Docking and scoring with alternative side-chain conformations

Distinct conformational and energetic features define the specific recognition of (di)aromatic peptide motifs by PEX14

The cycling import receptor PEX5 and its membrane-located binding partner PEX14 are key constituents of the peroxisomal import machinery. Upon recognition of newly synthesized cargo proteins carrying a peroxisomal targeting signal type 1 (PTS1) in the cytosol, the PEX5/cargo complex docks at the peroxisomal membrane by binding to PEX14. The PEX14 N-terminal domain (NTD) recognizes (di)aromatic peptides, mostly corresponding to Wxxx(F/Y)-motifs, with nano-to micromolar affinity. Human PEX5 possesses eight of these conserved motifs distributed within its 320-residue disordered N-terminal region. Here, we combine biophysical (ITC, NMR, CD), biochemical and computational methods to characterize the recognition of these (di)aromatic peptides motifs and identify key features that are recognized by PEX14. Notably, the eight motifs present in human PEX5 exhibit distinct affinities and energetic contributions for the interaction with the PEX14 NTD. Computational docking and analysis of the interactions of the (di)aromatic motifs identify the specific amino acids features that stabilize a helical conformation of the peptide ligands and mediate interactions with PEX14 NTD. We propose a refined consensus motif ExWΦxE(F/Y)Φ for high affinity binding to the PEX14 NTD and discuss conservation of the (di)aromatic peptide recognition by PEX14 in other species.

Introduction to the BioChemical Library (BCL): An Application-Based Open-Source Toolkit for Integrated Cheminformatics and Machine Learning in Computer-Aided Drug Discovery

The BioChemical Library (BCL) cheminformatics toolkit is an application-based academic open-source software package designed to integrate traditional small molecule cheminformatics tools with machine learning-based quantitative structure-activity/property relationship (QSAR/QSPR) modeling. In this pedagogical article we provide a detailed introduction to core BCL cheminformatics functionality, showing how traditional tasks (e.g., computing chemical properties, estimating druglikeness) can be readily combined with machine learning. In addition, we have included multiple examples covering areas of advanced use, such as reaction-based library design. We anticipate that this manuscript will be a valuable resource for researchers in computer-aided drug discovery looking to integrate modular cheminformatics and machine learning tools into their pipelines.

Protein Binding Pocket Optimization for Virtual High-Throughput Screening (vHTS) Drug Discovery

The virtual high-throughput screening (vHTS) approach has been widely used for large database screening to identify potential lead compounds for drug discovery. Due to its high computational demands, docking that allows receptor flexibility has been a challenging problem for virtual screening. Therefore, the selection of protein target conformations is crucial to produce useful vHTS results. Since only a single protein structure is used to screen large databases in most vHTS studies, the main challenge is to reduce false negative rates in selecting compounds for in vitro tests. False negatives are most likely to occur when using apo structures or homology models of protein targets due to the small volume of the binding pocket formed by incorrect side-chain conformations. Even holo protein structures can exhibit high false negative rates due to ligand-induced fit effects, since the shape of the binding pocket highly depends on its bound ligand. To reduce false negative rates and improve success rates for vHTS in drug discovery, we have developed a new Monte Carlo-based approach that optimizes the binding pocket of protein targets. This newly developed Monte Carlo pocket optimization (MCPO) approach was assessed on several datasets showing promising results. The binding pocket optimization approach could be a useful tool for vHTS-based drug discovery, especially in cases when only apo structures or homology models are available.

Key Features Relevant to Select Antigens and TCR From the MHC-Mismatched Repertoire to Treat Cancer

