Rational Approach to Optimizing Conformation-Switching Aptamers for Biosensing Applications

The utilization of structure-switching aptamers (SSAs) has enabled the development of novel sensing platforms for the sensitive and continuous detection of molecules. De novo development of SSAs, however, is complex and laborious. Here we describe a rational approach to SSA optimization that simultaneously improves aptamer binding affinity and introduces target-dependent conformation-switching for compatibility with real-world biosensor applications. Key structural features identified from NMR and computational modeling were used to optimize conformational switching in the presence of target, while large-scale, microarray-based mutation analysis was used to map regions of the aptamer permissive to mutation and identify combinations of mutations with stronger binding affinity. Optimizations were carried out in a relevant biofluid to ensure a seamless transition of the aptamer to a biosensing platform. Initial proof-of-concept for this approach is demonstrated with a cortisol binding aptamer but can easily be translated to other relevant aptamers. Cortisol is a hormone correlated with the stress response that has been associated with various medical conditions and is present at quantifiable levels in accessible biofluids. The ability to continuously track levels of stress in real-time via cortisol monitoring, which can be enabled by the aptamers reported here, is crucial for assessing human health and performance.

Optimized Charge/Hydrophobicity Balance of Antimicrobial Peptides Against Polymicrobial Abdominal Infections

Antimicrobial peptides (AMPs) potentially serve as ideal antimicrobial agents for the treatment of polymicrobial abdominal infections due to their broad-spectrum antimicrobial activity and excellent biocompatibility. However, the balance of chain length, positive charges, and hydrophobicity on the antimicrobial activity of AMPs are still far from being optimal. Herein, a series of AMPs ([KX]n -NH2 , X = Ile, Leu or Phe, n = 3, 4, 5, or 6) with varied charges and hydrophobicity for the treatment of polymicrobial abdominal infections are designed. Specifically, [KI]4 -NH2 peptide exhibits the best in vitro antimicrobial activity against Gram-positive and -negative bacteria, as well as fungal strains. Based on the good cell biocompatibility, [KI]4 -NH2 peptide is found to have negligible in vivo toxicity at the dosage of up to 28 mg kg-1 . Furthermore, great in vivo therapeutic efficacy of [KI]4 -NH2 peptide against S. typhimurium is demonstrated in the mice abdominal infection model. The design of short sequence of antimicrobial peptides with a charge/hydrophobicity balanced structures provides a simple and efficient strategy for potential clinical applications of antimicrobial peptide-based biomaterials in a variety of bacterial infection diseases.

Structural Optimization and Interaction Study of a DNA Aptamer to L1 Cell Adhesion Molecule

The L1 cell adhesion molecule (L1CAM) plays important roles in the development and plasticity of the nervous system as well as in tumor formation, progression, and metastasis. New ligands are necessary tools for biomedical research and the detection of L1CAM. Here, DNA aptamer yly12 against L1CAM was optimized to have much stronger binding affinity (10-24 fold) at room temperature and 37 °C via sequence mutation and extension. This interaction study revealed that the optimized aptamers (yly20 and yly21) adopted a hairpin structure containing two loops and two stems. The key nucleotides for aptamer binding mainly located in loop I and its adjacent area. Stem I mainly played the role of stabilizing the binding structure. The yly-series aptamers were demonstrated to bind the Ig6 domain of L1CAM. This study reveals a detailed molecular mechanism for the interaction between yly-series aptamers and L1CAM and provides guidance for drug development and detection probe design against L1CAM.

Optimized flip angle schemes for the split acquisition of fast spin-echo signals (SPLICE) sequence and application to diffusion-weighted imaging

PURPOSE

The diffusion-weighted SPLICE (split acquisition of fast spin-echo signals) sequence employs split-echo rapid acquisition with relaxation enhancement (RARE) readout to provide images almost free of geometric distortions. However, due to the varying T 2 $$ {}_2 $$ -weighting during k-space traversal, SPLICE suffers from blurring. This work extends a method for controlling the spatial point spread function (PSF) while optimizing the signal-to-noise ratio (SNR) achieved by adjusting the flip angles in the refocusing pulse train of SPLICE.

METHODS

An algorithm based on extended phase graph (EPG) simulations optimizes the flip angles by maximizing SNR for a flexibly chosen predefined target PSF that describes the desired k-space density weighting and spatial resolution. An optimized flip angle scheme and a corresponding post-processing correction filter which together achieve the target PSF was tested by healthy subject brain imaging using a clinical 1.5 T scanner.

