PubMed Health⌕ Search

PubMed · 16051818

Mutations that abrogate human immunodeficiency virus type 1 reverse transcriptase dimerization affect maturation of the reverse transcriptase heterodimer.

Abstract

The specific impact of mutations that abrogate human immunodeficiency virus type 1 (HIV-1) reverse transcriptase (RT) dimerization on virus replication is not known, as mutations shown previously to inhibit RT dimerization also impact Gag-Pol stability, resulting in pleiotropic effects on HIV-1 replication. We have previously characterized mutations at codon 401 in the HIV-1 RT tryptophan repeat motif that abrogate RT dimerization in vitro, leading to a loss in polymerase activity. The introduction of the RT dimerization-inhibiting mutations W401L and W401A into HIV-1 resulted in the formation of noninfectious viruses with reduced levels of both virion-associated and intracellular RT activity compared to the wild-type virus and the W401F mutant, which does not inhibit RT dimerization in vitro. Steady-state levels of the p66 and p51 RT subunits in viral lysates of the W401L and W401A mutants were reduced, but no significant decrease in Gag-Pol was observed compared to the wild type. In contrast, there was a decrease in processing of p66 to p51 in cell lysates for the dimerization-defective mutants compared to the wild type. The treatment of transfected cells with indinavir suggested that the HIV-1 protease contributed to the degradation of virion-associated RT subunits. These data demonstrate that mutations near the RT dimer interface that abrogate RT dimerization in vitro result in the production of replication-impaired viruses without detectable effects on Gag-Pol stability or virion incorporation. The inhibition of RT activity is most likely due to a defect in RT maturation, suggesting that RT dimerization represents a valid drug target for chemotherapeutic intervention.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Johanna Wapling, Katie L Moore, Secondo Sonza, Johnson Mak, Gilda Tachedjian. 2005. Mutations that abrogate human immunodeficiency virus type 1 reverse transcriptase dimerization affect maturation of the reverse transcriptase heterodimer.. https://doi.org/10.1128/jvi.79.16.10247-10257.2005

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related citations

Transgene sequence codon optimization and composition determines replication competence of self-amplifying RNA.

Self-amplifying RNA (saRNA) is an emerging RNA therapeutic modality that can facilitate higher magnitude and more durable protein expression at substantially lower doses than nonreplicating mRNA. Unlike conventional messenger RNA (mRNA), alphavirus-derived saRNA must support a replicase-driven RNA amplification step in addition to translation, raising the possibility that transgene coding sequences impose sequence-level constraints on replication. Here, saRNA replication was found to be dependent on the codon composition of the transgene; multiple therapeutic transgenes were replication defective despite an intact Venezuelan Equine Encephalitis Virus (VEEV)-derived saRNA backbone. Replication defects were rescued by synonymous codon re-optimization of the same transgenes, indicating that nucleotide-level features of the coding sequence, rather than the encoded protein, govern replication competence. Comparative compositional analyses identified a distinct signature associated with productive replication, characterized by elevated GC (>53%) and GC3 (>63%) content, higher codon adaptation to human (>0.75), and reduced UpA (<43/kb) and UpU (<41/kb) dinucleotide density. Moreover, deliberate compositional perturbation of an otherwise replication-competent transgene shifted these features and abolished replication, supporting a causal and combinatorial role for sequence composition in defining saRNA replication outcome. These findings define an underappreciated constraint in saRNA therapeutics and motivate saRNA-specific payload design frameworks that incorporate alphavirus-associated compositional biases during transgene sequence optimization.

Codon↗

CodonMoE: DNA language models for codon-dependent mRNA prediction.

MOTIVATION: Genomic language models (gLMs) face a fundamental efficiency challenge: one must either maintain separate specialized models for each biological modality (DNA and RNA) or develop large multimodal architectures. Both approaches impose significant computational burdens-modality-specific models require redundant infrastructure despite inherent biological connections, while multi-modal architectures demand increased parameter counts and extensive cross-modality pretraining. RESULTS: To address this limitation, we introduce CodonMoE (Adaptive Mixture of Codon Reformative Experts), a lightweight adapter that transforms DNA language models into effective RNA analyzers without RNA-specific pretraining. Our theoretical analysis establishes CodonMoE as a universal approximator at the codon level, capable of mapping arbitrary functions from codon sequences to codon-dependent RNA properties given sufficient expert capacity. Across four RNA prediction tasks spanning stability, expression, and regulation, DNA models augmented with CodonMoE significantly outperform their unmodified counterparts, with the HyenaDNA+CodonMoE series achieving state-of-the-art results using 80% fewer parameters than specialized RNA models. By maintaining sub-quadratic complexity while achieving superior performance, our approach provides a principled path toward unifying genomic language modeling, leveraging more abundant DNA data and reducing computational overhead while preserving modality-specific performance advantages. AVAILABILITY AND IMPLEMENTATION: Source code for the method and to reproduce the results is available at https://github.com/Kingsford-Group/CodonMoE.

Codon↗

CaLMPhosKAN: prediction of general phosphorylation sites in proteins via fusion of codon aware embeddings with amino acid aware embeddings and wavelet-based Kolmogorov-Arnold network.

MOTIVATION: The mapping from codon to amino acid is surjective due to codon degeneracy, suggesting that codon space might harbor higher information content. Embeddings from the codon language model have recently demonstrated success in various protein downstream tasks. However, predictive models for residue-level tasks such as phosphorylation sites, arguably the most studied Post-Translational Modification (PTM), and PTM sites prediction in general, have predominantly relied on representations in amino acid space. RESULTS: We introduce a novel approach for predicting phosphorylation sites by utilizing codon-level information through embeddings from the codon adaptation language model (CaLM), trained on protein-coding DNA sequences. Protein sequences are first reverse-translated into reliable coding sequences by mapping UniProt sequences to their corresponding NCBI reference sequences and extracting the exact coding sequences from their GenBank format using a dynamic programming-based global pairwise alignment. The resulting coding sequences are encoded using the CaLM encoder to generate codon-aware embeddings, which are subsequently integrated with amino acid-aware embeddings obtained from a protein language model, through an early fusion strategy. Next, a window-level representation of the site of interest, retaining the full sequence context, is constructed from the fused embeddings. A ConvBiGRU network extracts feature maps that capture spatiotemporal correlations between proximal residues within the window. This is followed by a prediction head based on a Kolmogorov-Arnold network (KAN) using the derivative of gaussian wavelet transform to generate the inference for the site. The overall model, dubbed CaLMPhosKAN, performs better than the existing approaches across multiple datasets. AVAILABILITY AND IMPLEMENTATION: CaLMPhosKAN is publicly available at https://github.com/KCLabMTU/CaLMPhosKAN.

Codon↗