Nucleic acid polymerases (Pols) are indispensable enzymes for life, health, and biotechnology because they mediate the flow of genetic information in biological systems. DNA and RNA polymerases (referred to as DNA Pols and RNA Pols, respectively) are responsible for the replication, repair, and transcription of DNA and RNA. Due to the critical roles these enzymes play in human diseases such as cancer and viral infections (e.g., COVID-19), as well as their extensive applications in synthetic gene polymers and DNA amplification, polymerases have become an important area of research. With the advancement of computational methods, researchers can now reveal atomic-level details of polymerase functions that are difficult to directly observe through experimental techniques.
Polymerases catalyze the stepwise addition of deoxyribonucleoside triphosphates (dNTPs) or ribonucleoside triphosphates (NTPs) to the 3' end of a growing primer guided by a template, extending the nucleic acid chain via nucleotide transfer reactions. Thus, two critical properties of polymerases are fidelity and processivity. Fidelity refers to the ability of a polymerase to select the correct (d)NTP to form Watson-Crick base pairs, while processivity refers to the average number of nucleotides inserted per binding event. Different polymerase families vary in their functional roles and fidelity/processivity characteristics. For example, A and B family DNA polymerases exhibit high fidelity and processivity in DNA repair and replication, whereas Y family DNA polymerases demonstrate lower fidelity and processivity in translesion synthesis processes.
While DNA polymerases exhibit structural diversity, they typically contain a catalytic domain resembling a right hand, further divided into palm, fingers, and thumb domains. In contrast, RNA polymerases are structurally more conserved, whether single-subunit or multi-subunit, containing seven conserved structural motifs (A to G). Despite structural diversity, all polymerases follow a common catalytic mechanism involving chemical reactions and physical processes such as substrate binding, conformational changes, and translocation along the primer template.
Fig. 1 Crystal structures of (a) DNA polymerase (Pol) I and (b) RNA Pol II (Geronimo I., et al. 2021).
The catalytic cycle begins with polymerase binding to the DNA substrate, followed by dNTP binding to form a ternary complex with the DNA/polymerase complex. This can induce open-to-closed conformational changes, accompanied by local rearrangements of the active site. After nucleotide insertion, pyrophosphate release occurs, followed by reverse conformational changes and translocation to free the active site for the next nucleotide insertion.
During nucleotide transfer reactions catalyzed by polymerases, a double metal ion mechanism is typically employed, where these two metal ions (MA and MB) coordinate with catalytic residues and incoming (d)NTP, lowering the pKa of the primer 3' oxygen, forming a nucleophilic species and initiating nucleotide transfer reactions.
Through X-ray crystallography and cryo-electron microscopy (cryo-EM) studies, scientists have obtained crystal structures of polymerases at each step of the catalytic cycle, elucidating the process of open-to-closed conformational changes. For instance, studies on DNA Pol β indicate that dNTP binding induces rotation of the N subdomain, triggering the transition of the polymerase from an open to a closed conformation, ultimately leading to pyrophosphate release.
Using time-resolved X-ray crystallography studies, researchers have for the first time monitored the process of phosphodiester bond formation in DNA Pol η in real time. The studies demonstrate that when Mg2+ ions occupy the two metal sites, proper alignment of the primer 3' oxygen and dNTP position occurs, confirming the proposed SN2 reaction mechanism.
Sugar moieties in the active site of polymerases typically adopt a C3'-endo conformation to accommodate catalysis needs. Despite the more common C2'-endo conformation in B-type DNA duplexes, studies suggest that the sugar ring at the primer terminus in polymerase active sites often adopts a C3'-endo conformation, which significantly influences catalytic efficiency.
For RNA-dependent RNA polymerases, particularly influenza virus RNA polymerase, cryo-EM captures structures in different states during the transcription cycle, showcasing complex structural rearrangements during catalysis. This structural knowledge aids in discovering new anti-influenza drugs.
Despite detailed structural insights into the polymerase catalytic cycle, many crucial chemical steps (such as 3' oxygen deprotonation and PPi release) remain incompletely resolved due to experimental limitations. Computational methods like hydrogen bond models (classical mechanisms), self-activation mechanisms (SAM), and water-mediated substrate assistance (WMSA) have been proposed to explain these processes. Different computational studies hold differing views on the mechanism of 3' oxygen deprotonation, but mechanisms proposed (like SAM) can account for the synchronicity of multi-step chemical and physical processes, providing new insights into polymerase function.
Time-resolved crystallography studies reveal the presence of a third metal ion in DNA polymerases crucial for phosphodiester bond formation. However, different computational studies yield varying results regarding its specific function. Some studies suggest the third metal ion reduces the energy barrier of the chemical reaction by stabilizing departing PPi products, while others argue its primary role is to stabilize the transition state post-chemical reaction.
Translocation steps in the polymerase catalytic cycle are crucial as they free the active site for the addition of new nucleotides. However, the specific mechanism and timing of translocation remain unclear. Molecular dynamics simulations indicate that RNA polymerases undergo translocation after PPi release, following a Brownian ratchet mechanism. Additionally, Markov state models and enhanced sampling methods are used to study specific steps of translocation.
HIV reverse transcriptase (HIV RT) is a well-known drug target, where computational methods such as docking, de novo design, and free energy perturbation (FEP) have successfully been applied to discover highly active new inhibitors. Currently, replicative polymerases are also targeted for anticancer drug development, with studies showing certain candidate drugs can enhance chemotherapy efficacy by inhibiting polymerase activity. Additionally, RNA-dependent RNA polymerases like the RdRp of SARS-CoV-2 are crucial targets for current drug development efforts, with scientists using MD simulations to understand their inhibition mechanisms to drive new drug discoveries.
While Y-family DNA polymerases excel in translesion synthesis, their lower processivity and activity limit their application in PCR. Through computational design and experimental validation, researchers have successfully improved the processivity and catalytic activity of these polymerases, enabling more effective amplification of damaged DNA samples.
To expand the genetic alphabet, scientists can store additional information in DNA by introducing unnatural base pairs (UBPs). This not only broadens their applications in molecular biology and biotechnology but also facilitates the production of aptamers binding proteins and cells, incorporation of non-natural amino acids into proteins, and generation of semi-synthetic organisms. However, finding or developing DNA polymerases capable of efficiently binding and extending UBPs remains a major challenge. Computational methods play a crucial role in understanding these polymerases' mechanisms.
Computational research has played a crucial role in deepening our understanding and predictive capabilities of polymerase catalytic mechanisms. By integrating experimental data and establishing molecular models, computational methods can uncover details of many chemical and physical steps, especially those processes that are challenging to capture directly through experiments. In the future, computational methods will continue to be pivotal in polymerase research, facilitating advancements in drug discovery, polymerase engineering, and other applications. We look forward to further advancing this fascinating research field through combined computational modeling and experimental studies.
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