Peptide binding prediction for the human class II MHC allele HLA-DP2:a molecular docking approach

MHC class II proteins bind oligopeptide fragments derived from proteolysis of pathogen antigens, presenting them at the cell surface for recognition by CD4+ T cells. Human MHC class II alleles are grouped into three loci: HLA-DP, HLA-DQ and HLA-DR. In contrast to HLA-DR and HLA-DQ, HLA-DP proteins have not been studied extensively, as they have been viewed as less important in immune responses than DRs and DQs. However, it is now known that HLA-DP alleles are associated with many autoimmune diseases. Quite recently, the X-ray structure of the HLA-DP2 molecule (DPA*0103, DPB1*0201) in complex with a self-peptide derived from the HLA-DR a-chain has been determined. In the present study, we applied a validated molecular docking protocol to a library of 247 modelled peptide-DP2 complexes, seeking to assess the contribution made by each of the 20 naturally occurred amino acids at each of the nine binding core peptide positions and the four flanking residues (two on both sides)

Peptide binding to HLA-DP proteins at pH 5.0 and pH 7.0:a quantitative molecular docking study

Background: HLA-DPs are class II MHC proteins mediating immune responses to many diseases. Peptides bind MHC class II proteins in the acidic environment within endosomes. Acidic pH markedly elevates association rate constants but dissociation rates are almost unchanged in the pH range 5.0 - 7.0. This pH-driven effect can be explained by the protonation/deprotonation states of Histidine, whose imidazole has a pKa of 6.0. At pH 5.0, imidazole ring is protonated, making Histidine positively charged and very hydrophilic, while at pH 7.0 imidazole is unprotonated, making Histidine less hydrophilic. We develop here a method to predict peptide binding to the four most frequent HLA-DP proteins: DP1, DP41, DP42 and DP5, using a molecular docking protocol. Dockings to virtual combinatorial peptide libraries were performed at pH 5.0 and pH 7.0. Results: The X-ray structure of the peptide - HLA-DP2 protein complex was used as a starting template to model by homology the structure of the four DP proteins. The resulting models were used to produce virtual combinatorial peptide libraries constructed using the single amino acid substitution (SAAS) principle. Peptides were docked into the DP binding site using AutoDock at pH 5.0 and pH 7.0. The resulting scores were normalized and used to generate Docking Score-based Quantitative Matrices (DS-QMs). The predictive ability of these QMs was tested using an external test set of 484 known DP binders. They were also compared to existing servers for DP binding prediction. The models derived at pH 5.0 predict better than those derived at pH 7.0 and showed significantly improved predictions for three of the four DP proteins, when compared to the existing servers. They are able to recognize 50% of the known binders in the top 5% of predicted peptides. Conclusions: The higher predictive ability of DS-QMs derived at pH 5.0 may be rationalised by the additional hydrogen bond formed between the backbone carbonyl oxygen belonging to the peptide position before p1 (p-1) and the protonated ε-nitrogen of His 79β. Additionally, protonated His residues are well accepted at most of the peptide binding core positions which is in a good agreement with the overall negatively charged peptide binding site of most MHC proteins

structural differences in kir3dl1 and lilrb1 interaction with hla b and the loading peptide polymorphisms in silico evidences

KIR3DL1 and LILRB1 interact with HLA class I. Using KIR3DL1/HLA-B interaction to set up the procedure, structural immune-informatics approaches have been performed in LILRB1/HLA-B alleles' combination also considering the contribution of the HLA bound peptide. All KIR3DL1 alleles interact strongly with HLA-B alleles carrying Bw4 epitope and negative charged amino acid residues in peptide position P8 disrupt KIR3DL1 binding. HLA-B alleles carrying Ile 194 show a higher strength of interaction with LILRB1 in all the analyzed haplotypes. Finally, we hypothesize a contribution of the amino acid at position 1 of the HLA bound peptide in the modulation of HLA-B/LILRB1 interaction

