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A study was conducted to develop novel protein parameters for the all-atom empirical energy function in the CHARMM program. The parameter evaluation focused on a self-consistent approach designed to realize a balance between the internal and interaction terms of the force field and interactions associated with solvents and solutes. The parameters were then obtained by fitting an extended set of experimental and ab initio results. In addition, internal parametrizations were selected to duplicate geometries from crystal structures, Raman spectroscopic data and ab initio computations.

The goal of this paper is predicting the conformational distributions of ligand binding sites using the AlphaFold2 (AF2) protein structure prediction program with stochastic subsampling of the multiple sequence alignment (MSA). We explored the opening of cryptic ligand binding sites in 16 proteins, where the closed and open conformations define the expected extreme points of the conformational variation. Due to the many structures of these proteins in the Protein Data Bank (PDB), we were able to study whether the distribution of X-ray structures affects the distribution of AF2 models. We have found that AF2 generates both a cluster of open and a cluster of closed models for proteins that have comparable numbers of open and closed structures in the PDB and not too many other conformations. This was observed even with default MSA parameters, thus without further subsampling. In contrast, with the exception of a single protein, AF2 did not yield multiple clusters of conformations for proteins that had imbalanced numbers of open and closed structures in the PDB, or had substantial numbers of other structures. Subsampling improved the results only for a single protein, but very shallow MSA led to incorrect structures. The ability of generating both open and closed conformations for six out of the 16 proteins agrees with the success rates of similar studies reported in the literature. However, we showed that this partial success is due to AF2 "remembering" the conformational distributions in the PDB and that the approach fails to predict rarely seen conformations., Competing Interests: Competing interests statement:The authors declare no competing interest.

Cryptic sites can expand the space of druggable proteins, but the potential usefulness of such sites needs to be investigated before any major effort. Given that the binding pockets are not formed, the druggability of such sites is not well understood. The analysis of proteins and their ligands shows that cryptic sites that are formed primarily by the motion of side chains moving out of the pocket to enable ligand binding generally do not bind drug-sized molecules with sufficient potency. By contrast, sites that are formed by loop or hinge motion are potentially valuable drug targets. Arguments are provided to explain the underlying causes in terms of classical enzyme inhibition theory and the kinetics of side chain motion and ligand binding., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Target identification and evaluation is a critical step in the drug discovery process. Although time-intensive and complex, the challenge becomes even more acute in the realm of infectious disease, where the rapid emergence of new viruses, the swift mutation of existing targets, and partial effectiveness of approved antivirals can lead to outbreaks of significant public health concern. The COVID-19 pandemic, caused by the SARS-CoV-2 virus, serves as a prime example of this, where despite the allocation of substantial resources, Paxlovid is currently the only effective treatment. In that case, significant effort pre-pandemic had been expended to evaluate the biological target for the closely related SARS-CoV. In this work, we utilize the computational hot spot mapping method, FTMove, to rapidly identify and rank binding sites for a set of nine SARS-CoV-2 drug/potential drug targets. FTMove takes into account protein flexibility by mapping binding site hot spots across an ensemble of structures for a given target. To assess the applicability of the FTMove approach to a wide range of drug targets for viral pathogens, we also carry out a comprehensive review of the known SARS-CoV-2 ligandable sites. The approach is able to identify the vast majority of all known sites and a few additional sites, which may in fact be yet to be discovered as ligandable. Furthermore, a UMAP analysis of the FTMove features for each identified binding site is largely able to separate predicted sites with experimentally known binders from those without known binders. These results demonstrate the utility of FTMove to rapidly identify actionable sites across a range of targets for a given indication. As such, the approach is expected to be particularly useful for assessing target binding sites for any emerging pathogen, as well as for indications in other disease areas, and providing actionable starting points for structure-based drug design efforts.

Objective: The rapid expansion of the biomedical literature challenges traditional review methods, especially during outbreaks of emerging infectious diseases when quick action is critical. Our study aims to explore the potential of ChatGPT to automate the biomedical literature review for rapid drug discovery., Materials and Methods: We introduce a novel automated pipeline helping to identify drugs for a given virus in response to a potential future global health threat. Our approach can be used to select PubMed articles identifying a drug target for the given virus. We tested our approach on two known pathogens: SARS-CoV-2, where the literature is vast, and Nipah, where the literature is sparse. Specifically, a panel of three experts reviewed a set of PubMed articles and labeled them as either describing a drug target for the given virus or not. The same task was given to the automated pipeline and its performance was based on whether it labeled the articles similarly to the human experts. We applied a number of prompt engineering techniques to improve the performance of ChatGPT., Results: Our best configuration used GPT-4 by OpenAI and achieved an out-of-sample validation performance with accuracy/F1-score/sensitivity/specificity of 92.87%/88.43%/83.38%/97.82% for SARS-CoV-2 and 87.40%/73.90%/74.72%/91.36% for Nipah., Conclusion: These results highlight the utility of ChatGPT in drug discovery and development and reveal their potential to enable rapid drug target identification during a pandemic-level health emergency., Competing Interests: Declaration of Competing Interest The authors declare no competing interests., (Copyright © 2024 Elsevier B.V. All rights reserved.)