Adoptive transfer of T cells transgenic for tumor-reactive T-cell receptors (TCR) is an attractive immunotherapeutic approach. However, clinical translation is so far limited due to challenges in the identification of suitable target antigens as well as TCRs that are concurrent safe and efficient. Definition of key characteristics relevant for effective and specific tumor rejection is essential to improve current TCR-based adoptive T-cell immunotherapies. We here characterized in-depth two TCRs derived from the human leukocyte antigen (HLA)-mismatched allogeneic repertoire targeting two different myeloperoxidase (MPO)-derived peptides presented by the same HLA-restriction element side by side comprising state of the art biochemical and cellular in vitro, in vivo, and in silico experiments. In vitro experiments reveal comparable functional avidities, off-rates, and cytotoxic activities for both TCRs. However, we observed differences especially with respect to cytokine secretion and cross-reactivity as well as in vivo activity. Biochemical and in silico analyses demonstrate different binding qualities of MPO-peptides to the HLA-complex determining TCR qualities. We conclude from our biochemical and in silico analyses of peptide-HLA-binding that rigid and high-affinity binding of peptides is one of the most important factors for isolation of TCRs with high specificity and tumor rejection capacity from the MHC-mismatched repertoire. Based on our results, we developed a workflow for selection of such TCRs with high potency and safety profile suitable for clinical translation.

Amino Acid Substitutions within HLA-B*27-Restricted T Cell Epitopes Prevent Recognition by Hepatitis Delta Virus-Specific CD8+ T Cells

Virus-specific CD8 T cell response seems to play a significant role in the outcome of hepatitis delta virus (HDV) infection. However, the HDV-specific T cell epitope repertoire and mechanisms of CD8 T cell failure in HDV infection have been poorly characterized. We therefore aimed to characterize HDV-specific CD8 T cell epitopes and the impacts of viral mutations on immune escape. In this study, we predicted peptide epitopes binding the most frequent human leukocyte antigen (HLA) types and assessed their HLA binding capacities. These epitopes were characterized in HDV-infected patients by intracellular gamma interferon (IFN-γ) staining. Sequence analysis of large hepatitis delta antigen (L-HDAg) and HLA typing were performed in 104 patients. The impacts of substitutions within epitopes on the CD8 T cell response were evaluated experimentally and by in silico studies. We identified two HLA-B*27-restricted CD8 T cell epitopes within L-HDAg. These novel epitopes are located in a relatively conserved region of L-HDAg. However, we detected molecular footprints within the epitopes in HLA-B*27-positive patients with chronic HDV infections. The variant peptides were not cross-recognized in HLA-B*27-positive patients with resolved HDV infections, indicating that the substitutions represent viral escape mutations. Molecular modeling of HLA-B*27 complexes with the L-HDAg epitope and its potential viral escape mutations indicated that the structural and electrostatic properties of the bound peptides differ considerably at the T cell receptor interface, which provides a possible molecular explanation for the escape mechanism. This viral escape from the HLA-B*27-restricted CD8 T cell response correlates with a chronic outcome of hepatitis D infection. T cell failure resulting from immune escape may contribute to the high chronicity rate in HDV infection.IMPORTANCE Hepatitis delta virus (HDV) causes severe chronic hepatitis, which affects 20 million people worldwide. Only a small number of patients are able to clear the virus, possibly mediated by a virus-specific T cell response. Here, we performed a systematic screen to define CD8 epitopes and investigated the role of CD8 T cells in the outcome of hepatitis delta and how they fail to eliminate HDV. Overall the number of epitopes identified was very low compared to other hepatotropic viruses. We identified, two HLA-B*27-restricted epitopes in patients with resolved infections. In HLA-B*27-positive patients with chronic HDV infections, however, we detected escape mutations within these identified epitopes that could lead to viral evasion of immune responses. These findings support evidence showing that HLA-B*27 is important for virus-specific CD8 T cell responses, similar to other viral infections. These results have implications for the clinical prognosis of HDV infection and for vaccine development.

Role of Supercomputers in Bioinformatics

Due to the involvement of effective and client-friendly components (i.e. supercomputers), rapid data analysis is being accomplished. In Bioinformatics, it is expanding many areas of research such as genomics, proteomics, metabolomics, etc. Structure-based drug design is one of the major areas of research to cure human malady. This chapter initiates a discussion on supercomputing in sequence analysis with a detailed table summarizing the software and Web-based programs used for sequence analysis. A brief talk on the supercomputing in virtual screening is given where the databases like DOCK, ZINC, EDULISS, etc. are introduced. As the chapter transitions to the next phase, the intricacies of advanced Quantitative Structure-Activity Relationship technologies like Fragment-Based 2D QSAR, Multiple-Field 3D QSAR, and Amino Acid-Based Peptide Prediction are put forth in a manner similar to the concept of abstraction. The supercomputing in docking studies is stressed where docking software for Protein-Ligand docking, Protein-Protein docking, and Multi-Protein docking are provided. The chapter ends with the applications of supercomputing in widely used microarray data analysis.

