In silico fragment-based drug design using a PASS approach

Fragment-based drug design integrates different methods to create novel ligands using fragment libraries focused on particular biological activities. Experimental approaches to the preparation of fragment libraries have some drawbacks caused by the need for target crystallization (X-ray and nuclear magnetic resonance) and careful immobilization (surface plasmon resonance). Molecular modelling (docking) requires accurate data on protein-ligand interactions, which are difficult to obtain for some proteins. The main drawbacks of QSAR application are associated with the need to collect large homogeneous datasets of chemical structures with experimentally determined self-consistent quantitative values (potency). We propose a ligand-based approach to the selection of fragments with positive contribution to biological activity, developed on the basis of the PASS algorithm. The robustness of the PASS algorithm for heterogeneous datasets has been shown earlier. PASS estimates qualitative (yes/no) prediction of biological activity spectra for over 4000 biological activities and, therefore, provides the basis for the preparation of a fragment library corresponding to multiple criteria. The algorithm for fragment selection has been validated using the fractions of intermolecular interactions calculated for known inhibitors of nine enzymes extracted from the Protein Data Bank database. The statistical significance of differences between fractions of intermolecular interactions corresponds, for several enzymes, to the estimated positive and negative contribution of fragments in enzyme inhibition.     

Relationship between Hot Spot Residues and Ligand Binding Hot Spots in Protein-Protein Interfaces

In the context of protein-protein interactions, the term "hot spot" refers to a residue or cluster of residues that makes a major contribution to the binding free energy, as determined by alanine scanning mutagenesis. In contrast, in pharmaceutical research, a hot spot is a site on a target protein that has high propensity for ligand binding and hence is potentially important for drug discovery. Here we examine the relationship between these two hot spot concepts by comparing alanine scanning data for a set of 15 proteins with results from mapping the protein surfaces for sites that can bind fragment-sized small molecules. We find the two types of hot spots are largely complementary; the residues protruding into hot spot regions identified by computational mapping or experimental fragment screening are almost always themselves hot spot residues as defined by alanine scanning experiments. Conversely, a residue that is found by alanine scanning to contribute little to binding rarely interacts with hot spot regions on the partner protein identified by fragment mapping. In spite of the strong correlation between the two hot spot concepts, they fundamentally differ, however. In particular, while identification of a hot spot by alanine scanning establishes the potential to generate substantial interaction energy with a binding partner, there are additional topological requirements to be a hot spot for small molecule binding. Hence, only a minority of hot spots identified by alanine scanning represent sites that are potentially useful for small inhibitor binding, and it is this subset that is identified by experimental or computational fragment screening.     

2D depiction of protein-ligand complexes

A method is presented for the automated preparation of schematic diagrams for protein-ligand complexes, in which the ligand is displayed in conventional 2D form, and the interactions to and between the residues in its vicinity are summarized in a concise and information-rich manner. The structural entities are arranged to maximize aesthetic ideals and to properly convey important distance relationships. The diagram is annotated with calculated hydrogen bonds, a substitution contour, solvent exposure, chelated metals, covalently bound linkages, pi-pi and pi-cation interactions, and, for series of complexes, conserved residues and interactions. Residues, cofactors, ions, and solvent components are drawn in cartoon form as adjuncts to the ligand. The method can be applied to aligned sets which contain multiple ligands, or multiple members of a protein family, in which case the ligand orientations and protein residue placement will show consistent trends throughout the series.     

乙型肝炎表面抗原片段三维结构的同源模建及其配体的设计

利用同源模建和分子动力学优化得到了一种乙肝表面抗原片段的三维结构.通过对活性部位的分析,设计了与抗原片段相结合的配体.讨论了Trp163,Trp165和Pro70对于紧密结合配体所起的重要作用,抗原片段与配体之间的氢键也决定了它们结合的相对位置.从复合物得到的结构信息将有助于揭示配体与乙肝抗原的作用机理,促进乙型肝炎病人诊断与治疗试剂的研制     

An analysis of the interactions between the Sem-5 SH3 domain and its ligands using molecular dynamics, free energy calculations, and sequence analysis.