The knowledge of ligand binding hot spots and of the important interactions within such hot spots is crucial for the design of lead compounds in the early stages of structure-based drug discovery. The computational solvent mapping server FTMap can reliably identify binding hot spots as consensus clusters, free energy minima that bind a variety of organic probe molecules. However, in its current implementation, FTMap provides limited information on regions within the hot spots that tend to interact with specific pharmacophoric features of potential ligands. E-FTMap is a new server that expands on the original FTMap protocol. E-FTMap uses 119 organic probes, rather than the 16 in the original FTMap, to exhaustively map binding sites, and identifies pharmacophore features as atomic consensus sites where similar chemical groups bind. We validate E-FTMap against a set of 109 experimentally derived structures of fragment-lead pairs, finding that highly ranked pharmacophore features overlap with the corresponding atoms in both fragments and lead compounds. Additionally, comparisons of mapping results to ensembles of bound ligands reveal that pharmacophores generated with E-FTMap tend to sample highly conserved protein-ligand interactions. E-FTMap is available as a web server at https://eftmap.bu.edu.

Introduction: A fundamental challenge in computational vaccinology is that most B-cell epitopes are conformational and therefore hard to predict from sequence alone. Another significant challenge is that a great deal of the amino acid sequence of a viral surface protein might not in fact be antigenic. Thus, identifying the regions of a protein that are most promising for vaccine design based on the degree of surface exposure may not lead to a clinically relevant immune response. Methods: Linear peptides selected by phage display experiments that have high affinity to the monoclonal antibody of interest ("mimotopes") usually have similar physicochemical properties to the antigen epitope corresponding to that antibody. The sequences of these linear peptides can be used to find possible epitopes on the surface of the antigen structure or a homology model of the antigen in the absence of an antigen-antibody complex structure. Results and Discussion: Herein we describe two novel methods for mapping mimotopes to epitopes. The first is a novel algorithm named MimoTree that allows for gaps in the mimotopes and epitopes on the antigen. More specifically, a mimotope may have a gap that does not match to the epitope to allow it to adopt a conformation relevant for binding to an antibody, and residues may similarly be discontinuous in conformational epitopes. MimoTree is a fully automated epitope detection algorithm suitable for the identification of conformational as well as linear epitopes. The second is an ensemble approach, which combines the prediction results from MimoTree and two existing methods., Competing Interests: Authors MS and AC were employed by Fractal Therapeutics Inc. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Li, Wilderotter, Stoddard, Van Egeren, Chakravarty and Joseph-McCarthy.)

The neural network-based program AlphaFold2 (AF2) provides high accuracy structure prediction for a large fraction of globular proteins. An important question is whether these models are accurate enough for reliably docking small ligands. Several recent papers and the results of CASP15 reveal that local conformational errors reduce the success rates of direct ligand docking. Here, we focus on the ability of the models to conserve the location of binding hot spots, regions on the protein surface that significantly contribute to the binding free energy of the protein-ligand interaction. Clusters of hot spots predict the location and even the druggability of binding sites, and hence are important for computational drug discovery. The hot spots are determined by protein mapping that is based on the distribution of small fragment-sized probes on the protein surface and is less sensitive to local conformation than docking. Mapping models taken from the AlphaFold Protein Structure Database show that identifying binding sites is more reliable than docking, but the success rates are still 5% to 10% lower than based on mapping X-ray structures. The drop in accuracy is particularly large for models of multidomain proteins. However, both the model binding sites and the mapping results can be substantially improved by generating AF2 models for the ligand binding domains of interest rather than the entire proteins and even more if using forced sampling with multiple initial seeds. The mapping of such models tends to reach the accuracy of results obtained by mapping the X-ray structures.

SARS-CoV-2, the virus that causes COVID-19, led to a global health emergency that claimed the lives of millions. Despite the widespread availability of vaccines, the virus continues to exist in the population in an endemic state which allows for the continued emergence of new variants. Most of the current vaccines target the spike glycoprotein interface of SARS-CoV-2, creating a selection pressure favoring viral immune evasion. Antivirals targeting other molecular interactions of SARS-CoV-2 can help slow viral evolution by providing orthogonal selection pressures on the virus. GRP78 is a host auxiliary factor that mediates binding of the SARS-CoV-2 spike protein to human cellular ACE2, the primary pathway of cell infection. As GRP78 forms a ternary complex with SARS-CoV-2 spike protein and ACE2, disrupting the formation of this complex is expected to hinder viral entry into host cells. Here, we developed a model of the GRP78-Spike RBD-ACE2 complex. We then used that model together with hot spot mapping of the GRP78 structure to identify the putative binding site for spike protein on GRP78. Next, we performed structure-based virtual screening of known drug/candidate drug libraries to identify binders to GRP78 that are expected to disrupt spike protein binding to the GRP78, and thereby preventing viral entry to the host cell. A subset of these compounds has previously been shown to have some activity against SARS-CoV-2. The identified hits are starting points for the further development of novel SARS-CoV-2 therapeutics, potentially serving as proof-of-concept for GRP78 as a potential drug target for other viruses., (© 2024. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

