Format:
1 Online-Ressource (xi, 418 Seiten)
ISBN:
9789811250477
Content:
The Pacific Symposium on Biocomputing (PSB) 2022 is an international, multidisciplinary conference for the presentation and discussion of current research in the theory and application of computational methods in problems of biological significance. Presentations are rigorously peer reviewed and are published in an archival proceedings volume. PSB 2022 will be held on January 3 - 7, 2022 in Kohala Coast, Hawaii. Tutorials and workshops will be offered prior to the start of the conference.PSB 2022 will bring together top researchers from the US, the Asian Pacific nations, and around the world to exchange research results and address open issues in all aspects of computational biology. It is a forum for the presentation of work in databases, algorithms, interfaces, visualization, modeling, and other computational methods, as applied to biological problems, with emphasis on applications in data-rich areas of molecular biology.The PSB has been designed to be responsive to the need for critical mass in sub-disciplines within biocomputing. For that reason, it is the only meeting whose sessions are defined dynamically each year in response to specific proposals. PSB sessions are organized by leaders of research in biocomputing's 'hot topics.' In this way, the meeting provides an early forum for serious examination of emerging methods and approaches in this rapidly changing field.
Content:
Intro -- Content -- Preface -- AI-DRIVEN ADVANCES IN MODELING OF PROTEIN STRUCTURE -- Session Introduction: AI-Driven Advances in Modeling of Protein Structure -- 1. A short retrospect -- 2. A brief outline of current research -- 3. Future developments (complexes, ligand interactions, other molecules, dynamics, language models, geometry models, sequence design) -- 4. What is needed for further progress? -- 5. Overview of papers in this session -- 5.1. Evaluating significance of training data selection in machine learning -- 5.2. Geometric pattern transferability -- 5.3. Supervised versus unsupervised sequence to contact learning -- 5.4. Side chain packing using SE(3) transformers -- 5.5. Feature selection in electrostatic representations of ligand binding sites -- References -- Training Data Composition Affects Performance of Protein Structure Analysis Algorithms -- 1. Introduction -- 2. Methods -- 2.1. Experimental Design -- 2.2. Task-specific Methods -- 3. Results -- 3.1. Performance on NMR and cryo-EM structures is consistently lower than performance on X-ray structures, independent of training set -- 3.2. Inclusion of NMR data in the training set improves performance on held-out NMR data and does not degrade performance on X-ray data -- 3.3. Known biochemical and biophysical effects are replicated in trained models -- 3.4. Downsampling X-ray structures during training negatively affects performance on all types of data -- 4. Conclusion -- 5. Acknowledgments -- References -- Transferability of Geometric Patterns from Protein Self-Interactions to Protein-Ligand Interactions -- 1. Introduction -- 2. Related Work -- 3. Methods -- 3.1. Datasets -- 3.2. Contact extraction -- 3.3. Representing contact geometry -- 4. Results -- 4.1. Protein self-contacts exhibit clear geometric clustering.
Note:
Description based on publisher supplied metadata and other sources
Additional Edition:
ISBN 9789811250460
Additional Edition:
Erscheint auch als Druck-Ausgabe ISBN 9789811250460
Language:
English
Keywords:
Biocomputer
;
Bioinformatik
;
Kongress
;
Konferenzschrift
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