The Src-homology-3 (SH3) domain of the Caenorhabditis elegans protein Sem-5 binds proline-rich sequences. It is reported that the SH3 domains broadly accept amide N-substituted residues instead of only recognizing prolines on the basis of side chain shape or rigidity. We have studied the interactions between Sem-5 and its ligands using molecular dynamics (MD), free energy calculations, and sequence analysis. Relative binding free energies, estimated by a method called MM/PBSA, between different substitutions at sites -1, 0, and +2 of the peptide are consistent with the experimental data. A new method to calculate atomic partial charges, AM1-BCC method, is also used in the binding free energy calculations for different N-substitutions at site -1. The results are very similar to those obtained from widely used RESP charges in the AMBER force field. AM1-BCC charges can be calculated more rapidly for any organic molecule than can the RESP charges. Therefore, their use can enable a broader and more efficient application of the MM/PBSA method in drug design. Examination of each component of the free energy leads to the construction of van der Waals interaction energy profiles for each ligand as well as for wild-type and mutant Sem-5 proteins. The profiles and free energy calculations indicate that the van der Waals interactions between the ligands and the receptor determine whether an N- or a Calpha-substituted residue is favored at each site. A VC value (defined as a product of the conservation percentage of each residue and its van der Waals interaction energy with the ligand) is used to identify several residues on the receptor that are critical for specificity and binding affinity. This VC value may have a potential use in identifying crucial residues for any ligand-protein or protein-protein system. Mutations at two of those crucial residues, N190 and N206, are examined. One mutation, N190I, is predicted to reduce the selectivity of the N-substituted residue at site -1 of the ligand and is shown to bind similarly with N- and Calpha-substituted residues at that site.     

Use of selective Trp side chain labeling to characterize protein-protein and protein-ligand interactions by NMR spectroscopy

Recent studies on amino acid occurrence in protein binding sites suggest that only a reduced number of residues are responsible for most interaction energy in protein-protein and protein-ligand interactions. Above all, tryptophan (Trp) seems to be the most frequent residue in protein's hot spots. Here we report a novel, efficient, and cost-effective method to selectively incorporate specific isotope labels into the side chains of Trp residues in recombinant proteins. We show that the method proposed allows selective NMR observation of Trp side chains that enables studies of ligand binding, protein-protein interactions, hydrogen binding, protein folding, and side chain dynamics. Examples with the protein BIR3 will be given.     

Comparative analysis of protein-bound ligand conformations with respect to catalyst's conformational space subsampling algorithms

We examined the quality of Catalyst's conformational model generation algorithm via a large scale study based on the crystal structures of a sample of 510 pharmaceutically relevant protein-ligand complexes extracted from the Protein Data Bank (PDB). Our results show that the tested algorithms implemented within Catalyst are able to produce high quality conformers, which in most of the cases are well suited for in silico drug research. Catalyst-specific settings were analyzed, such as the method used for the conformational model generation (FAST vs BEST) and the maximum number of generated conformers. By setting these options for higher fitting quality, the average RMS values describing the similarity of experimental and simulated conformers were improved from an RMS of 1.06 with max. 50 FAST generated conformers to an RMS of 0.93 with max. 255 BEST generated conformers, which represents an improvement by 12%. Each method provides best fitting conformers with an RMS value<1.50 in more than 80% of all cases. We analyzed the computing time/quality ratio of various conformational model generation settings and examined ligands in high energy conformations. Furthermore, properties of the same ligands in various proteins were investigated, and the fitting qualities of experimental conformations from the PDB and the Cambridge Structural Database (CSD) were compared. One of the most important conclusions of former studies, the fact that bioactive conformers often have energy high above that of global minima, was confirmed.     

