Esophageal balloon catheter system identification to improve respiratory effort time features and amplitude determination.

Objective. Understanding a patient's respiratory effort and mechanics is essential for the provision of individualized care during mechanical ventilation. However, measurement of transpulmonary pressure (the difference between airway and pleural pressures) is not easily performed in practice. While airway pressures are available on most mechanical ventilators, pleural pressures are measured indirectly by an esophageal balloon catheter. In many cases, esophageal pressure readings take other phenomena into account and are not a reliable measure of pleural pressure.Approach.A system identification approach was applied to provide accurate pleural measures from esophageal pressure readings. First, we used a closed pressurized chamber to stimulate an esophageal balloon and model its dynamics. Second, we created a simplified version of an artificial lung and tried the model with different ventilation configurations. For validation, data from 11 patients (five male and six female) were used to estimate respiratory effort profile and patient mechanics.Main results.After correcting the dynamic response of the balloon catheter, the estimates of resistance and compliance and the corresponding respiratory effort waveform were improved when compared with the adjusted quantities in the test bench. The performance of the estimated model was evaluated using the respiratory pause/occlusion maneuver, demonstrating improved agreement between the airway and esophageal pressure waveforms when using the normalized mean squared error metric. Using the corrected muscle pressure waveform, we detected start and peak times 130 ± 50 ms earlier and a peak amplitude 2.04 ± 1.46 cmH2O higher than the corresponding estimates from esophageal catheter readings.Significance.Compensating the acquired measurements with system identification techniques makes the readings more accurate, possibly better portraying the patient's situation for individualization of ventilation therapy.

Recent Advances and Challenges in Protein Structure Prediction.

Artificial intelligence has made significant advances in the field of protein structure prediction in recent years. In particular, DeepMind's end-to-end model, AlphaFold2, has demonstrated the capability to predict three-dimensional structures of numerous unknown proteins with accuracy levels comparable to those of experimental methods. This breakthrough has opened up new possibilities for understanding protein structure and function as well as accelerating drug discovery and other applications in the field of biology and medicine. Despite the remarkable achievements of artificial intelligence in the field, there are still some challenges and limitations. In this Review, we discuss the recent progress and some of the challenges in protein structure prediction. These challenges include predicting multidomain protein structures, protein complex structures, multiple conformational states of proteins, and protein folding pathways. Furthermore, we highlight directions in which further improvements can be conducted.

E2EDA: Protein Domain Assembly Based on End-to-End Deep Learning.

With the development of deep learning, almost all single-domain proteins can be predicted at experimental resolution. However, the structure prediction of multi-domain proteins remains a challenge. Achieving end-to-end protein domain assembly and further improving the accuracy of the full-chain modeling by accurately predicting inter-domain orientation while improving the assembly efficiency will provide significant insights into structure-based drug discovery. In this work, we propose an End-to-End Domain Assembly method based on deep learning, named E2EDA. We first develop RMNet, an EfficientNetV2-based deep learning model that fuses multiple features using an attention mechanism to predict inter-domain rigid motion. Then, the predicted rigid motions are transformed into inter-domain spatial transformations to directly assemble the full-chain model. Finally, the scoring strategy RMscore is designed to select the best model from multiple assembled models. The experimental results show that the average TM-score of the model assembled by E2EDA on the benchmark set (282) is 0.827, which is better than those of other domain assembly methods SADA (0.792) and DEMO (0.730). Meanwhile, on our constructed multi-domain data set from AlphaFold DB, the model reassembled by E2EDA is 7.0% higher in TM-score compared to the full-chain model predicted by AlphaFold2, indicating that E2EDA can capture more accurate inter-domain orientations to improve the quality of the model predicted by AlphaFold2. Furthermore, compared to SADA and AlphaFold2, E2EDA reduced the average runtime on the benchmark by 64.7% and 19.2%, respectively, indicating that E2EDA can significantly improve assembly efficiency through an end-to-end approach. The online server is available at http://zhanglab-bioinf.com/E2EDA.

Structural analogue-based protein structure domain assembly assisted by deep learning.

MOTIVATION: With the breakthrough of AlphaFold2, the protein structure prediction problem has made remarkable progress through deep learning end-to-end techniques, in which correct folds could be built for nearly all single-domain proteins. However, the full-chain modelling appears to be lower on average accuracy than that for the constituent domains and requires higher demand on computing hardware, indicating the performance of full-chain modelling still needs to be improved. In this study, we investigate whether the predicted accuracy of the full-chain model can be further improved by domain assembly assisted by deep learning. RESULTS: In this article, we developed a structural analogue-based protein structure domain assembly method assisted by deep learning, named SADA. In SADA, a multi-domain protein structure database was constructed for the full-chain analogue detection using individual domain models. Starting from the initial model constructed from the analogue, the domain assembly simulation was performed to generate the full-chain model through a two-stage differential evolution algorithm guided by the energy function with an inter-residue distance potential predicted by deep learning. SADA was compared with the state-of-the-art domain assembly methods on 356 benchmark proteins, and the average TM-score of SADA models is 8.1% and 27.0% higher than that of DEMO and AIDA, respectively. We also assembled 293 human multi-domain proteins, where the average TM-score of the full-chain model after the assembly by SADA is 1.1% higher than that of the model by AlphaFold2. To conclude, we find that the domains often interact in the similar way in the quaternary orientations if the domains have similar tertiary structures. Furthermore, homologous templates and structural analogues are complementary for multi-domain protein full-chain modelling. AVAILABILITY AND IMPLEMENTATION: http://zhanglab-bioinf.com/SADA. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

A sequential niche multimodal conformational sampling algorithm for protein structure prediction.

