Force spectroscopy studies on protein-ligand interactions: A single protein mechanics perspective

ABSTRACTCatch bonds are a form of mechanoregulation wherein protein-ligand interactions are strengthened by the application of dissociative tension. Currently, the best-characterized examples of catch bonds are between single protein-ligand pairs. The essential AAA (ATPase associated with diverse cellular activities) mechanoenzyme Mdn1 drives two separate steps in ribosome biogenesis, using its MIDAS domain to extract the ubiquitin-like (UBL) domain-containing proteins Rsa4 and Ytm1 from ribosomal precursors. However, it must subsequently release these assembly factors to reinitiate the enzymatic cycle. The mechanism underlying MIDAS-UBL switching between strongly- and weakly-bound states is unknown. Here, we use single-molecule optical tweezers to investigate the force-dependence of MIDAS-UBL binding. Parallel experiments with Rsa4 and Ytm1 show that forces up to ~4 pN, matching the magnitude of force produced by AAA proteins similar to Mdn1, enhance the MIDAS domain binding lifetime up to tenfold, and higher forces accelerate dissociation. Together, our studies indicate that Mdn1’s MIDAS domain forms catch bonds with more than one UBL-substrate, and provide insights into how mechanoregulation may contribute to the Mdn1 enzymatic cycle during ribosome biogenesis.

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Calculating the force-dependent unbinding rate of biological macromolecular bonds from the force-ramp optical trapping assays

10.1101/2021.09.20.461030 ◽

2021 ◽

Author(s):

Apurba Paul ◽

Joshua Alper

Keyword(s):

Optical Tweezers ◽

Single Molecule ◽

Force Spectroscopy ◽

Optical Tweezer ◽

Stochastic Simulations ◽

Cumulative Distribution ◽

Cellular Functions ◽

Single Molecule Force Spectroscopy ◽

Protein Ligand Interactions ◽

Ligand Interactions

AbstractThe non-covalent biological bonds that constitute protein-protein or protein-ligand interactions play crucial roles in many cellular functions, including mitosis, motility, and cell-cell adhesion. The effect of external force (F) on the unbinding rate (koff(F)) of macromolecular interactions is a crucial parameter to understanding the mechanisms behind these functions. Optical tweezer-based single-molecule force spectroscopy is frequently used to obtain quantitative force-dependent dissociation data on slip, catch, and ideal bonds. However, analyses of this data using dissociation time or dissociation force histograms often quantitatively compare bonds without fully characterizing their underlying biophysical properties. Additionally, the results of histogram-based analyses can depend on the rate at which force was applied during the experiment and the experiment’s sensitivity. Here, we present an analytically derived cumulative distribution function-like approach to analyzing force-dependent dissociation force spectroscopy data. We demonstrate the benefits and limitations of the technique using stochastic simulations of various bond types. We show that it can be used to obtain the detachment rate and force sensitivity of biological macromolecular bonds from force spectroscopy experiments by explicitly accounting for loading rate and noisy data. We also discuss the implications of our results on using optical tweezers to collect force-dependent dissociation data.

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Solvent Isotope Effects in Atomic Force Spectroscopy

Microscopy and Microanalysis ◽

10.1017/s143192760003035x ◽

2001 ◽

Vol 7 (S2) ◽

pp. 856-857

Author(s):

Jeffrey G. Forbes ◽

John P. Santos

Keyword(s):

Deuterium Oxide ◽

Isotope Effects ◽

Protein Ligand Interactions ◽

Atomic Force ◽

Single Protein ◽

Ligand Interactions ◽

Solvent Isotope ◽

Functionalized Tips ◽

Glass Surfaces ◽

Solvent Isotope Effects

The utility of the atomic force microscope (AFM) for measuring forces is now well established. The AFM is capable of measuring forces from tens of piconewtons to tens of nanonewtons. Systems ranging from the adhesion of clean or functionalized tips interacting with different surface, to singlemolecule measurements of the force required to rupture protein-ligand interactions, to the forced unfolding of single protein molecules have been studied. The adhesion of a clean silicon nitride tip to a clean glass surface is one of the simplest systems to study and was the first system to demonstrate quantized adhesion. AFM cantilevers will adhere to glass surfaces in the presence of water and the force required to separate the surfaces is on the order of 1-6 nN at low pH. The adhesion mechanism is believed to be due to hydrogen bonds formed between the silanol groups present on both the glass and AFM tip. Deuterium oxide (D2O) has physical properties significantly different from that of H2O; the density, melting temperature and heat capacity are all greater than that of water. These and the differences between H2O and D2O can be attributed to the lower vibrational frequency of the OD bond and the fact that O-D…O bonds are about lkJ/mol stronger than O-H…O bonds.