The rapid emergence of immune-evading viral variants of SARS-CoV-2 calls into question the practicality of a vaccine-only public-health strategy for managing the ongoing COVID-19 pandemic. It has been suggested that widespread vaccination is necessary to prevent the emergence of future immune-evading mutants. Here, we examined that proposition using stochastic computational models of viral transmission and mutation. Specifically, we looked at the likelihood of emergence of immune escape variants requiring multiple mutations and the impact of vaccination on this process. Our results suggest that the transmission rate of intermediate SARS-CoV-2 mutants will impact the rate at which novel immune-evading variants appear. While vaccination can lower the rate at which new variants appear, other interventions that reduce transmission can also have the same effect. Crucially, relying solely on widespread and repeated vaccination (vaccinating the entire population multiple times a year) is not sufficient to prevent the emergence of novel immune-evading strains, if transmission rates remain high within the population. Thus, vaccines alone are incapable of slowing the pace of evolution of immune evasion, and vaccinal protection against severe and fatal outcomes for COVID-19 patients is therefore not assured.

At the outset of an emergent viral respiratory pandemic, sequence data is among the first molecular information available. As viral attachment machinery is a key target for therapeutic and prophylactic interventions, rapid identification of viral "spike" proteins from sequence can significantly accelerate the development of medical countermeasures. For six families of respiratory viruses, covering the vast majority of airborne and droplet-transmitted diseases, host cell entry is mediated by the binding of viral surface glycoproteins that interact with a host cell receptor. In this report it is shown that sequence data for an unknown virus belonging to one of the six families above provides sufficient information to identify the protein(s) responsible for viral attachment. Random forest models that take as input a set of respiratory viral sequences can classify the protein as "spike" vs. non-spike based on predicted secondary structure elements alone (with 97.3% correctly classified) or in combination with N-glycosylation related features (with 97.0% correctly classified). Models were validated through 10-fold cross-validation, bootstrapping on a class-balanced set, and an out-of-sample extra-familial validation set. Surprisingly, we showed that secondary structural elements and N-glycosylation features were sufficient for model generation. The ability to rapidly identify viral attachment machinery directly from sequence data holds the potential to accelerate the design of medical countermeasures for future pandemics. Furthermore, this approach may be extendable for the identification of other potential viral targets and for viral sequence annotation in general in the future., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2023 Walker et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

In the face of a long-running pandemic, understanding the drivers of ongoing SARS-CoV-2 transmission is crucial for the rational management of COVID-19 disease burden. Keeping schools open has emerged as a vital societal imperative during the pandemic, but in-school transmission of SARS-CoV-2 can contribute to further prolonging the pandemic. In this context, the role of schools in driving SARS-CoV-2 transmission acquires critical importance. Here we model in-school transmission from first principles to investigate the effectiveness of layered mitigation strategies on limiting in-school spread. We examined the effect of masks and air quality (ventilation, filtration and ionizers) on steady-state viral load in classrooms, as well as on the number of particles inhaled by an uninfected person. The effectiveness of these measures in limiting viral transmission was assessed for variants with different levels of mean viral load (ancestral, Delta, Omicron). Our results suggest that a layered mitigation strategy can be used effectively to limit in-school transmission, with certain limitations. First, poorly designed strategies (insufficient ventilation, no masks, staying open under high levels of community transmission) will permit in-school spread even if some level of mitigation is present. Second, for viral variants that are sufficiently contagious, it may be difficult to construct any set of interventions capable of blocking transmission once an infected individual is present, underscoring the importance of other measures. Our findings provide practical recommendations; in particular, the use of a layered mitigation strategy that is designed to limit transmission, with other measures such as frequent surveillance testing and smaller class sizes (such as by offering remote schooling options to those who prefer it) as needed., Competing Interests: Authors AC and MS are employed by Fractal Therapeutics. Author RN is employed by Halozyme Therapeutics. Authors AC, MS, RN, DV, and DJ-M are shareholders in Fractal Therapeutics. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Van Egeren, Stoddard, Malakar, Ghosh, Acharya, Mainuddin, Majumdar, Luo, Nolan, Joseph-McCarthy, White, Hochberg, Basu and Chakravarty.)

