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Zhang, G.

Publications and source records attributed to Zhang, G..

At least 217 records · Page 12Linked to original sources
bioRxiv2023

SPDesign: protein sequence designer based on structural sequence profile using ultrafast shape recognition

Designing protein with specified structure and function involves a key component named sequence design, which can provide valuable insights into understanding the life systems as well for the diagnosis and therapy of diseases. Although deep learning methods have made great progress in protein sequence design, most of these studies focus on network structure optimization, while ignoring protein-specific physicochemical features. Inspired by the successful application of structure templates and pre-trained models in the field of protein structure prediction, we explored whether the representation of structural sequence profile can be used for protein sequence design. In this work, we proposed SPDesign, a method for protein sequence design based on structural sequence profile using ultrafast shape recognition. Given an input back-bone structure, SPDesign utilizes ultrafast shape recognition vectors to accelerate the search for similar protein structures (aka, structural analogs) in our in-house PAcluster80 structure database, and then extracts the sequence profile from the analogs through structure alignment. Combined with structural pre-trained knowledge and geometric features, they are further feed into an enhanced graph neural network to predict the sequence. Experimental results show that SPDesign significantly outperforms the state-of-the-art methods, such as ProteinMPNN, Pifold and LM-Design, leading to 21.89%, 15.54% and 11.4% accuracy gains in sequence recovery rate on CATH 4.2 benchmark, respectively. Encouraging results also have been achieved on the TS50 and TS500 benchmarks, with performance reaching 68.64% and 71.63%. Furthermore, detailed analysis conducted by the PDBench tool suggest that SPDesign performs well in subdivided structures such as buried residues and solenoid. More interestingly, we found that SPDesign can well reconstruct the sequences of some proteins that have similar structures but different sequences. Finally, the structural modeling verification experiment bears out that the sequences designed by our method can fold into the native structures more accurately.

bioinformatics↗
bioRxiv2023

Machine-learning-based Structural Analysis of Interactions between Antibodies and Antigens

Computational analysis of paratope-epitope interactions between antibodies and their corresponding antigens can facilitate our understanding of the molecular mechanism underlying humoral immunity and boost the design of new therapeutics for many diseases. The recent breakthrough in artificial intelligence has made it possible to predict protein-protein interactions and model their structures. Unfortunately, detecting antigen-binding sites associated with a specific antibody is still a challenging problem. To tackle this challenge, we implemented a deep learning model to characterize interaction patterns between antibodies and their corresponding antigens. With high accuracy, our model can distinguish between antibody-antigen complexes and other types of protein-protein complexes. More intriguingly, we can identify antigens from other common protein binding regions with an accuracy of higher than 70% even if we only have the epitope information. This indicates that antigens have distinct features on their surface that antibodies can recognize. Additionally, our model was unable to predict the partnerships between antibodies and their particular antigens. This result suggests that one antigen may be targeted by more than one antibody and that antibodies may bind to previously unidentified proteins. Taken together, our results support the precision of antibody-antigen interactions while also suggesting positive future progress in the prediction of specific pairing.

bioinformatics↗
bioRxiv2023

Predicting protein folding pathway using a novel folding force field model derived from known protein universe

The protein folding problem has emerged as a new challenge with the significant advances in deep learning driven protein structure prediction methods. While the structures of almost all known proteins have been successfully predicted, the process by which they fold remains an enigma. Understanding the intricate folding mechanism is of paramount importance, as it directly impacts the stable expression and biological function of proteins. Here, we propose FoldPAthreader, a protein folding pathway prediction method that designs a novel folding force field model by exploring the intrinsic relationship between protein evolutionary history and folding mechanisms from the known protein universe. Further, the folding force field is used to guide Monte Carlo conformational sampling, driving the protein chain fold into its native state by exploring a series of transition states and potential intermediates. On the 30 targets we collected, FoldPAthreader can successfully predict 70% of the proteins whose folding pathway is consistent with wet-lab experimental data. The results show that the folding force field can capture key dynamic features of hydrogen bonding and hydrophobic interactions. Importantly, for the widely studied BPTI and TIM proteins, the folding pathway predicted by FoldPAthreader have the same microscopic dynamic properties as those simulated by molecular dynamics. Significance StatementProtein folding is the process by which a protein acquires its functional conformations by gradually transforming from random coils into a specific three-dimensional structure. In the post-Alphafold2 era, functional analysis of protein macromolecules should not only rely on the final state structure, but should pay more attention to the structural folding process, that is, the various intermediate states formed during the folding process. At present, there is no folding force field specifically used for protein folding pathway prediction in computational biology. Here we extracted folding information from 100-million-level structure database and designed a new folding force field for folding pathway prediction, proving a hypothesis that the protein evolutionary history implicitly contains folding information of individual protein. This study may provide new insights into the understanding of protein folding mechanisms, which is expected to advance drug discovery.