Structural Differences in KIR3DL1 and LILRB1 Interaction with HLA-B and the Loading Peptide Polymorphisms: In Silico

KIR3DL1 and LILRB1 interact with HLA class I. Using KIR3DL1/HLA-B interaction to set up the procedure, structural immune-informatics approaches have been performed in LILRB1/HLA-B alleles’ combination also considering the contribution of the HLA bound peptide. All KIR3DL1 alleles interact strongly with HLA-B alleles carrying Bw4 epitope and negative charged amino acid residues in peptide position P8 disrupt KIR3DL1 binding. HLA-B alleles carrying Ile 194 show a higher strength of interaction with LILRB1 in all the analyzed haplotypes. Finally, we hypothesize a contribution of the amino acid at position 1 of the HLA bound peptide in the modulation of HLA-B/LILRB1 interaction

Icolos: a workflow manager for structure-based post-processing of de novo generated small molecules

A Summary: We present Icolos, a workflow manager written in Python as a tool for automating complex structure-based workflows for drug design. Icolos can be used as a standalone tool, for example in virtual screening campaigns, or can be used in conjunction with deep learning-based molecular generation facilitated for example by REINVENT, a previously published molecular de novo design package. In this publication, we focus on the internal structure and general capabilities of Icolos, using molecular docking experiments as an illustrative example

Link-INVENT: generative linker design with reinforcement learning

In this work, we present Link-INVENT as an extension to the existing de novo molecular design platform REINVENT. We provide illustrative examples on how Link-INVENT can be applied to fragment linking, scaffold hopping, and PROTAC design case studies where the desirable molecules should satisfy a combination of different criteria. With the help of reinforcement learning, the agent used by Link-INVENT learns to generate favourable linkers connecting molecular subunits that satisfy diverse objectives, facilitating practical application of the model for real-world drug discovery projects. We also introduce a range of linker-specific objectives in the Scoring Function of REINVENT. The code is freely available at https://github.com/MolecularAI/Reinvent

Transformer-based molecular optimization beyond matched molecular pairs

Molecular optimization aims to improve the drug profile of a starting molecule. It is a fundamental problem in drug discovery but challenging due to (i) the requirement of simultaneous optimization of multiple properties and (ii) the large chemical space to explore. Recently, deep learning methods have been proposed to solve this task by mimicking the chemist\u27s intuition in terms of matched molecular pairs (MMPs). Although MMPs is a widely used strategy by medicinal chemists, it offers limited capability in terms of exploring the space of structural modifications, therefore does not cover the complete space of solutions. Often more general transformations beyond the nature of MMPs are feasible and/or necessary, e.g. simultaneous modifications of the starting molecule at different places including the core scaffold. This study aims to provide a general methodology that offers more general structural modifications beyond MMPs. In particular, the same Transformer architecture is trained on different datasets. These datasets consist of a set of molecular pairs which reflect different types of transformations. Beyond MMP transformation, datasets reflecting general structural changes are constructed from ChEMBL based on two approaches: Tanimoto similarity (allows for multiple modifications) and scaffold matching (allows for multiple modifications but keep the scaffold constant) respectively. We investigate how the model behavior can be altered by tailoring the dataset while using the same model architecture. Our results show that the models trained on differently prepared datasets transform a given starting molecule in a way that it reflects the nature of the dataset used for training the model. These models could complement each other and unlock the capability for the chemists to pursue different options for improving a starting molecule

LibINVENT: Reaction-based Generative Scaffold Decoration for in Silico Library Design

Because of the strong relationship between the desired molecular activity and its structural core, the screening of focused, core-sharing chemical libraries is a key step in lead optimization. Despite the plethora of current research focused on in silico methods for molecule generation, to our knowledge, no tool capable of designing such libraries has been proposed. In this work, we present a novel tool for de novo drug design called LibINVENT. It is capable of rapidly proposing chemical libraries of compounds sharing the same core while maximizing a range of desirable properties. To further help the process of designing focused libraries, the user can list specific chemical reactions that can be used for the library creation. LibINVENT is therefore a flexible tool for generating virtual chemical libraries for lead optimization in a broad range of scenarios. Additionally, the shared core ensures that the compounds in the library are similar, possess desirable properties, and can also be synthesized under the same or similar conditions. The LibINVENT code is freely available in our public repository at https://github.com/MolecularAI/Lib-INVENT. The code necessary for data preprocessing is further available at: https://github.com/MolecularAI/Lib-INVENT-dataset

