Identifying Potential Molecular Targets in Fungi Based on (Dis)Similarities in Binding Site Architecture with Proteins of the Human Pharmacolome

Invasive fungal infections represent a public health problem that worsens over the years with the increasing resistance to current antimycotic agents. Therefore, there is a compelling medical need of widening the antifungal drug repertoire, following different methods such as drug repositioning, identification and validation of new molecular targets and developing new inhibitors against these targets. In this work we developed a structure-based strategy for drug repositioning and new drug design, which can be applied to infectious fungi and other pathogens. Instead of applying the commonly accepted off-target criterion to discard fungal proteins with close homologues in humans, the core of our approach consists in identifying fungal proteins with active sites that are structurally similar, but preferably not identical to binding sites of proteins from the so-called "human pharmacolome". Using structural information from thousands of human protein target-inhibitor complexes, we identified dozens of proteins in fungal species of the genera Histoplasma, Candida, Cryptococcus, Aspergillus and Fusarium, which might be exploited for drug repositioning and, more importantly, also for the design of new fungus-specific inhibitors. As a case study, we present the in vitro experiments performed with a set of selected inhibitors of the human mitogen-activated protein kinases 1/2 (MEK1/2), several of which showed a marked cytotoxic activity in different fungal species.

Data Mining Meets Machine Learning: A Novel ANN-based Multi-Body Interaction Docking Scoring Function (MBI-Score) based on Utilizing Frequent Geometric and Chemical Patterns of Interfacial Atoms in Native Protein-Ligand Complexes

Accurate prediction of binding poses is crucial to structure-based drug design. We employ two powerful artificial intelligence (AI) approaches, data-mining and machine-learning, to design artificial neural network (ANN) based pose-scoring function. It is a simple machine-learning-based statistical function that employs frequent geometric and chemical patterns of interacting atoms at protein-ligand interfaces. The patterns are derived by mining interfaces of "native" protein-ligand complexes. Each interface is represented by a graph where nodes are atoms and edges connect protein-ligand interfacial atoms located within certain cutoff distance of each other. Applying frequent subgraph mining to these interfaces provides "native" frequent patterns of interacting atoms. Subsequently, given a pose for a protein-ligand complex of interest, the pose-scoring function (the information-processing unit or neuron) calculates the degree of matching between the interaction patterns present at the pose's interface and the native frequent patterns. The pose-scoring function takes into account the frequency of occurrence of the matching native patterns, the size of the match, and the degree of geometrical similarity between pose-specific and matching native frequent patterns. This novel "multi-body interaction" pose-scoring function (MBI-Score) was validated using two databases, PDBbind and Astex-85, and it outperformed seven commonly used commercial scoring functions. MBI-Score is available at www.khashanlab.org/mbi-score.

Why not consider a spherical protein? Implications of backbone hydrogen bonding for protein structure and function

The intrinsic ability of protein structures to exhibit the geometric features required for molecular function in the absence of evolution is examined in the context of three systems: the reference set of real, single domain protein structures, a library of computationally generated, compact homopolypeptides, artificial structures with protein-like secondary structural elements, and quasi-spherical random proteins packed at the same density as proteins but lacking backbone secondary structure and hydrogen bonding. Without any evolutionary selection, the library of artificial structures has similar backbone hydrogen bonding, global shape, surface to volume ratio and statistically significant structural matches to real protein global structures. Moreover, these artificial structures have native like ligand binding cavities, and a tiny subset has interfacial geometries consistent with native-like protein-protein interactions and DNA binding. In contrast, the quasi-spherical random proteins, being devoid of secondary structure, have a lower surface to volume ratio and lack ligand binding pockets and intermolecular interaction interfaces. Surprisingly, these quasi-spherical random proteins exhibit protein like distributions of virtual bond angles and almost all have a statistically significant structural match to real protein structures. This implies that it is local chain stiffness, even without backbone hydrogen bonding, and compactness that give rise to the likely completeness of the library solved single domain protein structures. These studies also suggest that the packing of secondary structural elements generates the requisite geometry for intermolecular binding. Thus, backbone hydrogen bonding plays an important role not only in protein structure but also in protein function. Such ability to bind biological molecules is an inherent feature of protein structure; if combined with appropriate protein sequences, it could provide the non-zero background probability for low-level function that evolution requires for selection to occur.

Structural basis for a new mechanism of inhibition of HIV-1 integrase identified by fragment screening and structure-based design

BACKGROUND

HIV-1 integrase is a clinically validated therapeutic target for the treatment of HIV-1 infection, with one approved therapeutic currently on the market. This enzyme represents an attractive target for the development of new inhibitors to HIV-1 that are effective against the current resistance mutations.

METHODS

A fragment-based screening method employing surface plasmon resonance and NMR was initially used to detect interactions between integrase and fragments. The binding sites of the fragments were elucidated by crystallography and the structural information used to design and synthesize improved ligands.

RESULTS

The location of binding of fragments to the catalytic core of integrase was found to be in a previously undescribed binding site, adjacent to the mobile loop. Enzyme assays confirmed that formation of enzyme-fragment complexes inhibits the catalytic activity of integrase and the structural data was utilized to further develop these fragments into more potent novel enzyme inhibitors.

CONCLUSIONS

We have defined a new site in integrase as a valid region for the structure-based design of allosteric integrase inhibitors. Using a structure-based design process we have improved the activity of the initial fragments 45-fold.