bioinformatics↗
bioRxiv2023

Robust parametric UMAP for the analysis of single-cell data

The increasing throughput of single-cell technologies and the pace of data generation are enhancing the resolution at which we observe cell state transitions. The characterization and visualization of these transitions rely on the construction of a low dimensional embedding, which is usually done via non-parametric methods such as t-SNE or UMAP. However, existing approaches become more and more inefficient as the size of the data gets larger and larger. Here, we test the viability of using parametric methods for the fact that they can be trained with a small subset of the data and be applied to future data when needed. We observed that the recently developed parametric version of UMAP is generalizable and robust to dropout. Additionally, to certify the robustness of the model, we use the theoretical upper and lower bounds of the mapped coordinates in the UMAP space to regularize the training process.

bioinformatics↗
bioRxiv2023

USP7 Inhibition Promotes Mesenchymal Stem cell-Macrophage Crosstalk in Age-related Osteoporosis via Efferocytosis and Senolysis

Although elderly osteoporotic patients have similar implant survival rates compared to those of normal individuals, they require longer healing periods to achieve proper osseointegration. This may be related to chronic inflammatory responses and impaired stem cell repair functions in the osteoporotic bone microenvironment. Recently, the deubiquitinating enzyme, ubiquitin-specific peptidase 7 (USP7), was found to regulate macrophage immune response and modulate stem cell osteogenic differentiation. The selective inhibitor of USP7, P5091, has also been found to promote bone repair and homeostasis in osteoporotic conditions. However, the roles of USP7 and P5091 in osteoimmunology and dental implant osseointegration under senile osteoporotic conditions remain unclear. In this study, USP7 depletion and P5091 were showed to inhibit inflammation in senescent bone marrow derived macrophages (BMDMs) and promote osteogenic differentiation in aged BMSCs. Furthermore, mRNA-Seq revealed that USP7 depletion could enhance efferocytosis in senescent BMDMs through the EPSIN1/ low-density lipoprotein receptor-related protein 1 (LRP1) pathway and selectively induce apoptosis (senolysis) in aged BMSCs. In senile osteoporotic mice, we found that the osseointegration period was prolonged compared to young mice, and P5091 promoted the early stage of osseointegration, which may be related to macrophage efferocytosis around the implant. Collectively, this study suggests that USP7 inhibition may accelerate the osseointegration process in senile osteoporotic conditions by promoting macrophage efferocytosis and aged BMSCs apoptosis. This has implications for understanding the cellular interactions and signaling mechanisms in the peri-implant bone microenvironment under osteoporotic conditions. It may also provide clinical significance in developing new therapies to enhance osseointegration quality and shorten the edentulous period in elderly osteoporotic patients.

cell biology↗
bioRxiv2023

Neurophysiological Features of STN LFP underlying Sleep Fragmentation in Parkinson's Disease

