{"product_id":"protein-structure-prediction-methods-in-molecular-biology","title":"Protein Structure Prediction (Methods in Molecular Biology)","description":"\u003cp\u003eOur review of Protein Structure Prediction, Fourth Edition, finds it most useful for researchers and advanced students who need a practical, methods-focused reference for computational modeling. The book compiles up-to-date web servers and software workflows, including recent advances like residue-contact prediction via deep learning and cryo-EM integration, and the single biggest reason to buy is its emphasis on actionable implementation detail that helps readers reproduce and adapt techniques in their own projects.\u003c\/p\u003e\u003ch2\u003eKey Features\u003c\/h2\u003e\u003cul\u003e\n\u003cli\u003e\n\u003cstrong\u003eComprehensive coverage:\u003c\/strong\u003e Chapters survey a wide range of freely available web servers and software so researchers can choose appropriate tools for specific modeling tasks.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eDeep learning contacts:\u003c\/strong\u003e The book explains residue-contact prediction via deep learning, providing context on how these predictions improve structure modeling accuracy.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eDocking methods:\u003c\/strong\u003e Multiple protein docking models are described, helping readers understand options for modeling protein-protein interactions.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eCryo-EM techniques:\u003c\/strong\u003e Coverage of cryo-electron microscopy workflows shows how experimental density maps can be combined with computational models.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003ePractical implementation advice:\u003c\/strong\u003e Chapters include step-by-step details and tips that enable readers to reproduce results and adapt pipelines to their own datasets.\u003c\/li\u003e\n\u003c\/ul\u003e\u003ch2\u003eWho It's For\u003c\/h2\u003e\u003cp\u003eThis volume is best for graduate students, postdocs, and computational biologists who need a methods-oriented reference on current protein structure prediction tools and practical guidance on implementation. It suits labs that rely on academic web servers and open-source software rather than commercial suites.\u003c\/p\u003e\u003cp\u003eThose seeking an introductory overview of biology or a purely theoretical treatment should look elsewhere; this edition assumes familiarity with basic structural biology concepts and focuses on actionable, technical workflows rather than textbook basics.\u003c\/p\u003e\u003ch2\u003ePros \u0026amp; Cons\u003c\/h2\u003e\u003cp\u003e\u003cstrong\u003ePros\u003c\/strong\u003e\u003c\/p\u003e\u003cul\u003e\n\u003cli\u003ePractical, stepwise implementation tips make it easier to apply methods to real projects.\u003c\/li\u003e\n\u003cli\u003eUp-to-date inclusion of \u003cstrong\u003edeep learning\u003c\/strong\u003e contact prediction reflects current best practices.\u003c\/li\u003e\n\u003cli\u003eBalanced coverage of docking and \u003cstrong\u003ecryo-EM\u003c\/strong\u003e shows how computational and experimental approaches intersect.\u003c\/li\u003e\n\u003c\/ul\u003e\u003cp\u003e\u003cstrong\u003eCons\u003c\/strong\u003e\u003c\/p\u003e\u003cul\u003e\u003cli\u003eNot designed as a novice textbook; readers without prior structural biology background may need supplementary resources.\u003c\/li\u003e\u003c\/ul\u003e\u003ch2\u003eSpecifications\u003c\/h2\u003e\u003ctable\u003e\n\u003ctr\u003e\n\u003ctd\u003eTitle\u003c\/td\u003e\n\u003ctd\u003eProtein Structure Prediction, Fourth Edition\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eSeries\u003c\/td\u003e\n\u003ctd\u003eMethods in Molecular Biology\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eEditor \/ Brand\u003c\/td\u003e\n\u003ctd\u003eDaisuke Kihara\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eFocus areas\u003c\/td\u003e\n\u003ctd\u003eProtein modeling, docking, residue-contact prediction, cryo-EM\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eAudience\u003c\/td\u003e\n\u003ctd\u003eResearchers, graduate students, computational biologists\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eContent type\u003c\/td\u003e\n\u003ctd\u003ePractical methods and implementation advice\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/table\u003e\u003ch2\u003eOur Verdict\u003c\/h2\u003e\u003cp\u003eProtein Structure Prediction, Fourth Edition is a strong, practical resource for anyone who needs hands-on guidance with current computational modeling tools. Its emphasis on freely available web servers, \u003cstrong\u003edeep learning\u003c\/strong\u003e contact methods, and cryo-EM makes it good value for research groups and advanced students who want reproducible workflows rather than high-level theory.\u003c\/p\u003e\u003ch2\u003eFrequently Asked Questions\u003c\/h2\u003e\u003cp\u003e\u003cstrong\u003eDoes this edition cover deep learning methods?\u003c\/strong\u003e\u003cbr\u003eYes, it includes chapters on residue-contact prediction via deep learning and discusses their role in improving modeling accuracy.\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eIs this book suitable for beginners?\u003c\/strong\u003e\u003cbr\u003eIt is best suited to readers with prior structural biology knowledge; beginners may need an introductory text first.\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eAre the tools described freely available?\u003c\/strong\u003e\u003cbr\u003eThe review emphasizes web servers and software that are freely available to the academic community.\u003c\/p\u003e","brand":"Daisuke Kihara","offers":[{"title":"Default Title","offer_id":48258938994907,"sku":"1071607103","price":139.99,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0724\/1043\/1707\/files\/61VHWXjczPS._SL1180.jpg?v=1778308840","url":"https:\/\/gearmusthave.com\/products\/protein-structure-prediction-methods-in-molecular-biology","provider":"GearMustHave","version":"1.0","type":"link"}