Binding response: a descriptor for selecting ligand binding site on protein surfaces

A Computational Pipeline to Identify and Characterize Binding Sites and Interacting Chemotypes in SARS-CoV-2

Minimizing the human and economic costs of the COVID-19 pandemic and future pandemics requires the ability to develop and deploy effective treatments for novel pathogens as soon as possible after they emerge. To this end, we introduce a new computational pipeline for the rapid identification and characterization of binding sites in viral proteins along with the key chemical features, which we call chemotypes, of the compounds predicted to interact with those same sites. The composition of source organisms for the structural models associated with an individual binding site is used to assess the site's degree of structural conservation across different species, including other viruses and humans. We propose a search strategy for novel therapeutics that involves the selection of molecules preferentially containing the most structurally rich chemotypes identified by our algorithm. While we demonstrate the pipeline on SARS-CoV-2, it is generalizable to any new virus, as long as either experimentally solved structures for its proteins are available or sufficiently accurate predicted structures can be constructed.

Supramolecular assemblies involving biologically relevant antiparallel π-stacking and unconventional solvent driven structural topology in maleato and fumarato bridged Zn(ii) coordination polymers: antiproliferative evaluation and theoretical studies

In vitro anticancer activities have been explored in solvent driven maleato and fumarato bridged Zn(ii) coordination polymers involving energetically significant antiparallel π-stacking and enclathrated guest MeOH/H₂O moieties.

In Silico Target Druggability Assessment: From Structural to Systemic Approaches

This chapter will focus on today's in silico direct and indirect approaches to assess therapeutic target druggability. The direct approach tries to infer from the 3D structure the capacity of the target protein to bind small molecule in order to modulate its biological function. Algorithms to recognize and characterize the quality of the ligand interaction sites whether within buried protein cavities or within large protein-protein interface will be reviewed in the first part of the paper. In the case a ligand-binding site is already identified, indirect aspects of target druggability can be assessed. These indirect approaches focus first on target promiscuity and the potential difficulties in developing specific drugs. It is based on large-scale comparison of protein-binding sites. The second aspect concerns the capacity of the target to induce resistant pathway once it is inhibited or activated by a drug. The emergence of drug-resistant pathways can be assessed through systemic analysis of biological networks implementing metabolism and/or cell regulation signaling.

Novel Noncatalytic Substrate-Selective p38α-Specific MAPK Inhibitors with Endothelial-Stabilizing and Anti-Inflammatory Activity

The p38 MAPK family is composed of four kinases of which p38α/MAPK14 is the major proinflammatory member. These kinases contribute to many inflammatory diseases, but the currently available p38 catalytic inhibitors (e.g., SB203580) are poorly effective and cause toxicity. We reasoned that the failure of catalytic p38 inhibitors may derive from their activity against noninflammatory p38 isoforms (e.g., p38β/MAPK11) and loss of all p38α-dependent responses, including anti-inflammatory, counterregulatory responses via mitogen- and stress-activated kinase (MSK) 1/2 and Smad3. We used computer-aided drug design to target small molecules to a pocket near the p38α glutamate-aspartate (ED) substrate-docking site rather than the catalytic site, the sequence of which had only modest homology among p38 isoforms. We identified a lead compound, UM101, that was at least as effective as SB203580 in stabilizing endothelial barrier function, reducing inflammation, and mitigating LPS-induced mouse lung injury. Differential scanning fluorimetry and saturation transfer difference-nuclear magnetic resonance demonstrated specific binding of UM101 to the computer-aided drug design-targeted pockets in p38α but not p38β. RNA sequencing analysis of TNF-α-stimulated gene expression revealed that UM101 inhibited only 28 of 61 SB203580-inhibited genes and 7 of 15 SB203580-inhibited transcription factors, but spared the anti-inflammatory MSK1/2 pathway. We provide proof of principle that small molecules that target the ED substrate-docking site may exert anti-inflammatory effects similar to the catalytic p38 inhibitors, but their isoform specificity and substrate selectivity may confer inherent advantages over catalytic inhibitors for treating inflammatory diseases.

