PLoS Computational Biology最新文献

{"title":"Rapid prediction of key residues for foldability by machine learning model enables the design of highly functional libraries with hyperstable constrained peptide scaffolds.","authors":"Fei Cai, Yuehua Wei, Daniel Kirchhofer, Andrew Chang, Yingnan Zhang","doi":"10.1371/journal.pcbi.1012609","DOIUrl":"10.1371/journal.pcbi.1012609","url":null,"abstract":"<p><p>Peptides are an emerging modality for developing therapeutics that can either agonize or antagonize cellular pathways associated with disease, yet peptides often suffer from poor chemical and physical stability, which limits their potential. However, naturally occurring disulfide-constrained peptides (DCPs) and de novo designed Hyperstable Constrained Peptides (HCPs) exhibiting highly stable and drug-like scaffolds, making them attractive therapeutic modalities. Previously, we established a robust platform for discovering peptide therapeutics by utilizing multiple DCPs as scaffolds. However, we realized that those libraries could be further improved by considering the foldability of peptide scaffolds for library design. We hypothesized that specific sequence patterns within the peptide scaffolds played a crucial role in spontaneous folding into a stable topology, and thus, these sequences should not be subject to randomization in the original library design. Therefore, we developed a method for designing highly diverse DCP libraries while preserving the inherent foldability of each scaffold. To achieve this, we first generated a large-scale dataset from yeast surface display (YSD) combined with shotgun alanine scan experiments to train a machine-learning (ML) model based on techniques used for natural language understanding. Then we validated the ML model with experiments, showing that it is able to not only predict the foldability of peptides with high accuracy across a broad range of sequences but also pinpoint residues critical for foldability. Using the insights gained from the alanine scanning experiment as well as prediction model, we designed a new peptide library based on a de novo-designed HCP, which was optimized for enhanced folding efficiency. Subsequent panning trials using this library yielded promising hits having good folding properties. In summary, this work advances peptide or small protein domain library design practices. These findings could pave the way for the efficient development of peptide-based therapeutics in the future.</p>","PeriodicalId":20241,"journal":{"name":"PLoS Computational Biology","volume":"20 11","pages":"e1012609"},"PeriodicalIF":3.8,"publicationDate":"2024-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142668954","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"MFPSP: Identification of fungal species-specific phosphorylation site using offspring competition-based genetic algorithm.","authors":"Chao Wang, Quan Zou","doi":"10.1371/journal.pcbi.1012607","DOIUrl":"10.1371/journal.pcbi.1012607","url":null,"abstract":"<p><p>Protein phosphorylation is essential in various signal transduction and cellular processes. To date, most tools are designed for model organisms, but only a handful of methods are suitable for predicting task in fungal species, and their performance still leaves much to be desired. In this study, a novel tool called MFPSP is developed for phosphorylation site prediction in multi-fungal species. The amino acids sequence features were derived from physicochemical and distributed information, and an offspring competition-based genetic algorithm was applied for choosing the most effective feature subset. The comparison results shown that MFPSP achieves a more advanced and balanced performance to several state-of-the-art available toolkits. Feature contribution and interaction exploration indicating the proposed model is efficient in uncovering concealed patterns within sequence. We anticipate MFPSP to serve as a valuable bioinformatics tool and benefiting practical experiments by pre-screening potential phosphorylation sites and enhancing our functional understanding of phosphorylation modifications in fungi. The source code and datasets are accessible at https://github.com/AI4HKB/MFPSP/.