Ultra-fast evaluation of protein energies directly from sequence

The structure, function, stability, and many other properties of a protein in a fixed environment are fully specified by its sequence, but in a manner that is difficult to discern. We present a general approach for rapidly mapping sequences directly to their energies on a pre-specified rigid backbone, an important sub-problem in computational protein design and in some methods for protein structure prediction. The cluster expansion (CE) method that we employ can, in principle, be extended to model any computable or measurable protein property directly as a function of sequence. Here we show how CE can be applied to the problem of computational protein design, and use it to derive excellent approximations of physical potentials. The approach provides several attractive advantages. First, following a one-time derivation of a CE expansion, the amount of time necessary to evaluate the energy of a sequence adopting a specified backbone conformation is reduced by a factor of 10(7) compared to standard full-atom methods for the same task. Second, the agreement between two full-atom methods that we tested and their CE sequence-based expressions is very high (root mean square deviation 1.1-4.7 kcal/mol, R2 = 0.7-1.0). Third, the functional form of the CE energy expression is such that individual terms of the expansion have clear physical interpretations. We derived expressions for the energies of three classic protein design targets-a coiled coil, a zinc finger, and a WW domain-as functions of sequence, and examined the most significant terms. Single-residue and residue-pair interactions are sufficient to accurately capture the energetics of the dimeric coiled coil, whereas higher-order contributions are important for the two more globular folds. For the task of designing novel zinc-finger sequences, a CE-derived energy function provides significantly better solutions than a standard design protocol, in comparable computation time. Given these advantages, CE is likely to find many uses in computational structural modeling.     

Methane emissions reduce the radiative cooling effect of a subtropical estuarine mangrove wetland by half

The role of coastal mangrove wetlands in sequestering atmospheric carbon dioxide (CO₂) and mitigating climate change has received increasing attention in recent years. While recent studies have shown that methane (CH₄) emissions can potentially offset the carbon burial rates in low‐salinity coastal wetlands, there is hitherto a paucity of direct and year‐round measurements of ecosystem‐scale CH₄ flux (F_CH4) from mangrove ecosystems. In this study, we examined the temporal variations and biophysical drivers of ecosystem‐scale F_CH4 in a subtropical estuarine mangrove wetland based on 3 years of eddy covariance measurements. Our results showed that daily mangrove F_CH4 reached a peak of over 0.1 g CH₄‐C m^?2 day^?1 during the summertime owing to a combination of high temperature and low salinity, while the wintertime F_CH4 was negligible. In this mangrove, the mean annual CH₄ emission was 11.7 ± 0.4 g CH₄‐C m^–2 year^?1 while the annual net ecosystem CO₂ exchange ranged between ?891 and ?690 g CO₂‐C m^?2 year^?1, indicating a net cooling effect on climate over decadal to centurial timescales. Meanwhile, we showed that mangrove F_CH4 could offset the negative radiative forcing caused by CO₂ uptake by 52% and 24% over a time horizon of 20 and 100 years, respectively, based on the corresponding sustained‐flux global warming potentials. Moreover, we found that 87% and 69% of the total variance of daily F_CH4 could be explained by the random forest machine learning algorithm and traditional linear regression model, respectively, with soil temperature and salinity being the most dominant controls. This study was the first of its kind to characterize ecosystem‐scale F_CH4 in a mangrove wetland with long‐term eddy covariance measurements. Our findings implied that future environmental changes such as climate warming and increasing river discharge might increase CH₄ emissions and hence reduce the net radiative cooling effect of estuarine mangrove forests.     

Repeated horizontal gene transfer of GALactose metabolism genes violates Dollo’s law of irreversible loss

Dollo’s law posits that evolutionary losses are irreversible, thereby narrowing the potential paths of evolutionary change. While phenotypic reversals to ancestral states have been observed, little is known about their underlying genetic causes. The genomes of budding yeasts have been shaped by extensive reductive evolution, such as reduced genome sizes and the losses of metabolic capabilities. However, the extent and mechanisms of trait reacquisition after gene loss in yeasts have not been thoroughly studied. Here, through phylogenomic analyses, we reconstructed the evolutionary history of the yeast galactose utilization pathway and observed widespread and repeated losses of the ability to utilize galactose, which occurred concurrently with the losses of GALactose (GAL) utilization genes. Unexpectedly, we detected multiple galactose-utilizing lineages that were deeply embedded within clades that underwent ancient losses of galactose utilization. We show that at least two, and possibly three, lineages reacquired the GAL pathway via yeast-to-yeast horizontal gene transfer. Our results show how trait reacquisition can occur tens of millions of years after an initial loss via horizontal gene transfer from distant relatives. These findings demonstrate that the losses of complex traits and even whole pathways are not always evolutionary dead-ends, highlighting how reversals to ancestral states can occur.     

