Prediction of allosteric sites and signalling: insights from benchmarking datasets

Allostery is a pervasive mechanism which regulates the activity of proteins in living systems through binding of a molecule at a distant site from the orthosteric site of the protein. The universality of allosteric regulation complemented by the benefits of highly specific, potentially non-toxic and protein activity modulating allosteric drugs makes uncovering allosteric sites on proteins invaluable for drug discovery. However, there are few computational methods to effectively predict them. Bond-to-bond propensity analysis, a recently developed method, has successfully predicted allosteric sites for a diverse group of proteins with only the knowledge of the orthosteric sites and the corresponding ligands in 19 of 20 cases. The method is based on an energy-weighted atomistic protein graph and allows for computationally highly efficient analysis in atomistic detail. We here extended the analysis onto 432 structures of 146 proteins from two existing benchmarking datasets for allosteric proteins: ASBench and CASBench. We further refined the metrics to account for the cumulative effect of residues with high propensities and the crucial residues in a given site with two additional measures. The allosteric site is recovered for 95/113 proteins (99/118 structures) from ASBench and 32/33 proteins (304/314 structures) from CASBench, with the only a priori knowledge being the orthosteric site residues. Knowing the orthosteric ligands of the protein, the allosteric site is identified for 32/33 proteins (308/314 structures) from CASBench.

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Prediction of allosteric sites and mediating interactions through bond-to-bond propensities

10.1101/056275 ◽

2016 ◽

Author(s):

B.R.C. Amor ◽

M.T. Schaub ◽

S.N. Yaliraki ◽

M. Barahona

Keyword(s):

Active Site ◽

Linear Time ◽

Covalent Bond ◽

Allosteric Site ◽

Protein Activity ◽

Biochemical Processes ◽

Allosteric Proteins ◽

Allosteric Interactions ◽

Allosteric Sites ◽

Graph Theoretical Approach

Allosteric regulation is central to many biochemical processes. Allosteric sites provide a target to fine-tune protein activity, yet we lack computational methods to predict them. Here, we present an efficient graph-theoretical approach for identifying allosteric sites and the mediating interactions that connect them to the active site. Using an atomistic graph with edges weighted by covalent and non-covalent bond energies, we obtain a bond-to-bond propensity that quantifies the effect of instantaneous bond fluctuations propagating through the protein. We use this propensity to detect the sites and communication pathways most strongly linked to the active site, assessing their significance through quantile regression and comparison against a reference set of 100 generic proteins. We exemplify our method in detail with three well-studied allosteric proteins: caspase-1, CheY, and h-Ras, correctly predicting the location of the allosteric site and identifying key allosteric interactions. Consistent prediction of allosteric sites is then attained in a further set of 17 proteins known to exhibit allostery. Because our propensity measure runs in almost linear time, it offers a scalable approach to high-throughput searches for candidate allosteric sites.

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Fractal analysis in biology and medicine

Dalʹnevostočnyj medicinskij žurnal ◽

10.35177/1994-5191-2020-4-85-92 ◽

2020 ◽

pp. 85-92

Keyword(s):

Fractal Analysis ◽

A Priori ◽

Research Work ◽

Structure And Function ◽

Review Article ◽

Living Systems ◽

Modern Biology ◽

Additional Information ◽

Important Additional Information ◽

And Function

The review article discusses the possibilities of using fractal mathematical analysis to solve scientific and applied problems of modern biology and medicine. The authors show that only such an approach, related to the section of nonlinear mechanics, allows quantifying the chaotic component of the structure and function of living systems, that is a priori important additional information and expands, in particular, the possibilities of diagnostics, differential diagnosis and prediction of the course of physiological and pathological processes. A number of examples demonstrate the specific advantages of using fractal analysis for these purposes. The conclusion can be made that the expanded use of fractal analysis methods in the research work of medical and biological specialists is promising.

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Treatment of Ureteral Stent-Related Symptoms

Urologia Internationalis ◽

10.1159/000518387 ◽

2021 ◽

pp. 1-16

Author(s):

Angela Pecoraro ◽

Dario Peretti ◽

Zhe Tian ◽

Roberta Aimar ◽

Gabriel Niculescu ◽

...