DockStream: a docking wrapper to enhance de novo molecular design

Recently, we have released the de novo design platform REINVENT in version 2.0. This improved and extended iteration supports far more features and scoring function components, which allows bespoke and tailor-made protocols to maximize impact in small molecule drug discovery projects. A major obstacle of generative models is producing active compounds, in which predictive (QSAR) models have been applied to enrich target activity. However, QSAR models are inherently limited by their applicability domains. To overcome these limitations, we introduce a structure-based scoring component for REINVENT. DockStream is a flexible, stand-alone molecular docking wrapper that provides access to a collection of ligand embedders and docking backends. Using the benchmarking and analysis workflow provided in DockStream, execution and subsequent analysis of a variety of docking configurations can be automated. Docking algorithms vary greatly in performance depending on the target and the benchmarking and analysis workflow provides a streamlined solution to identifying productive docking configurations. We show that an informative docking configuration can inform the REINVENT agent to optimize towards improving docking scores using public data. With docking activated, REINVENT is able to retain key interactions in the binding site, discard molecules which do not fit the binding cavity, harness unused (sub-)pockets, and improve overall performance in the scaffold-hopping scenario. The code is freely available at https://github.com/MolecularAI/DockStream

Immunoinformatic Docking Approach for the Analysis of KIR3DL1/HLA-B Interaction

KIR3DL1 is among the most interesting receptors studied, within the killer immunoglobulin receptor (KIR) family. Human leukocyte antigen (HLA) class I Bw4 epitope inhibits strongly Natural Killer (NK) cell's activity through interaction with KIR3DL1 receptor, while Bw6 generally does not. This interaction has been indicated to play an important role in the immune control of different viral infectious diseases. However, the structural interaction between the KIR3DL1 receptor and different HLA-B alleles has been scarcely studied. To understand the complexity of KIR3DL1-HLA-B interaction, HLA-B alleles carrying Bw4/Bw6 epitope and KIR3DL1 * 001 allele in presence of different peptides has been evaluated by using a structural immunoinformatic approach. Different energy minimization force fields (ff) have been tested and NOVA ff enables the successful prediction of ligandreceptor interaction. HLA-B alleles carrying Bw4 epitope present the highest capability of interaction with KIR3DL1 * 001 compared to the HLA-B alleles presenting Bw6. The presence of the epitope Bw4 determines a conformational change which leads to a stronger interaction between nonpolymorphic arginine at position 79 of HLA-B and KIR3DL1 * 001 136-142 loop. The data shed new light on the modalities of KIR3DL1 interaction with HLA-B alleles essential for the modulation of NK immune-mediated response. Background Killer immunoglobulin receptor (KIR)3DL1 is among the most interesting receptors studied within the KIR family. It interacts with polymorphisms located on human leukocyte antigen (HLA)-A and HLA-B molecules that have been widely associated with the control of several infections and autoimmune disorders The KIRs/HLA-B complex crystal structure has been resolved and deposited in the Protein Data Bank The basis for this effect might be due to different, not mutually exclusive mechanisms: (i) direct steric effect, (ii) lack of complementarities between the peptide and KIR3DL1 * 001, and (iii) the induced conformational change in the Bw4 epitope BioMed Research International To assess the complexity of KIR3DL1-HLA-B interaction and its role in the immune regulation we studied the formation of complexes between different HLA-B alleles carrying Bw4/Bw6 and KIR3DL1 * 001 allele in presence of different peptides using a structural immunoinformatic approach. Material and Methods Seven HLA-B alleles have been