This work describes a computational methodology for the design of braces for adolescent idiopathic scoliosis. The proposed methodology relies on a personalized simulation model of the patient\'s trunk, and automatically searches for the brace geometry that optimizes the trade-off between clinical improvement and patient comfort. To do this, we introduce a formulation of differentiable biomechanics of the patient\'s trunk, the brace, and their interaction. We design a simulation model that is differentiable with respect to both the deformation state and the brace design parameters, and we show how this differentiable model is used for the efficient update of brace design parameters within a numerical optimization algorithm. To evaluate the proposed methodology, we have obtained trunk models with personalized geometry for five patients of adolescent idiopathic scoliosis, and we have designed Boston-type braces. In a simulation setting, the designed braces improve clinical metrics by 45% on average, under acceptable comfort conditions. In the future, the methodology can be extended beyond synthetic validation, and tested with physical braces on the actual patients.

Computer simulations are widely used for the selection of conditions for the synthesis of molecularly imprinted polymers and can rapidly reduce the experimental cycle time and save labor and materials. In this paper, estrone molecularly imprinted polymers (E1-MIPs) are designed at the M062X/6-311+G(d,p) level with itaconic acid (IA) as the functional monomer. The imprinted molar ratio between E1 and IA was optimized, cross-linkers and solvents were screened, and the nature of interactions between E1 and IA was explored. The simulated results showed that pentaerythritol triacrylate was the best cross-linker. Meanwhile, when the imprinted molar ratio between E1 and IA was 1:4, the E1-IA complex had the largest amount of hydrogen bonds, the lowest binding energy, and the strongest stability. Using the simulation results as guidance, the E1-MIPs were prepared to modify the electrons of a quartz crystal microbalance (QCM) sensor. The experimental studies showed that the E1-MIPs-QCM sensor had the highest adsorption capacity to E1 in comparison with their analogues, and the lowest detection value of the sensor was 16.00 μg/L. The computer simulations and experimental studies could provide guidance for synthesize novel E1-MIPs materials. It also could provide important references and directions for the application of E1-MIPs.

Molecular dynamics (MD) simulations of prepolymerization mixtures can provide detailed insights concerning the molecular-level mechanisms underlying the performance of molecularly imprinted polymers (MIPs) and can be used for the in silico screening of candidate polymer systems. Here, we describe the use of MD simulations of all-atom, all-component MIP prepolymerization mixtures and procedures for the evaluation of the simulation data using the Amber simulation software suite.

We present here a case study of an antibody-engineering platform that selects, modifies, and assembles antibody parts to construct novel antibodies. A salient feature of this platform includes the role of amino acid networks in optimizing framework regions (FRs) and complementarity determining regions (CDRs) to engineer new antibodies with desired structure-function relationships. The details of this approach are described in the context of its utility in engineering ZAb_FLEP, a potent anti-Zika virus antibody. ZAb_FLEP comprises of distinct parts, including heavy chain and light chain FRs and CDRs, with engineered features such as loop lengths and optimal epitope-paratope contacts. We demonstrate, with different test antibodies derived from different FR-CDR combinations, that despite these test antibodies sharing high overall sequence similarity, they yield diverse functional readouts. Furthermore, we show that strategies relying on one dimensional sequence similarity-based analyses of antibodies miss important structural nuances of the FR-CDR relationship, which is effectively addressed by the amino acid networks approach of this platform.

Pseudomonas putida KT2440 is a metabolically versatile, HV1-certified, genetically accessible, and thus interesting microbial chassis for biotechnological applications. However, its obligate aerobic nature hampers production of oxygen sensitive products and drives up costs in large scale fermentation. The inability to perform anaerobic fermentation has been attributed to insufficient ATP production and an inability to produce pyrimidines under these conditions. Addressing these bottlenecks enabled growth under micro-oxic conditions but does not lead to growth or survival under anoxic conditions.
Here, a data-driven approach was used to develop a rational design for a P. putida KT2440 derivative strain capable of anaerobic respiration. To come to the design, data derived from a genome comparison of 1628 Pseudomonas strains was combined with genome-scale metabolic modelling simulations and a transcriptome dataset of 47 samples representing 14 environmental conditions from the facultative anaerobe Pseudomonas aeruginosa.
The results indicate that the implementation of anaerobic respiration in P. putida KT2440 would require at least 49 additional genes of known function, at least 8 genes encoding proteins of unknown function, and 3 externally added vitamins.

Surfactant instillation into the lungs is used to treat several respiratory disorders such as neonatal respiratory distress syndrome (NRDS). The success of the treatments significantly depends on the uniformity of distribution of the instilled surfactant in airways. This is challenging to directly evaluate due to the inaccessibility of lung airways and great difficulty with imaging them. To tackle this problem, we developed a 3D physical model of human lung airway tree. Using a defined set of principles, we first generated computational models of eight generations of neonates\' tracheobronchial tree comprising the conducting zone airways. Similar to native lungs, these models contained continuously-branching airways that rotated in the 3D space and reduced in size with increase in the generation number. Then, we used additive manufacturing to generate physical airway tree models that precisely replicated the computational designs. We demonstrated the utility of the physical models to study surfactant delivery in the lungs and showed the effect of orientation of the airway tree in the gravitational field on the distribution of instilled surfactant between the left and right lungs and within each lung. Our 3D lung airway tree model offers a novel tool for quantitative studies of therapeutics delivery.

Operations research is a well-established field that uses computational systems to support decisions in business and public life. Good solutions to operations research problems can make a large difference to the efficient running of businesses and organisations and so the field often searches for new methods to improve these solutions. The high school timetabling problem is an example of an operations research problem and is a challenging task which requires assigning events and resources to time slots subject to a set of constraints. In this article, a new sequence-based selection hyper-heuristic is presented that produces excellent results on a suite of high school timetabling problems. In this study, we present an easy-to-implement, easy-to-maintain, and effective sequence-based selection hyper-heuristic to solve high school timetabling problems using a benchmark of unified real-world instances collected from different countries. We show that with sequence-based methods, it is possible to discover new best known solutions for a number of the problems in the timetabling domain. Through this investigation, the usefulness of sequence-based selection hyper-heuristics has been demonstrated and the capability of these methods has been shown to exceed the state of the art.

In our previous work, a NAD(H)-dependent carbonyl reductase (GoCR) was identified from Gluconobacter oxydans, which showed moderate to high enantiospecificity for the reduction of different kinds of prochiral ketones. In the present study, the crystal structure of GoCR was determined at 1.65Å resolution, and a computational strategy concerning substrate-enzyme docking and all-atom molecular dynamics (MD) simulation was established to help understand the molecular basis of enantiopreference and enantiorecognition for GoCR, and to further guide the design and engineering of GoCR enantioselectivity. For the reduction of ethyl 2-oxo-4-phenylbutyrate (OPBE), three binding pocket residues, Cys93, Tyr149, and Trp193 were predicted to play a critical role in determining the enantioselectivity. Through site-directed mutagenesis, single-point mutant W193A was constructed and proved to reduce OPBE to ethyl (R)-2-hydroxy-4-phenylbutyrate (R-HPBE) with a significantly improved ee of >99% compared to 43.2% for the wild type (WT). Furthermore, double mutant C93V/Y149A was proved to even invert the enantioselectivity of GoCR to afford S-HPBE at 79.8% ee.