MOTIVATION: Massive local minima on the protein energy landscape often cause traditional conformational sampling algorithms to be easily trapped in local basin regions, because they find it difficult to overcome high-energy barriers. Also, the lowest energy conformation may not correspond to the native structure due to the inaccuracy of energy models. This study investigates whether these two problems can be alleviated by a sequential niche technique without loss of accuracy. RESULTS: A sequential niche multimodal conformational sampling algorithm for protein structure prediction (SNfold) is proposed in this study. In SNfold, a derating function is designed based on the knowledge learned from the previous sampling and used to construct a series of sampling-guided energy functions. These functions then help the sampling algorithm overcome high-energy barriers and avoid the re-sampling of the explored regions. In inaccurate protein energy models, the high-energy conformation that may correspond to the native structure can be sampled with successively updated sampling-guided energy functions. The proposed SNfold is tested on 300 benchmark proteins, 24 CASP13 and 19 CASP14 FM targets. Results show that SNfold correctly folds (TM-score ≥ 0.5) 231 out of 300 proteins. In particular, compared with Rosetta restrained by distance (Rosetta-dist), SNfold achieves higher average TM-score and improves the sampling efficiency by more than 100 times. On several CASP FM targets, SNfold also shows good performance compared with four state-of-the-art servers in CASP. As a plug-in conformational sampling algorithm, SNfold can be extended to other protein structure prediction methods. AVAILABILITY AND IMPLEMENTATION: The source code and executable versions are freely available at https://github.com/iobio-zjut/SNfold. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Genetic variation of rs12918566 affects GRIN2A expression and is associated with spontaneous movement response during sevoflurane anesthesia induction.

N-methyl-D-aspartate (NMDA) receptors mediate excitatory neurotransmission in the nervous system and are preferentially inhibited by general anesthetics such as sevoflurane. Spontaneous movement is a common complication during sevoflurane anesthesia induction and seriously affects operations. In this study, we investigated the relationship between NMDA polymorphisms and spontaneous movement during sevoflurane induction. This prospective clinical study enrolled 393 patients undergoing sevoflurane anesthesia as part of their surgical routine. In the GRIN1, GRIN2A, and GRIN2B genes, 13 polymorphisms that form a heteromeric complex as part of the NMDA receptor were selected using Haploview and genotyped using matrix-assisted laser desorption ionization-time of flight mass spectrometry MassARRAY. Both RNAfold and Genotype-Tissue Expression portals were used to identify gene expression profiles. Our data showed that 35.8% of subjects exhibited spontaneous movement. The GRIN2A rs12918566 polymorphism was associated with spontaneous movement during sevoflurane induction. A logistic regression analysis of additive, dominant, and recessive models indicated a significant association (odds ratio [OR] (95% confidence limit [CI]): 0.58 (0.42-0.80), p = .00086; OR (95% CI): 0.51 (0.31-0.84), p = .0075, and OR (95% CI): 0.47 (0.27-0.81), p = .0060, respectively). After false discovery rate (FDR) correction, the additive model was still significant with a PFDR =0.010. Bioinformatics demonstrated that the rs12918566 genomic variation affected GRIN2A expression in brain tissue. We also revealed that GRIN2A rs12918566 was significantly associated with spontaneous movement during sevoflurane induction. We believe the NMDA receptor plays an important role in regulating the anesthetic effects of sevoflurane.

Association between well-characterized gene polymorphisms and the hypnosis response caused by sevoflurane-induced anaesthesia.

WHAT IS KNOWN AND OBJECTIVE: Sevoflurane is the most widely used volatile anaesthetic in clinical practice. It exhibits a hypnotic (unconsciousness) effect and causes a loss of reaction to noxious stimuli (immobility). However, to date, the mechanism of action of sevoflurane is poorly understood. In this study, we explored the effects of genetic variations on sevoflurane-induced hypnosis. METHODS: Sixty-six SNPs in 18 candidate genes were genotyped using MALDI-TOF MassARRAY in a discovery cohort containing 161 patients administered sevoflurane. Significant polymorphisms were assessed in a validation cohort containing 265 patients. RESULTS AND DISCUSSION: Three polymorphisms (GRIN1 rs28681971, rs79901440 and CHRNA7 rs72713539) were significantly associated with the time to loss of consciousness in patients treated with sevoflurane in the discovery cohort; among them, GRIN1 rs28681971 showed a significant association even after false discovery rate (FDR) correction (pFDR  = 0.039). Following the validation analysis, GRIN1 rs28681971 and rs79901440 showed statistical efficacy (pFDR  = 0.027, 0.034). Combined assessments and meta-analysis of the results of the two cohorts indicated that the C carriers of rs28681971 and T carriers of rs79901440 in GRIN1 require a longer time to achieve unconsciousness. WHAT IS NEW AND CONCLUSION: These findings suggest that GRIN1 polymorphisms are associated with sevoflurane-induced unconsciousness. Thus, the genotypes of GRIN1 may serve as novel and meaningful biomarkers for sevoflurane-induced unconsciousness.