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Biology subjects

Stein, R. R.

Publications and source records attributed to Stein, R. R..

4 recordsLinked to original sources

Experimental conditions to retrieve intrinsic cooperativity α directly from single binding assay data exemplified by the ternary complex formation of FKBP12, MAPRE1 and macrocyclic molecular glues

The incorporation of disease-relevant targets in a compound-dependent manner into ternary complexes with an assisting chaperone protein has become a common modality. Among the various types of ternary complex-forming compounds, molecular glues are of particular interest because they do not require significant binary (independent) target affinity. Instead, ternary complexes formed by molecular glues retrieve a significant part of their thermodynamic stability through newly induced chaperone-target or glue-target interactions that occur only in the ternary complex. These interactions lead to enhanced ligand binding - termed intrinsic cooperativity - which can be retrieved via the apparent cooperativity either by monitoring ligand binding through the chaperone or through the target protein. In this publication, the advantage of measuring the apparent cooperativity to determine the cooperativity by the weaker binding protein is discussed and illustrated using the example of ternary complexes between FKBP12, MAPRE1 and macrocyclic molecular glues derived from the rapamycin binding motif for FKBP12. Furthermore, the impact of the following three parameters on the apparent cooperativity is illustrated: 1.) the concentration of the monitoring protein, 2.) the excess of the counter protein, 3.) the affinity of the glue to the weaker binding protein in combination with the degree of intrinsic cooperativity . From this, experimental conditions to determine the intrinsic cooperativity with only one binding assay and without the need for a comprehensive mathematical model covering all simultaneous events under non-saturating conditions are highlighted. However, this framework requires a binding assay capable of measuring or at least estimating very weak binary affinities. If this is not possible for experimental reasons, but binding assays for both proteins are available within a normal bandwidth and the affinity to the stronger binding protein is not too high, it is discussed how the binding curve for the weaker binding protein in the presence of an excess of the weaker binding protein can be used to overcome the missing binary Kd for the weakly binding protein.

biochemistry↗

Protein structure from experimental evolution

Natural evolution encodes rich information about the structure and function of biomolecules in the genetic record. Previously, statistical analysis of co-variation patterns in natural protein families has enabled the accurate computation of 3D structures. Here, we explored whether similar information can be generated by laboratory evolution, starting from a single gene and performing multiple cycles of mutagenesis and functional selection. We evolved two bacterial antibiotic resistance proteins, {beta}-lactamase PSE1 and acetyltransferase AAC6, and obtained hundreds of thousands of diverse functional sequences. Using evolutionary coupling analysis, we inferred residue interactions in good agreement with contacts in the crystal structures, confirming genetic encoding of structural constraints in the selected sequences. Computational protein folding with contact constraints yielded 3D structures with the same fold as that of natural relatives. Evolution experiments combined with inference of residue interactions from sequence information opens the door to a new experimental method for the determination of protein structures.

evolutionary biology↗

Perturbation biology links temporal protein changes to drug responses in a melanoma cell line

Data-driven mathematical modeling of biological systems has enormous potential to understand and predict the interplay between molecular and phenotypic response to perturbation, and provides a rational approach to the nomination of therapies in complex diseases such as cancer. Melanoma is a particularly debilitating disease for which most therapies eventually fail as resistance to chemotherapy and targeted drugs develop. We have previously applied an iterative experimental-computational modeling approach, termed perturbation biology, to predict and test effective drug combinations in melanoma cell lines [1].\n\nIn this work, we extend our analysis framework to derive models of temporally-acquired perturbation data that do not require prior knowledge and explicit specification of the targets of individual drugs. Specifically, we characterize the response of the melanoma cell line A2058 to 54 cancer drug combinations at 8 logarithmically spaced time points from 10 minutes to 67 hours. At each time point, 124 antibodies of proteins and phospho-proteins with broad coverage of cancer-related pathways and two phenotypes (cell number and apoptosis) were measured. These data are used to infer interactions in ordinary differential equation-based models that capture temporal aspects of the drug perturbation data. This network representation of drug-protein, protein-protein, and protein-phenotype interactions can be used to identify new logical (not necessarily direct biochemical) interactions. The agreement between the predicted phenotypic response and corresponding data for unseen drug perturbations has a Pearsons correlation coefficient of 0.79. We further use model predictions to nominate effective combination therapies and perform experimental validation of the highest ranked combinations.\n\nThis new data-driven modeling framework is a step forward in perturbation biology as it incorporates the temporal aspect of data. This work therefore opens the door to a new understanding of dynamic drug responses at a molecular level.

systems biology↗

Protein Profiling In Cancer Cell Lines And Tumor Tissue Using Reverse Phase Protein Arrays

Reverse phase protein array (RPPA) technology is an antibody-based high-throughput assay for protein profiling of biological specimens that allows for many measurements with very small amounts of cell lysate. Here, we report the sensitivity, reproducibility, and accuracy of a particular RPPA platform called Zeptosens. We customized the RPPA protocol for our in-house setup, and measured more than 80 total protein and phospho-protein levels in various cancer samples, including cell lines, organoids, tumor chunks, core needle biopsies, and laser-capture microdissected tissue samples. We discuss pros and cons of the RPPA platform, and describe results from profiling 15 cancer cell line cells using RPPA.

systems biology↗