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Novel computational biology methods and their applications to drug discovery

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Frontiers in biology 2011年第4期6卷 289-299页

作者： Sharangdhar S.PHATAK Hoang T.TRAN Shuxing ZHANG School of Biomedical Informatics The University of Texas Health Science Center7000 Fannin StreetHoustonTX 77030USA The Integrated Molecular Discovery Laboratory Department of Experimental TherapeuticsThe University of Texas M.D.Anderson Cancer Center1515 Holcombe BlvdHoustonTX 77030USA

computational biology methods are now firmly entrenched in the drug discovery *** methods focus on modeling and simulations of biological systems to complement and direct conventional experimental *** important branches of computational biology include protein homology modeling and the computational biophysics method of molecular *** modeling methods attempt to accurately predict three-dimensional(3D)structures of uncrystallized proteins for subsequent structure-based drug design *** dynamics methods aim to elucidate the molecular motions of the static representations of crystallized protein *** this review we highlight recent novel methodologies in the field of homology modeling and molecular *** drug discovery applications using these methods conclude the review.

关键词： computational biology drug discovery homology modeling molecular dynamics structure-based drug design

Guest Editorial: Special Issue on Bioinformatics and computational biology

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Tsinghua Science and Technology 2012年第6期17卷 607-608页

作者： Yi Pan Zhipeng Cai Department of Computer Science Georgia State University Atlanta GA 30302 USA

Bioinformatics and computational biology research is fundamental to our understanding of complex biological systems, impacting the science and technology of fields ranging from agricultural and environmental sciences to pharmaceutical and medical sciences. It is one of the fastest developing research fields in the last two decades. High throughput biological data that are used to provide information at molecular and genetic level are rapidly generated. Almost all research problems in biological and medical sciences nowadays are computationally hard. computational techniques have become an indispensable tool to discover new biological insights. It has already enabled unprecedented advances in biological and medical sciences, and will continue to be a vibrant research area with broadening of computational techniques and new emerging challenges.

关键词： medical sciences science and technology computational biology biological systems biological data

computational biology Study of S100 Family with Suggestions for Crystallization

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Tsinghua Science and Technology 2001年第3期6卷 248-252页

作者：王钰叶盛阮戈江凡饶子和 Laboratory of Structural biology School of Life Sciences and Engineering Tsinghua University Beijing 100084 China

The S100 family is a class of calcium regulated proteins with EF hand. They are widely distributed and are implicated in diverse intracellular and extracellular physiological processes. A study of the S100 family using computational biology methods such as multiple sequence alignment, structural alignment and the construction of an evolutionary tree will promote understanding of S100 protein structures and their function, and could provide suggestions for crystallization. 

关键词： S100 family computational biology sequence alignment structural alignment crystallization

computational biology in topical bioactive peptide discovery for cosmeceutical application:a concise review

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Biomedical Engineering Communications 2023年第3期2卷 7-15页

作者： Xu-Hui Li Wen-Rou Su Fei-Fei Wang Ke Li Jing-Yong Zhu Si-Yu Zhu Si-Ning Kang Cong-Fen He Jun-Xiang Li Xiao Lin AGECODE R&D Center Yangtze Delta Region Institute of Tsinghua UniversityJiaxing 314000China Zhejiang Provincial Key Laboratory of Applied Enzymology Yangtze Delta Region Institute of Tsinghua UniversityJiaxing 314000China Yunnan Botanee Bio-technology Group Co. Ltd.Yunnan 650000China Yunnan Yunke Characteristic Plant Extraction Laboratory Co. Ltd.Yunnan 650000China Shenzhen Accompany Technology Co. LtdShenzhen 518000China College of Life Sciences and Medicine Zhejiang Sci-tech UniversityHangzhou 310018China Harvest Biotech(Zhejiang)Co. Ltd.Jiaxing 314000China School of Beijing Key Lab of Plant Resources Research and Development Beijing Technology and Business UniversityBeijing 100048China School of Life Sciences Northwestern Polytechnical UniversityXi’an 710072China

Regenerative medicine and anti-aging research have made great strides at the molecular and cellular levels in dermatology and the medical aesthetic field,targeting potential treatments with skin therapeutic and intervention pathways,which make it possible to develop effective skin regeneration and repair *** the rapid development of computational biology,bioinformatics as well as artificial intelligence(A.I.),the development of new ingredients for regenerative medicine has been greatly accelerated,and the success rate has been *** application cases have appeared in topical skin regeneration and repair *** review will briefly introduce the application of bioactive peptides in skin repair and anti-aging as emerging ingredients in cosmeceutics and emphasize how *** computational biology technology may accelerate the development of innovative peptide molecules and ultimately translate them into potential skin regenerative and anti-aging ***,two research routines have been summarized and current limitations as well as directions were discussed for border applications in future research.

