Benchmarking AlphaFold-Generated Structures of Chemokine-Chemokine Receptor Complexes.

AlphaFold and AlphaFold-Multimer have become two essential tools for the modeling of unknown structures of proteins and protein complexes. In this work, we extensively benchmarked the quality of chemokine-chemokine receptor structures generated by AlphaFold-Multimer against experimentally determined structures. Our analysis considered both the global quality of the model, as well as key structural features for chemokine recognition. To study the effects of template and multiple sequence alignment parameters on the results, a new prediction pipeline called LIT-AlphaFold (https://github.com/LIT-CCM-lab/LIT-AlphaFold) was developed, allowing extensive input customization. AlphaFold-Multimer correctly predicted differences in chemokine binding orientation and accurately reproduced the unique binding orientation of the CXCL12-ACKR3 complex. Further, the predictions of the full receptor N-terminus provided insights into a putative chemokine recognition site 0.5. The accuracy of chemokine N-terminus binding mode prediction varied between complexes, but the confidence score permitted the distinguishing of residues that were very likely well positioned. Finally, we generated a high-confidence model of the unsolved CXCL12-CXCR4 complex, which agreed with experimental mutagenesis and cross-linking data.

Comparing transmembrane protein structures with ATOLL.

SUMMARY: The 3D structure of transmembrane helices plays a key role in the function of membrane proteins. While visual inspection can usually discern the distinctive features of a helix bundle, simply translating them into a 2D diagram can be difficult. ATOLL (Aligned Transmembrane dOmains Layout fLattening) projects the helix bundle onto the lipid bilayer plane, thereby facilitating the comparison of different structures of the same membrane protein or structures of different membrane proteins. AVAILABILITY AND IMPLEMENTATION: ATOLL is a program written in Python3. The source code is freely available on the web at https://github.com/LIT-CCM-lab/ATOLL. ATOLL is implemented into a web server (https://atoll.drugdesign.unistra.fr/). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

LIT-PCBA: An Unbiased Data Set for Machine Learning and Virtual Screening.

Comparative evaluation of virtual screening methods requires a rigorous benchmarking procedure on diverse, realistic, and unbiased data sets. Recent investigations from numerous research groups unambiguously demonstrate that artificially constructed ligand sets classically used by the community (e.g., DUD, DUD-E, MUV) are unfortunately biased by both obvious and hidden chemical biases, therefore overestimating the true accuracy of virtual screening methods. We herewith present a novel data set (LIT-PCBA) specifically designed for virtual screening and machine learning. LIT-PCBA relies on 149 dose-response PubChem bioassays that were additionally processed to remove false positives and assay artifacts and keep active and inactive compounds within similar molecular property ranges. To ascertain that the data set is suited to both ligand-based and structure-based virtual screening, target sets were restricted to single protein targets for which at least one X-ray structure is available in complex with ligands of the same phenotype (e.g., inhibitor, inverse agonist) as that of the PubChem active compounds. Preliminary virtual screening on the 21 remaining target sets with state-of-the-art orthogonal methods (2D fingerprint similarity, 3D shape similarity, molecular docking) enabled us to select 15 target sets for which at least one of the three screening methods is able to enrich the top 1%-ranked compounds in true actives by at least a factor of 2. The corresponding ligand sets (training, validation) were finally unbiased by the recently described asymmetric validation embedding (AVE) procedure to afford the LIT-PCBA data set, consisting of 15 targets and 7844 confirmed active and 407,381 confirmed inactive compounds. The data set mimics experimental screening decks in terms of hit rate (ratio of active to inactive compounds) and potency distribution. It is available online at http://drugdesign.unistra.fr/LIT-PCBA for download and for benchmarking novel virtual screening methods, notably those relying on machine learning.

Local Interaction Density (LID), a Fast and Efficient Tool to Prioritize Docking Poses.

Ligand docking at a protein site can be improved by prioritizing poses by similarity to validated binding modes found in the crystal structures of ligand/protein complexes. The interactions formed in the predicted model are searched in each of the reference 3D structures, taken individually. We propose to merge the information provided by all references, creating a single representation of all known binding modes. The method is called LID, an acronym for Local Interaction Density. LID was benchmarked in a pose prediction exercise on 19 proteins and 1382 ligands using PLANTS as docking software. It was also tested in a virtual screening challenge on eight proteins, with a dataset of 140,000 compounds from DUD-E and PubChem. LID significantly improved the performance of the docking program in both pose prediction and virtual screening. The gain is comparable to that obtained with a rescoring approach based on the individual comparison of reference binding modes (the GRIM method). Importantly, LID is effective with a small number of references. LID calculation time is negligible compared to the docking time.

Do Fragments and Crystallization Additives Bind Similarly to Drug-like Ligands?

