Computational Alanine Scanning of Protein-Protein Interfaces

By Tanja Kortemme, David E. Kim, David Baker. 2004

555 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Materials Chemistry, Molecular Biology

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Citations in the corpus

Systematic Analysis of Helical Protein Interfaces Reveals Targets for Synthetic Inhibitors (2010, background): “Hot spot residues were predicted using a computational alanine scan.(29,30) Hot spot residues were defined as residues that upon mutation to alanine are predicted to decrease the binding energy by a threshold value ΔΔGbind ≥ 1.”
Better together: Elements of successful scientific software development in a distributed collaborative community (2020, methodology): “The Robetta server[54] was deployed in 2003 and is commonly used by scientists inside and outside of our community for fragment generation[55], domain parsing and structure prediction[56,57], alanine scanning[58], and DNA interface scanning[59].”
Protein–Protein Interaction Hotspots Carved into Sequences (2007, background): “The Baker lab has shown that given a 3-D structure of a protein complex, it is possible to predict the results of alanine scans specifically and accurately [39,40].”
Selectivity by Small-Molecule Inhibitors of Protein Interactions Can Be Driven by Protein Surface Fluctuations (2015, unknown): “First, the “Robetta” server (85, 86) is used to estimate the contribution to the binding free-energy of each interfacial sidechain (ΔΔGres) via computational alanine scanning.”
SnugDock: Paratope Structural Optimization during Antibody-Antigen Docking Compensates for Errors in Antibody Homology Models (2010, background): “High-resolution complex structures might next be used for computational alanine scanning [51], computational affinity maturation [52] or alteration of binding specificity [53].”
Peptiderive server: derive peptide inhibitors from protein–protein interactions (2016, methodology): “alanine scanning, by computation of energy changes (24,25) or by using machine learning approaches based on structure and sequence ((26–28); reviewed in (29)).”
Protein structure prediction and analysis using the Robetta server (2004, unknown): “Robetta includes a ‘computational interface alanine scanning’ ( 4 ) method that predicts the effects of truncation mutations on thestabilityofprotein‐proteincomplexesasdescribedinreferences (4,5).”
Harnessing protein folding neural networks for peptide–protein docking (2021, background): “The identification of these interface residues (and hotspots therein) provide a good starting point for further study using low throughput experiments (8).”
A Web Resource for Standardized Benchmark Datasets, Metrics, and Rosetta Protocols for Macromolecular Modeling and Design (2015, methodology): “Key results: Alanine scanning performance has not shown improvement when used with modern Rosetta score functions and aggressive side chain/backbone minimization methods; performance of the protocol described here is comparable to that shown in earlier publications [24,25] and available on the Robet”
Atomic-Accuracy Prediction of Protein Loop Structures through an RNA-Inspired Ansatz (2012, methodology): “An ensemble of 400 models is retained and typically includes models within 5 kBT of the lowest energy model at each stage, mimicking a thermal ensemble (here, a Rosetta energy unit is taken to be approximately one kBT [29]).”