Computer-Aided Drug Design Methods

Computational approaches are useful tools to interpret and guide experiments to expedite the antibiotic drug design process. Structure-based drug design (SBDD) and ligand-based drug design (LBDD) are the two general types of computer-aided drug design (CADD) approaches in existence. SBDD methods analyze macromolecular target 3-dimensional structural information, typically of proteins or RNA, to identify key sites and interactions that are important for their respective biological functions. Such information can then be utilized to design antibiotic drugs that can compete with essential interactions involving the target and thus interrupt the biological pathways essential for survival of the microorganism(s). LBDD methods focus on known antibiotic ligands for a target to establish a relationship between their physiochemical properties and antibiotic activities, referred to as a structure-activity relationship (SAR), information that can be used for optimization of known drugs or guide the design of new drugs with improved activity. In this chapter, standard CADD protocols for both SBDD and LBDD will be presented with a special focus on methodologies and targets routinely studied in our laboratory for antibiotic drug discoveries.

The Small Molecule IMR-1 Inhibits the Notch Transcriptional Activation Complex to Suppress Tumorigenesis

In many cancers, aberrant Notch activity has been demonstrated to play a role in the initiation and maintenance of the neoplastic phenotype and in cancer stem cells, which may allude to its additional involvement in metastasis and resistance to therapy. Therefore, Notch is an exceedingly attractive therapeutic target in cancer, but the full range of potential targets within the pathway has been underexplored. To date, there are no small-molecule inhibitors that directly target the intracellular Notch pathway or the assembly of the transcriptional activation complex. Here, we describe an in vitro assay that quantitatively measures the assembly of the Notch transcriptional complex on DNA. Integrating this approach with computer-aided drug design, we explored potential ligand-binding sites and screened for compounds that could disrupt the assembly of the Notch transcriptional activation complex. We identified a small-molecule inhibitor, termed Inhibitor of Mastermind Recruitment-1 (IMR-1), that disrupted the recruitment of Mastermind-like 1 to the Notch transcriptional activation complex on chromatin, thereby attenuating Notch target gene transcription. Furthermore, IMR-1 inhibited the growth of Notch-dependent cell lines and significantly abrogated the growth of patient-derived tumor xenografts. Taken together, our findings suggest that a novel class of Notch inhibitors targeting the transcriptional activation complex may represent a new paradigm for Notch-based anticancer therapeutics, warranting further preclinical characterization. Cancer Res; 76(12); 3593-603. ©2016 AACR.

Molecular modeling studies demonstrate key mutations that could affect the ligand recognition by influenza AH1N1 neuraminidase

The goal of this study was to identify neuraminidase (NA) residue mutants from human influenza AH1N1 using sequences from 1918 to 2012. Multiple alignment studies of complete NA sequences (5732) were performed. Subsequently, the crystallographic structure of the 1918 influenza (PDB ID: 3BEQ-A) was used as a wild-type structure and three-dimensional (3-D) template for homology modeling of the mutated selected NA sequences. The 3-D mutated NAs were refined using molecular dynamics (MD) simulations (50 ns). The refined 3-D models were used to perform docking studies using oseltamivir. Multiple sequence alignment studies showed seven representative mutations (A232V, K262R, V263I, T264V, S367L, S369N, and S369K). MD simulations applied to 3-D NAs showed that each NA had different active-site shapes according to structural surface visualization and docking results. Moreover, Cartesian principal component analyses (cPCA) show structural differences among these NA structures caused by mutations. These theoretical results suggest that the selected mutations that are located outside of the active site of NA could affect oseltamivir recognition and could be associated with resistance to oseltamivir.