Keyword(s):

Combination Therapy ◽

Randomized Clinical Trials ◽

Meta Analysis ◽

A Priori ◽

Cumulative Effect ◽

Risk Difference ◽

Ureteral Stent ◽

Pde Inhibitors ◽

Meta Analyses ◽

Alpha Antagonist

Background: The aim of the study was to assess the effectiveness of the main classes of drugs used at reducing morbidity related to ureteric stents. Summary: After establishing a priori protocol, a systematic electronic literature search was conducted in July 2019. The randomized clinical trials (RCTs) selection proceeded in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) guidelines and was registered (PROSPERO ID 178130). The risk of bias and the quality assessment of the included RCTs were performed. Ureteral Stent Symptom Questionnaire (USSQ), International Prostate Symptom Score (IPSS), and quality of life (QoL) were pooled for meta-analysis. Mean difference and risk difference were calculated as appropriate for each outcome to determine the cumulative effect size. Fourteen RCTs were included in the analysis accounting for 2,842 patients. Alpha antagonist, antimuscarinic, and phosphodiesterase (PDE) inhibitors significatively reduced all indexes of the USSQ, the IPSS and QoL scores relative to placebo. Conversely, combination therapy (alpha antagonist plus antimuscarinic) showed in all indexes of the USSQ, IPSS, and QoL over alpha antagonist or antimuscarinic alone. On comparison with alpha blockers, PDE inhibitors were found to be equally effective for urinary symptoms, general health, and body pain parameters, but sexual health parameters improved significantly with PDE inhibitors. Finally, antimuscarinic resulted in higher decrease in all indexes of the USSQ, the IPSS, and QoL relative to alpha antagonist. Key message: Relative to placebo, alpha antagonist alone, antimuscarinics alone, and PDE inhibitors alone have beneficial effect in reducing stent-related symptoms. Furthermore, there are significant advantages of combination therapy compared with monotherapy. Finally, PDE inhibitors are comparable to alpha antagonist, and antimuscarinic seems to be more effective than alpha antagonist alone.

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The structural basis for cancer drug interactions with the catalytic and allosteric sites of SAMHD1

10.1101/296624 ◽

2018 ◽

Author(s):

Kirsten M. Knecht ◽

Olga Buzovetsky ◽

Constanze Schneider ◽

Dominique Thomas ◽

Vishok Srikanth ◽

...

Keyword(s):

Cancer Therapy ◽

Genome Stability ◽

Viral Infections ◽

Structural Basis ◽

Cancer Drug ◽

Allosteric Site ◽

Nucleotide Analogues ◽

Future Design ◽

Nucleotide Analogue ◽

Allosteric Sites

AbstractSAMHD1 is a deoxynucleoside triphosphate triphosphohydrolase (dNTPase) that depletes cellular dNTPs in non-cycling cells to promote genome stability and to inhibit retroviral and herpes viral replication. In addition to being substrates, cellular nucleotides also allosterically regulate SAMHD1 activity. Recently, it was shown that high expression levels of SAMHD1 are also correlated with significantly worse patient responses to nucleotide analogue drugs important for treating a variety of cancers, including Acute Myeloid Leukemia (AML). In this study, we used biochemical, structural, and cellular methods to examine the interactions of various cancer drugs with SAMHD1. We found that both the catalytic and the allosteric sites of SAMHD1 are sensitive to sugar modifications of the nucleotide analogs, with the allosteric site being significantly more restrictive. We crystallized cladribine-TP, clofarabine-TP, fludarabine-TP, vidarabine-TP, cytarabine-TP, and gemcitabine-TP in the catalytic pocket of SAMHD1. We find that all of these drugs are substrates of SAMHD1 and that the efficacy of most of these drugs is affected by SAMHD1 activity. Of the nucleotide analogues tested, only cladribine-TP with a deoxyribose sugar efficiently induced the catalytically active SAMHD1 tetramer. Together, these results establish a detailed framework for understanding the substrate specificity and allosteric activation of SAMHD1 with regards to nucleotide analogues, which can be used to improve current cancer and antiviral therapies.SignificanceNucleoside analogue drugs are widely used to treat a variety of cancers and viral infections. With an essential role in regulating the nucleotide pool in the cell by degrading cellular nucleotides, SAMHD1 has the potential to decrease the cellular concentration of frequently prescribed nucleotide analogues and thereby decrease their clinical efficacy in cancer therapy. To improve future nucleotide analogue treatments, it is important to understand SAMHD1 interactions with these drugs. Our work thoroughly examines the extent to which nucleotide analogues interact with the catalytic and allosteric sites of SAMHD1. This work contributes to the assessment of SAMHD1 as a potential therapeutic target for cancer therapy and the future design of SAMHD1 modulators that might improve the efficacy of existing therapies.