</p>","PeriodicalId":20241,"journal":{"name":"PLoS Computational Biology","volume":"20 11","pages":"e1012607"},"PeriodicalIF":3.8,"publicationDate":"2024-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142668952","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Human perception of self-motion and orientation during galvanic vestibular stimulation and physical motion.","authors":"Aaron R Allred, Caroline R Austin, Lanna Klausing, Nicholas Boggess, Torin K Clark","doi":"10.1371/journal.pcbi.1012601","DOIUrl":"10.1371/journal.pcbi.1012601","url":null,"abstract":"<p><p>Galvanic vestibular stimulation (GVS) is an emergent tool for stimulating the vestibular system, offering the potential to manipulate or enhance processes relying on vestibular-mediated central pathways. However, the extent of GVS's influence on the perception of self-orientation pathways is not understood, particularly in the presence of physical motions. Here, we quantify roll tilt perception impacted by GVS during passive whole-body roll tilts in humans (N = 11). We find that GVS systematically amplifies and attenuates perceptions of roll tilt during physical tilt, dependent on the GVS waveform. Subsequently, we develop a novel computational model that predicts 6DoF self-motion and self-orientation perceptions for any GVS waveform and motion by modeling the vestibular afferent neuron dynamics modulated by GVS in conjunction with an observer central processing model. This effort provides a means to systematically alter spatial orientation perceptions using GVS during concurrent physical motion, and we find that irregular afferent dynamics alone best describe resultant perceptions.</p>","PeriodicalId":20241,"journal":{"name":"PLoS Computational Biology","volume":"20 11","pages":"e1012601"},"PeriodicalIF":3.8,"publicationDate":"2024-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142668885","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A dynamical systems model for the total fission rate in Drp1-dependent mitochondrial fission.","authors":"Anna K Leinheiser, Colleen C Mitchell, Ethan Rooke, Stefan Strack, Chad E Grueter","doi":"10.1371/journal.pcbi.1012596","DOIUrl":"10.1371/journal.pcbi.1012596","url":null,"abstract":"<p><p>Mitochondrial hyperfission in response to cellular insult is associated with reduced energy production and programmed cell death. Thus, there is a critical need to understand the molecular mechanisms coordinating and regulating the complex process of mitochondrial fission. We develop a nonlinear dynamical systems model of dynamin related protein one (Drp1)-dependent mitochondrial fission and use it to identify parameters which can regulate the total fission rate (TFR) as a function of time. The TFR defined from a nondimensionalization of the model undergoes a Hopf bifurcation with bifurcation parameter [Formula: see text] where [Formula: see text] is the total concentration of mitochondrial fission factor (Mff) and k+ and k- are the association and dissociation rate constants between oligomers on the outer mitochondrial membrane. The variable μ can be thought of as the maximum build rate over the disassembling rate of oligomers. Though the nondimensionalization of the system results in four dimensionless parameters, we found the TFR and the cumulative total fission (TF) depend strongly on only one, μ. Interestingly, the cumulative TF does not monotonically increase as μ increases. Instead it increases with μ to a certain point and then begins to decrease as μ continues to increase. This non-monotone dependence on μ suggests interventions targeting k+, k-, or [Formula: see text] may have a non-intuitive impact on the fission mechanism. Thus understanding the impact of regulatory parameters, such as μ, may assist future therapeutic target selection.</p>","PeriodicalId":20241,"journal":{"name":"PLoS Computational Biology","volume":"20 11","pages":"e1012596"},"PeriodicalIF":3.8,"publicationDate":"2024-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142668907","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A new look at TFPI inhibition of factor X activation.","authors":"Fabian Santiago, Amandeep Kaur, Shannon Bride, Dougald Monroe, Karin Leiderman, Suzanne Sindi","doi":"10.1371/journal.pcbi.1012509","DOIUrl":"10.1371/journal.pcbi.1012509","url":null,"abstract":"<p><p>Blood coagulation is a vital physiological process involving a complex network of biochemical reactions, which converge to form a blood clot that repairs vascular injury. This process unfolds in three phases: initiation, amplification, and propagation, ultimately leading to thrombin formation. Coagulation begins when tissue factor (TF) is exposed on an injured vessel's wall. The first step is when activated factor VII (VIIa) in the plasma binds to TF, forming complex TF:VIIa, which activates factor X. Activated factor X (Xa) is necessary for coagulation, so the regulation of its activation is crucial. Tissue Factor Pathway Inhibitor (TFPI) is a critical regulator of the initiation phase as it inhibits the activation of factor X. While