The development and deployment of several SARS-CoV-2 vaccines in a little over a year is an unprecedented achievement of modern medicine. The high levels of efficacy against transmission for some of these vaccines makes it feasible to use them to suppress SARS-CoV-2 altogether in regions with high vaccine acceptance. However, viral variants with reduced susceptibility to vaccinal and natural immunity threaten the utility of vaccines, particularly in scenarios where a return to pre-pandemic conditions occurs before the suppression of SARS-CoV-2 transmission. In this work we model the situation in the United States in May-June 2021, to demonstrate how pre-existing variants of SARS-CoV-2 may cause a rebound wave of COVID-19 in a matter of months under a certain set of conditions. A high burden of morbidity (and likely mortality) remains possible, even if the vaccines are partially effective against new variants and widely accepted. Our modeling suggests that variants that are already present within the population may be capable of quickly defeating the vaccines as a public health intervention, a serious potential limitation for strategies that emphasize rapid reopening before achieving control of SARS-CoV-2., Competing Interests: I have read the journal’s policy and the authors of this manuscript have the following competing interests: A.C. and M.S. are employees and shareholders of Fractal Therapeutics. D.V.E., A.N., B.Z., and D.J.-M. are shareholders of Fractal Therapeutics. This does not alter our adherence to PLOS ONE policies on sharing data and materials.

The rapid emergence and expansion of novel SARS-CoV-2 variants threatens our ability to achieve herd immunity for COVID-19. These novel SARS-CoV-2 variants often harbor multiple point mutations, conferring one or more evolutionarily advantageous traits, such as increased transmissibility, immune evasion and longer infection duration. In a number of cases, variant emergence has been linked to long-term infections in individuals who were either immunocompromised or treated with convalescent plasma. In this paper, we used a stochastic evolutionary modeling framework to explore the emergence of fitter variants of SARS-CoV-2 during long-term infections. We found that increased viral load and infection duration favor emergence of such variants. While the overall probability of emergence and subsequent transmission from any given infection is low, on a population level these events occur fairly frequently. Targeting these low-probability stochastic events that lead to the establishment of novel advantageous viral variants might allow us to slow the rate at which they emerge in the patient population, and prevent them from spreading deterministically due to natural selection. Our work thus suggests practical ways to achieve control of long-term SARS-CoV-2 infections, which will be critical for slowing the rate of viral evolution., (© 2021. The Author(s).)

The spike protein receptor-binding domain (RBD) of SARS-CoV-2 is the molecular target for many vaccines and antibody-based prophylactics aimed at bringing COVID-19 under control. Such a narrow molecular focus raises the specter of viral immune evasion as a potential failure mode for these biomedical interventions. With the emergence of new strains of SARS-CoV-2 with altered transmissibility and immune evasion potential, a critical question is this: how easily can the virus escape neutralizing antibodies (nAbs) targeting the spike RBD? To answer this question, we combined an analysis of the RBD structure-function with an evolutionary modeling framework. Our structure-function analysis revealed that epitopes for RBD-targeting nAbs overlap one another substantially and can be evaded by escape mutants with ACE2 affinities comparable to the wild type, that are observed in sequence surveillance data and infect cells in vitro. This suggests that the fitness cost of nAb-evading mutations is low. We then used evolutionary modeling to predict the frequency of immune escape before and after the widespread presence of nAbs due to vaccines, passive immunization or natural immunity. Our modeling suggests that SARS-CoV-2 mutants with one or two mildly deleterious mutations are expected to exist in high numbers due to neutral genetic variation, and consequently resistance to vaccines or other prophylactics that rely on one or two antibodies for protection can develop quickly -and repeatedly- under positive selection. Predicted resistance timelines are comparable to those of the decay kinetics of nAbs raised against vaccinal or natural antigens, raising a second potential mechanism for loss of immunity in the population. Strategies for viral elimination should therefore be diversified across molecular targets and therapeutic modalities., Competing Interests: I have read the journal’s policy and the authors of this manuscript have the following competing interests: A.C., M.S., and U.T. are employees and shareholders of Fractal Therapeutics. D.V.E., A.N., B.Z., and D.J.- M. are shareholders of Fractal Therapeutics. This does not alter our adherence to PLOS ONE policies on sharing data and materials.