BiPPred: Combined sequence- and structure-based prediction of peptide binding to the Hsp70 chaperone BiP

Substrate binding to Hsp70 chaperones is involved in many biological processes, and the identification of potential substrates is important for a comprehensive understanding of these events. We present a multi-scale pipeline for an accurate, yet efficient prediction of peptides binding to the Hsp70 chaperone BiP by combining sequence-based prediction with molecular docking and MMPBSA calculations. First, we measured the binding of 15mer peptides from known substrate proteins of BiP by peptide array (PA) experiments and performed an accuracy assessment of the PA data by fluorescence anisotropy studies. Several sequence-based prediction models were fitted using this and other peptide binding data. A structure-based position-specific scoring matrix (SB-PSSM) derived solely from structural modeling data forms the core of all models. The matrix elements are based on a combination of binding energy estimations, molecular dynamics simulations, and analysis of the BiP binding site, which led to new insights into the peptide binding specificities of the chaperone. Using this SB-PSSM, peptide binders could be predicted with high selectivity even without training of the model on experimental data. Additional training further increased the prediction accuracies. Subsequent molecular docking (DynaDock) and MMGBSA/MMPBSA-based binding affinity estimations for predicted binders allowed the identification of the correct binding mode of the peptides as well as the calculation of nearly quantitative binding affinities. The general concept behind the developed multi-scale pipeline can readily be applied to other protein-peptide complexes with linearly bound peptides, for which sufficient experimental binding data for the training of classical sequence-based prediction models is not available. Proteins 2016; 84:1390-1407. © 2016 Wiley Periodicals, Inc.

Building a virtual ligand screening pipeline using free software: a survey

Virtual screening, the search for bioactive compounds via computational methods, provides a wide range of opportunities to speed up drug development and reduce the associated risks and costs. While virtual screening is already a standard practice in pharmaceutical companies, its applications in preclinical academic research still remain under-exploited, in spite of an increasing availability of dedicated free databases and software tools. In this survey, an overview of recent developments in this field is presented, focusing on free software and data repositories for screening as alternatives to their commercial counterparts, and outlining how available resources can be interlinked into a comprehensive virtual screening pipeline using typical academic computing facilities. Finally, to facilitate the set-up of corresponding pipelines, a downloadable software system is provided, using platform virtualization to integrate pre-installed screening tools and scripts for reproducible application across different operating systems.

Insights into Protein-Ligand Interactions: Mechanisms, Models, and Methods

Molecular recognition, which is the process of biological macromolecules interacting with each other or various small molecules with a high specificity and affinity to form a specific complex, constitutes the basis of all processes in living organisms. Proteins, an important class of biological macromolecules, realize their functions through binding to themselves or other molecules. A detailed understanding of the protein–ligand interactions is therefore central to understanding biology at the molecular level. Moreover, knowledge of the mechanisms responsible for the protein-ligand recognition and binding will also facilitate the discovery, design, and development of drugs. In the present review, first, the physicochemical mechanisms underlying protein–ligand binding, including the binding kinetics, thermodynamic concepts and relationships, and binding driving forces, are introduced and rationalized. Next, three currently existing protein-ligand binding models—the “lock-and-key”, “induced fit”, and “conformational selection”—are described and their underlying thermodynamic mechanisms are discussed. Finally, the methods available for investigating protein–ligand binding affinity, including experimental and theoretical/computational approaches, are introduced, and their advantages, disadvantages, and challenges are discussed.

