ProteinsPlus: interactive analysis of protein–ligand binding interfaces

Abstract Due to the increasing amount of publicly available protein structures searching, enriching and investigating these data still poses a challenging task. The ProteinsPlus web service (https://proteins.plus) offers a broad range of tools addressing these challenges. The web interface to the tool collection focusing on protein–ligand interactions has been geared towards easy and intuitive access to a large variety of functionality for life scientists. Since our last publication, the ProteinsPlus web service has been extended by additional services as well as it has undergone substantial infrastructural improvements. A keyword search functionality was added on the start page of ProteinsPlus enabling users to work on structures without knowing their PDB code. The tool collection has been augmented by three tools: StructureProfiler validates ligands and active sites using selection criteria of well-established protein–ligand benchmark data sets, WarPP places water molecules in the ligand binding sites of a protein, and METALizer calculates, predicts and scores coordination geometries of metal ions based on surrounding complex atoms. Additionally, all tools provided by ProteinsPlus are available through a REST service enabling the automated integration in structure processing and modeling pipelines.

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Systematic domain-based aggregation of protein structures highlights DNA-, RNA- and other ligand-binding positions

Nucleic Acids Research ◽

10.1093/nar/gky1224 ◽

2018 ◽

Vol 47 (2) ◽

pp. 582-593 ◽

Cited By ~ 5

Author(s):

Shilpa Nadimpalli Kobren ◽

Mona Singh

Keyword(s):

Ligand Binding ◽

Small Molecules ◽

Binding Sites ◽

Protein Structures ◽

Complex Structures ◽

Disease Etiology ◽

Systematic Assessment ◽

Protein Ligand Interactions ◽

Ligand Binding Sites ◽

Ligand Interactions

Abstract Domains are fundamental subunits of proteins, and while they play major roles in facilitating protein–DNA, protein–RNA and other protein–ligand interactions, a systematic assessment of their various interaction modes is still lacking. A comprehensive resource identifying positions within domains that tend to interact with nucleic acids, small molecules and other ligands would expand our knowledge of domain functionality as well as aid in detecting ligand-binding sites within structurally uncharacterized proteins. Here, we introduce an approach to identify per-domain-position interaction ‘frequencies’ by aggregating protein co-complex structures by domain and ascertaining how often residues mapping to each domain position interact with ligands. We perform this domain-based analysis on ∼91000 co-complex structures, and infer positions involved in binding DNA, RNA, peptides, ions or small molecules across 4128 domains, which we refer to collectively as the InteracDome. Cross-validation testing reveals that ligand-binding positions for 2152 domains are highly consistent and can be used to identify residues facilitating interactions in ∼63–69% of human genes. Our resource of domain-inferred ligand-binding sites should be a great aid in understanding disease etiology: whereas these sites are enriched in Mendelian-associated and cancer somatic mutations, they are depleted in polymorphisms observed across healthy populations. The InteracDome is available at http://interacdome.princeton.edu.

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Systematic domain-based aggregation of protein structures highlights DNA-, RNA-, and other ligand-binding positions

10.1101/394494 ◽

2018 ◽

Author(s):

Shilpa Nadimpalli Kobren ◽

Mona Singh

Keyword(s):

Ligand Binding ◽

Small Molecules ◽

Binding Sites ◽

Protein Structures ◽

Complex Structures ◽

Disease Etiology ◽

Systematic Assessment ◽

Protein Ligand Interactions ◽

Ligand Binding Sites ◽

Ligand Interactions

AbstractDomains are fundamental subunits of proteins, and while they play major roles in facilitating protein–DNA, protein–RNA and other protein–ligand interactions, a systematic assessment of their various interaction modes is still lacking. A comprehensive resource identifying positions within domains that tend to interact with nucleic acids, small molecules and other ligands would expand our knowledge of domain functionality as well as aid in detecting ligand-binding sites within structurally uncharacterized proteins. Here we introduce an approach to identify per-domain-position interaction “propensities” by aggregating protein co-complex structures by domain and ascertaining how frequently residues mapping to each domain position interact with ligands. We perform this domain-based analysis on ∼82,000 co-complex structures, and infer positions involved in binding DNA, RNA, peptides, ions, or small molecules across 4,120 domains, which we refer to collectively as the InteracDome. Cross-validation testing reveals that ligand-binding positions for 1,327 domains can be confidently modeled and used to identify residues facilitating interactions in ∼60–69% of human genes. Our resource of domain-inferred ligand-binding sites should be a great aid in understanding disease etiology: whereas these sites are enriched in Mendelian-associated and cancer somatic mutations, they are depleted in polymorphisms observed across healthy populations. The InteracDome is available at http://interacdome.princeton.edu.