BackgroundSleep fragmentation is a persistent problem throughout the course of Parkinsons disease (PD). However, the related neurophysiological patterns and the underlying mechanisms remained unclear. MethodWe recorded subthalamic nucleus (STN) local field potentials (LFPs) using DBS with real-time wireless recording capacity from thirteen PD patients undergoing a one-night polysomnography recording, one month after DBS surgery before initial programming and when the patients were Off-Medication. The STN LFP features that characterized different sleep stages, correlated with arousal and sleep fragmentation index, and preceded stage transitions during N2 and REM sleep were analyzed. ResultsBoth beta and low gamma oscillations in NREM sleep increased with the severity of sleep disturbance (arousal index (ArI)-betaNREM: r=0.9,p=0.0001) and sleep fragmentation index (SFI)-betaNREM: r=0.6,p=0.0301; SFI-gammaNREM: r=0.6,p=0.0324). We next examined the low-to-high power ratio, which was the power ratio of theta oscillations to beta and low gamma oscillations, and found it to be an indicator of sleep fragmentation (ArI-LHPRNREM: r=-0.8,p=0.0053; ArI-LHPRREM: r=-0.6,p=0.0373; SFI-LHPRNREM: r=-0.7,p=0.0204; SFI-LHPRREM: r=-0.6,p=0.0428). In addition, long beta bursts (>0.25s) during NREM stage 2 were found preceding the completion of transition to stages with more cortical activities (towards Wake/N1/REM compared with towards N3 (p<0.01)) and negatively correlated with STN spindles, which were detected in STN LFPs with peak frequency distinguishable from long beta bursts (STN spindle: 11.5Hz, STN long beta bursts: 23.8Hz), in occupation during NREM sleep ({beta} = -0.24,p<0.001). ConclusionFeatures of STN LFPs help explain neurophysiological mechanisms underlying sleep fragmentations in PD, which can inform new intervention for sleep dysfunction. What is already known on this topicBeta oscillation, which is a biomarker for rigidity and bradykinesia during awake, significantly reduced during NREM sleep compared to REM or awake. Researches from MPTP non-human primate model suggested increased beta oscillation in basal ganglia contributed to insomnia in PD, however evidence in human patients is still lacking. What this study addsBeta and low gamma band activities in STN LFPs during sleep recorded from human PD patients correlated with severity of sleep impairment. The low-high power ratio can serve as a biomarker for sleep fragmentation. Besides, pathological beta bursts and physiological sleep spindles can be detected in STN LFP during NREM sleep and distinguishable from each other. How this study might affect research, practice or policyThese findings enhance our understanding of the electrophysiological mechanisms that underlie sleep fragmentation in Parkinsons disease. The study also has implications on the design of closed-loop DBS: we may need to take multiple frequency band activities into consideration and differentiate pathological beta oscillation from the sleep spindles.

physiology↗
bioRxiv2023

New insights into tissue-specific responses and interactive characteristics of crop-microbe "One Health" system to soil chromium and ofloxacin pollution

This study firstly investigated the tissue-specific responses and interactive characteristics of the crop-microbe system to co-pollution with ofloxacin (OFL) and chromium (Cr) in soil. The results emphasized the hormesis effect induced by low-dose OFL (1 mg L-1) on ginger plants subjected to soil Cr stress. However, high-dose OFL (100 mg L-1) and Cr co-stressed plants exhibited reduced growth, root activity, antioxidant enzyme activities and photosynthesis-fluorescence performances, while the reactive oxygen species (ROS) reflected by O2{middle dot}- and H2O2 significantly increased up to 43.34% and 78.63%, respectively, compared to other treatments. In addition, high-throughput sequencing indicated that OFL influenced rhizosphere microbial diversity, composition, and evolution, favoring Proteobacteria proliferation under co-pollution. Meanwhile, root exudate patterns shifted, with humic-like exudates, potentially interacting with pollutants and microbes. Notably, enrichments of antibiotic resistance gene (qnrS) in edible rhizome and potential pathogenic bacteria in ginger rhizosphere were observed under OFL and Cr co-pollution, raising environmental and food chain concerns. Through structural equation modeling, we quantitatively established correlations within soil-crop-microbe factors, emphasizing their interconnected nature. Overall, this study sheds light on the complex responses of the crop-microbe system to OFL and Cr co-pollution, highlighting the importance of understanding pollutant interactions for enhancing plant resilience and mitigating environmental risks. HighlightsO_LIRevealed a hormesis from low-dose OFL on Cr-polluted ginger, enhancing resilience. C_LIO_LIRhizosphere microbial composition shift under co-pollution, favoring Proteobacteria. C_LIO_LIqnrS and potential pathogens increased under co-pollution, posing food chain risks. C_LIO_LIStructural equation modeling quantified correlations in soil- crop-microbe factors. C_LIO_LIClarified crop-microbe "One Health" interactions role under Cr & OFL co-pollution. C_LI Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=128 SRC="FIGDIR/small/561380v1_ufig1.gif" ALT="Figure 1"> View larger version (61K): org.highwire.dtl.DTLVardef@bd3d20org.highwire.dtl.DTLVardef@1f8024eorg.highwire.dtl.DTLVardef@15f6154org.highwire.dtl.DTLVardef@ef0aeb_HPS_FORMAT_FIGEXP M_FIG C_FIG

