KiSSim: Predicting off-targets from structural similarities in the kinome

Protein kinases are among the most important drug targets because their dysregulation can cause cancer, inflammatory, and degenerative diseases. Developing selective inhibitors is challenging due to the highly conserved binding sites across the roughly 500 human kinases. Thus, detecting subtle similarities on a structural level can help to explain and predict off-targets among the kinase family. Here, we present the kinase-focused and subpocket-enhanced KiSSim fingerprint (Kinase Structural Similarity). The fingerprint builds on the KLIFS pocket definition, composed of 85 residues aligned across all available protein kinase structures, which enables residue-by-residue comparison without a computationally expensive alignment. The residues' physicochemical and spatial properties are encoded within their structural context including key subpockets at the hinge region, the DFG motif, and the front pocket. Since structure was found to contain information complementary to sequence, we used the fingerprint to calculate all-against-all similarities within the structurally covered kinome. Thereby, we could identify off-targets that are unexpected if solely considering the sequence-based kinome tree grouping; for example, Erlobinib’s known kinase off-targets SLK and LOK show high similarities to the key target EGFR (TK group) though belonging to the STE group. KiSSim reflects profiling data better or at least as well as other approaches such as KLIFS pocket sequence identity, KLIFS interaction fingerprints (IFPs), or SiteAlign. To rationalize observed (dis)similarities, the fingerprint values can be visualized in 3D by coloring structures with residue and feature resolution. We believe that the KiSSim fingerprint is a valuable addition to the kinase research toolbox to guide off-target and polypharmacology prediction. The method is distributed as an open-source Python package on GitHub and as conda package: https://github.com/volkamerlab/kissim

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Discovery of an Unexpected Similarity in Ligand Binding Between BRD4 and PPARγ

10.26434/chemrxiv.11472618.v1 ◽

2019 ◽

Author(s):

Lina Humbeck ◽

Jette Pretzel ◽

Saskia Spitzer ◽

Oliver Koch

Keyword(s):

Cancer Therapy ◽

Drug Targets ◽

Synergistic Effects ◽

Complex Structure ◽

Peroxisome Proliferator ◽

Resistance Development ◽

Peroxisome Proliferator Activated Receptor ◽

Starting Point ◽

Fundamental Research ◽

Important Drug

Knowledge about interrelationships between different proteins is crucial in fundamental research for the elucidation of protein networks and pathways. Furthermore, it is especially critical in chemical biology to identify further key regulators of a disease and to take advantage of polypharmacology effects. A comprehensive scaffold-based analysis uncovered an unexpected relationship between bromodomain-containing protein 4 (BRD4) and peroxisome-proliferator activated receptor gamma (PPARγ). They are both important drug targets for cancer therapy and many more important diseases. Both proteins share binding site similarities near a common hydrophobic subpocket which should allow the design of a polypharmacology-based ligand targeting both proteins. Such a dual-BRD4-PPARγ-modulator could show synergistic effects with a higher efficacy or delayed resistance development in, for example, cancer therapy. Thereon, a complex structure of sulfasalazine was obtained that involves two bromodomains and could be a potential starting point for the design of a bivalent BRD4 inhibitor.

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Crystal structure of SARS-CoV-2 main protease in complex with a Chinese herb inhibitor shikonin

10.1101/2020.06.16.155812 ◽

2020 ◽

Author(s):

Jian Li ◽

Xuelan Zhou ◽

Yan Zhang ◽

Fanglin Zhong ◽

Cheng Lin ◽

...

Keyword(s):

Crystal Structure ◽

Conformational Changes ◽

Drug Targets ◽

Chinese Herb ◽

Binding Modes ◽

High Potency ◽

Main Protease ◽

Covalent Inhibitors ◽

Catalytic Dyad ◽

Important Drug

AbstractMain protease (Mpro, also known as 3CLpro) has a major role in the replication of coronavirus life cycle and is one of the most important drug targets for anticoronavirus agents. Here we report the crystal structure of main protease of SARS-CoV-2 bound to a previously identified Chinese herb inhibitor shikonin at 2.45 angstrom resolution. Although the structure revealed here shares similar overall structure with other published structures, there are several key differences which highlight potential features that could be exploited. The catalytic dyad His41-Cys145 undergoes dramatic conformational changes, and the structure reveals an unusual arrangement of oxyanion loop stabilized by the substrate. Binding to shikonin and binding of covalent inhibitors show different binding modes, suggesting a diversity in inhibitor binding. As we learn more about different binding modes and their structure-function relationships, it is probable that we can design more effective and specific drugs with high potency that can serve as effect SARS-CoV-2 anti-viral agents.

