Binding site hit rates (blue columns, left axis) and BSSC (bound-state similarity coefficient) values (red lines, right axis) for the Bcl-xL ensemble (PDB ID 2m03, 20 models).

<p>Horizontal axes list model numbers, with the last column showing the averaged binding site hit rate and BSSC value. BSSC values are defined for three different ligand-bound structures of MDM2. <b>A.</b> MDM2 bound to the inhibitor ABT-737 (PDB ID 2yxj). <b>B.</b> MDM2 bound to a BAK peptide (PDB ID 1bxl).</p

Identification of binding sites.

<p><b>A.</b> Ligand-free MDM2 (1z1m, green) with p53 peptide (cyan) from the bound structure (3v3b). The top binding site predicted by FTSite (brown mesh) overlaps with the peptide in 18 of the 24 structures of 1z1m. <b>B.</b> Ligand-free Bcl-xL structure (2m03, green), with BAK peptide (cyan) from structure 1bxl, inhibitor ABT-737 (red sticks) from structure 2yxj, and the BIH SAHB peptide (magenta) binding to the close Bcl-xL homologue BAX (2k7w). The top predicted binding site (brown mesh) overlaps with the BAK peptide and ABT-737 in 6 of the 20 structures in 2m03, and with the BIH SAHB site in 9 of the 20 structures. <b>C.</b> Ligand-free EDC3 (4a53, green) with DCP2 peptide (cyan) from the structure 4a54. The top predicted binding site (brown mesh) overlaps with the peptide in all 20 structures in 4a53. <b>D.</b> Ligand-free MAGI1 PDZ1 (2kpk, green) with a C-terminal peptide of HPV16 E6 (cyan) from structure 2kpl. The top predicted binding site (brown mesh) overlaps with the peptide in 19 of the 20 structures in 2kpk. <b>E.</b> Ligand-free PSD95 PDZ1 (1iu2, green) with a peptide (cyan) from structure 1rgr. The top predicted binding site (brown mesh) overlaps with the peptide in 40 of the 50 structures in 1iu2).</p

Binding site hit rates (blue columns, left axis) and BSSC (bound-state similarity coefficient) values (red lines, right axis) for the ligand-free NMR structures of PSD-95 PDZ1, MAGI-1 PDZ1, and EDC3.

<p>Horizontal axes list model numbers, with the last column showing the averaged binding site hit rate and BSSC value. <b>A.</b> Unbound PSD-95 PDZ1 ensemble (PDB ID 1iu2, 50 models). BSSC values are defined for a peptide-bound structure (PDB ID 1rgr). <b>B.</b> Unbound MAGI-1 PDZ1 ensemble (PDB ID 2kpk, 20 models). BSSC values are defined for a peptide-bound structure (PDB ID 2kpl). <b>C.</b> Unbound EDC3 ensemble (PDB ID 4a53, 20 models). BSSC values are defined for a peptide-bound structure (PDB ID 4a54).</p

Protein targets and summary of results.

a<p>The number in parenthesis indicates the number of structures in the NMR ensemble.</p>b<p>In parenthesis we indicate the ligand bound to the protein.</p>c<p>BSSC denotes the bound-state similarity coefficient, which measures the similarity of each model to the bound state. The number of parenthesis is the rank based on the binding site hit rate (see Results).</p>d<p>R denotes the correlation coefficient between the binding site hit rate and BSSC.</p><p>Protein targets and summary of results.</p

Evidence of Conformational Selection Driving the Formation of Ligand Binding Sites in Protein-Protein Interfaces

<div><p>Many protein-protein interactions (PPIs) are compelling targets for drug discovery, and in a number of cases can be disrupted by small molecules. The main goal of this study is to examine the mechanism of binding site formation in the interface region of proteins that are PPI targets by comparing ligand-free and ligand-bound structures. To avoid any potential bias, we focus on ensembles of ligand-free protein conformations obtained by nuclear magnetic resonance (NMR) techniques and deposited in the Protein Data Bank, rather than on ensembles specifically generated for this study. The measures used for structure comparison are based on detecting binding hot spots, i.e., protein regions that are major contributors to the binding free energy. The main tool of the analysis is computational solvent mapping, which explores the surface of proteins by docking a large number of small “probe” molecules. Although we consider conformational ensembles obtained by NMR techniques, the analysis is independent of the method used for generating the structures. Finding the energetically most important regions, mapping can identify binding site residues using ligand-free models based on NMR data. In addition, the method selects conformations that are similar to some peptide-bound or ligand-bound structure in terms of the properties of the binding site. This agrees with the conformational selection model of molecular recognition, which assumes such pre-existing conformations. The analysis also shows the maximum level of similarity between unbound and bound states that is achieved without any influence from a ligand. Further shift toward the bound structure assumes protein-peptide or protein-ligand interactions, either selecting higher energy conformations that are not part of the NMR ensemble, or leading to induced fit. Thus, forming the sites in protein-protein interfaces that bind peptides and can be targeted by small ligands always includes conformational selection, although other recognition mechanisms may also be involved.</p></div

