Proteins are large, complex molecules that carry out most of the work in cells. They are composed of long chains of smaller units called amino acids, and the specific order of these amino acids determines a protein’s unique three-dimensional shape. This intricate shape dictates the protein’s specific function. Knowing a protein’s function is important for understanding how living systems operate, but experimentally determining the function of every protein is a time-consuming and expensive endeavor. This highlights the growing need for computational methods to predict protein functions efficiently.
Why Protein Function Matters
Proteins are involved in nearly every activity within a cell. They act as enzymes, accelerating chemical reactions, such as those involved in digestion and energy production. Proteins also provide structural support, forming components of cells and tissues, like actin and tubulin in the cytoskeleton.
They play roles in transporting molecules, such as hemoglobin carrying oxygen in the blood, and in defense, with antibodies protecting the body from foreign invaders. Proteins also regulate cellular processes by acting as signaling molecules and receptors, coordinating activities between different cells and organs. Understanding these diverse roles is important for comprehending both health and disease.
How Protein Functions Are Predicted
Sequence-based prediction is a common approach, comparing a new protein’s amino acid sequence to large databases of known sequences, such as UniProt. If a high degree of similarity or “homology” is found, it is inferred that the new protein likely shares a similar function to its known counterpart. Tools like BLAST are frequently used for this purpose, identifying similar proteins and suggesting potential functions based on sequence alignment.
Structure-based methods are used when a protein’s three-dimensional shape is known or can be accurately predicted. These methods identify specific regions on the protein surface, such as active sites or binding pockets, where interactions with other molecules occur. Algorithms analyze these structural features to infer function, as proteins with similar structures often perform similar roles. For instance, methods like Ligsite or CASTp identify cavities on the protein surface that are likely to bind small molecules, providing clues about the protein’s activity.
Proteins rarely work in isolation; instead, they often form complex networks of interactions within a cell. Interaction-based methods leverage this principle, inferring a protein’s function by examining its known or predicted interactions with other proteins. If a protein consistently interacts with a group of proteins that share a common function, it suggests that the uncharacterized protein might also be involved in that same process. Databases like STRING (Search Tool for the Retrieval of Interacting Genes/Proteins) collect and integrate known and predicted protein-protein associations, including both physical interactions and functional linkages, to build these networks.
Modern approaches incorporate machine learning and artificial intelligence (AI) for protein function prediction. These computational models learn from large datasets of known protein sequences, structures, and interactions to identify subtle patterns that human analysis might miss. Deep learning architectures, including convolutional neural networks and attention-based transformers, are effective at processing complex biological data. These AI tools can integrate information from multiple sources, such as sequence, structure, and interaction networks, to produce more accurate and comprehensive predictions of protein function.
Impact Across Science and Medicine
In drug discovery, understanding protein function is important for identifying potential drug targets. By predicting a protein’s role, researchers can determine its involvement in a disease and design new therapies that target or modulate its activity. For example, knowing the function of a disease-causing protein allows for the development of drugs that inhibit its harmful effects.
Protein function prediction also advances disease understanding by unraveling the molecular basis of conditions. Identifying dysfunctional proteins or altered protein functions can shed light on the underlying mechanisms of diseases. This insight can lead to the development of new diagnostic tools and more targeted treatments.
In biotechnology and bioengineering, predicting protein functions enables the design of new proteins for specific industrial applications. This includes creating new enzymes with enhanced activity or stability for processes like biofuel production or food processing. Engineered proteins can also be used to develop sensitive biosensors for medical diagnostics or environmental monitoring, and to improve crop resistance to pests and diseases.
Beyond these practical applications, protein function prediction accelerates fundamental biological research. It provides a way to gain insights into uncharacterized proteins emerging from genome sequencing projects. This helps researchers understand new biological pathways and cellular processes, expanding biological knowledge.
Current Limitations in Prediction
Despite advancements, several challenges persist in protein function prediction. A limitation is data scarcity; while millions of protein sequences are known, only a small fraction, less than 0.3%, have experimentally validated and annotated functions. This lack of experimental data can hinder the training and validation of prediction models, especially for new proteins without close relatives.
The complexity of protein function also poses a hurdle. Many proteins can perform multiple functions, or their function might depend on the specific cellular context, making a single prediction difficult. Even subtle differences in amino acid sequences or protein structures can lead to entirely different functions, which complicates predictions based solely on similarity.
Experimental validation is another bottleneck. While computational methods can generate hypotheses, these predictions require laboratory experiments to confirm their accuracy, which remains a time-consuming and labor-intensive process. Predicting the function of new proteins, those with no known structural or sequence similarity to existing proteins, is challenging and often requires new experimental approaches.