Communications in Information and Systems最新文献_第2页

Mathematical artificial intelligence design of mutation-proof COVID-19 monoclonal antibodies. 数学人工智能设计防突变的 COVID-19 单克隆抗体。

IF 0.6

Communications in Information and Systems Pub Date : 2022-01-01 Epub Date: 2022-07-22 DOI: 10.4310/cis.2022.v22.n3.a3

Jiahui Chen, Guo-Wei Wei

{"title":"Mathematical artificial intelligence design of mutation-proof COVID-19 monoclonal antibodies.","authors":"Jiahui Chen, Guo-Wei Wei","doi":"10.4310/cis.2022.v22.n3.a3","DOIUrl":"10.4310/cis.2022.v22.n3.a3","url":null,"abstract":"Emerging severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants have compromised existing vaccines and posed a grand challenge to coronavirus disease 2019 (COVID-19) prevention, control, and global economic recovery. For COVID-19 patients, one of the most effective COVID-19 medications is monoclonal antibody (mAb) therapies. The United States Food and Drug Administration (U.S. FDA) has given the emergency use authorization (EUA) to a few mAbs, including those from Regeneron, Eli Elly, etc. However, they are also undermined by SARS-CoV-2 mutations. It is imperative to develop effective mutation-proof mAbs for treating COVID-19 patients infected by all emerging variants and/or the original SARS-CoV-2. We carry out a deep mutational scanning to present the blueprint of such mAbs using algebraic topology and artificial intelligence (AI). To reduce the risk of clinical trial-related failure, we select five mAbs either with FDA EUA or in clinical trials as our starting point. We demonstrate that topological AI-designed mAbs are effective for variants of concerns and variants of interest designated by the World Health Organization (WHO), as well as the original SARS-CoV-2. Our topological AI methodologies have been validated by tens of thousands of deep mutational data and their predictions have been confirmed by results from tens of experimental laboratories and population-level statistics of genome isolates from hundreds of thousands of patients.","PeriodicalId":45018,"journal":{"name":"Communications in Information and Systems","volume":"22 3","pages":"339-361"},"PeriodicalIF":0.6,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9881605/pdf/nihms-1825681.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10695510","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

Carbohydrate-Protein Interactions: Advances and Challenges. 碳水化合物-蛋白质相互作用:进展与挑战。

IF 0.9

Communications in Information and Systems Pub Date : 2021-01-01 DOI: 10.4310/cis.2021.v21.n1.a7

Shuang Zhang, Kyle Yu Chen, Xiaoqin Zou

引用次数: 5

COVID-19 data sharing and collaboration COVID-19数据共享与协作

IF 0.9

Communications in Information and Systems Pub Date : 2021-01-01 DOI: 10.4310/CIS.2021.V21.N3.A1

D. Duncan

{"title":"COVID-19 data sharing and collaboration","authors":"D. Duncan","doi":"10.4310/CIS.2021.V21.N3.A1","DOIUrl":"https://doi.org/10.4310/CIS.2021.V21.N3.A1","url":null,"abstract":"There is an immediate need to study COVID-19, and the COVID-19 Data Archive (COVID-ARC) provides access to data along with user-friendly tools for researchers to perform analyses to better understand COVID-19 and encourage collaboration on this research. The COVID-19 pandemic has been spreading rapidly across the world, and there are still many unknowns about COVID-19. There is an urgent need for scientists around the world to work together to model the virus, study how the virus has changed and will change over time, understand how it spreads, and study transmission after vaccination. COVID-ARC can also prepare scientists for future pandemics by putting the infrastructure in place to enable researchers to aggregate data and perform analyses quickly in the event of an emergency. We have developed a platform of networked and centralized web-accessible data archives to store multimodal data related to COVID-19 and make them broadly available and accessible to the world-wide scientific community to expedite research in this area. COVID-ARC provides tools for researchers to visualize and analyze various types of data as well as a website with tools for training, announcements, virtual information sessions, and a knowledgebase wherein researchers post questions and receive answers from the community.","PeriodicalId":45018,"journal":{"name":"Communications in Information and Systems","volume":"1 1","pages":""},"PeriodicalIF":0.9,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70404859","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 5

SARS-CoV-2 becoming more infectious as revealed by algebraic topology and deep learning. 代数拓扑和深度学习揭示了SARS-CoV-2的传染性增强。

IF 0.9

Communications in Information and Systems Pub Date : 2021-01-01 Epub Date: 2021-02-08 DOI: 10.4310/cis.2021.v21.n1.a2

Jiahui Chen, Rui Wang, Guo-Wei Wei

引用次数: 0

A Bayes-inspired theory for optimally building an efficient coarse-grained folding force field. 一种贝叶斯启发理论，用于优化建立高效的粗粒度折叠力场。

IF 0.9

Communications in Information and Systems Pub Date : 2021-01-01 DOI: 10.4310/cis.2021.v21.n1.a4