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Calculating the force-dependent unbinding rate of biological macromolecular bonds from force-ramp optical trapping assays

10.21203/rs.3.rs-933021/v1 ◽

2021 ◽

Author(s):

Apurba Paul ◽

Joshua Alper

Keyword(s):

Optical Tweezers ◽

Single Molecule ◽

Force Spectroscopy ◽

Optical Tweezer ◽

Stochastic Simulations ◽

Cumulative Distribution ◽

Cellular Functions ◽

Single Molecule Force Spectroscopy ◽

Protein Ligand Interactions ◽

Ligand Interactions

Abstract The non-covalent biological bonds that constitute protein-protein or protein-ligand interactions play crucial roles in many cellular functions, including mitosis, motility, and cell-cell adhesion. The effect of external force (𝐹) on the unbinding rate (𝑘off(𝐹)) of macromolecular interactions is a crucial parameter to understanding the mechanisms behind these functions. Optical tweezer-based single-molecule force spectroscopy is frequently used to obtain quantitative force-dependent dissociation data on slip, catch, and ideal bonds. However, analyses of this data using dissociation time or dissociation force histograms often quantitatively compare bonds without fully characterizing their underlying biophysical properties. Additionally, the results of histogram-based analyses can depend on the rate at which force was applied during the experiment and the experiment’s sensitivity. Here, we present an analytically derived cumulative distribution function-like approach to analyzing force-dependent dissociation force spectroscopy data. We demonstrate the benefits and limitations of the technique using stochastic simulations of various bond types. We show that it can be used to obtain the detachment rate and force sensitivity of biological macromolecular bonds from force spectroscopy experiments by explicitly accounting for loading rate and noisy data. We also discuss the implications of our results on using optical tweezers to collect force-dependent dissociation data.

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Calculating the force-dependent unbinding rate of biological macromolecular bonds from force-ramp optical trapping assays

Scientific Reports ◽

10.1038/s41598-021-03690-1 ◽

2022 ◽

Vol 12 (1) ◽

Author(s):

Apurba Paul ◽

Joshua Alper

Keyword(s):

Optical Tweezers ◽

Single Molecule ◽

Force Spectroscopy ◽

Optical Tweezer ◽

Stochastic Simulations ◽

Cumulative Distribution ◽

Cellular Functions ◽

Single Molecule Force Spectroscopy ◽

Protein Ligand Interactions ◽

Ligand Interactions

AbstractThe non-covalent biological bonds that constitute protein–protein or protein–ligand interactions play crucial roles in many cellular functions, including mitosis, motility, and cell–cell adhesion. The effect of external force ($$F$$ F ) on the unbinding rate ($${k}_{\text{off}}\left(F\right)$$ k off F ) of macromolecular interactions is a crucial parameter to understanding the mechanisms behind these functions. Optical tweezer-based single-molecule force spectroscopy is frequently used to obtain quantitative force-dependent dissociation data on slip, catch, and ideal bonds. However, analyses of this data using dissociation time or dissociation force histograms often quantitatively compare bonds without fully characterizing their underlying biophysical properties. Additionally, the results of histogram-based analyses can depend on the rate at which force was applied during the experiment and the experiment’s sensitivity. Here, we present an analytically derived cumulative distribution function-like approach to analyzing force-dependent dissociation force spectroscopy data. We demonstrate the benefits and limitations of the technique using stochastic simulations of various bond types. We show that it can be used to obtain the detachment rate and force sensitivity of biological macromolecular bonds from force spectroscopy experiments by explicitly accounting for loading rate and noisy data. We also discuss the implications of our results on using optical tweezers to collect force-dependent dissociation data.

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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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Synthetic Peptide Arrays and Peptide Combinatorial Libraries for the Exploration of Protein-Ligand Interactions and the Design of Protein Inhibitors

Combinatorial Chemistry & High Throughput Screening ◽

10.2174/1386207053258497 ◽

2005 ◽

Vol 8 (2) ◽

pp. 135-143 ◽

Cited By ~ 25

Author(s):

Jutta Eichler

Keyword(s):

Synthetic Peptide ◽

Combinatorial Libraries ◽

Peptide Arrays ◽

Protein Inhibitors ◽

Protein Ligand Interactions ◽

Ligand Interactions

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Combiphore (Structure and Ligand Based Pharmacophore) - Approach for the Design of GPR40 Modulators in the Management of Diabetes

Current Drug Discovery Technologies ◽

10.2174/1570163815666181008165822 ◽

2020 ◽

Vol 17 (2) ◽

pp. 233-247

Author(s):

Krishna A. Gajjar ◽

Anuradha K. Gajjar

Keyword(s):