previous studies have proposed two pathways-direct and indirect binding-for TFPI's inhibitory role, the specific biochemical reactions and their rates remain ambiguous. Many existing mathematical models only assume an indirect pathway, which may be less effective under physiological flow conditions. In this study, we revisit datasets from two experiments focused on activated factor X formation in the presence of TFPI. We employ an adaptive Metropolis method for parameter estimation to reinvestigate a previously proposed biochemical scheme and corresponding rates for both inhibition pathways. Our findings show that both pathways are essential to replicate the static experimental results. Previous studies have suggested that flow itself makes a significant contribution to the inhibition of factor X activation. We added flow to this model with our estimated parameters to determine the contribution of the two inhibition pathways under these conditions. We found that direct binding of TFPI is necessary for inhibition under flow. The indirect pathway has a weaker inhibitory effect due to removal of solution phase inhibitory complexes by flow.</p>","PeriodicalId":20241,"journal":{"name":"PLoS Computational Biology","volume":"20 11","pages":"e1012509"},"PeriodicalIF":3.8,"publicationDate":"2024-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11567595/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142639589","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"GeM-LR: Discovering predictive biomarkers for small datasets in vaccine studies.","authors":"Lin Lin, Rachel L Spreng, Kelly E Seaton, S Moses Dennison, Lindsay C Dahora, Daniel J Schuster, Sheetal Sawant, Peter B Gilbert, Youyi Fong, Neville Kisalu, Andrew J Pollard, Georgia D Tomaras, Jia Li","doi":"10.1371/journal.pcbi.1012581","DOIUrl":"10.1371/journal.pcbi.1012581","url":null,"abstract":"<p><p>Despite significant progress in vaccine research, the level of protection provided by vaccination can vary significantly across individuals. As a result, understanding immunologic variation across individuals in response to vaccination is important for developing next-generation efficacious vaccines. Accurate outcome prediction and identification of predictive biomarkers would represent a significant step towards this goal. Moreover, in early phase vaccine clinical trials, small datasets are prevalent, raising the need and challenge of building a robust and explainable prediction model that can reveal heterogeneity in small datasets. We propose a new model named Generative Mixture of Logistic Regression (GeM-LR), which combines characteristics of both a generative and a discriminative model. In addition, we propose a set of model selection strategies to enhance the robustness and interpretability of the model. GeM-LR extends a linear classifier to a non-linear classifier without losing interpretability and empowers the notion of predictive clustering for characterizing data heterogeneity in connection with the outcome variable. We demonstrate the strengths and utility of GeM-LR by applying it to data from several studies. GeM-LR achieves better prediction results than other popular methods while providing interpretations at different levels.</p>","PeriodicalId":20241,"journal":{"name":"PLoS Computational Biology","volume":"20 11","pages":"e1012581"},"PeriodicalIF":3.8,"publicationDate":"2024-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11594404/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142626667","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Human-like dissociations between confidence and accuracy in convolutional neural networks.","authors":"Medha Shekhar, Dobromir Rahnev","doi":"10.1371/journal.pcbi.1012578","DOIUrl":"10.1371/journal.pcbi.1012578","url":null,"abstract":"<p><p>Prior research has shown that manipulating stimulus energy by changing both stimulus contrast and variability results in confidence-accuracy dissociations in humans. Specifically, even when performance is matched, higher stimulus energy leads to higher confidence. The most common explanation for this effect, derived from cognitive modeling, is the positive evidence heuristic where confidence neglects evidence that disconfirms the choice. However, an alternative explanation is the signal-and-variance-increase hypothesis, according to which these dissociations arise from changes in the separation and variance of perceptual representations. Because artificial neural networks lack built-in confidence heuristics, they can serve as a test for the necessity of confidence heuristics in explaining confidence-accuracy dissociations. Therefore, we