Validating Small-Molecule Force Fields for Macrocyclic Compounds Using NMR Data in Different Solvents.

Macrocycles are a promising class of compounds as therapeutics for difficult drug targets due to a favorable combination of properties: They often exhibit improved binding affinity compared to their linear counterparts due to their reduced conformational flexibility, while still being able to adapt to environments of different polarity. To assist in the rational design of macrocyclic drugs, there is need for computational methods that can accurately predict conformational ensembles of macrocycles in different environments. Molecular dynamics (MD) simulations remain one of the most accurate methods to predict ensembles quantitatively, although the accuracy is governed by the underlying force field. In this work, we benchmark four different force fields for their application to macrocycles by performing replica exchange with solute tempering (REST2) simulations of 11 macrocyclic compounds and comparing the obtained conformational ensembles to nuclear Overhauser effect (NOE) upper distance bounds from NMR experiments. Especially, the modern force fields OpenFF 2.0 and XFF yield good results, outperforming force fields like GAFF2 and OPLS/AA. We conclude that REST2 in combination with modern force fields can often produce accurate ensembles of macrocyclic compounds. However, we also highlight examples for which all examined force fields fail to produce ensembles that fulfill the experimental constraints.

A new antibiotic traps lipopolysaccharide in its intermembrane transporter.

Gram-negative bacteria are extraordinarily difficult to kill because their cytoplasmic membrane is surrounded by an outer membrane that blocks the entry of most antibiotics. The impenetrable nature of the outer membrane is due to the presence of a large, amphipathic glycolipid called lipopolysaccharide (LPS) in its outer leaflet1. Assembly of the outer membrane requires transport of LPS across a protein bridge that spans from the cytoplasmic membrane to the cell surface. Maintaining outer membrane integrity is essential for bacterial cell viability, and its disruption can increase susceptibility to other antibiotics2-6. Thus, inhibitors of the seven lipopolysaccharide transport (Lpt) proteins that form this transenvelope transporter have long been sought. A new class of antibiotics that targets the LPS transport machine in Acinetobacter was recently identified. Here, using structural, biochemical and genetic approaches, we show that these antibiotics trap a substrate-bound conformation of the LPS transporter that stalls this machine. The inhibitors accomplish this by recognizing a composite binding site made up of both the Lpt transporter and its LPS substrate. Collectively, our findings identify an unusual mechanism of lipid transport inhibition, reveal a druggable conformation of the Lpt transporter and provide the foundation for extending this class of antibiotics to other Gram-negative pathogens.

A novel antibiotic class targeting the lipopolysaccharide transporter.

Carbapenem-resistant Acinetobacter baumannii (CRAB) has emerged as a major global pathogen with limited treatment options1. No new antibiotic chemical class with activity against A. baumannii has reached patients in over 50 years1. Here we report the identification and optimization of tethered macrocyclic peptide (MCP) antibiotics with potent antibacterial activity against CRAB. The mechanism of action of this molecule class involves blocking the transport of bacterial lipopolysaccharide from the inner membrane to its destination on the outer membrane, through inhibition of the LptB2FGC complex. A clinical candidate derived from the MCP class, zosurabalpin (RG6006), effectively treats highly drug-resistant contemporary isolates of CRAB both in vitro and in mouse models of infection, overcoming existing antibiotic resistance mechanisms. This chemical class represents a promising treatment paradigm for patients with invasive infections due to CRAB, for whom current treatment options are inadequate, and additionally identifies LptB2FGC as a tractable target for antimicrobial drug development.

Discovery of Pyrido[2,3-b]indole Derivatives with Gram-Negative Activity Targeting Both DNA Gyrase and Topoisomerase IV.