Parameterization of the Hamiltonian Dielectric Solvent (HADES) Reaction-Field Method for the Solvation Free Energies of Amino Acid Side-Chain Analogs

Optimization of the Hamiltonian dielectric solvent (HADES) method for biomolecular simulations in a dielectric continuum is presented with the goal of calculating accurate absolute solvation free energies while retaining the model's accuracy in predicting conformational free-energy differences. The solvation free energies of neutral and polar amino acid side-chain analogs calculated by using HADES, which may optionally include nonpolar contributions, were optimized against experimental data to reach a chemical accuracy of about 0.5 kcal mol(-1). The new parameters were evaluated for charged side-chain analogs. The HADES results were compared with explicit-solvent, generalized Born, Poisson-Boltzmann, and QM-based methods. The potentials of mean force (PMFs) between pairs of side-chain analogs obtained by using HADES and explicit-solvent simulations were used to evaluate the effects of the improved parameters optimized for solvation free energies on intermolecular potentials.

GalaxyDock: protein-ligand docking with flexible protein side-chains

An important issue in developing protein-ligand docking methods is how to incorporate receptor flexibility. Consideration of receptor flexibility using an ensemble of precompiled receptor conformations or by employing an effectively enlarged binding pocket has been reported to be useful. However, direct consideration of receptor flexibility during energy optimization of the docked conformation has been less popular because of the large increase in computational complexity. In this paper, we present a new docking program called GalaxyDock that accounts for the flexibility of preselected receptor side-chains by global optimization of an AutoDock-based energy function trained for flexible side-chain docking. This method was tested on 3 sets of protein-ligand complexes (HIV-PR, LXRβ, cAPK) and a diverse set of 16 proteins that involve side-chain conformational changes upon ligand binding. The cross-docking tests show that the performance of GalaxyDock is higher or comparable to previous flexible docking methods tested on the same sets, increasing the binding conformation prediction accuracy by 10%-60% compared to rigid-receptor docking. This encouraging result suggests that this powerful global energy optimization method may be further extended to incorporate larger magnitudes of receptor flexibility in the future. The program is available at http://galaxy.seoklab.org/softwares/galaxydock.html .

Potential and limitations of ensemble docking

A major problem in structure-based virtual screening applications is the appropriate selection of a single or even multiple protein structures to be used in the virtual screening process. A priori it is unknown which protein structure(s) will perform best in a virtual screening experiment. We investigated the performance of ensemble docking, as a function of ensemble size, for eight targets of pharmaceutical interest. Starting from single protein structure docking results, for each ensemble size up to 500,000 combinations of protein structures were generated, and, for each ensemble, pose prediction and virtual screening results were derived. Comparison of single to multiple protein structure results suggests improvements when looking at the performance of the worst and the average over all single protein structures to the performance of the worst and average over all protein ensembles of size two or greater, respectively. We identified several key factors affecting ensemble docking performance, including the sampling accuracy of the docking algorithm, the choice of the scoring function, and the similarity of database ligands to the cocrystallized ligands of ligand-bound protein structures in an ensemble. Due to these factors, the prospective selection of optimum ensembles is a challenging task, shown by a reassessment of published ensemble selection protocols.

Drug design for ever, from hype to hope

In its first 25 years JCAMD has been disseminating a large number of techniques aimed at finding better medicines faster. These include genetic algorithms, COMFA, QSAR, structure based techniques, homology modelling, high throughput screening, combichem, and dozens more that were a hype in their time and that now are just a useful addition to the drug-designers toolbox. Despite massive efforts throughout academic and industrial drug design research departments, the number of FDA-approved new molecular entities per year stagnates, and the pharmaceutical industry is reorganising accordingly. The recent spate of industrial consolidations and the concomitant move towards outsourcing of research activities requires better integration of all activities along the chain from bench to bedside. The next 25 years will undoubtedly show a series of translational science activities that are aimed at a better communication between all parties involved, from quantum chemistry to bedside and from academia to industry. This will above all include understanding the underlying biological problem and optimal use of all available data.

Identifying continuous pores in protein structures with PROPORES by computational repositioning of gating residues

Proteins containing concavities such as pockets, cavities, and tunnels or pores perform important functions in ligand-induced signal transduction, enzymatic catalysis, and in facilitating the permeation of small molecules through membranes. Computational algorithms for identifying such shapes are therefore of great use for studying the mechanisms of these reactions. We developed the novel toolkit PROPORES for pore identification and applied our program to the systems aquaporin, tryptophan synthase, leucine transporter, and acetylcholinesterase. As a novel feature, the program checks whether access to occluded ligand binding pockets or blocked channels can be achieved by systematically rotating side chains of the gating residues. In this way, we obtain a more flexible view of the putative structural adaptability of protein structures. For the four systems mentioned, the new method was able to identify connections between pores that are separated in the X-ray structures or to connect internal pores with the protein surrounding. The software is available from http://gepard.bioinformatik.uni-saarland.de/software/propores/.