VISCANA: visualized cluster analysis of protein-ligand interaction based on the ab initio fragment molecular orbital method for virtual ligand screening

We have developed a visualized cluster analysis of protein-ligand interaction (VISCANA) that analyzes the pattern of the interaction of the receptor and ligand on the basis of quantum theory for virtual ligand screening. Kitaura et al. (Chem. Phys. Lett. 1999, 312, 319-324.) have proposed an ab initio fragment molecular orbital (FMO) method by which large molecules such as proteins can be easily treated with chemical accuracy. In the FMO method, a total energy of the molecule is evaluated by summation of fragment energies and interfragment interaction energies (IFIEs). In this paper, we have proposed a cluster analysis using the dissimilarity that is defined as the squared Euclidean distance between IFIEs of two ligands. Although the result of an ordered table by clustering is still a massive collection of numbers, we combine a clustering method with a graphical representation of the IFIEs by representing each data point with colors that quantitatively and qualitatively reflect the IFIEs. We applied VISCANA to a docking study of pharmacophores of the human estrogen receptor alpha ligand-binding domain (57 amino acid residues). By using VISCANA, we could classify even structurally different ligands into functionally similar clusters according to the interaction pattern of a ligand and amino acid residues of the receptor protein. In addition, VISCANA could estimate the correct docking conformation by analyzing patterns of the receptor-ligand interactions of some conformations through the docking calculation.     

Importance of accurate charges in molecular docking: quantum mechanical/molecular mechanical (QM/MM) approach

The extent to which accuracy of electric charges plays a role in protein-ligand docking is investigated through development of a docking algorithm, which incorporates quantum mechanical/molecular mechanical (QM/MM) calculations. In this algorithm, fixed charges of ligands obtained from force field parameterization are replaced by QM/MM calculations in the protein environment, treating only the ligands as the quantum region. The algorithm is tested on a set of 40 cocrystallized structures taken from the Protein Data Bank (PDB) and provides strong evidence that use of nonfixed charges is important. An algorithm, dubbed "Survival of the Fittest" (SOF) algorithm, is implemented to incorporate QM/MM charge calculations without any prior knowledge of native structures of the complexes. Using an iterative protocol, this algorithm is able in many cases to converge to a nativelike structure in systems where redocking of the ligand using a standard fixed charge force field exhibits nontrivial errors. The results demonstrate that polarization effects can play a significant role in determining the structures of protein-ligand complexes, and provide a promising start towards the development of more accurate docking methods for lead optimization applications.     

Estimating protein-ligand binding affinity using high-throughput screening by NMR

Many of today's drug discovery programs use high-throughput screening methods that rely on quick evaluations of protein activity to rank potential chemical leads. By monitoring biologically relevant protein-ligand interactions, NMR can provide a means to validate these discovery leads and to optimize the drug discovery process. NMR-based screens typically use a change in chemical shift or line width to detect a protein-ligand interaction. However, the relatively low throughput of current NMR screens and their high demand on sample requirements generally makes it impractical to collect complete binding curves to measure the affinity for each compound in a large and diverse chemical library. As a result, NMR ligand screens are typically limited to identifying candidates that bind to a protein and do not give any estimate of the binding affinity. To address this issue, a methodology has been developed to rank binding affinities for ligands based on NMR screens that use 1D (1)H NMR line-broadening experiments. This method was demonstrated by using it to estimate the dissociation equilibrium constants for twelve ligands with the protein human serum albumin (HSA). The results were found to give good agreement with previous affinities that have been reported for these same ligands with HSA.     

π-Hole Interactions Involving Nitro Aromatic Ligands in Protein Structures

Studying noncanonical intermolecular interactions between a ligand and a protein constitutes an emerging research field. Identifying synthetically accessible molecular fragments that can engage in intermolecular interactions is a key objective in this area. Here, it is shown that so-called “π-hole interactions” are present between the nitro moiety in nitro aromatic ligands and lone pairs within protein structures (water and protein carbonyls and sulfurs). Ample structural evidence was found in a PDB analysis and computations reveal interaction energies of about −5 kcal mol⁻¹ for ligand–protein π-hole interactions. Several examples are highlighted for which a π-hole interaction is implicated in the superior binding affinity or inhibition of a nitro aromatic ligand versus a similar non-nitro analogue. The discovery that π-hole interactions with nitro aromatics are significant within protein structures parallels the finding that halogen bonds are biologically relevant. This has implications for the interpretation of ligand–protein complexation phenomena, for example, involving the more than 50 approved drugs that contain a nitro aromatic moiety.     

