Ligand design by a combinatorial approach based on modeling and experiment: application to HLA-DR4

Solvation Free Energy as a Measure of Hydrophobicity: Application to Serine Protease Binding Interfaces

Solvation and hydrophobicity play a key role in a variety of biological mechanisms. In substrate binding, but also in structure-based drug design, the thermodynamic properties of water molecules surrounding a given protein are of high interest. One of the main algorithms devised in recent years to quantify thermodynamic properties of water is the grid inhomogeneous solvation theory (GIST), which calculates these features on a grid surrounding the protein. Despite the inherent advantages of GIST, the computational demand is a major drawback, as calculations for larger systems can take days or even weeks. Here, we present a GPU accelerated version of the GIST algorithm, which facilitates efficient estimates of solvation free energy even of large biomolecular interfaces. Furthermore, we show that GIST can be used as a reliable tool to evaluate protein surface hydrophobicity. We apply the approach on a set of nine different proteases calculating localized solvation free energies on the surface of the binding interfaces as a measure of their hydrophobicity. We find a compelling agreement with the hydrophobicity of their substrates, i.e., peptides, binding into the binding cleft, and thus our approach provides a reliable description of hydrophobicity characteristics of these biological interfaces.

Combinatorial chemistry in drug discovery

Several combinatorial methods have been developed to create focused or diverse chemical libraries with a wide range of linear or macrocyclic chemical molecules: peptides, non-peptide oligomers, peptidomimetics, small-molecules, and natural product-like organic molecules. Each combinatorial approach has its own unique high-throughput screening and encoding strategy. In this article, we provide a brief overview of combinatorial chemistry in drug discovery with emphasis on recently developed new technologies for design, synthesis, screening and decoding of combinatorial library. Examples of successful application of combinatorial chemistry in hit discovery and lead optimization are given. The limitations and strengths of combinatorial chemistry are also briefly discussed. We are now in a better position to truly leverage the power of combinatorial technologies for the discovery and development of next-generation drugs.

The Roles of Water in the Protein Matrix: A Largely Untapped Resource for Drug Discovery

The value of thoroughly understanding the thermodynamics specific to a drug discovery/design study is well known. Over the past decade, the crucial roles of water molecules in protein structure, function, and dynamics have also become increasingly appreciated. This Perspective explores water in the biological environment by adopting its point of view in such phenomena. The prevailing thermodynamic models of the past, where water was seen largely in terms of an entropic gain after its displacement by a ligand, are now known to be much too simplistic. We adopt a set of terminology that describes water molecules as being "hot" and "cold", which we have defined as being easy and difficult to displace, respectively. The basis of these designations, which involve both enthalpic and entropic water contributions, are explored in several classes of biomolecules and structural motifs. The hallmarks for characterizing water molecules are examined, and computational tools for evaluating water-centric thermodynamics are reviewed. This Perspective's summary features guidelines for exploiting water molecules in drug discovery.

Predicting fragment binding poses using a combined MCSS MM-GBSA approach

Improved methods are required to predict the position and orientation (pose) of binding to the target protein of low molecular weight compounds identified in fragment screening campaigns. This is particularly important to guide initial chemistry to generate structure-activity relationships for the cases where a high resolution structure cannot be obtained. We have assessed the benefit of an implicit solvent method for assessment of fragment binding poses generated by the Multiple Copy Simultaneous Search (MCSS) method in CHARMm. Additionally, the effect of using multiple receptor structures for a flexible receptor is investigated. The original MCSS performance -50% of fragment positions accurately predicted and scored - was increased up to 67% by scoring MCSS energy minima with a Molecular Mechanics Generalized Born approach with molecular volume integration and Surface Area model (MM-GBSA). The same increase in performance (but occasionally for different targets) was observed when using the docking program GOLD followed by MM-GBSA rescoring. The combined results from both methods resulted in a higher success rate emphasizing that a comparison of different docking methods can increase the correct identification of binding poses. For a receptor where multiple structures are available, Hsp90, the average performance on randomly adding receptor structures was also investigated. The results suggest that predictions using these docking methods can be used with some confidence to guide chemical optimization, if the structure of the target either remains relatively fixed on ligand binding, or if a number of crystal structures are available with diverse ligands bound and there is information on the positions of key water molecules in the binding site.

