GalaxyDomDock: An ab initio domain–domain docking web server for multi-domain protein structure prediction

Modeling and Analysis of HIV-1 Pol Polyprotein as a Case Study for Predicting Large Polyprotein Structures

Acquired immunodeficiency syndrome (AIDS) is caused by human immunodeficiency virus (HIV). HIV protease, reverse transcriptase, and integrase are targets of current drugs to treat the disease. However, anti-viral drug-resistant strains have emerged quickly due to the high mutation rate of the virus, leading to the demand for the development of new drugs. One attractive target is Gag-Pol polyprotein, which plays a key role in the life cycle of HIV. Recently, we found that a combination of M50I and V151I mutations in HIV-1 integrase can suppress virus release and inhibit the initiation of Gag-Pol autoprocessing and maturation without interfering with the dimerization of Gag-Pol. Additional mutations in integrase or RNase H domain in reverse transcriptase can compensate for the defect. However, the molecular mechanism is unknown. There is no tertiary structure of the full-length HIV-1 Pol protein available for further study. Therefore, we developed a workflow to predict the tertiary structure of HIV-1 NL4.3 Pol polyprotein. The modeled structure has comparable quality compared with the recently published partial HIV-1 Pol structure (PDB ID: 7SJX). Our HIV-1 NL4.3 Pol dimer model is the first full-length Pol tertiary structure. It can provide a structural platform for studying the autoprocessing mechanism of HIV-1 Pol and for developing new potent drugs. Moreover, the workflow can be used to predict other large protein structures that cannot be resolved via conventional experimental methods.

Recent Advances and Challenges in Protein Structure Prediction

Artificial intelligence has made significant advances in the field of protein structure prediction in recent years. In particular, DeepMind's end-to-end model, AlphaFold2, has demonstrated the capability to predict three-dimensional structures of numerous unknown proteins with accuracy levels comparable to those of experimental methods. This breakthrough has opened up new possibilities for understanding protein structure and function as well as accelerating drug discovery and other applications in the field of biology and medicine. Despite the remarkable achievements of artificial intelligence in the field, there are still some challenges and limitations. In this Review, we discuss the recent progress and some of the challenges in protein structure prediction. These challenges include predicting multidomain protein structures, protein complex structures, multiple conformational states of proteins, and protein folding pathways. Furthermore, we highlight directions in which further improvements can be conducted.

Peptide-based PROTACs: Current Challenges and Future Perspectives

Proteolysis-targeting chimeras (PROTACs) are an attractive means to target previously undruggable or drug-resistant mutant proteins. While small molecule-based PROTACs are stable and can cross cell membranes, there is limited availability of suitable small molecule warheads capable of recruiting proteins to an E3 ubiquitin ligase for degradation. With advances in structural biology and in silico protein structure prediction, it is now becoming easier to define highly selective peptides suitable for PROTAC design. As a result, peptide-based PROTACs are becoming a feasible proposition for targeting previously "undruggable" proteins not amenable to small molecule inhibition. In this review, we summarize recent progress in the design and application of peptide-based PROTACs as well as several practical approaches for obtaining candidate peptides for PROTACs. We also discuss the major hurdles preventing the translation of peptide-based PROTACs from bench to bedside, such as their delivery and bioavailability, with the aim of stimulating discussion about how best to accelerate the clinical development of peptide- based PROTACs in the near future.