Mapping fingerprints of 24 unbound MDM2 structures.

<p>In each plot, horizontal axis, MDM2 residues (E25-Y104); vertical axis, percentage of probe-residue contacts (0–20%). Residues within 4 Å from the p53 peptide (PDB 1ycr) are marked with red dots.</p

Binding site hit rates (blue columns, left axis) and BSSC (bound-state similarity coefficient) values (red lines, right axis) for the MDM2 ensemble (PDB ID 1z1m, 24 models).

<p>Horizontal axes list model numbers, with the last column showing the averaged binding site hit rate and BSSC value. BSSC values are defined for three different ligand-bound structures of MDM2. <b>A.</b> MDM2 bound to a p53 peptide (PDB ID 1ycr). <b>B.</b> MDM2 bound to the inhibitor Nutlin-2 (PDB ID 1rv1). <b>C.</b> MDM2 bound to a piperidinone derivative (PDB ID 2lzg).</p

A benchmark testing ground for integrating homology modeling and protein docking

Protein docking procedures carry out the task of predicting the structure of a protein-protein complex starting from the known structures of the individual protein components. More often than not, however, the structure of one or both components is not known, but can be derived by homology modeling on the basis of known structures of related proteins deposited in the Protein Data Bank (PDB). Thus, the problem is to develop methods that optimally integrate homology modeling and docking with the goal of predicting the structure of a complex directly from the amino acid sequences of its component proteins. One possibility is to use the best available homology modeling and docking methods. However, the models built for the individual subunits often differ to a significant degree from the bound conformation in the complex, often much more so than the differences observed between free and bound structures of the same protein, and therefore additional conformational adjustments, both at the backbone and side chain levels need to be modeled to achieve an accurate docking prediction. In particular, even homology models of overall good accuracy frequently include localized errors that unfavorably impact docking results. The predicted reliability of the different regions in the model can also serve as a useful input for the docking calculations. Here we present a benchmark dataset that should help to explore and solve combined modeling and docking problems. This dataset comprises a subset of the experimentally solved \u27target\u27 complexes from the widely used Docking Benchmark from the Weng Lab (excluding antibody-antigen complexes). This subset is extended to include the structures from the PDB related to those of the individual components of each complex, and hence represent potential templates for investigating and benchmarking integrated homology modeling and docking approaches. Template sets can be dynamically customized by specifying ranges in sequence similarity and in PDB release dates, or using other filtering options, such as excluding sets of specific structures from the template list. Multiple sequence alignments, as well as structural alignments of the templates to their corresponding subunits in the target are also provided. The resource is accessible online or can be downloaded at http://cluspro.org/benchmark, and is updated on a weekly basis in synchrony with new PDB releases

Improved cytosine base editors generated from TadA variants

Cytosine base editors (CBEs) enable programmable genomic C·G-to-T·A transition mutations and typically comprise a modified CRISPR–Cas enzyme, a naturally occurring cytidine deaminase, and an inhibitor of uracil repair. Previous studies have shown that CBEs utilizing naturally occurring cytidine deaminases may cause unguided, genome-wide cytosine deamination. While improved CBEs that decrease stochastic genome-wide off-targets have subsequently been reported, these editors can suffer from suboptimal on-target performance. Here, we report the generation and characterization of CBEs that use engineered variants of TadA (CBE-T) that enable high on-target C·G to T·A across a sequence-diverse set of genomic loci, demonstrate robust activity in primary cells and cause no detectable elevation in genome-wide mutation. Additionally, we report cytosine and adenine base editors (CABEs) catalyzing both A-to-I and C-to-U editing (CABE-Ts). Together with ABEs, CBE-Ts and CABE-Ts enable the programmable installation of all transition mutations using laboratory-evolved TadA variants with improved properties relative to previously reported CBEs