Small-molecule inhibitors of ERK-mediated immediate early gene expression and proliferation of melanoma cells expressing mutated BRaf

Constitutive activation of the extracellular-signal-regulated kinases 1 and 2 (ERK1/2) are central to regulating the proliferation and survival of many cancer cells. The current inhibitors of ERK1/2 target ATP binding or the catalytic site and are therefore limited in their utility for elucidating the complex biological roles of ERK1/2 through its phosphorylation and regulation of over 100 substrate proteins. To overcome this limitation, a combination of computational and experimental methods was used to identify low-molecular-mass inhibitors that are intended to target ERK1/2 substrate-docking domains and selectively interfere with ERK1/2 regulation of substrate proteins. In the present study, we report the identification and characterization of compounds with a thienyl benzenesulfonate scaffold that were designed to inhibit ERK1/2 substrates containing an F-site or DEF (docking site for ERK, FXF) motif. Experimental evidence shows the compounds inhibit the expression of F-site containing immediate early genes (IEGs) of the Fos family, including c-Fos and Fra1, and transcriptional regulation of the activator protein-1 (AP-1) complex. Moreover, this class of compounds selectively induces apoptosis in melanoma cells containing mutated BRaf and constitutively active ERK1/2 signalling, including melanoma cells that are inherently resistant to clinically relevant kinase inhibitors. These findings represent the identification and initial characterization of a novel class of compounds that inhibit ERK1/2 signalling functions and their potential utility for elucidating ERK1/2 and other signalling events that control the growth and survival of cancer cells containing elevated ERK1/2 activity.

Novel LRRK2 GTP-binding inhibitors reduced degeneration in Parkinson's disease cell and mouse models

Mutations in the leucine-rich repeat kinase-2 (LRRK2) gene cause autosomal-dominant Parkinson's disease (PD) and contribute to sporadic PD. LRRK2 contains Guanosine-5'-triphosphate (GTP) binding, GTPase and kinase activities that have been implicated in the neuronal degeneration of PD pathogenesis, making LRRK2, a potential drug target. To date, there is no disease-modifying drug to slow the neuronal degeneration of PD and no published LRRK2 GTP domain inhibitor. Here, the biological functions of two novel GTP-binding inhibitors of LRRK2 were examined in PD cell and mouse models. Through a combination of computer-aided drug design (CADD) and LRRK2 bio-functional screens, two novel compounds, 68: and 70: , were shown to reduce LRRK2 GTP binding and to inhibit LRRK2 kinase activity in vitro and in cultured cell assays. Moreover, these two compounds attenuated neuronal degeneration in human SH-SY5Y neuroblastoma cells and mouse primary neurons expressing mutant LRRK2 variants. Although both compounds inhibited LRRK2 kinase activity and reduced neuronal degeneration, solubility problems with 70: prevented further testing in mice. Thus, only 68: was tested in a LRRK2-based lipopolysaccharide (LPS)-induced pre-inflammatory mouse model. 68: reduced LRRK2 GTP-binding activity and kinase activity in brains of LRRK2 transgenic mice after intraperitoneal injection. Moreover, LPS induced LRRK2 upregulation and microglia activation in mouse brains. These findings suggest that disruption of GTP binding to LRRK2 represents a potential novel therapeutic approach for PD intervention and that these novel GTP-binding inhibitors provide both tools and lead compounds for future drug development.

Computational method to identify druggable binding sites that target protein-protein interactions

Protein-protein interactions are implicated in the pathogenesis of many diseases and are therefore attractive but challenging targets for drug design. One of the challenges in development is the identification of potential druggable binding sites in protein interacting interfaces. Identification of interface surfaces can greatly aid rational drug design of small molecules inhibiting protein-protein interactions. In this work, starting from the structure of a free monomer, we have developed a ligand docking based method, called "FindBindSite" (FBS), to locate protein-protein interacting interface regions and potential druggable sites in this interface. FindBindSite utilizes the results from docking a small and diverse library of small molecules to the entire protein structure. By clustering regions with the highest docked ligand density from FBS, we have shown that these high ligand density regions strongly correlate with the known protein-protein interacting surfaces. We have further predicted potential druggable binding sites on the protein surface using FBS, with druggability being defined as the site with high density of ligands docked. FBS shows a hit rate of 71% with high confidence and 93% with lower confidence for the 41 proteins used for predicting druggable binding sites on the protein-protein interface. Mining the regions of lower ligand density that are contiguous with the high scoring high ligand density regions from FBS, we were able to map 70% of the protein-protein interacting surface in 24 out of 41 structures tested. We also observed that FBS has limited sensitivity to the size and nature of the small molecule library used for docking. The experimentally determined hotspot residues for each protein-protein complex cluster near the best scoring druggable binding sites identified by FBS. These results validate the ability of our technique to identify druggable sites within protein-protein interface regions that have the maximal possibility of interface disruption.