Travis Hurst, Dong Zhang, Yuanzhe Zhou, Shi-Jie Chen

{"title":"A Bayes-inspired theory for optimally building an efficient coarse-grained folding force field.","authors":"Travis Hurst, Dong Zhang, Yuanzhe Zhou, Shi-Jie Chen","doi":"10.4310/cis.2021.v21.n1.a4","DOIUrl":"10.4310/cis.2021.v21.n1.a4","url":null,"abstract":"Because of their potential utility in predicting conformational changes and assessing folding dynamics, coarse-grained (CG) RNA folding models are appealing for rapid characterization of RNA molecules. Previously, we reported the iterative simulated RNA reference state (IsRNA) method for parameterizing a CG force field for RNA folding, which consecutively updates the simulation force field to reflect marginal distributions of folding coordinates in the structure database and extract various energy terms. While the IsRNA model was validated by showing close agreement between the IsRNA-simulated and experimentally observed distributions, here, we expand our theoretical understanding of the model and, in doing so, improve the parameterization process to optimize the subset of included folding coordinates, which leads to accelerated simulations. Using statistical mechanical theory, we analyze the underlying, Bayesian concept that drives parameterization of the energy function, providing a general method for developing predictive, knowledge-based, polymer force fields on the basis of limited data. Furthermore, we propose an optimal parameterization procedure, based on the principal of maximum entropy.","PeriodicalId":45018,"journal":{"name":"Communications in Information and Systems","volume":"21 1","pages":"65-83"},"PeriodicalIF":0.9,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8336718/pdf/nihms-1690260.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39280490","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

Scoring Functions for Protein-RNA Complex Structure Prediction: Advances, Applications, and Future Directions. 蛋白质- rna复合物结构预测的评分功能:进展、应用和未来方向。

IF 0.9

Communications in Information and Systems Pub Date : 2020-01-01 DOI: 10.4310/cis.2020.v20.n1.a1

Liming Qiu, Xiaoqin Zou

{"title":"Scoring Functions for Protein-RNA Complex Structure Prediction: Advances, Applications, and Future Directions.","authors":"Liming Qiu, Xiaoqin Zou","doi":"10.4310/cis.2020.v20.n1.a1","DOIUrl":"https://doi.org/10.4310/cis.2020.v20.n1.a1","url":null,"abstract":"Protein-RNA interaction is among the most essential of biological events in living cells, being involved in protein synthesizing, RNA processing and transport, DNA transcription, and regulation of gene expression, and many other critical bio-molecular activities. A thorough understanding of this interaction is of paramount importance in fundamental study of a variety of vital cellular processes and therapeutic application for remedy of a broad range of diseases. Experimental high-resolution 3D structure determination is the primary source of knowledge for protein-RNA complexes. However, due to technical limitations, the existing techniques for experimental structure determination couldn't match the demand from fast growing interest in academia and industry. This problem necessitates the alternative high-throughput computational method for protein-RNA complex structure prediction. Similar to the in silico methods used for protein-protein and protein-DNA interactions, a reliable prediction of protein-RNA complex structure requires a scoring function with commensurate discriminatory power. Derived from determined structures and purposed to predict the to-be-determined structures, the scoring function is not only a predictive tool but also a gauge of our knowledge of protein-RNA interaction. In this review, we present an overview of the status of existing scoring functions and the scientific principle behind their constructions as well as their strengths and limitations. Finally, we will discuss about future directions of the scoring function development for protein-RNA structure prediction.","PeriodicalId":45018,"journal":{"name":"Communications in Information and Systems","volume":"20 1","pages":"1-22"},"PeriodicalIF":0.9,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8049283/pdf/nihms-1690416.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38817789","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: 2

Generative network complex (GNC) for drug discovery. 用于药物发现的生成网络复合体（GNC）。

IF 0.9

Communications in Information and Systems Pub Date : 2019-01-01 DOI: 10.4310/cis.2019.v19.n3.a2

Christopher Grow, Kaifu Gao, Duc Duy Nguyen, Guo-Wei Wei

{"title":"Generative network complex (GNC) for drug discovery.","authors":"Christopher Grow, Kaifu Gao, Duc Duy Nguyen, Guo-Wei Wei","doi":"10.4310/cis.2019.v19.n3.a2","DOIUrl":"10.4310/cis.2019.v19.n3.a2","url":null,"abstract":"It remains a challenging task to generate a vast variety of novel compounds with desirable pharmacological properties. In this work, a generative network complex (GNC) is proposed as a new platform for designing novel compounds, predicting their physical and chemical properties, and selecting potential drug candidates that fulfill various druggable criteria such as binding affinity, solubility, partition coefficient, etc. We combine a SMILES string generator, which consists of an encoder, a drug-property controlled or regulated latent space, and a decoder, with verification deep neural networks, a target-specific three-dimensional (3D) pose generator, and mathematical deep learning networks to generate new compounds, predict their drug properties, construct 3D poses associated with target proteins, and reevaluate druggability, respectively. New compounds were generated in the latent space by either randomized output, controlled output, or optimized output. In our demonstration, 2.08 million and 2.8 million novel compounds are generated respectively for Cathepsin S and BACE targets. These new compounds are very different from the seeds and cover a larger chemical space. For potentially active compounds, their 3D poses are generated using a state-of-the-art method. The resulting 3D complexes are further evaluated for druggability by a championing deep learning algorithm based on algebraic topology, differential geometry, and algebraic graph theories. Performed on supercomputers, the whole process took less than one week. Therefore, our GNC is an efficient new paradigm for discovering new drug candidates.","PeriodicalId":45018,"journal":{"name":"Communications in Information and Systems","volume":"19 3","pages":"241-277"},"PeriodicalIF":0.9,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8274326/pdf/nihms-1069335.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39182616","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