The rise of multidrug resistant (MDR) Gram-negative (GN) pathogens and the decline of available antibiotics that can effectively treat these severe infections are a major threat to modern medicine. Developing novel antibiotics against MDR GN pathogens is particularly difficult as compounds have to permeate the GN double membrane, which has very different physicochemical properties, and have to circumvent a plethora of resistance mechanisms such as multiple efflux pumps and target modifications. The bacterial type II topoisomerases DNA gyrase (GyrA2B2) and Topoisomerase IV (ParC2E2) are highly conserved targets across all bacterial species and validated in the clinic by the fluoroquinolones. Dual inhibitors targeting the ATPase domains (GyrB/ParE) of type II topoisomerases can overcome target-based fluoroquinolone resistance. However, few ATPase inhibitors are active against GN pathogens. In this study, we demonstrated a successful strategy to convert a 2-carboxamide substituted azaindole chemical scaffold with only Gram-positive (GP) activity into a novel series with also potent activity against a range of MDR GN pathogens. By systematically fine-tuning the many physicochemical properties, we identified lead compounds such as 17r with a balanced profile showing potent GN activity, high aqueous solubility, and desirable PK features. Moreover, we showed the bactericidal efficacy of 17r using a neutropenic mouse thigh infection model.

A New Approach of Mitigating CYP3A4 Induction Led to the Discovery of Potent Hepatitis B Virus (HBV) Capsid Inhibitor with Optimal ADMET Profiles.

Described herein is a new approach to mitigate CYP3A4 induction. In this unconventional approach, a fine-tuning of the dihedral angle between the C4 phenyl and the dihydropyrimidine core of the heteroaryldihydropyrimidine (HAP) class of capsid inhibitors successfully altered the structure-activity-relationships (SARs) of the unwanted CYP3A4 induction and the desired HBV capsid inhibition to more favorable values. This eventually led to the discovery of a new capsid inhibitor with significantly reduced CYP3A4 induction, excellent anti-HBV activity, favorable preclinical PK/PD profiles, and no early safety flags.

Structure-based prediction of ligand-protein interactions on a genome-wide scale.

We report a template-based method, LT-scanner, which scans the human proteome using protein structural alignment to identify proteins that are likely to bind ligands that are present in experimentally determined complexes. A scoring function that rapidly accounts for binding site similarities between the template and the proteins being scanned is a crucial feature of the method. The overall approach is first tested based on its ability to predict the residues on the surface of a protein that are likely to bind small-molecule ligands. The algorithm that we present, LBias, is shown to compare very favorably to existing algorithms for binding site residue prediction. LT-scanner's performance is evaluated based on its ability to identify known targets of Food and Drug Administration (FDA)-approved drugs and it too proves to be highly effective. The specificity of the scoring function that we use is demonstrated by the ability of LT-scanner to identify the known targets of FDA-approved kinase inhibitors based on templates involving other kinases. Combining sequence with structural information further improves LT-scanner performance. The approach we describe is extendable to the more general problem of identifying binding partners of known ligands even if they do not appear in a structurally determined complex, although this will require the integration of methods that combine protein structure and chemical compound databases.

On-rate based optimization of structure-kinetic relationship--surfing the kinetic map.

In the lead discovery process residence time has become an important parameter for the identification and characterization of the most efficacious compounds in vivo. To enable the success of compound optimization by medicinal chemistry toward a desired residence time the understanding of structure-kinetic relationship (SKR) is essential. This article reviews various approaches to monitor SKR and suggests using the on-rate as the key monitoring parameter. The literature is reviewed and examples of compound series with low variability as well as with significant changes in on-rates are highlighted. Furthermore, findings of kinetic on-rate changes are presented and potential underlying rationales are discussed.

Toward a "structural BLAST": using structural relationships to infer function.

We outline a set of strategies to infer protein function from structure. The overall approach depends on extensive use of homology modeling, the exploitation of a wide range of global and local geometric relationships between protein structures and the use of machine learning techniques. The combination of modeling with broad searches of protein structure space defines a "structural BLAST" approach to infer function with high genomic coverage. Applications are described to the prediction of protein-protein and protein-ligand interactions. In the context of protein-protein interactions, our structure-based prediction algorithm, PrePPI, has comparable accuracy to high-throughput experiments. An essential feature of PrePPI involves the use of Bayesian methods to combine structure-derived information with non-structural evidence (e.g. co-expression) to assign a likelihood for each predicted interaction. This, combined with a structural BLAST approach significantly expands the range of applications of protein structure in the annotation of protein function, including systems level biological applications where it has previously played little role.

