Partitioning of Free Energies of Solvation into Fragment Contributions: Applications in Drug Design

Drugs targeting the four adenosine receptor (AR) subtypes can provide “soft" treatment of various significant diseases. Even for the two experimentally resolved AR subtypes the description of the orthosteric binding area and structure-activity relationships of ligands remains a demanding task due to the high similar amino acids sequence but also the broadness and flexibility of the ARs binding area. The identification of new pharmacophoric moieties and nanomolar leads and the exploration of their binding area with mutagenesis and state-of-the-art computational methods useful also for drug design purposes remains a challenging aim for all ARs. Here, we identified several low nanomolar ligands and potent competitive antagonists against A1R / A3R, containing the novel pyrazolo[3,4-c]pyridine pharmacophore for ARs, from a screen of an in-house library of only 52 compounds, originally designed for anti-proliferative activity. We identified L2-L10, A15, A17 with 3-aryl, 7-anilino and a electronegative group at 5-position as low micromolar to low nanomolar A1R / A3R antagonists. A17 has for A1R Kd = 5.62 nM and a residence time (RT) 41.33 min and for A3R Kd = 13.5 nM, RT = 47.23 min. The kinetic data showed that compared to the not potent or mediocre congeners the active compounds have similar association, for example at A1R Kon = 13.97 x106 M-1 (A17) vs Kon = 3.36 x106 M-1 (A26) but much lower dissociation rate Koff = 0.024 min-1 (A17) vs 0.134 min-1 (A26). Using molecular dynamics (MD) simulations and mutagenesis experiments we investigated the binding site of A17 showing that it can interact with an array of residues in transmembrane helix 5 (TM5), TM6, TM7 of A1R or A3R including residues E5.30, E5.28, T7.35 in A1R instead of Q5.28, V5.30 , L7.35 in A3R. A striking observation for drug design purposes is that for L2506.51A the binding affinity of A17 significantly increased at A1R. A17 provides a lead representative of a promising series and by means of the Thermodynamics Integration coupled with MD simulations (TI/MD) method, first applied here on whole GPCR- membrane system and showing a very good agreement between calculated and experimental relative binding free energies for A1R and A3R (spearman rank correlation p = 0.82 and 0.84, respectively), and kinetic experiments can lead to ligands with improved profile against ARs.

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Alchemical Absolute Protein-Ligand Binding Free Energies for Drug Design

Chemical Science ◽

10.1039/d1sc03472c ◽

2021 ◽

Author(s):

Yuriy Khalak ◽

Gary Tresdern ◽

Matteo Aldeghi ◽

Hannah Magdalena Baumann ◽

David L. Mobley ◽

...

Keyword(s):

Free Energy ◽

Drug Discovery ◽

Drug Design ◽

Ligand Binding ◽

Binding Free Energy ◽

Free Energy Calculations ◽

Free Energies ◽

Energy Calculations ◽

Binding Free Energy Calculations ◽

Binding Free Energies

The recent advances in relative protein-ligand binding free energy calculations have shown the value of alchemical methods in drug discovery. Accurately assessing absolute binding free energies, although highly desired, remains...

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Structural Perspective on Revealing and Altering Molecular Functions of Genetic Variants Linked with Diseases

International Journal of Molecular Sciences ◽

10.3390/ijms20030548 ◽

2019 ◽

Vol 20 (3) ◽

pp. 548 ◽

Cited By ~ 7

Author(s):

Yunhui Peng ◽

Emil Alexov ◽

Sankar Basu

Keyword(s):

