developed framework outperforms other LLM-based state-of-the-art approaches across the designed AGV-drone scheduling scenario and two benchmark combinatorial problems. The findings suggest that LLMs can serve not only as general-purpose optimizers but also as interpretable heuristic generators that adapt efficiently to complex and heterogeneous domains. Full article

Electroporation-mediated gene delivery is a cornerstone of synthetic biology, offering several advantages over other methods: higher efficiencies, broader applicability, and simpler sample preparation. Yet, electroporation protocols are often challenging to integrate into highly multiplexed workflows, owing to limitations in their scalability and tunability. These challenges ultimately increase the time and cost per transformation. As a result, rapidly screening genetic libraries, exploring combinatorial designs, or optimizing electroporation parameters requires extensive iterations, consuming large quantities of expensive custom-made DNA and cell lines or primary cells. To address these limitations, we have developed a High-Throughput Microfluidic Electroporation (HTME) platform that includes a 384-well electroporation plate (E-Plate) and control electronics capable of rapidly electroporating all wells in under a minute with individual control of each well. Fabricated using scalable and cost-effective printed-circuit-board (PCB) technology, the E-Plate significantly reduces consumable costs and reagent consumption by operating on nano to microliter volumes. Furthermore, individually addressable wells facilitate rapid exploration of large sets of experimental conditions to optimize electroporation for different cell types and plasmid concentrations/types. Use of the standard 384-well footprint makes the platform easily integrable into automated workflows, thereby enabling end-to-end automation. We demonstrate transformation of E. coli with pUC19 to validate the HTME’s core functionality, achieving at least a single colony forming unit in more than 99% of wells and confirming the platform’s ability to rapidly perform hundreds of electroporations with customizable conditions. This work highlights the HTME’s potential to significantly accelerate synthetic biology Design-Build-Test-Learn (DBTL) cycles by mitigating the transformation/transfection bottleneck. Full article

A novel algorithm was proposed for solving the max-cut problem, which seeks to identify the cut with the maximum weight in a given graph. Our technique is based on the bundle approach, applied to a newly formulated semidefinite relaxation. This research establishes the theoretical convergence of our approximation technique and presents the numerical results obtained on several large-scale graphs from the BiqMac library, specifically with 100, 250, and 500 nodes. The resulting performance was compared with that produced by two alternative semidefinite programming-based approximation methods, namely the BiqMac and BiqBin solvers, by comparing the CPU time and the number of function calls. The primary objective of this work was to enhance the scalability and computational efficiency in solving the max-cut problem, particularly for large-scale graph instances. Despite the development of numerous approximation algorithms, a persistent challenge lies in effectively handling problems with a large number of constraints. Our algorithm addresses this by integrating a novel semidefinite relaxation with a bundle-based optimization framework, achieving faster convergence and fewer function calls. These advancements mark a meaningful step forward in the efficient resolution of NP-hard combinatorial optimization problems. Full article

Phage display has advanced the discovery of peptides that selectively bind to a wide variety of cell surface molecules, offering new modalities to modulate disease-related protein–protein interactions (PPIs). These cell-binding peptides occupy a unique pharmaceutical space between small molecules and large biologics, and their growing popularity has opened up new avenues for targeting cell surface proteins that were previously considered undruggable. This work provides an overview of methods for identifying cell-selective peptides using phage display combinatorial libraries, covering in vitro, ex vivo, and in vivo biopanning approaches. It addresses key considerations in library design, including the peptide conformation (linear vs. cyclic) and length, and highlights examples of clinically approved peptides developed through phage display. It also discusses the on-phage chemical cyclization of peptides to overcome the limitations of genetically encoded disulfide bridges and emphasizes advances in combining next-generation sequencing (NGS) with phage display to improve peptide selection and analysis workflows. Furthermore, due to the often suboptimal binding affinity of peptides identified in phage display selections, this article discusses affinity maturation techniques, including random mutagenesis and rational design through structure–activity relationship (SAR) studies to optimize initial peptide candidates. By integrating these developments, this review outlines practical strategies and future directions for harnessing phage display in targeting challenging cell surface proteins. Full article

Fluorescent chemosensors are increasingly becoming relevant in recognition chemistry due to their sensitivity, selectivity, fast response time, real-time detection capability, and low cost. Boronic acids have been reported for the recognition of mycolactone, the cytotoxin responsible for tissue damage in Buruli ulcer disease. A library of fluorescent arylboronic acid chemosensors with various signaling moieties with certain beneficial photophysical characteristics (i.e., aminoacridine, aminoquinoline, azo, BODIPY, coumarin, fluorescein, and rhodamine variants) and a recognition moiety (i.e., boronic acid unit) were rationally designed and synthesised using combinatorial approaches, purified, and fully characterised using a set of complementary spectrometric and spectroscopic techniques such as NMR, LC-MS, FT-IR, and X-ray crystallography. In addition, a complete set of basic photophysical quantities such as absorption maxima (λ_abs^max), emission maxima (λ_em^max), Stokes shift (∆λ), molar extinction coefficient (ε), fluorescence quantum yield (Φ_F), and brightness were determined using UV-vis absorption and fluorescence emission spectroscopy techniques. The synthesised arylboronic acid chemosensors were investigated as chemosensors for mycolactone detection using the fluorescent-thin layer chromatography (f-TLC) method. Compound 7 (with a coumarin core) emerged the best (λ_abs^max = 456 nm, λ_em^max = 590 nm, ∆λ = 134 nm, ε = 52816 M⁻¹cm⁻¹, Φ_F = 0.78, and brightness = 41,197 M⁻¹cm⁻¹). Full article

