Natural ComputingPub Date : 2023-05-08DOI: 10.1007/s11047-023-09943-4
Juan Luis Filgueiras, Daniel Varela, José Santos
{"title":"Protein structure prediction with energy minimization and deep learning approaches.","authors":"Juan Luis Filgueiras, Daniel Varela, José Santos","doi":"10.1007/s11047-023-09943-4","DOIUrl":"10.1007/s11047-023-09943-4","url":null,"abstract":"<p><p>In this paper we discuss the advantages and problems of two alternatives for ab initio protein structure prediction. On one hand, recent approaches based on deep learning, which have significantly improved prediction results for a wide variety of proteins, are discussed. On the other hand, methods based on protein conformational energy minimization and with different search strategies are analyzed. In this latter case, our methods based on a memetic combination between differential evolution and the fragment replacement technique are included, incorporating also the possibility of niching in the evolutionary search. Different proteins have been used to analyze the pros and cons in both approaches, proposing possibilities of integration of both alternatives.</p>","PeriodicalId":49783,"journal":{"name":"Natural Computing","volume":" ","pages":"1-12"},"PeriodicalIF":2.1,"publicationDate":"2023-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10165305/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9708719","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Natural ComputingPub Date : 2022-03-01Epub Date: 2022-03-04DOI: 10.1007/s11047-022-09882-6
Jasmijn A Baaijens, Paola Bonizzoni, Christina Boucher, Gianluca Della Vedova, Yuri Pirola, Raffaella Rizzi, Jouni Sirén
{"title":"Computational graph pangenomics: a tutorial on data structures and their applications.","authors":"Jasmijn A Baaijens, Paola Bonizzoni, Christina Boucher, Gianluca Della Vedova, Yuri Pirola, Raffaella Rizzi, Jouni Sirén","doi":"10.1007/s11047-022-09882-6","DOIUrl":"10.1007/s11047-022-09882-6","url":null,"abstract":"<p><p>Computational pangenomics is an emerging research field that is changing the way computer scientists are facing challenges in biological sequence analysis. In past decades, contributions from combinatorics, stringology, graph theory and data structures were essential in the development of a plethora of software tools for the analysis of the human genome. These tools allowed computational biologists to approach ambitious projects at population scale, such as the 1000 Genomes Project. A major contribution of the 1000 Genomes Project is the characterization of a broad spectrum of genetic variations in the human genome, including the discovery of novel variations in the South Asian, African and European populations-thus enhancing the catalogue of variability within the reference genome. Currently, the need to take into account the high variability in population genomes as well as the specificity of an individual genome in a personalized approach to medicine is rapidly pushing the abandonment of the traditional paradigm of using a single reference genome. A graph-based representation of multiple genomes, or <i>a graph pangenome</i>, is replacing the linear reference genome. This means completely rethinking well-established procedures to analyze, store, and access information from genome representations. Properly addressing these challenges is crucial to face the computational tasks of ambitious healthcare projects aiming to characterize human diversity by sequencing 1M individuals (Stark et al. 2019). This tutorial aims to introduce readers to the most recent advances in the theory of data structures for the representation of graph pangenomes. We discuss efficient representations of <i>haplotypes</i> and the variability of <i>genotypes</i> in graph pangenomes, and highlight applications in solving computational problems in human and microbial (viral) pangenomes.</p>","PeriodicalId":49783,"journal":{"name":"Natural Computing","volume":"21 1","pages":"81-108"},"PeriodicalIF":1.7,"publicationDate":"2022-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10038355/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9199489","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}