NMR structure of lipoprotein YxeF from Bacillus subtilis reveals a calycin fold and distant homology with the lipocalin Blc from Escherichia coli.

The soluble monomeric domain of lipoprotein YxeF from the Gram positive bacterium B. subtilis was selected by the Northeast Structural Genomics Consortium (NESG) as a target of a biomedical theme project focusing on the structure determination of the soluble domains of bacterial lipoproteins. The solution NMR structure of YxeF reveals a calycin fold and distant homology with the lipocalin Blc from the Gram-negative bacterium E.coli. In particular, the characteristic ß-barrel, which is open to the solvent at one end, is extremely well conserved in YxeF with respect to Blc. The identification of YxeF as the first lipocalin homologue occurring in a Gram-positive bacterium suggests that lipocalins emerged before the evolutionary divergence of Gram positive and Gram negative bacteria. Since YxeF is devoid of the α-helix that packs in all lipocalins with known structure against the ß-barrel to form a second hydrophobic core, we propose to introduce a new lipocalin sub-family named 'slim lipocalins', with YxeF and the other members of Pfam family PF11631 to which YxeF belongs constituting the first representatives. The results presented here exemplify the impact of structural genomics to enhance our understanding of biology and to generate new biological hypotheses.

MarkUs: a server to navigate sequence-structure-function space.

We describe MarkUs, a web server for analysis and comparison of the structural and functional properties of proteins. In contrast to a 'structure in/function out' approach to protein function annotation, the server is designed to be highly interactive and to allow flexibility in the examination of possible functions, suggested either automatically by various similarity measures or specified by a user directly. This is combined with tools that allow a user to assess independently whether or not a suggested function is consistent with the bioinformatic and biophysical properties of a given query structure, further allowing the user to generate testable hypotheses. The server is available at http://wiki.c2b2.columbia.edu/honiglab_public/index.php/Software:Mark-Us.

Fragment-based de novo ligand design by multiobjective evolutionary optimization.

GANDI (Genetic Algorithm-based de Novo Design of Inhibitors) is a computational tool for automatic fragment-based design of molecules within a protein binding site of known structure. A genetic algorithm and a tabu search act in concert to join predocked fragments with a user-supplied list of fragments. A novel feature of GANDI is the simultaneous optimization of force field energy and a term enforcing 2D-similarity to known inhibitor(s) or 3D-overlap to known binding mode(s). Scaffold hopping can be promoted by tuning the relative weights of these terms. The performance of GANDI is tested on cyclin-dependent kinase 2 (CDK2) using a library of about 14 000 fragments and the binding mode of a known oxindole inhibitor to bias the design. Top ranking GANDI molecules are involved in one to three hydrogen bonds with the backbone polar groups in the hinge region of CDK2, an interaction pattern observed in potent kinase inhibitors. Notably, a GANDI molecule with very favorable predicted binding affinity shares a 2-N-phenyl-1,3-thiazole-2,4-diamine moiety with a known nanomolar inhibitor of CDK2. Importantly, molecules with a favorable GANDI score are synthetic accessible. In fact, eight of the 1809 molecules designed by GANDI for CDK2 are found in the ZINC database of commercially available compounds which also contains about 600 compounds with identical scaffolds as those in the top ranking GANDI molecules.

Discovery of kinase inhibitors by high-throughput docking and scoring based on a transferable linear interaction energy model.

The linear interaction energy method with continuum electrostatics (LIECE) is evaluated in depth on five kinases. The two multiplicative coefficients for the van der Waals energy and electrostatic free energy are shown to be transferable among different kinases. Moreover, good enrichment factors are obtained for a library of 40375 diverse compounds seeded with 73 known inhibitors of CDK2. Therefore, a general two-parameter LIECE model for kinases is derived by combining large data sets of inhibitors of CDK2, Lck, and p38. This two-parameter model is cross-validated on two kinases not used for fitting; it shows an average error of about 1.5 kcal/mol for the prediction of absolute binding affinity of 37 and 128 known inhibitors of EphB4 and EGFR, respectively. High-throughput docking and ranking by two-parameter LIECE models are shown to be able to identify novel low-micromolar EphB4 and CDK2 inhibitors of low-molecular weight (< or =355 g/mol).