Drug Design ◽

Structural Information ◽

Radical Change ◽

Biological Macromolecules ◽

Free Energies ◽

Biophysical Properties ◽

Structure Based Drug Design ◽

Promising Alternative ◽

Intrinsically Disordered ◽

Binding Free Energies

Structural information of biological macromolecules is crucial and necessary to deliver predictions about the effects of mutations—whether polymorphic or deleterious (i.e., disease causing), wherein, thermodynamic parameters, namely, folding and binding free energies potentially serve as effective biomarkers. It may be emphasized that the effect of a mutation depends on various factors, including the type of protein (globular, membrane or intrinsically disordered protein) and the structural context in which it occurs. Such information may positively aid drug-design. Furthermore, due to the intrinsic plasticity of proteins, even mutations involving radical change of the structural and physico–chemical properties of the amino acids (native vs. mutant) can still have minimal effects on protein thermodynamics. However, if a mutation causes significant perturbation by either folding or binding free energies, it is quite likely to be deleterious. Mitigating such effects is a promising alternative to the traditional approaches of designing inhibitors. This can be done by structure-based in silico screening of small molecules for which binding to the dysfunctional protein restores its wild type thermodynamics. In this review we emphasize the effects of mutations on two important biophysical properties, stability and binding affinity, and how structures can be used for structure-based drug design to mitigate the effects of disease-causing variants on the above biophysical properties.

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Novel Pyrazolo[3,4-c]pyridine Antagonists with Nanomolar affinity for A1 / A3 Adenosine Receptors: Binding Kinetics and Exploration of their Binding Profile Using Mutagenesis Experiments, MD simulations and TI/MD calculations

10.26434/chemrxiv-2021-j18mg-v3 ◽

2021 ◽

Author(s):

Margarita Stampelou ◽

Anna Suchankova ◽

Efpraxia Tzortzini ◽

Lakshiv Dhingra ◽

Kerry Barkan ◽

...

Keyword(s):

Drug Design ◽

Transmembrane Helix ◽

Rank Correlation ◽

Membrane System ◽

Md Simulations ◽

Dissociation Rate ◽

The Novel ◽

Free Energies ◽

Electronegative Group ◽

Good Agreement

Drugs targeting the four adenosine receptor (AR) subtypes can provide “soft" treatment of various significant diseases. Even for the two experimentally resolved AR subtypes the description of the orthosteric binding area and structure-activity relationships of ligands remains a demanding task due to the high similar amino acids sequence but also the broadness and flexibility of the ARs binding area. The identification of new pharmacophoric moieties and nanomolar leads and the exploration of their binding area with mutagenesis and state-of-the-art computational methods useful also for drug design purposes remains a challenging aim for all ARs. Here, we identified several low nanomolar ligands and potent competitive antagonists against A1R / A3R, containing the novel pyrazolo[3,4-c]pyridine pharmacophore for ARs, from a screen of an in-house library of only 52 compounds, originally designed for anti-proliferative activity. We identified L2-L10, A15, A17 with 3-aryl, 7-anilino and a electronegative group at 5-position as low micromolar to low nanomolar A1R / A3R antagonists. A17 has for A1R Kd = 5.62 nM and a residence time (RT) 41.33 min and for A3R Kd = 13.5 nM, RT = 47.23 min. The kinetic data showed that compared to the not potent or mediocre congeners the active compounds have similar association, for example at A1R Kon = 13.97 x106 M-1 (A17) vs Kon = 3.36 x106 M-1 (A26) but much lower dissociation rate Koff = 0.024 min-1 (A17) vs 0.134 min-1 (A26). Using molecular dynamics (MD) simulations and mutagenesis experiments we investigated the binding site of A17 showing that it can interact with an array of residues in transmembrane helix 5 (TM5), TM6, TM7 of A1R or A3R including residues E5.30, E5.28, T7.35 in A1R instead of Q5.28, V5.30 , L7.35 in A3R. A striking observation for drug design purposes is that for L2506.51A the binding affinity of A17 significantly increased at A1R. A17 provides a lead representative of a promising series and by means of the Thermodynamics Integration coupled with MD simulations (TI/MD) method, first applied here on whole GPCR- membrane system and showing a very good agreement between calculated and experimental relative binding free energies for A1R and A3R (spearman rank correlation p = 0.82 and 0.84, respectively), and kinetic experiments can lead to ligands with improved profile against ARs.