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19F-NMR in Target-based Drug Discovery

Current Medicinal Chemistry ◽

10.2174/0929867326666190610160534 ◽

2019 ◽

Vol 26 (26) ◽

pp. 4964-4983 ◽

Cited By ~ 7

Author(s):

CongBao Kang

Keyword(s):

Drug Discovery ◽

Conformational Changes ◽

Protein Structures ◽

19F Nmr ◽

Binding Affinities ◽

Protein Ligand Interactions ◽

Compound Libraries ◽

Ligand Interactions ◽

Reference Ligand ◽

Hit Identification

Solution NMR spectroscopy plays important roles in understanding protein structures, dynamics and protein-protein/ligand interactions. In a target-based drug discovery project, NMR can serve an important function in hit identification and lead optimization. Fluorine is a valuable probe for evaluating protein conformational changes and protein-ligand interactions. Accumulated studies demonstrate that 19F-NMR can play important roles in fragment- based drug discovery (FBDD) and probing protein-ligand interactions. This review summarizes the application of 19F-NMR in understanding protein-ligand interactions and drug discovery. Several examples are included to show the roles of 19F-NMR in confirming identified hits/leads in the drug discovery process. In addition to identifying hits from fluorinecontaining compound libraries, 19F-NMR will play an important role in drug discovery by providing a fast and robust way in novel hit identification. This technique can be used for ranking compounds with different binding affinities and is particularly useful for screening competitive compounds when a reference ligand is available.

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Identification of protein pockets and cavities by Euclidean Distance Transform

10.7287/peerj.preprints.27314v1 ◽

2018 ◽

Author(s):

Sebastian Daberdaku

Keyword(s):

Ligand Binding ◽

Active Sites ◽

Euclidean Distance ◽

Protein Structures ◽

Ligand Docking ◽

Distance Transform ◽

Structure Based Drug Design ◽

Distance Map ◽

Euclidean Distance Transform ◽

Protein Pockets

Protein pockets and cavities usually coincide with the active sites of biological processes, and their identification is significant since it constitutes an important step for structure-based drug design and protein-ligand docking applications. This paper presents a novel purely geometric algorithm for the detection of ligand binding protein pockets and cavities based on the Euclidean Distance Transform (EDT). The EDT can be used to compute the Solvent-Excluded surface for any given probe sphere radius value at high resolutions and in a timely manner. The algorithm is adaptive to the specific candidate ligand: it computes two voxelised protein surfaces using two different probe sphere radii depending on the shape of the candidate ligand. The pocket regions consist of the voxels located between the two surfaces, which exhibit a certain minimum depth value from the outer surface. The distance map values computed by the EDT algorithm during the second surface computation can be used to elegantly determine the depth of each candidate pocket and to rank them accordingly. Cavities on the other hand, are identified by scanning the inside of the protein for voids. The algorithm determines and outputs the best k candidate pockets and cavities, i.e. the ones that are more likely to bind to the given ligand. The method was applied to a representative set of protein-ligand complexes and their corresponding unbound protein structures to evaluate its ligand binding site prediction capabilities, and was shown to outperform most of the previously developed purely geometric pocket and cavity search methods.

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Improving protein–ligand binding prediction by considering the bridging water molecules in Autodock

Journal of Theoretical and Computational Chemistry ◽

10.1142/s0219633619500275 ◽

2019 ◽

Vol 18 (05) ◽

pp. 1950027 ◽

Cited By ~ 1

Author(s):

Qiangna Lu ◽

Lian-Wen Qi ◽

Jinfeng Liu

Keyword(s):