physiology↗
bioRxiv2023

AutoMolDesigner for Antibiotic Discovery: An AI-based Open-source Software for Automated Design of Small-molecule Antibiotics

Discovery of small-molecule antibiotics with novel chemotypes serves as one of the essential strategies to address antibiotic resistance. Although a considerable number of computational tools committed to molecular design have been reported, there is a deficit in the holistic and efficient tool specifically developed for small-molecule antibiotic discovery. To address this issue, we report AutoMolDesigner, a computational modeling software dedicated to small-molecule antibiotic design. It is a generalized framework comprising two functional modules, i.e., generative deep learning-enabled molecular generation and automated machine learning based-antibacterial activity/property prediction, wherein individually trained models and curated datasets are out-of-the-box for whole cell-based antibiotic screening and design. It is open-source thus allows for the incorporation of new features for flexible use. Unlike most software programs based on Linux and command lines, this application equipped with Qt-based graphical user interface can be run on personal computers with multiple operating systems, making it much easier to use for experimental scientists. The software and related materials are freely available at GitHub (https://github.com/taoshen99/AutoMolDesigner) and Zenodo (https://zenodo.org/record/8366085).

bioinformatics↗
bioRxiv2023

Accurate somatic variant calling performance using Avidity Sequencing with Burning Rock OncoScreenTM Plus panel

BackgroundNext-generation sequencing (NGS) comprehensive genomic profiling panels provide targeted genome-wide detection of the somatic variant landscape of various cancer types. Recently, an innovative sequencing technology, Avidity Sequencing from Element Biosciences, has emerged to provide economical option for mid-throughput sequencing, with high quality (Q40). AimsThe aim of this study was to evaluate the performance characteristics of AVITI sequencing by avidity on detection of somatic variants from reference samples, and to compare the variant detection concordance between AVITI and NovaSeq sequencing platforms by evaluating the variant callings of 518 cancer-related genes using Burning Rocks OncoScreenTM Plus panel. MethodsContrived commercial reference control samples (harboring known variants) and clinical Formalin-Fixed Paraffin-Embedded tissue (FFPE) samples were examined to evaluate the variant detection performance of two sequencing platforms, with respects to Single Nucleotide Variant (SNV), Insertion-Deletion (Indel), Structure Variant (SV/Fusion), Copy Number Variation (CNV), Microsatellite Instability (MSI), and Tumor Mutation Burden (TMB). Variant specific QC metrics were developed to evaluate sequencing-level and variant calling level accuracy. All samples were processed utilizing the OncoScreenTM Plus assay, including library preparation and bioinformatics post sequencing data analysis. ResultsThe OncoScreenTM Plus assay is compatible with the AVITI system. Samples prepared with the OncoScreenTM Plus panel and sequenced on the AVITI system has successfully detected cancer related variants including SNV/Indels, SV/Fusion, CNVs, MSIs, and TMB, which were highly concordant with reference controls. In addition, the AVITI system produced a higher overall quality score, index assignment rate, mean target coverage, and lower optical duplication rate AVITI than NovaSeq system. ConclusionThe new sequencing technology Avidity from AVITI system can be applied seamlessly to current high-performance targeted oncology assays to reliably identify somatic variants in a flexible and cost-effective manner.

genomics↗
bioRxiv2023

Evaluating the Effectiveness of a Common Approach to Artifact Correction and Rejection in Event-related Potential Research