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Practical protocols for calculations of protein-ligand binding free energies in daily drug-design projects using AMBER GPU-TI with ff14SB/GAFF

10.1021/scimeetings.0c04749 ◽

2020 ◽

Author(s):

Xibing He ◽

Junmei Wang

Keyword(s):

Drug Design ◽

Ligand Binding ◽

Free Energies ◽

Design Projects ◽

Binding Free Energies

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Computational Modeling of Solvent Effects on Protein-Ligand Interactions Using Fully Polarizable Continuum Model and Rational Drug Design

Communications in Computational Physics ◽

10.4208/cicp.130911.121011s ◽

2013 ◽

Vol 13 (1) ◽

pp. 31-60 ◽

Cited By ~ 6

Author(s):

Fang Zheng ◽

Chang-Guo Zhan

Keyword(s):

Drug Design ◽

Computational Modeling ◽

Molecular Species ◽

Solvent Effects ◽

Polarizable Continuum Model ◽

Rational Drug Design ◽

Free Energies ◽

Protein Ligand Interactions ◽

Rational Drug ◽

Ligand Interactions

AbstractThis is a brief review of the computational modeling of protein-ligand interactions using a recently developed fully polarizable continuum model (FPCM) and rational drug design. Computational modeling has become a powerful tool in understanding detailed protein-ligand interactions at molecular level and in rational drug design. To study the binding of a protein with multiple molecular species of a ligand, one must accurately determine both the relative free energies of all of the molecular species in solution and the corresponding microscopic binding free energies for all of the molecular species binding with the protein. In this paper, we aim to provide a brief overview of the recent development in computational modeling of the solvent effects on the detailed protein-ligand interactions involving multiple molecular species of a ligand related to rational drug design. In particular, we first briefly discuss the main challenges in computational modeling of the detailed protein-ligand interactions involving the multiple molecular species and then focus on the FPCM model and its applications. The FPCM method allows accurate determination of the solvent effects in the first-principles quantum mechanism (QM) calculations on molecules in solution. The combined use of the FPCM-based QM calculations and other computational modeling and simulations enables us to accurately account for a protein binding with multiple molecular species of a ligand in solution. Based on the computational modeling of the detailed protein-ligand interactions, possible new drugs may be designed rationally as either small-molecule ligands of the protein or engineered proteins that bind/metabolize the ligand. The computational drug design has successfully led to discovery and development of promising drugs.

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Novel Pyrazolo[3,4-c]pyridine Antagonists with Nanomolar affinity for A1 / A3 Adenosine Receptors: Binding Kinetics and Exploration of their Binding Profile Using Mutagenesis Experiments, MD simulations and TI/MD calculations

10.26434/chemrxiv-2021-j18mg-v4 ◽

2021 ◽

Author(s):

Margarita Stampelou ◽

Anna Suchankova ◽

Efpraxia Tzortzini ◽

Lakshiv Dhingra ◽

Kerry Barkan ◽

...

Keyword(s):