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Structural biology and structure–function relationships of membrane proteins

Biochemical Society Transactions ◽

10.1042/bst20180269 ◽

2018 ◽

Vol 47 (1) ◽

pp. 47-61 ◽

Cited By ~ 7

Author(s):

Rosana Reis ◽

Isabel Moraes

Keyword(s):

Membrane Proteins ◽

Structure Function ◽

Structural Biology ◽

Drug Targets ◽

Three Dimensional ◽

Data Bank ◽

Technical Advances ◽

Medical Importance ◽

G Protein Coupled ◽

Important Drug

Abstract The study of structure–function relationships of membrane proteins (MPs) has been one of the major goals in the field of structural biology. Many Noble Prizes regarding remarkable accomplishments in MP structure determination and biochemistry have been awarded over the last few decades. Mutations or improper folding of these proteins are associated with numerous serious illnesses. Therefore, as important drug targets, the study of their primary sequence and three-dimensional fold, combined with cell-based assays, provides vital information about their structure–function relationships. Today, this information is vital to drug discovery and medicine. In the last two decades, many have been the technical advances and breakthroughs in the field of MP structural biology that have contributed to an exponential growth in the number of unique MP structures in the Protein Data Bank. Nevertheless, given the medical importance and many unanswered questions, it will never be an excess of MP structures, regardless of the method used. Owing to the extension of the field, in this brief review, we will only focus on structure–function relationships of the three most significant pharmaceutical classes: G protein-coupled receptors, ion channels and transporters.

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Improving extraction and post-purification concentration of membrane proteins

The Analyst ◽

10.1039/c7an01470h ◽

2018 ◽

Vol 143 (6) ◽

pp. 1378-1386 ◽

Cited By ~ 9

Author(s):

Hasin Feroz ◽

HyeYoung Kwon ◽

Jing Peng ◽

Hyeonji Oh ◽

Bryan Ferlez ◽

...

Keyword(s):

Pharmaceutical Industry ◽

Membrane Proteins ◽

Drug Targets ◽

Functional Protein ◽

High Yields ◽

Important Drug

Membrane proteins (MPs), despite being critically important drug targets for the pharmaceutical industry, are difficult to study due to challenges in obtaining high yields of functional protein.

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PyLipID: A Python package for analysis of protein-lipid interactions from MD simulations

10.1101/2021.07.14.452312 ◽

2021 ◽

Author(s):

Wanling Song ◽

Robin A. Corey ◽

Bertie Ansell ◽

Keith Cassidy ◽

Michael Horrell ◽

...

Keyword(s):

Molecular Dynamics ◽

Membrane Proteins ◽

Molecular Dynamics Simulations ◽

Binding Sites ◽

Objective Assessment ◽

Source Distribution ◽

Lipid Interactions ◽

Dynamics Simulations ◽

Python Package

Lipids play important modulatory and structural roles for membrane proteins. Molecular dynamics simulations are frequently used to provide insights into the nature of these protein-lipid interactions. Systematic comparative analysis requires tools that provide algorithms for objective assessment of such interactions. We introduce PyLipID, a python package for the identification and characterization of specific lipid interactions and binding sites on membrane proteins from molecular dynamics simulations. PyLipID uses a community analysis approach for binding site detection, calculating lipid residence times for both the individual protein residues and the detected binding sites. To assist structural analysis, PyLipID produces representative bound lipid poses from simulation data, using a density-based scoring function. To estimate residue contacts robustly, PyLipID uses a dual-cutoff scheme to differentiate between lipid conformational rearrangements whilst bound from full dissociation events. In addition to the characterization of protein-lipid interactions, PyLipID is applicable to analysis of the interactions of membrane proteins with other ligands. By combining automated analysis, efficient algorithms, and open-source distribution, PyLipID facilitates the systematic analysis of lipid interactions from large simulation datasets of multiple species of membrane proteins.

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An automated platform for structural analysis of membrane proteins through serial crystallography

10.1101/2021.06.03.446146 ◽

2021 ◽

Author(s):

Robert D Healey ◽

Shibom Basu ◽

Anne-Sophie Humm ◽

Cedric Leyrat ◽

Xiaojing Cong ◽

...