evaluated according to carrying Bw4/Bw6 epitope supertype Homology modeling has been applied using HLA-B57 KIR3DL1 complex as a template (PDB ID: 3VH8). A MOTIF alignment All models were subjected to energy minimization by using Yet Another Scientific Artificial Reality Application, ("YASARA" http://www.yasara.com/) and the implemented AMBER and NOVA force fields (ff). The same minimization protocol was used with both force fields. This is represented by a combination of steepest descent performed at the beginning and followed up by a simulated annealing. The calculation is performed until the convergence criteria are met or the maximum number of steps is exceeded. To study differences between AMBER and NOVA ff and to prevent bias due to a low number of data points, HLA-B alleles analyzed have at least 100 data points for both binders (EC 50 < 500 nm) and nonbinders (EC 50 > 20000 nm) peptides in the IEDB database (HLA-B * 35:01: binders = 162, non-binders = 108; HLA-B * 51:01: binders = 142, nonbinders = 220; HLA-B * 57:01: binders = 144, non-binders = 205; HLA-B * 58:01: binders = 154, non-binders = 516) HLA-B KIR3DL1 complexes were therefore analyzed using NOVA ff for minimization followed by AMBER ff for energy calculation as previously described The binding free energy (Δ ) has been calculated as the sum of the interactive and solvation energies. KIR3DL1 binding free energy has been calculated as the difference between KIR3DL1-HLA-B-peptide complex and the sum of HLApeptide complex and KIR3DL1 alone as previously reported The difference in the KIR3DL1 binding free energy (ΔΔ ) in presence of different HLA bound peptides has been calculated as differences between the Δ of the KIR3DL1/ HLA-peptide-(substituted) complex and the KIR3DL1/HLApeptide-(reference) complex as previously reported The more negative is the value the stronger is the predicted KIR3DL1 interaction. Statistical analysis has been performed using the paired nonparametric Wilcoxon test, on GraphPad Prism 5.0 (San Diego, CA, USA). Graphics are represented as whiskers of 10-90 percentiles of the distribution obtained for the 180 singleamino acid substitutions in the bound peptide to HLA-B allele. Results and Discussion Immunoinformatic Approach. To select the best strategy for evaluation of the HLA-peptide interaction with KIR3DL1 receptor, different energy minimization ff have been tested in parallel. Among them, AMBER and NOVA ff are the most used in bioinformatics approach The use of AMBER allowed a greater distortion than NOVA in the cocrystallization structure of HLA-B * 57:01-KIR3DL1 when used for energy minimization (AMBER minimized structure RMSD = 0.725; NOVA minimized structure RMSD = 0.573). This is in agreement with previous observation of other molecular systems comparing the two ff Role of HLA Bw4/Bw6 Epitope in KIR3DL1 * 001 Interaction. To evaluate the contribution of Bw4 and Bw6 epitopes together with the peptide substitution in KIR3DL1/HLA interaction, different HLA-B alleles carrying Bw4 or Bw6 epitope have been analyzed The presence of epitope Bw4 determines a higher strength of interaction with KIR3DL1 * 001 allele respect to epitope Bw6 ( < 0.0001, The distribution of the ΔΔ for all the substituted peptides for each HLA-B allele has been further analyzed. HLA-B * 07:02 has the weakest interaction with KIR3DL1 * 001 followed by HLA-B * 35:01 as previously reported in experimental data obtained through flow cytometry and cytotoxic activity assay Conclusions Here we confirm for the first time via structural immunoinformatic approach, the highest interaction of HLA-B alleles carrying Bw4 epitope with KIR3DL1 * 001 compared to epitope Bw6. The presence of epitope Bw4 is determining a conformational change which strengthens the interaction between the nonpolymorphic HLA-B Arg79 residue and KIR3DL1 * 001 136-142 loop. In conclusion, the study of KIR3DL1-HLA-B aims to explain the mechanisms of immune modulation of NK cells immune response and may provide the basis for the design of more specific immunotherapeutic approaches in infectious diseases and immune disorders