Design of glycopeptides used to investigate class II MHC binding and T-cell responses associated with autoimmune arthritis

The glycopeptide fragment CII259–273 from type II collagen (CII) binds to the murine A^q and human DR4 class II Major Histocompatibility Complex (MHC II) proteins, which are associated with development of murine collagen-induced arthritis (CIA) and rheumatoid arthritis (RA), respectively. It has been shown that CII259–273 can be used in therapeutic vaccination of CIA. This glycopeptide also elicits responses from T-cells obtained from RA patients, which indicates that it has an important role in RA as well. We now present a methodology for studies of (glyco)peptide-receptor interactions based on a combination of structure-based virtual screening, ligand-based statistical molecular design and biological evaluations. This methodology included the design of a CII259–273 glycopeptide library in which two anchor positions crucial for binding in pockets of A^q and DR4 were varied. Synthesis and biological evaluation of the designed glycopeptides provided novel structure-activity relationship (SAR) understanding of binding to A^q and DR4. Glycopeptides that retained high affinities for these MHC II proteins and induced strong responses in panels of T-cell hybridomas were also identified. An analysis of all the responses revealed groups of glycopeptides with different response patterns that are of high interest for vaccination studies in CIA. Moreover, the SAR understanding obtained in this study provides a platform for the design of second-generation glycopeptides with tuned MHC affinities and T-cell responses.

Fragment-based drug design

Fragment-based drug design (FBDD), which is comprised of both fragment screening and the use of fragment hits to design leads, began more than 15 years ago and has been steadily gaining in popularity and utility. Its origin lies on the fact that the coverage of chemical space and the binding efficiency of hits are directly related to the size of the compounds screened. Nevertheless, FBDD still faces challenges, among them developing fragment screening libraries that ensure optimal coverage of chemical space, physical properties and chemical tractability. Fragment screening also requires sensitive assays, often biophysical in nature, to detect weak binders. In this chapter we will introduce the technologies used to address these challenges and outline the experimental advantages that make FBDD one of the most popular new hit-to-lead process.

De novo design: balancing novelty and confined chemical space

IMPORTANCE OF THE FIELD

De novo drug design serves as a tool for the discovery of new ligands for macromolecular targets as well as optimization of known ligands. Recently developed tools aim to address the multi-objective nature of drug design in an unprecedented manner.

AREAS COVERED IN THIS REVIEW

This article discusses recent advances in de novo drug design programs and accessory programs used to evaluate compounds post-generation.

WHAT THE READER WILL GAIN

The reader is introduced to the challenges inherent in de novo drug design and will become familiar with current trends in de novo design. Furthermore, the reader will be better prepared to assess the value of a tool, and be equipped to design more elegant tools in the future.

TAKE HOME MESSAGE

De novo drug design can assist in the efficient discovery of new compounds with a high affinity for a given target. The inclusion of existing chemoinformatic methods with current structure-based de novo design tools provides a means of enhancing the therapeutic value of these generated compounds.

Identifying druggable disease-modifying gene products

Many disease genes encode proteins that are difficult to target directly using small molecule drugs. Improvements in libraries based on synthetic compounds, natural products, and other types of molecules may ultimately allow some challenging proteins to be successfully targeted; however, these developments alone are unlikely to be sufficient. A complementary strategy exploits the functional interconnectivity of intracellular networks to find druggable targets lying upstream, downstream, or in parallel to a disease-causing gene, where modulation can influence the disease process indirectly. These targets can be selected using prior knowledge of disease-associated pathways or identified using phenotypic chemical and genetic screens in model organisms and cells. These approaches should facilitate the identification of effective drug targets for many genetic disorders.

Three-dimensional model of lanosterol 14 alpha-demethylase from Cryptococcus neoformans: active-site characterization and insights into azole binding

Cryptococcus neoformans is one of the most important causes of life-threatening fungal infections in immunocompromised patients. Lanosterol 14 alpha-demethylase (CYP51) is the target of azole antifungal agents. This study describes, for the first time, the 3-dimensional model of CYP51 from Cryptococcus neoformans (CnCYP51). The model was further refined by energy minimization and molecular-dynamics simulations. The active site of CnCYP51 was well characterized by multiple-copy simultaneous-search calculations, and four functional regions important for rational drug design were identified. The mode of binding of the natural substrate and azole antifungal agents with CnCYP51 was identified by flexible molecular docking. A G484S substitution mechanism for azole resistance in CnCYP51, which might be important for the conformation of the heme environment, is suggested.

