Computation-Driven Discovery of Cryptic Allosteric Sites and Novel Modulators of Monoamine Transporters


Cryptic allosteric sites are binding sites on proteins that are not readily apparent or are inactive under normal physiological conditions. These sites are named “cryptic” because they usually hidden within the protein’s structure and may become accessible or active only under conditions, such as conformational changes induced by the binding of a ligand at another site (often the orthosteric site) or by post-translational modifications.

The identification of cryptic allosteric sites can have significant implications for drug discovery for example, drugs designed to target cryptic allosteric sites can be highly selective, as these sites are often unique to specific types of receptors. Additionally, because allosteric modulators do not compete with endogenous ligands at the orthosteric site, they may produce fewer side effects. Moreover, targeting cryptic allosteric sites can lead to the development of new classes of therapeutics, particularly for disorders where conventional drugs targeting the orthosteric sites have limited efficacy or unacceptable side effects. Scientists use various advanced techniques, such as X-ray crystallography, cryo-electron microscopy, NMR spectroscopy, and computational modeling, to discover and characterize these hidden sites. The dynamic nature of proteins means that these sites may become ‘druggable’ under the right conditions, providing novel pathways for intervention in various diseases affecting the brain, such as neurological disorders and psychiatric conditions.

Monoamine transporters (MATs), which include serotonin transporter (SERT), are essential components of synaptic transmissions in the central nervous system, and they play a critical role in regulating various complex behaviors. The importance of MATs is underscored by the use of drugs like selective serotonin reuptake inhibitors (SSRIs) as first-line treatments for neurological conditions such as major depression and anxiety disorders. However, traditional drugs that target MATs by binding to their orthosteric sites can lead to unwanted side effects due to their competitive binding nature. Therefore, there is a growing interest in developing small-molecule modulators that can target allosteric sites on MATs, as these can potentially provide more specific and effective therapeutic options while minimizing side effects.

In a new study by Dr. Gao Tu, Dr. Binbin Xu, Dr. Ding Luo, Dr. Jin Liu and led by Professor Weiwei Xue from the School of Pharmaceutical Sciences at Chongqing University employed advanced computational techniques to shed light on the structural dynamics of SERT and the binding of serotonin to SERT in various conformations. The research begins with the observation that SERT exists in an ensemble of different conformations during serotonin reuptake, each with its unique structural characteristics. These conformations include the outward-open conformation without substrate bound (OOapo), outward-open, outward-occluded, and inward-open conformations bound by serotonin (OOholo, OCholo, and IOholo, respectively).

The research team used successfully induced-fit docking Gaussian-accelerated molecular dynamics (IFD-GaMD) simulation to characterize these fundamental states of SERT. The simulations provide insights into how serotonin interacts with SERT in different conformations, shedding light on the binding mechanisms. Furthermore, the authors identified key residues that are crucial for the interaction between serotonin and SERT, helping us understand the molecular basis of serotonin binding. Moreover, they also identified potential druggable sites on SERT, including cryptic allosteric sites. These sites represent promising targets for the development of new drugs that could modulate SERT with an allosteric mechanism. The study identifies 11 potential druggability sites, including 5 cryptic allosteric sites, through a site-finding algorithm. “The multiple conformation states of SERT characterized using the IFD-GaMD approach is almost identical to the recently released experimental structures,” said Professor Weiwei Xue. “The identified druggability sites on the transporter, especially the cryptic ones will provide a new way for structure-based virtual screening of novel modulators to regulate the transporter’s function.”

It is noteworthy to mention another study by Professor Weiwei Xue and colleagues extended their research and constructed a model of human dopamine transporter (hDAT) based on the Cryo-EM structure of a similar protein, the human serotonin transporter (hSERT), using the IFD-GaMD approach. This helped in identifying stable states of hDAT that are suitable for drug targeting. The research is published in Journal of Chemical Information and Modeling. The hDAT regulates the levels of dopamine by reuptaking it from the synapse into neurons. This function is crucial for normal brain activity and is a potential therapeutic target for diseases like depression, Parkinson’s disease, and drug addiction. Researchers performed virtual screening on a large chemical library, leading to the identification of potential compounds that might interact with the hDAT. From this screening, 10 compounds were tested in vitro, resulting in the discovery of a specific compound, Z1078601926, which allosterically inhibits hDAT when used with an orthosteric ligand (a molecule that binds directly to the dopamine transporter), nomifensine. Further GaMD simulations and free energy analysis helped to understand how Z1078601926 works in conjunction with nomifensine to inhibit hDAT, suggesting a potential synergistic effect. Indeed, the identified compound not only serves as a promising starting point for developing new treatments targeting hDAT but also validates the use of structure-based methods for finding new allosteric modulators for hDAT and possibly other therapeutic targets.

The new studies by Professor Weiwei Xue and colleagues provides valuable knowledge into the structural dynamics and binding mechanisms of serotonin with SERT and coorborated their research in applying the knowledge on another receptor hDAT. It also highlights the potential for designing novel molecules that can modulate SERT with greater specificity and fewer side effects than traditional drugs. The computational approach used by the authors offers a cost-effective and efficient way to study complex biological processes, contributing to the ongoing efforts to develop more effective treatments for depression and anxiety disorders. Ultimately, this research has the potential to impact the field of medicine by paving the way for the development of safer and more targeted therapies for mental health conditions.

Computation-Driven Discovery of Cryptic Allosteric Sites and Novel Modulators of Monoamine Transporters - Medicine Innovates

About the author

Dr.Weiwei Xue is an Associate Professor of Pharmaceutical Sciences at Chongqing University. He received a bachelor’s degree in Chemistry (2006) and a Ph.D. in Cheminformatics (2014) from Lanzhou University. He worked as a visiting scholar in the Institute for Protein Design at the University of Washington (2018-2019). The research in Dr. Xue’s Lab is focused on developing disease- and therapeutic-related bioinformatics databases and tools, and combing artificial intelligence and molecular modeling approaches to design innovative small molecules or protein binders against molecular targets of complex diseases, including psychiatric disorders, viral infection, and cancer. He has published more than 90 peer-reviewed papers in the area of bioinformatics and computational drug design ( He is also an editorial-board-member of Computers in Biology and Medicine.


Tu G, Xu B, Luo D, Liu J, Liu Z, Chen G, Xue W. Multi-state Model-Based Identification of Cryptic Allosteric Sites on Human Serotonin Transporter. ACS Chem Neurosci. 2023 ;14(9):1686-1694. doi: 10.1021/acschemneuro.3c00155.

Go To ACS Chem Neurosci.

Deng S, Zhang H, Gou R, Luo D, Liu Z, Zhu F, Xue W. Structure-Based Discovery of a Novel Allosteric Inhibitor against Human Dopamine Transporter. J Chem Inf Model. 2023 63(14):4458-4467. doi: 10.1021/acs.jcim.3c00477.

Go To J Chem Inf Model.