QRB Discovery最新文献_第6页

Sulfur-mediated chalcogen versus hydrogen bonds in proteins: a see-saw effect in the conformational space. 蛋白质中硫介导的硫与氢键：构象空间中的跷跷板效应。

QRB Discovery Pub Date : 2023-04-27 eCollection Date: 2023-01-01 DOI: 10.1017/qrd.2023.3

Vishal Annasaheb Adhav, Sanket Satish Shelke, Pananghat Balanarayan, Kayarat Saikrishnan

{"title":"Sulfur-mediated chalcogen versus hydrogen bonds in proteins: a see-saw effect in the conformational space.","authors":"Vishal Annasaheb Adhav, Sanket Satish Shelke, Pananghat Balanarayan, Kayarat Saikrishnan","doi":"10.1017/qrd.2023.3","DOIUrl":"10.1017/qrd.2023.3","url":null,"abstract":"Divalent sulfur (S) forms a chalcogen bond (Ch-bond) via its σ-holes and a hydrogen bond (H-bond) via its lone pairs. The relevance of these interactions and their interplay for protein structure and function is unclear. Based on the analyses of the crystal structures of small organic/organometallic molecules and proteins and their molecular electrostatic surface potential, we show that the reciprocity of the substituent-dependent strength of the σ-holes and lone pairs correlates with the formation of either Ch-bond or H-bond. In proteins, cystines preferentially form Ch-bonds, metal-chelated cysteines form H-bonds, while methionines form either of them with comparable frequencies. This has implications for the positioning of these residues and their role in protein structure and function. Computational analyses reveal that the S-mediated interactions stabilise protein secondary structures by mechanisms such as helix capping and protecting free β-sheet edges by negative design. The study highlights the importance of S-mediated Ch-bond and H-bond for understanding protein folding and function, the development of improved strategies for protein/peptide structure prediction and design and structure-based drug discovery.","PeriodicalId":34636,"journal":{"name":"QRB Discovery","volume":"4 ","pages":"e5"},"PeriodicalIF":0.0,"publicationDate":"2023-04-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10411326/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9976166","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Chasing collective variables using temporal data-driven strategies. 利用时间数据驱动策略追逐集体变量。

QRB Discovery Pub Date : 2023-01-06 eCollection Date: 2023-01-01 DOI: 10.1017/qrd.2022.23

Haochuan Chen, Christophe Chipot

{"title":"Chasing collective variables using temporal data-driven strategies.","authors":"Haochuan Chen, Christophe Chipot","doi":"10.1017/qrd.2022.23","DOIUrl":"10.1017/qrd.2022.23","url":null,"abstract":"The convergence of free-energy calculations based on importance sampling depends heavily on the choice of collective variables (CVs), which in principle, should include the slow degrees of freedom of the biological processes to be investigated. Autoencoders (AEs), as emerging data-driven dimension reduction tools, have been utilised for discovering CVs. AEs, however, are often treated as black boxes, and what AEs actually encode during training, and whether the latent variables from encoders are suitable as CVs for further free-energy calculations remains unknown. In this contribution, we review AEs and their time-series-based variants, including time-lagged AEs (TAEs) and modified TAEs, as well as the closely related model variational approach for Markov processes networks (VAMPnets). We then show through numerical examples that AEs learn the high-variance modes instead of the slow modes. In stark contrast, time series-based models are able to capture the slow modes. Moreover, both modified TAEs with extensions from slow feature analysis and the state-free reversible VAMPnets (SRVs) can yield orthogonal multidimensional CVs. As an illustration, we employ SRVs to discover the CVs of the isomerizations of N-acetyl-N'-methylalanylamide and trialanine by iterative learning with trajectories from biased simulations. Last, through numerical experiments with anisotropic diffusion, we investigate the potential relationship of time-series-based models and committor probabilities.","PeriodicalId":34636,"journal":{"name":"QRB Discovery","volume":"4 ","pages":"e2"},"PeriodicalIF":0.0,"publicationDate":"2023-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10411323/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9976165","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Computational prediction of ω-transaminase selectivity by deep learning analysis of molecular dynamics trajectories. 基于分子动力学轨迹深度学习分析的ω-转氨酶选择性计算预测。