The success of fragment-based drug design (FBDD) hinges upon the optimization of low-molecular-weight compounds (MW < 300 Da) with weak binding affinities to lead compounds with high affinity and selectivity. Usually, structural information from fragment-protein complexes is used to develop ideas about the binding mode of similar but drug-like molecules. In this regard, crystallization additives such as cryoprotectants or buffer components, which are highly abundant in crystal structures, are frequently ignored. Thus, the aim of this study was to investigate the information present in protein complexes with fragments as well as those with additives and how they relate to the binding modes of their drug-like counterparts. We present a thorough analysis of the binding modes of crystallographic additives, fragments, and drug-like ligands bound to four diverse targets of wide interest in drug discovery and highly represented in the Protein Data Bank: cyclin-dependent kinase 2, ß-secretase 1, carbonic anhydrase 2, and trypsin. We identified a total of 630 unique molecules bound to the catalytic binding sites, among them 31 additives, 222 fragments, and 377 drug-like ligands. In general, we observed that, independent of the target, protein-fragment interaction patterns are highly similar to those of drug-like ligands and mostly cover the residues crucial for binding. Crystallographic additives are also able to show conserved binding modes and recover the residues important for binding in some of the cases. Moreover, we show evidence that the information from fragments and drug-like ligands can be applied to rescore docking poses in order to improve the prediction of binding modes.

Comprehensive analysis of commercial fragment libraries.

Screening of fragment libraries is a valuable approach to the drug discovery process. The quality of the library is one of the keys to success, and more particularly the design or choice of a library has to meet the specificities of the research program. In this study, we made an inventory of the commercial fragment libraries and we established a methodology which allows any library to be positioned in relation to the complete offer currently on the market, by addressing the following questions: does this chemical library look like another chemical library? What is the coverage of the current chemical space by this chemical library? What are the characteristic structural features of the fragments of this chemical library? We based our analysis on 2D and 3D chemical descriptors, framework class generation and the generative topographic map. We identified 59 270 scaffolds, which can be searched in a dedicated web site (https://gtmfrag.drugdesign.unistra.fr) and developed a model which accounts for fragment diversity while being easy to interpret (download at 10.5281/zenodo.5534434).

Modeling of CCR5 Recognition by HIV-1 gp120: How the Viral Protein Exploits the Conformational Plasticity of the Coreceptor.

The chemokine receptor CCR5 is a key player in HIV-1 infection. The cryo-EM 3D structure of HIV-1 envelope glycoprotein (Env) subunit gp120 in complex with CD4 and CCR5 has provided important structural insights into HIV-1/host cell interaction, yet it has not explained the signaling properties of Env nor the fact that CCR5 exists in distinct forms that show distinct Env binding properties. We used classical molecular dynamics and site-directed mutagenesis to characterize the CCR5 conformations stabilized by four gp120s, from laboratory-adapted and primary HIV-1 strains, and which were previously shown to bind differentially to distinct CCR5 forms and to exhibit distinct cellular tropisms. The comparative analysis of the simulated structures reveals that the different gp120s do indeed stabilize CCR5 in different conformational ensembles. They differentially reorient extracellular loops 2 and 3 of CCR5 and thus accessibility to the transmembrane binding cavity. They also reshape this cavity differently and give rise to different positions of intracellular ends of transmembrane helices 5, 6 and 7 of the receptor and of its third intracellular loop, which may in turn influence the G protein binding region differently. These results suggest that the binding of gp120s to CCR5 may have different functional outcomes, which could result in different properties for viruses.

Binding mode information improves fragment docking.

Docking is commonly used in drug discovery to predict how ligand binds to protein target. Best programs are generally able to generate a correct solution, yet often fail to identify it. In the case of drug-like molecules, the correct and incorrect poses can be sorted by similarity to the crystallographic structure of the protein in complex with reference ligands. Fragments are particularly sensitive to scoring problems because they are weak ligands which form few interactions with protein. In the present study, we assessed the utility of binding mode information in fragment pose prediction. We compared three approaches: interaction fingerprints, 3D-matching of interaction patterns and 3D-matching of shapes. We prepared a test set composed of high-quality structures of the Protein Data Bank. We generated and evaluated the docking poses of 586 fragment/protein complexes. We observed that the best approach is twice as accurate as the native scoring function, and that post-processing is less effective for smaller fragments. Interestingly, fragments and drug-like molecules both proved to be useful references. In the discussion, we suggest the best conditions for a successful pose prediction with the three approaches.

Structural Insights on Fragment Binding Mode Conservation.

Aiming at a deep understanding of fragment binding to ligandable targets, we performed a large scale analysis of the Protein Data Bank. Binding modes of 1832 drug-like ligands and 1079 fragments to 235 proteins were compared. We observed that the binding modes of fragments and their drug-like superstructures binding to the same protein are mostly conserved, thereby providing experimental evidence for the preservation of fragment binding modes during molecular growing. Furthermore, small chemical changes in the fragment are tolerated without alteration of the fragment binding mode. The exceptions to this observation generally involve conformational variability of the molecules. Our data analysis also suggests that, provided enough fragments have been crystallized within a protein, good interaction coverage of the binding pocket is achieved. Last, we extended our study to 126 crystallization additives and discuss in which cases they provide information relevant to structure-based drug design.