Eyeblinks and other large artifacts can create two major problems in event-related potential (ERP) research, namely confounds and increased noise. Here, we developed a method for assessing the effectiveness of artifact correction and rejection methods at minimizing these two problems. We then used this method to assess a common artifact minimization approach, in which independent component analysis (ICA) is used to correct ocular artifacts, and artifact rejection is used to reject trials with extreme values resulting from other sources (e.g., movement artifacts). This approach was applied to data from five common ERP components (P3b, N400, N170, mismatch negativity, and error-related negativity). Four common scoring methods (mean amplitude, peak amplitude, peak latency, and 50% area latency) were examined for each component. We found that eyeblinks differed systematically across experimental conditions for several of the components. We also found that artifact correction was reasonably effective at minimizing these confounds, although it did not usually eliminate them completely. In addition, we found that the rejection of trials with extreme voltage values was effective at reducing noise, with the benefits of eliminating these trials outweighing the reduced number of trials available for averaging. For researchers who are analyzing similar ERP components and participant populations, this combination of artifact correction and rejection approaches should minimize artifact-related confounds and lead to improved data quality. Researchers who are analyzing other components or participant populations can use the method developed in this study to determine which artifact minimization approaches are effective in their data.

neuroscience↗
bioRxiv2023

In situ cryoET reveals inhibitor-induced perturbations in Pf80S GTPase interactions

Malaria parasites rely heavily on rapid, high fidelity protein synthesis to infect and replicate in human erythrocytes, making translation an attractive target for new antimalarials. Here, we have determined in situ structures of Pf80S ribosomes in thirteen conformational and compositional states from cryoFIB-milled Plasmodium falciparum-infected human erythrocytes across the stages of asexual intraerythrocytic parasite replication. We observe eight active translation intermediates, enabling us to define the native malarial translation elongation cycle, which surprisingly features a bifurcation at the decoding stage of the cycle that has not previously been described. Examination of perturbations in the distribution of ribosomes among these states in the presence of a malaria-specific translation inhibitor suggests that the inhibitor impedes PfeEF2 and PfeEF1 interactions with the ribosome. We integrated our in situ cryoET data with proteomic and ultrastructural data to arrive at a deeper understanding of malarial translation, which will inform development of new therapies.

biophysics↗
bioRxiv2023

The 14-3-3/spastin pathway coordinates neurite regeneration and recovery of spinal cord injury by targeting microtubule dynamics

Axon regeneration is abortive in the central nervous system following injury. Orchestrating microtubule dynamics has emerged as a promising approach to improve axonal regeneration. The microtubule severing enzyme spastin is essential for axonal development and regeneration through remodeling of microtubule arrangement. To date, however, little is known regarding the mechanisms underlying spastin action in neural regeneration after spinal cord injury. Here, we use glutathione transferase pulldown and immunoprecipitation assays to demonstrate that 14-3-3 interacts with spastin, both in vivo and in vitro, via spastin Ser233 phosphorylation. Moreover, we show that 14-3-3 protects spastin from degradation by inhibiting the ubiquitination pathway and upregulates the spastin-dependent severing ability. Furthermore, the 14-3-3 agonist Fusicoccin (FC-A) promotes neurite outgrowth and regeneration in vitro which needs spastin activation. Western blot and immunofluorescence results revealed that 14-3-3 protein is upregulated in the neuronal compartment after spinal cord injury in vivo. In addition, administration of FC-A not only promotes locomotor recovery, but also nerve regeneration following spinal cord injury in both contusion and lateral hemisection models; however, application of spastin inhibitor spastazoline successfully reverses these phenomena. Taken together, these results indicate that 14-3-3 is a molecular switch that regulates spastin protein levels, and the small molecule 14-3-3 agonist FC-A effectively mediates the recovery of spinal cord injury in mice which requires spastin participation. HighlightsO_LIThe formation of the 14-3-3 and spastin protein complex requires phosphorylation of spastin at the S233 site. C_LIO_LI14-3-3 is involved in the phosphorylation-ubiquitination crosstalk of spastin, thus impacting the protein stability. C_LIO_LI14-3-3 agonists Fusicoccin-A can facilitate the repair of spinal cord injury in mice. C_LIO_LIActivation of spastin function is a prerequisite for nerve regeneration and recovery of spinal cord injury in mice. C_LI Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=129 SRC="FIGDIR/small/550600v3_ufig1.gif" ALT="Figure 1"> View larger version (66K): org.highwire.dtl.DTLVardef@ebabb6org.highwire.dtl.DTLVardef@5651caorg.highwire.dtl.DTLVardef@113df6borg.highwire.dtl.DTLVardef@1b0ea5a_HPS_FORMAT_FIGEXP M_FIG C_FIG

neuroscience↗
bioRxiv2023

Functional analysis of Cdc20 reveals a critical role of CRY box in mitotic checkpoint signaling