Rapid identification of ligand-binding sites by using an assignment-free NMR approach

In this study, we developed an assignment-free approach for rapid identification of ligand-binding sites in target proteins by using NMR. With a sophisticated cell-free stable isotope-labeling procedure that introduces (15)N- or (13)C-labels to specific atoms of target proteins, this approach requires only a single series of ligand titrations with labeled targets. Using titration data, ligand-binding sites in the target protein can be identified without time-consuming assignment procedures. We demonstrated the feasibility of this approach by using structurally well-characterized interactions between mitogen-activated protein (MAP) kinase p38α and its inhibitor 2-amino-3-benzyloxypyridine. Furthermore, we confirmed the recently proposed fatty acid binding to p38α and confirmed the fatty acid-binding site in the MAP kinase insert region.

Expanding the number of 'druggable' targets: non-enzymes and protein-protein interactions

Following sequencing and assembly of the human genome, the preferred methods for identification of new drug targets have changed dramatically. Modern tactics such as genome-wide association studies (GWAS) and deep sequencing are fundamentally different from the pharmacology-guided approaches used previously, in which knowledge of small molecule ligands acting at their cellular targets was the primary discovery engine. A consequence of the 'target-first, pharmacology-second' strategy is that many predicted drug targets are non-enzymes, such as scaffolding, regulatory or structural proteins, and their activities are often dependent on protein-protein interactions (PPIs). These types of targets create unique challenges to drug discovery efforts because enzymatic turnover cannot be used as a convenient surrogate for compound potency. Moreover, it is often challenging to predict how ligand binding to non-enzymes might affect changes in protein function and/or pathobiology. Thus, in the postgenomic era, targets might be strongly implicated by molecular biology-based methods, yet they often later earn the designation of 'undruggable'. Can the scope of available targets be widened to include these promising, but challenging, non-enzymes? In this review, we discuss advances in high-throughput screening (HTS) technology and chemical library design that are emerging to deal with these challenges.

Structure-based target druggability assessment

The focus of this chapter is on the important concepts behind the in silico techniques that are used today to assess target druggability. The first step of the assessment consists of finding cavity space in the protein using 2D and/or 3D topological concepts. These concepts underlie the geometry and energy-based pocketfinder algorithms. Analysis pursues on the physico-chemical complementarity between the binding site and the drug like molecule. Geometrical and molecular flexibility aspect are also included in this assessment. The presence of hot interaction spots are shown to be particularly important for targeting protein-protein interactions. Finally, binding site promiscuity can be assessed by large scale structural comparison with other targets. Common chemical features amongst protein cavities can predict potential cross-reactivity with unwanted targets.

Comparison of different ranking methods in protein-ligand binding site prediction

In recent years, although many ligand-binding site prediction methods have been developed, there has still been a great demand to improve the prediction accuracy and compare different prediction algorithms to evaluate their performances. In this work, in order to improve the performance of the protein-ligand binding site prediction method presented in our former study, a comparison of different binding site ranking lists was studied. Four kinds of properties, i.e., pocket size, distance from the protein centroid, sequence conservation and the number of hydrophobic residues, have been chosen as the corresponding ranking criterion respectively. Our studies show that the sequence conservation information helps to rank the real pockets with the most successful accuracy compared to others. At the same time, the pocket size and the distance of binding site from the protein centroid are also found to be helpful. In addition, a multi-view ranking aggregation method, which combines the information among those four properties, was further applied in our study. The results show that a better performance can be achieved by the aggregation of the complementary properties in the prediction of ligand-binding sites.

