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Abstract MotivationForecasting the synergistic effects of drug combinations facilitates drug discovery and development, especially regarding cancer therapeutics. While numerous computational methods have emerged, most of them fall short in fully modeling the relationships among clinical entities including drugs, cell lines, and diseases, which hampers their ability to generalize to drug combinations involving unseen drugs. These relationships are complex and multidimensional, requiring sophisticated modeling to capture nuanced interplay that can significantly influence therapeutic efficacy. ResultsWe present a novel deep hypergraph learning method named Heterogeneous Entity Representation for MEdicinal Synergy (HERMES) prediction to predict the synergistic effects of anti-cancer drugs. Heterogeneous data sources, including drug chemical structures, gene expression profiles, and disease clinical semantics, are integrated into hypergraph neural networks equipped with a gated residual mechanism to enhance high-order relationship modeling. HERMES demonstrates state-of-the-art performance on two benchmark datasets, significantly outperforming existing methods in predicting the synergistic effects of drug combinations, particularly in cases involving unseen drugs. Availability and implementationThe source code is available at https://github.com/Christina327/HERMES. 

Bioluminescence is a fascinating natural phenomenon, wherein organisms produce light through specific biochemical reactions. Among these organisms, Renilla luciferase (RLuc) derived from the sea pansy Renilla reniformis is notable for its blue light emission and has potential applications in bioluminescent tagging. Our study focuses on RLuc8, a variant of RLuc with eight amino acid substitutions. Recent studies have shown that the luminescent emitter coelenteramide can adopt multiple protonation states, which may be influenced by nearby residues at the enzyme's active site, demonstrating a complex interplay between protein structure and bioluminescence. Herein, using the quantum mechanical consistent force field method and the semimacroscopic protein dipole-Langevin dipole method with linear response approximation, we show that the phenolate state of coelenteramide in RLuc8 is the primary light-emitting species in agreement with experimental results. Our calculations also suggest that the proton transfer (PT) from neutral coelenteramide to Asp162 plays a crucial role in the bioluminescence process. Additionally, we reproduced the observed emission maximum for the amide anion in RLuc8-D120A and the pyrazine anion in the presence of a Na+ counterion in RLuc8-D162A, suggesting that these are the primary emitters. Furthermore, our calculations on the neutral emitter in the engineered AncFT-D160A enzyme, structurally akin to RLuc8-D162A but with a considerably blue-shifted emission peak, aligned with the observed data, possibly explaining the variance in emission peaks. Overall, this study demonstrates an effective approach to investigate chromophores' bimolecular states while incorporating the PT process in emission spectra calculations, contributing valuable insights for future studies of PT in photoproteins. 

ABSTRACT Understanding how the intrinsic ability of populations and species to meet shifting selective demands shapes evolutionary patterns over both short and long timescales is a major question in biology. One major axis of evolutionary flexibility can be measured by phenotypic integration and modularity. The strength, scale, and structure of integration may constrain or catalyze evolution in the face of new selective pressures. We analyze a dataset of seven leaf measurements across Vitaceae to examine how correlations in trait divergence are linked to transitions between freezing and nonfreezing habitats. We assess this by applying a custom algorithm to compare the timing of habitat shifts to changes in the structure of evolutionary trait correlation at discrete points along a phylogeny. We also explore these patterns in relation to lineage diversification rates to understand how and whether patterns in the evolvability of complex multivariate phenotypes are linked to higher‐level macroevolutionary dynamics. We found that shifts in the structure, but not the overall strength, of phylogenetic integration of leaves precipitate colonization of freezing climates. Lineages that underwent associated shifts in leaf trait integration and subsequent movement into freezing habitats also displayed lower turnover and higher net diversification, suggesting a link among shifting vectors of selection, internal constraint, and lineage persistence in the face of changing environments. 