Evolution of Coagulation-Fragmentation Stochastic Processes Using Accurate Chemical Master Equation Approach. 基于精确化学主方程方法的凝固-破碎随机过程演化。

IF 0.9

Communications in Information and Systems Pub Date : 2019-01-01 Epub Date: 2019-04-18 DOI: 10.4310/cis.2019.v19.n1.a3

Farid Manuchehrfar, Wei Tian, Tom Chou, Jie Liang

{"title":"Evolution of Coagulation-Fragmentation Stochastic Processes Using Accurate Chemical Master Equation Approach.","authors":"Farid Manuchehrfar, Wei Tian, Tom Chou, Jie Liang","doi":"10.4310/cis.2019.v19.n1.a3","DOIUrl":"https://doi.org/10.4310/cis.2019.v19.n1.a3","url":null,"abstract":"Coagulation and fragmentation (CF) is a fundamental process in which smaller particles attach to each other to form larger clusters while existing clusters break up into smaller particles . It is a ubiquitous process that plays important roles in many physical and biological phenomena. CF is typically a stochastic process that often occurs in confined spaces with a limited number of available particles . Here, we study the CF process formulated with the discrete Chemical Master Equation (dCME). Using the newly developed Accurate Chemical Master Equation (ACME) method, we examine the time-dependent behavior of the CF system. We investigate the effects of a number of important factors that influence the overall behavior of the system, including the dimensionality, the ratio of attachment to detachment rates among clusters, and the initial conditions. By comparing CF in one and three dimensions, we conclude that systems in three dimensions are more likely to form large clusters. We also demonstrate how the ratio of the attachment to detachment rates affects the dynamics and the steady-state of the system. Finally, we demonstrate the relationship between the formation of large clusters and the initial condition.","PeriodicalId":45018,"journal":{"name":"Communications in Information and Systems","volume":"19 1","pages":"37-55"},"PeriodicalIF":0.9,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8378664/pdf/nihms-1707545.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39333873","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

Review of quantitative systems pharmacological modeling in thrombosis. 血栓形成定量系统药理模型研究进展。

IF 0.9

Communications in Information and Systems Pub Date : 2019-01-01 Epub Date: 2019-12-06 DOI: 10.4310/cis.2019.v19.n3.a1

Limei Cheng, Guo-Wei Wei, Tarek Leil

引用次数: 3

Divide-and-conquer strategy for large-scale Eulerian solvent excluded surface 大规模欧拉溶剂排除表面的分治策略

IF 0.9

Communications in Information and Systems Pub Date : 2018-09-12 DOI: 10.4310/CIS.2018.v18.n4.a5

Rundong Zhao, Menglun Wang, Y. Tong, G. Wei

{"title":"Divide-and-conquer strategy for large-scale Eulerian solvent excluded surface","authors":"Rundong Zhao, Menglun Wang, Y. Tong, G. Wei","doi":"10.4310/CIS.2018.v18.n4.a5","DOIUrl":"https://doi.org/10.4310/CIS.2018.v18.n4.a5","url":null,"abstract":"Motivation\u0000Surface generation and visualization are some of the most important tasks in biomolecular modeling and computation. Eulerian solvent excluded surface (ESES) software provides analytical solvent excluded surface (SES) in the Cartesian grid, which is necessary for simulating many biomolecular electrostatic and ion channel models. However, large biomolecules and/or fine grid resolutions give rise to excessively large memory requirements in ESES construction. We introduce an out-of-core and parallel algorithm to improve the ESES software.\u0000\u0000\u0000Results\u0000The present approach drastically improves the spatial and temporal efficiency of ESES. The memory footprint and time complexity are analyzed and empirically verified through extensive tests with a large collection of biomolecule examples. Our results show that our algorithm can successfully reduce memory footprint through a straightforward divide-and-conquer strategy to perform the calculation of arbitrarily large proteins on a typical commodity personal computer. On multi-core computers or clusters, our algorithm can reduce the execution time by parallelizing most of the calculation as disjoint subproblems. Various comparisons with the state-of-the-art Cartesian grid based SES calculation were done to validate the present method and show the improved efficiency. This approach makes ESES a robust software for the construction of analytical solvent excluded surfaces.\u0000\u0000\u0000Availability and implementation\u0000http://weilab.math.msu.edu/ESES.","PeriodicalId":45018,"journal":{"name":"Communications in Information and Systems","volume":"18 4 1","pages":"299-329"},"PeriodicalIF":0.9,"publicationDate":"2018-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49172497","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3