Bacterial persisters are a quasidormant subpopulation of cells that are tolerant to antibiotic treatment. The combination of the aminoglycoside tobramycin with fumarate as an antibacterial potentiator utilizes an antipersister strategy that is aimed at reducing recurrent Pseudomonas aeruginosa infections by enhancing the killing of P. aeruginosa persisters. Stationary-phase cultures of P. aeruginosa were used to generate persister cells. A range of tobramycin concentrations was tested with a range of metabolite concentrations to determine the potentiation effect of the metabolite under a variety of conditions, including a range of pH values and in the presence of azithromycin or cystic fibrosis (CF) patient sputum. In addition, 96-well dish biofilm and colony biofilm assays were performed, and the cytotoxicity of the tobramycin-fumarate combination was determined utilizing a lactate dehydrogenase (LDH) assay. Enhanced killing of up to 6 orders of magnitude of P. aeruginosa persisters over a range of CF isolates, including mucoid and nonmucoid strains, was observed for the tobramycin-fumarate combination compared to killing with tobramycin alone. Furthermore, significant fumarate-mediated potentiation was seen in the presence of azithromycin or CF patient sputum. Fumarate also reduced the cytotoxicity of tobramycin-treated P. aeruginosa to human epithelial airway cells. Finally, in mucoid and nonmucoid CF isolates, complete eradication of P. aeruginosa biofilm was observed in the colony biofilm assay due to fumarate potentiation. These data suggest that a combination of tobramycin with fumarate as an antibacterial potentiator may be an attractive therapeutic for eliminating recurrent P. aeruginosa infections in CF patients through the eradication of bacterial persisters., (Copyright © 2017 American Society for Microbiology.)

The SZMAP method computes binding free energies and the corresponding thermodynamic components for water molecules in the binding site of a protein structure [ SZMAP, 1.0.0 ; OpenEye Scientific Software Inc. : Santa Fe, NM, USA , 2011 ]. In this work, the ability of SZMAP to predict water structure and thermodynamic stability is examined for the X-ray crystal structures of a series of protein-ligand complexes. SZMAP results correlate with higher-level replica exchange thermodynamic integration double decoupling calculations of the absolute free energy of bound waters in the test set complexes. In addition, SZMAP calculations show good agreement with experimental data in terms of water conservation (across multiple crystal structures) and B-factors over a subset of the test set. In particular, the SZMAP neutral entropy difference term calculated at crystallographic water positions within each of the complex structures correlates well with whether that crystallographic water is conserved or displaceable. Furthermore, the calculated entropy of the water probe relative to the continuum shows a significant degree of correlation with the B-factors associated with the oxygen atoms of the water molecules. Taken together, these results indicate that SZMAP is capable of quantitatively predicting water positions and their energetics and is potentially a useful tool for determining which waters to attempt to displace, maintain, or build in through water-mediated interactions when evolving a lead series during a drug discovery program.

Proteins are dynamic molecules, and understanding their movements, especially as they relate to molecular recognition and protein-ligand interactions, poses a significant challenge to structure-based drug discovery. In most instances, protein flexibility is underrepresented in computer-aided drug design due to uncertainties on how it should be accurately modeled as well as the computational cost associated with attempting to incorporate flexibility in the calculations. One approach that aims to address these issues is ensemble-based docking. With this technique, ligands are docked to an ensemble of rigid protein conformations. Molecular dynamics (MD) simulations can be used to generate the ensemble of protein conformations for the subsequent docking. Here we present a novel approach that uses biased-MD simulations to generate the docking ensemble. The MD simulations are biased toward an initial protein-ligand X-ray complex structure. The biasing maintains some of the original crystallographic pocket-ligand information and thereby enhances sampling of the more relevant conformational space of the protein. Resulting trajectories are clustered to select a representative set of protein conformations, and ligands are docked to that reduced set of conformations. Cross-docking to this ensemble and then selecting the lowest scoring pose enables reliable identification of the correct binding mode. Various levels of biasing are investigated, and the method is validated for cyclin-dependent kinase 2 and factor Xa.

Fragment-based lead discovery and design has and continues to show increasing promise in drug discovery. In this article, the current state of the art in terms of hot-spot characterization, fragment screening techniques, and fragment-based design is discussed. Three overall fragment-based lead generation strategies are explored and involve the chemical biology characterization of biological targets via fragment screening, fragment screening as a complementary approach to high-throughput screening of drug-like compounds, and direct fragment-based drug discovery, respectively. The evolution and development of fragment libraries is described. With an emphasis on computational approaches and the strategies applied at AstraZeneca, the review illustrates how integration of data from one regime can inform the design of experiments in the other, ultimately leading to the discovery of high quality chemical matter.

Thymidylate kinase (TMK) is an essential enzyme in bacterial DNA synthesis. The deoxythymidine monophosphate (dTMP) substrate binding pocket was targeted in a rational-design, structure-supported effort, yielding a unique series of antibacterial agents showing a novel, induced-fit binding mode. Lead optimization, aided by X-ray crystallography, led to picomolar inhibitors of both Streptococcus pneumoniae and Staphylococcus aureus TMK. MICs < 1 μg/mL were achieved against methicillin-resistant S. aureus (MRSA), S. pneumoniae, and vancomycin-resistant Enterococcus (VRE). Log D adjustments yielded single diastereomers 14 (TK-666) and 46, showing a broad antibacterial spectrum against Gram-positive bacteria and excellent selectivity against the human thymidylate kinase ortholog.