QRB Discovery Pub Date : 2023-01-01 DOI: 10.1017/qrd.2022.22

Carlos Ramírez-Palacios, Siewert J Marrink

{"title":"Computational prediction of ω-transaminase selectivity by deep learning analysis of molecular dynamics trajectories.","authors":"Carlos Ramírez-Palacios, Siewert J Marrink","doi":"10.1017/qrd.2022.22","DOIUrl":"https://doi.org/10.1017/qrd.2022.22","url":null,"abstract":"We previously presented a computational protocol to predict the enzymatic (enantio)selectivity of an ω-transaminase towards a set of ligands (Ramírez-Palacios et al. (2021) Journal of Chemical Information and Modeling 61(11), 5569-5580) by counting the number of binding poses present in molecular dynamics (MD) simulations that met a defined set of geometric criteria. The geometric criteria consisted of a hand-crafted set of distances, angles and dihedrals deemed to be important for the enzymatic reaction to take place. In this work, the MD trajectories are reanalysed using a deep-learning approach to predict the enantiopreference of the enzyme without the need for hand-crafted criteria. We show that a convolutional neural network is capable of classifying the trajectories as belonging to the 'reactive' or 'non-reactive' enantiomer (binary classification) with a good accuracy (>0.90). The new method reduces the computational cost of the methodology, because it does not necessitate the sampling approach from the previous work. We also show that analysing how neural networks reach specific decisions can aid hand-crafted approaches (e.g. definition of near-attack conformations, or binding poses).","PeriodicalId":34636,"journal":{"name":"QRB Discovery","volume":"4 ","pages":"e1"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10392675/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9925965","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Mouth breathing, dry air, and low water permeation promote inflammation, and activate neural pathways, by osmotic stresses acting on airway lining mucus. 口呼吸，干燥的空气，低水渗透促进炎症，并激活神经通路，渗透应力作用于气道粘膜。

QRB Discovery Pub Date : 2023-01-01 DOI: 10.1017/qrd.2023.1

David A Edwards, Kian Fan Chung

{"title":"Mouth breathing, dry air, and low water permeation promote inflammation, and activate neural pathways, by osmotic stresses acting on airway lining mucus.","authors":"David A Edwards, Kian Fan Chung","doi":"10.1017/qrd.2023.1","DOIUrl":"https://doi.org/10.1017/qrd.2023.1","url":null,"abstract":"Respiratory disease and breathing abnormalities worsen with dehydration of the upper airways. We find that humidification of inhaled air occurs by evaporation of water over mucus lining the upper airways in such a way as to deliver an osmotic force on mucus, displacing it towards the epithelium. This displacement thins the periciliary layer of water beneath mucus while thickening topical water that is partially condensed from humid air on exhalation. With the rapid mouth breathing of dry air, this condensation layer, not previously reported while common to transpiring hydrogels in nature, can deliver an osmotic compressive force of up to around 100 cm H2O on underlying cilia, promoting adenosine triphosphate secretion and activating neural pathways. We derive expressions for the evolution of the thickness of the condensation layer, and its impact on cough frequency, inflammatory marker secretion, cilia beat frequency and respiratory droplet generation. We compare our predictions with human clinical data from multiple published sources and highlight the damaging impact of mouth breathing, dry, dirty air and high minute volume on upper airway function. We predict the hypertonic (or hypotonic) saline mass required to reduce (or amplify) dysfunction by restoration (or deterioration) of the structure of ciliated and condensation water layers in the upper airways and compare these predictions with published human clinical data. Preserving water balance in the upper airways appears critical in light of contemporary respiratory health challenges posed by the breathing of dirty and dry air.","PeriodicalId":34636,"journal":{"name":"QRB Discovery","volume":"4 ","pages":"e3"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10392678/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10294233","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Origins of life: first came evolutionary dynamics. 生命的起源:首先是进化动力学。