Accurate chromosome segregation is coordinated by the spindle assembly checkpoint (SAC) through its effector the mitotic checkpoint complex (MCC), to inhibit the anaphase-promoting complex or cyclosome (APC/C). Cdc20 is an essential mitotic regulator since it promotes mitotic exit through activating the APC/C and monitors kinetochore-microtubule attachment through activating the SAC. The proper functioning of Cdc20 requires multiple interactions with APC/C and MCC subunits. To functionally assess each of these interactions within cells requires efficient depletion of endogenous Cdc20, which is highly difficult to achieve by RNAi. Here we generated Cdc20 RNAi sensitive cell lines by CRISPR/Cas9 which display a penetrant metaphase arrest phenotype by a single RNAi treatment. In this null background, we accurately measured the contribution of each known motif of Cdc20 on APC/C and SAC activation. The CRY box, a previously identified degron was found to be critical for the SAC by promoting the MCC formation and stabilizing the interaction between the MCC and APC/C. These data reveal additional regulatory components within the SAC and establish a novel method to interrogate Cdc20 function.

cell biology↗
bioRxiv2023

Carbonate dissolution without acid: carbonate hydrolysis, catalyzed by photosynthetic microorganisms, in deteriorating stone monuments

Rapidly deteriorating stone (marble) monuments are generally blamed on acid rain. We suggest an alternative, not necessarily exclusive, culprit: rock-inhabiting cyanobacteria and microalgae, which may enter via fissures and dissolve carbonates as they propagate under the translucent stone surface. Cyanobacteria and algae absorb CO2 and HCO - and, in so doing, catalyze the reaction between carbonates and water itself. The resultant calcium hydroxide is expected to react with atmospheric CO2 and with adhered dust on its way out. We tested this hypothesis at the Forbidden City in Beijing, China, by inspecting stone monuments (dolomitic marble) for telltale signs of colonization and by studying an exfoliation crust with molecular and imaging techniques. The results, reported here, are consistent. Deterioration began in, and spread from, stone joints, cracks, and shattered stone edges. A cyanobacterial biofilm visible to the naked eye was present under the deteriorating stone surface. Colonized mineral grains were dissolved in a surface-controlled manner, i.e. along crystallographic and twinning planes. Secondary calcite, as well as clay minerals, were detected in the crust.

ecology↗
bioRxiv2023

Incorporating physics to overcome data scarcity in predictive modeling of protein function: a case study of BK channels