Ligand Binding ◽

Considerable Improvement ◽

Ligand Docking ◽

Water Molecules ◽

Binding Modes ◽

Binding Prediction ◽

Docking Simulations ◽

Protein Ligand Interactions ◽

Ligand Interactions ◽

Docking Program

Water plays a significant role in determining the protein–ligand binding modes, especially when water molecules are involved in mediating protein–ligand interactions, and these important water molecules are receiving more and more attention in recent years. Considering the effects of water molecules has gradually become a routine process for accurate description of the protein–ligand interactions. As a free docking program, Autodock has been most widely used in predicting the protein–ligand binding modes. However, whether the inclusion of water molecules in Autodock would improve its docking performance has not been systematically investigated. Here, we incorporate important bridging water molecules into Autodock program, and systematically investigate the effectiveness of these water molecules in protein–ligand docking. This approach was evaluated using 18 structurally diverse protein–ligand complexes, in which several water molecules bridge the protein–ligand interactions. Different treatment of water molecules were tested by using the fixed and rotatable water molecules, and a considerable improvement in successful docking simulations was found when including these water molecules. This study illustrates the necessity of inclusion of water molecules in Autodock docking, and emphasizes the importance of a proper treatment of water molecules in protein–ligand binding predictions.

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High-resolution cryo-EM proteasome structures in drug development

Acta Crystallographica Section D Structural Biology ◽

10.1107/s2059798317007021 ◽

2017 ◽

Vol 73 (6) ◽

pp. 522-533 ◽

Cited By ~ 7

Author(s):

Edward P. Morris ◽

Paula C. A. da Fonseca

Keyword(s):

Structure Determination ◽

Protein Structures ◽

Three Dimensional ◽

Specific Inhibitor ◽

Particle Analysis ◽

20S Proteasome ◽

Ligand Specificity ◽

Protein Ligand Interactions ◽

Ligand Interactions ◽

Dimensional Reconstruction

With the recent advances in biological structural electron microscopy (EM), protein structures can now be obtained by cryo-EM and single-particle analysis at resolutions that used to be achievable only by crystallographic or NMR methods. We have explored their application to study protein–ligand interactions using the human 20S proteasome, a well established target for cancer therapy that is also being investigated as a target for an increasing range of other medical conditions. The map of a ligand-bound human 20S proteasome served as a proof of principle that cryo-EM is emerging as a realistic approach for more general structural studies of protein–ligand interactions, with the potential benefits of extending such studies to complexes that are unfavourable to other methods and allowing structure determination under conditions that are closer to physiological, preserving ligand specificity towards closely related binding sites. Subsequently, the cryo-EM structure of thePlasmodium falciparum20S proteasome, with a new prototype specific inhibitor bound, revealed the molecular basis for the ligand specificity towards the parasite complex, which provides a framework to guide the development of highly needed new-generation antimalarials. Here, the cryo-EM analysis of the ligand-bound human andP. falciparum20S proteasomes is reviewed, and a complete description of the methods used for structure determination is provided, including the strategy to overcome the bias orientation of the human 20S proteasome on electron-microscope grids and details of theicr3dsoftware used for three-dimensional reconstruction.

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Estimation of Binding Rates and Affinities from Multiensemble Markov Models and Ligand Decoupling

10.26434/chemrxiv.14728206.v1 ◽

2021 ◽

Author(s):

Yunhui Ge ◽

Vincent Voelz

Keyword(s):

Ligand Binding ◽

Markov Models ◽

Time Correlation ◽

Test System ◽

Binding Affinities ◽

Limited Data ◽

Markov State Model ◽

Protein Ligand Interactions ◽

Ligand Interactions ◽

Kinetics Of

Accurate and efficient simulation of the thermodynamics and kinetics of protein-ligand interactions is crucial for computational drug discovery. Multiensemble Markov Model (MEMM) estimators can provide estimates of both binding rates and affinities from collections of short trajectories, but have not been systematically explored for situations when a ligand is decoupled through scaling of non-bonded interactions. In this work, we compare the performance of two MEMM approaches for estimating ligand binding affinities and rates: (1) the transition-based reweighting analysis method (TRAM) and (2) a Maximum Caliber (MaxCal) based method. As a test system, we construct a small host-guest system where the ligand is a single uncharged Lennard-Jones (LJ) particle, and the receptor is an 11-particle icosahedral pocket made from the same atom type. To realistically mimic a protein-ligand binding system, the LJ ε parameter was tuned, and the system placed in a periodic box with 860 TIP3P water molecules. A benchmark was performed using over 80 μs of unbiased simulation, and an 18-state Markov state model used to estimate reference binding affinities and rates. We then tested the performance of TRAM and MaxCal when challenged with limited data. Both TRAM and MaxCal approaches perform better than conventional MSMs, with TRAM showing better convergence and accuracy. We find that subsampling of trajectories to remove time correlation improves the accuracy of both TRAM and MaxCal, and that in most cases only a single biased ensemble to enhance sampled transitions is required to make accurate estimates.