tested whether confidence-accuracy dissociations induced by stimulus energy manipulations emerge naturally in convolutional neural networks (CNNs). We found that, across three different energy manipulations, CNNs produced confidence-accuracy dissociations similar to those found in humans. This effect was present for a range of CNN architectures from shallow 4-layer networks to very deep ones, such as VGG-19 and ResNet-50 pretrained on ImageNet. Further, we traced back the reason for the confidence-accuracy dissociations in all CNNs to the same signal-and-variance increase that has been proposed for humans: higher stimulus energy increased the separation and variance of evidence distributions in the CNNs' output layer leading to higher confidence even for matched accuracy. These findings cast doubt on the necessity of the positive evidence heuristic to explain human confidence and establish CNNs as promising models for testing cognitive theories of human behavior.</p>","PeriodicalId":20241,"journal":{"name":"PLoS Computational Biology","volume":"20 11","pages":"e1012578"},"PeriodicalIF":3.8,"publicationDate":"2024-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11594416/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142626673","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"AI-powered simulation-based inference of a genuinely spatial-stochastic gene regulation model of early mouse embryogenesis.","authors":"Michael Alexander Ramirez Sierra, Thomas R Sokolowski","doi":"10.1371/journal.pcbi.1012473","DOIUrl":"https://doi.org/10.1371/journal.pcbi.1012473","url":null,"abstract":"<p><p>Understanding how multicellular organisms reliably orchestrate cell-fate decisions is a central challenge in developmental biology, particularly in early mammalian development, where tissue-level differentiation arises from seemingly cell-autonomous mechanisms. In this study, we present a multi-scale, spatial-stochastic simulation framework for mouse embryogenesis, focusing on inner cell mass (ICM) differentiation into epiblast (EPI) and primitive endoderm (PRE) at the blastocyst stage. Our framework models key regulatory and tissue-scale interactions in a biophysically realistic fashion, capturing the inherent stochasticity of intracellular gene expression and intercellular signaling, while efficiently simulating these processes by advancing event-driven simulation techniques. Leveraging the power of Simulation-Based Inference (SBI) through the AI-driven Sequential Neural Posterior Estimation (SNPE) algorithm, we conduct a large-scale Bayesian inferential analysis to identify parameter sets that faithfully reproduce experimentally observed features of ICM specification. Our results reveal mechanistic insights into how the combined action of autocrine and paracrine FGF4 signaling coordinates stochastic gene expression at the cellular scale to achieve robust and reproducible ICM patterning at the tissue scale. We further demonstrate that the ICM exhibits a specific time window of sensitivity to exogenous FGF4, enabling lineage proportions to be adjusted based on timing and dosage, thereby extending current experimental findings and providing quantitative predictions for both mutant and wild-type ICM systems. Notably, FGF4 signaling not only ensures correct EPI-PRE lineage proportions but also enhances ICM resilience to perturbations, reducing fate-proportioning errors by 10-20% compared to a purely cell-autonomous system. Additionally, we uncover a surprising role for variability in intracellular initial conditions, showing that high gene-expression heterogeneity can improve both the accuracy and precision of cell-fate proportioning, which remains robust when fewer than 25% of the ICM population experiences perturbed initial conditions. Our work offers a comprehensive, spatial-stochastic description of the biochemical processes driving ICM differentiation and identifies the necessary conditions for its robust unfolding. It also provides a framework for future exploration of similar spatial-stochastic systems in developmental biology.