The relationship between the sequence identities of alpha helical proteins in the PDB and the molecular similarities of their ligands.

This paper considers the relationship between the percentage sequence identities of protein chains and the molecular similarities of the ligands they bind. Among a set of alpha helical proteins from the PDB, it is found that related proteins tend to bind similar ligands. Furthermore, the property of binding similar ligands can be used to define the categories of "like" and "unlike" pairs of protein chains, separated by an approximate cutoff at a sequence identity of, or somewhat above, 45%. Similarly, the property of binding related protein chains can be used to define "low" and "high" similarity pairs of ligand residues, with a cutoff at a Tanimoto score of 0.70. The ligands bound to two "like" protein chains are five times more likely to be of high similarity than would be expected if protein sequence identity and ligand molecular similarity were independent variables. Nonetheless, the nature of the PDB means that it is unclear whether the same conclusions would be reached with a data set representing an unbiased sample of all protein-ligand complexes in a living cell. The construction of an appropriate data set for such a study represents a significant challenge.     

Identification of ligand binding sites on proteins using a multi-scale approach

Identification of a ligand binding site on a protein is pivotal to drug discovery. To date, no reliable and computationally feasible general approach to this problem has been published. Here we present an automated efficient method for determining binding sites on proteins for potential ligands without any a priori knowledge. Our method is based upon the multiscale concept where we deal with a hierarchy of models generated using a k-means clustering algorithm for the potential ligand. This is done in a simple approach whereby a potential ligand is represented by a growing number of feature points. At each increasing level of detail, a pruning of potential binding site is performed. A nonbonding energy function is used to score the interactions between molecules at each step. The technique was successfully employed to seven protein-ligand complexes. In the current paper we show that the algorithm considerably reduces the computational effort required to solve this problem. This approach offers real opportunities for exploiting the large number of structures that will evolve from structural genomics.     

Formal Estimation of Errors in Computed Absolute Interaction Energies of Protein-ligand Complexes

A largely unsolved problem in computational biochemistry is the accurate prediction of binding affinities of small ligands to protein receptors. We present a detailed analysis of the systematic and random errors present in computational methods through the use of error probability density functions, specifically for computed interaction energies between chemical fragments comprising a protein-ligand complex. An HIV-II protease crystal structure with a bound ligand (indinavir) was chosen as a model protein-ligand complex. The complex was decomposed into twenty-one (21) interacting fragment pairs, which were studied using a number of computational methods. The chemically accurate complete basis set coupled cluster theory (CCSD(T)/CBS) interaction energies were used as reference values to generate our error estimates. In our analysis we observed significant systematic and random errors in most methods, which was surprising especially for parameterized classical and semiempirical quantum mechanical calculations. After propagating these fragment-based error estimates over the entire protein-ligand complex, our total error estimates for many methods are large compared to the experimentally determined free energy of binding. Thus, we conclude that statistical error analysis is a necessary addition to any scoring function attempting to produce reliable binding affinity predictions.     