There is an urgent need for new antibacterials that pinpoint novel targets and thereby avoid existing resistance mechanisms. We have created novel synthetic antibacterials through structure-based drug design that specifically target bacterial thymidylate kinase (TMK), a nucleotide kinase essential in the DNA synthesis pathway. A high-resolution structure shows compound TK-666 binding partly in the thymidine monophosphate substrate site, but also forming new induced-fit interactions that give picomolar affinity. TK-666 has potent, broad-spectrum Gram-positive microbiological activity (including activity against methicillin-resistant Staphylococcus aureus and vancomycin-resistant Enterococcus), bactericidal action with rapid killing kinetics, excellent target selectivity over the human ortholog, and low resistance rates. We demonstrate in vivo efficacy against S. aureus in a murine infected-thigh model. This work presents the first validation of TMK as a compelling antibacterial target and provides a rationale for pursuing novel clinical candidates for treating Gram-positive infections through TMK.

An NMR fragment screening dataset with known binders and decoys was used to evaluate the ability of docking and re-scoring methods to identify fragment binders. Re-scoring docked poses using the Molecular Mechanics Poisson-Boltzmann Surface Area (MM-PBSA) implicit solvent model identifies additional active fragments relative to either docking or random fragment screening alone. Early enrichment, which is clearly most important in practice for selecting relatively small sets of compounds for experimental testing, is improved by MM-PBSA re-scoring. In addition, the value in MM-PBSA re-scoring of docked poses for virtual screening may be in lessening the effect of the variation in the protein complex structure used.

Acidic mammalian chitinase (AMCase) is a member of the glycosyl hydrolase 18 family (EC 3.2.1.14) that has been implicated in the pathophysiology of allergic airway disease such as asthma. Small molecule inhibitors of AMCase were identified using a combination of high-throughput screening, fragment screening, and virtual screening techniques and characterized by enzyme inhibition and NMR and Biacore binding experiments. X-ray structures of the inhibitors in complex with AMCase revealed that the larger more potent HTS hits, e.g. 5-(4-(2-(4-bromophenoxy)ethyl)piperazine-1-yl)-1H-1,2,4-triazol-3-amine 1, spanned from the active site pocket to a hydrophobic pocket. Smaller fragments identified by FBS occupy both these pockets independently and suggest potential strategies for linking fragments. Compound 1 is a 200 nM AMCase inhibitor which reduced AMCase enzymatic activity in the bronchoalveolar lavage fluid in allergen-challenged mice after oral dosing.

Fragment-based drug discovery approaches allow for a greater coverage of chemical space and generally produce high efficiency ligands. As such, virtual and experimental fragment screening are increasingly being coupled in an effort to identify new leads for specific therapeutic targets. Fragment docking is employed to create target-focussed subset of compounds for testing along side generic fragment libraries. The utility of the program Glide with various scoring schemes for fragment docking is discussed. Fragment docking results for two test cases, prostaglandin D2 synthase and DNA ligase, are presented and compared to experimental screening data. Self-docking, cross-docking, and enrichment studies are performed. For the enrichment runs, experimental data exists indicating that the docking decoys in fact do not inhibit the corresponding enzyme being examined. Results indicate that even for difficult test cases fragment docking can yield enrichments significantly better than random.

Acidic mammalian chitinase (AMCase) is a mammalian chitinase that has been implicated in allergic asthma. One of only two active mammalian chinases, AMCase, is distinguished from other chitinases by several unique features. Here, we present the novel structure of the AMCase catalytic domain, both in the apo form and in complex with the inhibitor methylallosamidin, determined to high resolution by X-ray crystallography. These results provide a structural basis for understanding some of the unique characteristics of this enzyme, including the low pH optimum and the preference for the beta-anomer of the substrate. A triad of polar residues in the second-shell is found to modulate the highly conserved chitinase active site. As a novel target for asthma therapy, structural details of AMCase activity will help guide the future design of specific and potent AMCase inhibitors.

The protein kinase C (PKC) family of serine/threonine kinases is implicated in a wide variety of cellular processes. The PKC theta (PKCtheta) isoform is involved in TCR signal transduction and T cell activation and regulates T cell mediated diseases, including lung inflammation and airway hyperresponsiveness. Thus inhibition of PKCtheta enzyme activity by a small molecule represents an attractive strategy for the treatment of asthma. A PKCtheta high-throughput screening (HTS) campaign led to the identification of 4-(3-bromophenylamino)-5-(3,4-dimethoxyphenyl)-3-pyridinecarbonitrile 4a, a low microM ATP competitive PKCtheta inhibitor. Structure based hit-to-lead optimization led to the identification of 5-(3,4-dimethoxyphenyl)-4-(1H-indol-5-ylamino)-3-pyridinecarbonitrile 4p, a 70 nM PKCtheta inhibitor. Compound 4p was selective for inhibition of novel PKC isoforms over a panel of 21 serine/threonine, tyrosine, and phosphoinositol kinases, in addition to the conventional and atypical PKCs, PKCbeta, and PKCzeta, respectively. Compound 4p also inhibited IL-2 production in antiCD3/anti-CD28 activated T cells enriched from splenocytes.