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The structural basis for cancer drug interactions with the catalytic and allosteric sites of SAMHD1

Proceedings of the National Academy of Sciences ◽

10.1073/pnas.1805593115 ◽

2018 ◽

Vol 115 (43) ◽

pp. E10022-E10031 ◽

Cited By ~ 11

Author(s):

Kirsten M. Knecht ◽

Olga Buzovetsky ◽

Constanze Schneider ◽

Dominique Thomas ◽

Vishok Srikanth ◽

...

Keyword(s):

Genome Stability ◽

Structural Basis ◽

Cancer Drug ◽

Allosteric Site ◽

Nucleotide Analogs ◽

Antiviral Therapies ◽

Allosteric Sites ◽

Catalytically Active ◽

Deoxynucleoside Triphosphate ◽

Patient Responses

SAMHD1 is a deoxynucleoside triphosphate triphosphohydrolase (dNTPase) that depletes cellular dNTPs in noncycling cells to promote genome stability and to inhibit retroviral and herpes viral replication. In addition to being substrates, cellular nucleotides also allosterically regulate SAMHD1 activity. Recently, it was shown that high expression levels of SAMHD1 are also correlated with significantly worse patient responses to nucleotide analog drugs important for treating a variety of cancers, including acute myeloid leukemia (AML). In this study, we used biochemical, structural, and cellular methods to examine the interactions of various cancer drugs with SAMHD1. We found that both the catalytic and the allosteric sites of SAMHD1 are sensitive to sugar modifications of the nucleotide analogs, with the allosteric site being significantly more restrictive. We crystallized cladribine-TP, clofarabine-TP, fludarabine-TP, vidarabine-TP, cytarabine-TP, and gemcitabine-TP in the catalytic pocket of SAMHD1. We found that all of these drugs are substrates of SAMHD1 and that the efficacy of most of these drugs is affected by SAMHD1 activity. Of the nucleotide analogs tested, only cladribine-TP with a deoxyribose sugar efficiently induced the catalytically active SAMHD1 tetramer. Together, these results establish a detailed framework for understanding the substrate specificity and allosteric activation of SAMHD1 with regard to nucleotide analogs, which can be used to improve current cancer and antiviral therapies.

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Discovery of cryptic allosteric sites using reversed allosteric communication by a combined computational and experimental strategy

Chemical Science ◽

10.1039/d0sc05131d ◽

2021 ◽

Vol 12 (1) ◽

pp. 464-476

Author(s):

Duan Ni ◽

Jiacheng Wei ◽

Xinheng He ◽

Ashfaq Ur Rehman ◽

Xinyi Li ◽

...

Keyword(s):

Md Simulations ◽

Allosteric Site ◽

Experimental Strategy ◽

Allosteric Communication ◽

Allosteric Sites

Using reversed allosteric communication, we performed MD simulations, MSMs, and mutagenesis experiments, to discover allosteric sites. It reproduced the known allosteric site for MDL-801 on Sirt6 and uncovered a novel cryptic allosteric Pocket X.

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Unraveling allosteric landscapes of allosterome with ASD

Nucleic Acids Research ◽

10.1093/nar/gkz958 ◽

2019 ◽

Cited By ~ 1

Author(s):

Xinyi Liu ◽

Shaoyong Lu ◽

Kun Song ◽

Qiancheng Shen ◽

Duan Ni ◽

...

Keyword(s):

Drug Discovery ◽

Protein Function ◽

Target Identification ◽

Allosteric Regulation ◽

Biological Research ◽

Missense Mutations ◽

Allosteric Site ◽

Comprehensive Information ◽

Allosteric Sites ◽

Human Proteins

Abstract Allosteric regulation is one of the most direct and efficient ways to fine-tune protein function; it is induced by the binding of a ligand at an allosteric site that is topographically distinct from an orthosteric site. The Allosteric Database (ASD, available online at http://mdl.shsmu.edu.cn/ASD) was developed ten years ago to provide comprehensive information related to allosteric regulation. In recent years, allosteric regulation has received great attention in biological research, bioengineering, and drug discovery, leading to the emergence of entire allosteric landscapes as allosteromes. To facilitate research from the perspective of the allosterome, in ASD 2019, novel features were curated as follows: (i) >10 000 potential allosteric sites of human proteins were deposited for allosteric drug discovery; (ii) 7 human allosterome maps, including protease and ion channel maps, were built to reveal allosteric evolution within families; (iii) 1312 somatic missense mutations at allosteric sites were collected from patient samples from 33 cancer types and (iv) 1493 pharmacophores extracted from allosteric sites were provided for modulator screening. Over the past ten years, the ASD has become a central resource for studying allosteric regulation and will play more important roles in both target identification and allosteric drug discovery in the future.

