Expanding FTMap for Fragment-Based Identification of Pharmacophore Regions in Ligand Binding Sites.

The knowledge of ligand binding hot spots and of the important interactions within such hot spots is crucial for the design of lead compounds in the early stages of structure-based drug discovery. The computational solvent mapping server FTMap can reliably identify binding hot spots as consensus clusters, free energy minima that bind a variety of organic probe molecules. However, in its current implementation, FTMap provides limited information on regions within the hot spots that tend to interact with specific pharmacophoric features of potential ligands. E-FTMap is a new server that expands on the original FTMap protocol. E-FTMap uses 119 organic probes, rather than the 16 in the original FTMap, to exhaustively map binding sites, and identifies pharmacophore features as atomic consensus sites where similar chemical groups bind. We validate E-FTMap against a set of 109 experimentally derived structures of fragment-lead pairs, finding that highly ranked pharmacophore features overlap with the corresponding atoms in both fragments and lead compounds. Additionally, comparisons of mapping results to ensembles of bound ligands reveal that pharmacophores generated with E-FTMap tend to sample highly conserved protein-ligand interactions. E-FTMap is available as a web server at https://eftmap.bu.edu.

Conservation of Hot Spots and Ligand Binding Sites in Protein Models by AlphaFold2.

The neural network-based program AlphaFold2 (AF2) provides high accuracy structure prediction for a large fraction of globular proteins. An important question is whether these models are accurate enough for reliably docking small ligands. Several recent papers and the results of CASP15 reveal that local conformational errors reduce the success rates of direct ligand docking. Here, we focus on the ability of the models to conserve the location of binding hot spots, regions on the protein surface that significantly contribute to the binding free energy of the protein-ligand interaction. Clusters of hot spots predict the location and even the druggability of binding sites, and hence are important for computational drug discovery. The hot spots are determined by protein mapping that is based on the distribution of small fragment-sized probes on the protein surface and is less sensitive to local conformation than docking. Mapping models taken from the AlphaFold Protein Structure Database show that identifying binding sites is more reliable than docking, but the success rates are still 5% to 10% lower than based on mapping X-ray structures. The drop in accuracy is particularly large for models of multidomain proteins. However, both the model binding sites and the mapping results can be substantially improved by generating AF2 models for the ligand binding domains of interest rather than the entire proteins and even more if using forced sampling with multiple initial seeds. The mapping of such models tends to reach the accuracy of results obtained by mapping the X-ray structures.

Accurate ligand-protein docking in CASP15 using the ClusPro LigTBM server.

In the ligand prediction category of CASP15, the challenge was to predict the positions and conformations of small molecules binding to proteins that were provided as amino acid sequences or as models generated by the AlphaFold2 program. For most targets, we used our template-based ligand docking program ClusPro ligTBM, also implemented as a public server available at https://ligtbm.cluspro.org/. Since many targets had multiple chains and a number of ligands, several templates, and some manual interventions were required. In a few cases, no templates were found, and we had to use direct docking using the Glide program. Nevertheless, ligTBM was shown to be a very useful tool, and by any ranking criteria, our group was ranked among the top five best-performing teams. In fact, all the best groups used template-based docking methods. Thus, it appears that the AlphaFold2-generated models, despite the high accuracy of the predicted backbone, have local differences from the x-ray structure that make the use of direct docking methods more challenging. The results of CASP15 confirm that this limitation can be frequently overcome by homology-based docking.

The use of Yttrium-90 Ibritumomab Tiuxetan ((90)Y-IT) as a consolidation therapy in high-risk patients with diffuse large B-cell lymphoma ineligible for autologous stem-cell transplantation.

AIM OF THE STUDY: To evaluate the efficacy and safety of Yttrium-90 Ibritumomab Tiuxetan ((90)Y-IT) as a consolidation therapy in the management of DLBCL. MATERIAL AND METHODS: Patients with primary refractory or high-risk DLBCL (n = 18), ineligible for autologous stem-cell transplantation, were included in a retrospective study performed at three centers by the Polish Lymphoma Research Group (PLRG). All patients (mean age 61, range 35-82) either didn't achieve a complete response or didn't complete the scheduled therapy due to its complications. Response rates (CR, PR, SD, PD) according to Cheson criteria, overall survival (OS), progression-free survival (PFS) and adverse effects of radioimmunotherapy were analyzed. RESULTS: Consolidation radioimmunotherapy increased the CR rate from 38% (n = 7) to 82% (n = 15). One patient remained in PR, one patient remained in SD, while one patient remained in PD. During a median follow-up of five years, 11 patients (62%) were alive with no recurrence, 4 patients (22%) were alive with relapse while 3 patients (16%) died. There was no statistically significant difference in PFS between those in CR and those in PR before (90)Y-IT. CONCLUSIONS: Radioimmunotherapy is an effective consolidation therapy for high risk/refractory DLBCL patients and worthy of further investigation in prospective trials.