Quantitative Analysis of Torso FDG-PET Scans by Using Anatomical Standardization of Normal Cases from Thorough Physical Examinations

Understanding of standardized uptake value (SUV) of 2-deoxy-2-[18F]fluoro-d-glucose positron emission tomography (FDG-PET) depends on the background accumulations of glucose because the SUV often varies the status of patients. The purpose of this study was to develop a new method for quantitative analysis of SUV of FDG-PET scan images. The method included an anatomical standardization and a statistical comparison with normal cases by using Z-score that are often used in SPM or 3D-SSP approach for brain function analysis. Our scheme consisted of two approaches, which included the construction of a normal model and the determination of the SUV scores as Z-score index for measuring the abnormality of an FDG-PET scan image. To construct the normal torso model, all of the normal images were registered into one shape, which indicated the normal range of SUV at all voxels. The image deformation process consisted of a whole body rigid registration of shoulder to bladder region and liver registration and a non-linear registration of body surface by using the thin-plate spline technique. In order to validate usefulness of our method, we segment suspicious regions on FDG-PET images manually, and obtained the Z-scores of the regions based on the corresponding voxels that stores the mean and the standard deviations from the normal model. We collected 243 (143 males and 100 females) normal cases to construct the normal model. We also extracted 432 abnormal spots from 63 abnormal cases (73 cancer lesions) to validate the Z-scores. The Z-scores of 417 out of 432 abnormal spots were higher than 2.0, which statistically indicated the severity of the spots. In conclusions, the Z-scores obtained by our computerized scheme with anatomical standardization of torso region would be useful for visualization and detection of subtle lesions on FDG-PET scan images even when the SUV may not clearly show an abnormality.     

Peroxisome-targeted and tandem repeat multimer expressions of human antimicrobial peptide LL37 in Pichia pastoris

Although the human antimicrobial peptide LL37 has a broad spectrum of antimicrobial activities, it easily damages host cells following heterologous expressions. This study attempted two strategies to alleviate its damage to host cells when expressed in Pichia pastoris using the AOX1 promoter. Tandem repeat multimers of LL37 were first designed, and secretion expression strains GS115-9K-(DPLL37DP)_n (n?=?2, 4, 6 and 8) containing different copies of the LL37 gene were constructed. However, LL37 tandems still killed the cells after 96?hr of induction. Subsequently, peroxisome-targeted expression was performed by adding a peroxisomal targeting signal 1 (SKL) at the C-terminus of LL37. The LL37 expression strain GS115-3.5K-LL37-SKL showed no significant inhibition in the cells after induction. Antibacterial activity assays showed that the recombinant LL37 expressed in peroxisomes had good antimicrobial activities. Then, a strain GS115-3.5K-LL37-GFP-SKL producing LL37, green fluorescent protein, and SKL fusion proteins was constructed, and the fusion protein was confirmed to be targeting the peroxisomes. However, protein extraction analysis indicated that most of the fusion proteins were still located in the cell debris after cell disruption, and further studies are required to extract more proteins from the peroxisome membrane.     

Protein folding and binding in confined spaces and in crowded solutions

Simple theoretical models are presented to illustrate the effects of spatial confinement and macromolecular crowding on the equilibria and rates of protein folding and binding. Confinement is expected to significantly stabilize the folded state, but for crowding only a marginal effect on protein stability is expected. In confinement the unfolded chain is restricted to a cage but in crowding the unfolded chain may explore different interstitial voids. Because confinement and crowding eliminate the more expanded conformations of the unfolded state, folding from the compact unfolded state is expected to speed up. Crowding will shift the binding equilibrium of proteins toward the bound state. The significant slowing down in protein diffusion by crowding, perhaps beneficial for chaperonin action, could result in a decrease in protein binding rates.