</p>","PeriodicalId":20241,"journal":{"name":"PLoS Computational Biology","volume":"20 11","pages":"e1012473"},"PeriodicalIF":3.8,"publicationDate":"2024-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142626630","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Linear-regression-based algorithms can succeed at identifying microbial functional groups despite the nonlinearity of ecological function.","authors":"Yuanchen Zhao, Otto X Cordero, Mikhail Tikhonov","doi":"10.1371/journal.pcbi.1012590","DOIUrl":"10.1371/journal.pcbi.1012590","url":null,"abstract":"<p><p>Microbial communities play key roles across diverse environments. Predicting their function and dynamics is a key goal of microbial ecology, but detailed microscopic descriptions of these systems can be prohibitively complex. One approach to deal with this complexity is to resort to coarser representations. Several approaches have sought to identify useful groupings of microbial species in a data-driven way. Of these, recent work has claimed some empirical success at de novo discovery of coarse representations predictive of a given function using methods as simple as a linear regression, against multiple groups of species or even a single such group (the ensemble quotient optimization (EQO) approach). Modeling community function as a linear combination of individual species' contributions appears simplistic. However, the task of identifying a predictive coarsening of an ecosystem is distinct from the task of predicting the function well, and it is conceivable that the former could be accomplished by a simpler methodology than the latter. Here, we use the resource competition framework to design a model where the \"correct\" grouping to be discovered is well-defined, and use synthetic data to evaluate and compare three regression-based methods, namely, two proposed previously and one we introduce. We find that regression-based methods can recover the groupings even when the function is manifestly nonlinear; that multi-group methods offer an advantage over a single-group EQO; and crucially, that simpler (linear) methods can outperform more complex ones.</p>","PeriodicalId":20241,"journal":{"name":"PLoS Computational Biology","volume":"20 11","pages":"e1012590"},"PeriodicalIF":3.8,"publicationDate":"2024-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11588209/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142626687","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Mechanical stress and anionic lipids synergistically stabilize an atypical structure of the angiotensin II type 1 receptor (AT1).","authors":"Rym Ben Boubaker, Daniel Henrion, Marie Chabbert","doi":"10.1371/journal.pcbi.1012559","DOIUrl":"10.1371/journal.pcbi.1012559","url":null,"abstract":"<p><p>Environmental factors, including mechanical stress and surrounding lipids, can influence the response of GPCRs, such as the mechanosensitive angiotensin II type 1 receptor (AT1). To investigate the impact of these factors on AT1 activation, we developed a steered molecular dynamics simulations protocol based on quaternion formalism. In this protocol, a pulling force was applied to the N-terminus of transmembrane helix 6 (TM6) to induce the TM6 opening characteristic of activation. Subsequently, the simulations were continued without constraints to allow the receptor to relax around the novel TM6 conformation under different conditions. We analyzed the responses of AT1 to membrane stretching, modeled by applying surface tension, in different bilayers. In phosphocholine bilayers without surface tension, we could observe a transient atypical structure of AT1, with an outward TM7 conformation, at the beginning of the activation process. This atypical structure then evolved toward a pre-active structure with outward TM6 and inward TM7. Strikingly, the presence of anionic phosphoglycerol lipids and application of surface tension synergistically favored the atypical structure, which led to an increase in the cross-section area of the receptor intracellular domain. Lipid internalization and H-bonds between lipid heads and the receptor C-terminus increased in phosphoglycerol vs phosphocholine bilayers, but did not depend on surface tension. The difference in the cross-section area of the atypical and pre-active conformations makes the conformational transition sensitive to lateral pressure, and favors the atypical conformation upon surface tension. Anionic lipids act as allosteric modulators of the conformational transition, by stabilizing the atypical conformation. These findings contribute to decipher the mechanisms underlying AT1 activation, highlighting the influence of environmental factors on GPCR responses. Moreover, our results reveal the existence of intermediary conformations that depend on receptor environment and could be targeted in drug design efforts.</p>","PeriodicalId":20241,"journal":{"name":"PLoS Computational Biology","volume":"20 11","pages":"e1012559"},"PeriodicalIF":3.8,"publicationDate":"2024-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11560033/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142626691","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}