This paper presents a theoretical framework for constructing generalized weighted Voronoi diagrams (GWVDs) of weighted points and straight-line segments (“sites”) in the Euclidean plane, based on lower envelopes constructed in three-dimensional space. Central to our approach is an algebraic distance function that defines the minimum weighted distance from a point to a site. We also introduce a parameterization for the bisectors, ensuring a precise representation of Voronoi edges. The connection to lower envelopes allows us to derive (almost tight) bounds on the combinatorial complexity of a GWVD. We conclude with a short discussion of implementation strategies, ranging from leveraging computational geometry libraries to employing graphics hardware for approximate solutions. Full article

Drug intake, its absorption in the body, removal, and various side effects are factors considered when designing the drugs. Here, the in silico tools act as virtual shortcuts, assisting in the prediction of several important physicochemical properties like polar surface area (PSA), molecular weight, and molecular flexibility, etc., to evaluate probable drug leads as potential drug candidates. These tools also play a vital role in the prediction of the bioactivity score of probable drug leads against various human receptors. This paper presents a virtual combinatorial library of selected thiosemicarbazones (TSCs) and their metal complexes. Different properties like bioactivity score, physicochemical, distribution, absorption, excretion, metabolism, and toxicity (ADMET) parameters were assessed. By using ChemDraw Ultra 12.0, the structures of ligands and complexes were drawn and downloaded in PDB format. Physicochemical parameters were calculated using online softwares viz. Molinspiration and SwissADME, and ADMET properties were calculated using admetSAR (2.0). Molecular docking was performed using PyRx Python Prescription 0.8. with Janus Kinase and Transforming Growth Factor Beta (Tgf-β). Janus Kinase and Tgf-β are some cytokines involved in cell development, proliferation, and cell death. Three important TSCs, i.e., salicyldehyde thiosemicarbazone, acenaphthenequinone thiosemicarbazone, 2-chloronicotinic thiosemicarbazone, and their virtually designed complexes exhibited appreciable in silico results. Most ligands and complexes had good bioactivity values against all the biological targets. Full article

Background/objectives: Streptococcus agalactiae (or Group B Streptococcus, GBS) is a major cause of neonatal meningitis globally. There are 10 serotypes of GBS, which are distinguished by their capsular polysaccharide (CPS) structure, with serotypes Ia, Ib, II, III, IV and V responsible for up to 99% of infections. Currently, there are no licensed vaccines against GBS. The most developed candidates are glycoconjugate vaccines, which can be highly effective but are also expensive to produce by existing approaches and unaffordable for many parts of the world. Biosynthesis of recombinant glycans and glycoconjugates in tractable strains of bacteria offers a low-cost alternative approach to current chemical conjugation methods. Methods: In this study, we apply combinatorial hierarchical DNA assembly to the heterologous biosynthesis of GBS III, IV and V CPSs in E. coli. Each gene was removed from its native regulation, paired with synthetic regulatory elements and rebuilt from the bottom up to generate libraries of reconstituted pathways. These pathways were screened for glycan biosynthesis using serotype-specific antisera. Results: We identified several configurations that successfully biosynthesised the GBS CPSs. Furthermore, we exploited the conserved nature of the GBS CPS biosynthesis loci and the flexibility of modular DNA assembly by constructing hybrid pathways from a minimal pool of glycosyltransferase genes. We show that transferase genes with homologous function can be used interchangeably between pathways, obviating the need to clone a complete locus for each new CPS assembly. Conclusions: In conclusion, we report the first demonstration of heterologous GBS CPS IV and V biosynthesis in E. coli, a key milestone towards the development of low-cost recombinant multivalent GBS glycoconjugate vaccines. Full article

Integer linear programs (ILPs) and mixed integer programs (MIPs) often have multiple distinct optimal solutions, yet the widely used Gurobi optimization solver returns certain solutions at disproportionately high frequencies. This behavior is disadvantageous, as, in fields such as biomedicine, the identification and analysis of distinct optima yields valuable domain-specific insights that inform future research directions. In the present work, we introduce MORSE (Multiple Optima via Random Sampling and careful choice of the parameter Epsilon), a randomized, parallelizable algorithm to efficiently generate multiple optima for ILPs. MORSE maps multiplicative perturbations to the coefficients in an instance’s objective function, generating a modified instance that retains an optimum of the original problem. We formalize and prove the above claim in some practical conditions. Furthermore, we prove that for 0/1 selection problems, MORSE finds each distinct optimum with equal probability. We evaluate MORSE using two measures; the number of distinct optima found in r independent runs, and the diversity of the list (with repetitions) of solutions by average pairwise Hamming distance and Shannon entropy. Using these metrics, we provide empirical results demonstrating that MORSE outperforms the Gurobi method and unweighted variations of the MORSE method on a set of 20 Mixed Integer Programming Library (MIPLIB) instances and on a combinatorial optimization problem in cancer genomics. Full article

