Since time immemorial humans have been using plants to treat various ailments. From using aloe vera on sunburns to eating ginger for stomach upset, there is seemingly no end to what problems plants have an answer for. Despite the almost infinite chemical compounds that plants have to offer, there is simply not enough time to research them all. 

Traditionally, plant medicinal research revolved around creating extracts that were tested in labs on various biological systems. This testing, while useful, would take years to produce a single medicine that could be marketed for consumer use. With the increasing prevalence of new or developing illnesses, this technique could easily be outpaced by the mutational ability of new viruses and diseases. Recently, the integration of computational science to address these shortcomings of traditional biological work has created numerous breakthroughs in the effort of discovering new drugs. If you are interested in how scientists have used computational biology to study plant compounds without stepping foot in a lab, keep reading! 

Computational Biology Outpaces Typical Lab Work 

According to Carnegie Mellon, computational biology seeks to address biological systems through the development and use of computer modeling. Put simply, it takes real biological data to power the pursuit of new knowledge. It can also take the form of large databases upon which new data can be compared and validated. For instance, computational biology can take known sequences of genes to search for similar sequences in separate genomes. Letting computers take over the grunt work frees time for researchers to work, hypothesize, and analyze data. 

According to a review article by Wang et al., computational biology offers multiple avenues that can outpace typical lab work. Simplifying their work, the most basic computational approaches to discovering new plant drugs rely on the idea of genetic conservation. In basic terms, genes with more related sequences are likely have related functions. In its most crude form, this assumption can be used in computational tools to compare new plant sequences to databases of known plant genes. Any similarities can then be flagged and brought into the lab for study. 

Compared to old methods that required testing each plant individually, new computational work allows us to canvas large amounts of plants, giving us better insights into which groups may have the compounds we are looking for. 

You may be asking, “This seems too good to be true?”, or “Aren’t there flaws with these processes?” 

As with any new process, these basic approaches to computationally discovering plant compounds have considerable drawbacks. Notably, genes usually aren’t so simple in terms of a sequence equating to a specific function. Small changes in genes that would still make them flag under computational searches could alter their actual function to a large degree. Likewise, orthologs (or genes in different species that arose from a common ancestor) can differ in function while retaining similar sequences over time. 

Rather than seeing these issues as immovable obstacles, researchers have been working to pioneer and tailor computational methods to address them head-on. 

New Breakthroughs in Molecular Docking and Modeling 

Finding similar gene sequences or sequences near each other is a great metric to discover new genes in a known pathway, however, it doesn’t necessarily answer the question, “How can we tailor searches to look for specific drugs that interact with human illness?” This is where the process of molecular modeling and docking studies comes into play. 

Molecular docking is a tool that takes on another level of complexity. It uses computer algorithms to analyze molecular structures and characteristics to predict how chemicals will react. For molecular plant studies, this computational tool connects all pieces of the puzzle, from finding new gene sequences to analyzing if their metabolites will affect human illness. Although molecular docking is a very new field of study, it has spearheaded the effort for drug discovery, fueling the fire of medicinal breakthroughs.