Connectivity and binding‐site recognition: Applications relevant to drug design

Here, we describe a family of methods based on residue–residue connectivity for characterizing binding sites and apply variants of the method to various types of protein–ligand complexes including proteases, allosteric‐binding sites, correctly and incorrectly docked poses, and inhibitors of protein–protein interactions. Residues within ligand‐binding sites have about 25% more contact neighbors than surface residues in general; high‐connectivity residues are found in contact with the ligand in 84% of all complexes studied. In addition, a k‐means algorithm was developed that may be useful for identifying potential binding sites with no obvious geometric or connectivity features. The analysis was primarily carried out on 61 protein–ligand structures from the MEROPS protease database, 250 protein–ligand structures from the PDBSelect (25%), and 30 protein–protein complexes. Analysis of four proteases with crystal structures for multiple bound ligands has shown that residues with high connectivity tend to have less variable side‐chain conformation. The relevance to drug design is discussed in terms of identifying allosteric‐binding sites, distinguishing between alternative docked poses and designing protein interface inhibitors. Taken together, this data indicate that residue–residue connectivity is highly relevant to medicinal chemistry. © 2010 Wiley Periodicals, Inc. J Comput Chem, 2010     

Molecular dynamics simulation study on the interaction of KRN 7000 and three analogues with human CD1d

Many analogues of KRN 7000, a synthetic glycolipid (α-galactosylceramide) exhibiting immuno-stimulatory activity and antitumor properties, were previously synthesized and tested in order to understand the reasons for the resulting biological activity and Th1/Th2 cytokine profile. Principles have been established for the interaction of such glycolipids with the human CD1d molecule but the exact mechanism by which different ligands with the same polar head elicit distinct biological responses remains unclear. Based on these experiments and on the available crystal structures, protein-ligand interactions are explored using molecular dynamics simulations. Hydrogen bond interactions are examined with regard to the polar group orientation. The influence of modulations on the dynamic behavior of the CD1d-glycolipid complex is addressed. Overall, our data support the mechanism by which the shortening of the α-GalCer sphingosine chain causes a significant twist of the CD1d α1 helix structure from residue Phe84 that affects the position of CD1d residues involved in the TCR recognition.     

Hydration in drug design. 1. Multiple hydrogen-bonding features of water molecules in mediating protein-ligand interactions

Summary Water is known to play an important rôle in the recognition and stabilization of the interaction between a ligand and its site. This has important implications for drug design. Analyses of 19 high-resolution crystal structures of protein-ligand complexes reveal the multiple hydrogen-bonding feature of water molecules mediating protein-ligand interactions. Most of the water molecules (nearly 80%) involved in bridging the protein and the ligand can make three or more hydrogen bonds when distance and bond angles are used as criteria to define hydrogen-bonding interactions. Isotropic B-factors have been used to take into account the mobility of water molecules. The water molecules at binding sites bridge the protein and ligand, and interact with other water molecules to form a complex network of interconnecting hydrogen bonds. Some water molecules at the site do not directly bridge between the protein and the ligand, but may contribute indirectly to the stability of the complex by holding bridging water molecules in the right position through a network of hydrogen bonds. These water networks are probably crucial for the stability of the protein-ligand complex and are important for any site-directed drug design strategies.     

Multiple ligand docking by Glide: implications for virtual second-site screening

Performance of Glide was evaluated in a sequential multiple ligand docking paradigm predicting the binding modes of 129 protein-ligand complexes crystallized with clusters of 2-6 cooperative ligands. Three sampling protocols (single precision-SP, extra precision-XP, and SP without scaling ligand atom radii-SP hard) combined with three different scoring functions (GlideScore, Emodel and Glide Energy) were tested. The effects of ligand number, docking order and druglikeness of ligands and closeness of the binding site were investigated. On average 36?% of all structures were reproduced with RMSDs lower than 2??. Correctly docked structures reached 50?% when docking druglike ligands into closed binding sites by the SP hard protocol. Cooperative binding to metabolic and transport proteins can dramatically alter pharmacokinetic parameters of drugs. Analyzing the cytochrome P450 subset the SP hard protocol with Emodel ranking reproduced two-thirds of the structures well. Multiple ligand binding is also exploited by the fragment linking approach in lead discovery settings. The HSP90 subset from real life fragment optimization programs revealed that Glide is able to reproduce the positions of multiple bound fragments if conserved water molecules are considered. These case studies assess the utility of Glide in sequential multiple docking applications.