QRB Discovery Pub Date : 2023-01-01 DOI: 10.1017/qrd.2023.2

Charles Kocher, Ken A Dill

引用次数: 1

Computer-aided comprehensive explorations of RNA structural polymorphism through complementary simulation methods. 通过互补模拟方法对 RNA 结构多态性进行计算机辅助综合探索。

QRB Discovery Pub Date : 2022-10-17 eCollection Date: 2022-01-01 DOI: 10.1017/qrd.2022.19

Konstantin Röder, Guillaume Stirnemann, Pietro Faccioli, Samuela Pasquali

引用次数: 0

Towards design of drugs and delivery systems with the Martini coarse-grained model. 利用马蒂尼粗粒度模型设计药物和输送系统。

QRB Discovery Pub Date : 2022-10-12 eCollection Date: 2022-01-01 DOI: 10.1017/qrd.2022.16

Lisbeth R Kjølbye, Gilberto P Pereira, Alessio Bartocci, Martina Pannuzzo, Simone Albani, Alessandro Marchetto, Brian Jiménez-García, Juliette Martin, Giulia Rossetti, Marco Cecchini, Sangwook Wu, Luca Monticelli, Paulo C T Souza

{"title":"Towards design of drugs and delivery systems with the Martini coarse-grained model.","authors":"Lisbeth R Kjølbye, Gilberto P Pereira, Alessio Bartocci, Martina Pannuzzo, Simone Albani, Alessandro Marchetto, Brian Jiménez-García, Juliette Martin, Giulia Rossetti, Marco Cecchini, Sangwook Wu, Luca Monticelli, Paulo C T Souza","doi":"10.1017/qrd.2022.16","DOIUrl":"10.1017/qrd.2022.16","url":null,"abstract":"Coarse-grained (CG) modelling with the Martini force field has come of age. By combining a variety of bead types and sizes with a new mapping approach, the newest version of the model is able to accurately simulate large biomolecular complexes at millisecond timescales. In this perspective, we discuss possible applications of the Martini 3 model in drug discovery and development pipelines and highlight areas for future development. Owing to its high simulation efficiency and extended chemical space, Martini 3 has great potential in the area of drug design and delivery. However, several aspects of the model should be improved before Martini 3 CG simulations can be routinely employed in academic and industrial settings. These include the development of automatic parameterisation protocols for a variety of molecule types, the improvement of backmapping procedures, the description of protein flexibility and the development of methodologies enabling efficient sampling. We illustrate our view with examples on key areas where Martini could give important contributions such as drugs targeting membrane proteins, cryptic pockets and protein-protein interactions and the development of soft drug delivery systems.","PeriodicalId":34636,"journal":{"name":"QRB Discovery","volume":"3 ","pages":"e19"},"PeriodicalIF":0.0,"publicationDate":"2022-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10392664/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10301374","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Applications of machine learning in computer-aided drug discovery. 机器学习在计算机辅助药物研发中的应用。

QRB Discovery Pub Date : 2022-09-01 eCollection Date: 2022-01-01 DOI: 10.1017/qrd.2022.12

Sm Bargeen Alam Turzo, Eric R Hantz, Steffen Lindert

引用次数: 0

Integrative structural modelling and visualisation of a cellular organelle. 细胞器的综合结构建模和可视化。

QRB Discovery Pub Date : 2022-08-09 eCollection Date: 2022-01-01 DOI: 10.1017/qrd.2022.10

Ludovic Autin, Brett A Barbaro, Andrew I Jewett, Axel Ekman, Shruti Verma, Arthur J Olson, David S Goodsell

引用次数: 0

Erratum: Comparing 2 crystal structures and 12 AlphaFold2-predicted human membrane glucose transporters and their water-soluble glutamine, threonine and tyrosine variants - CORRIGENDUM. 勘误：比较 2 个晶体结构和 12 个 AlphaFold2 预测的人类膜葡萄糖转运体及其水溶性谷氨酰胺、苏氨酸和酪氨酸变体 - CORRIGENDUM。

QRB Discovery Pub Date : 2022-07-21 eCollection Date: 2022-01-01 DOI: 10.1017/qrd.2022.8

Eva Smorodina, Fei Tao, Rui Qing, David Jin, Steve Yang, Shuguang Zhang

引用次数: 0