The Traveling Salesman Problem (TSP) is a classical discrete combinatorial optimization problem that is widely applied in various domains, including robotics, transportation, networking, etc. Although existing studies have provided extensive discussions of the TSP, the issues of improving convergence and optimization capability are still open. In this study, we aim to address this issue by proposing a new algorithm named IDINFO (Improved version of the discretized INFO). The proposed IDINFO is an extension of the INFO (weighted mean of vectors) algorithm in discrete space with optimized searching strategies. It applies the multi-strategy search and a threshold-based 2-opt and 3-opt local search to improve the local searching ability and avoid the issue of local optima of the discretized INFO. We use the TSPLIB library to estimate the performance of the IDINFO for the TSP. Our algorithm outperforms the existing representative algorithms (e.g., PSM, GWO, DSMO, DJAYA, AGA, CNO_PSO, Neural-3-OPT, and LIH) when tested against multiple benchmark sets. Its effectiveness was also verified in the real world in solving the TSP in short-distance delivery. Full article

Rising antifungal resistance prompted the World Health Organization and the Food and Agriculture Organization to bring attention to the consequences of this threat to human, animal, and environmental health, and food security. In addition, there is an alarming cross-species pathogenicity. New antifungal agents are urgently needed, preferably with a low induction of antimicrobial resistance (AMR). Among the most promising novel antimicrobials are the host-defense peptides, which present potent anti-infective properties and elicit low or negligible AMR. The rapid creation of libraries of host-defense peptides is highlighted by the synthesis of analogs of the immunomodulator and antimicrobial peptide rigin. Starting from smaller fragments incorporating hydroxyproline customizable units, which can be selectively cleaved and modified to give different lateral chains and N-substituents, two fragment libraries were built. Then the fragments were combined to give a library of rigin analogs, some of which displayed a potent antifungal activity not observed in the natural peptide. Surprisingly, the most active ones were N-substituted and lateral-chain protected analogs, while the free cationic peptides displayed low direct activity. This work shows that the strategy of combining site-selective peptide modification and a combinatorial approach can provide peptide-diverse libraries, where unexpected drug leads may be identified. Full article

The LLL (Lenstra–Lenstra–Lovász) algorithm is an important method for lattice basis reduction and has broad applications in computer algebra, cryptography, number theory, and combinatorial optimization. However, current LLL algorithms face challenges such as inadequate adaptation to domestic supercomputers and low efficiency. To enhance the efficiency of the LLL algorithm in practical applications, this research focuses on parallel optimization of the LLL_FP (LLL double-precision floating-point type) algorithm from the NTL library on the domestic Tianhe supercomputer using the Phytium ARM V8 processor. The optimization begins with the vectorization of the Gram–Schmidt coefficient calculation and row transformation using the SIMD instruction set of the Phytium chip, which significantly improve computational efficiency. Further assembly-level optimization fully utilizes the low-level instructions of the Phytium processor, and this increases execution speed. In terms of memory access, data prefetch techniques were then employed to load necessary data in advance before computation. This will reduce cache misses and accelerate data processing. To further enhance performance, loop unrolling was applied to the core loop, which allows more operations per loop iteration. Experimental results show that the optimized LLL_FP algorithm achieves up to a 42% performance improvement, with a minimum improvement of 34% and an average improvement of 38% in single-core efficiency compared to the serial LLL_FP algorithm. This study provides a more efficient solution for large-scale lattice basis reduction and demonstrates the potential of the LLL algorithm in ARM V8 high-performance computing environments. Full article

Ras proteins are pivotal in the regulation of cell proliferation signals, and their dysregulation is intricately linked to the pathogenesis of various malignancies. Peptide inhibitors hold distinct advantages in targeting Ras proteins, attributable to their extensive binding domains, which result from the smooth surfaces of the proteins. The array of specific strategies includes the employment of full hydrocarbon chains, cyclic peptides, linear peptides, and N-terminal nucleation polypeptides. These methods effectively suppress the Ras signaling pathway through distinct mechanisms, highlighting their potential as anti-neoplastic agents. Moreover, cutting-edge methodologies, including the N-terminal aspartate nucleation strategy and the utilization of hydrocarbon-stapled peptides, are transforming the landscape of therapeutics aimed at Ras proteins. These innovations highlight the promise of peptide libraries and combinatorial chemistry in augmenting binding affinity, specificity, and cellular permeability, which are pivotal for the development of potent anti-cancer agents. The incorporation of dual therapeutic strategies, such as the synergy between peptide inhibitors and conventional chemotherapy or the use of radiotherapy enhancers, emerges as a compelling strategy to bolster the efficacy of cancer treatments targeting the Ras-MAPK pathway. Furthermore, recent studies have demonstrated that Ras-targeting stabilized peptides can amplify the radio-sensitivity of cancer cells, offering an innovative approach to enhance the efficacy of radiation therapy within cancer management. Full article