Machine learning has played transformative roles in numerous chemical and biophysical problems such as protein folding where large amount of data exists. Nonetheless, many important problems remain challenging for data-driven machine learning approaches due to the limitation of data scarcity. One approach to overcome data scarcity is to incorporate physical principles such as through molecular modeling and simulation. Here, we focus on the big potassium (BK) channels that play important roles in cardiovascular and neural systems. Many mutants of BK channel are associated with various neurological and cardiovascular diseases, but the molecular effects are unknown. The voltage gating properties of BK channels have been characterized for 473 site-specific mutations experimentally over the last three decades; yet, these functional data by themselves remain far too sparse to derive a predictive model of BK channel voltage gating. Using physics-based modeling, we quantify the energetic effects of all single mutations on both open and closed states of the channel. Together with dynamic properties derived from atomistic simulations, these physical descriptors allow the training of random forest models that could reproduce unseen experimentally measured shifts in gating voltage, {Delta}V1/2, with a RMSE [~] 32 mV and correlation coefficient of R [~] 0.7. Importantly, the model appears capable of uncovering nontrivial physical principles underlying the gating of the channel, including a central role of hydrophobic gating. The model was further evaluated using four novel mutations of L235 and V236 on the S5 helix, mutations of which are predicted to have opposing effects on V1/2 and suggest a key role of S5 in mediating voltage sensor-pore coupling. The measured {Delta}V1/2 agree quantitatively with prediction for all four mutations, with a high correlation of R = 0.92 and RMSE = 18 mV. Therefore, the model can capture nontrivial voltage gating properties in regions where few mutations are known. The success of predictive modeling of BK voltage gating demonstrates the potential of combining physics and statistical learning for overcoming data scarcity in nontrivial protein function prediction. Author SummaryDeep machine learning has brought many exciting breakthroughs in chemistry, physics and biology. These models require large amount of training data and struggle when the data is scarce. The latter is true for predictive modeling of the function of complex proteins such as ion channels, where only hundreds of mutational data may be available. Using the big potassium (BK) channel as a biologically important model system, we demonstrate that a reliable predictive model of its voltage gating property could be derived from only 473 mutational data by incorporating physics-derived features, which include dynamic properties from molecular dynamics simulations and energetic quantities from Rosetta mutation calculations. We show that the final random forest model captures key trends and hotspots in mutational effects of BK voltage gating, such as the important role of pore hydrophobicity. A particularly curious prediction is that mutations of two adjacent residues on the S5 helix would always have opposite effects on the gating voltage, which was confirmed by experimental characterization of four novel mutations. The current work demonstrates the importance and effectiveness of incorporating physics in predictive modeling of protein function with scarce data.

bioinformatics↗
bioRxiv2023

Optimal Filters for ERP Research II: Recommended Settings for Seven Common ERP Components

In research with event-related potentials (ERPs), aggressive filters can substantially improve the signal-to-noise ratio and maximize statistical power, but they can also produce significant waveform distortion. Although this tradeoff has been well documented, the field lacks recommendations for filter cutoffs that quantitatively address both of these competing considerations. To fill this gap, we quantified the effects of a broad range of low-pass filter and high-pass filter cutoffs for seven common ERP components (P3b, N400, N170, N2pc, mismatch negativity, error-related negativity, and lateralized readiness potential) recorded from a set of neurotypical young adults. We also examined four common scoring methods (mean amplitude, peak amplitude, peak latency, and 50% area latency). For each combination of component and scoring method, we quantified the effects of filtering on data quality (noise level and signal-to-noise ratio) and waveform distortion. This led to recommendations for optimal low-pass and high-pass filter cutoffs. We repeated the analyses after adding artificial noise to provide recommendations for datasets with moderately greater noise levels. For researchers who are analyzing data with similar ERP components, noise levels, and participant populations, using the recommended filter settings should lead to improved data quality and statistical power without creating problematic waveform distortion.

neuroscience↗
bioRxiv2023

Identifying Membrane Protein-Lipid Interactions with Lipidomic Lipid Exchange-Mass Spectrometry

Lipids can play important roles in modulating membrane protein structure and function. However, it is challenging to identify natural lipids bound to membrane proteins in complex bilayers. Here, we developed lipidomic lipid exchange-mass spectrometry (LX-MS) to study the lipid affinity for membrane proteins on a lipidomic scale. We first mix membrane protein nanodiscs with empty nanodiscs that have no embedded membrane proteins. After allowing lipids to passively exchange between the two populations, we separate the two types of nanodiscs and perform lipidomic analysis on each with liquid chromatography and mass spectrometry. Enrichment of lipids in the membrane protein nanodiscs reveals the affinity of individual lipids for binding the target membrane protein. We apply this approach to study three membrane proteins. With the E. coli ammonium transporter AmtB and aquaporin AqpZ in nanodiscs with E. coli polar lipid extract, we detected binding of cardiolipin and phosphatidyl-glycerol lipids to the proteins. With the acetylcholine receptor in nanodiscs with brain polar lipid extract, we discovered a complex set of lipid interactions that depended on the head group and tail composition. Overall, lipidomic LX-MS provides a detailed understanding of the lipid binding affinity and thermodynamics for membrane proteins in complex bilayers and provides a unique perspective on the chemical environment surrounding membrane proteins.

biophysics↗