A novel algorithm for the connecting of fragment molecules is presented and validated for a number of test systems. Within the CONFIRM (Connecting Fragments Found in Receptor Molecules) approach a pre-prepared library of bridges is searched to extract those which match a search criterion derived from known experimental or computational binding information about fragment molecules within a target binding site. The resulting bridge 'hits' are then connected, in an automated fashion, to the fragments and docked into the target receptor. Docking poses are assessed in terms of root-mean-squared deviation from the known positions of the fragment molecules, as well as docking score should known inhibitors be available. The creation of the bridge library, the full details and novelty of the CONFIRM algorithm, and the general applicability of this approach within the field of fragment-based de novo drug design are discussed.

Gangliosides are key players in neuronal inhibition, with antibody-mediated clustering of gangliosides blocking neurite outgrowth in cultures and axonal regeneration post injury. In this study we show that the ganglioside GT1b can form a complex with the Nogo-66 receptor NgR1. The interaction is shown by analytical ultracentrifugation sedimentation and is mediated by the sialic acid moiety on GT1b, with mutations in FRG motifs on NgR1 attenuating the interaction. One FRG motif was developed into a cyclic peptide (N-AcCLQKFRGSSC-NH(2)) antagonist of GT1b, reversing the GT1b antibody inhibition of cerebellar granule cell neurite outgrowth. Interestingly, the peptide also antagonizes neurite outgrowth inhibition mediated by soluble forms of the myelin-associated glycoprotein (MAG). Structure function analysis of the peptide point to the conserved FRG triplet being the minimal functional motif, and mutations within this motif inhibit NgR1 binding to both GT1b and MAG. Finally, using gene ablation, we show that the cerebellar neuron response to GT1b antibodies and soluble MAG is indeed dependent on NgR1 function. The results suggest that gangliosides inhibit neurite outgrowth by interacting with FRG motifs in the NgR1 and that this interaction can also facilitate the binding of MAG to the NgR1. Furthermore, the results point to a rational strategy for developing novel ganglioside antagonists.

Target-based virtual screening is increasingly used to generate leads for targets for which high quality three-dimensional (3D) structures are available. To allow large molecular databases to be screened rapidly, a tiered scoring scheme is often employed whereby a simple scoring function is used as a fast filter of the entire database and a more rigorous and time-consuming scoring function is used to rescore the top hits to produce the final list of ranked compounds. Molecular mechanics Poisson-Boltzmann surface area (MM-PBSA) approaches are currently thought to be quite effective at incorporating implicit solvation into the estimation of ligand binding free energies. In this paper, the ability of a high-throughput MM-PBSA rescoring function to discriminate between correct and incorrect docking poses is investigated in detail. Various initial scoring functions are used to generate docked poses for a subset of the CCDC/Astex test set and to dock one set of actives/inactives from the DUD data set. The effectiveness of each of these initial scoring functions is discussed. Overall, the ability of the MM-PBSA rescoring function to (i) regenerate the set of X-ray complexes when docking the bound conformation of the ligand, (ii) regenerate the X-ray complexes when docking conformationally expanded databases for each ligand which include "conformation decoys" of the ligand, and (iii) enrich known actives in a virtual screen for the mineralocorticoid receptor in the presence of "ligand decoys" is assessed. While a pharmacophore-based molecular docking approach, PhDock, is used to carry out the docking, the results are expected to be general to use with any docking method.

Protein tyrosine phosphatase 1B (PTP1B) is a negative regulator of the insulin and leptin receptor pathways and thus an attractive therapeutic target for diabetes and obesity. Starting with a high micromolar lead compound, structure-based optimization of novel PTP1B inhibitors by extension of the molecule from the enzyme active site into the second phosphotyrosine binding site is described. Medicinal chemistry, guided by X-ray complex structure and molecular modeling, has yielded low nanomolar PTP1B inhibitors in an efficient manner. Compounds from this chemical series were found to be actively transported into hepatocytes. This active uptake into target tissues could be one of the possible avenues to overcome the poor membrane permeability of PTP1B inhibitors.