Evaluation of geometric complementarity between molecular surfaces using compactly supported radial basis functions

One of the challenges faced by all molecular docking algorithms is that of being able to discriminate between correct results and false positives obtained in the simulations. The scoring or energetic function is the one that must fulfill this task. Several scoring functions have been developed and new methodologies are still under development. In this paper, we have employed the Compactly Supported Radial Basis Functions (CSRBF) to create analytical representations of molecular surfaces, which are then included as key components of a new scoring function for molecular docking. The method proposed here achieves a better ranking of the solutions produced by the program DOCK, as compared with the ranking done by its native contact scoring function. Our new analytical scoring function based on CSRBF can be easily included in different available docking programs as a reliable and quick filter in large-scale docking simulations.

A novel approach to local similarity of protein binding sites substantially improves computational drug design results

We present a novel notion of binding site local similarity based on the analysis of complete protein environments of ligand fragments. Comparison of a query protein binding site (target) against the 3D structure of another protein (analog) in complex with a ligand enables ligand fragments from the analog complex to be transferred to positions in the target site, so that the complete protein environments of the fragment and its image are similar. The revealed environments are similarity regions and the fragments transferred to the target site are considered as binding patterns. The set of such binding patterns derived from a database of analog complexes forms a cloud-like structure (fragment cloud), which is a powerful tool for computational drug design. It has been shown on independent test sets that the combined use of a traditional energy-based score together with the cloud-based score responsible for the quality of embedding of a ligand into the fragment cloud improves the self-docking and screening results dramatically. The usage of a fragment cloud as a source of positioned molecular fragments fitting the binding protein environment has been validated by reproduction of experimental ligand optimization results.

Setting up a large set of protein-ligand PDB complexes for the development and validation of knowledge-based docking algorithms

BACKGROUND

The number of algorithms available to predict ligand-protein interactions is large and ever-increasing. The number of test cases used to validate these methods is usually small and problem dependent. Recently, several databases have been released for further understanding of protein-ligand interactions, having the Protein Data Bank as backend support. Nevertheless, it appears to be difficult to test docking methods on a large variety of complexes. In this paper we report the development of a new database of protein-ligand complexes tailored for testing of docking algorithms.

METHODS

Using a new definition of molecular contact, small ligands contained in the 2005 PDB edition were identified and processed. The database was enriched in molecular properties. In particular, an automated typing of ligand atoms was performed. A filtering procedure was applied to select a non-redundant dataset of complexes. Data mining was performed to obtain information on the frequencies of different types of atomic contacts. Docking simulations were run with the program DOCK.

RESULTS

We compiled a large database of small ligand-protein complexes, enriched with different calculated properties, that currently contains more than 6000 non-redundant structures. As an example to demonstrate the value of the new database, we derived a new set of chemical matching rules to be used in the context of the program DOCK, based on contact frequencies between ligand atoms and points representing the protein surface, and proved their enhanced efficiency with respect to the default set of rules included in that program.

CONCLUSION

The new database constitutes a valuable resource for the development of knowledge-based docking algorithms and for testing docking programs on large sets of protein-ligand complexes. The new chemical matching rules proposed in this work significantly increase the success rate in DOCKing simulations. The database developed in this work is available at http://cimlcsext.cim.sld.cu:8080/screeningbrowser/.

Structure and Molecular Interactions of a Unique Antitumor Antibody Specific for N-Glycolyl GM3

N-glycolyl GM3 ganglioside is an attractive target antigen for cancer immunotherapy, because this epitope is a molecular marker of certain tumor cells and not expressed in normal human tissues. The murine monoclonal antibody 14F7 specifically recognizes N-glycolyl GM3 and shows no cross-reactivity with the abundant N-acetyl GM3 ganglioside, a close structural homologue of N-glycolyl GM3. Here, we report the crystal structure of the 14F7 Fab fragment at 2.5 A resolution and its molecular model with the saccharide moiety of N-glycolyl GM3, NeuGcalpha3Galbeta4Glcbeta. Fab 14F7 contains a very long CDR H3 loop, which divides the antigen-binding site of this antibody into two subsites. In the docking model, the saccharide ligand is bound to one of these subsites, formed solely by heavy chain residues. The discriminative feature of N-glycolyl GM3 versus N-acetyl GM3, its hydroxymethyl group, is positioned in a hydrophilic cavity, forming hydrogen bonds with the carboxyl group of Asp H52, the indole NH of Trp H33 and the hydroxyl group of Tyr H50. For the hydrophobic methyl group of N-acetyl GM3, this environment would not be favorable, explaining why the antibody specifically recognizes N-glycolyl GM3, but not N-acetyl GM3. Mutation of Asp H52 to hydrophobic residues of similar size completely abolished binding. Our model of the antibodycarbohydrate complex is consistent with binding data for several tested glycolipids as well as for a variety of 14F7 mutants with replaced VL domains.

Analysis of protein structures reveals regions of rare backbone conformation at functional sites

Regions of rare conformation were located in 300 protein crystal structures representing seven major protein folds. A distance matrix algorithm was used to search rapidly for 9-residue fragments of rare backbone conformation using a comparison to a relational database of encoded fragments derived from the database of nonredundant structures. Rare fragments were found in 61% of the analyzed protein structures. Detailed analysis was performed for 78 proteins of different folds. The rare fragments were located near functional sites in 72% of the protein structures. The rare fragments often formed parts of ligand-binding sites (59%), protein-protein interfaces (8%), and domain-domain contacts (5%). Of the remaining structures, 5% had a high average B-factor or high local B-factors. Statistical analysis suggests that the association between ligands and rare regions does not occur by chance alone. The present study is likely to underestimate the number of functional sites, because not all analyzed protein structures contained a ligand. The results suggest that rapid searches for regions with rare local backbone conformations can assist in prediction of functional sites in novel proteins.