MM-PBSA Captures Key Role of Intercalating Water Molecules at a Protein−Protein Interface

The calculation of protein interaction energetics is of fundamental interest, yet accurate quantities are difficult to obtain due to the complex and dynamic nature of protein interfaces. This is further complicated by the presence of water molecules, which can exhibit transient interactions of variable duration and strength with the protein surface. The T-cell receptor (TCR) and its staphylococcal enterotoxin 3 (SEC3) binding partner are well-characterized examples of a protein−protein interaction system exhibiting interfacial plasticity, cooperativity, and additivity among mutants. Specifically engineered mutants induce intercalating interfacial water molecules, which subsequently enhance protein−protein binding affinity. In this work, we perform a set of molecular mechanics (MM) Poisson−Boltzmann (PB) surface area (SA) calculations on the wild type and two mutant TCR-SEC3 systems and show that the method is able to discriminate between weak and strong binders only when key explicit water molecules are included in the analysis. The results presented here point to the promise of MM-PBSA toward rationalizing molecular recognition at protein−protein interfaces, while establishing a general approach to handle explicit interfacial water molecules in such calculations

Additional file 2: of System-level analysis of metabolic trade-offs during anaerobic photoheterotrophic growth in Rhodopseudomonas palustris

RP model and gene knockout results. (XLSX 133 kb

Additional file 3: of System-level analysis of metabolic trade-offs during anaerobic photoheterotrophic growth in Rhodopseudomonas palustris

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Additional file 1: of System-level analysis of metabolic trade-offs during anaerobic photoheterotrophic growth in Rhodopseudomonas palustris

System-level Analyses of robustness of metabolism to environmental and genetic perturbations. (DOCX 3179 kb

Conflict of Interest Form from <i>MET</i> Exon 14 Mutation Encodes an Actionable Therapeutic Target in Lung Adenocarcinoma

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Supplementary Figure 1: A survey of MET mutations and expression across TCGA datasets from diverse cancer types. Supplementary figure 2, MET exon 14 status and trypsin assay Supplementary Figure 3 MET half-life assay results Supplementary Figure 4 tumor grade and size changes Supplementary Figure 5: Acquired resistance alleles are in trans</p

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