Rational design of allosteric modulators: Challenges and successes

Chatzigoulas, Alexios; Cournia, Zoe

doi:10.1002/wcms.1529

Cited by 51 publications

(55 citation statements)

References 516 publications

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“…[32][33][34][35] In addition, the lack of structural mechanistic knowledge hinders compound modification and lead optimization. 28,33,[36][37][38] Taken together, these factors invoke a demand for more powerful methods that provide in-depth insight into allosterism and can guide allosteric drug design.…”

Section: Introductionmentioning

confidence: 99%

Along the allostery stream: Recent advances in computational methods for allosteric drug discovery

Chai

Wang

et al. 2021

WIREs Comput Mol Sci

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Allostery is a universal, biological phenomenon in which orthosteric sites are finetuned by topologically distal allosteric sites triggered by perturbations, such as ligand binding, residue mutations, or post-translational modifications. Allosteric regulation is implicated in a variety of physiological and pathological conditions and is thus emerging as a novel avenue for drug discovery. Allosteric drugs have traditionally been discovered by serendipity through large-scale experimental screening. Recently, we have witnessed significant progress in biophysics, particularly in structural bioinformatics, which has facilitated the in-depth characterization of allosteric effects and the accurate detection of allosteric residues and exosites. These advances improve our understanding of allosterism and promote allosteric drug discovery, thereby revolutionizing the shift from the traditional serendipitous route used to discover allosteric drugs to the updated path centered on rational structure-based design. In this review, recent advances in computational methods applied to allosteric drug discovery are summarized. We comprehensively review these achievements along various levels of allosteric events, from the construction of allosteric databases to the identification and analysis of allosteric residues, signals, sites, and modulators. We expect to increase the awareness of the discovery of allosteric drugs using structure-based computational methods.

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Section: Introductionmentioning

confidence: 99%

Along the allostery stream: Recent advances in computational methods for allosteric drug discovery

Chai

Wang

et al. 2021

WIREs Comput Mol Sci

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“…For example, lipid binding is a mechanism that activates PI3Kα, and a hotspot cancer mutation on this enzyme acts by altering the interaction between PI3Kα and the membrane by allosterically enhancing its activity [ 17 ]. We strongly believe that allosteric binding pockets at the protein-membrane interface could modulate the activity of protein function representing a novel therapeutic strategy by disrupting the protein-membrane interactions [ 17 , 54 ].…”

Section: Discussionmentioning

confidence: 99%

Predicting protein–membrane interfaces of peripheral membrane proteins using ensemble machine learning

Chatzigoulas

Cournia

2022

Briefings in Bioinformatics

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Abnormal protein–membrane attachment is involved in deregulated cellular pathways and in disease. Therefore, the possibility to modulate protein–membrane interactions represents a new promising therapeutic strategy for peripheral membrane proteins that have been considered so far undruggable. A major obstacle in this drug design strategy is that the membrane-binding domains of peripheral membrane proteins are usually unknown. The development of fast and efficient algorithms predicting the protein–membrane interface would shed light into the accessibility of membrane–protein interfaces by drug-like molecules. Herein, we describe an ensemble machine learning methodology and algorithm for predicting membrane-penetrating amino acids. We utilize available experimental data from the literature for training 21 machine learning classifiers and meta-classifiers. Evaluation of the best ensemble classifier model accuracy yields a macro-averaged F1 score = 0.92 and a Matthews correlation coefficient = 0.84 for predicting correctly membrane-penetrating amino acids on unknown proteins of a validation set. The python code for predicting protein–membrane interfaces of peripheral membrane proteins is available at https://github.com/zoecournia/DREAMM.

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“…These approaches comprise the forecast of allosteric sites, the affinity of allosteric ligand-protein interactions, allosteric communication pathways, allosteric conformational changes, allosteric mutations, and virtual screening of allosteric modulators, such as ASD and ASBench. 370 Besides, some of these approaches have been built as dedicated web understanding has recently promoted the resurgence of covalent targeting approaches and their successful incorporation into the libraries of drug discovery. [372][373][374] Covalent mechanisms offer many superiorities, including unmatched efficacy and selectivity, extended duration of action, and favorable pharmacokinetic profiles.…”

Section: Allosteric Methodsmentioning

confidence: 99%

“…Fortunately, as a feasible substitute for experimental methods, computer‐aided drug design (CADD) methods are powerful approaches with outstanding performance in the field of allosteric methodology. These approaches comprise the forecast of allosteric sites, the affinity of allosteric ligand‐protein interactions, allosteric communication pathways, allosteric conformational changes, allosteric mutations, and virtual screening of allosteric modulators, such as ASD and ASBench 370 . Besides, some of these approaches have been built as dedicated web servers or tools to promote use by medicinal chemists for exploring the allosteric mechanisms and searching for allosteric modulators, such tools include Allosite, AllosMod, AlloSigMA, Alloscore, AlloMAPS and AlloDrivers 371 …”

Section: Emerging Solutionsmentioning

confidence: 99%

Targeting key proteins involved in transcriptional regulation for cancer therapy: Current strategies and future prospective

Pei

Guo

Peng

et al. 2022

Medicinal Research Reviews

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The key proteins involved in transcriptional regulation play convergent roles in cellular homeostasis, and their dysfunction mediates aberrant gene expressions that underline the hallmarks of tumorigenesis. As tumor progression is dependent on such abnormal regulation of transcription, it is important to discover novel chemical entities as antitumor drugs that target key tumor‐associated proteins involved in transcriptional regulation. Despite most key proteins (especially transcription factors) involved in transcriptional regulation are historically recognized as undruggable targets, multiple targeting approaches at diverse levels of transcriptional regulation, such as epigenetic intervention, inhibition of DNA‐binding of transcriptional factors, and inhibition of the protein–protein interactions (PPIs), have been established in preclinically or clinically studies. In addition, several new approaches have recently been described, such as targeting proteasomal degradation and eliciting synthetic lethality. This review will emphasize on accentuating these developing therapeutic approaches and provide a thorough conspectus of the drug development to target key proteins involved in transcriptional regulation and their impact on future oncotherapy.

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Rational design of allosteric modulators: Challenges and successes

Cited by 51 publications

References 516 publications

Along the allostery stream: Recent advances in computational methods for allosteric drug discovery

Along the allostery stream: Recent advances in computational methods for allosteric drug discovery

Predicting protein–membrane interfaces of peripheral membrane proteins using ensemble machine learning

Targeting key proteins involved in transcriptional regulation for cancer therapy: Current strategies and future prospective

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