Abstract PremiseThe preservation of plant tissues in ethanol is conventionally viewed as problematic. Here, we show that leaf preservation in ethanol combined with proteinase digestion can provide high‐quality DNA extracts. Additionally, as a pretreatment, ethanol can facilitate DNA extraction for recalcitrant samples. MethodsDNA was isolated from leaves preserved with 96% ethanol or from silica‐desiccated leaf samples and herbarium fragments that were pretreated with ethanol. DNA was extracted from herbarium tissues using a special ethanol pretreatment protocol, and these extracts were compared with those obtained using the standard cetyltrimethylammonium bromide (CTAB) method. ResultsDNA extracted from tissue preserved in, or pretreated with, ethanol was less fragmented than DNA from tissues without pretreatment. Adding proteinase digestion to the lysis step increased the amount of DNA obtained from the ethanol‐pretreated tissues. The combination of the ethanol pretreatment with liquid nitrogen freezing and a sorbitol wash prior to cell lysis greatly improved the quality and yield of DNA from the herbarium tissue samples. DiscussionThis study critically reevaluates the consequences of ethanol for plant tissue preservation and expands the utility of pretreatment methods for molecular and phylogenomic studies. 

Laboratory evolution combined with computational enzyme design provides the opportunity to generate novel biocatalysts. Nevertheless, it has been challenging to understand how laboratory evolution optimizes designer enzymes by introducing seemingly random mutations. A typical enzyme optimized with laboratory evolution is the abiological Kemp eliminase, initially designed by grafting active site residues into a natural protein scaffold. Here, we relate the catalytic power of laboratory-evolved Kemp eliminases to the statistical energy ( E MaxEnt ) inferred from their natural homologous sequences using the maximum entropy model. The E MaxEnt of designs generated by directed evolution is correlated with enhanced activity and reduced stability, thus displaying a stability-activity trade-off. In contrast, the E MaxEnt for mutants in catalytic-active remote regions (in which remote residues are important for catalysis) is strongly anticorrelated with the activity. These findings provide an insight into the role of protein scaffolds in the adaption to new enzymatic functions. It also indicates that the valley in the E MaxEnt landscape can guide enzyme design for abiological catalysis. Overall, the connection between laboratory and natural evolution contributes to understanding what is optimized in the laboratory and how new enzymatic function emerges in nature, and provides guidance for computational enzyme design. 

Although computational enzyme design is of great importance, the advances utilizing physics-based approaches have been slow, and further progress is urgently needed. One promising direction is using machine learning, but such strategies have not been established as effective tools for predicting the catalytic power of enzymes. Here, we show that the statistical energy inferred from homologous sequences with the maximum entropy (MaxEnt) principle significantly correlates with enzyme catalysis and stability at the active site region and the more distant region, respectively. This finding decodes enzyme architecture and offers a connection between enzyme evolution and the physical chemistry of enzyme catalysis, and it deepens our understanding of the stability–activity trade-off hypothesis for enzymes. Overall, the strong correlations found here provide a powerful way of guiding enzyme design. 

A variety of signals, including inflammasome activation, trigger the formation of large transmembrane pores by gasdermin D (GSDMD). There are primarily two functions of the GSDMD pore, to drive lytic cell death, known as pyroptosis, and to permit the release of leaderless interleukin-1 (IL-1) family cytokines, a process that does not require pyroptosis. We are interested in the mechanism by which the GSDMD pore channels IL-1 release from living cells. Recent studies revealed that electrostatic interaction, in addition to cargo size, plays a critical role in GSDMD-dependent protein release. Here, we determined computationally that to enable electrostatic filtering against pro-IL-1β, acidic lipids in the membrane need to effectively neutralize positive charges in the membrane-facing patches of the GSDMD pore. In addition, we predicted that salt has an attenuating effect on electrostatic filtering and then validated this prediction using a liposome leakage assay. A calibrated electrostatic screening factor is necessary to account for the experimental observations, suggesting that ion distribution within the pore may be different from the bulk solution. Our findings corroborate the electrostatic influence of IL-1 transport exerted by the GSDMD pore and reveal extrinsic factors, including lipid and salt, that affect the electrostatic environment.