Keyword(s):

Membrane Proteins ◽

Membrane Protein ◽

High Throughput ◽

Drug Targets ◽

Protein Crystallization ◽

Integral Membrane Protein ◽

Structural Level ◽

Ligand Discovery ◽

Serial Crystallography ◽

Automated Platform

Membrane proteins are central to many pathophysiological processes yet remain very difficult to analyze at a structural level. Moreover, high-throughput structure-based drug discovery has not yet been exploited for membrane proteins due to lack of automation. Here, we present a facile and versatile platform for in meso membrane protein crystallization, enabling rapid atomic structure determination at both cryogenic and room temperature and in a single support. We apply this approach to two human integral membrane proteins, which allowed us to capture different conformational states of intramembrane enzyme-product complexes and analyze the structural dynamics of the ADIPOR2 integral membrane protein. Finally, we demonstrate an automated pipeline combining high-throughput microcrystal soaking, automated laser-based harvesting and serial crystallography enabling screening of small molecule libraries with membrane protein crystals grown in meso. This approach brings badly needed automation for this important class of drug targets and enables high-throughput structure-based ligand discovery with membrane proteins.

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The crystal structure of mycobacterial epoxide hydrolase A

Scientific Reports ◽

10.1038/s41598-020-73452-y ◽

2020 ◽

Vol 10 (1) ◽

Author(s):

Eike C. Schulz ◽

Sara R. Henderson ◽

Boris Illarionov ◽

Thomas Crosskey ◽

Stacey M. Southall ◽

...

Keyword(s):

Crystal Structure ◽

Substrate Specificity ◽

Drug Targets ◽

Epoxide Hydrolase ◽

Sequence Similarity ◽

Structural Similarity ◽

High Sequence Similarity ◽

High Structural Similarity ◽

Potential Drug Targets ◽

Insight Into

Abstract The human pathogen Mycobacterium tuberculosis is the causative agent of tuberculosis resulting in over 1 million fatalities every year, despite decades of research into the development of new anti-TB compounds. Unlike most other organisms M. tuberculosis has six putative genes for epoxide hydrolases (EH) of the α/β-hydrolase family with little known about their individual substrates, suggesting functional significance for these genes to the organism. Due to their role in detoxification, M. tuberculosis EH’s have been identified as potential drug targets. Here, we demonstrate epoxide hydrolase activity of M. thermoresistibile epoxide hydrolase A (Mth-EphA) and report its crystal structure in complex with the inhibitor 1,3-diphenylurea at 2.0 Å resolution. Mth-EphA displays high sequence similarity to its orthologue from M. tuberculosis and generally high structural similarity to α/β-hydrolase EHs. The structure of the inhibitor bound complex reveals the geometry of the catalytic residues and the conformation of the inhibitor. Comparison to other EHs from mycobacteria allows insight into the active site plasticity with respect to substrate specificity. We speculate that mycobacterial EHs may have a narrow substrate specificity providing a potential explanation for the genetic repertoire of epoxide hydrolase genes in M. tuberculosis.

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A machine learning-based chemoproteomic approach to identify drug targets and binding sites in complex proteomes

Nature Communications ◽

10.1038/s41467-020-18071-x ◽

2020 ◽

Vol 11 (1) ◽

Cited By ~ 4

Author(s):

Ilaria Piazza ◽

Nigel Beaton ◽

Roland Bruderer ◽

Thomas Knobloch ◽

Crystel Barbisan ◽

...

Keyword(s):

Machine Learning ◽

Binding Sites ◽

Drug Targets

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Targeting the Class A Carbapenemase GES-5 via Virtual Screening

Biomolecules ◽

10.3390/biom10020304 ◽

2020 ◽

Vol 10 (2) ◽

pp. 304

Author(s):

Raphael Klein ◽

Laura Cendron ◽

Martina Montanari ◽

Pierangelo Bellio ◽

Giuseppe Celenza ◽

...

Keyword(s):

Antibiotic Resistance ◽

Drug Targets ◽

Antimicrobial Treatment ◽

Compound Library ◽

In Silico Screening ◽

Class A ◽

Biochemical Assays ◽

Competitive Inhibitors ◽

Important Drug

The worldwide spread of β-lactamases able to hydrolyze last resort carbapenems contributes to the antibiotic resistance problem and menaces the successful antimicrobial treatment of clinically relevant pathogens. Class A carbapenemases include members of the KPC and GES families. While drugs against KPC-type carbapenemases have recently been approved, for GES-type enzymes, no inhibitors have yet been introduced in therapy. Thus, GES carbapenemases represent important drug targets. Here, we present an in silico screening against the most prevalent GES carbapenemase, GES-5, using a lead-like compound library of commercially available compounds. The most promising candidates were selected for in vitro validation in biochemical assays against recombinant GES-5 leading to four derivatives active as high micromolar competitive inhibitors. For the best inhibitors, the ability to inhibit KPC-2 was also evaluated. The discovered inhibitors constitute promising starting points for hit to lead optimization.

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