RESULTS

Brain images showed a clear and consistent improvement over those obtained with a standard constant flip angle scheme. SNR was increased and apparent diffusion coefficient estimates were more accurate. For a modified Hann k-space weighting example, considerable benefits resulted from acquisition weighting by flip angle control.

CONCLUSION

The presented flexible method for optimizing SPLICE flip angle schemes offers improved MR image quality of geometrically accurate diffusion-weighted images that makes the sequence a strong candidate for radiotherapy planning or stereotactic surgery.

Encoding capability prediction of acquisition schedules in CEST MR fingerprinting for pH quantification

PURPOSE

To identify a reliable metric for predicting the encoding capability of CEST MR fingerprinting acquisition schedules for pH quantification, which may facilitate CEST MR fingerprinting protocol optimization.

METHODS

Numerical simulations and Cr phantom MRI experiments were conducted at 3 Tesla under representative CEST MR fingerprinting sampling scenarios, including the pseudorandomization of imaging parameters (e.g., saturation power B₁ , saturation frequency offset, saturation time, and relaxation time), and variation of the maximum saturation power B_1max , B₁ number, and sampling pattern. The CEST effect at 2 ppm was measured using asymmetry analysis and matched to a predefined dictionary to determine the pH. The pH quantification error was assessed using RMSE. Three metrics, namely the Cramer-Rao bound, dot product, and Euclidean distance, were calculated for each sampling scenario, and their relationships with the pH RMSE were investigated to examine their effectiveness for predicting the encoding capability of sampling schedules for pH quantification.

RESULTS

Both simulation and phantom studies revealed that the Cramer-Rao bound metric consistently exhibited superior performance for predicting the pH quantification error. Although dot product exhibited good encoding capability prediction in most sampling scenarios, it failed in the scenario with varied B₁ numbers. In contrast, Euclidean distance exhibited the worst performance among the 3 metrics in all scenarios.

CONCLUSION

Superior over dot product and Euclidean distance, the Cramer-Rao bound metric may reliably predicting the encoding capability of CEST MR fingerprinting sampling strategies and may be useful for guiding CEST MRI protocol optimization.

Advanced sequence optimization for the high efficient yield of human group A rotavirus VP6 recombinant protein in Escherichia coli and its use as immunogen

Rotavirus is the important etiological agents of infectious diarrhea among children under 5 years old. Rotaviruses are divided into 10 serogroups (A-J) and each group is based on genetic properties of major structural protein VP6. We designed a novel VP6 sequence optimization to increase the expression level of this protein. Numerous factors such as codon adaptation index, codon pair bias, and guanine-cytosine content were adapted based on Escherichiacoli codon usage. In addition, the ribosome binding site (RBS) of pET-15b was redesigned by the RBS calculator and the secondary structure of VP6 messenger RNA was optimized in the whole length of the coding sequence. Various factors including isopropyl beta- d-thiogalactoside (IPTG) concentration, temperature, and induction time were analyzed for the optimization of the best expression in E. coli by sodium dodecyl sulfate-polyacrylamide gel electrophoresis and western blotting. The recombinant VP6 (rVP6) protein was purified by the Ni-sepharose and then the hyperimmune sera were generated against rVP6 in rabbits. Among three different temperatures, IPTG concentrations, and postinductions, the level of rVP6 was higher at 37°C, 1 mM of IPTG, and 8 h, respectively. Also, the high expression level of rVP6 was obtained in the insoluble aggregate form (43.8 g/L). After purification, the yield of rVP6 was 10.83 g/L. The rVP6 specific antiserum was confirmed by both immunofluorescent and western blotting. The versatile sequence optimization was the reason to produce a high level of rVP6 compared to other reports and can potentially apply to produce cheaper commercial kits to diagnose serological tests and new rotavirus vaccines.