Molecular Docking ◽

Structural Information ◽

Docking Studies ◽

Structural Motif ◽

Roc Curve Analysis ◽

Ligand Complex ◽

Discovery Studio ◽

Protein Ligand Interactions ◽

Ligand Interactions ◽

Pharmacophore Models

Background: Pharmacophore mapping and molecular docking can be synergistically integrated to improve the drug design and discovery process. A rational strategy, combiphore approach, derived from the combined study of Structure and Ligand based pharmacophore has been described to identify novel GPR40 modulators. Methods: DISCOtech module from Discovery studio was used for the generation of the Structure and Ligand based pharmacophore models which gave hydrophobic aromatic, ring aromatic and negative ionizable as essential pharmacophoric features. The generated models were validated by screening active and inactive datasets, GH scoring and ROC curve analysis. The best model was exposed as a 3D query to screen the hits from databases like GLASS (GPCR-Ligand Association), GPCR SARfari and Mini-Maybridge. Various filters were applied to retrieve the hit molecules having good drug-like properties. A known protein structure of hGPR40 (pdb: 4PHU) having TAK-875 as ligand complex was used to perform the molecular docking studies; using SYBYL-X 1.2 software. Results and Conclusion: Clustering both the models gave RMSD of 0.89. Therefore, the present approach explored the maximum features by combining both ligand and structure based pharmacophore models. A common structural motif as identified in combiphore for GPR40 modulation consists of the para-substituted phenyl propionic acid scaffold. Therefore, the combiphore approach, whereby maximum structural information (from both ligand and biological protein) is explored, gives maximum insights into the plausible protein-ligand interactions and provides potential lead candidates as exemplified in this study.

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Xanthine Oxidase and Transforming Growth Factor Beta-activated Kinase 1: potential Targets for Gout Intervention

Current Rheumatology Reviews ◽

10.2174/1573397116666201126162202 ◽

2020 ◽

Vol 16 ◽

Author(s):

Rajesh Basnet ◽

Sandhya Khadka ◽

Buddha Bahadur Basnet ◽

Til Bahadur Basnet ◽

Buddhi Bal Chidi ◽

...

Keyword(s):

Growth Factor ◽

Xanthine Oxidase ◽

Transforming Growth Factor Beta ◽

Transforming Growth Factor ◽

Developed Countries ◽

Structure Based Drug Design ◽

Protein Ligand Interactions ◽

Ligand Interactions ◽

Moderate Condition ◽

Growth Factor Beta

Background: Gout, an inflammatory arthritis, caused by the deposition of monosodium urate crystals into affected joints and other tissues has become one of the major health problems of today's world. The main risk factor for gout is hyperuricemia, which may be caused by excessive or insufficient excretion of uric acid. The incidence is usually in the age group of 30- 50 years, commonly in males. In developed countries, the incidence of gout ranges from 1 to 4%. Despite effective treatments, there has been an increase in the number of cases over the past few decades. Objective: In recent years, the development of targeted drugs in gout has made significant achievements. The global impact of gout continues to increase, and as a result, the focus of disease-modifying therapies remains elusive. In addition, the characterization of available instrumental compounds is urgently needed to explore the use of novel selective and key protein-ligand interactions for the effective treatment of gout. Xanthine oxidase (XO) is a key target in gout to consider the use of XO inhibitors in patients with mild to moderate condition, however, the costs are high and no other direct progress has been made. Despite many XO inhibitors, a selective potent inhibitor for XO is limited. Likewise, in recent years, attention has been focused on different strategies for the discovery and development of new selectivity ligands against transforming growth factor beta-activated kinase 1 (TAK1), a potential therapeutic target for gout. Therefore the insight on human XO structure and TAK1 provides a clue into protein-ligand interactions and provides the basis for molecular modeling and structure-based drug design. Conclusion: In this review, we briefly introduce the clinical characteristics, the development of crystal, inhibitors, and crystal structure of XO and TAK1 protein.

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ASFP (Artificial Intelligence based Scoring Function Platform): a web server for the development of customized scoring functions

Journal of Cheminformatics ◽

10.1186/s13321-021-00486-3 ◽

2021 ◽

Vol 13 (1) ◽

Author(s):

Xujun Zhang ◽

Chao Shen ◽

Xueying Guo ◽

Zhe Wang ◽

Gaoqi Weng ◽

...

Keyword(s):

High Efficiency ◽

Low Cost ◽

Pearson Correlation ◽

Scoring Function ◽

Web Server ◽

Scoring Functions ◽

Protein Ligand Interactions ◽

Prediction Module ◽

Ligand Interactions ◽

Benchmark Datasets

AbstractVirtual screening (VS) based on molecular docking has emerged as one of the mainstream technologies of drug discovery due to its low cost and high efficiency. However, the scoring functions (SFs) implemented in most docking programs are not always accurate enough and how to improve their prediction accuracy is still a big challenge. Here, we propose an integrated platform called ASFP, a web server for the development of customized SFs for structure-based VS. There are three main modules in ASFP: (1) the descriptor generation module that can generate up to 3437 descriptors for the modelling of protein–ligand interactions; (2) the AI-based SF construction module that can establish target-specific SFs based on the pre-generated descriptors through three machine learning (ML) techniques; (3) the online prediction module that provides some well-constructed target-specific SFs for VS and an additional generic SF for binding affinity prediction. Our methodology has been validated on several benchmark datasets. The target-specific SFs can achieve an average ROC AUC of 0.973 towards 32 targets and the generic SF can achieve the Pearson correlation coefficient of 0.81 on the PDBbind version 2016 core set. To sum up, the ASFP server is a powerful tool for structure-based VS.

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