Understanding how gene-level mutations affect the binding affinity of protein–protein interactions is a key issue of protein engineering. Due to the complexity of the problem, using physical force field to predict the mutation-induced binding free-energy change remains challenging. In this work, we present a renewed approach to calculate the impact of gene mutations on the binding affinity through the structure-based profiling of protein–protein interfaces, where the binding free-energy change (ΔΔG) is counted as the logarithm of relative probability of mutant amino acids over wild-type ones in the interface alignment matrix; three pseudo-counts are introduced to alleviate the limit of the current interface library. Compared with a previous profile score that was based on the log-odds likelihood calculation, the correlation between predicted and experimental ΔΔG of single-site mutations is increased in this approach from 0.33 to 0.68. The structure-based profile score is found complementary to the physical potentials, where a linear combination of the profile score with the FoldX potential could increase the ΔΔG correlation from 0.46 to 0.74. It is also shown that the profile score is robust for counting the coupling effect of multiple individual mutations. For the mutations involving more than two mutation sites where the correlation between FoldX and experimental data vanishes, the profile-based calculation retains a strong correlation with the experimental measurements.

The geological storage of CO 2 in deep saline formations is increasing seen as a viable strategy to reduce the release of greenhouse gases to the atmosphere. There are numerous sedimentary basins in China, in which a number of suitable CO 2 geologic reservoirs are potentially available. To identify the multi-phase processes, geochemical changes and mineral alteration, and CO 2 trapping mechanisms after CO 2 injection, reactive geochemical transport simulations using a simple 2D model were performed. Mineralogical composition and water chemistry from a deep saline formation of Songliao Basin were used. Results indicate that different storage forms of CO 2 vary with time. In the CO 2 injection period, a large amount of CO 2 remains as a free supercritical phase (gas trapping), and the amount dissolved in the formation water (solubility trapping) gradually increases. Later, gas trapping decreases, solubility trapping increases significantly due to migration and diffusion of the CO 2 plume, and the amount trapped by carbonate minerals increases gradually with time. The residual CO 2 gas keeps dissolving into groundwater and precipitating carbonate minerals. For the Songliao Basin sandstone, variations in the reaction rate and abundance of chlorite, and plagioclase composition affect significantly the estimates of mineral alteration and CO 2 storage in different trapping mechanisms. The effect of vertical permeability and residual gas saturation on the 2 overall storage is smaller compared to the geochemical factors. However, they can affect the spatial distribution of the injected CO 2 in the formations. The CO 2 mineral trapping capacity could be in the order of ten kilogram per cubic meter medium for the Songliao Basin sandstone, and may be higher depending on the composition of primary aluminosilicate minerals especially the content of Ca, Mg, and Fe.

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Polygonum multiflorum (PM) is a well‐known Chinese herbal medicine that has been reported to induce inflammation‐associated idiosyncratic liver injury. This study aimed to identify the genetic basis of susceptibility to PM‐drug‐induced liver injury (PM‐DILI) and to develop biological markers for predicting the risk of PM‐DILI in humans. The major histocompatibility complex (MHC) regions of 11 patients with PM‐DILI were sequenced, and all human leukocyte antigen (HLA)–type frequencies were compared to the Han‐MHC database. An independent replication study that included 15 patients with PM‐DILI, 33 patients with other DILI, and 99 population controls was performed to validate the candidate allele by HLA‐B PCR sequence‐based typing. A prospective cohort study that included 72 outpatients receiving PM for 4 weeks was designed to determine the influence of the risk allele on PM‐DILI. In the pilot study, the frequency of HLA‐B*35:01 was 45.4% in PM‐DILI patients compared with 2.7% in the Han Chinese population (odds ratio [OR], 30.4; 95% confidence interval [CI], 11.7‐77.8; P = 1.9 × 10−10). In the independent replication study and combined analyses, a logistic regression model confirmed that HLA‐B*35:01 is a high‐risk allele of PM‐DILI (PM‐DILI versus other DILI, OR, 86.5; 95% CI, 14.2‐527.8, P = 1.0 × 10−6; and PM‐DILI versus population controls, OR, 143.9; 95% CI, 30.1‐687.5, P = 4.8 × 10−10). In the prospective cohort study, an asymptomatic increase in transaminase levels was diagnosed in 6 patients, representing a significantly higher incidence (relative risk, 8.0; 95% CI, 1.9‐33.2; P < 0.02) in the HLA‐B*35:01 carriers (37.5%) than in the noncarriers (4.7%). Conclusion: The HLA‐B*35:01 allele is a genetic risk factor for PM‐DILI and a potential biomarker for predicting PM‐DILI in humans.

Despite the large number of noncoding RNAs in human genome and their roles in many diseases include cancer, we know very little about them due to lack of structural clues. The centerpiece of the structural clues is the full RNA base-pairing structure of secondary and tertiary contacts that can be precisely obtained only from costly and time-consuming 3D structure determination. Here, we performed deep mutational scanning of self-cleaving CPEB3 ribozyme by error-prone PCR and showed that a library of &lt;5 × 104 single-to-triple mutants is sufficient to infer 25 of 26 base pairs including non-nested, nonhelical, and noncanonical base pairs with both sensitivity and precision at 96%. Such accurate inference was further confirmed by a twister ribozyme at 100% precision with only noncanonical base pairs as false negatives. The performance was resulted from analyzing covariation-induced deviation of activity by utilizing both functional and nonfunctional variants for unsupervised classification, followed by Monte Carlo (MC) simulated annealing with mutation-derived scores. Highly accurate inference can also be obtained by combining MC with evolution/direct coupling analysis, R-scape or epistasis analysis. The results highlight the usefulness of deep mutational scanning for high-accuracy structural inference of self-cleaving ribozymes with implications for other structured RNAs that permit high-throughput functional selections.

Refining modelled structures to approach experimental accuracy is one of the most challenging problems in molecular biology. Despite many years’ efforts, the progress in protein or RNA structure refinement has been slow because the global minimum given by the energy scores is not at the experimentally determined “native” structure. Here, we propose a fully knowledge-based energy function that captures the full orientation dependence of base–base, base–oxygen and oxygen–oxygen interactions with the RNA backbone modelled by rotameric states and internal energies. A total of 4000 quantum-mechanical calculations were performed to reweight base–base statistical potentials for minimizing possible effects of indirect interactions. The resulting BRiQ knowledge-based potential, equipped with a nucleobase-centric sampling algorithm, provides a robust improvement in refining near-native RNA models generated by a wide variety of modelling techniques.

As a primary diagnostic tool for cardiac diseases, electrocardiogram (ECG) signals are often contaminated by various kinds of noise, such as baseline wander, electrode contact noise and motion artifacts. In this paper, we propose a contractive denoising technique to improve the performance of current denoising auto-encoders (DAEs) for ECG signal denoising. Based on the Frobenius norm of the Jacobean matrix for the learned features with respect to the input, we develop a stacked contractive denoising auto-encoder (CDAE) to build a deep neural network (DNN) for noise reduction, which can significantly improve the expression of ECG signals through multi-level feature extraction. The proposed method is evaluated on ECG signals from the bench-marker MIT-BIH Arrhythmia Database, and the noises come from the MIT-BIH noise stress test database. The experimental results show that the new CDAE algorithm performs better than the conventional ECG denoising method, specifically with more than 2.40 dB improvement in the signal-to-noise ratio (SNR) and nearly 0.075 to 0.350 improvements in the root mean square error (RMSE).

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