Balancing target flexibility and target denaturation in computational fragment-based inhibitor discovery

Accounting for target flexibility and selecting "hot spots" most likely to be able to bind an inhibitor continue to be challenges in the field of structure-based drug design, especially in the case of protein-protein interactions. Computational fragment-based approaches using molecular dynamics (MD) simulations are a promising emerging technology having the potential to address both of these challenges. However, the optimal MD conditions permitting sufficient target flexibility while also avoiding fragment-induced target denaturation remain ambiguous. Using one such technology (Site Identification by Ligand Competitive Saturation, SILCS), conditions were identified to either prevent denaturation or identify and exclude trajectories in which subtle but important denaturation was occurring. The target system used was the well-characterized protein cytokine IL-2, which is involved in a protein-protein interface and, in its unliganded crystallographic form, lacks surface pockets that can serve as small-molecule binding sites. Nonetheless, small-molecule inhibitors have previously been discovered that bind to two "cryptic" binding sites that emerge only in the presence of ligand binding, highlighting the important role of IL-2 flexibility. Using the above conditions, SILCS with hydrophobic fragments was able to identify both sites based on favorable fragment binding while avoiding IL-2 denaturation. An important additional finding was that acetonitrile, a water-miscible fragment, fails to identify either site yet can induce target denaturation, highlighting the importance of fragment choice.

A new protein-ligand binding sites prediction method based on the integration of protein sequence conservation information

Background

Prediction of protein-ligand binding sites is an important issue for protein function annotation and structure-based drug design. Nowadays, although many computational methods for ligand-binding prediction have been developed, there is still a demanding to improve the prediction accuracy and efficiency. In addition, most of these methods are purely geometry-based, if the prediction methods improvement could be succeeded by integrating physicochemical or sequence properties of protein-ligand binding, it may also be more helpful to address the biological question in such studies.

Results

In our study, in order to investigate the contribution of sequence conservation in binding sites prediction and to make up the insufficiencies in purely geometry based methods, a simple yet efficient protein-binding sites prediction algorithm is presented, based on the geometry-based cavity identification integrated with sequence conservation information. Our method was compared with the other three classical tools: PocketPicker, SURFNET, and PASS, and evaluated on an existing comprehensive dataset of 210 non-redundant protein-ligand complexes. The results demonstrate that our approach correctly predicted the binding sites in 59% and 75% of cases among the TOP1 candidates and TOP3 candidates in the ranking list, respectively, which performs better than those of SURFNET and PASS, and achieves generally a slight better performance with PocketPicker.

Conclusions

Our work has successfully indicated the importance of the sequence conservation information in binding sites prediction as well as provided a more accurate way for binding sites identification.

POSE SCALING: GEOMETRICAL ASSESSMENT OF LIGAND BINDING POSES

A descriptor, the pose scaling factor, is proposed to quantitatively evaluate the geometrical match between a ligand and a target binding site. The pose scaling factor can be used to readily rank results of target-based in silico database screening or docking on large numbers of compounds. Such an approach will be of utility in the development and refinement of docking algorithms.

AADS--an automated active site identification, docking, and scoring protocol for protein targets based on physicochemical descriptors

We report here a robust automated active site detection, docking, and scoring (AADS) protocol for proteins with known structures. The active site finder identifies all cavities in a protein and scores them based on the physicochemical properties of functional groups lining the cavities in the protein. The accuracy realized on 620 proteins with sizes ranging from 100 to 600 amino acids with known drug active sites is 100% when the top ten cavity points are considered. These top ten cavity points identified are then submitted for an automated docking of an input ligand/candidate molecule. The docking protocol uses an all atom energy based Monte Carlo method. Eight low energy docked structures corresponding to different locations and orientations of the candidate molecule are stored at each cavity point giving 80 docked structures overall which are then ranked using an effective free energy function and top five structures are selected. The predicted structure and energetics of the complexes agree quite well with experiment when tested on a data set of 170 protein-ligand complexes with known structures and binding affinities. The AADS methodology is implemented on an 80 processor cluster and presented as a freely accessible, easy to use tool at http://www.scfbio-iitd.res.in/dock/ActiveSite_new.jsp .

