Daniel‐Adriano Silva
Molecular Biology · 2016–2020
A researcher in the Science corpus: 9 papers; trusts 24 researchers and is trusted by 4 researchers here, as inferred from the citation graph.
Subfields
- Molecular Biology — 421 citations into it; #8,642 of 17,773 by trust here, #6,535 by citations
- Materials Chemistry — 199 citations into it; #8,063 of 11,513 by trust here, #7,227 by citations
- Computational Biology & Bioinformatics — 73 citations into it
- Spectroscopy — 49 citations into it
- Computational Chemistry & Drug Discovery — 39 citations into it
- Ecology — 10 citations into it
- Biomedical Engineering — 6 citations into it
Whom Daniel‐Adriano Silva trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Andrew Leaver-Fay
- David Baker
- Michael D. Tyka
- Adrian A. Canutescu
- Amelie Stein
- Daniel J. Mandell
- Carol A. Rohl
- Sarel Jacob Fleishman
- Scott E. Boyken
- N. Koga
Who trusts Daniel‐Adriano Silva overall
Papers from Daniel‐Adriano Silva's perspective
Ordered by how strongly the researchers Daniel‐Adriano Silva trusts endorse each paper. Same corpus, a different order for every researcher.
- Alternate states of proteins revealed by detailed energy landscape mapping
- Modeling T cell receptor recognition of CD1-lipid and MR1-metabolite complexes
- T-Cell Transfer Therapy Targeting Mutant KRAS in Cancer
- Systematic Comparison of Amber and Rosetta Energy Functions for Protein Structure Evaluation.
- Repurposing Citizen Science Games as Software Tools for Professional Scientists
- A flexible docking approach for prediction of T cell receptor–peptide–MHC complexes
- Can Self-Inhibitory Peptides be Derived from the Interfaces of Globular Protein-Protein Interactions?
- Systematic Analysis of Helical Protein Interfaces Reveals Targets for Synthetic Inhibitors
- RosettaHoles: Rapid assessment of protein core packing for structure prediction, refinement, design, and validation
- Predicting the Tolerated Sequences for Proteins and Protein Interfaces Using RosettaBackrub Flexible Backbone Design
Their papers
- Principles for designing proteins with cavities formed by curved β-sheets (2017) — 130 citations
- De novo protein design by citizen scientists (2019) — 131 citations
- Comprehensive computational design of ordered peptide macrocycles (2017) — 183 citations
- Macromolecular modeling and design in Rosetta: recent methods and frameworks (2020) — 783 citations
- De novo design of covalently constrained mesosize protein scaffolds with unique tertiary structures (2017) — 48 citations
- Essentials of de novo protein design: Methods and applications (2018) — 54 citations
- De novo design of potent and selective mimics of IL-2 and IL-15 (2019) — 474 citations
- Massively parallel de novo protein design for targeted therapeutics (2017) — 438 citations
- Motif-Driven Design of Protein-Protein Interfaces. (2016) — 48 citations