Tumor progression loci-2 (Tpl2) (Cot/MAP3K8) is a serine/threonine kinase in the MAP3K family directly upstream of MEK. Recent studies using Tpl2 knockout mice have indicated an important role for Tpl2 in the lipopolysaccharide (LPS) induced production of tumor necrosis factor alpha (TNF-alpha) and other proinflammatory cytokines involved in diseases such as rheumatoid arthritis. Initial 4-anilino-6-aminoquinoline-3-carbonitrile leads showed poor selectivity for Tpl2 over epidermal growth factor receptor (EGFR) kinase. Using molecular modeling and crystallographic data of the EGFR kinase domain with and without an EGFR kinase-specific 4-anilinoquinazoline inhibitor (erlotinib, Tarceva), we hypothesized that we could diminish the inhibition of EGFR kinase by substitution at the C-8 position of our 4-anilino-6-aminoquinoline-3-carbonitrile leads. The 8-substituted-4-anilino-6-aminoquinoline-3-carbonitriles were prepared from the appropriate 2-substituted 4-nitroanilines. Modifications to the C-6 and C-8 positions led to the identification of compounds with increased inhibition of TNF-alpha release from LPS-stimulated rat and human blood, and these analogues were also highly selective for Tpl2 kinase over EGFR kinase. Further structure-activity based modifications led to the identification of 8-bromo-4-(3-chloro-4-fluorophenylamino)-6-[(1-methyl-1H-imidazol-4-yl)methylamino]quinoline-3-carbonitrile, which demonstrated in vitro as well as in vivo efficacy in inhibition of LPS-induced TNF-alpha production.

A series of beta-sulfonyl hydroxamate TACE inhibitors, bearing a butynylamino or a butynyloxy P1' group, was designed and synthesized. Of the compounds investigated, 22 has excellent potency against isolated TACE enzyme, shows good selectivity over MMP-2 and MMP-13, and oral activity in an in vivo mouse model of TNF-alpha production.

Structure-based methods were used to design beta-sulfone 3,3-piperidine hydroxamates as TACE inhibitors with the aim of improving selectivity for TACE versus MMP-13. Several compounds in this series were synthesized and evaluated in enzymatic and cell-based assays. These analogs exhibit excellent in vitro potency against isolated TACE enzyme and show good selectivity for TACE over the related metalloproteases MMP-2, -13, and -14.

Combinatorial synthesis and large scale screening methods are being used increasingly in drug discovery, particularly for finding novel lead compounds. Although these "random" methods sample larger areas of chemical space than traditional synthetic approaches, only a relatively small percentage of all possible compounds are practically accessible. It is therefore helpful to select regions of chemical space that have greater likelihood of yielding useful leads. When three-dimensional structural data are available for the target molecule this can be achieved by applying structure-based computational design methods to focus the combinatorial library. This is advantageous over the standard usage of computational methods to design a small number of specific novel ligands, because here computation is employed as part of the combinatorial design process and so is required only to determine a propensity for binding of certain chemical moieties in regions of the target molecule. This paper describes the application of the Multiple Copy Simultaneous Search (MCSS) method, an active site mapping and de novo structure-based design tool, to design a focused combinatorial library for the class II MHC protein HLA-DR4. Methods for the synthesizing and screening the computationally designed library are presented; evidence is provided to show that binding was achieved. Although the structure of the protein-ligand complex could not be determined, experimental results including cross-exclusion of a known HLA-DR4 peptide ligand (HA) by a compound from the library. Computational model building suggest that at least one of the ligands designed and identified by the methods described binds in a mode similar to that of native peptides.

The following account describes our systematic effort to replace one of the carboxylate groups of our diacid thiophene PTP1B inhibitors. Active hits were validated using enzymatic assays before pursuing efforts to improve the potency. Only when the C2 carboxylic acid was replaced with another ionizable functional group was reversible and competitive inhibition retained. Use of a tetrazole ring or 1,2,5-thiadiazolidine-3-one-1,1-dioxide as a carboxylate mimetic led to the discovery of two unique starting series that showed improved permeability (PAMPA) and potency of the order of 300nM. The SAR from these efforts underscores some of the major challenges in developing small molecule inhibitors for PTP1B.

Structure-based lead optimization approaches are increasingly playing a role in the drug-discovery process. Recent advances in 'high-throughput' molecular docking methods and examples of their successful use in lead optimization are reviewed. Measures of docking accuracy, scoring function comparisons, and consensus approaches are discussed. Differences in docking protocols typically used for lead optimization versus lead generation are highlighted; this section includes a discussion of the latest methods for the incorporation of protein flexibility. New approaches developed specifically for the design of combinatorial libraries as well as those designed or used for 'fragment' versus lead optimization are presented. Finally, potential future improvements to the technology are outlined.

By focusing on the P1 portion of the piperidine beta-sulfone ligands we identified a motif that induces selectivity and resulted in a series of TACE inhibitors that demonstrated excellent in vitro potency against isolated TACE enzyme and excellent selectivity over MMPs 1, 2, 9, 13, and 14.