Structure-based druggability assessment--identifying suitable targets for small molecule therapeutics

A target is druggable if it can be modulated in vivo by a drug-like molecule. The general properties of oral drugs are summarized by the 'rule of 5' which specifies parameters related to size and lipophilicity. Structure-based target druggability assessment consists of predicting ligand-binding sites on the protein that are complementary to these drug-like properties. Automated identification of ligand-binding sites can use geometrical considerations alone or include specific physicochemical properties of the protein surface. Features of a pocket's size and shape, together with measures of its hydrophobicity, are most informative in identifying suitable drug-binding pockets. The recent availability of several validation sets of druggable versus undruggable targets has helped fuel the development of more elaborate methods.

Beyond structural genomics: computational approaches for the identification of ligand binding sites in protein structures

Structural genomics projects have revealed structures for a large number of proteins of unknown function. Understanding the interactions between these proteins and their ligands would provide an initial step in their functional characterization. Binding site identification methods are a fast and cost-effective way to facilitate the characterization of functionally important protein regions. In this review we describe our recently developed methods for binding site identification in the context of existing methods. The advantage of energy-based approaches is emphasized, since they provide flexibility in the identification and characterization of different types of binding sites.

Prediction of ligand-binding sites of proteins by molecular docking calculation for a random ligand library

A new approach to predicting the ligand-binding sites of proteins was developed, using protein-ligand docking computation. In this method, many compounds in a random library are docked onto the whole protein surface. We assumed that the true ligand-binding site would exhibit stronger affinity to the compounds in the random library than the other sites, even if the random library did not include the ligand corresponding to the true binding site. We also assumed that the affinity of the true ligand-binding site would be correlated to the docking scores of the compounds in the random library, if the ligand-binding site was correctly predicted. We call this method the molecular-docking binding-site finding (MolSite) method. The MolSite method was applied to 89 known protein-ligand complex structures extracted from the Protein Data Bank, and it predicted the correct binding sites with about 80-99% accuracy, when only the single top-ranked site was adopted. In addition, the average docking score was weakly correlated to the experimental protein-ligand binding free energy, with a correlation coefficient of 0.44.

Protein-protein binding site prediction by local structural alignment

Generalization of an earlier algorithm has led to the development of new local structural alignment algorithms for prediction of protein-protein binding sites. The algorithms use maximum cliques on protein graphs to define structurally similar protein regions. The search for structural neighbors in the new algorithms has been extended to all the proteins in the PDB and the query protein is compared to more than 60,000 proteins or over 300,000 single-chain structures. The resulting structural similarities are combined and used to predict the protein binding sites. This study shows that the location of protein binding sites can be predicted by comparing only local structural similarities irrespective of general protein folds.

Drug-like density: a method of quantifying the "bindability" of a protein target based on a very large set of pockets and drug-like ligands from the Protein Data Bank

One approach to estimating the "chemical tractability" of a candidate protein target where we know the atomic resolution structure is to examine the physical properties of potential binding sites. A number of other workers have addressed this issue. We characterize ~290,000 "pockets" from ~42,000 protein crystal structures in terms of a three parameter "pocket space": volume, buriedness, and hydrophobicity. A metric DLID (drug-like density) measures how likely a pocket is to bind a drug-like molecule. This is calculated from the count of other pockets in its local neighborhood in pocket space that contain drug-like cocrystallized ligands and the count of total pockets in the neighborhood. Surprisingly, despite being defined locally, a global trend in DLID can be predicted by a simple linear regression on log(volume), buriedness, and hydrophobicity. Two levels of simplification are necessary to relate the DLID of individual pockets to "targets": taking the best DLID per Protein Data Bank (PDB) entry (because any given crystal structure can have many pockets), and taking the median DLID over all PDB entries for the same target (because different crystal structures of the same protein can vary because of artifacts and real conformational changes). We can show that median DLIDs for targets that are detectably homologous in sequence are reasonably similar and that median DLIDs correlate with the "druggability" estimate of Cheng et al. (Nature Biotechnology 2007, 25, 71-75).