关键词： computational biology artificial intelligence cosmeceutical bioactive peptide regenerative medicine

Reconstructing cell lineage trees with genomic barcoding:approaches and applications

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Journal of Genetics and Genomics 2024年第1期51卷 35-47页

作者： Zizhang Li Wenjing Yang Peng Wu Yuyan Shan Xiaoyu Zhang Feng Chen Junnan Yang Jian-Rong Yang Advanced Medical Technology Center The First Affiliated HospitalZhongshan School of MedicineSun Yat-sen UniversityGuangzhouGuangdong 510080China Department of Genetics and Biomedical Informatics Zhongshan School of MedicineSun Yat-sen UniversityGuangzhouGuangdong 510080China Key Laboratory of Tropical Disease Control Ministry of EducationSun Yat-sen UniversityGuangzhouGuangdong 510080China

In multicellular organisms,developmental history of cell divisions and functional annotation of terminal cells can be organized into a cell lineage tree(CLT).The reconstruction of the CLT has long been a major goal in developmental biology and other related *** technological advancements,especially those in editable genomic barcodes and single-cell high-throughput sequencing,have sparked a new wave of experimental methods for reconstructing *** we review the existing experimental approaches to the reconstruction of CLT,which are broadly categorized as either image-based or DNA barcode-based *** addition,we present a summary of the related literature based on the biological insight pro-vided by the obtained ***,we discuss the challenges that will arise as more and better CLT data become available in the near *** barcoding-based CLT reconstructions and analyses,due to their wide applicability and high scalability,offer the potential for novel biological discoveries,especially those related to general and systemic properties of the developmental process.

关键词： Cell lineage tree Developmental regulation Biotechnology computational biology

Evolutionary Safe Padé Approximation Scheme for Dynamical Study of Nonlinear Cervical Human Papilloma Virus Infection Model

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Computer Modeling in Engineering & Sciences 2024年第9期140卷 2275-2296页

作者： Javaid Ali Armando Ciancio Kashif Ali Khan Nauman Raza Haci Mehmet Baskonus Muhammad Luqman Zafar-Ullah Khan Department of Mathematics Govt.Graduate College TownshipLahore54770Pakistan Universitàdegli Studi di Messina Dipartimento di Scienze BiomedicheOdontoiatriche e delle Immagini Morfologiche e FunzionaliMessina98122Italy Department of Mathematics University of Engineering and TechnologyLahore54890Pakistan Department of Mathematics University of the PunjabLahore54590Pakistan Mathematics Research Center Near East University TRNCMersin 10Nicosia99138Turkey Department of Mathematics and Science Education Faculty of EducationHarran UniversitySanliurfa63190Turkey Department of Dermatology Rashid Latif Medical CollegeLahore54600Pakistan

This study proposes a structure-preserving evolutionary framework to find a semi-analytical approximate solution for a nonlinear cervical cancer epidemic(CCE)*** underlying CCE model lacks a closed-form exact *** solutions obtained through traditional finite difference schemes do not ensure the preservation of the model’s necessary properties,such as positivity,boundedness,and ***,the development of structure-preserving semi-analytical approaches is always *** research introduces an intelligently supervised computational paradigm to solve the underlying CCE model’s physical properties by formulating an equivalent unconstrained optimization ***-free safe Padérational functions approximate the mathematical shape of state variables,while the model’s physical requirements are treated as problem *** primary model of the governing differential equations is imposed to minimize the error between approximate *** evolutionary algorithm,the Genetic Algorithm with Multi-Parent Crossover(GA-MPC),executes the optimization *** resulting method is the Evolutionary Safe PadéApproximation(ESPA)*** proof of unconditional convergence of the ESPA scheme on the CCE model is supported by numerical *** performance of the ESPA scheme on the CCE model is thoroughly investigated by considering various orders of non-singular Padéapproximants.