Efficient incorporation of protein flexibility and dynamics into molecular docking simulations

Flexibility and dynamics are protein characteristics that are essential for the process of molecular recognition. Conformational changes in the protein that are coupled to ligand binding are described by the biophysical models of induced fit and conformational selection. Different concepts that incorporate protein flexibility into protein-ligand docking within the context of these two models are reviewed. Several computational studies that discuss the validity and possible limitations of such approaches will be presented. Finally, different approaches that incorporate protein dynamics, e.g., configurational entropy, and solvation effects into docking will be highlighted.

Computational design of protein-ligand interfaces: potential in therapeutic development

Computational design of protein-ligand interfaces finds optimal amino acid sequences within a small-molecule binding site of a protein for tight binding of a specific small molecule. It requires a search algorithm that can rapidly sample the vast sequence and conformational space, and a scoring function that can identify low energy designs. This review focuses on recent advances in computational design methods and their application to protein-small molecule binding sites. Strategies for increasing affinity, altering specificity, creating broad-spectrum binding, and building novel enzymes from scratch are described. Future prospects for applications in drug development are discussed, including limitations that will need to be overcome to achieve computational design of protein therapeutics with novel modes of action.

DynaDock: A new molecular dynamics-based algorithm for protein-peptide docking including receptor flexibility

Molecular docking programs play an important role in drug development and many well-established methods exist. However, there are two situations for which the performance of most approaches is still not satisfactory, namely inclusion of receptor flexibility and docking of large, flexible ligands like peptides. In this publication a new approach is presented for docking peptides into flexible receptors. For this purpose a two step procedure was developed: first, the protein-peptide conformational space is scanned and approximate ligand poses are identified and second, the identified ligand poses are refined by a new molecular dynamics-based method, optimized potential molecular dynamics (OPMD). The OPMD approach uses soft-core potentials for the protein-peptide interactions and applies a new optimization scheme to the soft-core potential. Comparison with refinement results obtained by conventional molecular dynamics and a soft-core scaling approach shows significant improvements in the sampling capability for the OPMD method. Thus, the number of starting poses needed for successful refinement is much lower than for the other methods. The algorithm was evaluated on 15 protein-peptide complexes with 2-16mer peptides. Docking poses with peptide RMSD values <2.10 A from the equilibrated experimental structures were obtained in all cases. For four systems docking into the unbound receptor structures was performed, leading to peptide RMSD values <2.12 A. Using a specifically fitted scoring function in 11 of 15 cases the best scoring poses featured a peptide RMSD < or = 2.10 A.

Conformational transitions upon ligand binding: holo-structure prediction from apo conformations

Biological function of proteins is frequently associated with the formation of complexes with small-molecule ligands. Experimental structure determination of such complexes at atomic resolution, however, can be time-consuming and costly. Computational methods for structure prediction of protein/ligand complexes, particularly docking, are as yet restricted by their limited consideration of receptor flexibility, rendering them not applicable for predicting protein/ligand complexes if large conformational changes of the receptor upon ligand binding are involved. Accurate receptor models in the ligand-bound state (holo structures), however, are a prerequisite for successful structure-based drug design. Hence, if only an unbound (apo) structure is available distinct from the ligand-bound conformation, structure-based drug design is severely limited. We present a method to predict the structure of protein/ligand complexes based solely on the apo structure, the ligand and the radius of gyration of the holo structure. The method is applied to ten cases in which proteins undergo structural rearrangements of up to 7.1 A backbone RMSD upon ligand binding. In all cases, receptor models within 1.6 A backbone RMSD to the target were predicted and close-to-native ligand binding poses were obtained for 8 of 10 cases in the top-ranked complex models. A protocol is presented that is expected to enable structure modeling of protein/ligand complexes and structure-based drug design for cases where crystal structures of ligand-bound conformations are not available.