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Phytoconstituents from Markhamia Tomentosa Bind To HPV Oncoprotein with Apoptogenic Potential: A Molecular Modeling Approach

Annals of Science and Technology ◽

10.2478/ast-2021-0008 ◽

2021 ◽

Vol 0 (0) ◽

Author(s):

Mutiat B. Ibrahim ◽

Adeola T. Kola-Mustapha ◽

Niyi S. Adelakun ◽

Neil A. Koorbanally

Keyword(s):

Drug Targets ◽

Active Sites ◽

Caspase 3 ◽

Cancer Cell Line ◽

Hpv 16 ◽

Protein Ligand Interactions ◽

Ligand Interactions ◽

Potent Growth ◽

The Stability

Abstract Markhamia tomentosa crude extract and fractions exhibited potent growth inhibitory effects capable to induce apoptosis in cervical (HeLa) cancer cell line via in vitro model. Presently, interaction of M. tomentosa phytoconstituents with molecular drug targets to exert its anticancer property is evaluated via in silico study. Identified phytoconstituents from M. tomentosa were retrieved from PubChem database and docked in active sites of HPV 16 E6, caspase -3 and caspase -8 targets using AutoDockVina from PyRx software. Screening for druglikeness; and absorption, distribution, metabolism, excretion, and toxicity (ADMET) predictions was carried out with the use of SwissADME and pkCSM web servers. Standard melphalan and co-crystallized ligands of caspases -3 and -8 enzymes were used to validate protein-ligand interactions. Molecular dynamic simulation was used to validate the stability of the hit molecules complexed with caspases -3 and -8. All identified phytoconstituents from M. tomentosa showed binding affinity for HPV with docking scores range of - 5.4 to -2.6 kcal/mol. Ajugol, carnosol, luteolin and phytol showed good docking energy range of -6.8 to -3.6 kcal/mol; and -4.8 to -1.9 kcal/mol for the active sites of caspases -3 and -8 targets respectively. Based on docking scores; drug-likeliness; and ADMET predictions; luteolin and carnosol were selected as hit compounds. These molecules were found to be stable within the binding site of caspase -3 target throughout the 40ns simulation time. These findings identified hit ligands from M. tomentosa phytoconstituents that inhibit HPV 16 E6 oncogene expression with stimulation of caspases -3 and -8 targets.

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A blueprint for high affinity SARS-CoV-2 Mpro inhibitors from activity-based compound library screening guided by analysis of protein dynamics

10.1101/2020.12.14.422634 ◽

2020 ◽

Author(s):

Jonas Gossen ◽

Simone Albani ◽

Anton Hanke ◽

Benjamin P. Joseph ◽

Cathrine Bergh ◽

...

Keyword(s):

Ligand Binding ◽

Binding Site ◽

Active Sites ◽

Chemical Space ◽

Protein Structures ◽

Rational Drug Design ◽

In Vitro Assays ◽

Strong Basis ◽

High Affinity ◽

Compound Libraries

AbstractThe SARS-CoV-2 coronavirus outbreak continues to spread at a rapid rate worldwide. The main protease (Mpro) is an attractive target for anti-COVID-19 agents. Unfortunately, unexpected difficulties have been encountered in the design of specific inhibitors. Here, by analyzing an ensemble of ~30,000 SARS-CoV-2 Mpro conformations from crystallographic studies and molecular simulations, we show that small structural variations in the binding site dramatically impact ligand binding properties. Hence, traditional druggability indices fail to adequately discriminate between highly and poorly druggable conformations of the binding site. By performing ~200 virtual screenings of compound libraries on selected protein structures, we redefine the protein’s druggability as the consensus chemical space arising from the multiple conformations of the binding site formed upon ligand binding. This procedure revealed a unique SARS-CoV-2 Mpro blueprint that led to a definition of a specific structure-based pharmacophore. The latter explains the poor transferability of potent SARS-CoV Mpro inhibitors to SARS-CoV-2 Mpro, despite the identical sequences of the active sites. Importantly, application of the pharmacophore predicted novel high affinity inhibitors of SARS-CoV-2 Mpro, that were validated by in vitro assays performed here and by a newly solved X-ray crystal structure. These results provide a strong basis for effective rational drug design campaigns against SARS-CoV-2 Mpro and a new computational approach to screen protein targets with malleable binding sites.

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