Protein pockets: inventory, shape, and comparison

The shape of the protein surface dictates what interactions are possible with other macromolecules, but defining discrete pockets or possible interaction sites remains difficult. First, there is the problem of defining the extent of the pocket. Second, one has to characterize the shape of each pocket. Third, one needs to make quantitative comparisons between pockets on different proteins. An elegant solution to these problems is to sort all surface and solvent points by travel depth and then collect a hierarchical tree of pockets. The connectivity of the tree is determined via the deepest saddle points between each pair of neighboring pockets. The resulting pocket surfaces tessellate the entire protein surface, producing a complete inventory of pockets. This method of identifying pockets also allows one to easily compute important shape metrics, including the problematic pocket volume, surface area, and mouth size. Pockets are also annotated with their lining residue lists and polarity and with other residue-based properties. Using this tree and the various shape metrics pockets can be merged, grouped, or filtered for further analysis. Since this method includes the entire surface, it guarantees that any pocket of interest will be found among the output pockets, unlike all previous methods of pocket identification. The resulting hierarchy of pockets is easy to visualize and aids users in higher level analysis. Comparison of pockets is done by using the shape metrics, avoiding the complex shape alignment problem. Example applications show that the method facilitates pocket comparison along mutational or time-dependent series. Pockets from families of proteins can be examined using multiple pocket tree alignments to see how ligand binding sites or how other pockets have changed with evolution. Our method is called CLIPPERS for complete liberal inventory of protein pockets elucidating and reporting on shape.

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.

Druggable pockets and binding site centric chemical space: a paradigm shift in drug discovery

Detection, comparison and analyses of binding pockets are pivotal to structure-based drug design endeavors, from hit identification, screening of exosites and de-orphanization of protein functions to the anticipation of specific and non-specific binding to off- and anti-targets. Here, we analyze protein-ligand complexes and discuss methods that assist binding site identification, prediction of druggability and binding site comparison. The full potential of pockets is yet to be harnessed, and we envision that better understanding of the pocket space will have far-reaching implications in the field of drug discovery, such as the design of pocket-specific compound libraries and scoring functions.

Computational approaches to identifying and characterizing protein binding sites for ligand design

Given the three-dimensional structure of a protein, how can one find the sites where other molecules might bind to it? Do these sites have the properties necessary for high affinity binding? Is this protein a suitable target for drug design? Here, we discuss recent developments in computational methods to address these and related questions. Geometric methods to identify pockets on protein surfaces have been developed over many years but, with new algorithms, their performance is still improving. Simulation methods show promise in accounting for protein conformational variability to identify transient pockets but lack the ease of use of many of the (rigid) shape-based tools. Sequence and structure comparison approaches are benefiting from the constantly increasing size of sequence and structure databases. Energetic methods can aid identification and characterization of binding pockets, and have undergone recent improvements in the treatment of solvation and hydrophobicity. The "druggability" of a binding site is still difficult to predict with an automated procedure. The methodologies available for this purpose range from simple shape and hydrophobicity scores to computationally demanding free energy simulations.

Development of extracellular signal-regulated kinase inhibitors

Activation of the extracellular signal-regulated kinase (ERK) signaling pathway has been implicated in mediating a diverse array of cellular functions including cell differentiation, proliferation, and inflammatory responses. In this review, we will discuss approaches to identify inhibitors of ERK proteins through targeting ATP-dependent and ATP-independent mechanisms. Given the diversity of ERK substrates and the importance of ERK signaling in normal cell functions, emphasis will be placed on the methods for identifying small molecular weight compounds that are substrate selective through ATP-independent interactions and potentially relevant to inflammatory processes. The approach for selective targeting of ERK substrates takes advantage of the basic understanding of unique ERK docking domains that are thought to interact with specific amino acid sequences on substrate proteins. Computer aided drug design (CADD) can facilitate the high throughput screening of millions of compounds with the potential for selective interactions with ERK docking domains and disruption of substrate interactions. As such, the CADD approach significantly reduces the number of compounds that will be evaluated in subsequent biological assays and greatly increases the hit rate of biologically active compounds. The potentially active compounds are evaluated for ERK protein binding using spectroscopic and structural biology methods. Compounds that show ERK interactions are then tested for their ability to inhibit substrate interactions and phosphorylation as well as ERK-dependent functions in whole organism or cell-based assays. Finally, the relevance of substrate-selective ERK inhibitors in the context of inflammatory disease will be discussed.

