Biology subjects

Lei Xie

Publications and source records attributed to Lei Xie.

4 recordsLinked to original sources

bioRxiv2016

Harnessing Big Data for Systems Pharmacology

Systems pharmacology aims to holistically understand genetic, molecular, cellular, organismal, and environmental mechanisms of drug actions through developing mechanistic or predictive models. Data-driven modeling plays a central role in systems pharmacology, and has already enabled biologists to generate novel hypotheses. However, more is needed. The drug response is associated with genetic/epigenetic variants and environmental factors, is coupled with molecular conformational dynamics, is affected by possible off-targets, is modulated by the complex interplay of biological networks, and is dependent on pharmacokinetics. Thus, in order to gain a comprehensive understanding of drug actions, systems pharmacology requires integration of models across data modalities, methodologies, organismal hierarchies, and species. This imposes a great challenge on model management, integration, and translation. Here, we discuss several upcoming issues in systems pharmacology and potential solutions to them using big data technology. It will allow systems pharmacology modeling to be findable, accessible, interoperable, reusable, reliable, interpretable, and actionable.

Pharmacology and Toxicology↗

bioRxiv2016

Insights into the binding mode of MEK type-III inhibitors. A step towards discovering and designing allosteric kinase inhibitors across the human kinome

Protein kinases are critical drug targets for treating a large variety of human diseases. Type-I and type-II kinase inhibitors frequently exhibit off-target toxicity or lead to mutation acquired resistance. Two highly specific allosteric type-III MEK-targeted drugs, Trametinib and Cobimetinib, offer a new approach. Thus, understanding the binding mechanism of existing type-III kinase inhibitors will provide insights for designing new type-III kinase inhibitors. In this work we have systematically studied the binding mode of MEK-targeted type-III inhibitors using structural systems pharmacology and molecular dynamics simulation. Our studies provide detailed sequence, structure, interaction-fingerprint, pharmacophore and binding-site information on the binding characteristics of MEK type-III kinase inhibitors. We propose that the helix-folding activation loop is a hallmark allosteric binding site for type-III inhibitors. Subsequently we screened and predicted allosteric binding sites across the human kinome, suggesting other kinases as potential targets suitable for type-III inhibitors. Our findings will provide new insights into the design of potent and selective kinases inhibitors.\n\nAuthor SummaryHuman protein kinases represent a large protein family relevant to many diseases, especially cancers, and have become important drug targets. However, developing the desired selective kinase-targeted inhibitors remain challenging. Kinase allosteric inhibitors provide that opportunity, but, to date, few have been designed other than MEK inhibitors. To more efficiently develop kinase allosteric inhibitors, we systematically studied the binding mode of the MEK type-III allosteric kinase inhibitors using structural system pharmacology and molecular dynamics approaches. New insights into the binding mode and mechanism of type-III inhibitors were revealed that may facilitate the design of new prospective type-III kinase inhibitors.

Pharmacology and Toxicology↗

bioRxiv2016

The Plasmodium falciparum drugome and its polypharmacological implications

Malaria is a disease contracted by over 200 million people each year, mostly in developing countries. The primary causative agent, Plasmodium falciparum (P. falciparum) has shown increased resistance to existing drugs, hence new treatments are needed quickly. To this end we performed a high-throughput systems-level analysis, mapping existing FDA drugs with the potential for repurposing against targets from the P. falciparum structural proteome. The resulting P. falciparum drugome (P.falciparum-drugome) was used to prioritize potential new anti-malaria candidate targets and highlight some novel FDA approved drugs that have apparent anti-malaria effects for possible use as multi-target therapeutics.

Systems Biology↗

bioRxiv2015

Toward high-throughput predictive modeling of protein binding/unbinding kinetics

One of the unaddressed challenges in drug discovery is that drug potency determined in vitro is not a reliable indicator of drug activity in humans. Accumulated evidences suggest that in vivo activity is more strongly correlated with the binding/unbinding kinetics than the equilibrium thermodynamics of protein-ligand interactions (PLI). However, existing experimental and computational techniques are insufficient in studying the molecular details of kinetics process of PLI. Consequently, we not only have limited mechanistic understanding of the kinetic process but also lack a practical platform for the high-throughput screening and optimization of drug leads based on their kinetic properties. Here we address this unmet need by integrating energetic and conformational dynamic features derived from molecular modeling with multi-task learning. To test our method, HIV-1 protease is used as a model system. Our integrated model provides us with new insights into the molecular determinants of kinetics of PLI. We find that the coherent coupling of conformational dynamics between protein and ligand may play a critical role in determining the kinetic rate constants of PLI. Furthermore, we demonstrate that the relative movement of normal nodes of amino acids upon ligand binding is an important feature to capture conformational dynamics of the binding/unbinding kinetics. Coupled with the multi-task learning, we can predict combined kon and koff accurately with an accuracy of 74.35%. Thus, it is possible to screen and optimize compounds based on their binding/unbinding kinetics. The further development of such computational tools will bridge one of the critical missing links in drug discovery.

Bioinformatics↗