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PASSer: Prediction of Allosteric Sites Server

10.26434/chemrxiv.13360535.v1 ◽

2020 ◽

Author(s):

Hao Tian ◽

Xi Jiang ◽

Peng Tao

Keyword(s):

Drug Discovery ◽

Prior Information ◽

Gradient Boosting ◽

Command Line ◽

Allosteric Site ◽

Command Line Interface ◽

And Topology ◽

Extreme Gradient Boosting ◽

Allosteric Sites ◽

Allosteric Mechanisms

Allostery is considered important in regulating protein's activity. Drug development depends on the understanding of allosteric mechanisms, especially the identification of allosteric sites, which is prerequisite in drug discovery and design. Many computational methods have been developed for allosteric site prediction using pocket features and dynamics information. Here, we provide a novel ensembled model, consisting of eXtreme gradient boosting (XGBoost) and graph convolutional neural network (GCNN) to predict allosteric sites. Our model can learn both physical properties and topology structure without any prior information and exhibited good performance under several indicators. Prediction results have shown that 84.9% of allosteric pockets in the testing proteins appeared in the top 3 positions. The PASSer: Protein Allosteric Sites Server (https://passer.smu.edu), along with a command line interface (CLI, https://github.com/smutaogroup/passerCLI) provide insights for further analysis in drug discovery.

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PASSer2.0: Accurate Prediction of Protein Allosteric Sites Through Automated Machine Learning

10.33774/chemrxiv-2021-q4319-v2 ◽

2021 ◽

Author(s):

Sian Xiao ◽

Hao Tian ◽

Peng Tao

Keyword(s):

Machine Learning ◽

Drug Discovery ◽

Computational Model ◽

Protein Dynamics ◽

Allosteric Site ◽

Multiple Indicators ◽

Machine Learning Model ◽

Fundamental Process ◽

Allosteric Sites ◽

Automated Machine Learning

Allostery is a fundamental process in regulating proteins’ activity. The discovery, design and development of allosteric drugs demand for better identification of allosteric sites. Several computational methods have been developed previously to predict allosteric sites using static pocket features and protein dynamics. Here, we present a computational model using automated machine learning for allosteric site prediction. Our model, PASSer2.0, advanced the previous results and performed well across multiple indicators with 89.2% of allosteric pockets appeared among the top 3 positions. The trained machine learning model has been integrated with the Protein Allosteric Sites Server (https://passer.smu.edu) to facilitate allosteric drug discovery.

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Molecular Design of a “Two-in-One” Orthosteric-Allosteric Chimeric Mutant Selective EGFR Inhibitor

10.26434/chemrxiv.13416452 ◽

2020 ◽

Author(s):

Florian Wittlinger ◽

david heppner ◽

Ciric To ◽

Marcel Guenther ◽

Bo Hee Shin ◽

...

Keyword(s):

Molecular Design ◽

Growth Factor Receptor ◽

Kinase Domain ◽

Egfr Inhibitor ◽

Allosteric Site ◽

Generation Agent ◽

Design And Synthesis ◽

Activating Mutations ◽

Allosteric Sites ◽

Successive Generations

Inhibitors developed to target the epidermal growth factor receptor (EGFR) are an effective therapy for patients with non-small cell lung cancer harbouring drug-sensitive activating mutations in the EGFR kinase domain. Drug resistance due to treatment-acquired mutations within the receptor itself has motivated development of successive generations of inhibitors that bind in the ATP-site, and third-generation agent osimertinib is now a first-line treatment for this disease. More recently, allosteric inhibitors have been developed to overcome the C797S mutation that confers resistance to osimertinib. In this study, we present the rational structure-guided design and synthesis of a mutant-selective EGFR inhibitor that spans the ATPand allosteric sites. The lead compound consists of a pyridinyl imidazole scaffold that binds irreversibly in the orthosteric site fused with a benzylisoindolinedione occupying the allosteric site. The compound potently inhibits enzymatic activity in L858R/T790M/C797S mutant EGFR (4.9 nM), with relative sparing of wild-type EGFR (47 nM). Additionally, this compound achieves cetuximab-independent, mutant-selective cellular efficacy on the L858R and L858R/T790M variants

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