关键词： Nonlinear cervical cancer epidemic non-singular Padéapproximants approximate solutions computational biology

Enhanced Temporal Correlation for Universal Lesion Detection

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Computer Modeling in Engineering & Sciences 2024年第3期138卷 3051-3063页

作者： Muwei Jian Yue Jin Hui Yu School of Computer Science and Technology Shandong University of Finance and EconomicsJinanChina School of Information Science and Technology Linyi UniversityLinyiChina School of Creative Technologies University of PortsmouthPortsmouthUK

Universal lesion detection(ULD)methods for computed tomography(CT)images play a vital role in the modern clinical medicine and intelligent *** is well known that single 2D CT slices lack spatial-temporal characteristics and contextual information compared to 3D CT ***,3D CT blocks necessitate significantly higher hardware resources during the learning ***,efficiently exploiting temporal correlation and spatial-temporal features of 2D CT slices is crucial for ULD *** this paper,we propose a ULD network with the enhanced temporal correlation for this purpose,named *** designed TCE module is applied to enrich the discriminate feature representation of multiple sequential CT ***,we employ multi-scale feature maps to facilitate the localization and detection of lesions in various *** experiments are conducted on the DeepLesion benchmark demonstrate that thismethod achieves 66.84%and 78.18%for FS@0.5 and FS@1.0,respectively,outperforming compared state-of-the-art methods.

关键词： Universal lesion detection computational biology medical computing deep learning enhanced temporal correlation

Decoding Retinoblastoma: Differential Gene Expression

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International Journal of Clinical Medicine 2024年第4期15卷 177-196页

作者： Ahmed Jasim Mahmood Al-Mashhadani Franko Shehaj Lianhong Zhou Department of Ophthalmology Renmin Hospital of Wuhan University Wuhan China

Background: Retinoblastoma, the most common intraocular pediatric cancer, presents complexities in its genetic landscape that necessitate a deeper understanding for improved therapeutic interventions. This study leverages computational tools to dissect the differential gene expression profiles in retinoblastoma. Methods: Employing an in silico approach, we analyzed gene expression data from public repositories by applying rigorous statistical models, including limma and de seq 2, for identifying differentially expressed genes DEGs. Our findings were validated through cross-referencing with independent datasets and existing literature. We further employed functional annotation and pathway analysis to elucidate the biological significance of these DEGs. Results: Our computational analysis confirmed the dysregulation of key retinoblastoma-associated genes. In comparison to normal retinal tissue, RB1 exhibited a 2.5-fold increase in expression (adjusted p Conclusions: Our analysis reinforces the critical genetic alterations known in retinoblastoma and unveils new avenues for research into the disease’s molecular basis. The discovery of chemoresistance markers and immune-related genes opens potential pathways for personalized treatment strategies. The study’s outcomes emphasize the power of in silico analyses in unraveling complex cancer genomics.

关键词： Retinoblastoma Gene Expression In Silico Study Differentially Expressed Genes Chemoresistance Immune Response computational biology

mvPPT:A Highly Efficient and Sensitive Pathogenicity Prediction Tool for Missense Variants

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Genomics, Proteomics & Bioinformatics 2023年第2期21卷 414-426页

作者： Shi-Yuan Tong Ke Fan Zai-Wei Zhou Lin-Yun Liu Shu-Qing Zhang Yinghui Fu Guang-Zhong Wang Ying Zhu Yong-Chun Yu Jing'an District Central Hospital of Shanghai State Key Laboratory of Medical NeurobiologyMOEFrontiers Center for Brain ScienceInstitutes of Brain ScienceFudan UniversityShanghai 200032China Shanghai Xunyin Biotechnology Co. Ltd.Shanghai 201802China CAS Key Laboratory of computational biology Shanghai Institute of Nutrition and HealthUniversity of Chinese Academy of SciencesChinese Academy of SciencesShanghai 200031China Huashan Hospital State Key Laboratory of Medical NeurobiologyMOE Frontiers Center for Brain ScienceInstitutes of Brain ScienceFudan UniversityShanghai 200032China

Next-generation sequencing technologies both boost the discovery of variants in the human genome and exacerbate the challenges of pathogenic variant *** this study,we developed Pathogenicity Prediction Tool for missense variants(mvPPT),a highly sensitive and accurate missense variant classifier based on gradient *** adopts high-confidence training sets with a wide spectrum of variant profiles,and extracts three categories of features,including scores from existing prediction tools,frequencies(allele frequencies,amino acid frequencies,and genotype frequencies),and genomic *** with established predictors,mvPPT achieves superior performance in all test sets,regardless of data *** addition,our study also provides guidance for training set and feature selection strategies,as well as reveals highly relevant features,which may further provide biological insights into variant pathogenicity.

关键词： Machine learning Missensevariant Genomics computational biology Pathogenicityprediction