In silico analysis of antibody-carbohydrate interactions and its application to xenoreactive antibodies

Antibody-carbohydrate interactions play central roles in stimulating adverse immune reactions. The most familiar example of such a process is the reaction observed in ABO-incompatible blood transfusion and organ transplantation. The ABO blood groups are defined by the presence of specific carbohydrates expressed on the surface of red blood cells. Preformed antibodies in the incompatible recipient (i.e., different blood groups) recognize cells exhibiting host-incompatible ABO system antigens and proceed to initiate lysis of the incompatible cells. Pig-to-human xenotransplantation presents a similar immunological barrier. Antibodies present in humans recognize carbohydrate antigens on the surface of pig organs as foreign and proceed to initiate hyperacute xenograft rejection. The major carbohydrate xenoantigens all bear terminal Gal alpha(1,3)Gal epitopes (or alphaGal). In this study, we have developed and validated a site mapping technique to investigate protein-ligand recognition and applied it to antibody-carbohydrate systems. This site mapping technique involves the use of molecular docking to generate a series of antibody-carbohydrate complexes, followed by analysis of the hydrogen bonding and van der Waals interactions occurring in each complex. The technique was validated by application to a series of antibody-carbohydrate crystal structures. In each case, the majority of interactions made in the crystal structure complex were able to be reproduced. The technique was then applied to investigate xenoantigen recognition by a panel of monoclonal anti-alphaGal antibodies. The results indicate that there is a significant overlap of the antibody regions engaging the xenoantigens across the panel. Likewise, similar regions of the xenoantigens interact with the antibodies.

Fpocket: an open source platform for ligand pocket detection

Background

Virtual screening methods start to be well established as effective approaches to identify hits, candidates and leads for drug discovery research. Among those, structure based virtual screening (SBVS) approaches aim at docking collections of small compounds in the target structure to identify potent compounds. For SBVS, the identification of candidate pockets in protein structures is a key feature, and the recent years have seen increasing interest in developing methods for pocket and cavity detection on protein surfaces.

Results

Fpocket is an open source pocket detection package based on Voronoi tessellation and alpha spheres built on top of the publicly available package Qhull. The modular source code is organised around a central library of functions, a basis for three main programs: (i) Fpocket, to perform pocket identification, (ii) Tpocket, to organise pocket detection benchmarking on a set of known protein-ligand complexes, and (iii) Dpocket, to collect pocket descriptor values on a set of proteins. Fpocket is written in the C programming language, which makes it a platform well suited for the scientific community willing to develop new scoring functions and extract various pocket descriptors on a large scale level. Fpocket 1.0, relying on a simple scoring function, is able to detect 94% and 92% of the pockets within the best three ranked pockets from the holo and apo proteins respectively, outperforming the standards of the field, while being faster.

Conclusion

Fpocket provides a rapid, open source and stable basis for further developments related to protein pocket detection, efficient pocket descriptor extraction, or drugablity prediction purposes. Fpocket is freely available under the GNU GPL license at .

Protein surface conservation in binding sites

An algorithm is described which uses the conservation of the 3D structure of protein surfaces, as opposed to their sequences, to detect protein-protein binding sites. The protein in which protein-protein binding sites are sought is compared with structures of multiple structurally related proteins and the surface that is conserved at least once is considered to be a part of the binding site. The binding site predictions obtained in this way for a set of protein-protein complexes correspond well with the actual protein-protein binding sites. A comparison of this method with an algorithm using the support vector machine approach for predicting protein-protein binding sites shows structural conservation to be an important characteristic that distinguishes binding sites from the remainder of protein surfaces.