Homology modeling and molecular dynamics simulation of N-myristoyltransferase from protozoan parasites: active site characterization and insights into rational inhibitor design

Myristoyl-CoA:protein N-myristoyltransferase (NMT) is a cytosolic monomeric enzyme that catalyzes the transfer of the myristoyl group from myristoyl-CoA to the N-terminal glycine of a number of eukaryotic cellular and viral proteins. Recent experimental data suggest NMT from parasites could be a promising new target for the design of novel antiparasitic agents with new mode of action. However, the active site topology and inhibitor specificity of these enzymes remain unclear. In this study, three-dimensional models of NMT from Plasmodium falciparum (PfNMT), Leishmania major (LmNMT) and Trypanosoma brucei (TbNMT) were constructed on the basis of the crystal structures of fungal NMTs using homology modeling method. The models were further refined by energy minimization and molecular dynamics simulations. The active sites of PfNMT, LmNMT and TbNMT were characterized by multiple copy simultaneous search (MCSS). MCSS functional maps reveal that PfNMT, LmNMT and TbNMT share a similar active site topology, which is defined by two hydrophobic pockets, a hydrogen-bonding (HB) pocket, a negatively-charged HB pocket and a positively-charged HB pocket. Flexible docking approaches were then employed to dock known inhibitors into the active site of PfNMT. The binding mode, structure-activity relationships and selectivity of inhibitors were investigated in detail. From the results of molecular modeling, the active site architecture and certain key residues responsible for inhibitor binding were identified, which provided insights for the design of novel inhibitors of parasitic NMTs.

Class II HLA-peptide binding prediction using structural principles

The precise prediction of class II human leukocyte antigen (HLA) peptide binding finds application in epitope design for the development of vaccines and diagnostics of diseases associated with CD4+ T-cellular immunity. HLA II binding peptides have an extended conformation at the binding groove unlike class I. This increases peptide binding combinations of varying length at the groove, having an eventual effect in the host immune response to infectious agents. Here we describe the development of a prediction model using information gleaned from HLA II-peptide (HLA II-p) structural data. We created a manually curated dataset of 15 HLA II-p structural complexes from Protein databank (PDB). The dataset was used to develop virtual binding pockets for accommodating HLA-II-specific short peptides. The binding of peptides to the virtual pockets is estimated using the Q matrix (a quantitative matrix based on amino acid residue properties). Internal cross-validation of the model using the 15 HLA II-p structural complexes produced an accuracy of 53% with a sensitivity of 53%. The model was further evaluated using a dataset of 3676 class II-specific peptides consisting of 1188 binders and 2488 nonbinders derived from MHCBN (a database of HLA binders and nonbinders). The model produced an accuracy of 53% with 70.8% specificity and 27.6% sensitivity. The positive predictive value (PPV) was 62% and the negative predictive value (NPV) 58%. A 62% PPV suggests that the model fairly predicts a good number of binders among predicted binders and thus that the success rate among predicted binder for further verification is good. The described model is simple and rapid, with large HLA allele coverage representing the sampled global population, despite weak prediction accuracy. The ability of the model to predict a wide array of defined class II alleles is found to be applicable for proteome-wide scanning of parasitic genomes.

Automated clustering of probe molecules from solvent mapping of protein surfaces: new algorithms applied to hot-spot mapping and structure-based drug design

Use of solvent mapping, based on multiple-copy minimization (MCM) techniques, is common in structure-based drug discovery. The minima of small-molecule probes define locations for complementary interactions within a binding pocket. Here, we present improved methods for MCM. In particular, a Jarvis-Patrick (JP) method is outlined for grouping the final locations of minimized probes into physical clusters. This algorithm has been tested through a study of protein-protein interfaces, showing the process to be robust, deterministic, and fast in the mapping of protein "hot spots." Improvements in the initial placement of probe molecules are also described. A final application to HIV-1 protease shows how our automated technique can be used to partition data too complicated to analyze by hand. These new automated methods may be easily and quickly extended to other protein systems, and our clustering methodology may be readily incorporated into other clustering packages.