Drug Design ◽

Transmembrane Helix ◽

Rank Correlation ◽

Membrane System ◽

Md Simulations ◽

Dissociation Rate ◽

The Novel ◽

Free Energies ◽

Electronegative Group ◽

Good Agreement

Drugs targeting the four adenosine receptor (AR) subtypes can provide “soft" treatment of various significant diseases. Even for the two experimentally resolved AR subtypes the description of the orthosteric binding area and structure-activity relationships of ligands remains a demanding task due to the high similar amino acids sequence but also the broadness and flexibility of the ARs binding area. The identification of new pharmacophoric moieties and nanomolar leads and the exploration of their binding area with mutagenesis and state-of-the-art computational methods useful also for drug design purposes remains a challenging aim for all ARs. Here, we identified several low nanomolar ligands and potent competitive antagonists against A1R / A3R, containing the novel pyrazolo[3,4-c]pyridine pharmacophore for ARs, from a screen of an in-house library of only 52 compounds, originally designed for anti-proliferative activity. We identified L2-L10, A15, A17 with 3-aryl, 7-anilino and a electronegative group at 5-position as low micromolar to low nanomolar A1R / A3R antagonists. A17 has for A1R Kd = 5.62 nM and a residence time (RT) 41.33 min and for A3R Kd = 13.5 nM, RT = 47.23 min. The kinetic data showed that compared to the not potent or mediocre congeners the active compounds have similar association, for example at A1R Kon = 13.97 x106 M-1 (A17) vs Kon = 3.36 x106 M-1 (A26) but much lower dissociation rate Koff = 0.024 min-1 (A17) vs 0.134 min-1 (A26). Using molecular dynamics (MD) simulations and mutagenesis experiments we investigated the binding site of A17 showing that it can interact with an array of residues in transmembrane helix 5 (TM5), TM6, TM7 of A1R or A3R including residues E5.30, E5.28, T7.35 in A1R instead of Q5.28, V5.30 , L7.35 in A3R. A striking observation for drug design purposes is that for L2506.51A the binding affinity of A17 significantly increased at A1R. A17 provides a lead representative of a promising series and by means of the Thermodynamics Integration coupled with MD simulations (TI/MD) method, first applied here on whole GPCR- membrane system and showing a very good agreement between calculated and experimental relative binding free energies for A1R and A3R (spearman rank correlation p = 0.82 and 0.84, respectively), and kinetic experiments can lead to ligands with improved profile against ARs.

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Structural Perspective on Revealing and Altering Molecular Functions of Genetic Variants Linked with Diseases

10.20944/preprints201812.0273.v3 ◽

2019 ◽

Author(s):

Yunhui Peng ◽

Emil Alexov ◽

Sankar Basu

Keyword(s):

Drug Design ◽

Structural Information ◽

Chemical Properties ◽

Radical Change ◽

Biological Macromolecules ◽

Free Energies ◽

Structure Based Drug Design ◽

Promising Alternative ◽

Intrinsically Disordered ◽

Binding Free Energies

Structural information of biological macromolecules is crucial and necessary to deliver predictions about the effects of mutations—whether polymorphic or deleterious (i.e., disease causing), wherein, thermodynamic parameters, namely, folding and binding free energies potentially serve as effective biomarkers. It may be emphasized that the effect of a mutation depends on various factors, including the type of protein (globular, membrane or intrinsically disordered protein) and the structural context to which it occurs. Such information may positively aid drug-design. Furthermore, due to the intrinsic plasticity of proteins, even mutations involving radical change of the structural and physico-chemical properties of the amino acids (native vs. mutant) can still have minimal effects of protein thermodynamics. However, if a mutation causes significant perturbation of either folding or binding free energies, it is quite likely to be deleterious. Mitigating such effects is a promising alternative to the traditional approaches of designing inhibitors. This can be done by structure-based in silico screening of small molecules for which binding to the dysfunctional protein restores its wild type thermodynamics. In this review we emphasize on the effects of mutations on two important biophysical characteristics, stability and binding affinity, and how structures can be used for structure-based drug design to mitigate the effects of disease-causing variants on the above biophysical characteristics.

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D3R Grand Challenge 4: Blind Prediction of Protein-Ligand Poses, Affinity Rankings, and Relative Binding Free Energies

10.26434/chemrxiv.11363006.v1 ◽

2019 ◽

Author(s):

conor parks ◽

Zied Gaieb ◽

Michael Chiu ◽

Huanwang Yang ◽

Chenghua Shao ◽

...

Keyword(s):

Drug Design ◽

Crystal Structures ◽

Best Practice ◽

Grand Challenge ◽

Pose Prediction ◽

Free Energies ◽

Design Data ◽

Data Resource ◽

Practice Methods ◽

Stage 1

<div>The Drug Design Data Resource (D3R) aims to identify best practice methods for computer aided drug design through blinded ligand pose prediction and affinity challenges. Herein, we report on the results of Grand Challenge 4 (GC4). GC4 focused on proteins beta secretase 1 and Cathepsin S, and was run in an analogous manner to prior challenges. In Stage 1, participant ability to predict the pose and affinity of BACE1 ligands were assessed. Following the completion of Stage 1, all BACE1 co-crystal structures were released, and Stage 2 tested affinity rankings with co-crystal structures. We provide an analysis of the results and discuss insights into determined best practice methods.<br></div>

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