Protein sequence design by conformational landscape optimization

The protein design problem is to identify an amino acid sequence that folds to a desired structure. Given Anfinsen's thermodynamic hypothesis of folding, this can be recast as finding an amino acid sequence for which the desired structure is the lowest energy state. As this calculation involves not only all possible amino acid sequences but also, all possible structures, most current approaches focus instead on the more tractable problem of finding the lowest-energy amino acid sequence for the desired structure, often checking by protein structure prediction in a second step that the desired structure is indeed the lowest-energy conformation for the designed sequence, and typically discarding a large fraction of designed sequences for which this is not the case. Here, we show that by backpropagating gradients through the transform-restrained Rosetta (trRosetta) structure prediction network from the desired structure to the input amino acid sequence, we can directly optimize over all possible amino acid sequences and all possible structures in a single calculation. We find that trRosetta calculations, which consider the full conformational landscape, can be more effective than Rosetta single-point energy estimations in predicting folding and stability of de novo designed proteins. We compare sequence design by conformational landscape optimization with the standard energy-based sequence design methodology in Rosetta and show that the former can result in energy landscapes with fewer alternative energy minima. We show further that more funneled energy landscapes can be designed by combining the strengths of the two approaches: the low-resolution trRosetta model serves to disfavor alternative states, and the high-resolution Rosetta model serves to create a deep energy minimum at the design target structure.

Designing protein structures and complexes with the molecular modeling program Rosetta

Proteins perform an amazingly diverse set of functions in all aspects of life. Critical to the function of many proteins are the highly specific three-dimensional structures they adopt. For this reason, there is strong interest in learning how to rationally design proteins that adopt user-defined structures. Over the last 25 years, there has been significant progress in the field of computational protein design as rotamer-based sequence optimization protocols have enabled accurate design of protein tertiary and quaternary structure. In this award article, I will summarize how the molecular modeling program Rosetta is used to design new protein structures and describe how we have taken advantage of this capability to create proteins that have important applications in research and medicine. I will highlight three protein design stories: the use of protein interface design to create therapeutic bispecific antibodies, the engineering of light-inducible proteins that can be used to recruit proteins to specific locations in the cell, and the de novo design of new protein structures from pieces of naturally occurring proteins.

Enhancing heterologous expression in Chlamydomonas reinhardtii by transcript sequence optimization

Various species of microalgae have recently emerged as promising host-organisms for use in biotechnology industries due to their unique properties. These include efficient conversion of sunlight into organic compounds, the ability to grow in extreme conditions and the occurrence of numerous post-translational modification pathways. However, the inability to obtain high levels of nuclear heterologous gene expression in microalgae hinders the development of the entire field. To overcome this limitation, we analyzed different sequence optimization algorithms while studying the effect of transcript sequence features on heterologous expression in the model microalga Chlamydomonas reinhardtii, whose genome consists of rare features such as a high GC content. Based on the analysis of genomic data, we created eight unique sequences coding for a synthetic ferredoxin-hydrogenase enzyme, used here as a reporter gene. Following in silico design, these synthetic genes were transformed into the C. reinhardtii nucleus, after which gene expression levels were measured. The empirical data, measured in vivo show a discrepancy of up to 65-fold between the different constructs. In this work we demonstrate how the combination of computational methods and our empirical results enable us to learn about the way gene expression is encoded in the C. reinhardtii transcripts. We describe the deleterious effect on overall expression of codons encoding for splicing signals. Subsequently, our analysis shows that utilization of a frequent subset of preferred codons results in elevated transcript levels, and that mRNA folding energy in the vicinity of translation initiation significantly affects gene expression.

A "fuzzy"-logic language for encoding multiple physical traits in biomolecules

To carry out their activities, biological macromolecules balance different physical traits, such as stability, interaction affinity, and selectivity. How such often opposing traits are encoded in a macromolecular system is critical to our understanding of evolutionary processes and ability to design new molecules with desired functions. We present a framework for constraining design simulations to balance different physical characteristics. Each trait is represented by the equilibrium fractional occupancy of the desired state relative to its alternatives, ranging from none to full occupancy, and the different traits are combined using Boolean operators to effect a "fuzzy"-logic language for encoding any combination of traits. In another paper, we presented a new combinatorial backbone design algorithm AbDesign where the fuzzy-logic framework was used to optimize protein backbones and sequences for both stability and binding affinity in antibody-design simulation. We now extend this framework and find that fuzzy-logic design simulations reproduce sequence and structure design principles seen in nature to underlie exquisite specificity on the one hand and multispecificity on the other hand. The fuzzy-logic language is broadly applicable and could help define the space of tolerated and beneficial mutations in natural biomolecular systems and design artificial molecules that encode complex characteristics.